A Digital Platform with Activity Tracking for Energy Management Support in Long COVID: A Randomised Controlled Trial

A Digital Platform with Activity Tracking for Energy Management Support in Long COVID: A Randomised Controlled Trial | 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 A Digital Platform with Activity Tracking for Energy Management Support in Long COVID: A Randomised Controlled Trial Lawrence Hayes, Nilihan Sanal-Hayes, Jacqueline Mair, Antonio Dello Iacono, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5951040/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Feb, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract People with long COVID (LC) report worsening symptoms after activity, like post-exertional malaise (PEM) in chronic fatigue syndrome (CFS). The National Institute for Health and Care Excellence (NICE) recommends ‘energy management’ for CFS, but at the time of writing, how people with LC would respond to energy management was unknown. In a 6-month pragmatic decentralised randomised controlled trial (RCT), we compared a just-in-time intervention to support energy management in adults with LC to standard care. Participants were randomised to receive either the ‘Pace Me’ app and a wearable activity tracker (intervention) or an app only with data entry screens (control). The intervention group received just-in-time messages on PEM management when they reached 50%, 75%, and 100% of their daily ‘activity allowance’. The primary outcome was PEM measured by the DePaul Symptom Questionnaire-Post-Exertional Malaise (DSQ-PEM). Of 368 participants assessed for eligibility, 250 participants were randomised 1:1, but 36 control and eight intervention participants were lost to follow-up. 12 control and 24 intervention participants were excluded from analysis due to missing data. 84 intervention participants and 77 control participants were analysed. There was no time by group interaction for the DSQ-PEM. The intervention group value was 48 (95% CI 44–53) pre-intervention and 46 (95% CI 41–51) post-intervention (arbitrary units). The control group value was 47 (95% CI 42–52) pre-intervention and 44 (95% CI 39–49) post-intervention (interaction effect p = 0.614, η²p = 0.002; trivial). No individual question exhibited an interaction effect (P > 0.05). Digitally supported energy management in people with LC had no effect on PEM compared to standard care. Although the intervention had no additional effect compared to control, the substantial recovery rate in LC may have masked intervention effects. Therefore, future studies should consider this energy management framework in conditions without such recovery rates, such as CFS. Health sciences/Medical research/Outcomes research Health sciences/Signs and symptoms/Fatigue Long COVID post-acute sequelae of SARS-CoV-2 (PASC) symptoms digital health just-in-time intervention energy management pacing Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Following the outbreak of the Sars-Cov-2 (COVID-19) virus in March 2020, a significant proportion of those who became infected experienced persistent symptoms lasting several months commonly known as long COVID (LC). Symptoms of LC include pain, lethargy, exercise intolerance, myalgia, cognitive impairment, and fatigue 1,2 . Symptoms could be disabling even if initial effects were mild, and could occur immediately after the infection or some weeks later 3 . These accounts appear similar to post-exertional malaise (PEM; described as an intolerance to mental and physical exertion, triggering an aggravation of symptoms typically lasting > 14 hours up to several days 4 ) reported by people with conditions such as myalgic encephalitis/chronic fatigue syndrome (ME/CFS). LC is associated with highly variable symptom loads, fluctuating between periods of relative alleviation and exacerbation, often preceded by exertion 5 . Our Public Patient Involvement (PPI) group and others 6 describe 'push-crash' cycles where a perception of getting better leads to a push to return to normal activities precipitating a subsequent crash with severe worsening of symptoms, similar to PEM. Early work confirmed that 50% of people with LC meet the technical definition of PEM lasting > 14 hours, while nearly all report some PEM-like symptoms lasting up to 14 hours 6,7 . However, the cause of persistent symptoms is not well understood, and a clear pathophysiological mechanism remains elusive. This is particularly true for those whose initial symptoms were mild and for whom there are no health records since most did not attend hospital 5 or receive any COVID-19 treatment that might explain prolonged rehabilitation (e.g. mechanical ventilation). Without a clear underlying mechanism, treatment options have focussed on symptom management to help people with LC manage activities of daily living (ADL). Building on the similarities in symptoms between LC and ME/CFS, the most common symptom management strategy used in LC support is energy management, the only support strategy currently recommended for ME/CFS 8 . Energy management uses techniques such as planning, feedback, and activity diaries to help individuals manage their ‘energy envelope’ to limit the debilitating consequences of their condition by reducing or avoiding periods of symptom exacerbation 9–11 . Energy management is suitable for self-management and is associated with higher adherence and greater patient-reported benefit 12 . However, despite multiple underlying theories 9,13 , concrete steps for symptom self-management are rare. Energy management also requires participants to recall and compare two poorly described constructs; 'energy availability' and 'energy use', which our PPI group and evidence from the ME/CFS literature