Wearables-defined sedentary behaviour clusters reveal distinct symptom profiles in youth with severe mental illness

Wearables-defined sedentary behaviour clusters reveal distinct symptom profiles in youth with severe mental illness | 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 Article Wearables-defined sedentary behaviour clusters reveal distinct symptom profiles in youth with severe mental illness Parisa Fani-Molky, Daniel Talbot, Felicia Cao, Matthew N Ahmadi, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9397165/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Wearable devices enable continuous monitoring of biometric information and may objectively monitor symptoms of severe mental illness. In this study, a wearable device was integrated into youth community mental health settings and used to explore activity and sleep patterns. Of 45 participants (median age=19yrs, 67% female), the largest diagnostic group was affective disorders alone (42%), followed by personality diagnoses (with or without affective diagnoses) (35.6%). Three statistically significant clusters based on sedentary behaviour (SB) were identified using hierarchical cluster analysis. Participants with lowest SB (Cluster 1) had the lowest functional impairment but the highest reported symptom severity. Conversely, the most sedentary group (Cluster 3) had the highest functional impairment but the lowest symptom burden. Using the sleep regularity index, no significant group differences were found. SB recorded via wearable devices may discriminate functional and symptom severity profiles across clusters and serve as an objective measure of clinical progress. Health sciences/Diseases Health sciences/Health care Biological sciences/Psychology Social science/Psychology Wearable Electronic Devices Community Mental Health Services Remote Patient Monitoring Mental Disorders Sedentary Behaviour Sleep Figures Figure 1 Introduction Sleep and physical activity are fundamental pillars of both physical and mental health. They share a bidirectional relationship, whereby each influences the other and together play a critical role in improving outcomes for individuals with severe mental illness (SMI) 1 – 3 . Individuals with SMI, such as schizophrenia, bipolar affective disorder, major depressive disorder or personality disorders, often experience disrupted sleep patterns and decreased physical activity 4 . This is likely due to a combination of biological, psychological, and social factors, including but not limited to circadian rhythm disruption 5 , medication side effects (notably sedation and weight gain) 6 – 8 , and motivational deficits 3 , 4 , 9 . These disturbances are not merely secondary symptoms but may alter different stages of illness 10 , contribute directly to poorer cognitive functioning 11 , poorer emotional regulation 12 , and quality of life 13 , as well as to the markedly reduced life expectancy observed in this population 14 , 15 . A key indicator of healthy sleep is the Sleep Regularity Index (SRI), which measures the consistency of an individual’s daily sleep patterns 16 . SRI is defined as the average likelihood that an individual will be in the same state (either asleep or awake) at two time points 24 hours apart 16 . A high SRI indicates a more regular sleep pattern (for example, sleeping from 10 pm to 7 am every day), while a low SRI suggests an irregular sleep pattern 16 . However, to calculate an SRI, continuous monitoring over several nights—at minimum 2 to 3 in a row—is necessary to establish a consistent pattern 16 , 17 . A UK Biobank study of over 79,000 participants found that higher SRI scores were independently associated with lower risk of depression and anxiety, even after controlling for sleep duration 18 . These findings suggest that when people sleep may be as important as how long they sleep. Mechanistically, disrupted sleep–wake regularity may worsen psychiatric symptoms through dysregulated circadian processes, impaired emotion regulation, and altered monoamine neurotransmitter functioning 5 , 19 . Thus, sleep regularity represents a critical yet underutilised marker of mental health. Similarly, insufficient physical activity and excessive sedentary behaviour (SB) are strongly linked to adverse physical and psychological outcomes 3 , 20 . The World Health Organisation 21 recommends that adults engage in at least 150 minutes of moderate or vigorous physical activity each week, and limit sedentary time. However, 34% of young Australians aged 18–24 fall short of these guidelines 22 and globally, physical inactivity is on the rise 23 , 24 . A meta-analysis by Vancampfort, et al. 25 demonstrated that individuals with SMI spend significantly more time sedentary and engage in less moderate to vigorous physical activity (MVPA) than those without a mental health diagnosis, with community-based patients often less active than inpatients. In addition, a cross-sectional study across 23 countries found that, compared to outpatients, psychiatric inpatients were both more sedentary and more physically active—possibly reflecting structured inpatient exercise groups alongside more time spent resting 26 . This pattern highlights the need for structured, community-level strategies to promote movement and address the physical health disparities driving excess morbidity and mortality in SMI populations 27 . Sleep and physical activity are closely interlinked domains interacting through shared neurobiological, physiological and behavioural pathways 28 . Both are regulated by the circadian system, and disruption in one can dysregulate the other, leading to cascading effects on mood, cognition, and motivation 19 , 29 , 30 . Reduced physical activity contributes to circadian misalignment and sleep quality, while irregular sleep patterns can impair energy levels, motivation to be active, and daylight exposure, all core components of healthy circadian rhythms 31 , 32 . These reciprocal disruptions are further compounded in SMI by psychotropic medication side effects, altered dopamine and serotonin signalling, and inflammatory processes that together contribute to fatigue, anhedonia, and amotivation 33 , 34 . Consequently, examining sleep and activity jointly, as complementary behavioural indicators, may offer a more comprehensive picture of functional impairment and provide an insight into possible treatment approaches in youth with SMI. Wearable devices, such as a wristwatch, offer a promising, non-invasive means of monitoring sleep and activity 35 , 36 . By enabling continuous, longitudinal assessment, wearables allow clinicians and researchers to detect subtle changes in behaviour and functioning that may be overlooked in brief clinical encounters, an approach particularly valuable in community mental health (CMH) settings 37 . Our present study draws on data from the unWIRED study 38 , which integrated wearable monitoring into routine CMH care for youth with SMI. Using hierarchical clustering, we explored the clinical and functional profiles of participants based on their SRI and SB derived from wearables. By identifying behavioural phenotypes linked to clinical outcomes, the findings will inform the development of targeted, data-driven digital interventions to enhance recovery and promote long-term well-being in community mental health care. Results A total of 48 participants enrolled in the unWIRED study. Two participants were removed as they did not record any data with the wearable device. One participant was removed due to a protocol deviation. Demographic information of the 45 study participants is outlined in Table 1 . The median age was 19 years (range 16–24), with a female predominance in the cohort (66.7%). The majority of participants were of Southeast/northeast Asian descent (37.8%) or European (31.1%). At time of recruitment, 19 (42.2%) of participants had completed secondary school, and 8 (17.8%) had completed tertiary education. The primary source of social support for most participants was family (40.0%) or friends (28.9%). Table 1 Demographics of unWIRED participants. Variable Median (IQR) Range Age 19 (18–21) 16–24 Variable n (%) Gender 45 Male 13 28.9 Female 30 66.7 Other 2 4.4 Ethnicity Southeast/Northeast Asian 17 37.8 European 14 31.1 Middle Eastern 3 6.7 Southern/Central Asian 3 6.7 Central/South American 2 4.4 African 1 2.2 Multiple selections 5 11.1 Schooling Partial Secondary 18 40.0 Complete Secondary 19 42.2 Tertiary 8 17.8 Main social support Family/Relative 18 40.0 Friend/Roommate 13 28.9 Partner 6 13.3 Other 3 6.7 No one 2 4.4 Unanswered 3 6.7 Diagnosis Affective diagnosis only 19 42.2 Personality ± affective 16 35.6 Psychosis (alone or with any other diagnosis) 10 22.2 Medications a Antipsychotic 26 57.8 Low dose (CPZE < 200mg/day) 18 69.2 High dose (CPZE ≥ 200mg/day) 7 26.9 Clozapine 1 3.9 Antidepressant 26 57.8 Mood stabiliser 10 22.2 Benzodiazepine 3 6.7 Stimulant 3 6.7 Clonidine 1 2.2 Beta blocker 1 2.2 Anticholinergic or Antihistamine 2 4.4 Melatonin 4 8.9 No Medication or Unknown 3 6.7 a. The total will not equal 100%, as some participants have received multiple medications simultaneously. CPZE: chlorpromazine equivalents; IQR: Interquartile range. This cohort consisted mainly of participants with affective diagnoses (42.2%), followed by those with personality diagnoses (with or without affective diagnoses) (35.6%). Only 10 participants (22.2%) had a primary psychotic illness. The most common psychotropic agents used were antidepressants (57.8%) and antipsychotics (57.8%). One participant was on clozapine. In this study, we focus solely on participants' baseline psychometrics (Table 2 ). The median baseline SOFAS score is 55, indicating moderate difficulty in social and occupational functioning 39 . Depression severity using the DASS-21 indicated moderate levels of depression, moderate levels of anxiety, but normal levels of stress based on the established cut-offs 40 . The median total BASIS mean score is 1.8, with 85.7% of the cohort scoring equal to or above the general population mean of 0.67 41 . The lowest PSQI total score is seven, suggesting that all participants met the cut-off for sleep disturbance 42 . Table 2 Clinical and actigraphy measures. Variable Median (IQR) Range Baseline Psychometrics SOFAS 55.0 (50.0–60.0) 40.0–81.0 DASS-21 39.0 (24.0-44.8) 0.0–60.0 Depression 14.0 (7.3–17.5) 0.0–21.0 Anxiety 10.0 (5.0-14.5) 0.0–20.0 Stress 13.5 (9.3–17) 0.0–21.0 BASIS-24 a 1.8 (1.2–2.1) 0.4–2.9 Depression/Functioning 2.7 (2-3.1) 0.2-4.0 Relationships 1.9 (1.2–2.4) 0.2–2.8 Self-Harm 1.5 (0.0-2.9) 0.0–4.0 Psychosis 1.0 (0.0-1.5) 0.0-2.8 Emotional Lability 2.3 (2.0–3.0) 0.0-3.7 Substance Abuse 0.3 (0.0-0.8) 0.0-3.3 PSQI total score 13.0 (11.0-15.8) 7.0–18.0 Sleep efficiency score (%) b 90.9 (75.0-100.0) 23.3–100.0 Actigraphy SB (mins) c 367.7 (288.9-474.6) 117.7-619.8 MVPA (bouts) c 15.0 (4.5–22.0) 0.0-100.5 MVPA duration (mins) c 17.1 (3.3–38.9) 0.0-151.3 SRI (%) d 56.3 (51.7–65.1) 25.2–84.0 Total sleep time (hrs) c 6.7 (5.6–7.8) 4.5–10.9 a. The overall BASIS-24 score and its subscales are calculated as the mean for each participant and are reported here as the cohort median of the BASIS-24 mean scores. b. Calculated based on the self-reported PSQI: (hours spent sleeping/total time in bed) x 100. c. For each participant, aggregated medians (based on total wearable time) were calculated. d. Overall calculation of each participant’s sleep regularity over the course of the study. BASIS-24: Behaviour And Symptom Identification