Psychometric Assessment of the EQ-5D-3L And ReQoL Measures in Secure Care Patients with an Intellectual Disability: Reliability, Agreement, Construct Validity, and Responsiveness

Psychometric Assessment of the EQ-5D-3L And ReQoL Measures in Secure Care Patients with an Intellectual Disability: Reliability, Agreement, Construct Validity, and Responsiveness | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Psychometric Assessment of the EQ-5D-3L And ReQoL Measures in Secure Care Patients with an Intellectual Disability: Reliability, Agreement, Construct Validity, and Responsiveness Matthew Franklin, Jennifer Condie, Katie Aafjes-van Doorn, Paula Foscarini-Craggs, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9022589/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract PURPOSE. People with intellectual disabilities (PID) are a vulnerable, marginalised group for whom mental ill-health is common. PID who commit criminal offences may require secure inpatient care, within which aggression and violence cause major problems stemming in-part from patients’ psychological distress. To conduct economic evaluations of interventions to aid secure care PID, utility-based measures (e.g., EQ-5D, ReQoL-UI) must be sensitive and responsive to changes in aggression and psychological distress. Within secure care PID, we assess the psychometric properties of the self-reported and proxy-reported EQ-5D-3L (generic health) and ReQoL-UI (recovery-focussed quality-of-life) alongside the clinician-reported MOAS (aggression) and self-reported BSI (psychological distress). We use the self-reported EQ-5D-3L adapted for PID; the ReQoL-10 was also assessed. METHODS. The SCHEMA trial collected measures at baseline, 19- and 38-weeks post-baseline. EQ-5D-3L and ReQoL measures’ inter-rater reliability and agreement between self- and proxy-responses was assessed, with construct validity and responsiveness judged against MOAS and BSI scores and cut-offs, e.g., with/out aggression or psychological distress. RESULTS. Forty-seven adults were randomised (intervention, 24; control, 23). The psychometric results were notably influenced by using self- or proxy-responses. The EQ-5D-3L suggested little heterogeneity among responders. Inter-rater reliability/agreement was: EQ-5D-3L, poor/none; ReQoL-UI, poor/minimal; ReQoL-10, moderate/weak-moderate. The ReQoL-UI/-10 had better construct validity with the MOAS and BSI than the EQ-5D-3L. Responsiveness tended to be larger for the proxy-reported ReQoL-10, albeit trivial to small across all measures. CONCLUSION. Despite the small sample size common with PID research, this study suggests the ReQoL measures might be preferred over the EQ-5D-3L in secure care PID. intellectual disability learning disability secure care recovery-focussed quality of life ReQoL-UI ReQoL-10 EuroQol EQ-5D-3L adapted EQ-5D-3L EQ-5D-3L-ID 1. INTRODUCTION People with intellectual disabilities (PID) – a term we use to encapsulate people with learning disabilities (global developmental delay) and/or borderline intellectual functioning (BIF; low IQ) – are a vulnerable group for whom mental ill-health is common [ 1 ]. PID who commit criminal offences may require secure inpatient care, as services that treat people who pose a risk to themselves or others. Aggression and violence cause major problems in psychiatric and secure inpatient care, stemming in-part from patients’ psychological distress and poor mental health [ 2 ]. An international review indicated that secure care patients are more likely to be violent than those in other psychiatric unit types, with 69% of assaults against England’s National Health Service (NHS) staff occurring in mental health or intellectual disability (ID) settings [ 3 ]. Developing cost-effective interventions in secure care remains a priority, to aid PID and care staff [4; 5]. However, such PID research comes with challenges including unknown psychometric properties of health-related quality-of-life (HRQoL) measures for outcome assessment, to more general PID research recruitment and conduct issues [ 6 – 8 ]. Assessing cost-effectiveness using quality-adjusted life years (QALYs) is recommended by the National Institute for Health and Care Excellence (NICE) for England and Wales alongside health technology assessment (HTA) agencies internationally [9; 10]. The ‘quality-adjustment’ part involves quantifying HRQoL, typically using generic preference-based (aka., utility-weighted) measures, e.g., EuroQol’s EQ-5D measures [ 9 – 11 ]. An alternative is the Recovering Quality-of-life (ReQoL) utility index (ReQoL-UI) based on the ReQoL 10-item (ReQoL-10), which focusses on personal mental health recovery [12; 13]. There is growing psychometric evidence for the ReQoL compared to the EQ-5D measures in mental health populations [14; 15]; a published trial assessed the EQ-5D five-level (EQ-5D-5L) versus ReQoL-UI for economic evaluation [ 16 ]. None of these case studies involved PID. PID research evidence is lacking for several reasons. First and foremost, low PID research involvement can stem from active or passive participation exclusion, alongside cognitive and intellectual challenges with research consent processes; these problems are compounded in secure care settings where there are further involvement barriers, leading to small sample sizes impacting PID research conduct and publishing [ 17 – 20 ]. More specific to outcomes research, outcome measures often include complex wording / concepts / constructs that PID may not fully comprehend, impacting the measure’s perceived face validity due to responder understanding/comprehension [7; 21; 22]. Relatedly, proxy-completion (e.g., by a carer or clinician on behalf of the patient) may be required alongside / instead of self-completion, which often requires the proxy to respond as if they were the patient (i.e., to reflect the patient’s perceived response) which can be difficult for proxies to do accurately [ 23 – 26 ]. The EQ-5D three-level version (EQ-5D-3L) has been adapted for completion by people with mild to moderate ID (EQ-5D-3L-ID), which could improve PID self-completion face validity [ 7 ]. However, the ReQoL measures do not have an ID-adapted nor proxy-version, meaning the standard ReQoL measures are used. There is currently no psychometric evidence of the EQ-5D-3L-ID and ReQoL measures in PID as an overlooked and marginalised group. Our aim is to assess the psychometric properties (inter-rater reliability and agreement, construct validity, and responsiveness) of the self-reported EQ-5D-3L-ID, proxy-completed EQ-5D-3L Proxy Version 1, and self- and proxy-completed ReQoL-10/-UI in secure care PID. We assess these measures against each other (e.g., self- vs proxy-responses), and against the clinician-reported Modified Overt Aggression Scale (MOAS) and self-reported Brief Symptom Inventory (BSI) reflecting aggression and psychological distress, respectively, as key constructs of interest in secure care settings. Subsequently we use these results to discuss the appropriateness of the EQ-5D-3L(-ID) and ReQoL measures for capturing HRQoL and conducting economic evaluation in secure care settings for PID. 2. METHODS 2.1. Data source Data were obtained from SCHEMA (Secure Care Hospital Evaluation of Manualized interpersonal Art-psychotherapy), a two-arm, parallel-group, unblinded, individually randomised controlled trial (RCT) comparing manualized interpersonal art psychotherapy alongside usual care versus usual care only in secure care PID [ 27 ]. The trial’s eligibility criteria and consenting process in described in Supplementary Appendix S1. The study protocol and related trial documents were approved by the London-City & East research ethics committee (REC ID: 23/LO/0026; IRAS project ID: 319325) [ 27 ]. Table 1 ’s described outcome measures were collected at baseline, 19-, and 38-weeks post-baseline. 2.2. Outcome measures 2.2.1. EQ-5D-3L adapted for adults with intellectual disabilities and proxy-version The EQ-5D measures are often the HTA reference case internationally as generic health measures with five dimensions/items [9; 10; 28]: mobility; self-care; usual activity; pain/discomfort; anxiety/depression. The EQ-5D-3L has three severity levels from 1 (best state) to 3 (worst state). UK utility score range: -0.594 (worst state) to 1 (best state) [ 29 ]. The self-reported EQ-5D-3L-ID involved changes in wording, language, structure, and images compared to the original EQ-5D-3L to support completion by PID [ 7 ]. EQ-5D-3L Proxy Version 1 was completed by ward staff, i.e., responding to the questionnaire by providing their own impression of the respondent’s health [ 30 ]. For descriptive purposes, we refer to just the EQ-5D-3L when discussing both self- and proxy-reported measures. 2.2.2. Recovering Quality-of-life 10-item (ReQoL-10) and Utility Index (ReQoL-UI) The ReQoL measures were developed using qualitative and quantitative techniques capturing the recovering quality-of-life concerns of mental health service users [ 12 ]. The ReQoL-10 includes six positively (items: 2, 4, 5, 7, 8, 10) and four negatively (items: 1, 3, 6, 9) worded mental health items plus one physical health item across seven themes: autonomy; wellbeing; hope; activity; belonging and relationships; self-perception; physical health [ 12 ]. Each item is scored from 0 (worst state) to 4 (best state); summary score range: 0 (worst state) to 40 (best state) The ReQoL-UI can be assigned to six-items (items: 3, 5, 6, 7, 9, 10) and one physical health item of the ReQoL-10, while retaining the original seven themes [ 13 ]. UK utility score range: −0.195 (worst state) to 1 (best state) [11; 13]. Neither an ID-adapted nor proxy-reported ReQoL-10 exists. Thus, for SCHEMA the original ReQoL-10 was self- and proxy-reported. The proxy ReQoL-10 and EQ-5D-3L were completed, where possible, by the same ward staff member across all time points. 2.2.3. Modified Overt Aggression Scale (MOAS) The MOAS is an observer-rated measure that assesses the frequency and severity of aggressive incidents over the past week [ 31 ]. The scale has four categories scored from 0 (no events) to 4 (most severe form of aggression), each with a severity weight (i.e., x1 to x4): verbal aggression (x1), aggression against property (x2), auto-aggression (against self; x3), and physical aggression (against others; x4). The summed scores across categories are multiplied by the category severity weight to produce the weighted / total MOAS score, with a higher score indicating more aggressive behavior [ 31 ]. The MOAS is completed by a research nurse/clinical support in the SCHEMA trial. 2.2.4. Brief Symptom Inventory (BSI) The BSI is a 53-item self-report instrument to assess psychological distress [ 32 ]. Participants rate the distress associated with a specific problem over the past 7 days, from 0 (not at all) to 4 (extremely). For the SCHEMA trial, a visual analogue scale with different emotional / distressed face visuals aided participants indicate their response within nine primary symptom dimensions (see Table 1 ). For the symptom dimension subscale, scores are summed across the relevant items then divided by the number of items in the respective dimension (e.g., somatization, 7-items) [ 32 ]. Items 11, 25, 39, and 52 do not factor into any dimension, but are included when calculating Grand Total Scores (GTS) including the Global Severity Index (GSI), Positive Symptom Total (PST), and Positive Symptom Distress Index (PSDI) for which higher scores represent worse states (see Table 1 ) [ 32 ]. 2.3. Statistical analyses Each analysis uses all observed cases, with relevant sample sizes ( N ) presented in the result tables. Inter-rater reliability and agreement, and construct validity are assessed based on the whole cohort’s baseline data; responsiveness is assessed across each time-point for the whole cohort. Statistical significance (SS), p-value < 0.05. Analyses are conducted in Stata 19 [ 33 ]. 2.3.1. Inter-rater reliability and agreement Inter-rater reliability measures consistency in rankings between raters (i.e., self- vs proxy), whereas inter-rater agreement measures if raters assign the exact same score. In the SCHEMA trial, each subject is rated dominantly by a different proxy alongside the patient’s rating of their own HRQoL, i.e., unique patient-proxy pairings. Therefore, for assessing reliability, one-way random-effects models estimate intraclass correlation coefficients (ICCs) for the continuous utility/summary scores, with weighted Kappa statistics for ordinal item scores [ 34 – 37 ]. To assess agreement, proportion of ‘agreed’ responses (i.e., raters provide the same score) and weighted Brennan and Prediger (BP) coefficients are used, alongside Bland-Altman plots. Linear weights are used to penalise disagreement proportionally, whereas quadratic weights penalise greater disagreement more severely [36; 38]. In cases of symmetrical imbalance / low variation in responses, reliability can be low despite high agreement. Relatedly, the BP coefficient is equivalent to the prevalence-adjusted and bias-adjusted kappa (PABAK), correcting for symmetrical imbalances in the Kappa statistic. We report and reflect on both (e.g., ICC/Kappa and BP) as recommended [ 39 – 44 ]. 2.3.2. Construct validity Construct validity assesses the extent to which a measure reflects HRQoL differences hypothesised to exist. This is important for HRQoL measures, as their values should reflect HRQoL factors associated with the evaluated condition/treatment. Construct validity is assessed despite no ‘gold standard’ HRQoL measure, given difficulties in any given indicator(s) capturing health’s full impact on people’s lives. Thus, we assess indicators to suggest, but cannot fully prove, construct validity, i.e., convergent and known-group validity. Convergent validity assesses to what extent measure scores converge together. Spearman’s rank absolute correlation strength (ACS) and associated p-value non-parametrically indicate the degree to which instruments measure related factors [ 45 ]. Locally weighted scatterplot smoothing (LOWESS) techniques regress lines of central tendency between two variables, plotted on a scatterplot to non-parametrically visualise their relationship [ 46 ]. Known-group validity assesses the extent to which instrument scores differ between groups that are expected to differ, measured using Cohen’s d standardised absolute effect sizes (AES), i.e., mean score difference between two adjacent severity subgroups divided by the standard deviation of scores for the milder group [45; 47]. The non-parametric Kruskal Wallis test complements the AES to suggest statistically significant difference between groups. 2.3.3. Responsiveness Responsiveness is important for health economics and outcomes research, as any change in health must be reflected by change in the chosen HRQoL measure. For example, if health (or other relevant construct) changes following an intervention, but the HRQoL score does not change, the HRQoL measure may not be responsive to actual changes in health. To measure responsiveness, we examined floor (worst possible score) and ceiling (best possible score) effects, which affect the measure’s ability to detect deterioration or improvements in health, respectively. We also examined the magnitude of change in scores over time, as a crude indicator of responsiveness, using standardised response means (SRMs), i.e., divide the mean change by the change standard deviation [45; 47]. 3. RESULTS 3.1. Descriptive statistics Overall, 50 people consented to the SCHEMA trial. Three people withdrew before randomisation, leaving 47 people (24 intervention-arm: 23 control-arm) for our analyses: mean age, 36.9 years (range: 21 to 59), with 91.5% male. A Consort diagram, baseline descriptive statistics and measure score histograms are in Supplementary Appendix 1–3. Table 2 presents baseline number of responders and measure scores across the whole cohort, and across all time-points in Table 3 . At baseline, there was very little missing data. The EQ-5D-3L-ID was completed by 40 (85.1%) participants; however, this was due to trial sites initially using the non-ID-adapted EQ-5D-3L for 7 participants, thus represents a trial error. Generally, the ReQoL measures followed by the BSI suggests the SCHEMA study sample are more heterogenous than the EQ-5D-3L or MOAS (Table 2 ). Focussing on the proxy-responses for descriptive purposes, the EQ-5D-3L suggests the 47 participants can be categorised into 14 unique health states which each have a unique score. Relatedly, the MOAS summary scores suggest the 47 participants can be categorised into 16 unique health states, with 10 (summary) or 11 (weighted) unique summary scores. In comparison, the ReQoL-10 and ReQoL-UI suggests the 47 participants can be categorised into 47 or 46 unique health states, with 20 or 46 unique summary scores, respectively. 3.2. Inter-rater reliability and agreement Table 4 ICC results suggest that on average the inter-rater reliability is poor between the self- and proxy-reported scores for the EQ-5D-3L and ReQoL-UI, whereas reliability for the ReQoL-10 was moderate albeit with broad 95% confidence intervals. At the item-level, the proportion of ‘agreed’ responses were higher for the EQ-5D-3L’s physical health items (e.g., ‘Mobility’, 90% agreed; ‘Self-care’, 77.5% agreed; ‘Usual activities’, 67.5% agreed) than any of the mental health items across HRQoL measures. Additionally, there was little variation in such physical health items with the mean tending to ‘no problem’, leading to the ‘Kappa paradox’ at the item-level, i.e., high agreement conversely matched with low reliability [39; 41]. Here, the Kappa paradox indicates patients and proxies are disproportionality focussing on a single score (i.e., no problem) within the EQ-5D-3L physical health items. Similarly for the BP and Kappa, the linear and quadratic weighted statistics are the same for these EQ-5D-3L physical health items, indicating constant proportional disagreement in item response, again pointing to a lack of variability. In comparison, the higher quadratic than linearly weighted Kappa and BP statistics for the ReQoL items suggest that while there is disagreement between raters, the disagreements are primarily small/adjacent (e.g., rating a 2 instead of a 3) rather than large (e.g., rating a 1 instead of a 4) with better variability than for the EQ-5D-3L. Overall, across HRQoL measures, agreement tended to be higher than reliability. For the EQ-5D-3L, such agreement stemmed from a lack of response variation at the item-level potentially explaining the bigger separation in reliability and agreement statistics. This contributes to overall poorer agreement and reliability at the EQ-5D-3L utility score-level than the ReQoL measures utility/summary score. Complementary Bland-Altman plots are presented in Supplementary Appendix S4. 3.3. Construct validity Table 5 ACS results indicate that the clinician-reported MOAS had higher correlation with the proxy- than self-reported EQ-5D-3L and ReQoL-10, but the opposite (albeit with smaller differences) for the ReQoL-UI. Across all HRQoL measures, the MOAS summary/weighted score and verbal items generally had the higher/moderate ACS that was SS, apart from with the self-reported EQ-5D-3L-ID which was weak and non-SS. The ACS with the self-reported BSI was higher with the self- than proxy-reported EQ-5D-3L and ReQoL measures, so opposite to the relationship with the MOAS for the EQ-5D-3L and ReQoL-10. The BSI particularly had strong and SS ACS with the self-reported ReQoL-UI and ReQoL-10, which is not surprising given these measures are both self-reported and focussed particularly on mental health. These ACS results are complemented and reinforced by the LOWESS graphs presented in Supplementary Appendix S5. Table 6 AES results for the MOAS aggression cut-offs suggest a similar result to the ACS: effect sizes were notably larger for the proxy- than self-reported EQ-5D-3L, with the same for the ReQoL-10 albeit a smaller difference, although this result was reversed for the ReQoL-UI. AES were largest (0.951) for the proxy-reported ReQoL-10 and smallest (0.223) for the self-reported EQ-5D-3L-ID. Across BSI subscales, AES varied depending on HRQoL measure used and who completed the measure. The ReQoL-UI/-10 self-reported version tended to have the largest AES across all BSI symptomatic subscales. Overall, ACS and AES results suggest a similar story, particularly emphasising a disconnect between self- and proxy-responses. Also, weak-SS AES between MOAS and proxy-reported HRQoL measures, but strong-SS ACS between BSI and self-reported HRQoL measures. The HRQoL measures often had large AES between MOAS aggression and BSI symptomatic cut-off groups, with the ReQoL-10 notably having larger AES than its utility-based counterpart with differing performance between the ReQoL-UI and EQ-5D-3L depending on who completed the measure and the specific aggression / symptomatic comparison. 3.4. Responsiveness Table 3 suggests differences in score change direction between measures. For example, the self-reported EQ-5D-3L and ReQoL-UI suggested mean HRQoL deterioration since baseline, whereas the proxy-reported version of the same measures suggested mean improvement. The MOAS and BSI (not PSDI at 19-weeks) also suggested aggression and psychological distress improvement since baseline, as did the self- and proxy-reported ReQoL-10. Interestingly, the self-reported ReQoL-UI suggested mean deterioration despite being elicited from the ReQoL-10 which suggested mean improvement since baseline. Additionally, responsiveness differed dependent on time-points being compared (Table 3 , i.e., 19-weeks versus 38-weeks, compared to baseline or previous data collection timepoint). Overall, responsiveness tended to be larger for the ReQoL-10 proxy-report, albeit trivial to small across all measures. Relatedly, ceiling effects at baseline (Table 2 ) occurred in a lower proportion of responders for the ReQoL-UI/-10 than EQ-5D-3L. 4. DISCUSSION Despite the small sample size common for PID research, our results revealed a range of policy and practice relevant insights. First, the self-reported EQ-5D-3L-ID and proxy-reported EQ-5D-3L Proxy Version 1 has poor to no agreement / reliability / correlation; though, this result can in-part be attributed to a lack of variability in responses particularly for the physical health items, followed by a higher degree in utility score differences when there is a difference between self- and proxy-responses (i.e., a 1 unit difference in item score disagreement proportionally translates to larger/smaller utility score changes due to the utility value set). Thus, this indicates known issues with lack of EQ-5D-3L response options, particularly in condition areas and populations for which it has poor construct validity and responsiveness [ 48 – 50 ]. The EQ-5D-3L was chosen for the SCHEMA trial as it has been adapted for PID, with no similar adaption for the EQ-5D-5L. Though the EQ-5D-3L-ID may have improved face validity of self-responses, the three-level version itself causes other issues to do with response heterogeneity impacting agreement and reliability statistics, alongside potentially its construct validity and responsiveness. Relatedly, the observer-reported MOAS had a stronger relationship with proxy-reported EQ-5D-3L than the self-reported EQ-5D-3L-ID, which makes sense due to potentially better alignment between observer and proxy responses; however, these MOAS correlations were more similar across the self- and proxy-reported ReQoL-10/-UI. When reflecting on the self-reported BSI though, correlations were stronger with self-reported HRQoL measures than proxy-reported; again, indicating the responder has a key influence on the psychometric results. The known-group validity results echo the convergent validity results when assessing between those with aggression / psychiatric symptom distress compared to those without. Overall, it seems apparent that the person completing the measure (e.g., self- vs proxy-response) is a key driver of the observed between-measure relationship, which has implications when relying on different perspectives to represent different but important constructs to evaluate the (cost-)effectiveness of care interventions [ 23 – 26 ]. Regardless, the ReQoL-10/-UI had better construct validity with both the MOAS and BSI. For the MOAS, the relationship was fairly similar between the self- and proxy-reported ReQoL-10/-UI, while being stronger than for the EQ-5D-3L. A notably stronger relationship was estimated between self-reported BSI and ReQoL-10/-UI, which was stronger than with the proxy-reported ReQoL-10/-UI; again, emphasising who was completing the measure in-part driving the relationship strength. Overall, if aggression and/or psychological distress is of interest when capturing HRQoL in secure care patients (as key outcomes for the SCHEMA trial), then the ReQoL-10/-UI is recommended over the EQ-5D-3L based on our psychometric results. These ReQoL results seem reasonably robust despite, but still notably influenced by, who is completing the measure (i.e. self- vs proxy-response). Using the EQ-5D-3L-ID may have improved face validity of self-responses but brought with it known issues with having only three response levels that may have been in-part avoided by using the EQ-5D-5L. Although we hypothesise the lack of variability in physical health items would have perpetuated if the EQ-5D-5L has been used, when disagreement in item scores was small (as was more common) this would have had a smaller proportional impact on utility scores, potentially marginally improving the reliability and agreement statistics particularly at the utility score level. 4.1. National implications for research, care, and policy across England and Wales Our results highlight a range of considerations when using and interpreting scores from the EQ-5D-3L and ReQoL-UI (-10), and their subsequent effect on HRQoL/clinical assessment and economic evaluation evidence. The EQ-5D measures are recommended for economic evaluation by HTA agencies such as NICE for England and Wales [ 10 ]. However, NICE’s method guide (Section 4.3.10) states other measures can be used when supported by psychometric evidence [ 10 ]. Our study suggests that in secure care patients, specifically PID, that the ReQoL-10/-UI has notably better psychometric properties than the EQ-5D-3L across all areas we assessed, noting for construct validity this relies on aggression and psychological distress being important outcomes of interest. The ReQoL measures have been recommended for mental health settings by NHS England [ 51 ]. NICE has abstained from recommending preference-based measures other than the EQ-5D measures, despite the EQ-5D having known insensitivities and a growing body of literature indicating the relative benefits of the ReQoL-10/-UI in mental health populations, albeit the evidence is mixed in common mental disorders [15; 52–55]. There are also empirically evidenced potential implications when using the ReQoL-UI for economic evaluation if physical and mental health do not change over the trial time horizon [15; 16]. 