find challenging, especially when dealing with impaired cognition and memory 14 . Additionally, the dynamic nature of energy management requires recalling activities that might have triggered previous bouts of PEM-like symptoms, even if they occurred weeks or months earlier. Despite its widespread use, evidence to support the efficacy of energy management in preventing bouts of PEM is extremely limited 11,15 . Furthermore, there are no contemporary trials that leverage mobile and wearable technology, which could address many of the challenges associated with managing and tracking activity to manage LC symptoms 11,15 . The aim of this trial was to determine if activity tracking combined with just-in-time messages helped people with LC implement energy management and reduce incidents of symptom exacerbation. We hypothesised a priori that a just-in-time intervention to assist energy management (intervention group) would reduce frequency or severity of PEM compared to usual care (control). METHODS Study Design The study was designed as a pragmatic single-centre randomised controlled trial. The intervention was a remotely delivered, just-in-time energy management support programme designed to reduce the frequency or severity of PEM experienced by individuals experiencing LC. The trial ran between January 2022 and September 2023. The study was approved by the University of the West of Scotland Institutional Ethics Board (approval number 16638) and the trial was registered with ICTRN (ISRCTN16033549; https://www.isrctn.com/ISRCTN16033549 ). Participants Inclusion criteria Eligible participants had to be adults (≥ 18yrs) reporting persistent symptoms following a COVID-19 infection, which interfered with daily activities (in line with NICE guidelines 2021). Many of those eligible to participate in the trial had contracted COVID-19 before home-based testing kits were available and before polymerase chain reaction (PCR) testing was made publicly available. Consequently, we accepted self-reported persistent symptoms of ≥ 8 weeks following an infection consistent with COVID-19. Participants had to recover at home (e.g. access to their GP but no ongoing clinical care related to their COVID-19 infection) and have access to a smartphone with Android version 6 or iOS version 10 or higher. Exclusion Criteria Participants were excluded if they (1) had insufficient English language to understand messages, (2) had no smartphone access, (3) were participating in another LC intervention, (4) had impaired cognitive function which compromises comprehension of study information or messaging, (5) were receiving therapies known to cause symptom exacerbations (e.g. chemotherapy) or aimed at treating LC, (6) or (6) were receiving ongoing care for LC via primary or secondary care services. Recruitment At the time of recruitment, the UK was engaged in COVID-19 related lockdowns. Individuals expressing interest to trial information distributed via social media were contacted via telephone or video conferencing for a briefing which included screening for inclusion and exclusion criteria, a verbal overview of the study and an opportunity to ask questions. Participants were provided with an information sheet and re-contacted at least 7-days later to provide a further opportunity to ask questions and, if willing, enrol in the trial. Participants provided written informed consent and were allocated an enrolment number. Recruitment was facilitated by our partner organisation, Long COVID Scotland, and involved promotion of the trial via online social groups, social media, print media, a study website and meetings with Long COVID Scotland members. The trial targeted people who had not been hospitalised following their COVID-19 infection, therefore there was no data linkage or recruitment via primary or secondary care. Randomisation and masking A secure third-party service (studyrandomizer.com) was used to randomise 250 participants 1:1 into two evenly distributed study arms (intervention or control) with each arm consisting of 125 participants (50% of the total sample; equal allocation). Randomisation into the respective arms was conducted using a permuted block algorithm, with a fixed block size of 125 participants. Participants randomised to the intervention group received usual care with just-in-time messaging support, and the control group received usual care only (Fig. 1). NS-H generated the sequence, enrolled participants, and assigned them to the trial groups. NS-H was the senior post-doctoral trial manager for the rest of the trial. Blinding was impossible due to the requirements of the experimental group. Both groups were required to download an app. The intervention group downloaded the Pace Me app which included energy management support and data collection instruments, whereas the control group’s app only contained data collection instruments. Participants created an account, logged in, provided additional e-consent (ensuring participants understood their interaction with the app), and undertook baseline assessments. Screengrabs of checkbox completion and digital signatures were captured and securely stored separate to other study data. Participants self-reported gender at their initial meeting at enrolment. ***INSERT FIG 1 NEAR HERE*** Procedures Following randomisation, a study start date approximately one week later was agreed. This provided time for an activity tracker (Fitbit Charge 5, Fitbit Inc., San Francisco, CA, USA) to be posted to intervention group participants. Subsequently, an online meeting was scheduled to assist participants in downloading the Pace Me app, registering an account and completing the e-consent, and turning off all notifications from Fitbit that would encourage additional