Scale; DASS-21: Depression, Anxiety and Stress Scale − 21 Items; IQR: Interquartile range; MVPA: moderate to vigorous physical activity; PSQI: Pittsburgh Sleep Quality Index; SB: sedentary behaviour; SOFAS: Social and Occupational Functioning Assessment Scale; SRI: sleep regularity index. Table 2 also outlines the actigraphy variables discussed in this study. The wearable was worn on average for 32.9 days, approximately 17.9 hours per day, covering 979 nights included in the sleep analysis, along with 261 weekends. The average non wear time was 6.1 hours per day. The cohort's median MVPA was 15.0 bouts (Interquartile Range (IQR) = 4.5–22.0) across a median MVPA duration of 17.1 minutes (IQR = 3.3–38.9), and the median SB was 367.7 minutes (IQR = 288.9-474.6). The median total sleep time was 6.7 hours (IQR = 5.6–7.8) a day. The median SRI of the cohort is 56.3 (IQR = 51.7–65.1), well below the established cut-off of 70 43 . Cluster analysis based on SRI and SB revealed three distinct clusters (Fig. 1 ). The Kruskal-Wallis test determined there was no significant difference between clusters when looking at SRI, MVPA and total sleep time, but there was a significant difference in the SB between the three clusters (p < .001) (Table 3 ). Cluster 1 has the lowest SB (mean (M) = 262.9 minutes, standard deviation (SD) = 51.3), Cluster 2 has an intermediate SB (M = 412.6 minutes, SD = 49.0), and Cluster 3 has the highest SB (M = 570.7 minutes, SD = 26.7) (Fig. 1 and Table 4 ). Dunn’s post-hoc tests with Bonferroni correction showed that SB differed significantly between Cluster 1 and Cluster 2 ( adjusted p < .001 ) and between Cluster 1 and Cluster 3 ( adjusted p < .001 ). The comparison between Cluster 2 and Cluster 3 is trending towards statistical significance after correction ( adjusted p = .060 ). Analysis based on SRI and MVPA did not reveal any distinct clusters (Supplementary Fig. 2). Table 3 Kruskal–Wallis test of wearable measures results across clusters. Variable Kruskal-Wallis chi-squared DF P value SB (mins) a 37.644 2 0.000* MVPA (bouts) a 2.403 2 0.301 MVPA duration (mins) a 1.899 2 0.387 SRI (%) b 0.327 2 0.849 Total sleep time (hrs) a 0.105 2 0.949 a. For each participant, aggregated medians (based on total wearable time) were calculated. b. Overall calculation of each participant’s sleep regularity over the course of the study. SB: sedentary behaviour; SRI: sleep regularity index; MVPA: moderate to vigorous physical activity. Table 4 Cluster analysis based on SRI and SB Variable Cluster 1 Cluster 2 Cluster 3 n 19 18 8 Age median (IQR) 18.0 (17.0-19.5) 20.5 (18.0-21.8) 19.5 (18.8–22.3) Female 12 12 6 Male 5 6 2 Other gender 2 0 0 Diagnosis Affective only 8 7 4 Personality ± affective 9 6 1 Psychosis (alone or with any other diagnosis) 2 5 3 Baseline psychometrics SOFAS ≤ 60 (%) a 58.3 69.2 100.0 PSQI total score median (IQR) b 13.0 (13.0–16.0) 12.0 (11.0–16.0) 11.5 (9.8–14.0) PSQI sleep efficiency (%) mean (± SD) c 81.9 (21.7) 83.1 (22.4) 88.0 (13.7) DASS (%) d Depression 72.2 50.0 37.5 Anxiety 66.7 43.8 25.0 Stress 22.2 6.2 12.5 BASIS-24 (%) e Depression / Functioning 66.7 56.2 50.0 Relationships 55.6 50.0 37.5 Self-harm 72.2 50.0 37.5 Psychosis 72.2 50.0 25.0 Emotional lability 72.2 56.2 62.5 Substance abuse 72.2 37.5 50.0 Actigraphy - Mean (± SD) SB (mins) f 262.9 (51.3) 412.6 (49.0) 570.7 (26.7) MVPA (bouts) f 18.5 (16.0) 22.7 (26.6) 13.3 (20.0) MVPA duration (mins) f 42.2 (49.3) 25.0 (27.4) 12.9 (15.3) SRI (%) g 55.3 (14.7) 57.9 (12.7) 58.2 (8.2) Total sleep time (hours) f 6.6 (1.3) 7.0 (1.9) 6.9 (1.5) a. Scores ≤ 60 indicate moderate to severe social and functional impairment 39 . The table shows the percentage of participants in each cluster who scored at or below this level. b. A PSQI score greater than five suggests significant sleep difficulties 42 . c. Calculated based on the self-reported PSQI: (hours spent sleeping/total time in bed) x 100 d. Moderate or greater severity was defined as Depression ≥ 14, Anxiety ≥ 10, and Stress ≥ 19 40 . The table shows the percentage of participants in each cluster who meet or exceed these thresholds. e. For each subscale, a mean score was calculated and compared with the overall cohort median. The table shows the percentage of participants within each cluster whose subscale mean was greater than or equal to the cohort median. f. For each participant, aggregated medians (based on total wearable time) were calculated. g. Overall calculation of each participant’s sleep regularity over the course of the study. BASIS-24: Behaviour And Symptom Identification Scale; DASS-21: Depression, Anxiety and Stress Scale − 21 Items; IQR: Interquartile range; MVPA: moderate to vigorous physical activity; PSQI: Pittsburgh Sleep Quality Index; SB: sedentary behaviour; SD: standard deviation; SOFAS: Social and Occupational Functioning Assessment Scale; SRI: sleep regularity index Table 4 shows the demographic, clinical and wearable data for each cluster. Cluster 1 had the highest symptom burden in all categories of DASS-21 and BASIS-24, but the lowest functional impairment on the SOFAS (58.3% SOFAS ≤ 60). Cluster 2 had an intermediate profile with 69.2% showing functional impairment (SOFAS ≤ 60) and intermediate scores on DASS-21 and BASIS-24. Cluster 3 had the most functional impairment (100% SOFAS ≤ 60), but lower symptom severity across all DASS-21 and BASIS-24 categories. Discussion Our study examined wearable-derived activity patterns among youth with SMI and identified three distinct clusters of SB. The clustering approach revealed a paradoxical association between symptom burden and functioning: participants in the least sedentary cluster (Cluster 1) reported higher distress on the DASS-21 and BASIS-24 but demonstrated better functioning on the SOFAS. Conversely, those with the most sedentary behaviour (Cluster 3) reported lower self-reported symptoms yet showed the greatest functional impairment. This paradoxical finding suggests that improvements in symptoms may not directly translate into functional recovery, and that social and occupational functioning may lag behind symptomatic remission. Alternatively, more sedentary participants may report fewer symptoms because they are withdrawn from situations that exacerbate them 44 . Meanwhile, the less sedentary individuals may be re-engaging in work or social activities, which may increase awareness of symptoms or lead to exacerbation 44 . The participants in this study also demonstrated elevated levels of psychological distress in the BASIS-24, which assesses a broad spectrum of mental health symptoms. The cohort median of the BASIS-24 mean score is 1.8, with 85.7% of participants scoring at or above the established general population mean of 0.67 41 . Thus, this group experienced considerably greater psychopathology than typically seen in the general population, thus it is not surprising that functional recovery may follow a different trajectory. Youth with SMI may continue to struggle with demoralisation, reduced motivation, and a lack of daily structure 45 , which may manifest as physical inactivity and social withdrawal. Additionally, this cohort of young people with SMI had a low SRI (median 56.3 [IQR = 51.7–65.1]) compared with the established baseline, indicating that this group consists of irregular sleepers 43 .This is further reflected in the average for each cluster, indicating that despite SB and differences in clinical profiles, sleep irregularity is consistent. Given the overall severity of psychological challenges in this cohort, this finding aligns with the available literature, highlighting the importance of the association between low SRI and increased severity of psychological symptoms 18 , 46 . This underscores the importance of sleep as a target of clinical intervention, specifically a focus on improving SRI in conjunction to addressing psychological symptoms which may impair implementing such interventions. In this context, SB may serve as a sensitive indicator of ongoing functional impairment, capturing aspects of recovery that traditional symptom-focused assessments or clinician ratings may overlook. SB therefore offers a valuable behavioural signal that can help differentiate between symptomatic improvement and functional recovery and help signal the need for more assertive intervention. In view of this, wearable devices offer a feasible and cost-effective method for capturing objective behavioural data, including SB 47 . Wearables address limitations of traditional self-report measures, such as subjective reporting due to limited insight, recency bias or time constraints in CMH settings 36 . Thus, wearables can enable more targeted and responsive interventions to improve both mental and physical health outcomes. Several limitations should be considered when interpreting these findings. Firstly, the sample size is relatively small, particularly in relation to Cluster 3, and only baseline psychometric data are examined, which may limit generalisability. However, the study includes a diverse group of youth with SMI in a community setting, providing valuable preliminary insights. In addition, while wearable devices offer passive, high-resolution movement data, wearing the device correctly for a sufficient period is crucial for collecting high-quality interpretable data 47 . Our participants wore their device for nearly 33 days which is longer than in most comparable studies 48 , 49 , however even longer periods of use are necessary if wearables are to be used for long term monitoring. Psychopathology that may be relevant to the behaviours observed such as negative symptoms was also not explicitly measured. Negative symptoms, such as amotivation and anhedonia are common in psychotic and mood disorders and may also contribute to ongoing inactivity 50 . Future research with larger, longitudinal samples rated for negative symptoms, is necessary to explore how they interact with SB over time. In this study, we focus solely on sleep and activity measures; however, integrating other physiological indicators, such as heart rate or electrodermal activity, may identify subgroups who appear clinically well but show increased physiological arousal, providing insights into hidden anxiety or stress 51 . Finally, wearables cannot distinguish between different types of activity (e.g., social/purposeful versus solitary or non-goal-directed movement) 52 ; thus, incorporating contextual information, such as ecological momentary assessments, would improve understanding. Nonetheless, wearables offer invaluable insights that traditional methods cannot replicate 47 , 52 . In summary, the findings highlight the potential for SB to serve not only as a behavioural marker of functional impairment but also as a target for intervention in youth with SMI. Given that this cohort (aged 16–24) is in a formative developmental phase, essential for educational achievement, social growth, and identity development 53 , interventions targeting SB may yield broad and sustained benefits. Reducing SB may contribute to improved mental health, physical health, and social and occupational functioning, supporting a more holistic model of recovery 27 . Future work could leverage wearable data to inform personalised interventions such as structured physical activity programs, social participation initiatives, or community-based skill development supports. Additionally, integrating wearable data with ecological momentary assessments and clinical evaluations can offer a comprehensive view of a young person’s