4.2. International generalisability, policy, and ethical considerations Forensic mental health services (i.e., mental health in the legal system) which includes secure care is a policy relevant consideration internationally [ 56 ]. Across 18 European countries, Sampson et al. [ 56 ] highlighted the trade-off between political pressures to contain dangerous mentally disordered offenders (MDOs) for ensuring public safety and ethical debates regarding long-term forensic mental health care [ 57 ]. Compared to the general population, a larger proportion of people who have contact with the legal system have borderline to mild ID, e.g., UK’s criminal justice system: borderline to mild ID, ≈ 40%; mild ID, ≈ 20% mild [58; 59]. Given the interconnection between ID and poor mental health as wider determinants of violent behaviour leading to contacts with the justice system, there is a suggested moral and ethical requirement to deliver appropriate care to such populations in forensic mental health services [ 5 ]. Although HRQoL and outcomes research conducted within secure care PID might seem a small part of a bigger problem, it highlights why such research is required to ensure that new care interventions are evaluated appropriately to inform resource allocation problems across countries. Despite our study’s small sample size, common for PID research, the results suggest the ReQoL measures offer a more appropriate measure than the EQ-5D-3L for assessing HRQoL and associated economic evaluation. Thus, while following NICE’s and other HTA agencies’ EQ-5D reference case may seem the path of less resistance, it can potentially lead to inappropriate evidence informing resource allocation in such settings. 4.3. Limitations Mode of measure administration for self-completion depended on the participant, i.e., some participants completed measures themselves whereas others had the questions read to them. Due to the small sample, we couldn’t explore how this impacted the psychometric results. Additionally, our study did not include a qualitive investigation to understand if the EQ-5D-3L-ID did improve response face validity compared to the ReQoL with no ID-adaptation. For our study, there is no specific indication of concern for the ReQoL self- and proxy-responses, noting that we saw stronger relationships when the responder was the same across measures. A notable limitation is SCHEMA’s small sample size. For statistical analyses such as our psychometric assessments, any comparisons that utilise variations in the data are going to be hampered by small sample sizes. Despite this, there is still a case to conduct and publish such results, recognising that the small sample likely increases result uncertainty but can still be informative to decision-makers if the results are considered not misleading. SCHEMA was challenging research from the beginning given PID are a marginalised group, secure care research is limited, with study recruitment and conduct being difficult in both. Thus, despite the small sample, the results are potentially important and informative for ongoing and future research in this internationally policy relevant but often overlooked group and setting. 5. CONCLUSION Our study provides evidence to recommend the ReQoL measures over the EQ-5D-3L for HRQoL assessment and economic evaluations in secure care PID, particularly when aggression and psychological distress are important outcomes. Declarations Ethics approval. The study protocol and related trial documents were approved by the London-City & East research ethics committee (REC ID: 23/LO/0026; IRAS project ID: 319325) Funding. The SCHEMA trial and analysis was funded by the Health Education England (HEE) / National Institute for Health and Care Research (NIHR) Integrated Clinical and Practitioner Academic (ICA) programme (NIHR award identifier: NIHR301264). The views expressed are those of the author(s) and not necessarily those of the NIHR. The funding agreement ensured the authors’ independence in developing the purview of the manuscript, writing, and publishing the manuscript. Author Contribution Concept and design: MF, SH. Manuscript drafting: MF, SH. Analyses and result presentation: MF. Critical revisions for important intellectual content: MF, SH, JC, KAD, PFC, TLH, AI, IM, RM, ER, MR, SR, AZ. Obtaining funding: SH, MF. All authors reviewed and approved the final manuscript. Acknowledgement We thank the participants of the SCHEMA trial for being involved in the research. Data Availability If there is interest in using the SCHEMA trial data for secondary uses, please contact the SCHEMA trial Chief Investigator, Simon Hackett. References Cooper, S. A., Smiley, E., Morrison, J., Williamson, A., & Allan, L. (2007). Mental ill-health in adults with intellectual disabilities: prevalence and associated factors. The British journal of psychiatry , 190 (1), 27–35. Iozzino, L., Ferrari, C., Large, M., Nielssen, O., & De Girolamo, G. (2015). Prevalence and risk factors of violence by psychiatric acute inpatients: a systematic review and meta-analysis. PloS one , 10(6), e0128536. Marangozov, R., Manzoni, C., & Pike, G. (2017). Royal college of nursing employment survey 2017 . Institute for Employment Studies. Morrissey, C., Langdon, P. E., Geach, N., Chester, V., Ferriter, M., Lindsay, W. R., McCarthy, J., Devapriam, J., Walker, D. M., & Duggan, C. (2017). A systematic review and synthesis of outcome domains for use within forensic services for people with intellectual disabilities. BJPsych Open , 3 (1), 41–56. Tully, J., Hafferty, J., Whiting, D., Dean, K., & Fazel, S. (2024). Forensic mental health: envisioning a more empirical future. The Lancet Psychiatry , 11 (11), 934–942. Foscarini-Craggs, P., Iranpour, A., Aafjes-van, D., Franklin, M., Harrison, T., McKinnon, I., McNamara, R., Randell, E., Rose, S., & Zubala, A. (2026). Working with underrepresented groups: Lessons from the SCHEMA Trial. Trials. O’Dwyer, J. L., Bryant, L. D., Hulme, C., Kind, P., & Meads, D. M. (2024). Adapting the EQ-5D-3L for adults with mild to moderate learning disabilities. Health and Quality of Life Outcomes , 22 (1), 37. Bishop, R., Laugharne, R., Shaw, N., Russell, A. M., Goodley, D., Banerjee, S., Clack, E., SpeakUp, C. H. A. M. P. S., & Shankar, R. (2024). The inclusion of adults with intellectual disabilities in health research–challenges, barriers and opportunities: a mixed-method study among stakeholders in England. Journal of Intellectual Disability Research , 68 (2), 140–149. Rowen, D., Azzabi Zouraq, I., Chevrou-Severac, H., & van Hout, B. (2017). International regulations and recommendations for utility data for health technology assessment. Pharmacoeconomics , 35 (Suppl 1), 11–19. NICE (2022). NICE technology appraisal and highly specialised technologies guidance: the manual. Retrieved 24 Dec 2025, from https://www.nice.org.uk/process/pmg36 Karimi, M., & Brazier, J. (2016). Health, health-related quality of life, and quality of life: what is the difference? Pharmacoeconomics , 34 (7), 645–649. Keetharuth, A. D., Brazier, J., Connell, J., Bjorner, J. B., Carlton, J., Buck, E. T., Ricketts, T., McKendrick, K., Browne, J., & Croudace, T. (2018). Recovering Quality of Life (ReQoL): a new generic self-reported outcome measure for use with people experiencing mental health difficulties. The British Journal of Psychiatry , 212 (1), 42–49. Keetharuth, A. D., Rowen, D., Bjorner, J. B., & Brazier, J. (2021). Estimating a preference-based index for mental health from the recovering quality of life measure: valuation of recovering quality of life utility index. Value in Health , 24 (2), 281–290. Xu, R. H., Keetharuth, A. D., Wang, L., Cheung, A. W., & Wong, E. L. (2022). Measuring health-related quality of life and well-being: a head-to-head psychometric comparison of the EQ-5D-5L, ReQoL-UI and ICECAP-A. The European Journal of Health Economics , 23 (2), 165–176. Franklin, M., Enrique, A., Palacios, J., & Richards, D. (2021). Psychometric assessment of EQ-5D-5L and ReQoL measures in patients with anxiety and depression: construct validity and responsiveness. Quality of Life Research , 30 (9), 2633–2647. Franklin, M., Hunter, R. M., Enrique, A., Palacios, J., & Richards, D. (2022). Estimating cost-effectiveness using alternative preference-based scores and within-trial methods: exploring the dynamics of the quality-adjusted life-year using the EQ-5D 5-level version and recovering quality of life utility index. Value in Health , 25 (6), 1018–1029. Abbing, A., Haeyen, S., Nyapati, S., Verboon, P., & Hooren, S. (2023). Effectiveness and mechanisms of the arts therapies in forensic care. A systematic review, narrative synthesis, and meta analysis. Frontiers in Psychiatry , 14 , 1128252. McIntosh, L. G., Janes, S., O'Rourke, S., & Thomson, L. D. (2021). Effectiveness of psychological and psychosocial interventions for forensic mental health inpatients: A meta-analysis. Aggression and Violent Behavior , 58 , 101551. Brooker, K., van Dooren, K., Tseng, C. H., McPherson, L., Lennox, N., & Ware, R. (2015). Out of sight, out of mind? The inclusion and identification of people with intellectual disability in public health research. Perspectives in public health , 135 (4), 204–211. Feldman, M. A., Bosett, J., Collet, C., & Burnham-Riosa, P. (2014). Where are persons with intellectual disabilities in medical research? A survey of published clinical trials. Journal of Intellectual Disability Research , 58 (9), 800–809. Hackett, S. S., Zubala, A., Aafjes-van Doorn, K., Chadwick, T., Harrison, T. L., Bourne, J., Freeston, M., Jahoda, A., Taylor, J. L., & Ariti, C. (2020). A randomised controlled feasibility study of interpersonal art psychotherapy for the treatment of aggression in people with intellectual disabilities in secure care. Pilot and Feasibility Studies , 6 (1), 180. Connell, J., Carlton, J., Grundy, A., Taylor Buck, E., Keetharuth, A. D., Ricketts, T., Barkham, M., Robotham, D., Rose, D., & Brazier, J. (2018). The importance of content and face validity in instrument development: lessons learnt from service users when developing the Recovering Quality of Life measure (ReQoL). Quality of life research , 27 (7), 1893–1902. Lapin, B. R., Thompson, N. R., Schuster, A., Honomichl, R., & Katzan, I. L. (2021). The validity of proxy responses on patient-reported outcome measures: Are proxies a reliable alternative to stroke patients’ self-report? Quality of Life Research , 30 (6), 1735–1745. Claes, C., Vandevelde, S., Van Hove, G., Van Loon, J., Verschelden, G., & Schalock, R. (2012). Relationship between self-report and proxy ratings on assessed personal quality of life‐related outcomes. Journal of Policy and Practice in intellectual disabilities , 9 (3), 159–165. Li, M., Harris, I., & Lu, Z. K. (2015). Differences in proxy-reported and patient-reported outcomes: assessing health and functional status among medicare beneficiaries. BMC medical research methodology , 15 (1), 62. Griffiths, A. W., Smith, S. J., Martin, A., Meads, D., Kelley, R., & Surr, C. A. (2020). Exploring self-report and proxy-report quality-of-life measures for people living with dementia in care homes. Quality of Life Research , 29 (2), 463–472. Hackett, S. S., Foscarini-Craggs, P., Aafjes-van Doorn, K., Franklin, M., Riaz, M., Zubala, A., Condie, J., McKinnon, I., Iranpour, A., & Harrison, T. L. (2025). Secure care (forensic) hospital evaluation of manualised interpersonal art-psychotherapy (SCHEMA): A randomised controlled trial protocol (Vol. 5). NIHR Open Research. Devlin, N. J., & Brooks, R. (2017). EQ-5D and the EuroQol group: past, present and future. Applied health economics and health policy , 15 (2), 127–137. Dolan, P. (1997). Modeling valuations for EuroQol health states. Medical care , 35 (11), 1095–1108. EuroQol Research Foundation (2025). Proxy version 1 (Frequently Asked Questions). Retrieved 28 Feb 2025, from https://euroqol.org/faq/proxy-version-1/ Oliver, P., Crawford, M., Rao, B., Reece, B., & Tyrer, P. (2007). Modified Overt Aggression Scale (MOAS) for people with intellectual disability and aggressive challenging behaviour: a reliability study. Journal of Applied Research in Intellectual Disabilities , 20 (4), 368–372. Derogatis, L. R. (1993). Brief Symptom Inventory: Administration, scoring, and procedures manual . National Computer Systems (NCS). StataCorp. (2025). Stata 19. College Station . StataCorp LLC. McHugh, M. L. (2012). Interrater reliability: the kappa statistic. Biochemia medica , 22 (3), 276–282. Koo, T. K., & Li, M. Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of chiropractic medicine , 15 (2), 155–163. Sim, J., & Wright, C. C. (2005). The kappa statistic in reliability studies: use, interpretation, and sample size requirements. Physical therapy , 85 (3), 257–268. Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. biometrics, 159–174. Vanbelle, S., & Albert, A. (2009). A note on the linearly weighted kappa coefficient for ordinal scales. Statistical Methodology , 6 (2), 157–163. Derksen, B. M., Bruinsma, W., Goslings, J. C., & Schep, N. W. (2024). The kappa paradox explained. The Journal of hand surgery , 49 (5), 482–485. Brennan, R. L., & Prediger, D. J. (1981). Coefficient kappa: Some uses, misuses, and alternatives. Educational and psychological measurement , 41 (3), 687–699. Flight, L., & Julious, S. A. (2015). The disagreeable behaviour of the kappa statistic. Pharmaceutical statistics , 14 (1), 74–78. Vach, W. (2005). The dependence of Cohen's kappa on the prevalence does not matter. Journal of clinical epidemiology , 58 (7), 655–661. Byrt, T., Bishop, J., & Carlin, J. B. (1993). Bias, prevalence and kappa. Journal of clinical epidemiology , 46 (5), 423–429. Klein, D. (2018). Implementing a general framework for assessing interrater agreement in Stata. The Stata Journal , 18 (4), 871–901. Cohen, J. (1992). Quantitative methods in psychology: A power primer. Psychological Bulletin , 112 , 1155–1159. Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American statistical association , 74 (368), 829–836. Middel, B., & van Sonderen, E. (2001). How to interpret the magnitude of change in health-related quality of life? A study on the use of Cohen’s thresholds for effect size estimates. Assessment of change in clinical evaluation . University of Groningen. Thompson, A. J., & Turner, A. J. (2020). A comparison of the EQ-5D-3L and EQ-5D-5L. Pharmacoeconomics , 38 (6), 575–591. Janssen, M. F., Bonsel, G. J., & Luo, N. (2018). Is EQ-5D-5L better than EQ-5D-3L? A head-to-head comparison of descriptive systems and value sets from seven countries. Pharmacoeconomics , 36 (6), 675–697. Janssen, M., Pickard, A. S., Golicki, D., Gudex, C., Niewada, M., Scalone, L., Swinburn, P., & Busschbach, J. (2013). Measurement properties of the EQ-5D-5L compared to the EQ-5D-3L across eight patient groups: a multi-country study. Quality of life research , 22 (7), 1717–1727. NHS England (2024). Implementation guidance 2024 – psychological therapies for severe mental health problems. Retrieved 17 Feb 2026. Payakachat, N., Ali, M. M., & Tilford, J. M. (2015). Can the EQ-5D detect meaningful change? A systematic review. Pharmacoeconomics , 33 (11), 1137–1154. Feng, Y. S., Kohlmann, T., Janssen, M. F., & Buchholz, I. (2021). Psychometric properties of the EQ-5D-5L: a systematic review of the literature. Quality of Life Research , 30 (3), 647–673. Mulhern, B., Mukuria, C., Barkham, M., Knapp, M., Byford, S., & Brazier, J. (2014). Using generic preference-based measures in mental health: psychometric validity of the EQ-5D and SF-6D. The British Journal of Psychiatry , 205 (3), 236–243. Papaioannou, D., Brazier, J., & Parry, G. (2011). How valid and responsive are generic health status measures, such as EQ-5D and SF-36, in schizophrenia? A systematic review. Value in Health , 14 (6), 907–920. Sampson, S., Edworthy, R., Völlm, B., & Bulten, E. (2016). Long-term forensic mental health services: an exploratory comparison of 18 European countries. International Journal of Forensic Mental Health , 15 (4), 333–351. Völlm, B., Bartlett, P., & McDonald, R. (2016). Ethical issues of long-term forensic psychiatric care. Ethics Medicine and Public Health , 2 (1), 36–44. Nieuwenhuis, J. G., Lepping, P., Mulder, N. L., Nijman, H. L., Veereschild, M., & Noorthoorn, E. O. (2021). Increased prevalence of intellectual disabilities in higher-intensity mental healthcare settings. BJPsych Open , 7(3), e83. Seelen-de Lang, B. L., Smits, H. J., Penterman, B. J., Noorthoorn, E. O., Nieuwenhuis, J. G., & Nijman, H. L. (2019). Screening for intellectual disabilities and borderline intelligence in Dutch outpatients with severe mental illness. Journal of Applied Research in Intellectual Disabilities , 32 (5), 1096–1102. EuroQol Research Foundation (2018). EQ-5D-3L User Guide. Retrieved 28 February 2025, from https://euroqol.org/publications/user-guides Kay, S. R., Wolkenfeld, F., & Murrill, L. M. (1988). Profiles of aggression among psychiatric patients: I. Nature and prevalence. The Journal of nervous and mental disease , 176 (9), 539–546. Tyrer, P., Oliver-Africano, P. C., Ahmed, Z., Bouras, N., Cooray, S., Deb, S., Murphy, D., Hare, M., Meade, M., & Reece, B. (2008). Risperidone, haloperidol, and placebo in the treatment of aggressive challenging behaviour in patients with intellectual disability: a randomised controlled trial. The Lancet , 371 (9606), 57–63. Kellett, S., Beail, N., Newman, D. W., & Hawes, A. (2004). The factor structure of the Brief Symptom Inventory: Intellectual disability evidence. Clinical Psychology & Psychotherapy: An International Journal of Theory & Practice , 11 (4), 275–281. Kellett, S., Beail, N., Newman, D. W., & Frankish, P. (2003). Utility of the Brief Symptom Inventory in the assessment of psychological distress. Journal of Applied Research in Intellectual Disabilities , 16 (2), 127–134. Tables Table 1. Description of outcomes measures and associated scores Long name Short name Responder Construct Scoring type No. items / domains Item score Floor/ worst Ceiling/ best Cut-offs and description Key Refs. EQ-5D three-level version: adapted for adults with mild to moderate learning disabilities and Proxy version 1 EQ-5D-3L: Adapted version (EQ-5D-3L-LD) and Proxy version 1 Adapted version: self-completed Proxy-version: ward staff Preference-based generic health as of today UK preference-based / utility value set 5 / 5 3-point scale: 1 (no problem) to 3 (extreme/ unable) -0.594 1 N/A [7; 29; 30; 60] Recovering Quality of Life—Utility Index ReQoL-UI Derived from ReQoL-10 Preference-based recovery-focussed quality of life in mental health service users over the last week UK preference-based / utility value set 7 / 2 5-point scale: 0 (worst state) to 4 (best state) − 0.195 1 N/A [13] Recovering Quality of Life—10 item ReQoL-10 ReQoL-10: self-completed or proxy-completed by ward staff Recovery-focussed quality of life in mental health service users over the last week Summary: aggregated score across item scores 10 / 1 5-point scale: 0 (worst state) to 4 (best state) 0 40 < 24, clinical range; ≥ 24, general population [12] Modified Overt Aggression Scale MOAS Proxy-completed: research nurse/ clinical support Frequency and severity of aggressive incidents over the past week Summary: aggregated item scores across item Weighted: item scores multiplied by domain weight then aggregated 4 / 4 Mutually inclusive 5-point scale: 0 (no events) to 4 (most severe) Severity weights: verbal (x1), property (x2), auto (x3), physical (x4) Item: 10 Summary: 40 Weighted: 100 0 0 0, no aggression; ≥ 1, aggression [31; 61; 62] Brief Symptom Inventory: Global Severity Index BSI: GSI Self-completed Psychological distress and symptom severity over last 7 days Average score across all completed items 53 / 9 5-point scale: 0 (not at all) to 4 (extremely) 4 0 T-score ≥ 63, clinical (not used – see footnote) [32; 63; 64] Positive Symptom Total PST BSI sub-score Number of experienced symptoms over last 7 days Summed number of items with non-zero response 53 / 9 5-point scale: 0 (not at all) to 4 (extremely) 53 0 T-score ≥ 63, clinical (not used – see footnote) [32; 63; 64] Positive Symptom Distress Index PSDI BSI sub-score Average level of distress with experienced symptoms over last 7 days Summed score of items with non-zero response divided by PST 53 / 9 5-point scale: 0 (not at all) to 4 (extremely) 4 0 T-score ≥ 63, clinical (not used – see footnote) [32; 63; 64] Somatization - BSI symptom dimension subscale Somatic symptoms due to psychological distress Averaged score across dimension items 7 / 1 5-point scale: 0 (not at all) to 4 (extremely) 4 0 0, not symptomatic 1≥, symptomatic [32] Obsessive-compulsive - BSI symptom dimension subscale Obsessive thoughts and compulsive behaviours Averaged score across dimension items 6 / 1 5-point scale: 0 (not at all) to 4 (extremely) 4 0 0, not symptomatic 1≥, symptomatic [32] Interpersonal sensitivity - BSI symptom dimension subscale Accurately assess others' abilities, states, and traits Averaged score across dimension items 4 / 1 5-point scale: 0 (not at all) to 4 (extremely) 4 0 0, not symptomatic 1≥, symptomatic [32] Depression - BSI symptom dimension subscale depressed mood or/and loss of pleasure / interest in activities Averaged score across dimension items 6 / 1 5-point scale: 0 (not at all) to 4 (extremely) 4 0 0, not symptomatic 1≥, symptomatic [32] Anxiety - BSI symptom dimension subscale Stress / worry affecting daily life with lack of control Averaged score across dimension items 6 / 1 5-point scale: 0 (not at all) to 4 (extremely) 4 0 0, not symptomatic 1≥, symptomatic [32] Hostility - BSI symptom dimension subscale Emotionally charged aggressive behaviour Averaged score across dimension items 5 / 1 5-point scale: 0 (not at all) to 4 (extremely) 4 0 0, not symptomatic 1≥, symptomatic [32] Phobic anxiety - BSI symptom dimension subscale Excessive / persistent fear of a specific object, situation, or activity Averaged score across dimension items 5 / 1 5-point scale: 0 (not at all) to 4 (extremely) 4 0 0, not symptomatic 1≥, symptomatic [32] Paranoid ideation - BSI symptom dimension subscale Persistent thoughts of suspicion and distrust Averaged score across dimension items 5 / 1 5-point scale: 0 (not at all) to 4 (extremely) 4 0 0, not symptomatic 1≥, symptomatic [32] Psychoticism - BSI symptom dimension subscale Personality trait involving aggression, impulsivity, and antisocialism Averaged score across dimension items 5 / 1 5-point scale: 0 (not at all) to 4 (extremely) 4 0 0, not symptomatic 1≥, symptomatic [32] BSI items for symptom dimension. somatization (7-items: 2, 7, 23, 29, 30, 33, and 37), obsessive-compulsive (6-items: 5, 15, 26, 27, 32, and 36), interpersonal sensitivity (5-items 20, 21, 22, and 42), depression (6-items 9, 16, 17, 18, 35, and 50), anxiety (6-items 1, 12, 19, 38, 45, and 49), hostility (5-items: 6, 13, 40, 41, and 46), phobic anxiety (5-items: 8, 28, 31, 43, and 47), paranoid ideation (5-items: 4, 10, 24, 48, and 51), and psychoticism (5-items: 3, 14, 34, 44, and 53) BSI T-score for Grand Total Score (GTS) cut-offs: we opted to not use the GTS T-score cut-offs due to access restrictions (e.g., additional cost) for the population norm reference material that permit us to operationalise the T-scores. Subsequently, we focussed on the symptom dimension subscales when assessing known-group validity. Table 2. Outcome measure scores, floor and ceiling effects at baseline across trial-arms Measure Score N (%) Mean Median SD P. worst score P. best score O worst score O best score N worst score (%) N best score (%) UHSP US EQ-5D-3L Self (Adapted) 40 (85.1%) 0.839 0.883 0.193 -0.594 1 0.255 1.000 0 (0.0) 15 (37.5) 12 12 Proxy (Version 1) 47 (100%) 0.832 0.848 0.179 -0.594 1 0.291 1.000 0 (0.0) 17 (36.2) 14 14 ReQoL-UI Self 47 (100%) 0.874 0.921 0.143 − 0.195 1 0.421 1.000 0 (0.0) 6 (12.8) 42 42 Proxy 47 (100%) 0.848 0.885 0.131 − 0.195 1 0.372 0.995 0 (0.0) 0 (0.0) 46 46 ReQoL-10 Self 47 (100%) 27.809 29.000 8.123 0 40 3.000 40.000 0 (0.0) 3 (6.4) 45 24 Proxy 47 (100%) 26.191 26.000 6.002 0 40 15.000 38.000 0 (0.0) 0 (0.0) 47 20 MOAS Summary 47 (100%) 1.830 1.000 3.435 40 0 16.000 0.000 0 (0.0) 22 (46.8) 16 10 Weighted 47 (100%) 2.787 1.000 6.241 100 0 37.000 0.000 0 (0.0) 22 (46.8) 16 11 Verbal 47 (100%) 1.277 0.000 2.384 10 0 10.000 0.000 2 (4.3) 25 (53.2) 8 7 Property 47 (100%) 0.255 0.000 0.943 10 0 6.000 0.000 0 (0.0) 41 (87.2) 4 4 Auto 47 (100%) 0.191 0.000 0.900 10 0 6.000 0.000 0 (0.0) 43 (91.5) 3 3 Physical 47 (100%) 0.106 0.000 0.312 10 0 1.000 0.000 0 (0.0) 42 (89.4) 2 2 BSI GSI 47 (100%) 0.860 0.811 0.697 4 0 2.755 0.000 0 (0.0) 4 (8.5) 44 39 PST 47 (100%) 20.277 19.000 14.316 53 0 53.000 0.000 1 (2.1) 4 (8.5) 44 30 PSDI 47 (100%) 1.920 2.042 0.812 4 0 3.478 0.000 0 (0.0) 4 (8.5) 44 40 Somatization 47 (100%) 0.571 0.286 0.642 4 0 2.143 0.000 0 (0.0) 14 (29.8) 32 14 Obsessive-compulsive 47 (100%) 1.043 0.833 0.895 4 0 3.167 0.000 0 (0.0) 11 (23.4) 37 16 Interpersonal sensitivity 47 (100%) 0.989 0.750 1.039 4 0 3.500 0.000 0 (0.0) 15 (31.9) 29 13 Depression 46 (97.9%) 0.899 0.583 0.908 4 0 3.000 0.000 0 (0.0) 12 (26.1) 34 15 Anxiety 47 (100%) 0.915 0.833 0.934 4 0 3.667 0.000 0 (0.0) 14 (29.8) 31 16 Hostility 47 (100%) 0.762 0.400 0.756 4 0 2.400 0.000 0 (0.0) 12 (25.5) 27 13 Phobic anxiety 47 (100%) 0.906 0.600 0.890 4 0 3.400 0.000 0 (0.0) 14 (29.8) 31 14 Paranoid ideation 47 (100%) 1.119 0.800 1.062 4 0 4.000 0.000 2 (4.3) 11 (23.4) 33 15 Psychoticism 47 (100%) 0.677 0.400 0.838 4 0 3.600 0.000 0 (0.0) 16 (34.0) 29 13 Acronyms. BSI, Brief Symptom Inventory; EQ-5D-3L, EQ-5D Three-Level version; GSI, Global Severity Index; N, number of responder; O. observed; P. possible; PDSI, Positive Symptom Distress Index; PST, Positive Symptom Total; ReQoL-UI(-10), Recovering Quality of Life—Utility Index (10 item); SD, standard deviation; UHSP, Unique Health State Profile; US, Unique Score. Possible (P.) Score & Observed (O.) Score . The table shows the possible (P.) floor/worst and ceiling/best scores as well as the observed (O.) worst and best scores achieved by the respondents; these are shown rather