exercise. Intervention participants entered their corresponding activity tracker number to activate the intervention features of the app (including real time tracking of energy management), while control participants only had access to in app questionnaires (Fig. 2). All participants completed questionnaires at the online meeting, to ensure baseline data capture before starting the intervention or control part of the experiment. All participants received automated alerts to remind them when they were due to complete specific questionnaires and reminders if they remained uncompleted a week after the intended completion time. ***INSERT FIG 2 NEAR HERE*** Intervention Design The Pace Me app aimed to support activity pacing in individuals with long COVID. The intervention design was informed by the Behaviour Change Wheel (BCW) 16 , evidence from literature 11,17 , and PPI feedback. The final design used goal setting, action planning, self-monitoring, feedback, prompts, and personalisation to assist users in managing energy levels. The intervention integrated heart rate and step count feedback using Fitbit Charge 5, logging of PEM, logging of symptoms, and support messaging when patients exceeded their energy allowance (see supplementary information 1 for full details of the intervention). The intervention used a personalised activity allowance, initially set to no more than 30 minutes of activity above 60% of their age-predicted heart rate maximum. This allowance was iteratively adjusted in response to participants’ activity and PEM reports. If participants reported PEM and had exceeded the allowance, the allowance remained unchanged (i.e. the PEM may be due to poor pacing). If participants reported PEM but had remained within their allowance for the preceding 3 days, the allowance was reduced in a stepwise fashion (i.e. activity allowance may be too large to prevent PEM). Conversely, the allowance was increased if participants did not report PEM for three consecutive weeks. For participants with unstable heart rates (e.g. autonomic issues), step counts were used to set activity allowance. These limits were not intended as targets but as a guide for energy management. The Pace Me app featured a home screen that displayed real-time tracking showing how much of that day’s energy allowance participants had used. Visualisations and emojis showed when participants were most at risk of exceeding their limits and provided feedback to help participants understand their pacing patterns. Participants received personalised notifications as their activity approached specific thresholds, capped at three per day to avoid overwhelming users. Notifications were triggered when participants reached 50%, 75%, and 100% of their activity allowance. They included an alert message and support tips sourced from individuals with experience in pacing strategies for managing energy (Fig. 3). ***INSERT FG 3 NEAR HERE*** Outcomes The primary outcome was frequency and severity of PEM assessed using the DePaul Symptom Questionnaire - PEM (DSQ-PEM) 4 (LC-COS Post-exertion symptoms), pre- and post-intervention. Questions 1–5 were measured on a five-point Likert scale with a ‘frequency’ domain (0 = none of the time, 1 = a little of the time, 2 = about half the time, 3 = most of the time, and 4 = all of the time) and a ‘severity’ domain. (0 = symptom not present, 1 = mild, 2 = moderate, 3 = severe, and 4 = very severe). Questions 6–8 and 10 were dichotomous yes/no responses, and question 9 asked ‘if you feel worse after activities, how long does this last?’ with six options: ≤1 h, 2–3 h, 4–10 h, 11–13 h, 14–23 h, or ≥ 24 h. Secondary outcomes were 12-Item Short Form Health Survey (SF-12), EuroQol 5-Dimension Health Questionnaire (EQ5D), Pain Visual Analogue Scale (VAS), Patient Health Questionnaire-4 (PHQ4), Fatigue Severity Scale (7-item version; FSS-7), Medical Research Council Breathlessness Questionnaire (MRC BQ), Symbol Digit Modalities Test (SDMT) total correct (out of 60), total time for correct answers only, average time per correct answer. Statistical analysis To determine sample size, our primary outcome variable was the DSQ-PEM. Using previous work, a minimum clinically relevant difference can be estimated as a change of 13 points on a 100-point scale 18 . Assuming a standard deviation (SD) of 25 18 , this resulted in a pairwise effect size of d = 0.5 (Cohen’s f = 0.25). We calculated our desired sample size for a two-way mixed-model (within- and between-subjects) analysis of variance (ANOVA). Using the WebPower package in R Studio, and the wp.rmanova function, with two groups, two time points, a medium effect size ( f = 0.25), assuming sphericity, an alpha of 0.05, desired statistical power of 0.9, testing for an interaction effect, the total n was 170 (85 per group). Consequently, we aimed to recruit 125 participants per group to allow for 30% drop-out. A post-hoc power calculation resulted in observed power of 0.88 with our sample size of 161, an effect size of f = 0.25, and an alpha level of 0.05. All analyses were conducted using Jamovi version 2.3.21. Data were tested for for normal distribution and homogeneity of variance to confirm parametric assumptions were met. Data are presented in text and tables as means and 95% confidence intervals (CI) unless otherwise stated. Because of randomisation, we did not undertake analysis of baseline equivalence, since the null hypothesis must be true and any differences due to chance 19 . Only participants who completed follow-up testing were included in analysis (i.e. per protocol analysis). The effect of the energy management intervention on main and secondary outcomes was examined using two-way mixed-model ANOVA with condition (intervention or