mental health and functioning, providing a digital tool to predict clinical deterioration for youth in the community. Methods Study Design: This study used data from the unWIRED study 38 , which integrated wearable devices into the care of young adults with SMI. The unWIRED study protocol was registered with the Australian New Zealand Clinical Trials Registry (ANZCTR) on 01/06/2020 (ACTRN12620000642987). Ethics has been approved by the Western Sydney Local Health District Human Research Ethics Committee (2019/ETH13581) for participants 18 and older, and by the Sydney Local Health District Human Research Ethics Committee (2021/ETH11253) for participants 16-17 years old. All participants provided written, informed consent prior to enrolment. Participants: Participants aged 16–25 years were recruited from CMH services in Sydney, Australia. Eligibility criteria included 1) a confirmed diagnosis of SMI (e.g., schizophrenia spectrum disorder, bipolar disorder, major depressive disorder, or personality disorder) established through routine clinical assessment, 2) current engagement with CMH services, and 3) capacity to consent to the study. Non-English-speaking participants or those with a greater than mild developmental disability were excluded from this study. Participants were followed for up to six months and not compensated for their involvement. Clinical measurements: Clinician recorded measures Clinical assessments were conducted at baseline and 6 months, and clinician-reported assessments were conducted at various timepoints throughout the study. Case managers and clinical staff recorded the Social and Occupational Functioning Assessment Scale (SOFAS) 39 for each participant. The SOFAS score ranges from 0 to 100, with higher scores indicating better overall functioning in daily life, including social situations, occupation, or school 39 . Each participant is assigned a SOFAS score at baseline, and a score of equal to or less than 60 indicates moderate to severe impairment in functioning, or worse 39 . Clinicians also reviewed patients’ electronic medical records to record medication history, and from which chlorpromazine dose equivalents were calculated for antipsychotic medications 54 . Self-reported questionnaires The Depression Anxiety Stress Scales–21 (DASS-21) measured symptoms of depression, anxiety, and stress. Participants rated the extent to which each statement applied to them during the past week using a 4-point Likert scale ranging from 0 ( “Did not apply to me at all” ) to 3 (“Applied to me very much, or most of the time” ) 40 . Example items include “I found it hard to wind down” (stress) and “I felt that life was meaningless” (depression). Baseline scores were obtained for each subscale, with moderate or greater symptom severity defined as depression scores of ≥ 14, anxiety scores of ≥ 10, and stress scores of ≥ 19 40 . Cronbach’s alpha for each subscale ranges from α = 0.90-0.95, demonstrating high reliability 55 . The Behavior and Symptom Identification Scale–24 (BASIS-24) assessed a broad range of mental health symptoms across six domains: depression/functioning, interpersonal relationships, self-harm, emotional lability, psychosis, and substance use 56 . Participants responded using a 5-point Likert scale ranging from 0 to 4, indicating how frequently they experienced each problem in the past week. Example items include “How often did you have mood swings?” and “How often did you try to hide your drinking or drug use?” A mean overall BASIS-24 score was calculated for each participant and compared with the established population normative mean of 0.67 41 . As subscale norms are not well established, a mean score was computed for each subscale and compared with the study cohort median to evaluate relative symptom severity within each domain. The BASIS-24 has demonstrated satisfactory reliability (subscales' coefficient α = 0.75-0.91) 41 . The Pittsburgh Sleep Quality Index (PSQI) evaluated self-reported sleep quality and disturbances over the past month across seven components, yielding a global score ranging from 0 to 21 42 . Example items include “During the past month, how often have you had trouble sleeping because you could not get to sleep within 30 minutes?” and “During the past month, how would you rate your overall sleep quality?” Scores greater than five indicate clinically significant sleep difficulties (Buysse et al., 1989). Sleep efficiency (%) was calculated for each participant using the following formula: (hours slept / total time in bed) × 100. Values exceeding 100% were rounded down to 100 to correct for overestimation. Healthy sleep is considered to be at 85% or greater, with scores <80% indicative of sleep disturbance 42 . Wearable Measurements: All participants were asked to wear the Empatica Embrace2 wearable device that continuously recorded their electrodermal activity, actigraphy and body temperature 57 . The Embrace2 contains a tri-axial accelerometer that measures movement patterns, with a battery life of around 48 hours 57 . The physiological data were collected in 30 second epochs. The data from the Empatica device were uploaded to the Empatica server and exported both to the mobile device of the participants, who saw the data displayed via Empatica’s proprietary app, and via a bespoke data pipeline to the Sydney Informatics Hub and Mackenzie Wearables Research Hub for data analysis, transformation, and visualisation. Actigraphy was calibrated for signal drift 58 and converted into sleep measures 59 and non-wear as detected 60 using previously validated Random Forest activity classifier 61 used in prior studies 62 . In this paper, we report on MVPA (bouts and duration in minutes), SB (in minutes), total sleep time (hours) and SRI. For each participant, aggregated medians (based on total wearable time) for SB, MVPA, and total sleep time were calculated. The SRI is an overall calculation of each participant’s sleep regularity over the course of the study. A score of 100 is a perfect SRI, with consistent sleep-wake patterns and the same sleep onset and offset times over the course of the study. An SRI of zero indicates random sleeping patterns (i.e., inconsistent sleep-wake times), with no predictable pattern from day to day. A standard threshold of SRI < 70 has been established to identify irregular sleepers 43 . Thus, any participant with an SRI <70 would be classified as having inconsistent sleep patterns. Statistical Analysis: Statistical analyses were conducted using RStudio (version 4.3.2) 63 . For participant characteristics, all continuous variables were summarised using descriptive statistics and categorical variables were summarised using frequency measures. Participants were hierarchically clustered into three groups based on their SRI and the median SB measured with the wearable device. The optimal number of clusters was determined using visual inspection of the dendrogram (Supplementary Figure 1). To compare clinical and demographic variables across the identified clusters, the non-parametric Kruskal-Wallis test was used. Post-hoc pairwise comparisons were conducted using Dunn’s test for multiple comparisons. Declarations Data availability: The data that support the findings of this study are not openly available due to reasons of sensitivity, ensuring privacy and ethical compliance. Data can be available from the corresponding author upon reasonable request. Code availability: The underlying code for this study is not publicly available but may be made available to qualified researchers on reasonable request from the corresponding author. Acknowledgements: We would like to thank the participants and clinicians at Prevention Early Intervention and Recovery Service (PEIRS), Canterbury and Camperdown community mental health teams, who made this study possible. We also thank the teams at the Sydney Informatics Hub and Mackenzie Wearables Research Hub@the Charles Perkins Centre for their assistance with data storage and pre-processing. Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author contributions: PFM: conceptualization, methodology, formal analysis, writing—original draft, review & editing. DT, RF, DJ, AC: data curation, investigation, writing—review & editing. FC: Formal analysis, writing—review & editing. MNA and ES: methodology, data curation, writing—review & editing. CB: supervision, writing—review & editing. BK: conceptualization, investigation, supervision, writing—review & editing. CH and AH: conceptualization, methodology, investigation, supervision, writing—review & editing. CH and AH contributed equally to this work and share last authorship. All authors reviewed and approved the final manuscript. Competing Interests: Professor Anthony Harris has received consultancy fees from Boehringer Ingelheim. Professor Anthony Harris and Beth Kotze were the recipient of an investigator-initiated grant from the Balnaves Foundation. He was an investigator on an industry sponsored trial by Alto Neuroscience. He is the recipient of funding from the Australian Research Council, the Medical Research Futures Fund and the National Health and Medical Research Council. He is a director of Mind Australia, a leading non-government organisation. References Baglioni, C. et al. Sleep and mental disorders: A meta-analysis of polysomnographic research. Psychol Bull 142 , 969-990 (2016). https://doi.org/10.1037/bul0000053 Scott, A. J., Webb, T. L., Martyn-St James, M., Rowse, G. & Weich, S. Improving sleep quality leads to better mental health: A meta-analysis of randomised controlled trials. Sleep Med Rev 60 , 101556 (2021). https://doi.org/10.1016/j.smrv.2021.101556 Pearce, M. et al. Association Between Physical Activity and Risk of Depression: A Systematic Review and Meta-analysis. JAMA Psychiatry 79 , 550-559 (2022). https://doi.org/10.1001/jamapsychiatry.2022.0609 Firth, J. et al. 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National, regional, and global trends in insufficient physical activity among adults from 2000 to 2022: a pooled analysis of 507 population-based surveys with 5.7 million participants. The Lancet Global Health 12 , e1232-e1243 (2024). https://doi.org/10.1016/S2214-109X(24)00150-5 Ramírez Varela, A. et al. Low global physical activity despite two decades of policy progress. Nature Health 1 , 338-354 (2026). https://doi.org/10.1038/s44360-025-00044-3 Vancampfort, D. et al. Sedentary behavior and physical activity levels in people with schizophrenia, bipolar disorder and major depressive disorder: a global systematic review and meta-analysis. World Psychiatry 16 , 308-315 (2017). https://doi.org/10.1002/wps.20458 Castro Monteiro, F. et al. Physical activity and sedentary behavior levels among individuals with mental illness: A cross-sectional study from 23 countries. PLoS One 19 , e0301583 (2024). https://doi.org/10.1371/journal.pone.0301583 Stubbs, B. et al. Integrating Physical Activity Into Routine Psychiatric Care: A Review. JAMA Psychiatry (2026). https://doi.org/10.1001/jamapsychiatry.2026.0026 Huang, B. H., Hamer, M., Duncan, M. J., Cistulli, P. A. & Stamatakis, E. The bidirectional association between sleep and physical activity: A 6.9 years longitudinal analysis of 38,601 UK Biobank participants. Prev Med 143 , 106315 (2021). https://doi.org/10.1016/j.ypmed.2020.106315 Lopresti, A. L., Hood, S. D. & Drummond, P. D. A review of lifestyle factors that contribute to important pathways associated with major depression: diet, sleep and exercise. Journal of affective disorders 148 , 12-27 (2013). Healy, K. L., Morris, A. R. & Liu, A. C. Circadian synchrony: sleep, nutrition, and physical activity. Frontiers in network physiology 1 , 732243 (2021). Kang, S. J. et al. Integrative modeling of accelerometry-derived sleep, physical activity, and circadian rhythm domains with current or remitted major depression. JAMA psychiatry 81 , 911-918 (2024). Liu, D., He, J. & Li, H. The relationship between adolescents’ physical activity, circadian rhythms, and sleep. Frontiers in Psychiatry 15 , 1415985 (2024). Lucido, M. J. et al. Aiding and Abetting Anhedonia: Impact of Inflammation on the Brain and Pharmacological Implications. Pharmacol Rev 73 , 1084-1117 (2021). https://doi.org/10.1124/pharmrev.120.000043 Wu, C., Mu, Q., Gao, W. & Lu, S. The characteristics of anhedonia in depression: a review from a clinically oriented perspective. Translational Psychiatry 15 , 90 (2025). https://doi.org/10.1038/s41398-025-03310-w Hickey, B. A. et al. Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review. Sensors (Basel) 21 (2021). https://doi.org/10.3390/s21103461 Johnston, D. et al. Integrating smartwatches in community mental health services for severe mental illness for detecting relapse and informing future intervention: A case series. Early Interv Psychiatry 18 , 471-477 (2024). https://doi.org/10.1111/eip.13529 Byrne, S., Kotze, B., Ramos, F., Casties, A. & Harris, A. Using a mobile health device to manage severe mental illness in the community: What is the potential and what are the challenges? Aust N Z J Psychiatry 54 , 964-969 (2020). https://doi.org/10.1177/0004867420945782 Byrne, S. et al. Integrating a Mobile Health Device Into a Community Youth Mental Health Team to Manage Severe Mental Illness: Protocol for a Randomized Controlled Trial. JMIR Res Protoc 9 , e19510 (2020). https://doi.org/10.2196/19510 Morosini, P. L., Magliano, L., Brambilla, L., Ugolini, S. & Pioli, R. Development, reliability and acceptability of a new version of the DSM-IV Social and Occupational Functioning Assessment Scale (SOFAS) to assess routine social funtioning. Acta psychiatrica Scandinavica 101 , 323-329 (2000). https://doi.org/10.1034/j.1600-0447.2000.101004323.x Lovibond, S. H., Lovibond, P. F. & Psychology Foundation of, A. Psychology Foundation monograph (Psychology Foundation of Australia, Sydney, N.S.W, 1995). Cameron, I. M. et al. Psychometric properties of the BASIS-24© (Behaviour and Symptom Identification Scale-Revised) Mental Health Outcome Measure. Int J Psychiatry Clin Pract 11 , 36-43 (2007). https://doi.org/10.1080/13651500600885531 Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R. & Kupfer, D. J. The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry research 28 , 193-213 (1989). https://doi.org/10.1016/0165-1781(89)90047-4 Windred, D. P. et al. Objective assessment of sleep regularity in 60 000 UK Biobank participants using an open-source package. Sleep 44 (2021). https://doi.org/10.1093/sleep/zsab254 Peçanha, A., Silveira, B., Krahe, T. E. & Landeira Fernandez, J. Can social isolation alleviate symptoms of anxiety and depression disorders? Frontiers in Psychiatry Volume 16 - 2025 (2025). https://doi.org/10.3389/fpsyt.2025.1561916 Fervaha, G., Foussias, G., Agid, O. & Remington, G. Motivational deficits in early schizophrenia: prevalent, persistent, and key determinants of functional outcome. Schizophr Res 166 , 9-16 (2015). https://doi.org/10.1016/j.schres.2015.04.040 Maki, K. A. et al. Sleep regularity and duration are associated with depression severity in a nationally representative United States sample. Neurobiol Sleep Circadian Rhythms 19 , 100133 (2025). https://doi.org/10.1016/j.nbscr.2025.100133 Chee, M. W. et al. World Sleep Society recommendations for the use of wearable consumer health trackers that monitor sleep. Sleep Med 131 , 106506 (2025). https://doi.org/10.1016/j.sleep.2025.106506 Bladon, S. et al. A systematic review of passive data for remote monitoring in psychosis and schizophrenia. NPJ Digit Med 8 , 62 (2025). https://doi.org/10.1038/s41746-025-01451-2 Hassan, L. et al. Utility of Consumer-Grade Wearable Devices for Inferring Physical and Mental Health Outcomes in Severe Mental Illness: Systematic Review. JMIR Ment Health 12 , e65143 (2025). https://doi.org/10.2196/65143 Strauss, G. P. & Cohen, A. S. A Transdiagnostic Review of Negative Symptom Phenomenology and Etiology. Schizophr Bull 43 , 712-719 (2017). https://doi.org/10.1093/schbul/sbx066 McDuff, D. et al. Evidence of differences in diurnal electrodermal, temperature and heart rate patterns by mental health status in free-living data. BMJ Ment Health 28 (2025). https://doi.org/10.1136/bmjment-2024-301307 Canali, S., Schiaffonati, V. & Aliverti, A. Challenges and recommendations for wearable devices in digital health: Data quality, interoperability, health equity, fairness. PLOS Digit Health 1 , e0000104 (2022). https://doi.org/10.1371/journal.pdig.0000104 Kemp, L. et al. The Impact of Positive and Adverse Experiences in Adolescence on Health and Wellbeing Outcomes in Early Adulthood. Int J Environ Res Public Health 21 (2024). https://doi.org/10.3390/ijerph21091147 Leucht, S., Samara, M., Heres, S. & Davis, J. M. Dose Equivalents for Antipsychotic Drugs: The DDD Method. Schizophr Bull 42 Suppl 1 , S90-94 (2016). https://doi.org/10.1093/schbul/sbv167 Crawford, J. R. & Henry, J. D. The Depression Anxiety Stress Scales (DASS): normative data and latent structure in a large non-clinical sample. Br J Clin Psychol 42 , 111-131 (2003). https://doi.org/10.1348/014466503321903544 Eisen, S. V., Wilcox, M., Leff, H. S., Schaefer, E. & Culhane, M. A. Assessing behavioral health outcomes in outpatient programs: Reliability and validity of the BASIS-32. The Journal of Behavioral Health Services & Research 26 , 5-17 (1999). https://doi.org/https://doi.org/10.1007/BF02287790 Empatica Inc. embrace 2 , ( Ahmadi, M. et al. Impact of physical activity patterns on major adverse cardiovascular events in adults with hypertension. British Journal of Sports Medicine , bjsports-2025-2021 (2026). https://doi.org/10.1136/bjsports-2025-109894 van Hees, V. T. et al. Estimating sleep parameters using an accelerometer without sleep diary. Scientific Reports 8 (2018). https://doi.org/10.1038/s41598-018-31266-z Ahmadi, M., Nathan, N., Sutherland, R., Wolfenden, L. & Trost, S. Non-wear or sleep? Evaluation of five non-wear detection algorithms for raw accelerometer data. Journal of Sports Sciences 38 , 399-404 (2020). https://doi.org/10.1080/02640414.2019.1703301 Chowdhury, A. K., Tjondronegoro, D., Chandran, V. & Trost, S. G. Ensemble Methods for Classification of Physical Activities from Wrist Accelerometry. Medicine and Science in Sports and Exercise 49 , 1965-1973 (2017). https://doi.org/10.1249/MSS.0000000000001291 Zask, A. et al. The effects of active classroom breaks on moderate to vigorous physical activity, behaviour and performance in a Northern NSW primary school: A quasi-experimental study. Health Promotion Journal of Australia 34 , 799-808 (2023). https://doi.org/10.1002/hpja.688 _R: A Language and Environment for Statistical Computing_. (R Foundation for Statistical Computing, 2023). Additional Declarations Competing interest reported. Professor Anthony Harris has received consultancy fees from Boehringer Ingelheim. Professors Anthony Harris and Beth Kotze were the recipient of an investigator-initiated grant from the Balnaves Foundation. He was an investigator on an industry sponsored trial by Alto Neuroscience. He is the recipient of funding from the Australian Research Council, the Medical Research Futures Fund and the National Health and Medical Research Council. He is a director of Mind Australia, a leading non-government organisation. 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SRI compared to SB.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9397165/v1/8e23e9b639345a7eb89f0153.jpeg"},{"id":108180885,"identity":"6c422cd1-3b91-4746-9119-e47692514d3d","added_by":"auto","created_at":"2026-04-30 08:54:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":798068,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9397165/v1/f9171942-e224-45fd-8760-dc586badd920.pdf"},{"id":107832790,"identity":"1a621333-6eeb-4d2c-8c19-0699a69a4981","added_by":"auto","created_at":"2026-04-26 15:36:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":277723,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationSBpaperPFM13.04.2026.docx","url":"https://assets-eu.researchsquare.com/files/rs-9397165/v1/869cf66070ca6f24cefe5780.docx"}],"financialInterests":"Competing interest reported. Professor Anthony Harris has received consultancy fees from Boehringer Ingelheim. Professors Anthony Harris and Beth Kotze were the recipient of an investigator-initiated grant from the Balnaves Foundation. He was an investigator on an industry sponsored trial by Alto Neuroscience. He is the recipient of funding from the Australian Research Council, the Medical Research Futures Fund and the National Health and Medical Research Council. He is a director of Mind Australia, a leading non-government organisation.","formattedTitle":"Wearables-defined sedentary behaviour clusters reveal distinct symptom profiles in youth with severe mental illness","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSleep and physical activity are fundamental pillars of both physical and mental health. They share a bidirectional relationship, whereby each influences the other and together play a critical role in improving outcomes for individuals with severe mental illness (SMI)\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Individuals with SMI, such as schizophrenia, bipolar affective disorder, major depressive disorder or personality disorders, often experience disrupted sleep patterns and decreased physical activity\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. This is likely due to a combination of biological, psychological, and social factors, including but not limited to circadian rhythm disruption\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, medication side effects (notably sedation and weight gain)\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, and motivational deficits\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. These disturbances are not merely secondary symptoms but may alter different stages of illness\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, contribute directly to poorer cognitive functioning\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, poorer emotional regulation\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, and quality of life\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, as well as to the markedly reduced life expectancy observed in this population\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA key indicator of healthy sleep is the Sleep Regularity Index (SRI), which measures the consistency of an individual\u0026rsquo;s daily sleep patterns\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. SRI is defined as the average likelihood that an individual will be in the same state (either asleep or awake) at two time points 24 hours apart\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. A high SRI indicates a more regular sleep pattern (for example, sleeping from 10 pm to 7 am every day), while a low SRI suggests an irregular sleep pattern\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, to calculate an SRI, continuous monitoring over several nights\u0026mdash;at minimum 2 to 3 in a row\u0026mdash;is necessary to establish a consistent pattern\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. A UK Biobank study of over 79,000 participants found that higher SRI scores were independently associated with lower risk of depression and anxiety, even after controlling for sleep duration\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. These findings suggest that \u003cem\u003ewhen\u003c/em\u003e people sleep may be as important as \u003cem\u003ehow long\u003c/em\u003e they sleep. Mechanistically, disrupted sleep\u0026ndash;wake regularity may worsen psychiatric symptoms through dysregulated circadian processes, impaired emotion regulation, and altered monoamine neurotransmitter functioning\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Thus, sleep regularity represents a critical yet underutilised marker of mental health.