than possible and observed minimum and maximum scores due to the fact that for the EQ-5D and ReQoL measures a higher score is a better state, whereas for the MOAS and BSI scores the opposite is true (i.e. a higher score is a worst state) Unique Health Status Profile (UHSP). The UHSP is based on a measure’s descriptive system and for our analyses is used to assess the ability to quantify heterogeneity of a specific measure or subscale. For example, the EQ-5D-5L and ReQoL-UI questionnaires produces a 5-digit or 7-digit health state profile, respectively, that represents the level of reported problems on each of the 5 or 7 dimensions of health, for example, 11223 for the EQ-5D-5L or 1112234 for the ReQoL-UI. UHSP refers to the number of UHSPs represented by the group of participants on that specific measure, for example, 12 self-reported EQ-5D-3L health state profiles compared with 42 self-reported ReQoL-UI health state profiles are represented by the participant sample. Thus, the self-reported ReQoL-UI suggests the sample are more heterogenous than the EQ-5D-3L. Unique Score (US). US refers to the number of USs represented by the sample. For example, the ReQoL-UI UHSP and US are equal such that each health state is represented by a US. For the ReQoL-10, MOAS, and BSI measures (e.g., using a summary score from a Likert item-score), the US < UHSP as some health states are represented by the same score . For example, although the self-reported ReQoL-10 represents 45 UHSPs in the study sample, many of these UHSPs are represented by the same summary score thus although the UHSPs suggests the sample are particularly heterogenous (i.e., representing 45 UHSPs), as many of these UHSPs have the same score, the US suggests they are less heterogenous (i.e., 24 US). Within statistical analysis using summary scores, it is the USs, not the UHSPs, that dictates the variation in the analysis. Table 3. Observed measure summary scores, number of responders, and standardised response means across time-points Measure t i Time-point (t i ) Dif. time-points, t i – t 0 Dif. time-points, t i – t i-1 N (%) Mean (SD) N (%) Mean (SD) SRM (p-value) N (%) Mean (SD) SRM (p-value) MOAS t 0 47 (100) 1.830 (3.435) - - - - - - summary t 1 39 (83) 1.308 (2.903) 39 (83) -0.590 (4.745) -0.124 (0.028) 39 (83) -0.590 (4.745) -0.124 (0.028) t 2 31 (66) 0.806 (1.424) 31 (66) -1.032 (3.497) -0.295 (0.066) 30 (64) -0.500 (2.862) -0.175 (0.798) MOAS t 0 47 (100) 2.787 (6.241) - - - - - - weighted t 1 39 (83) 2.590 (6.021) 39 (83) -0.308 (9.384) -0.033 (0.072) 39 (83) -0.308 (9.384) -0.033 (0.072) t 2 31 (66) 1.387 (2.604) 31 (66) -1.516 (7.127) -0.213 (0.202) 30 (64) -1.167 (5.682) -0.205 (0.488) BSI t 0 47 (100) 0.860 (0.697) - - - - - - GSI t 1 37 (79) 0.796 (0.815) 37 (79) -0.132 (0.598) -0.221 (0.200) 37 (79) -0.132 (0.598) -0.221 (0.200) t 2 34 (72) 0.858 (0.847) 34 (72) -0.074 (0.634) -0.117 (0.259) 33 (70) 0.047 (0.373) 0.127 (0.688) BSI t 0 47 (100) 20.277 (14.316) - - - - - - PST t 1 37 (79) 18.730 (15.951) 37 (79) -2.541 (12.036) -0.211 (0.341) 37 (79) -2.541 (12.036) -0.211 (0.341) t 2 34 (72) 19.676 (16.503) 34 (72) -1.941 (10.765) -0.180 (0.211) 33 (70) 0.212 (7.940) 0.027 (0.361) BSI t 0 47 (100) 1.920 (0.812) - - - - - - PSDI t 1 37 (79) 2.051 (0.822) 37 (79) 0.042 (0.826) 0.051 (0.892) 37 (79) 0.042 (0.826) 0.051 (0.892) t 2 34 (72) 1.804 (0.963) 34 (72) -0.163 (0.751) -0.217 (0.228) 33 (70) -0.169 (0.849) -0.199 (0.707) EQ-5D-3L-LD t 0 40 (85) 0.839 (0.193) - - - - - - Self-report t 1 33 (70) 0.801 (0.280) 31 (66) -0.005 (0.274) -0.017 (0.512) 31 (66) -0.005 (0.274) -0.017 (0.512) t 2 30 (64) 0.772 (0.330) 28 (60) -0.054 (0.300) -0.179 (0.926) 29 (62) -0.038 (0.292) -0.131 (0.874) EQ-5D-3L-LD t 0 47 (100) 0.832 (0.179) - - - - - - Proxy-report t 1 39 (83) 0.844 (0.116) 39 (83) 0.023 (0.187) 0.123 (0.877) 39 (83) 0.023 (0.187) 0.123 (0.877) t 2 34 (72) 0.840 (0.147) 34 (72) 0.031 (0.201) 0.153 (0.395) 34 (72) 0.008 (0.173) 0.047 (0.719) ReQoL-UI t 0 47 (100) 0.874 (0.143) - - - - - - Self-report t 1 37 (79) 0.835 (0.191) 37 (79) -0.024 (0.145) -0.167 (0.184) 37 (79) -0.024 (0.145) -0.167 (0.184) t 2 34 (72) 0.840 (0.223) 34 (72) -0.016 (0.203) -0.079 (0.383) 33 (70) -0.001 (0.146) -0.009 (0.526) ReQoL-UI t 0 47 (100) 0.848 (0.131) - - - - - - Proxy-report t 1 39 (83) 0.896 (0.095) 39 (83) 0.062 (0.156) 0.397 (0.011) 39 (83) 0.062 (0.156) 0.397 (0.011) t 2 34 (72) 0.856 (0.121) 34 (72) 0.029 (0.140) 0.205 (0.447) 34 (72) -0.040 (0.137) -0.295 (0.075) ReQoL-10 t 0 47 (100) 27.809 (8.123) - - - - - - Self-report t 1 37 (79) 28.189 (5.758) 37 (79) 0.973 (7.251) 0.134 (0.768) 37 (79) 0.973 (7.251) 0.134 (0.768) t 2 34 (72) 27.647 (8.655) 34 (72) 0.176 (7.814) 0.023 (0.784) 33 (70) -0.636 (6.025) -0.106 (0.950) ReQoL-10 t 0 47 (100) 26.191 (6.002) - - - - - - Proxy-report t 1 39 (83) 28.487 (7.323) 39 (83) 2.513 (8.188) 0.307 (0.052) 39 (83) 2.513 (8.188) 0.307 (0.052) t 2 34 (72) 26.382 (7.080) 34 (72) 0.765 (5.275) 0.145 (0.504) 34 (72) -2.088 (6.956) -0.300 (0.090) Acronyms. BSI, Brief Symptom Inventory; EQ-5D-3L, EQ-5D Three-Level version; GSI, Global Severity Index; N, number of responder; PDSI, Positive Symptom Distress Index; PST, Positive Symptom Total; ReQoL-UI(-10), Recovering Quality of Life—Utility Index (10 item); SD, standard deviation. t = time point , whereby: t 0 = baseline; t 1 = 19 weeks; t 2 = 38 weeks. N(%) states the number of people who completed the measure at the specific time-point, or at two given time-points e.g. relative to t 0 (baseline) or t i (whereby i is any time-point denoted as 1–2) Cohen’s SRM cut-off: < 0.2, trivial; 0.2 < 0.5, small; 0.5 1 means the change in score between time-points is larger than one standard deviation Table 4: Interrater agreement between self-reported and proxy-reported EQ-5D-3L and ReQoL measure scores at baseline Measure Mean (SD) score Agreed Proxy-report N (%) Inter-rater agreement (95%CI) (Pairs, N[%]) Patient Proxy Difference N (%) Higher, Lower, N BP (Linear) BP (Quadratic) Kappa (Linear) Kappa (Quadratic) / ICC EQ-5D-3L (40 [85.1]) 0.84 (0.19) 0.82 (0.19) -0.02 (0.28) 5 (12.5) 16 (40.0) 19 (47.5) 0.186 (0.000, 0.444) 0.186 (0.000, 0.662) N/A ICC: 0.000 (0.000, 0.259) - Mobility 1.05 (0.22) 1.10 (0.30) 0.05 (0.32) 36 (90.0) 3 (7.5) 1 (2.5) 0.800 (0.606, 0.994) 0.800 (0.606, 0.994) 0.286 (0.000, 0.808) 0.286 (0.000, 0.808) - Self care 1.03 (0.16) 1.25 (0.44) 0.23 (0.42) 31 (77.5) 9 (22.5) 0 (0.0) 0.550 (0.280, 0.821) 0.550 (0.280, 0.821) 0.143 (0.000, 0.408) 0.143 (0.000, 0.408) - Usual Act 1.35 (0.48) 1.13 (0.34) -0.23 (0.53) 27 (67.5) 2 (5.0) 11 (27.5) 0.350 (0.047, 0.653) 0.350 (0.047, 0.653) 0.161 (0.000, 0.449) 0.161 (0.000, 0.449) - Pain/ Dis 1.30 (0.52) 1.18 (0.39) -0.13 (0.65) 26 (65.0) 5 (12.5) 9 (22.5) 0.578 (0.384, 0.772) 0.681 (0.502, 0.861) 0.010 (0.000, 0.302) 0.000 (0.000, 0.259) - Anx/ Dep 1.38 (0.59) 1.55 (0.64) 0.18 (0.87) 18 (45.0) 14 (35.0) 8 (20.0) 0.296 (0.071, 0.523) 0.419 (0.167, 0.671) 0.000 (0.000, 0.222) 0.000 (0.000, 0.274) ReQoL-10 (47 [100]) 27.81 (8.12) 26.19 (6.00) -1.62 (6.82) 6 (12.8) 16 (34.0) 25 (53.2) 0.463 (0.341, 0.586) 0.708 (0.599, 0.816) N/A ICC: 0.532 (0.294, 0.709) ReQoL-UI (47 [100]) 0.87 (0.14) 0.85 (0.13) 0.03 (0.17) 1 (2.1) 17 (36.2) 29 (61.7) 0.219 (0.000, 0.509) 0.211 (0.000, 0.757) N/A ICC: 0.221 (0.000, 0.475) - Starting daily tasks 2.98 (1.26) 2.81 (1.24) -0.17 (1.54) 15 (31.9) 14 (29.8) 18 (28.3) 0.309 (0.113, 0.505) 0.415 (0.140, 0.690) 0.164 (0.000, 0.351) 0.245 (0.000, 0.521) - Trust others 2.40 (1.41) 2.30 (1.08) -0.11 (1.58) 17 (36.2) 13 (27.7) 17 (36.2) 0.295 (0.094, 0.496) 0.211 (0.000, 0.504) 0.193 (0.000, 0.401) 0.211 (0.000, 0.504) - Unable to cope 2.74 (1.24) 2.38 (1.13) -0.36 (1.45) 15 (31.2) 12 (25.5) 20 (42.6) 0.322 (0.135, 0.509) 0.452 (0.196, 0.709) 0.188 (0.000, 0.381) 0.244 (0.000, 0.538) - Doing things 2.66 (1.32) 2.74 (1.13) 0.09 (1.56) 16 (34.0) 16 (34.0) 15 (31.9) 0.309 (0.109, 0.508) 0.404 (0.129, 0.680) 0.169 (0.000, 0.391) 0.199 (0.000, 0.528) - Happy 2.60 (1.30) 2.36 (1.09) -0.23 (1.20) 19 (40.4) 10 (21.3) 18 (38.3) 0.481 (0.318, 0.645) 0.633 (0.448, 0.818) 0.381 (0.212, 0.551) 0.488 (0.251, 0.725) - Life not worth living 3.34 (1.01) 3.38 (0.97) 0.04 (1.16) 28 (59.6) 19 (21.3) 9 (19.2) 0.455 (0.236, 0.675) 0.472 (0.209, 0.736) 0.266 (0.041, 0.492) 0.309 (0.036, 0.583) - Enjoyment 3.02 (1.24) 2.85 (1.00) -0.17 (1.32) 17 (36.2) 12 (25.5) 18 (38.3) 0.415 (0.242, 0.588) 0.564 (0.362, 0.765) 0.220 (0.004, 0.436) 0.307 (0.000, 0.618) - Hopeful future 2.55 (1.54) 2.49 (1.20) -0.06 (1.76) 11 (23.4) 17 (36.2) 19 (40.4) 0.162 (0.000, 0.369) 0.239 (0.000, 0.563) 0.125 (0.000, 0.339) 0.185 (0.000, 0.501) - Loneliness 2.85 (1.22) 2.55 (1.19) -0.30 (1.27) 18 (38.3) 11 (23.4) 18 (38.3) 0.415 (0.251, 0.579) 0.585 (0.423, 0.747) 0.294 (0.109, 0.479) 0.434 (0.208, 0.659) - Confidence 2.66 (1.29) 2.32 (0.98) -0.34 (1.39) 18 (38.3) 10 (21.3) 19 (40.4) 0.362 (0.180, 0.543) 0.500 (0.305, 0.695) 0.217 (0.007, 0.427) 0.255 (0.000, 0.547) - Physical 3.26 (1.07) 2.89 (1.07) -0.36 (1.29) 22 (46.8) 7 (14.9) 18 (38.3) 0.481 (0.289, 0.674) 0.559 (0.292, 0.826) 0.280 (0.081, 0.480) 0.256 (0.000, 0.547) Acronyms. BP, Brennan and Prediger (BP) coefficient; CI, confidence interval; ICC, intraclass correlation coefficients; EQ-5D-3L, EQ-5D Three-Level version; ReQoL-UI(-10), Recovering Quality of Life—Utility Index (10 item); SD, standard deviation. Footnote. Interrater reliability assessed for: continuous data (i.e., utility and summary scores), one-way random effects model; ordinal data (i.e., item scores), weighted Kappa statistic. Interrater agreement assessed for: continuous and ordinal data, weighted Brennan and Prediger (BP) coefficient. Weights include linear (i.e., each 1-point deviation weighted equally) or quadratic (i.e., larger deviations carry a larger weight) weights. Linear and quadratic weights produce the same statistics when any disagreement is only 1-point and/or only two categories are involved in the analysis (e.g., responses fall into only two categories despite there being more categories). Estimates including 95% CIs are clipped at the lower limit (i.e., 0) as negative agreement and reliability has little informative interpretation. ICC for continuous scores and weighted kappa (quadratic weights) are grouped in the same column mainly for presentation reasons; though the weighted kappa with quadratic weights has been shown to be equivalent to the ICC when estimated using a two-way analysis of variance (ANOVA), noting this study used a one-way random effects model to estimate the ICC [38] . Cut-offs. Continuous (ICC [35] ): ≤ 0.5, poor; 0.5 < 0.75, moderate; 0.75<0.9, good/strong; ≥ 0.9, excellent. Ordinal (Kappa / BP [34; 36; 37] ) : ≤ 0.2, none; 0.2<0.4, minimal; 0.4<0.6, weak; 0.6<0.8, moderate; 0.8 <, strong. NB: various cut-offs exist for both the ICC and Kappa statistic. For the ICC, we opted for the widely cited cut-offs by Koo and Li [35]. For the weighted Kappa, the cut-off ranges tend to be similar across studies, but the labels vary; thus we opted for the labelling by McHugh [34] albeit we dropped the “>90, almost perfect” label as often such a higher level label doesn’t exist for other suggested cut-offs. For the BP coefficient, we choose the same cut-offs as for the Kappa, as the BP coefficient is equivalent to the prevalence-adjusted and bias-adjusted kappa (PABAK) [44]; subsequently, we use the same cut-offs but with the Kappa statistic suggested to represent ‘reliability’ and the BP coefficient representing ‘agreement’, though we recognise that descriptions of the Kappa statistic as representing ‘reliability’ or ‘agreement’ is mixed in the empirical literature. Table 5: Correlation coefficient matrix between measure scores at baseline Measure Measures Spearman’s rank correlation coefficient ( p -value) EQ-5D-3L utility ReQoL-UI utility ReQoL-10 summary Self-reported Proxy-reported Self-reported Proxy-reported Self-reported Proxy-reported EQ-5D-3L utility Self-reported (Adapted) - -0.022 (0.892) 0.605 (0.000) 0.141 (0.384) 0.545 (0.000) 0.374 (0.018) Proxy-reported (Proxy V1) -0.022 (0.892) - 0.174 (0.240) 0.610 (0.000) 0.211 (0.154) 0.446 (0.002) ReQoL-UI utility Self-reported 0.605 (0.000) 0.174 (0.240) - 0.469 (0.001) 0.874 (0.000) 0.567 (0.000) Proxy-reported 0.141 (0.384) 0.610 (0.000) 0.469 (0.001) - 0.465 (0.001) 0.762 (0.000) ReQoL-10 summary Self-reported 0.545 (0.000) 0.211 (0.154) 0.874 (0.000) 0.465 (0.001) - 0.537 (0.000) Proxy-reported 0.374 (0.018) 0.446 (0.002) 0.567 (0.000) 0.762 (0.000) 0.537 (0.000) - MOAS Summary -0.112 (0.490) -0.377 (0.010) -0.384 (0.008) -0.337 (0.021) -0.360 (0.013) -0.445 (0.002) (Clinician-reported) Weighted -0.083 (0.608) -0.365 (0.013) -0.360 (0.013) -0.343 (0.019) -0.337 (0.021) -0.435 (0.002) Verbal -0.205 (0.204) -0.395 (0.007) -0.429 (0.003) -0.334 (0.022) -0.401 (0.006) -0.466 (0.001) Property 0.050 (0.773) -0.260 (0.081) -0.181 (0.226) -0.178 (0.234) -0.082 (0.586) -0.213 (0.152) Auto 0.067 (0.731) 0.085 (0.586) -0.023 (0.883) -0.160 (0.292) -0.030 (0.845) -0.159 (0.295) Physical -0.174 (0.301) -0.248 (0.098) -0.300 (0.039) -0.265 (0.072) -0.173 (0.249) -0.344 (0.016) BSI GSI -0.555 (0.000) -0.274 (0.062) -0.729 (0.000) -0.451 (0.002) -0.736 (0.000) -0.463 (0.001) (Self-reported) PST -0.562 (0.000) -0.242 (0.102) -0.715 (0.000) -0.445 (0.002) -0.713 (0.000) -0.454 (0.002) PSDI -0.308 (0.053) -0.368 (0.011) -0.564 (0.000) -0.369 (0.011) -0.579 (0.000) -0.348 (0.017) Somatization -0.634 (0.000) -0.339 (0.020) -0.504 (0.000) -0.412 (0.004) -0.493 (0.001) -0.436 (0.002) Obsessive-compulsive -0.476 (0.002) -0.198 (0.181) -0.527 (0.000) -0.330 (0.024) -0.560 (0.000) -0.313 (0.032) Interpersonal sensitivity -0.491 (0.002) -0.214 (0.148) -0.579 (0.000) -0.372 (0.011) -0.617 (0.000) -0.363 (0.013) Depression -0.516 (0.001) -0.183 (0.222) -0.666 (0.000) -0.363 (0.014) -0.759 (0.000) -0.432 (0.003) Anxiety -0.423 (0.007) -0.208 (0.160) -0.645 (0.000) -0.357 (0.014) -0.550 (0.000) -0.277 (0.060) Hostility -0.396 (0.012) -0.288 (0.050) -0.687 (0.000) -0.415 (0.004) -0.617 (0.000) -0.457 (0.001) Phobic anxiety -0.282 (0.078) -0.395 (0.006) -0.474 (0.001) -0.432 (0.003) -0.513 (0.000) -0.396 (0.006) Paranoid ideation -0.565 (0.000) -0.148 (0.321) -0.634 (0.000) -0.308 (0.035) -0.660 (0.000) -0.424 (0.003) Psychoticism -0.359 (0.024) -0.206 (0.165) -0.550 (0.000) -0.227 (0.125) -0.608 (0.000) -0.246 (0.095) Acronyms. BSI, Brief Symptom Inventory; EQ-5D-3L, EQ-5D three-level; GSI, Global Severity Index; MOAS, Modified Overt Aggression Scale; ReQoL-10(-UI), Recovering Quality of Life 10-item (Utility Index); PST, Positive Symptom Total; PSDI, Positive Symptom Distress Index. Cohen’s ACS cut-offs. Cohen’s Absolute Correlation Strength (ACS) cut-offs: weak, < 0.3; moderate, 0.3 < 0.5; strong, ≥ 0.5. Table 6. Testing known-group validity based on MOAS summary score and BSI symptom cut-off groups at baseline Measures Groups, score range N (%) EQ-5D-3L-LD Self-reported EQ-5D-3L-LD Proxy-reported ReQoL-UI Self-reported ReQoL-UI Proxy-reported ReQoL-10 Self-reported ReQoL-10 Proxy-reported Mean (SD) ES (p-value) Mean (SD) ES (p-value) Mean (SD) ES (p-value) Mean (SD) ES (p-value) Mean (SD) ES (p-value) Mean (SD) ES (p-value) MOAS No aggression., 0 22 (46.8) 0.863 (0.184) 0.896 (0.154) 0.915 (0.116) 0.880 (0.132) 31.045 (6.708) 28.955 (5.473) summary score Aggression, ≥ 1 25 (53.2) 0.819 (0.203) 0.223 0.777 (0.184) 0.696 0.838 (0.156) 0.558 0.819 (0.126) 0.467 24.960 (8.309) 0.800 23.760 (5.449) 0.951 (0.455) (0.020) (0.018) (0.023) (0.009) (0.004) BSI Not symptomatic, 0 14 (29.8) 0.955 (0.089) 0.903 (0.137) 0.924 (0.117) 0.907 (0.075) 31.571 (9.395) 29.214 (5.873) Somatization Symptomatic, ≥ 1 33 (70.2) 0.789 (0.206) 0.920 0.803 (0.188) 0.573 0.853 (0.149) 0.504 0.822 (0.142) 0.670 26.212 (7.083) 0.685 24.909 (5.664) 0.752 (0.003) (0.070) (0.027) (0.022) (0.008) (0.021) BSI Not symptomatic, 0 11 (23.4) 0.970 (0.060) 0.880 (0.103) 0.957 (0.071) 0.907 (0.080) 34.000 (7.335) 29.273 (5.867) Obsessive Symptomatic, ≥ 1 36 (76.6) 0.801 (0.202) 0.931 0.818 (0.196) 0.346 0.849 (0.150) 0.792 0.829 (0.139) 0.608 25.917 (7.458) 1.088 25.250 (5.798) 0.692 compulsive (0.005) (0.538) (0.002) (0.049) (0.001) (0.053) BSI Not symptomatic, 0 15 (31.9) 0.942 (0.096) 0.860 (0.127) 0.959 (0.060) 0.879 (0.140) 33.400 (6.843) 28.800 (5.784) Interpersonal Symptomatic, ≥ 1 32 (68.1) 0.789 (0.209) 0.846 0.820 (0.200) 0.221 0.834 (0.153) 0.948 0.833 (0.126) 0.353 25.188 (7.385) 1.137 24.969 (5.789) 0.662 sensitivity (0.009) (0.784) (0.000) (0.066) (0.000) (0.052) BSI Not symptomatic, 0 12 (25.5) 0.949 (0.085) 0.869 (0.139) 0.965 (0.036) 0.885 (0.153) 34.750 (4.393) 30.167 (4.933) Depression Symptomatic, ≥ 1 35 (74.5) 0.802 (0.206) 0.796 0.820 (0.191) 0.276 0.843 (0.153) 0.916 0.835 (0.123) 0.380 25.429 (7.758) 1.316 24.829 (5.778) 0.956 (0.021) (0.600) (0.001) (0.028) (0.000) (0.008) BSI Not symptomatic, 0 14 (29.8) 0.913 (0.095) 0.893 (0.134) 0.957 (0.061) 0.891 (0.142) 32.714 (7.108) 29.071 (5.298) Anxiety Symptomatic, ≥ 1 33 (70.2) 0.803 (0.219) 0.580 0.807 (0.191) 0.490 0.839 (0.153) 0.884 0.829 (0.124) 0.483 25.727 (7.703) 0.927 24.970 (5.934) 0.712 (0.116) (0.137) (0.000) (0.011) (0.003) (0.043) BSI Not symptomatic, 0 12 (25.5) 0.910 (0.104) 0.897 (0.120) 0.964 (0.035) 0.847 (0.211) 33.833 (5.289) 28.917 (6.007) Hostility Symptomatic, ≥ 1 35 (74.5) 0.815 (0.211) 0.497 0.810 (0.192) 0.487 0.843 (0.153) 0.906 0.848 (0.094) -0.006 25.743 (7.939) 1.096 25.257 (5.792) 0.626 (0.206) (0.205) (0.001) (0.118) (0.002) (0.079) BSI Not symptomatic, 0 14 (29.8) 0.890 (0.104) 0.903 (0.113) 0.954 (0.061) 0.897 (0.157) 32.857 (6.803) 29.500 (5.360) Phobic Symptomatic, ≥ 1 33 (70.2) 0.817 (0.219) 0.376 0.802 (0.194) 0.575 0.840 (0.154) 0.847 0.827 (0.115) 0.548 25.667 (7.757) 0.960 24.788 (5.770) 0.833 anxiety (0.535) (0.077) (0.002) (0.002) (0.002) (0.016) BSI Not symptomatic, 0 11 (23.4) 0.974 (0.052) 0.863 (0.096) 0.962 (0.042) 0.850 (0.154) 34.455 (5.027) 27.636 (5.537) Paranoid Symptomatic, ≥ 1 36 (76.6) 0.800 (0.202) 0.963 0.823 (0.198) 0.220 0.847 (0.152) 0.842 0.847 (0.126) 0.028 25.778 (7.834) 1.188 25.750 (6.143) 0.314 ideation (0.003) (0.990) (0.004) (0.669) (0.001) (0.321) BSI Not symptomatic, 0 16 (34.0) 0.865 (0.252) 0.893 (0.105) 0.925 (0.141) 0.862 (0.141) 32.750 (6.094) 27.438 (5.955) Psychoticism Symptomatic, ≥ 1 31 (66.0) 0.823 (0.152) 0.211 0.801 (0.202) 0.521 0.848 (0.139) 0.553 0.840 (0.127) 0.162 25.258 (7.929) 1.017 25.548 (6.021) 0.315 (0.059) (0.209) (0.005) (0.357) (0.002) (0.381) Acronyms. BSI, Brief Symptom Inventory; EQ-5D-3L, EQ-5D three-level; ES, effect size (Cohen’s d ); GSI, Global Severity Index; MOAS, Modified Overt Aggression Scale; N, number of people; ReQoL-10(-UI), Recovering Quality of Life 10-item (Utility Index); SD, standard deviation; PST, Positive Symptom Total; PSDI, Positive Symptom Distress Index. Cohen’s AES cut-off: trivial, < 0.2; small, 0.2 < 0.5; medium, 0.5 1 means the difference between the two means is larger than one standard deviation. ESs are relative to less severe group. p -values: calculated from the non-parametric Kruskal Wallis test to suggest if there is a statistically significant difference between two or more known-groups based on the scores used as a complement to assessing ES. Additional Declarations No competing interests reported. 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17:09:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9022589/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9022589/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104781601,"identity":"2b570b31-d852-40ed-8225-4bb0ce1820ce","added_by":"auto","created_at":"2026-03-17 07:55:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3133009,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9022589/v1/bea67f3d-7ef2-4b28-bf4a-88b979a16f0b.pdf"},{"id":104522092,"identity":"98bdb613-f394-4fd1-b76b-b2399931e46e","added_by":"auto","created_at":"2026-03-12 20:11:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4266547,"visible":true,"origin":"","legend":"","description":"","filename":"2.AppendiciesPIDEQ5D3LReQoLpsychometrics020326.docx","url":"https://assets-eu.researchsquare.com/files/rs-9022589/v1/06e2e45501788a83c2b0bd90.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Psychometric Assessment of the EQ-5D-3L And ReQoL Measures in Secure Care Patients with an Intellectual Disability: Reliability, Agreement, Construct Validity, and Responsiveness","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003ePeople with intellectual disabilities (PID) \u0026ndash; a term we use to encapsulate people with learning disabilities (global developmental delay) and/or borderline intellectual functioning (BIF; low IQ) \u0026ndash; are a vulnerable group for whom mental ill-health is common [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. PID who commit criminal offences may require secure inpatient care, as services that treat people who pose a risk to themselves or others. Aggression and violence cause major problems in psychiatric and secure inpatient care, stemming in-part from patients\u0026rsquo; psychological distress and poor mental health [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. An international review indicated that secure care patients are more likely to be violent than those in other psychiatric unit types, with 69% of assaults against England\u0026rsquo;s National Health Service (NHS) staff occurring in mental health or intellectual disability (ID) settings [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Developing cost-effective interventions in secure care remains a priority, to aid PID and care staff [4; 5]. However, such PID research comes with challenges including unknown psychometric properties of health-related quality-of-life (HRQoL) measures for outcome assessment, to more general PID research recruitment and conduct issues [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAssessing cost-effectiveness using quality-adjusted life years (QALYs) is recommended by the National Institute for Health and Care Excellence (NICE) for England and Wales alongside health technology assessment (HTA) agencies internationally [9; 10]. The \u0026lsquo;quality-adjustment\u0026rsquo; part involves quantifying HRQoL, typically using generic preference-based (aka., utility-weighted) measures, e.g., EuroQol\u0026rsquo;s EQ-5D measures [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. An alternative is the Recovering Quality-of-life (ReQoL) utility index (ReQoL-UI) based on the ReQoL 10-item (ReQoL-10), which focusses on personal mental health recovery [12; 13]. There is growing psychometric evidence for the ReQoL compared to the EQ-5D measures in mental health populations [14; 15]; a published trial assessed the EQ-5D five-level (EQ-5D-5L) versus ReQoL-UI for economic evaluation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. None of these case studies involved PID.\u003c/p\u003e \u003cp\u003ePID research evidence is lacking for several reasons. First and foremost, low PID research involvement can stem from active or passive participation exclusion, alongside cognitive and intellectual challenges with research consent processes; these problems are compounded in secure care settings where there are further involvement barriers, leading to small sample sizes impacting PID research conduct and publishing [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. More specific to outcomes research, outcome measures often include complex wording / concepts / constructs that PID may not fully comprehend, impacting the measure\u0026rsquo;s perceived face validity due to responder understanding/comprehension [7; 21; 22]. Relatedly, proxy-completion (e.g., by a carer or clinician on behalf of the patient) may be required alongside / instead of self-completion, which often requires the proxy to respond as if they were the patient (i.e., to reflect the patient\u0026rsquo;s perceived response) which can be difficult for proxies to do accurately [\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The EQ-5D three-level version (EQ-5D-3L) has been adapted for completion by people with mild to moderate ID (EQ-5D-3L-ID), which could improve PID self-completion face validity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, the ReQoL measures do not have an ID-adapted nor proxy-version, meaning the standard ReQoL measures are used. There is currently no psychometric evidence of the EQ-5D-3L-ID and ReQoL measures in PID as an overlooked and marginalised group.\u003c/p\u003e \u003cp\u003eOur aim is to assess the psychometric properties (inter-rater reliability and agreement, construct validity, and responsiveness) of the self-reported EQ-5D-3L-ID, proxy-completed EQ-5D-3L Proxy Version 1, and self- and proxy-completed ReQoL-10/-UI in secure care PID. We assess these measures against each other (e.g., self- vs proxy-responses), and against the clinician-reported Modified Overt Aggression Scale (MOAS) and self-reported Brief Symptom Inventory (BSI) reflecting aggression and psychological distress, respectively, as key constructs of interest in secure care settings. Subsequently we use these results to discuss the appropriateness of the EQ-5D-3L(-ID) and ReQoL measures for capturing HRQoL and conducting economic evaluation in secure care settings for PID.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data source\u003c/h2\u003e \u003cp\u003eData were obtained from SCHEMA (Secure Care Hospital Evaluation of Manualized interpersonal Art-psychotherapy), a two-arm, parallel-group, unblinded, individually randomised controlled trial (RCT) comparing manualized interpersonal art psychotherapy alongside usual care versus usual care only in secure care PID [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe trial\u0026rsquo;s eligibility criteria and consenting process in described in Supplementary Appendix S1. The study protocol and related trial documents were approved by the London-City \u0026amp; East research ethics committee (REC ID: 23/LO/0026; IRAS project ID: 319325) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026rsquo;s described outcome measures were collected at baseline, 19-, and 38-weeks post-baseline.