control) as the between-subjects factor and time (pre- and post-intervention) as a within subjects factor. Alpha level is reported as exact p values and not described dichotomously as 'significant' or otherwise as recommended by the American Statistical Association 20 . We expressed effect sizes from the ANOVA as partial eta-squared (η²p), with values of 0.01, 0.06, and 0.14 interpreted as small, moderate, and large, respectively 21 . For categorical data, (DSQ-PEM questions 6–10) we used McNemar’s Test for paired samples (pre- to post- intervention), or Chi squared test for between group effects (intervention vs. control). Role of the funding source The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. RESULTS Of 368 participants assessed for eligibility, 55 were deemed ineligible, therefore 313 participants were recruited but 63 withdrew prior to randomisation. In total, 250 participants were randomised (125 per group), but 36 control and eight intervention participants were lost to follow-up. At the analysis stage, 12 control and 24 intervention participants were excluded from analysis due to missing data. Therefore, 84 intervention participants and 77 control participants completed the six-month follow-up and post-intervention data collection for the primary endpoint. Some participants completed the primary endpoint but not secondary outcome questionnaires, despite reminders. Therefore, the sample size for each outcome variable is reported in tables. Using the sum of the DSQ-PEM questions 1–5 expressed on a 100-point scale (as per our power calculation), the intervention group value was 48 (95% CI 44–53) pre-intervention and 46 (95% CI 41–51) post-intervention. The control group value was 47 (95% CI 42–52) pre-intervention and 44 (95% CI 39–49) post-intervention (interaction effect p = 0.614, η²p = 0.002; trivial). Analysis of individual questions are reported in Table 1 . There was a between group effect for question 1 frequency response, but no time or interaction effects were observed. For questions 6–8, and 10, the proportion of subjects who reported yes or no did not differ by time except for question 7 whereby the intervention group exhibited a higher proportion of yes responses pre-intervention compared to post-intervention. For question 9, there was a within-group effect for time, whereby participants felt worse after activities but there were no between group differences. There were no other observed effects. Table 1 DePaul Symptom Questionnaire - Post-exertional Malaise (DSQ-PEM) question responses for intervention and control groups. Baseline Post Repeated measures analysis of variance (ANOVA) Mean (95% CI) Mean (95% CI) p value time (η²p) p value group (η²p) p value interaction (η²p) DSQ-PEM Q1 severity Intervention (n = 84) 2.1 (1.9–2.5) 2.2 (1.9–2.5) p = 0.072 (η²p = 0.020; small effect) p = 0.354 (η²p = 0.005; trivial effect) p = 0.058 (η²p = 0.022; small effect) Control (n = 77) 2.3 (1.9–2.6) 1.8 (1.5–2.1) Q1 frequency Intervention (n = 84) 3.0 (2.7–3.3) 2.8 (2.5–3.1) p = 0.063 (η²p = 0.022; small effect) p = 0.039 (η²p = 0.027; small effect) p = 0.704 (η²p = 0.001; trivial effect) Control (n = 77) 2.6 (2.4–2.9) 2.4 (2.1–2.7) Q2 severity Intervention (n = 84) 2.1 (1.8–2.4) 2.0 (1.7–2.3) p = 0.380 (η²p = 0.005; trivial effect) p = 0.716 (η²p = 0.001; trivial effect) p = 0.656 (η²p = 0.001; trivial effect) Control (n = 77) 2.2 (1.9–2.5) 2.0 (1.7–2.4) Q2 frequency Intervention (n = 84) 2.9 (2.6–3.2) 2.7 (2.4-3.0) p = 0.694 (η²p = 0.001; trivial effect) p = 0.711 (η²p = 0.001; trivial effect) p = 0.316 (η²p = 0.006; trivial effect) Control (n = 77) 2.7 (2.4-3.0) 2.8 (2.5–3.1) Q3 severity Intervention (n = 84) 1.6 (1.3-2.0) 1.9 (1.5–2.2) p = 0.380 (η²p = 0.005; trivial effect) p = 0.333 (η²p = 0.006; trivial effect) p = 0.623 (η²p = 0.002; trivial effect) Control (n = 77) 2.1 (1.8–2.5) 2.0 (1.6–2.3) Q3 frequency Intervention (n = 84) 2.5 (2.2–2.8) 2.4 (2.2–2.7) p = 0.460 (η²p = 0.003; trivial effect) p = 0.890 (η²p = 0.000; trivial effect) p = 0.856 (η²p = 0.000; trivial effect) Control (n = 77) 2.5 (2.2–2.8) 2.5 (2.1–2.7) Q4 severity Intervention (n = 84) 2.1 (1.8–2.4) 1.9 (1.6–2.2) p = 0.311 (η²p = 0.006; trivial effect) p = 0.317 (η²p = 0.002; trivial effect) p = 0.269 (η²p = 0.008; trivial effect) Control (n = 77) 2.2 (1.8–2.5) 2.0 (1.7–2.3) Q4 frequency Intervention (n = 84) 2.9 (2.6–3.2) 2.7 (2.4-3.0) p = 0.681 (η²p = 0.001; trivial effect) p = 0.322 (η²p = 0.006; trivial effect) p = 0.164 (η²p = 0.012; small effect) Control (n = 77) 2.6 (2.2–2.9) 2.7 (2.4-3.0) Q5 severity Intervention (n = 84) 2.0 (1.7–2.3) 1.9 (1.6–2.2) p = 0.080 (η²p = 0.019; small effect) p = 0.861 (η²p = 0.000; trivial effect) p = 0.262 (η²p = 0.008; trivial effect) Control (n = 77) 2.1 (1.8–2.5) 1.8 (1.5–2.1) Q5 frequency Intervention (n = 84) 2.6 (2.3–2.9) 2.4 (2.0-2.7) p = 0.552 (η²p = 0.002; trivial effect) p = 0.411 (η²p = 0.004; trivial effect) p = 0.368 (η²p = 0.005; trivial effect). Control (n = 77) 2.3 (2.0-2.6) 2.3 (2.0-2.7) McNemar’s test Chi squared test Count (%) Count (%) Within group p value ( X 2 ) Between group p value ( X 2 ) Q6 response Intervention (pre n = 84, post n = 83) 9 yes (11%) 75 no (89%) 12 yes (14%) 71 no (86%) p = 0.439 (0.600) Pre; p = 1.000 (0.0.00) Post; p = 0.787 (0.073) Control (pre n = 77, post n = 77) 8 yes (10%) 69 no (90%) 10 yes (13%) 67 no (87%) p = 0.251 (1.32) Q7 response Intervention (pre n = 84, post n = 83) 82 yes (98%) 2 no (2%) 72 yes (87%) 11 no (13%) p = 0.007 (7.36) Pre; p = 0.266 (1.24) Post; p = 0.596 (0.280) Control (pre n = 77, post n = 76) 73 yes (95%) 4 no (5%) 68 yes (89%) 8 no (11%) p = 0.206 (1.60) Q8 response Intervention (pre n = 84, post n = 84) 71 yes (90%) 13 no (1 0%) 65 yes (77%) 19 no (23%) p = 0.157 (2.00) Pre; p = 0.465 (0.533) Post; p = 0.258 (1.28) Control (pre n = 77, post n = 77) 69 yes (90%) 8 no (10%) 65 yes (84%) 12 no (16%) p = 0.206 (1.60) Q 9 response Intervention (pre n = 84, post n = 82) 1 reported ≤ 1 hr (1%) 5 reported 2–3 hr (6%) 21reported 4–10 hr (25%) 2 reported 11–13 hr (2%) 7 reported 14–23 hr (8% 48reported ≥ 24 hr (57%) 2 reported ≤ 1 hr (2%) 8 reported 2–3 hr (10%) 15 reported 4–10 hr (18%) 1 reported 11–13 hr (1%) 15 reported 14–23 hr (18%) 41 reported ≥ 24 hr (50%) p < 0.001 (74.3) Pre; p = 0.861 (1.91) Post; p = 0.667 (3.21) Control (pre n = 77, post n = 76) 2 reported ≤ 1 hr (3%) 5 reported 2–3 hr (6%) 13 reported 4–10 hr (17%) 2 reported 11–13 hr (3%) 7 reported 14–23 hr (9% 48 reported ≥ 24 hr (62%) 2 reported ≤ 1 hr (3%) 5 reported 2–3 hr (7%) 10 reported 4–10 hr (13%) 2 reported 11–13 hr (3%) 10 reported 14–23 hr (13% 47 reported ≥ 24 hr (62%) p < 0.001 (69.3) Q10 response Intervention (pre n = 82, post n = 80) 75 yes (91%) 7 no (9%) 74 yes (93%) 6 no (8%) p = 0.198 (4.67) Pre; p = 0.712 (0.136) Post; p = 0.453 (1.58) Control (pre n = 77, post n = 77) 61 yes (90%) 7 no (10%) 63 yes (86%) 10 no (14%) p = 0.317 (1.00) ***INSERT Table 1 NEAR HERE*** Secondary outcomes are reported in Table 2 . No interaction effects were evident, suggesting the energy management intervention was not superior to control. Some effects for time were evident, suggesting an improvement in secondary outcomes from baseline to post-intervention follow-up in both groups. Table 2 Sf12 = 12-Item Short Form Health Survey, EQ5D: EuroQol 5-Dimension Health Questionnaire, Pain VAS: Pain Visual Analogue Scale, PHQ4: Patient Health Questionnaire-4, FSS (7 item): Fatigue Severity Scale (7-item version), MRC BQ = Medical Research Council Breathlessness Questionnaire, SEMCD = Self-Efficacy to Manage Chronic Disease, SDMT = Symbol Digit Modalities Test, TC = total correct (out of 60), TTCA = total time for correct answers only, TPCA = average time per correct answer. Baseline Post Repeated measures analysis of variance (ANOVA) Mean (95% CI) Mean (95% CI) p value time (η²p) p value group (η²p) p value interaction (η²p) HRQoL SF12 - Physical heath Intervention (n = 53) 54 (54–54) 54 (54–55) 0.754 (0.001; trivial) 0.969 (0.000; trivial) 0.349 (0.009; trivial) Control (n = 50) 54 (54–54) 54 (54–54) SF12 - Mental Health Intervention (n = 53) 59 (59–59) 59 (59–59) 0.003 (0.086; moderate) 0.241 (0.014; small) 0.531 (0.004; trivial) Control (n = 50) 59 (59–60) 59 (59–60) EQ5D Anxiety Intervention (n = 84) 1.4 (1.2–1.6) 1.2 (1.0-1.5) 0.075 (0.020; small) 0.583 (0.002; trivial) 0.957 (0.000; trivial) Control (n = 73) 1.3 (1.2–1.6) 1.2 (0.9–1.4) EQ5D Mobility Intervention (n = 84) 1.3 (1.1–1.6) 1.5 (1.2–1.7) 0.416 (0.004; trivial) 0.906 (0.000; trivial) 0.543 (0.002; trivial) Control (n = 73) 1.5 (1.3–1.7) 1.5 (1.2–1.7) EQ5D Pain Intervention (n = 84) 1.9 (1.7–2.1) 1.8 (1.6-2.0) 0.721 (0.001; trivial) 0.933 (0.000; trivial) 0.569 (0.002; trivial) Control (n = 73) 1.8 (1.6-2.0) 1.8 (1.6–2.1) EQ5D Self-care Intervention (n = 84) 1.0 (0.8–1.2) 1.0 (0.8–1.2) 0.431 (0.004; trivial) 0.803 (0.000; trivial) 0.180 (0.012; small) Control (n = 73) 1.0 (0.8–1.2) 0.9 (0.7–1.1) EQ5D Usual activities Intervention (n = 84) 2.5 (2.3–2.7) 2.3 (2.1–2.6) 0.003 (0.056; small) 0.530 (0.003; trivial) 0.657 (0.001; trivial) Control (n = 73) 2.6 (2.4–2.8) 2.4 (2.1–2.6) EQ5D Index Intervention (n = 84) 0.38 (0.31–0.44) 0.38 (0.30–0.45) 0.456 (0.004; trivial) 0.324 (0.006; trivial) 0.178 (0.012; small) Control (n = 73) 0.43 (0.37–0.49) 0.39 (0.31–0.47) Pain VAS Intervention (n = 78) 46 (41–51) 46 (40–52) 0.721 (0.001; trivial) 0.933 (0.000; trivial) 0.569 (0.002; trivial) Control (n = 71) 46 (40–51) 44 (38–51) Anxiety and Depression PHQ4 Intervention (n = 74) 5.3 (4.7-6.0) 4.7 (3.9–5.5) 0.002 (0.065; small) 0.686 (0.001; trivial) 0.764 (0.001; trivial) Control (n = 74) 5.2 (4.6–5.9) 4.5 (3.6–5.3) Fatigue FSS − 7 Intervention (n = 76) 6.3 (6.1–6.4) 6.0 (5.8–6.3) < 0.001 (0.118; large) 0.693 (0.001; trivial) 0.808 (0.000; trivial) Control (n = 71) 6.4 (6.3–6.6) 6.0 (5.8–6.4) Breathlessness MRC BQ Intervention (n = 84) 1.6 (1.4–1.8) 1.4 (1.3–1.6) 0.037 (0.028; small) 0.723 (0.001; trivial) 0.369 (0.005; trivial) Control (n = 74) 1.6 (1.4–1.7) 1.5 (1.3–1.6) Self-Management SEMCD Intervention (n = 81) 26 (25–28) 31 (28–33) 0.431 (0.004; trivial) 0.803 (0.000; trivial) 0.180 (0.012; small) Control (n = 72) 26 (24–28) 31 (27–33) Cognitive Function SDMT TC (/60) Intervention (n = 82) 58 (57–59) 59 (59–60) 0.154 (0.013; small) 0.355 (0.005; trivial) 0.107 (0.016; small) Control (n = 77) 58 (57–59) 59 (58–59) SDMT TTCA (s) Intervention (n = 82) 133 (126–141) 132 (113–151) 0.250 (0.008; trivial) 0.476 (0.003; trivial) 0.119 (0.015; small) Control (n = 77) 146 (129–163) 127 (120–134) SDMT TPCA (s) Intervention (n = 82) 2.3 (2.2–2.5) 2.2 (1.9–2.6) 0.154 (0.013; small) 0.355 (0.005; trivial) 0.107 (0.016; small) Control (n = 77) 2.5 (2.2–2.9) 2.2 (2.0-2.3) ***INSERT Table 2 NEAR HERE*** DISCUSSION This is the first study of its kind to examine digital technology to support energy management in conditions with PEM. We rigorously tested guided energy management in this randomised controlled trial (RCT), the first since the landmark PACE Trial, published over a decade ago 22 . The main finding of this study was that a just-in-time intervention in conjunction with a wearable activity tracker to support energy management in people with LC was not more effective than usual care. There were no intervention effects on the primary outcome, DSQ-PEM score, for questions 1-5 (or the sum of 1-5). Both groups experienced an improvement in secondary outcomes, including mental health, severity of problems in usual activities, anxiety and depression, fatigue, and breathlessness at follow up. This indicates regression to the mean, whereby both groups improved over time. As a result, we must reject our hypothesis that a just-in-time intervention to assist energy management would reduce frequency or severity of PEM compared to controls. Nevertheless, the intervention group reported fewer ‘yes’ responses to question 7 ‘do you experience a worsening of your fatigue/energy related illness after engaging in minimal physical effort?’ over time, while no differences were observed for the control group. Additionally, responses to question 9 whereby participants indicated whether they felt worse after activity indicated an improvement over time in the intervention group. This suggests that the intervention group perceived less PEM symptoms due to physical effort over the course of the intervention. However, given these findings are not corroborated by other items from the DSQ-PEM these findings should be interpreted with caution. Several factors may explain the lack of intervention efficacy. At the time of study development, LC was a relatively new condition, with most individuals having been initially infected during the first wave of COVID-19. There was an emerging view that LC symptoms were very similar to other post-viral conditions such as ME/CFS, such that they were almost indistinguishable as separate conditions 23 . Consequently, the intervention was developed under the assumption that LC would resemble ME/CFS and that individuals would experience prolonged and potentially persistent symptoms. This assumption holds for a subset of LC patients. For instance, members of our PPI group have been managing PEM and other symptom exacerbations since their initial infection in 2020. However, new evidence suggests that most individuals recover from LC, albeit very slowly, over a period of several months. For example, in a separate study, we tracked around 250 individuals for 9 months after their initial COVID-19 infection, generating early prospective data on the transition from acute infection to LC 24 . In that study, most experienced symptoms for four to seven months, with around 3% experiencing longer-lasting symptoms at follow up. Similarly, Oliveira et al. 25 tracked 34 LC patients for 12 months, finding that many initially met the case definition for ME/CFS, but most showed symptom improvement by the 12-month follow-up. Jason et al. 26 also found that over five months, most LC patients reported improvements in symptoms, particularly sleep and incidence of PEM. A consequence of these findings is that, for most individuals (excluding a small proportion who may develop a more persistent post-viral condition), LC differs from conditions like ME/CFS, where recovery can take years or may not occur at all. Consequently, the effectiveness of this type of activity-tracking just-in-time intervention is less certain for individuals with ME/CFS, who are unlikely to experience significant recovery or symptom reduction within the timeframe of such trials. There are some limitations of this trial that should be noted. At the time of recruitment, there was no agreed case definition for LC. Moreover, several of our participants had LC from presumed COVID-19 infection early in the pandemic, before home testing kits were available. We excluded hospitalised individuals who may have undergone PCR confirmation early in the pandemic. Consequently, definitive diagnosis of the participants’ condition was not possible. While ascertainment bias, for this reason, is both common and well-established in LC research 27 , it remains a limitation that should be considered. Nevertheless, robust randomisation and allocation concealment should prevent systematic effect in the resulting data. A second limitation, although necessary, was that we provided activity trackers to the intervention group but not the control group. The reason we believed this necessary was provision of a wearable would be in itself a form of intervention. As such, it is possible that similar frequency and severity of PEM existed between the two groups despite differences in physical activity, but due to our design we cannot examine this. Future work may wish to attempt to control for this limitation. Similarly, it is possible that some control participants used their own activity tracker or app to modify and manage their activity. For example, part way through the trial a commercial app aimed at energy management was launched and some control participants may have decided to use that app, introducing contamination bias. Thirdly, while two-arm RCTs are excellent for determining efficacy of an intervention, they are less helpful in providing data for optimisation. This optimisation is crucial for enhancing effectiveness, which is often only achieved through repeated testing and refinement. Our qualitative work indicated participants viewed the Pace Me platform positively, gaining a sense of insight, validation, and control over their condition 28 . Therefore, although our framework was not successful at reducing PEM in people with LC in 2022 and 2023, we suggest this should be developed and refined for deployment in different settings or population, much like the model of drug repurposing. Despite the limitations described above, the present study had a significant number of strengths. Firstly, we tested a novel method of energy management utilising technology, meaning this study is transformative compared with previous work which relied on journalling and activity planning. This project provided a user-friendly platform that simplified complex tasks of tracking activity, comparing PEM events, and influencing daily activity decisions for the end user. We hope that despite the null findings