\u003c/p\u003e \u003cp\u003eSimilarly, insufficient physical activity and excessive sedentary behaviour (SB) are strongly linked to adverse physical and psychological outcomes\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The World Health Organisation\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e recommends that adults engage in at least 150 minutes of moderate or vigorous physical activity each week, and limit sedentary time. However, 34% of young Australians aged 18\u0026ndash;24 fall short of these guidelines\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e and globally, physical inactivity is on the rise\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. A meta-analysis by Vancampfort, et al. \u003csup\u003e25\u003c/sup\u003e demonstrated that individuals with SMI spend significantly more time sedentary and engage in less moderate to vigorous physical activity (MVPA) than those without a mental health diagnosis, with community-based patients often less active than inpatients. In addition, a cross-sectional study across 23 countries found that, compared to outpatients, psychiatric inpatients were both more sedentary and more physically active\u0026mdash;possibly reflecting structured inpatient exercise groups alongside more time spent resting\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. This pattern highlights the need for structured, community-level strategies to promote movement and address the physical health disparities driving excess morbidity and mortality in SMI populations\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSleep and physical activity are closely interlinked domains interacting through shared neurobiological, physiological and behavioural pathways\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Both are regulated by the circadian system, and disruption in one can dysregulate the other, leading to cascading effects on mood, cognition, and motivation\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Reduced physical activity contributes to circadian misalignment and sleep quality, while irregular sleep patterns can impair energy levels, motivation to be active, and daylight exposure, all core components of healthy circadian rhythms\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. These reciprocal disruptions are further compounded in SMI by psychotropic medication side effects, altered dopamine and serotonin signalling, and inflammatory processes that together contribute to fatigue, anhedonia, and amotivation\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Consequently, examining sleep and activity jointly, as complementary behavioural indicators, may offer a more comprehensive picture of functional impairment and provide an insight into possible treatment approaches in youth with SMI.\u003c/p\u003e \u003cp\u003eWearable devices, such as a wristwatch, offer a promising, non-invasive means of monitoring sleep and activity\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. By enabling continuous, longitudinal assessment, wearables allow clinicians and researchers to detect subtle changes in behaviour and functioning that may be overlooked in brief clinical encounters, an approach particularly valuable in community mental health (CMH) settings\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Our present study draws on data from the unWIRED study\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, which integrated wearable monitoring into routine CMH care for youth with SMI. Using hierarchical clustering, we explored the clinical and functional profiles of participants based on their SRI and SB derived from wearables. By identifying behavioural phenotypes linked to clinical outcomes, the findings will inform the development of targeted, data-driven digital interventions to enhance recovery and promote long-term well-being in community mental health care.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 48 participants enrolled in the unWIRED study. Two participants were removed as they did not record any data with the wearable device. One participant was removed due to a protocol deviation. Demographic information of the 45 study participants is outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age was 19 years (range 16\u0026ndash;24), with a female predominance in the cohort (66.7%). The majority of participants were of Southeast/northeast Asian descent (37.8%) or European (31.1%). At time of recruitment, 19 (42.2%) of participants had completed secondary school, and 8 (17.8%) had completed tertiary education. The primary source of social support for most participants was family (40.0%) or friends (28.9%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics of unWIRED participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (18\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoutheast/Northeast Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle Eastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern/Central Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral/South American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple selections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartial Secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplete Secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain social support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily/Relative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFriend/Roommate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnanswered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAffective diagnosis only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonality\u0026thinsp;\u0026plusmn;\u0026thinsp;affective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychosis (alone or with any other diagnosis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedications\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntipsychotic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLow dose (CPZE \u0026lt;\u0026thinsp;200mg/day)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e18\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e69.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHigh dose (CPZE \u0026ge;\u0026thinsp;200mg/day)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e26.9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eClozapine\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e3.9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntidepressant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMood stabiliser\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenzodiazepine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStimulant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClonidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta blocker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnticholinergic or Antihistamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMelatonin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Medication or Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003ea. The total will not equal 100%, as some participants have received multiple medications simultaneously.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eCPZE: chlorpromazine equivalents; IQR: Interquartile range.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis cohort consisted mainly of participants with affective diagnoses (42.2%), followed by those with personality diagnoses (with or without affective diagnoses) (35.6%). Only 10 participants (22.2%) had a primary psychotic illness. The most common psychotropic agents used were antidepressants (57.8%) and antipsychotics (57.8%). One participant was on clozapine.\u003c/p\u003e \u003cp\u003eIn this study, we focus solely on participants' baseline psychometrics (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The median baseline SOFAS score is 55, indicating moderate difficulty in social and occupational functioning\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Depression severity using the DASS-21 indicated moderate levels of depression, moderate levels of anxiety, but normal levels of stress based on the established cut-offs\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The median total BASIS mean score is 1.8, with 85.7% of the cohort scoring equal to or above the general population mean of 0.67\u003csup\u003e41\u003c/sup\u003e. The lowest PSQI total score is seven, suggesting that all participants met the cut-off for sleep disturbance\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical and actigraphy measures.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline Psychometrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.0 (50.0\u0026ndash;60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.0\u0026ndash;81.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDASS-21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.0 (24.0-44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0\u0026ndash;60.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDepression\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e14.0 (7.3\u0026ndash;17.5)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.0\u0026ndash;21.0\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAnxiety\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e10.0 (5.0-14.5)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.0\u0026ndash;20.0\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eStress\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e13.5 (9.3\u0026ndash;17)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.0\u0026ndash;21.0\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBASIS-24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.8 (1.2\u0026ndash;2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4\u0026ndash;2.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDepression/Functioning\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e2.7 (2-3.1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.2-4.0\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRelationships\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1.9 (1.2\u0026ndash;2.4)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.2\u0026ndash;2.8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSelf-Harm\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1.5 (0.0-2.9)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.0\u0026ndash;4.0\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePsychosis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1.0 (0.0-1.5)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.0-2.8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEmotional Lability\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e2.3 (2.0\u0026ndash;3.0)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.0-3.7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSubstance Abuse\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.3 (0.0-0.8)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.0-3.3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI total score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.0 (11.0-15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.0\u0026ndash;18.