\u003c/p\u003e \u003cp\u003e\u0026lt;Table-1\u0026gt;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Outcome measures\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. EQ-5D-3L adapted for adults with intellectual disabilities and proxy-version\u003c/h2\u003e \u003cp\u003eThe EQ-5D measures are often the HTA reference case internationally as generic health measures with five dimensions/items [9; 10; 28]: mobility; self-care; usual activity; pain/discomfort; anxiety/depression. The EQ-5D-3L has three severity levels from 1 (best state) to 3 (worst state). UK utility score range: -0.594 (worst state) to 1 (best state) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe self-reported EQ-5D-3L-ID involved changes in wording, language, structure, and images compared to the original EQ-5D-3L to support completion by PID [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. EQ-5D-3L Proxy Version 1 was completed by ward staff, i.e., responding to the questionnaire by providing their own impression of the respondent\u0026rsquo;s health [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. For descriptive purposes, we refer to just the EQ-5D-3L when discussing both self- and proxy-reported measures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Recovering Quality-of-life 10-item (ReQoL-10) and Utility Index (ReQoL-UI)\u003c/h2\u003e \u003cp\u003eThe ReQoL measures were developed using qualitative and quantitative techniques capturing the recovering quality-of-life concerns of mental health service users [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The ReQoL-10 includes six positively (items: 2, 4, 5, 7, 8, 10) and four negatively (items: 1, 3, 6, 9) worded mental health items plus one physical health item across seven themes: autonomy; wellbeing; hope; activity; belonging and relationships; self-perception; physical health [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Each item is scored from 0 (worst state) to 4 (best state); summary score range: 0 (worst state) to 40 (best state)\u003c/p\u003e \u003cp\u003eThe ReQoL-UI can be assigned to six-items (items: 3, 5, 6, 7, 9, 10) and one physical health item of the ReQoL-10, while retaining the original seven themes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. UK utility score range: \u0026minus;0.195 (worst state) to 1 (best state) [11; 13].\u003c/p\u003e \u003cp\u003eNeither an ID-adapted nor proxy-reported ReQoL-10 exists. Thus, for SCHEMA the original ReQoL-10 was self- and proxy-reported. The proxy ReQoL-10 and EQ-5D-3L were completed, where possible, by the same ward staff member across all time points.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Modified Overt Aggression Scale (MOAS)\u003c/h2\u003e \u003cp\u003eThe MOAS is an observer-rated measure that assesses the frequency and severity of aggressive incidents over the past week [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The scale has four categories scored from 0 (no events) to 4 (most severe form of aggression), each with a severity weight (i.e., x1 to x4): verbal aggression (x1), aggression against property (x2), auto-aggression (against self; x3), and physical aggression (against others; x4). The summed scores across categories are multiplied by the category severity weight to produce the weighted / total MOAS score, with a higher score indicating more aggressive behavior [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The MOAS is completed by a research nurse/clinical support in the SCHEMA trial.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4. Brief Symptom Inventory (BSI)\u003c/h2\u003e \u003cp\u003eThe BSI is a 53-item self-report instrument to assess psychological distress [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Participants rate the distress associated with a specific problem over the past 7 days, from 0 (not at all) to 4 (extremely). For the SCHEMA trial, a visual analogue scale with different emotional / distressed face visuals aided participants indicate their response within nine primary symptom dimensions (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For the symptom dimension subscale, scores are summed across the relevant items then divided by the number of items in the respective dimension (e.g., somatization, 7-items) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Items 11, 25, 39, and 52 do not factor into any dimension, but are included when calculating Grand Total Scores (GTS) including the Global Severity Index (GSI), Positive Symptom Total (PST), and Positive Symptom Distress Index (PSDI) for which higher scores represent worse states (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Statistical analyses\u003c/h2\u003e \u003cp\u003eEach analysis uses all observed cases, with relevant sample sizes (\u003cem\u003eN\u003c/em\u003e) presented in the result tables. Inter-rater reliability and agreement, and construct validity are assessed based on the whole cohort\u0026rsquo;s baseline data; responsiveness is assessed across each time-point for the whole cohort. Statistical significance (SS), p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Analyses are conducted in Stata 19 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Inter-rater reliability and agreement\u003c/h2\u003e \u003cp\u003eInter-rater reliability measures consistency in rankings between raters (i.e., self- vs proxy), whereas inter-rater agreement measures if raters assign the exact same score. In the SCHEMA trial, each subject is rated dominantly by a different proxy alongside the patient\u0026rsquo;s rating of their own HRQoL, i.e., unique patient-proxy pairings. Therefore, for assessing reliability, one-way random-effects models estimate intraclass correlation coefficients (ICCs) for the continuous utility/summary scores, with weighted Kappa statistics for ordinal item scores [\u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. To assess agreement, proportion of \u0026lsquo;agreed\u0026rsquo; responses (i.e., raters provide the same score) and weighted Brennan and Prediger (BP) coefficients are used, alongside Bland-Altman plots. Linear weights are used to penalise disagreement proportionally, whereas quadratic weights penalise greater disagreement more severely [36; 38].\u003c/p\u003e \u003cp\u003eIn cases of symmetrical imbalance / low variation in responses, reliability can be low despite high agreement. Relatedly, the BP coefficient is equivalent to the prevalence-adjusted and bias-adjusted kappa (PABAK), correcting for symmetrical imbalances in the Kappa statistic. We report and reflect on both (e.g., ICC/Kappa and BP) as recommended [\u003cspan additionalcitationids=\"CR40 CR41 CR42 CR43\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Construct validity\u003c/h2\u003e \u003cp\u003eConstruct validity assesses the extent to which a measure reflects HRQoL differences hypothesised to exist. This is important for HRQoL measures, as their values should reflect HRQoL factors associated with the evaluated condition/treatment. Construct validity is assessed despite no \u0026lsquo;gold standard\u0026rsquo; HRQoL measure, given difficulties in any given indicator(s) capturing health\u0026rsquo;s full impact on people\u0026rsquo;s lives. Thus, we assess indicators to suggest, but cannot fully prove, construct validity, i.e., convergent and known-group validity.\u003c/p\u003e \u003cp\u003eConvergent validity assesses to what extent measure scores converge together. Spearman\u0026rsquo;s rank absolute correlation strength (ACS) and associated p-value non-parametrically indicate the degree to which instruments measure related factors [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Locally weighted scatterplot smoothing (LOWESS) techniques regress lines of central tendency between two variables, plotted on a scatterplot to non-parametrically visualise their relationship [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKnown-group validity assesses the extent to which instrument scores differ between groups that are expected to differ, measured using Cohen\u0026rsquo;s d standardised absolute effect sizes (AES), i.e., mean score difference between two adjacent severity subgroups divided by the standard deviation of scores for the milder group [45; 47]. The non-parametric Kruskal Wallis test complements the AES to suggest statistically significant difference between groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Responsiveness\u003c/h2\u003e \u003cp\u003eResponsiveness is important for health economics and outcomes research, as any change in health must be reflected by change in the chosen HRQoL measure. For example, if health (or other relevant construct) changes following an intervention, but the HRQoL score does not change, the HRQoL measure may not be responsive to actual changes in health.\u003c/p\u003e \u003cp\u003eTo measure responsiveness, we examined floor (worst possible score) and ceiling (best possible score) effects, which affect the measure\u0026rsquo;s ability to detect deterioration or improvements in health, respectively. We also examined the magnitude of change in scores over time, as a crude indicator of responsiveness, using standardised response means (SRMs), i.e., divide the mean change by the change standard deviation [45; 47].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Descriptive statistics\u003c/h2\u003e \u003cp\u003eOverall, 50 people consented to the SCHEMA trial. Three people withdrew before randomisation, leaving 47 people (24 intervention-arm: 23 control-arm) for our analyses: mean age, 36.9 years (range: 21 to 59), with 91.5% male. A Consort diagram, baseline descriptive statistics and measure score histograms are in Supplementary Appendix 1\u0026ndash;3.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents baseline number of responders and measure scores across the whole cohort, and across all time-points in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. At baseline, there was very little missing data. The EQ-5D-3L-ID was completed by 40 (85.1%) participants; however, this was due to trial sites initially using the non-ID-adapted EQ-5D-3L for 7 participants, thus represents a trial error.\u003c/p\u003e \u003cp\u003eGenerally, the ReQoL measures followed by the BSI suggests the SCHEMA study sample are more heterogenous than the EQ-5D-3L or MOAS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Focussing on the proxy-responses for descriptive purposes, the EQ-5D-3L suggests the 47 participants can be categorised into 14 unique health states which each have a unique score. Relatedly, the MOAS summary scores suggest the 47 participants can be categorised into 16 unique health states, with 10 (summary) or 11 (weighted) unique summary scores. In comparison, the ReQoL-10 and ReQoL-UI suggests the 47 participants can be categorised into 47 or 46 unique health states, with 20 or 46 unique summary scores, respectively.\u003c/p\u003e \u003cp\u003e\u0026lt;Table-2\u0026thinsp;\u0026gt;\u0026thinsp;\u0026lt;\u0026thinsp;Table-3\u0026gt;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Inter-rater reliability and agreement\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e ICC results suggest that on average the inter-rater reliability is poor between the self- and proxy-reported scores for the EQ-5D-3L and ReQoL-UI, whereas reliability for the ReQoL-10 was moderate albeit with broad 95% confidence intervals.\u003c/p\u003e \u003cp\u003eAt the item-level, the proportion of \u0026lsquo;agreed\u0026rsquo; responses were higher for the EQ-5D-3L\u0026rsquo;s physical health items (e.g., \u0026lsquo;Mobility\u0026rsquo;, 90% agreed; \u0026lsquo;Self-care\u0026rsquo;, 77.5% agreed; \u0026lsquo;Usual activities\u0026rsquo;, 67.5% agreed) than any of the mental health items across HRQoL measures. Additionally, there was little variation in such physical health items with the mean tending to \u0026lsquo;no problem\u0026rsquo;, leading to the \u0026lsquo;Kappa paradox\u0026rsquo; at the item-level, i.e., high agreement conversely matched with low reliability [39; 41]. Here, the Kappa paradox indicates patients and proxies are disproportionality focussing on a single score (i.e., no problem) within the EQ-5D-3L physical health items. Similarly for the BP and Kappa, the linear and quadratic weighted statistics are the same for these EQ-5D-3L physical health items, indicating constant proportional disagreement in item response, again pointing to a lack of variability. In comparison, the higher quadratic than linearly weighted Kappa and BP statistics for the ReQoL items suggest that while there is disagreement between raters, the disagreements are primarily small/adjacent (e.g., rating a 2 instead of a 3) rather than large (e.g., rating a 1 instead of a 4) with better variability than for the EQ-5D-3L.\u003c/p\u003e \u003cp\u003eOverall, across HRQoL measures, agreement tended to be higher than reliability. For the EQ-5D-3L, such agreement stemmed from a lack of response variation at the item-level potentially explaining the bigger separation in reliability and agreement statistics. This contributes to overall poorer agreement and reliability at the EQ-5D-3L utility score-level than the ReQoL measures utility/summary score. Complementary Bland-Altman plots are presented in Supplementary Appendix S4.\u003c/p\u003e \u003cp\u003e\u0026lt;Table-4\u0026gt;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Construct validity\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e ACS results indicate that the clinician-reported MOAS had higher correlation with the proxy- than self-reported EQ-5D-3L and ReQoL-10, but the opposite (albeit with smaller differences) for the ReQoL-UI. Across all HRQoL measures, the MOAS summary/weighted score and verbal items generally had the higher/moderate ACS that was SS, apart from with the self-reported EQ-5D-3L-ID which was weak and non-SS.\u003c/p\u003e \u003cp\u003eThe ACS with the self-reported BSI was higher with the self- than proxy-reported EQ-5D-3L and ReQoL measures, so opposite to the relationship with the MOAS for the EQ-5D-3L and ReQoL-10. The BSI particularly had strong and SS ACS with the self-reported ReQoL-UI and ReQoL-10, which is not surprising given these measures are both self-reported and focussed particularly on mental health. These ACS results are complemented and reinforced by the LOWESS graphs presented in Supplementary Appendix S5.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e AES results for the MOAS aggression cut-offs suggest a similar result to the ACS: effect sizes were notably larger for the proxy- than self-reported EQ-5D-3L, with the same for the ReQoL-10 albeit a smaller difference, although this result was reversed for the ReQoL-UI. AES were largest (0.951) for the proxy-reported ReQoL-10 and smallest (0.223) for the self-reported EQ-5D-3L-ID. Across BSI subscales, AES varied depending on HRQoL measure used and who completed the measure. The ReQoL-UI/-10 self-reported version tended to have the largest AES across all BSI symptomatic subscales.\u003c/p\u003e \u003cp\u003eOverall, ACS and AES results suggest a similar story, particularly emphasising a disconnect between self- and proxy-responses. Also, weak-SS AES between MOAS and proxy-reported HRQoL measures, but strong-SS ACS between BSI and self-reported HRQoL measures. The HRQoL measures often had large AES between MOAS aggression and BSI symptomatic cut-off groups, with the ReQoL-10 notably having larger AES than its utility-based counterpart with differing performance between the ReQoL-UI and EQ-5D-3L depending on who completed the measure and the specific aggression / symptomatic comparison.\u003c/p\u003e \u003cp\u003e\u0026lt;Table-5\u0026thinsp;\u0026gt;\u0026thinsp;\u0026lt;\u0026thinsp;Table-6\u0026gt;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Responsiveness\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e suggests differences in score change direction between measures. For example, the self-reported EQ-5D-3L and ReQoL-UI suggested mean HRQoL deterioration since baseline, whereas the proxy-reported version of the same measures suggested mean improvement. The MOAS and BSI (not PSDI at 19-weeks) also suggested aggression and psychological distress improvement since baseline, as did the self- and proxy-reported ReQoL-10. Interestingly, the self-reported ReQoL-UI suggested mean deterioration despite being elicited from the ReQoL-10 which suggested mean improvement since baseline. Additionally, responsiveness differed dependent on time-points being compared (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, i.e., 19-weeks versus 38-weeks, compared to baseline or previous data collection timepoint). Overall, responsiveness tended to be larger for the ReQoL-10 proxy-report, albeit trivial to small across all measures. Relatedly, ceiling effects at baseline (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) occurred in a lower proportion of responders for the ReQoL-UI/-10 than EQ-5D-3L.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eDespite the small sample size common for PID research, our results revealed a range of policy and practice relevant insights. First, the self-reported EQ-5D-3L-ID and proxy-reported EQ-5D-3L Proxy Version 1 has poor to no agreement / reliability / correlation; though, this result can in-part be attributed to a lack of variability in responses particularly for the physical health items, followed by a higher degree in utility score differences when there is a difference between self- and proxy-responses (i.e., a 1 unit difference in item score disagreement proportionally translates to larger/smaller utility score changes due to the utility value set). Thus, this indicates known issues with lack of EQ-5D-3L response options, particularly in condition areas and populations for which it has poor construct validity and responsiveness [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The EQ-5D-3L was chosen for the SCHEMA trial as it has been adapted for PID, with no similar adaption for the EQ-5D-5L. Though the EQ-5D-3L-ID may have improved face validity of self-responses, the three-level version itself causes other issues to do with response heterogeneity impacting agreement and reliability statistics, alongside potentially its construct validity and responsiveness.\u003c/p\u003e \u003cp\u003eRelatedly, the observer-reported MOAS had a stronger relationship with proxy-reported EQ-5D-3L than the self-reported EQ-5D-3L-ID, which makes sense due to potentially better alignment between observer and proxy responses; however, these MOAS correlations were more similar across the self- and proxy-reported ReQoL-10/-UI. When reflecting on the self-reported BSI though, correlations were stronger with self-reported HRQoL measures than proxy-reported; again, indicating the responder has a key influence on the psychometric results. The known-group validity results echo the convergent validity results when assessing between those with aggression / psychiatric symptom distress compared to those without. Overall, it seems apparent that the person completing the measure (e.g., self- vs proxy-response) is a key driver of the observed between-measure relationship, which has implications when relying on different perspectives to represent different but important constructs to evaluate the (cost-)effectiveness of care interventions [\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRegardless, the ReQoL-10/-UI had better construct validity with both the MOAS and BSI. For the MOAS, the relationship was fairly similar between the self- and proxy-reported ReQoL-10/-UI, while being stronger than for the EQ-5D-3L. A notably stronger relationship was estimated between self-reported BSI and ReQoL-10/-UI, which was stronger than with the proxy-reported ReQoL-10/-UI; again, emphasising who was completing the measure in-part driving the relationship strength.\u003c/p\u003e \u003cp\u003eOverall, if aggression and/or psychological distress is of interest when capturing HRQoL in secure care patients (as key outcomes for the SCHEMA trial), then the ReQoL-10/-UI is recommended over the EQ-5D-3L based on our psychometric results. These ReQoL results seem reasonably robust despite, but still notably influenced by, who is completing the measure (i.e. self- vs proxy-response). Using the EQ-5D-3L-ID may have improved face validity of self-responses but brought with it known issues with having only three response levels that may have been in-part avoided by using the EQ-5D-5L. Although we hypothesise the lack of variability in physical health items would have perpetuated if the EQ-5D-5L has been used, when disagreement in item scores was small (as was more common) this would have had a smaller proportional impact on utility scores, potentially marginally improving the reliability and agreement statistics particularly at the utility score level.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.1. National implications for research, care, and policy across England and Wales\u003c/h2\u003e \u003cp\u003eOur results highlight a range of considerations when using and interpreting scores from the EQ-5D-3L and ReQoL-UI (-10), and their subsequent effect on HRQoL/clinical assessment and economic evaluation evidence. The EQ-5D measures are recommended for economic evaluation by HTA agencies such as NICE for England and Wales [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, NICE\u0026rsquo;s method guide (Section 4.3.10) states other measures can be used when supported by psychometric evidence [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Our study suggests that in secure care patients, specifically PID, that the ReQoL-10/-UI has notably better psychometric properties than the EQ-5D-3L across all areas we assessed, noting for construct validity this relies on aggression and psychological distress being important outcomes of interest.\u003c/p\u003e \u003cp\u003eThe ReQoL measures have been recommended for mental health settings by NHS England [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. NICE has abstained from recommending preference-based measures other than the EQ-5D measures, despite the EQ-5D having known insensitivities and a growing body of literature indicating the relative benefits of the ReQoL-10/-UI in mental health populations, albeit the evidence is mixed in common mental disorders [15; 52\u0026ndash;55]. There are also empirically evidenced potential implications when using the ReQoL-UI for economic evaluation if physical and mental health do not change over the trial time horizon [15; 16].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.2. International generalisability, policy, and ethical considerations\u003c/h2\u003e \u003cp\u003eForensic mental health services (i.e., mental health in the legal system) which includes secure care is a policy relevant consideration internationally [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Across 18 European countries, Sampson et al. [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] highlighted the trade-off between political pressures to contain dangerous mentally disordered offenders (MDOs) for ensuring public safety and ethical debates regarding long-term forensic mental health care [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Compared to the general population, a larger proportion of people who have contact with the legal system have borderline to mild ID, e.g., UK\u0026rsquo;s criminal justice system: borderline to mild ID, \u0026asymp;\u0026thinsp;40%; mild ID, \u0026asymp;\u0026thinsp;20% mild [58; 59]. Given the interconnection between ID and poor mental health as wider determinants of violent behaviour leading to contacts with the justice system, there is a suggested moral and ethical requirement to deliver appropriate care to such populations in forensic mental health services [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although HRQoL and outcomes research conducted within secure care PID might seem a small part of a bigger problem, it highlights why such research is required to ensure that new care interventions are evaluated appropriately to inform resource allocation problems across countries. Despite our study\u0026rsquo;s small sample size, common for PID research, the results suggest the ReQoL measures offer a more appropriate measure than the EQ-5D-3L for assessing HRQoL and associated economic evaluation. Thus, while following NICE\u0026rsquo;s and other HTA agencies\u0026rsquo; EQ-5D reference case may seem the path of less resistance, it can potentially lead to inappropriate evidence informing resource allocation in such settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Limitations\u003c/h2\u003e \u003cp\u003eMode of measure administration for self-completion depended on the participant, i.e., some participants completed measures themselves whereas others had the questions read to them. Due to the small sample, we couldn\u0026rsquo;t explore how this impacted the psychometric results.\u003c/p\u003e \u003cp\u003eAdditionally, our study did not include a qualitive investigation to understand if the EQ-5D-3L-ID did improve response face validity compared to the ReQoL with no ID-adaptation. For our study, there is no specific indication of concern for the ReQoL self- and proxy-responses, noting that we saw stronger relationships when the responder was the same across measures.\u003c/p\u003e \u003cp\u003eA notable limitation is SCHEMA\u0026rsquo;s small sample size. For statistical analyses such as our psychometric assessments, any comparisons that utilise variations in the data are going to be hampered by small sample sizes. Despite this, there is still a case to conduct and publish such results, recognising that the small sample likely increases result uncertainty but can still be informative to decision-makers if the results are considered not misleading. SCHEMA was challenging research from the beginning given PID are a marginalised group, secure care research is limited, with study recruitment and conduct being difficult in both. Thus, despite the small sample, the results are potentially important and informative for ongoing and future research in this internationally policy relevant but often overlooked group and setting.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eOur study provides evidence to recommend the ReQoL measures over the EQ-5D-3L for HRQoL assessment and economic evaluations in secure care PID, particularly when aggression and psychological distress are important outcomes.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval.\u003c/h2\u003e \u003cp\u003eThe study protocol and related trial documents were approved by the London-City \u0026amp; East research ethics committee (REC ID: 23/LO/0026; IRAS project ID: 319325)\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding.\u003c/h2\u003e \u003cp\u003e The SCHEMA trial and analysis was funded by the Health Education England (HEE) / National Institute for Health and Care Research (NIHR) Integrated Clinical and Practitioner Academic (ICA) programme (NIHR award identifier: NIHR301264). The views expressed are those of the author(s) and not necessarily those of the NIHR. The funding agreement ensured the authors\u0026rsquo; independence in developing the purview of the manuscript, writing, and publishing the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConcept and design: MF, SH. Manuscript drafting: MF, SH. Analyses and result presentation: MF. Critical revisions for important intellectual content: MF, SH, JC, KAD, PFC, TLH, AI, IM, RM, ER, MR, SR, AZ. Obtaining funding: SH, MF. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e We thank the participants of the SCHEMA trial for being involved in the research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eIf there is interest in using the SCHEMA trial data for secondary uses, please contact the SCHEMA trial Chief Investigator, Simon Hackett.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCooper, S. A., Smiley, E., Morrison, J., Williamson, A., \u0026amp; Allan, L. (2007). Mental ill-health in adults with intellectual disabilities: prevalence and associated factors. \u003cem\u003eThe British journal of psychiatry\u003c/em\u003e, \u003cem\u003e190\u003c/em\u003e(1), 27\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIozzino, L., Ferrari, C., Large, M., Nielssen, O., \u0026amp; De Girolamo, G. (2015). Prevalence and risk factors of violence by psychiatric acute inpatients: a systematic review and meta-analysis. \u003cem\u003ePloS one\u003c/em\u003e, 10(6), e0128536.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarangozov, R., Manzoni, C., \u0026amp; Pike, G. (2017). \u003cem\u003eRoyal college of nursing employment survey 2017\u003c/em\u003e. Institute for Employment Studies.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorrissey, C., Langdon, P. E., Geach, N., Chester, V., Ferriter, M., Lindsay, W. R., McCarthy, J., Devapriam, J., Walker, D. M., \u0026amp; Duggan, C. (2017). A systematic review and synthesis of outcome domains for use within forensic services for people with intellectual disabilities. \u003cem\u003eBJPsych Open\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1), 41\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTully, J., Hafferty, J., Whiting, D., Dean, K., \u0026amp; Fazel, S. (2024). Forensic mental health: envisioning a more empirical future. \u003cem\u003eThe Lancet Psychiatry\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(11), 934\u0026ndash;942.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFoscarini-Craggs, P., Iranpour, A., Aafjes-van, D., Franklin, M., Harrison, T., McKinnon, I., McNamara, R., Randell, E., Rose, S., \u0026amp; Zubala, A. (2026). Working with underrepresented groups: Lessons from the SCHEMA Trial. Trials.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Dwyer, J. L., Bryant, L. D., Hulme, C., Kind, P., \u0026amp; Meads, D. M. (2024). Adapting the EQ-5D-3L for adults with mild to moderate learning disabilities. \u003cem\u003eHealth and Quality of Life Outcomes\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(1), 37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBishop, R., Laugharne, R., Shaw, N., Russell, A. M., Goodley, D., Banerjee, S., Clack, E., SpeakUp, C. H. A. M. P. S., \u0026amp; Shankar, R. (2024). The inclusion of adults with intellectual disabilities in health research\u0026ndash;challenges, barriers and opportunities: a mixed-method study among stakeholders in England. \u003cem\u003eJournal of Intellectual Disability Research\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e(2), 140\u0026ndash;149.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRowen, D., Azzabi Zouraq, I., Chevrou-Severac, H., \u0026amp; van Hout, B. (2017). International regulations and recommendations for utility data for health technology assessment. \u003cem\u003ePharmacoeconomics\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(Suppl 1), 11\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNICE (2022). NICE technology appraisal and highly specialised technologies guidance: the manual. Retrieved 24 Dec 2025, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nice.org.uk/process/pmg36\u003c/span\u003e\u003cspan address=\"https://www.nice.org.uk/process/pmg36\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarimi, M., \u0026amp; Brazier, J. (2016). Health, health-related quality of life, and quality of life: what is the difference? \u003cem\u003ePharmacoeconomics\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(7), 645\u0026ndash;649.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeetharuth, A. D., Brazier, J., Connell, J., Bjorner, J. B., Carlton, J., Buck, E. T., Ricketts, T., McKendrick, K., Browne, J., \u0026amp; Croudace, T. (2018). Recovering Quality of Life (ReQoL): a new generic self-reported outcome measure for use with people experiencing mental health difficulties. \u003cem\u003eThe British Journal of Psychiatry\u003c/em\u003e, \u003cem\u003e212\u003c/em\u003e(1), 42\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeetharuth, A. D., Rowen, D., Bjorner, J. B., \u0026amp; Brazier, J. (2021). Estimating a preference-based index for mental health from the recovering quality of life measure: valuation of recovering quality of life utility index. \u003cem\u003eValue in Health\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(2), 281\u0026ndash;290.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, R. H., Keetharuth, A. D., Wang, L., Cheung, A. W., \u0026amp; Wong, E. L. (2022). Measuring health-related quality of life and well-being: a head-to-head psychometric comparison of the EQ-5D-5L, ReQoL-UI and ICECAP-A. \u003cem\u003eThe European Journal of Health Economics\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(2), 165\u0026ndash;176.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFranklin, M., Enrique, A., Palacios, J., \u0026amp; Richards, D. (2021). Psychometric assessment of EQ-5D-5L and ReQoL measures in patients with anxiety and depression: construct validity and responsiveness. \u003cem\u003eQuality of Life Research\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(9), 2633\u0026ndash;2647.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFranklin, M., Hunter, R. M., Enrique, A., Palacios, J., \u0026amp; Richards, D. (2022). Estimating cost-effectiveness using alternative preference-based scores and within-trial methods: exploring the dynamics of the quality-adjusted life-year using the EQ-5D 5-level version and recovering quality of life utility index. \u003cem\u003eValue in Health\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(6), 1018\u0026ndash;1029.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbbing, A., Haeyen, S., Nyapati, S., Verboon, P., \u0026amp; Hooren, S. (2023). Effectiveness and mechanisms of the arts therapies in forensic care. A systematic review, narrative synthesis, and meta analysis. \u003cem\u003eFrontiers in Psychiatry\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e, 1128252.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcIntosh, L. G., Janes, S., O'Rourke, S., \u0026amp; Thomson, L. D. (2021). Effectiveness of psychological and psychosocial interventions for forensic mental health inpatients: A meta-analysis. \u003cem\u003eAggression and Violent Behavior\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e, 101551.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrooker, K., van Dooren, K., Tseng, C. H., McPherson, L., Lennox, N., \u0026amp; Ware, R. (2015). Out of sight, out of mind? The inclusion and identification of people with intellectual disability in public health research. \u003cem\u003ePerspectives in public health\u003c/em\u003e, \u003cem\u003e135\u003c/em\u003e(4), 204\u0026ndash;211.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeldman, M. A., Bosett, J., Collet, C., \u0026amp; Burnham-Riosa, P. (2014). Where are persons with intellectual disabilities in medical research? A survey of published clinical trials. \u003cem\u003eJournal of Intellectual Disability Research\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e(9), 800\u0026ndash;809.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHackett, S. S., Zubala, A., Aafjes-van Doorn, K., Chadwick, T., Harrison, T. L., Bourne, J., Freeston, M., Jahoda, A., Taylor, J. L., \u0026amp; Ariti, C. (2020). A randomised controlled feasibility study of interpersonal art psychotherapy for the treatment of aggression in people with intellectual disabilities in secure care. \u003cem\u003ePilot and Feasibility Studies\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 180.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConnell, J., Carlton, J., Grundy, A., Taylor Buck, E., Keetharuth, A. D., Ricketts, T., Barkham, M., Robotham, D., Rose, D., \u0026amp; Brazier, J. (2018). The importance of content and face validity in instrument development: lessons learnt from service users when developing the Recovering Quality of Life measure (ReQoL). \u003cem\u003eQuality of life research\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(7), 1893\u0026ndash;1902.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLapin, B. R., Thompson, N. R., Schuster, A., Honomichl, R., \u0026amp; Katzan, I. L. (2021). The validity of proxy responses on patient-reported outcome measures: Are proxies a reliable alternative to stroke patients\u0026rsquo; self-report? \u003cem\u003eQuality of Life Research\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(6), 1735\u0026ndash;1745.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClaes, C., Vandevelde, S., Van Hove, G., Van Loon, J., Verschelden, G., \u0026amp; Schalock, R. (2012). Relationship between self-report and proxy ratings on assessed personal quality of life‐related outcomes. \u003cem\u003eJournal of Policy and Practice in intellectual disabilities\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(3), 159\u0026ndash;165.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, M., Harris, I., \u0026amp; Lu, Z. K. (2015). Differences in proxy-reported and patient-reported outcomes: assessing health and functional status among medicare beneficiaries. \u003cem\u003eBMC medical research methodology\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGriffiths, A. W., Smith, S. J., Martin, A., Meads, D., Kelley, R., \u0026amp; Surr, C. A. (2020). Exploring self-report and proxy-report quality-of-life measures for people living with dementia in care homes. \u003cem\u003eQuality of Life Research\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(2), 463\u0026ndash;472.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHackett, S. S., Foscarini-Craggs, P., Aafjes-van Doorn, K., Franklin, M., Riaz, M., Zubala, A., Condie, J., McKinnon, I., Iranpour, A., \u0026amp; Harrison, T. L. (2025). \u003cem\u003eSecure care (forensic) hospital evaluation of manualised interpersonal art-psychotherapy (SCHEMA): A randomised controlled trial protocol\u003c/em\u003e (Vol. 5). NIHR Open Research.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDevlin, N. J., \u0026amp; Brooks, R. (2017). EQ-5D and the EuroQol group: past, present and future. \u003cem\u003eApplied health economics and health policy\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(2), 127\u0026ndash;137.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDolan, P. (1997). Modeling valuations for EuroQol health states. \u003cem\u003eMedical care\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(11), 1095\u0026ndash;1108.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEuroQol Research Foundation (2025). Proxy version 1 (Frequently Asked Questions). Retrieved 28 Feb 2025, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://euroqol.org/faq/proxy-version-1/\u003c/span\u003e\u003cspan address=\"https://euroqol.org/faq/proxy-version-1/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliver, P., Crawford, M., Rao, B., Reece, B., \u0026amp; Tyrer, P. (2007). Modified Overt Aggression Scale (MOAS) for people with intellectual disability and aggressive challenging behaviour: a reliability study. \u003cem\u003eJournal of Applied Research in Intellectual Disabilities\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(4), 368\u0026ndash;372.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDerogatis, L. R. (1993). \u003cem\u003eBrief Symptom Inventory: Administration, scoring, and procedures manual\u003c/em\u003e. National Computer Systems (NCS).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStataCorp. (2025). \u003cem\u003eStata 19. College Station\u003c/em\u003e. StataCorp LLC.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcHugh, M. L. (2012). Interrater reliability: the kappa statistic. \u003cem\u003eBiochemia medica\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(3), 276\u0026ndash;282.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoo, T. K., \u0026amp; Li, M. Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. \u003cem\u003eJournal of chiropractic medicine\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(2), 155\u0026ndash;163.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSim, J., \u0026amp; Wright, C. C. (2005). The kappa statistic in reliability studies: use, interpretation, and sample size requirements. \u003cem\u003ePhysical therapy\u003c/em\u003e, \u003cem\u003e85\u003c/em\u003e(3), 257\u0026ndash;268.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLandis, J. R., \u0026amp; Koch, G. G. (1977). The measurement of observer agreement for categorical data. biometrics, 159\u0026ndash;174.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVanbelle, S., \u0026amp; Albert, A. (2009). A note on the linearly weighted kappa coefficient for ordinal scales. \u003cem\u003eStatistical Methodology\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(2), 157\u0026ndash;163.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDerksen, B. M., Bruinsma, W., Goslings, J. C., \u0026amp; Schep, N. W. (2024). The kappa paradox explained. \u003cem\u003eThe Journal of hand surgery\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e(5), 482\u0026ndash;485.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrennan, R. L., \u0026amp; Prediger, D. J. (1981). Coefficient kappa: Some uses, misuses, and alternatives. \u003cem\u003eEducational and psychological measurement\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(3), 687\u0026ndash;699.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlight, L., \u0026amp; Julious, S. A. (2015). The disagreeable behaviour of the kappa statistic. \u003cem\u003ePharmaceutical statistics\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 74\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVach, W. (2005). The dependence of Cohen's kappa on the prevalence does not matter. \u003cem\u003eJournal of clinical epidemiology\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e(7), 655\u0026ndash;661.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eByrt, T., Bishop, J., \u0026amp; Carlin, J. B. (1993). Bias, prevalence and kappa. \u003cem\u003eJournal of clinical epidemiology\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(5), 423\u0026ndash;429.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlein, D. (2018). Implementing a general framework for assessing interrater agreement in Stata. \u003cem\u003eThe Stata Journal\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(4), 871\u0026ndash;901.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen, J. (1992). Quantitative methods in psychology: A power primer. \u003cem\u003ePsychological Bulletin\u003c/em\u003e, \u003cem\u003e112\u003c/em\u003e, 1155\u0026ndash;1159.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. \u003cem\u003eJournal of the American statistical association\u003c/em\u003e, \u003cem\u003e74\u003c/em\u003e(368), 829\u0026ndash;836.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiddel, B., \u0026amp; van Sonderen, E. (2001). How to interpret the magnitude of change in health-related quality of life? A study on the use of Cohen\u0026rsquo;s thresholds for effect size estimates. \u003cem\u003eAssessment of change in clinical evaluation\u003c/em\u003e. University of Groningen.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson, A. J., \u0026amp; Turner, A. J. (2020). A comparison of the EQ-5D-3L and EQ-5D-5L. \u003cem\u003ePharmacoeconomics\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(6), 575\u0026ndash;591.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanssen, M. F., Bonsel, G. J., \u0026amp; Luo, N. (2018). Is EQ-5D-5L better than EQ-5D-3L? A head-to-head comparison of descriptive systems and value sets from seven countries. \u003cem\u003ePharmacoeconomics\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(6), 675\u0026ndash;697.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanssen, M., Pickard, A. S., Golicki, D., Gudex, C., Niewada, M., Scalone, L., Swinburn, P., \u0026amp; Busschbach, J. (2013). Measurement properties of the EQ-5D-5L compared to the EQ-5D-3L across eight patient groups: a multi-country study. \u003cem\u003eQuality of life research\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(7), 1717\u0026ndash;1727.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNHS England (2024). Implementation guidance 2024 \u0026ndash; psychological therapies for severe mental health problems. Retrieved 17 Feb 2026.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePayakachat, N., Ali, M. M., \u0026amp; Tilford, J. M. (2015). Can the EQ-5D detect meaningful change? A systematic review. \u003cem\u003ePharmacoeconomics\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(11), 1137\u0026ndash;1154.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng, Y. S., Kohlmann, T., Janssen, M. F., \u0026amp; Buchholz, I. (2021). Psychometric properties of the EQ-5D-5L: a systematic review of the literature. \u003cem\u003eQuality of Life Research\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(3), 647\u0026ndash;673.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMulhern, B., Mukuria, C., Barkham, M., Knapp, M., Byford, S., \u0026amp; Brazier, J. (2014). Using generic preference-based measures in mental health: psychometric validity of the EQ-5D and SF-6D. \u003cem\u003eThe British Journal of Psychiatry\u003c/em\u003e, \u003cem\u003e205\u003c/em\u003e(3), 236\u0026ndash;243.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapaioannou, D., Brazier, J., \u0026amp; Parry, G. (2011). How valid and responsive are generic health status measures, such as EQ-5D and SF-36, in schizophrenia? A systematic review. \u003cem\u003eValue in Health\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(6), 907\u0026ndash;920.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSampson, S., Edworthy, R., V\u0026ouml;llm, B., \u0026amp; Bulten, E. (2016). Long-term forensic mental health services: an exploratory comparison of 18 European countries. \u003cem\u003eInternational Journal of Forensic Mental Health\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(4), 333\u0026ndash;351.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV\u0026ouml;llm, B., Bartlett, P., \u0026amp; McDonald, R. (2016). Ethical issues of long-term forensic psychiatric care. \u003cem\u003eEthics Medicine and Public Health\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1), 36\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNieuwenhuis, J. G., Lepping, P., Mulder, N. L., Nijman, H. L., Veereschild, M., \u0026amp; Noorthoorn, E. O. (2021). Increased prevalence of intellectual disabilities in higher-intensity mental healthcare settings. \u003cem\u003eBJPsych Open\u003c/em\u003e, 7(3), e83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeelen-de Lang, B. L., Smits, H. J., Penterman, B. J., Noorthoorn, E. O., Nieuwenhuis, J. G., \u0026amp; Nijman, H. L. (2019). Screening for intellectual disabilities and borderline intelligence in Dutch outpatients with severe mental illness. \u003cem\u003eJournal of Applied Research in Intellectual Disabilities\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(5), 1096\u0026ndash;1102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEuroQol Research Foundation (2018). EQ-5D-3L User Guide. Retrieved 28 February 2025, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://euroqol.org/publications/user-guides\u003c/span\u003e\u003cspan address=\"https://euroqol.org/publications/user-guides\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKay, S. R., Wolkenfeld, F., \u0026amp; Murrill, L. M. (1988). Profiles of aggression among psychiatric patients: I. Nature and prevalence. \u003cem\u003eThe Journal of nervous and mental disease\u003c/em\u003e, \u003cem\u003e176\u003c/em\u003e(9), 539\u0026ndash;546.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTyrer, P., Oliver-Africano, P. C., Ahmed, Z., Bouras, N., Cooray, S., Deb, S., Murphy, D., Hare, M., Meade, M., \u0026amp; Reece, B. (2008). Risperidone, haloperidol, and placebo in the treatment of aggressive challenging behaviour in patients with intellectual disability: a randomised controlled trial. \u003cem\u003eThe Lancet\u003c/em\u003e, \u003cem\u003e371\u003c/em\u003e(9606), 57\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKellett, S., Beail, N., Newman, D. W., \u0026amp; Hawes, A. (2004). The factor structure of the Brief Symptom Inventory: Intellectual disability evidence. \u003cem\u003eClinical Psychology \u0026amp; Psychotherapy: An International Journal of Theory \u0026amp; Practice\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(4), 275\u0026ndash;281.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKellett, S., Beail, N., Newman, D. W., \u0026amp; Frankish, P. (2003). Utility of the Brief Symptom Inventory in the assessment of psychological distress. \u003cem\u003eJournal of Applied Research in Intellectual Disabilities\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(2), 127\u0026ndash;134.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDescription of outcomes measures and associated scores\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLong name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShort name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponder\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstruct\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScoring type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. items / domains\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFloor/ worst\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCeiling/ best\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCut-offs and description\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKey Refs.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eEQ-5D three-level version: adapted for adults with mild to moderate learning disabilities and Proxy version 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEQ-5D-3L:\u003c/p\u003e\n \u003cp\u003eAdapted version (EQ-5D-3L-LD) and Proxy version 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAdapted version: self-completed\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eProxy-version: ward staff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ePreference-based generic health as of today\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eUK preference-based / utility value set\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5 / 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3-point scale:\u003c/p\u003e\n \u003cp\u003e1 (no problem) to\u003c/p\u003e\n \u003cp\u003e3 (extreme/ unable)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e[7; 29; 30; 60]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eRecovering Quality of Life\u0026mdash;Utility Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eReQoL-UI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eDerived from ReQoL-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ePreference-based recovery-focussed quality of life in mental health service users over the last week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eUK preference-based / utility value set\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e7 / 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5-point scale:\u003c/p\u003e\n \u003cp\u003e0 (worst state) to\u003c/p\u003e\n \u003cp\u003e4 (best state)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus; 0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e[13]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eRecovering Quality of Life\u0026mdash;10 item\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eReQoL-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eReQoL-10: self-completed or proxy-completed by ward staff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eRecovery-focussed quality of life in mental health service users over the last week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSummary: aggregated score across item scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e10 / 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5-point scale:\u003c/p\u003e\n \u003cp\u003e0 (worst state) to\u003c/p\u003e\n \u003cp\u003e4 (best state)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026thinsp;\u0026lt;\u0026thinsp;24, clinical range;\u003c/p\u003e\n \u003cp\u003e\u0026thinsp;\u0026ge;\u0026thinsp;24, general population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e[12]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eModified Overt Aggression Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eMOAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eProxy-completed: research nurse/ clinical support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eFrequency and severity of aggressive incidents over the past week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSummary: aggregated item scores across item\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eWeighted: item scores multiplied by domain weight then aggregated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4 / 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eMutually inclusive 5-point scale:\u003c/p\u003e\n \u003cp\u003e0 (no events) to\u003c/p\u003e\n \u003cp\u003e4 (most severe)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSeverity weights:\u003c/p\u003e\n \u003cp\u003everbal (x1),\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eproperty (x2),\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eauto (x3),\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ephysical (x4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eItem: 10\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSummary:\u003c/p\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eWeighted: 