herein, this energy management framework can be employed for other PEM-experiencing conditions such as lupus, ME/CFS, multiple sclerosis (MS), rheumatoid arthritis, etc. Advances in digital technologies have opened unprecedented opportunities to deliver effective and scalable behaviour change interventions and just-in-time adaptive interventions (JITAIs) have existed for almost a decade 29 , but until now, had not been applied to conditions with PEM. Therefore, implementing just-in-time energy management frameworks in these conditions could have significant impact given the scalability and inclusivity of remote support 30 . This step-wise change in how energy management can be delivered could result in positive individual, societal, and economic impacts. In conclusion, this study found a lack of treatment effect for those who received a just-in-time energy management intervention. Importantly, there were no adverse incidents in the intervention group, addressing a potential concern raised by our PPI group regarding the use of activity trackers in this population. This equivalence, along with the absence of adverse incidents, demonstrates that the intervention was safe and feasible. Future work should explore efficacy of similar apps in patients with conditions that are not expected to recover such as ME/CFS. Declarations AUTHOR CONTRIBUTIONS ACCORDING TO THE CREDIT TAXONOMY Conceptualisation: Nicholas F Sculthorpe; Methodology: Nilihan EM Sanal-Hayes, Lawrence D Hayes, Jacqueline L Mair, Joanne Ingram, Jane Ormerod, David Carless, Natlie Hilliard, Marie Mclaughlin, Rachel Meach, Nicholas F Sculthorpe; Formal analysis and investigation: Nilihan EM Sanal-Hayes, Nicholas F Sculthorpe; Investigation: Nilihan EM Sanal-Hayes, Nicholas F Sculthorpe; Resources: Nilihan EM Sanal-Hayes, Lawrence D Hayes, Jacqueline L Mair, Joanne Ingram, Jane Ormerod, David Carless, Natlie Hilliard, Marie Mclaughlin, Rachel Meach, Nicholas F Sculthorpe; Writing - original draft preparation: Nilihan EM Sanal-Hayes, Lawrence D Hayes, Jacqueline L Mair, Antonio Dello Iacono, Joanne Ingram, Nicholas F Sculthorpe; Writing - review and editing: Nilihan EM Sanal-Hayes, Jacqueline L Mair, Lawrence D Hayes, Antonio Dello Iacono, Joanne Ingram, Jane Ormerod, David Carless, Natlie Hilliard, Marie Mclaughlin, Rachel Meach, Nicholas F Sculthorpe; Visualization: Nicholas F Sculthorpe; Supervision: Nicholas F Sculthorpe; Project administration: Nilihan EM Sanal-Hayes, Lawrence D Hayes, Jacqueline L Mair, Antonio Dello Iacono, Joanne Ingram, Jane Ormerod, David Carless, Natlie Hilliard, Marie Mclaughlin, Rachel Meach, Nicholas F Sculthorpe; Funding acquisition: Lawrence D Hayes, Jacqueline L Mair, Joanne Ingram, Jane Ormerod, David Carless, Natlie Hilliard, Nicholas F Sculthorpe DECLARATION OF INTERESTS We declare no competing interests. DATA SHARING Data collected for this study, including individual anonymised participant data and a data dictionary defining each field in the set will be made available to others upon reasonable request. ACKNOWLEDGEMENTS This study was funded by the National Institute for Health and Care Research (NIHR) (Research Award COV-LT2-0010). The views expressed in this publication are those of the authors and not necessarily those of NIHR or the Department of Health and Social Care. Funding: National Institute for Health and Care Research References Greenhalgh T, Knight M, A’Court C, Buxton M, Husain L. Management of post-acute covid-19 in primary care. BMJ 2020; 370 : m3026. Hayes LD, Ingram J, Sculthorpe NF. More Than 100 Persistent Symptoms of SARS-CoV-2 (Long COVID): A Scoping Review. Frontiers in Medicine 2021; 8 . https://www.frontiersin.org/articles/10.3389/fmed.2021.750378 (accessed Feb 15, 2024). Sleat D, Wain R, Miller B. Long Covid: Reviewing the Science and Assessing the Risk. Tony Blair Institute for Global Change, 2020 https://institute.global/policy/long-covid-reviewing-science-and-assessing-risk (accessed Dec 9, 2020). Cotler J, Holtzman C, Dudun C, Jason L. A Brief Questionnaire to Assess Post-Exertional Malaise. Diagnostics 2018; 8 : 66. NIHR. Living with Covid19. National Institute for Health Research, 2020 DOI:10.3310/themedreview_41169. Vernon SD, Hartle M, Sullivan K, et al. Post-exertional malaise among people with long COVID compared to myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Work 2023; 74 : 1179–86. Wormgoor MEA, Rodenburg SC. Focus on post-exertional malaise when approaching ME/CFS in specialist healthcare improves satisfaction and reduces deterioration. Front Neurol 2023; 14 . DOI:10.3389/fneur.2023.1247698. NICE. Myalgic encephalomyelitis (or encephalopathy)/chronic fatigue syndrome: diagnosis and management. chronic fatigue syndrome 2021; : 87. Goudsmit EM, Nijs J, Jason LA, Wallman KE. Pacing as a strategy to improve energy management in myalgic encephalomyelitis/chronic fatigue syndrome: a consensus document. Disabil Rehabil 2012; 34 : 1140–7. The Spoon Theory written by Christine Miserandino. But You Dont Look Sick? support for those with invisible illness or chronic illness. 