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSleep efficiency score (%)\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e90.9 (75.0-100.0)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e23.3\u0026ndash;100.0\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActigraphy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSB (mins)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e367.7 (288.9-474.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e117.7-619.8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVPA (bouts)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.0 (4.5\u0026ndash;22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0-100.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVPA duration (mins)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.1 (3.3\u0026ndash;38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0-151.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRI (%)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.3 (51.7\u0026ndash;65.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.2\u0026ndash;84.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal sleep time (hrs)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.7 (5.6\u0026ndash;7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.5\u0026ndash;10.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003ea. The overall BASIS-24 score and its subscales are calculated as the mean for each participant and are reported here as the cohort median of the BASIS-24 mean scores.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eb. Calculated based on the self-reported PSQI: (hours spent sleeping/total time in bed) x 100.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003ec. For each participant, aggregated medians (based on total wearable time) were calculated.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003ed. Overall calculation of each participant\u0026rsquo;s sleep regularity over the course of the study.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eBASIS-24: Behaviour And Symptom Identification Scale; DASS-21: Depression, Anxiety and Stress Scale\u0026thinsp;\u0026minus;\u0026thinsp;21 Items; IQR: Interquartile range; MVPA: moderate to vigorous physical activity; PSQI: Pittsburgh Sleep Quality Index; SB: sedentary behaviour; SOFAS: Social and Occupational Functioning Assessment Scale; SRI: sleep regularity index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e also outlines the actigraphy variables discussed in this study. The wearable was worn on average for 32.9 days, approximately 17.9 hours per day, covering 979 nights included in the sleep analysis, along with 261 weekends. The average non wear time was 6.1 hours per day. The cohort's median MVPA was 15.0 bouts (Interquartile Range (IQR)\u0026thinsp;=\u0026thinsp;4.5\u0026ndash;22.0) across a median MVPA duration of 17.1 minutes (IQR\u0026thinsp;=\u0026thinsp;3.3\u0026ndash;38.9), and the median SB was 367.7 minutes (IQR\u0026thinsp;=\u0026thinsp;288.9-474.6). The median total sleep time was 6.7 hours (IQR\u0026thinsp;=\u0026thinsp;5.6\u0026ndash;7.8) a day. The median SRI of the cohort is 56.3 (IQR\u0026thinsp;=\u0026thinsp;51.7\u0026ndash;65.1), well below the established cut-off of 70\u003csup\u003e43\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCluster analysis based on SRI and SB revealed three distinct clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Kruskal-Wallis test determined there was no significant difference between clusters when looking at SRI, MVPA and total sleep time, but there was a significant difference in the SB between the three clusters \u003cem\u003e(p \u0026lt; .001)\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Cluster 1 has the lowest SB (mean (M)\u0026thinsp;=\u0026thinsp;262.9 minutes, standard deviation (SD)\u0026thinsp;=\u0026thinsp;51.3), Cluster 2 has an intermediate SB (M\u0026thinsp;=\u0026thinsp;412.6 minutes, SD\u0026thinsp;=\u0026thinsp;49.0), and Cluster 3 has the highest SB (M\u0026thinsp;=\u0026thinsp;570.7 minutes, SD\u0026thinsp;=\u0026thinsp;26.7) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Dunn\u0026rsquo;s post-hoc tests with Bonferroni correction showed that SB differed significantly between Cluster 1 and Cluster 2 (\u003cem\u003eadjusted p \u0026lt; .001\u003c/em\u003e) and between Cluster 1 and Cluster 3 (\u003cem\u003eadjusted p \u0026lt; .001\u003c/em\u003e). The comparison between Cluster 2 and Cluster 3 is trending towards statistical significance after correction (\u003cem\u003eadjusted p = .060\u003c/em\u003e). Analysis based on SRI and MVPA did not reveal any distinct clusters (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKruskal\u0026ndash;Wallis test of wearable measures results across clusters.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKruskal-Wallis chi-squared\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSB (mins)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVPA (bouts) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVPA duration (mins) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRI (%)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal sleep time (hrs) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ea. For each participant, aggregated medians (based on total wearable time) were calculated.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eb. Overall calculation of each participant\u0026rsquo;s sleep regularity over the course of the study.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSB: sedentary behaviour; SRI: sleep regularity index; MVPA: moderate to vigorous physical activity.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCluster analysis based on SRI and SB\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCluster 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCluster 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCluster 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.0 (17.0-19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.5 (18.0-21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.5 (18.8\u0026ndash;22.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAffective only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonality\u0026thinsp;\u0026plusmn;\u0026thinsp;affective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychosis (alone or with any other diagnosis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBaseline psychometrics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFAS\u0026thinsp;\u0026le;\u0026thinsp;60 (%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI total score median (IQR)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.0 (13.0\u0026ndash;16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.0 (11.0\u0026ndash;16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.5 (9.8\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI sleep efficiency (%) mean (\u0026plusmn;\u0026thinsp;SD)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.9 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.1 (22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.0 (13.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eDASS (%)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBASIS-24 (%)\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression / Functioning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationships\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-harm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmotional lability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubstance abuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eActigraphy - Mean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSB (mins)\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e262.9 (51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e412.6 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e570.7 (26.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVPA (bouts)\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.5 (16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.7 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.3 (20.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVPA duration (mins)\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.2 (49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.0 (27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.9 (15.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRI (%)\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.3 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.9 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.2 (8.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal sleep time (hours)\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.6 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.0 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.9 (1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ea. Scores\u0026thinsp;\u0026le;\u0026thinsp;60 indicate moderate to severe social and functional impairment \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The table shows the percentage of participants in each cluster who scored at or below this level.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eb. A PSQI score greater than five suggests significant sleep difficulties \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ec. Calculated based on the self-reported PSQI: (hours spent sleeping/total time in bed) x 100\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ed. Moderate or greater severity was defined as Depression\u0026thinsp;\u0026ge;\u0026thinsp;14, Anxiety\u0026thinsp;\u0026ge;\u0026thinsp;10, and Stress\u0026thinsp;\u0026ge;\u0026thinsp;19 \u003csup\u003e40\u003c/sup\u003e. The table shows the percentage of participants in each cluster who meet or exceed these thresholds.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ee. For each subscale, a mean score was calculated and compared with the overall cohort median. The table shows the percentage of participants within each cluster whose subscale mean was greater than or equal to the cohort median.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ef. For each participant, aggregated medians (based on total wearable time) were calculated.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eg. Overall calculation of each participant\u0026rsquo;s sleep regularity over the course of the study.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBASIS-24: Behaviour And Symptom Identification Scale; DASS-21: Depression, Anxiety and Stress Scale\u0026thinsp;\u0026minus;\u0026thinsp;21 Items; IQR: Interquartile range; MVPA: moderate to vigorous physical activity; PSQI: Pittsburgh Sleep Quality Index; SB: sedentary behaviour; SD: standard deviation; SOFAS: Social and Occupational Functioning Assessment Scale; SRI: sleep regularity index\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the demographic, clinical and wearable data for each cluster. Cluster 1 had the highest symptom burden in all categories of DASS-21 and BASIS-24, but the lowest functional impairment on the SOFAS (58.3% SOFAS\u0026thinsp;\u0026le;\u0026thinsp;60). Cluster 2 had an intermediate profile with 69.2% showing functional impairment (SOFAS\u0026thinsp;\u0026le;\u0026thinsp;60) and intermediate scores on DASS-21 and BASIS-24. Cluster 3 had the most functional impairment (100% SOFAS\u0026thinsp;\u0026le;\u0026thinsp;60), but lower symptom severity across all DASS-21 and BASIS-24 categories.