100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0, no aggression; \u0026ge;\u0026thinsp;1, aggression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e[31; 61; 62]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eBrief Symptom Inventory: Global Severity Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBSI: GSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSelf-completed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ePsychological distress and symptom severity over last 7 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAverage score across all completed items\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e53 / 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5-point scale:\u003c/p\u003e\n \u003cp\u003e0 (not at all) to 4 (extremely)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eT-score \u0026ge;\u0026thinsp;63, clinical\u003c/p\u003e\n \u003cp\u003e(not used \u0026ndash; see footnote)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e[32; 63; 64]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cul\u003e\n \u003cli\u003ePositive Symptom Total\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003ePST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBSI sub-score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eNumber of experienced symptoms over last 7 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSummed number of items with non-zero response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e53 / 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5-point scale:\u003c/p\u003e\n \u003cp\u003e0 (not at all) to 4 (extremely)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eT-score \u0026ge;\u0026thinsp;63, clinical\u003c/p\u003e\n \u003cp\u003e(not used \u0026ndash; see footnote)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e[32; 63; 64]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cul\u003e\n \u003cli\u003ePositive Symptom Distress Index\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003ePSDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBSI sub-score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAverage level of distress with experienced symptoms over last 7 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSummed score of items with non-zero response divided by PST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e53 / 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5-point scale:\u003c/p\u003e\n \u003cp\u003e0 (not at all) to 4 (extremely)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eT-score \u0026ge;\u0026thinsp;63, clinical\u003c/p\u003e\n \u003cp\u003e(not used \u0026ndash; see footnote)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e[32; 63; 64]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eSomatization\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBSI symptom dimension subscale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eSomatic symptoms due to psychological distress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAveraged score across dimension items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e7 / 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5-point scale:\u003c/p\u003e\n \u003cp\u003e0 (not at all) to 4 (extremely)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0, not symptomatic\u003c/p\u003e\n \u003cp\u003e1\u0026ge;, symptomatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e[32]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eObsessive-compulsive\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBSI symptom dimension subscale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eObsessive thoughts and compulsive behaviours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAveraged score across dimension items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6 / 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5-point scale:\u003c/p\u003e\n \u003cp\u003e0 (not at all) to 4 (extremely)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0, not symptomatic\u003c/p\u003e\n \u003cp\u003e1\u0026ge;, symptomatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e[32]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eInterpersonal sensitivity\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBSI symptom dimension subscale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAccurately assess others\u0026apos; abilities, states, and traits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAveraged score across dimension items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4 / 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5-point scale:\u003c/p\u003e\n \u003cp\u003e0 (not at all) to 4 (extremely)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0, not symptomatic\u003c/p\u003e\n \u003cp\u003e1\u0026ge;, symptomatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e[32]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eDepression\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBSI symptom dimension subscale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003edepressed mood or/and loss of pleasure / interest in activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAveraged score across dimension items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6 / 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5-point scale:\u003c/p\u003e\n \u003cp\u003e0 (not at all) to 4 (extremely)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0, not symptomatic\u003c/p\u003e\n \u003cp\u003e1\u0026ge;, symptomatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e[32]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eAnxiety\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBSI symptom dimension subscale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eStress / worry affecting daily life with lack of control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAveraged score across dimension items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6 / 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5-point scale:\u003c/p\u003e\n \u003cp\u003e0 (not at all) to 4 (extremely)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0, not symptomatic\u003c/p\u003e\n \u003cp\u003e1\u0026ge;, symptomatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e[32]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eHostility\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBSI symptom dimension subscale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eEmotionally charged aggressive behaviour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAveraged score across dimension items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5 / 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5-point scale:\u003c/p\u003e\n \u003cp\u003e0 (not at all) to 4 (extremely)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0, not symptomatic\u003c/p\u003e\n \u003cp\u003e1\u0026ge;, symptomatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e[32]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cul\u003e\n \u003cli\u003ePhobic anxiety\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBSI symptom dimension subscale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eExcessive / persistent fear of a specific object, situation, or activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAveraged score across dimension items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5 / 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5-point scale:\u003c/p\u003e\n \u003cp\u003e0 (not at all) to 4 (extremely)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0, not symptomatic\u003c/p\u003e\n \u003cp\u003e1\u0026ge;, symptomatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e[32]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eParanoid ideation\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBSI symptom dimension subscale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ePersistent thoughts of suspicion and distrust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAveraged score across dimension items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5 / 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5-point scale:\u003c/p\u003e\n \u003cp\u003e0 (not at all) to 4 (extremely)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0, not symptomatic\u003c/p\u003e\n \u003cp\u003e1\u0026ge;, symptomatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e[32]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cul\u003e\n \u003cli\u003ePsychoticism\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBSI symptom dimension subscale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ePersonality trait involving aggression, impulsivity, and antisocialism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAveraged score across dimension items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5 / 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5-point scale:\u003c/p\u003e\n \u003cp\u003e0 (not at all) to 4 (extremely)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0, not symptomatic\u003c/p\u003e\n \u003cp\u003e1\u0026ge;, symptomatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e[32]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eBSI items for symptom dimension.\u0026nbsp;\u003c/strong\u003esomatization (7-items: 2, 7, 23, 29, 30, 33, and 37), obsessive-compulsive (6-items: 5, 15, 26, 27, 32, and 36), interpersonal sensitivity (5-items 20, 21, 22, and 42), depression (6-items 9, 16, 17, 18, 35, and 50), anxiety (6-items 1, 12, 19, 38, 45, and 49), hostility (5-items: 6, 13, 40, 41, and 46), phobic anxiety (5-items: 8, 28, 31, 43, and 47), paranoid ideation (5-items: 4, 10, 24, 48, and 51), and psychoticism (5-items: 3, 14, 34, 44, and 53)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBSI T-score for Grand Total Score (GTS) cut-offs:\u003c/strong\u003e we opted to not use the GTS T-score cut-offs due to access restrictions (e.g., additional cost) for the population norm reference material that permit us to operationalise the T-scores. Subsequently, we focussed on the symptom dimension subscales when assessing known-group validity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e \u003cstrong\u003eOutcome measure scores, floor and ceiling effects at baseline across trial-arms\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScore\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP. worst score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP. best score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eO worst score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eO best score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN worst\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003escore (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN best\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003escore (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUHSP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEQ-5D-3L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSelf (Adapted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e40 (85.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e15 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eProxy (Version 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e17 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReQoL-UI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSelf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026minus; 0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e6 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eProxy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026minus; 0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReQoL-10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSelf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e27.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e29.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e8.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e40.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e3 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eProxy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e26.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e26.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e15.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e38.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMOAS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSummary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e16.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e22 (46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eWeighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e37.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e22 (46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eVerbal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e10.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e25 (53.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eProperty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e41 (87.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eAuto\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e43 (91.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ePhysical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e42 (89.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBSI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eGSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e4 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ePST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e20.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e19.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e14.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e53.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e4 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ePSDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e4 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSomatization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e14 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eObsessive-compulsive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e11 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eInterpersonal sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e15 (31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e46 (97.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e12 (26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e14 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eHostility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e12 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ePhobic anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e14 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eParanoid ideation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e4.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e11 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ePsychoticism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e47 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e16 (34.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAcronyms.\u0026nbsp;\u003c/strong\u003eBSI, Brief Symptom Inventory;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eEQ-5D-3L, EQ-5D Three-Level version; GSI, Global Severity Index; N, number of responder; O. observed; P. possible; PDSI, Positive Symptom Distress Index; PST, Positive Symptom Total; ReQoL-UI(-10), Recovering Quality of Life\u0026mdash;Utility Index (10 item); SD, standard deviation; UHSP, Unique Health State Profile; US, Unique Score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePossible (P.) Score \u0026amp; Observed (O.) Score\u003c/strong\u003e. The table shows the possible (P.) floor/worst and ceiling/best scores as well as the observed (O.) worst and best scores achieved by the respondents; these are shown rather than possible and observed minimum and maximum scores due to the fact that for the EQ-5D and ReQoL measures a higher score is a better state, whereas for the MOAS and BSI scores the opposite is true (i.e. a higher score is a worst state)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnique Health Status Profile (UHSP).\u0026nbsp;\u003c/strong\u003eThe UHSP is based on a measure\u0026rsquo;s descriptive system and for our analyses is used to assess the ability to quantify heterogeneity of a specific measure or subscale.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eFor example, the EQ-5D-5L and ReQoL-UI questionnaires produces a 5-digit or 7-digit health state profile, respectively, that represents the level of reported problems on each of the 5 or 7 dimensions of health, for example, 11223 for the EQ-5D-5L or 1112234 for the ReQoL-UI. UHSP refers to the number of UHSPs represented by the group of participants on that specific measure, for example, 12 self-reported EQ-5D-3L health state profiles compared with 42 self-reported ReQoL-UI health state profiles are represented by the participant sample. Thus, the self-reported ReQoL-UI suggests the sample are more heterogenous than the EQ-5D-3L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnique Score (US).\u0026nbsp;\u003c/strong\u003eUS refers to the number of USs represented by the sample. For example, the ReQoL-UI UHSP and US are equal such that each health state is represented by a US. For the ReQoL-10, MOAS, and BSI measures (e.g., using a summary score from a Likert item-score), the US \u0026lt; UHSP as some health states are represented by the same score\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eFor example, although the self-reported ReQoL-10 represents\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e45 UHSPs in the study sample, many of these UHSPs are represented by the same summary score thus although the UHSPs suggests the sample are particularly heterogenous (i.e., representing 45 UHSPs), as many of these UHSPs have the same score, the US suggests they are less heterogenous (i.e., 24 US). Within statistical analysis using summary scores, it is the USs, not the UHSPs, that dictates the variation in the analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eObserved measure summary scores, number of responders, and standardised response means across time-points\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003ei\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime-point (t\u003csub\u003ei\u003c/sub\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDif. time-points, t\u003csub\u003ei\u003c/sub\u003e \u0026ndash; t\u003csub\u003e0\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDif. time-points, t\u003csub\u003ei\u003c/sub\u003e \u0026ndash; t\u003csub\u003ei-1\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSRM (p-value)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSRM (p-value)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMOAS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e0\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e47 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.830 (3.435)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003esummary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e39 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.308 (2.903)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e39 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.590 (4.745)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.124 (0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e39 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.590 (4.745)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.124 (0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e31 (66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.806 (1.424)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e31 (66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-1.032 (3.497)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.295 (0.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e30 (64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.500 (2.862)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.175 (0.798)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMOAS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e0\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e47 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.787 (6.241)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eweighted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e39 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.590 (6.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e39 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.308 (9.384)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.033 (0.072)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e39 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.308 (9.384)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.033 (0.072)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e31 (66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.387 (2.604)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e31 (66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-1.516 (7.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.213 (0.202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e30 (64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-1.167 (5.682)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.205 (0.488)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBSI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e0\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e47 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.860 (0.697)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGSI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.796 (0.815)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.132 (0.598)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.221 (0.200)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.132 (0.598)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.221 (0.200)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.858 (0.847)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.074 (0.634)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.117 (0.259)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e33 (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.047 (0.373)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.127 (0.688)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBSI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e0\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e47 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e20.277 (14.316)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e18.730 (15.951)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-2.541 (12.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.211 (0.341)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-2.541 (12.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.211 (0.341)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e19.676 (16.503)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-1.941 (10.765)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.180 (0.211)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e33 (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.212 (7.940)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.027 (0.361)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBSI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e0\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e47 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.920 (0.812)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.051 (0.822)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.042 (0.826)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.051 (0.892)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.042 (0.826)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.051 (0.892)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.804 (0.963)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.163 (0.751)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.217 (0.228)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e33 (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.169 (0.849)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.199 (0.707)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEQ-5D-3L-LD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e0\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e40 (85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.839 (0.193)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-report\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e33 (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.801 (0.280)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e31 (66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.005 (0.274)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.017 (0.512)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e31 (66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.005 (0.274)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.017 (0.512)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e30 (64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.772 (0.330)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e28 (60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.054 (0.300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.179 (0.926)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e29 (62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.038 (0.292)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.131 (0.874)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEQ-5D-3L-LD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e0\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e47 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.832 (0.179)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProxy-report\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e39 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.844 (0.116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e39 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.023 (0.187)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.123 (0.877)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e39 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.023 (0.187)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.123 (0.877)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.840 (0.147)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.031 (0.201)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.153 (0.395)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.008 (0.173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.047 (0.719)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReQoL-UI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e0\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e47 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.874 (0.143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-report\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.835 (0.191)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.024 (0.145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.167 (0.184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.024 (0.145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.167 (0.184)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.840 (0.223)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.016 (0.203)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.079 (0.383)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e33 (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.001 (0.146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.009 (0.526)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReQoL-UI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e0\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e47 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.848 (0.131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProxy-report\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e39 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.896 (0.095)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e39 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.062 (0.156)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.397 (0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e39 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.062 (0.156)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.397 (0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.856 (0.121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.029 (0.140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.205 (0.447)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.040 (0.