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study examined wearable-derived activity patterns among youth with SMI and identified three distinct clusters of SB. The clustering approach revealed a paradoxical association between symptom burden and functioning: participants in the least sedentary cluster (Cluster 1) reported higher distress on the DASS-21 and BASIS-24 but demonstrated better functioning on the SOFAS. Conversely, those with the most sedentary behaviour (Cluster 3) reported lower self-reported symptoms yet showed the greatest functional impairment. This paradoxical finding suggests that improvements in symptoms may not directly translate into functional recovery, and that social and occupational functioning may lag behind symptomatic remission. Alternatively, more sedentary participants may report fewer symptoms because they are withdrawn from situations that exacerbate them\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Meanwhile, the less sedentary individuals may be re-engaging in work or social activities, which may increase awareness of symptoms or lead to exacerbation\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe participants in this study also demonstrated elevated levels of psychological distress in the BASIS-24, which assesses a broad spectrum of mental health symptoms. The cohort median of the BASIS-24 mean score is 1.8, with 85.7% of participants scoring at or above the established general population mean of 0.67\u003csup\u003e41\u003c/sup\u003e. Thus, this group experienced considerably greater psychopathology than typically seen in the general population, thus it is not surprising that functional recovery may follow a different trajectory. Youth with SMI may continue to struggle with demoralisation, reduced motivation, and a lack of daily structure\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, which may manifest as physical inactivity and social withdrawal.\u003c/p\u003e \u003cp\u003eAdditionally, this cohort of young people with SMI had a low SRI (median 56.3 [IQR\u0026thinsp;=\u0026thinsp;51.7\u0026ndash;65.1]) compared with the established baseline, indicating that this group consists of irregular sleepers\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.This is further reflected in the average for each cluster, indicating that despite SB and differences in clinical profiles, sleep irregularity is consistent. Given the overall severity of psychological challenges in this cohort, this finding aligns with the available literature, highlighting the importance of the association between low SRI and increased severity of psychological symptoms\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. This underscores the importance of sleep as a target of clinical intervention, specifically a focus on improving SRI in conjunction to addressing psychological symptoms which may impair implementing such interventions.\u003c/p\u003e \u003cp\u003eIn this context, SB may serve as a sensitive indicator of ongoing functional impairment, capturing aspects of recovery that traditional symptom-focused assessments or clinician ratings may overlook. SB therefore offers a valuable behavioural signal that can help differentiate between symptomatic improvement and functional recovery and help signal the need for more assertive intervention. In view of this, wearable devices offer a feasible and cost-effective method for capturing objective behavioural data, including SB\u003csup\u003e47\u003c/sup\u003e. Wearables address limitations of traditional self-report measures, such as subjective reporting due to limited insight, recency bias or time constraints in CMH settings\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Thus, wearables can enable more targeted and responsive interventions to improve both mental and physical health outcomes.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered when interpreting these findings. Firstly, the sample size is relatively small, particularly in relation to Cluster 3, and only baseline psychometric data are examined, which may limit generalisability. However, the study includes a diverse group of youth with SMI in a community setting, providing valuable preliminary insights. In addition, while wearable devices offer passive, high-resolution movement data, wearing the device correctly for a sufficient period is crucial for collecting high-quality interpretable data\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Our participants wore their device for nearly 33 days which is longer than in most comparable studies\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, however even longer periods of use are necessary if wearables are to be used for long term monitoring. Psychopathology that may be relevant to the behaviours observed such as negative symptoms was also not explicitly measured. Negative symptoms, such as amotivation and anhedonia are common in psychotic and mood disorders and may also contribute to ongoing inactivity\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Future research with larger, longitudinal samples rated for negative symptoms, is necessary to explore how they interact with SB over time.\u003c/p\u003e \u003cp\u003eIn this study, we focus solely on sleep and activity measures; however, integrating other physiological indicators, such as heart rate or electrodermal activity, may identify subgroups who appear clinically well but show increased physiological arousal, providing insights into hidden anxiety or stress \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Finally, wearables cannot distinguish between different types of activity (e.g., social/purposeful versus solitary or non-goal-directed movement)\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e; thus, incorporating contextual information, such as ecological momentary assessments, would improve understanding. Nonetheless, wearables offer invaluable insights that traditional methods cannot replicate\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn summary, the findings highlight the potential for SB to serve not only as a behavioural marker of functional impairment but also as a target for intervention in youth with SMI. Given that this cohort (aged 16\u0026ndash;24) is in a formative developmental phase, essential for educational achievement, social growth, and identity development\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, interventions targeting SB may yield broad and sustained benefits. Reducing SB may contribute to improved mental health, physical health, and social and occupational functioning, supporting a more holistic model of recovery\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Future work could leverage wearable data to inform personalised interventions such as structured physical activity programs, social participation initiatives, or community-based skill development supports. Additionally, integrating wearable data with ecological momentary assessments and clinical evaluations can offer a comprehensive view of a young person\u0026rsquo;s mental health and functioning, providing a digital tool to predict clinical deterioration for youth in the community.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Design:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study used data from the unWIRED study\u003csup\u003e38\u003c/sup\u003e, which integrated wearable devices into the care of young adults with SMI. The unWIRED study protocol was registered with the Australian New Zealand Clinical Trials Registry (ANZCTR) on 01/06/2020 (ACTRN12620000642987). Ethics has been approved by the Western Sydney Local Health District Human Research Ethics Committee (2019/ETH13581) for participants 18 and older, and by the Sydney Local Health District Human Research Ethics Committee (2021/ETH11253) for participants 16-17 years old. All participants provided written, informed consent prior to enrolment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants:\u003c/p\u003e\n\u003cp\u003eParticipants aged 16\u0026ndash;25 years were recruited from CMH services in Sydney, Australia. Eligibility criteria included 1) a confirmed diagnosis of SMI (e.g., schizophrenia spectrum disorder, bipolar disorder, major depressive disorder, or personality disorder) established through routine clinical assessment, 2) current engagement with CMH services, and 3) capacity to consent to the study. Non-English-speaking participants or those with a greater than mild developmental disability were excluded from this study. Participants were followed for up to six months and not compensated for their involvement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClinical measurements:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eClinician recorded measures\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eClinical assessments were conducted at baseline and 6 months, and clinician-reported assessments were conducted at various timepoints throughout the study. Case managers and clinical staff recorded the Social and Occupational Functioning Assessment Scale (SOFAS)\u003csup\u003e39\u003c/sup\u003e for each participant. The SOFAS score ranges from 0 to 100, with higher scores indicating better overall functioning in daily life, including social situations, occupation, or school\u003csup\u003e39\u003c/sup\u003e. Each participant is assigned a SOFAS score at baseline, and a score of equal to or less than 60 indicates moderate to severe impairment in functioning, or worse\u003csup\u003e39\u003c/sup\u003e. Clinicians also reviewed patients\u0026rsquo; electronic medical records to record medication history, and from which chlorpromazine dose equivalents were calculated for antipsychotic medications\u003csup\u003e54\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSelf-reported questionnaires\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;Depression Anxiety Stress Scales\u0026ndash;21 (DASS-21)\u0026nbsp;measured\u0026nbsp;symptoms of\u0026nbsp;depression, anxiety, and stress. Participants rated the extent to which each statement applied to them\u0026nbsp;during\u0026nbsp;the past week using a\u0026nbsp;4-point Likert scale\u0026nbsp;ranging from\u0026nbsp;0 (\u003cem\u003e\u0026ldquo;Did not apply to me at all\u0026rdquo;\u003c/em\u003e) to 3 \u003cem\u003e(\u0026ldquo;Applied to me very much, or most of the time\u0026rdquo;\u003c/em\u003e)\u003csup\u003e40\u003c/sup\u003e. Example items include \u003cem\u003e\u0026ldquo;I found it hard to wind down\u0026rdquo;\u003c/em\u003e (stress) and \u003cem\u003e\u0026ldquo;I felt that life was meaningless\u0026rdquo;\u003c/em\u003e (depression). Baseline scores were obtained for each subscale, with moderate or greater symptom severity defined as depression scores of \u0026ge; 14, anxiety scores of \u0026ge; 10, and stress scores of \u0026ge; 19\u003csup\u003e40\u003c/sup\u003e. Cronbach\u0026rsquo;s alpha for each subscale ranges from \u003cem\u003e\u0026alpha;\u0026nbsp;\u003c/em\u003e= 0.90-0.95,\u0026nbsp;demonstrating high reliability\u003csup\u003e55\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Behavior and Symptom Identification Scale\u0026ndash;24 (BASIS-24) assessed a broad range of mental health symptoms across six domains: depression/functioning, interpersonal relationships, self-harm, emotional lability, psychosis, and substance use \u003csup\u003e56\u003c/sup\u003e. Participants responded using a 5-point Likert scale ranging from 0 to 4, indicating how frequently they experienced each problem in the past week. Example items include \u003cem\u003e\u0026ldquo;How often did you have mood swings?\u0026rdquo;\u003c/em\u003e and \u003cem\u003e\u0026ldquo;How often did you try to hide your drinking or drug use?\u0026rdquo;\u003c/em\u003e A mean overall BASIS-24 score was calculated for each participant and compared with the established population normative mean of 0.67\u003csup\u003e41\u003c/sup\u003e. As subscale norms are not well established, a mean score was computed for each subscale and compared with the study cohort median to evaluate relative symptom severity within each domain. The BASIS-24 has demonstrated satisfactory reliability (subscales\u0026apos; coefficient \u003cem\u003e\u0026alpha;\u0026nbsp;\u003c/em\u003e= 0.75-0.91)\u003csup\u003e41\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;Pittsburgh Sleep Quality Index (PSQI)\u0026nbsp;evaluated self-reported sleep quality and disturbances over the past month across seven components, yielding a global score ranging from 0 to 21\u003csup\u003e42\u003c/sup\u003e. \u0026nbsp;Example items include \u003cem\u003e\u0026ldquo;During the past month, how often have you had trouble sleeping because you could not get to sleep within 30 minutes?\u0026rdquo;\u003c/em\u003e and \u003cem\u003e\u0026ldquo;During the past month, how would you rate your overall sleep quality?\u0026rdquo;\u003c/em\u003e Scores greater than five indicate clinically significant sleep difficulties (Buysse et al., 1989). Sleep efficiency (%) was calculated for each participant using the following formula: (hours slept / total time in bed) \u0026times; 100. Values exceeding 100% were rounded down to 100 to correct for overestimation. Healthy sleep is considered to be at 85% or greater, with scores \u0026lt;80% indicative of sleep disturbance\u003csup\u003e42\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWearable Measurements:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll participants were asked to wear the Empatica Embrace2 wearable device that continuously recorded their electrodermal activity, actigraphy and body temperature\u003csup\u003e57\u003c/sup\u003e. The Embrace2 contains a tri-axial accelerometer that measures movement patterns, with a battery life of around 48 hours\u003csup\u003e57\u003c/sup\u003e. The physiological data were collected in 30 second epochs. The data from the Empatica device were uploaded to the Empatica server and exported both to the mobile device of the participants, who saw the data displayed via Empatica\u0026rsquo;s proprietary app, and via a bespoke data pipeline to the Sydney Informatics Hub and Mackenzie Wearables Research Hub for data analysis, transformation, and visualisation. Actigraphy was calibrated for signal drift\u003csup\u003e58\u003c/sup\u003e and converted into sleep measures\u003csup\u003e59\u003c/sup\u003e and non-wear as detected\u003csup\u003e60\u003c/sup\u003e using previously validated Random Forest activity classifier\u003csup\u003e61\u003c/sup\u003e used in prior studies\u003csup\u003e62\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this paper, we report on MVPA (bouts and duration in minutes), SB (in minutes), total sleep time (hours) and SRI. For each participant, aggregated medians (based on total wearable time) for SB, MVPA, and total sleep time were calculated. The SRI is an overall calculation of each participant\u0026rsquo;s sleep regularity over the course of the study. A score of 100 is a perfect SRI, with consistent sleep-wake patterns and the same sleep onset and offset times over the course of the study. An SRI of zero indicates random sleeping patterns (i.e., inconsistent sleep-wake times), with no predictable pattern from day to day. A standard threshold of SRI \u0026lt; 70 has been established to identify irregular sleepers \u003csup\u003e43\u003c/sup\u003e. Thus, any participant with an SRI \u0026lt;70 would be classified as having inconsistent sleep patterns.\u003c/p\u003e\n\u003cp\u003eStatistical Analysis:\u003c/p\u003e\n\u003cp\u003eStatistical analyses were conducted using RStudio (version 4.3.2)\u003csup\u003e63\u003c/sup\u003e. For participant characteristics, all continuous variables were summarised using descriptive statistics and categorical variables were summarised using frequency measures. Participants were hierarchically clustered into three groups based on their SRI and the median SB measured with the wearable device. The optimal number of clusters was determined using visual inspection of the dendrogram (Supplementary Figure 1). To compare clinical and demographic variables across the identified clusters, the non-parametric Kruskal-Wallis test was used. Post-hoc pairwise comparisons were conducted using Dunn\u0026rsquo;s test for multiple comparisons.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are not openly available due to reasons of sensitivity, ensuring privacy and ethical compliance. Data can be available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe underlying code for this study is not publicly available but may be made available to qualified researchers on reasonable request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the participants and clinicians at Prevention Early Intervention and Recovery Service (PEIRS), Canterbury and Camperdown community mental health teams, who made this study possible. We also thank the teams at the Sydney Informatics Hub and Mackenzie Wearables Research Hub@the Charles Perkins Centre for their assistance with data storage and pre-processing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePFM: conceptualization, methodology, formal analysis, writing\u0026mdash;original draft, review \u0026amp; editing. DT, RF, DJ, AC: data curation, investigation, writing\u0026mdash;review \u0026amp; editing. FC: Formal analysis, writing\u0026mdash;review \u0026amp; editing. MNA and ES: methodology, data curation, writing\u0026mdash;review \u0026amp; editing. CB: supervision, writing\u0026mdash;review \u0026amp; editing. BK: conceptualization, investigation, supervision, writing\u0026mdash;review \u0026amp; editing. CH and AH: conceptualization, methodology, investigation, supervision, writing\u0026mdash;review \u0026amp; editing. CH and AH contributed equally to this work and share last authorship. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProfessor Anthony Harris has received consultancy fees from Boehringer Ingelheim. Professor Anthony Harris and Beth Kotze were the recipient of an investigator-initiated grant from the Balnaves Foundation. He was an investigator on an industry sponsored trial by Alto Neuroscience. He is the recipient of funding from the Australian Research Council, the Medical Research Futures Fund and the National Health and Medical Research Council. He is a director of Mind Australia, a leading non-government organisation.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBaglioni, C.\u003cem\u003e et al.\u003c/em\u003e Sleep and mental disorders: A meta-analysis of polysomnographic research. \u003cem\u003ePsychol Bull\u003c/em\u003e \u003cstrong\u003e142\u003c/strong\u003e, 969-990 (2016). https://doi.org/10.1037/bul0000053\u003c/li\u003e\n\u003cli\u003eScott, A. J., Webb, T. L., Martyn-St James, M., Rowse, G. \u0026amp; Weich, S. 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(R Foundation for Statistical Computing, 2023).\u003c/li\u003e\n\u003c/ol\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":"npj-exercise-medicine-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Exercise Medicine and Health](https://www.nature.com/npjexercisemed/)","snPcode":"44437","submissionUrl":"https://submission.nature.com/new-submission/44437/3","title":"npj Exercise Medicine and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Wearable Electronic Devices, Community Mental Health Services, Remote Patient Monitoring, Mental Disorders, Sedentary Behaviour, Sleep","lastPublishedDoi":"10.21203/rs.3.rs-9397165/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9397165/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWearable devices enable continuous monitoring of biometric information and may objectively monitor symptoms of severe mental illness. In this study, a wearable device was integrated into youth community mental health settings and used to explore activity and sleep patterns. Of 45 participants (median age=19yrs, 67% female), the largest diagnostic group was affective disorders alone (42%), followed by personality diagnoses (with or without affective diagnoses) (35.6%). Three statistically significant clusters based on sedentary behaviour (SB) were identified using hierarchical cluster analysis. Participants with lowest SB (Cluster 1) had the lowest functional impairment but the highest reported symptom severity. Conversely, the most sedentary group (Cluster 3) had the highest functional impairment but the lowest symptom burden. Using the sleep regularity index, no significant group differences were found. SB recorded via wearable devices may discriminate functional and symptom severity profiles across clusters and serve as an objective measure of clinical progress.\u003c/p\u003e","manuscriptTitle":"Wearables-defined sedentary behaviour clusters reveal distinct symptom profiles in youth with severe mental illness","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-26 15:36:37","doi":"10.21203/rs.3.rs-9397165/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-07T14:13:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252380143683602796768348499846991136070","date":"2026-04-23T17:18:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114381336179276351103676077360343946739","date":"2026-04-21T11:52:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-16T11:43:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-16T11:18:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-16T03:12:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Exercise Medicine and Health","date":"2026-04-12T23:32:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-exercise-medicine-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Exercise Medicine and Health](https://www.nature.com/npjexercisemed/)","snPcode":"44437","submissionUrl":"https://submission.nature.com/new-submission/44437/3","title":"npj Exercise Medicine and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5ead091d-be65-47a9-adb3-814a1285d2b5","owner":[],"postedDate":"April 26th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-07T14:13:06+00:00","index":36,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66900312,"name":"Health sciences/Diseases"},{"id":66900313,"name":"Health sciences/Health care"},{"id":66900314,"name":"Biological sciences/Psychology"},{"id":66900315,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-04-26T15:36:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-26 15:36:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9397165","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9397165","identity":"rs-9397165","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}