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.295 (0.075)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReQoL-10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e0\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e47 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e27.809 (8.123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-report\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e28.189 (5.758)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.973 (7.251)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.134 (0.768)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.973 (7.251)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.134 (0.768)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e27.647 (8.655)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.176 (7.814)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.023 (0.784)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e33 (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.636 (6.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.106 (0.950)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReQoL-10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e0\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e47 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e26.191 (6.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProxy-report\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e39 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e28.487 (7.323)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e39 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.513 (8.188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.307 (0.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e39 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.513 (8.188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.307 (0.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e26.382 (7.080)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.765 (5.275)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.145 (0.504)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-2.088 (6.956)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.300 (0.090)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAcronyms.\u0026nbsp;\u003c/strong\u003eBSI, Brief Symptom Inventory;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eEQ-5D-3L, EQ-5D Three-Level version; GSI, Global Severity Index; N, number of responder; PDSI, Positive Symptom Distress Index; PST, Positive Symptom Total; ReQoL-UI(-10), Recovering Quality of Life\u0026mdash;Utility Index (10 item); SD, standard deviation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026thinsp;=\u0026thinsp;time point\u003c/strong\u003e, whereby: \u003cem\u003et\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;baseline; \u003cem\u003et\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;19 weeks; \u003cem\u003et\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;38\u0026nbsp;weeks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eN(%)\u003c/strong\u003e states the number of people who completed the measure at the specific time-point, or at two given time-points e.g. relative to \u003cem\u003et\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e (baseline) or \u003cem\u003et\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e (whereby \u003cem\u003ei\u003c/em\u003e is any time-point denoted as 1\u0026ndash;2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCohen\u0026rsquo;s SRM cut-off:\u0026thinsp;\u0026lt;\u0026thinsp;\u003c/strong\u003e0.2, trivial; 0.2\u0026thinsp;\u0026lt;\u0026thinsp;0.5, small; 0.5\u0026thinsp;\u0026lt;\u0026thinsp;0.8, medium;\u0026thinsp;\u0026ge;\u0026thinsp;0.8, large; an SRM of\u0026thinsp;\u0026gt;\u0026thinsp;1 means the change in score between time-points is larger than one standard deviation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eInterrater agreement between self-reported and proxy-reported EQ-5D-3L and ReQoL measure scores at baseline\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD) score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAgreed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProxy-report N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInter-rater agreement (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(Pairs, N[%])\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProxy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigher,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower, N\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBP (Linear)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBP (Quadratic)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKappa (Linear)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKappa (Quadratic) / ICC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEQ-5D-3L (40 [85.1])\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.84 (0.19)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.82 (0.19)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.02 (0.28)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5 (12.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e16 (40.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e19 (47.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.186 (0.000, 0.444)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.186 (0.000, 0.662)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN/A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eICC: 0.000 (0.000, 0.259)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e- Mobility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.05 (0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.10 (0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.05 (0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36 (90.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.800 (0.606, 0.994)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.800 (0.606, 0.994)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.286 (0.000, 0.808)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.286 (0.000, 0.808)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e- Self care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.03 (0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.25 (0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.23 (0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31 (77.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.550 (0.280, 0.821)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.550 (0.280, 0.821)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.143 (0.000, 0.408)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.143 (0.000, 0.408)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e- Usual Act\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.35 (0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.13 (0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.23 (0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27 (67.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.350 (0.047, 0.653)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.350 (0.047, 0.653)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.161 (0.000, 0.449)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.161 (0.000, 0.449)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e- Pain/ Dis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.30 (0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.18 (0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.13 (0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26 (65.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.578 (0.384, 0.772)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.681 (0.502, 0.861)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.010 (0.000, 0.302)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000 (0.000, 0.259)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e- Anx/ Dep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.38 (0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.55 (0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.18 (0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.296 (0.071, 0.523)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.419 (0.167, 0.671)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000 (0.000, 0.222)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000 (0.000, 0.274)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReQoL-10 (47 [100])\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e27.81 (8.12)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e26.19 (6.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.62 (6.82)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e6 (12.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e16 (34.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e25 (53.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.463 (0.341, 0.586)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.708 (0.599, 0.816)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN/A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eICC: 0.532 (0.294, 0.709)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReQoL-UI (47 [100])\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.87 (0.14)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.85 (0.13)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03 (0.17)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1 (2.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e17 (36.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e29 (61.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.219 (0.000, 0.509)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.211 (0.000, 0.757)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN/A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eICC: 0.221 (0.000, 0.475)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e- Starting daily tasks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.98 (1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.81 (1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.17 (1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (28.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.309 (0.113, 0.505)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.415 (0.140, 0.690)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.164 (0.000, 0.351)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.245 (0.000, 0.521)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e- Trust others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.40 (1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.30 (1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.11 (1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.295 (0.094, 0.496)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.211 (0.000, 0.504)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.193 (0.000, 0.401)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.211 (0.000, 0.504)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e- Unable to cope\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.74 (1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.38 (1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.36 (1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 (42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.322 (0.135, 0.509)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.452 (0.196, 0.709)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.188 (0.000, 0.381)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.244 (0.000, 0.538)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e- Doing things\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.66 (1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.74 (1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09 (1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (34.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (34.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.309 (0.109, 0.508)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.404 (0.129, 0.680)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.169 (0.000, 0.391)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.199 (0.000, 0.528)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e- Happy\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.60 (1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.36 (1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.23 (1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.481 (0.318, 0.645)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.633 (0.448, 0.818)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.381 (0.212, 0.551)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.488 (0.251, 0.725)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e- Life not worth living\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.34 (1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.38 (0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04 (1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28 (59.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.455 (0.236, 0.675)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.472 (0.209, 0.736)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.266 (0.041, 0.492)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.309 (0.036, 0.583)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e- Enjoyment\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.02 (1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.85 (1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.17 (1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.415 (0.242, 0.588)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.564 (0.362, 0.765)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.220 (0.004, 0.436)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.307 (0.000, 0.618)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e- Hopeful future\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.55 (1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.49 (1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.06 (1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.162 (0.000, 0.369)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.239 (0.000, 0.563)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.125 (0.000, 0.339)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.185 (0.000, 0.501)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e- Loneliness\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.85 (1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.55 (1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.30 (1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.415 (0.251, 0.579)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.585 (0.423, 0.747)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.294 (0.109, 0.479)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.434 (0.208, 0.659)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e- Confidence\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.66 (1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.32 (0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.34 (1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.362 (0.180, 0.543)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.500 (0.305, 0.695)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.217 (0.007, 0.427)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.255 (0.000, 0.547)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e- Physical\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.26 (1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.89 (1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.36 (1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22 (46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.481 (0.289, 0.674)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.559 (0.292, 0.826)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.280 (0.081, 0.480)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.256 (0.000, 0.547)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAcronyms.\u0026nbsp;\u003c/strong\u003eBP,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eBrennan and Prediger (BP) coefficient; CI, confidence interval; ICC, intraclass correlation coefficients; EQ-5D-3L, EQ-5D Three-Level version; ReQoL-UI(-10), Recovering Quality of Life\u0026mdash;Utility Index (10 item); SD, standard deviation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFootnote.\u003c/strong\u003e Interrater reliability assessed for: continuous data (i.e., utility and summary scores), one-way random effects model; ordinal data (i.e., item scores), weighted Kappa statistic. Interrater agreement assessed for: continuous and ordinal data, weighted Brennan and Prediger (BP) coefficient. Weights include linear (i.e., each 1-point deviation weighted equally) or quadratic (i.e., larger deviations carry a larger weight) weights. Linear and quadratic weights produce the same statistics when any disagreement is only 1-point and/or only two categories are involved in the analysis (e.g., responses fall into only two categories despite there being more categories). Estimates including 95% CIs are clipped at the lower limit (i.e., 0) as negative agreement and reliability has little informative interpretation. ICC for continuous scores and weighted kappa (quadratic weights) are grouped in the same column mainly for presentation reasons; though the weighted kappa with quadratic weights has been shown to be equivalent to the ICC when estimated using a two-way analysis of variance (ANOVA), noting this study used a one-way random effects model to estimate the ICC [38] .\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCut-offs. Continuous (ICC\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e[35]\u003c/strong\u003e\u003cstrong\u003e):\u0026nbsp;\u003c/strong\u003e\u0026le; 0.5, poor; 0.5\u0026thinsp;\u0026lt;\u0026thinsp;0.75, moderate; 0.75\u0026lt;0.9, good/strong; \u0026ge; 0.9, excellent. \u003cstrong\u003eOrdinal (Kappa / BP \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e[34; 36; 37]\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e: \u0026le; 0.2, none; 0.2\u0026lt;0.4, minimal; 0.4\u0026lt;0.6, weak; 0.6\u0026lt;0.8, moderate; 0.8 \u0026lt;, strong. \u003cstrong\u003eNB:\u003c/strong\u003e\u0026nbsp; various cut-offs exist for both the ICC and Kappa statistic. For the ICC, we opted for the widely cited cut-offs by Koo and Li [35]. \u0026nbsp;For the weighted Kappa, the cut-off ranges tend to be similar across studies, but the labels vary; \u0026nbsp;thus we opted for the labelling by McHugh [34] albeit we dropped the \u0026ldquo;\u0026gt;90, almost perfect\u0026rdquo; label as often such a higher level label doesn\u0026rsquo;t exist for other suggested cut-offs. For the BP coefficient, we choose the same cut-offs as for the Kappa, as the BP coefficient is equivalent to the prevalence-adjusted and bias-adjusted kappa (PABAK) [44]; subsequently, we use the same cut-offs but with the Kappa statistic suggested to represent \u0026lsquo;reliability\u0026rsquo; and the BP coefficient representing \u0026lsquo;agreement\u0026rsquo;, though we recognise that descriptions of the Kappa statistic as representing \u0026lsquo;reliability\u0026rsquo; or \u0026lsquo;agreement\u0026rsquo; is mixed in the empirical literature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCorrelation coefficient matrix between measure scores at baseline\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"931\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 645px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpearman\u0026rsquo;s rank correlation coefficient (\u003cem\u003ep\u003c/em\u003e-value)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEQ-5D-3L utility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReQoL-UI utility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReQoL-10 summary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-reported\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProxy-reported\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-reported\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProxy-reported\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-reported\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProxy-reported\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEQ-5D-3L utility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eSelf-reported (Adapted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.022 (0.892)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.605 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.141 (0.384)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.545 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.374 (0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eProxy-reported (Proxy V1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.022 (0.892)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.174 (0.240)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.610 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.211 (0.154)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.446 (0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReQoL-UI utility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eSelf-reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.605 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.174 (0.240)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.469 (0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.874 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.567 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eProxy-reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.141 (0.384)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.610 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.469 (0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.465 (0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.762 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReQoL-10 summary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eSelf-reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.545 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.211 (0.154)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.874 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.465 (0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.537 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eProxy-reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.374 (0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.446 (0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.567 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.762 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.537 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMOAS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eSummary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.112 (0.490)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.377 (0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e-0.384 (0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.337 (0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.360 (0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.445 (0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(Clinician-reported)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eWeighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.083 (0.608)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.365 (0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e-0.360 (0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.343 (0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.337 (0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.435 (0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eVerbal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.205 (0.204)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.395 (0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e-0.429 (0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.334 (0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.401 (0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.466 (0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eProperty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.050 (0.773)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.260 (0.081)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e-0.181 (0.226)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.178 (0.234)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.082 (0.586)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.213 (0.152)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eAuto\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.067 (0.731)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.085 (0.586)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e-0.023 (0.883)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.160 (0.292)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.030 (0.845)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.159 (0.295)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003ePhysical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.174 (0.301)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.248 (0.098)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e-0.300 (0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.265 (0.072)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.173 (0.249)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.344 (0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBSI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eGSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.555 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.274 (0.062)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e-0.729 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.451 (0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.736 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.463 (0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(Self-reported)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003ePST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.562 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.242 (0.102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e-0.715 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.445 (0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.713 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.454 (0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003ePSDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.308 (0.053)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.368 (0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e-0.564 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.369 (0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.579 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.348 (0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eSomatization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.634 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.339 (0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e-0.504 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.412 (0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.493 (0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.436 (0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eObsessive-compulsive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.476 (0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.198 (0.181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e-0.527 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.330 (0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.560 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.313 (0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eInterpersonal sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.491 (0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.214 (0.148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e-0.579 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.372 (0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.617 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.363 (0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.516 (0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.183 (0.222)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e-0.666 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.363 (0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.759 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.432 (0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.423 (0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.208 (0.160)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e-0.645 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.357 (0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.550 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.277 (0.060)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eHostility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.396 (0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.288 (0.050)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e-0.687 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.415 (0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.617 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.457 (0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003ePhobic anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.282 (0.078)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.395 (0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e-0.474 (0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.432 (0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.513 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.396 (0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eParanoid ideation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.565 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.148 (0.321)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e-0.634 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.308 (0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.660 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.424 (0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003ePsychoticism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.359 (0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-0.206 (0.165)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e-0.550 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.227 (0.125)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.608 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.246 (0.095)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAcronyms.\u0026nbsp;\u003c/strong\u003eBSI, Brief Symptom Inventory; EQ-5D-3L, EQ-5D three-level; GSI, Global Severity Index;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eMOAS, Modified Overt Aggression Scale;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eReQoL-10(-UI), Recovering Quality of Life 10-item (Utility Index); PST, Positive Symptom Total; PSDI, Positive Symptom Distress Index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCohen\u0026rsquo;s ACS cut-offs.\u0026nbsp;\u003c/strong\u003eCohen\u0026rsquo;s Absolute Correlation Strength (ACS) cut-offs: weak,\u0026thinsp;\u0026lt;\u0026thinsp;0.3; moderate, 0.3\u0026thinsp;\u0026lt;\u0026thinsp;0.5; strong,\u0026thinsp;\u0026ge;\u0026thinsp;0.5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTesting known-group validity based on MOAS summary score and BSI symptom cut-off groups at baseline\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroups,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003escore range\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEQ-5D-3L-LD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-reported\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEQ-5D-3L-LD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eProxy-reported\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReQoL-UI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-reported\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReQoL-UI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eProxy-reported\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReQoL-10\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-reported\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReQoL-10\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eProxy-reported\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eES\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(p-value)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eES\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(p-value)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eES\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(p-value)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eES\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(p-value)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eES\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(p-value)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eES\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(p-value)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eMOAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eNo aggression., 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e22 (46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.863 (0.184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.896 (0.154)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.915 (0.116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.880 (0.132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e31.045 (6.708)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e28.955 (5.473)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003esummary score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eAggression, \u0026ge; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e25 (53.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.819 (0.203)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.777 (0.184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.838 (0.156)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.819 (0.126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e24.960 (8.309)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e23.760 (5.449)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.455)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eNot symptomatic, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e14 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.955 (0.089)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.903 (0.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.924 (0.117)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.907 (0.075)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e31.571 (9.395)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e29.214 (5.873)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eSomatization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eSymptomatic, \u0026ge; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e33 (70.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.789 (0.206)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.803 (0.188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.853 (0.149)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.822 (0.142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e26.212 (7.083)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e24.909 (5.664)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.070)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eNot symptomatic, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e11 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.970 (0.060)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.880 (0.103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.957 (0.071)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.907 (0.080)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e34.000 (7.335)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e29.273 (5.867)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eObsessive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eSymptomatic, \u0026ge; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e36 (76.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.801 (0.202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.818 (0.196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.849 (0.150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.829 (0.139)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e25.917 (7.458)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e25.250 (5.798)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ecompulsive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.538)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.049)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.053)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eNot symptomatic, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e15 (31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.942 (0.096)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.860 (0.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.959 (0.060)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.879 (0.140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e33.400 (6.843)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e28.800 (5.784)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003eInterpersonal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eSymptomatic, \u0026ge; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e32 (68.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.789 (0.209)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.820 (0.200)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.834 (0.153)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.833 (0.126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e25.188 (7.385)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e24.969 (5.789)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.662\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003esensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.784)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eNot symptomatic, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e12 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.949 (0.085)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.869 (0.139)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.965 (0.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.885 (0.153)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e34.750 (4.393)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e30.167 (4.933)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eSymptomatic, \u0026ge; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e35 (74.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.802 (0.206)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.820 (0.191)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.843 (0.153)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.835 (0.123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e25.429 (7.758)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e24.829 (5.778)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.600)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eNot symptomatic, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e14 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.913 (0.095)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.893 (0.134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.957 (0.061)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.891 (0.142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e32.714 (7.108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e29.071 (5.298)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eSymptomatic, \u0026ge; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e33 (70.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.803 (0.219)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.807 (0.191)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.839 (0.153)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.829 (0.124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e25.727 (7.703)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e24.970 (5.934)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eNot symptomatic, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e12 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.910 (0.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.897 (0.120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.964 (0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.847 (0.211)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e33.833 (5.289)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e28.917 (6.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eHostility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eSymptomatic, \u0026ge; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e35 (74.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.815 (0.211)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.810 (0.192)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.843 (0.153)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.848 (0.094)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e25.743 (7.939)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e25.257 (5.792)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.206)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.079)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eNot symptomatic, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e14 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.890 (0.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.903 (0.113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.954 (0.061)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.897 (0.157)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e32.857 (6.803)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e29.500 (5.360)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ePhobic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eSymptomatic, \u0026ge; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e33 (70.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.817 (0.219)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.802 (0.194)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.840 (0.154)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.827 (0.115)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e25.667 (7.757)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e24.788 (5.770)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eanxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.535)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.077)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eNot symptomatic, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e11 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.974 (0.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.863 (0.096)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.962 (0.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.850 (0.154)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e34.455 (5.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e27.636 (5.537)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eParanoid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eSymptomatic, \u0026ge; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e36 (76.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.800 (0.202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.823 (0.198)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.847 (0.152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.847 (0.126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e25.778 (7.834)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e25.750 (6.143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eideation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.669)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.321)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eNot symptomatic, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e16 (34.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.865 (0.252)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.893 (0.105)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.925 (0.141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.862 (0.141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e32.750 (6.094)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e27.438 (5.955)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ePsychoticism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eSymptomatic, \u0026ge; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e31 (66.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.823 (0.152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.801 (0.202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.848 (0.139)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.840 (0.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e25.258 (7.929)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e25.548 (6.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.059)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.209)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.357)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e(0.381)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAcronyms.\u0026nbsp;\u003c/strong\u003eBSI, Brief Symptom Inventory; EQ-5D-3L, EQ-5D three-level; ES, effect size (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e); GSI, Global Severity Index; MOAS, Modified Overt Aggression Scale; N, number of people; ReQoL-10(-UI), Recovering Quality of Life 10-item (Utility Index); SD, standard deviation; PST, Positive Symptom Total; PSDI, Positive Symptom Distress Index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCohen\u0026rsquo;s AES cut-off:\u0026nbsp;\u003c/strong\u003etrivial,\u0026thinsp;\u0026lt;\u0026thinsp;0.2; small, 0.2\u0026thinsp;\u0026lt;\u0026thinsp;0.5; medium, 0.5\u0026thinsp;\u0026lt;\u0026thinsp;0.8; large;\u0026thinsp;\u0026ge;\u0026thinsp;0.8; an ES of\u0026thinsp;\u0026gt;\u0026thinsp;1 means the difference between the two means is\u0026nbsp;larger than one\u0026nbsp;standard deviation. ESs are relative to less severe group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-values:\u0026nbsp;\u003c/strong\u003ecalculated from the non-parametric Kruskal Wallis test to suggest if there is a statistically significant difference between two or more known-groups based on the scores used as a complement to assessing ES.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"quality-of-life-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"qure","sideBox":"Learn more about [Quality of Life Research](https://www.springer.com/journal/11136)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/qure/default.aspx","title":"Quality of Life Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"intellectual disability, learning disability, secure care, recovery-focussed quality of life, ReQoL-UI, ReQoL-10, EuroQol, EQ-5D-3L adapted, EQ-5D-3L, EQ-5D-3L-ID","lastPublishedDoi":"10.21203/rs.3.rs-9022589/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9022589/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePURPOSE.\u003c/strong\u003e People with intellectual disabilities (PID) are a vulnerable, marginalised group for whom mental ill-health is common. PID who commit criminal offences may require secure inpatient care, within which aggression and violence cause major problems stemming in-part from patients’ psychological distress. To conduct economic evaluations of interventions to aid secure care PID, utility-based measures (e.g., EQ-5D, ReQoL-UI) must be sensitive and responsive to changes in aggression and psychological distress.\u003c/p\u003e\n\u003cp\u003eWithin secure care PID, we assess the psychometric properties of the self-reported and proxy-reported EQ-5D-3L (generic health) and ReQoL-UI (recovery-focussed quality-of-life) alongside the clinician-reported MOAS (aggression) and self-reported BSI (psychological distress). We use the self-reported EQ-5D-3L adapted for PID; the ReQoL-10 was also assessed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODS.\u003c/strong\u003e The SCHEMA trial collected measures at baseline, 19- and 38-weeks post-baseline. EQ-5D-3L and ReQoL measures’ inter-rater reliability and agreement between self- and proxy-responses was assessed, with construct validity and responsiveness judged against MOAS and BSI scores and cut-offs, e.g., with/out aggression or psychological distress.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS.\u003c/strong\u003e Forty-seven adults were randomised (intervention, 24; control, 23). The psychometric results were notably influenced by using self- or proxy-responses. The EQ-5D-3L suggested little heterogeneity among responders. Inter-rater reliability/agreement was: EQ-5D-3L, poor/none; ReQoL-UI, poor/minimal; ReQoL-10, moderate/weak-moderate. The ReQoL-UI/-10 had better construct validity with the MOAS and BSI than the EQ-5D-3L. Responsiveness tended to be larger for the proxy-reported ReQoL-10, albeit trivial to small across all measures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSION.\u003c/strong\u003e Despite the small sample size common with PID research, this study suggests the ReQoL measures might be preferred over the EQ-5D-3L in secure care PID.\u003c/p\u003e","manuscriptTitle":"Psychometric Assessment of the EQ-5D-3L And ReQoL Measures in Secure Care Patients with an Intellectual Disability: Reliability, Agreement, Construct Validity, and Responsiveness","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 20:11:37","doi":"10.21203/rs.3.rs-9022589/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-08T09:15:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-05T21:45:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-24T03:13:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43396557706146188008943721260013366121","date":"2026-03-15T17:09:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220228482028543703769653242563526725225","date":"2026-03-12T04:24:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-09T22:12:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-04T04:42:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-04T04:40:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Quality of Life Research","date":"2026-03-03T16:51:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"quality-of-life-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"qure","sideBox":"Learn more about [Quality of Life Research](https://www.springer.com/journal/11136)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/qure/default.aspx","title":"Quality of Life Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d144d6a2-d6d4-408a-b904-f4d8cd6fab1a","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T09:51:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 20:11:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9022589","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9022589","identity":"rs-9022589","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}