2013; published online April 25. https://butyoudontlooksick.com/articles/written-by-christine/the-spoon-theory/ (accessed Feb 13, 2024). Sanal-Hayes NEM, Mclaughlin M, Hayes LD, et al. A scoping review of ‘Pacing’ for management of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS): lessons learned for the long COVID pandemic. Journal of Translational Medicine 2023; 21 : 720. Geraghty K, Hann M, Kurtev S. Myalgic encephalomyelitis/chronic fatigue syndrome patients’ reports of symptom changes following cognitive behavioural therapy, graded exercise therapy and pacing treatments: Analysis of a primary survey compared with secondary surveys. J Health Psychol 2017; 24 : 1318–33. Jason L, Muldowney K, Torres-Harding S. The Energy Envelope Theory and myalgic encephalomyelitis/chronic fatigue syndrome. AAOHN J 2008; 56 : 189–95. Jason L, Benton M, Torres-Harding S, Muldowney K. The impact of energy modulation on physical functioning and fatigue severity among patients with ME/CFS. Patient Education and Counseling 2009; 77 : 237–41. Sanal-Hayes NEM, Mclaughlin M, Mair JL, et al. ‘Pacing’ for management of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS): a systematic review and meta-analysis. Fatigue: Biomedicine, Health & Behavior 2024; : 1–18. Michie S, Atkins L, West R. The Behaviour Change Wheel: A Guide to Designing Interventions. Silverback Publishing, 2014. Mair JL, Salamanca-Sanabria A, Augsburger M, et al. Effective Behavior Change Techniques in Digital Health Interventions for the Prevention or Management of Noncommunicable Diseases: An Umbrella Review. Annals of Behavioral Medicine 2023; 57 : 817–35. Jason L, Ohanian D, Brown A, et al. Differentiating Multiple Sclerosis from Myalgic Encephalomyelitis and Chronic Fatigue Syndrome. Insights Biomed 2017; 2 : 11. Moher D, Hopewell S, Schulz KF, et al. CONSORT 2010 Explanation and Elaboration: updated guidelines for reporting parallel group randomised trials. BMJ 2010; 340 : c869. Hurlbert SH, Levine RA, Utts J. Coup de Grâce for a Tough Old Bull: “Statistically Significant” Expires. The American Statistician 2019; 73 : 352–7. Lakens D. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol 2013; 4 . DOI:10.3389/fpsyg.2013.00863. White P, Chalder T, Sharpe M. The PACE trial: results of a large trial of nonpharmacological treatments. Journal of psychosomatic research 2011; 70 : 622‐. Sukocheva OA, Maksoud R, Beeraka NM, et al. Analysis of post COVID-19 condition and its overlap with myalgic encephalomyelitis/chronic fatigue syndrome. J Adv Res 2022; 40 : 179–96. Sculthorpe NF, McLaughlin M, Cerexhe L, et al. Tracking Persistent Symptoms in Scotland (TraPSS): a longitudinal prospective cohort study of COVID-19 recovery after mild acute infection. BMJ Open 2025; 15 : e086646. Oliveira CR, Jason LA, Unutmaz D, Bateman L, Vernon SD. Improvement of Long COVID symptoms over one year. Front Med (Lausanne) 2022; 9 : 1065620. Jason LA, Islam M, Conroy K, et al. COVID-19 Symptoms Over Time: Comparing Long-Haulers to ME/CFS. Fatigue 2021; 9 : 59–68. Høeg TB, Ladhani S, Prasad V. How methodological pitfalls have created widespread misunderstanding about long COVID. BMJ Evid Based Med 2024; 29 : 142–6. Meach R, Carless D, Sanal-Hayes NEM, et al. An Adaptive Pacing Intervention for Adults Living With Long COVID: A Narrative Study of Patient Experiences of Using the PaceMe app. Journal of Patient Experience 2024; 11 : 23743735241272158. Rabbi M, Pfammatter A, Zhang M, Spring B, Choudhury T. Automated personalized feedback for physical activity and dietary behavior change with mobile phones: a randomized controlled trial on adults. JMIR Mhealth Uhealth 2015; 3 : e42. Perez MV, Mahaffey KW, Hedlin H, et al. Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. N Engl J Med 2019; 381 : 1909–17. Additional Declarations There is NO Competing Interest. Supplementary Files SUPPPLEMENTARYFIGUREdayswithoutpem.tif supplementary information 2: days without PEM SUPPLEMENTARYfiguredataflow.jpg supplementary information 1: details of the intervention Cite Share Download PDF Status: Published Journal Publication published 02 Feb, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5951040","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":412660729,"identity":"f35d531c-f9bf-41f8-bb23-53b48e645e1f","order_by":0,"name":"Lawrence 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12:36:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5951040/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5951040/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-025-64831-y","type":"published","date":"2026-02-02T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80251747,"identity":"8698732b-7c9c-4650-943c-6a385dc42676","added_by":"auto","created_at":"2025-04-09 17:24:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53650,"visible":true,"origin":"","legend":"\u003cp\u003eCONSORT flow diagram of study recruitment.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5951040/v1/ebadf2d6d03a6ee987afb31f.png"},{"id":80251968,"identity":"1a5babb3-3b66-481c-b852-14a545136ac6","added_by":"auto","created_at":"2025-04-09 17:32:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1193839,"visible":true,"origin":"","legend":"\u003cp\u003eExample of the in-app display for the experimental group with energy management support.\u003c/p\u003e","description":"","filename":"figure2pacingdisplay.png","url":"https://assets-eu.researchsquare.com/files/rs-5951040/v1/dd830f1bd3f84c83b4f46d7d.png"},{"id":80251749,"identity":"79ee43fe-f18f-4cb2-a439-6322188c48c7","added_by":"auto","created_at":"2025-04-09 17:24:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":237814,"visible":true,"origin":"","legend":"\u003cp\u003eThree examples of energy management alerts received by the experimental group. The image displays the alerts when expanded (i.e. pressed on the phone home screen and opened the Pace Me app).\u003c/p\u003e","description":"","filename":"figure3pacingalerts.png","url":"https://assets-eu.researchsquare.com/files/rs-5951040/v1/36c54fb68b381f6fd1c2a021.png"},{"id":101739714,"identity":"d3e5b579-28c7-4e83-9478-8bc65e2417bc","added_by":"auto","created_at":"2026-02-03 08:05:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3885441,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5951040/v1/97af04f4-bfad-4f15-9b90-94fcfae9d4ea.pdf"},{"id":80251972,"identity":"5c255c0f-7b3d-4d45-80fe-6c1737e69c42","added_by":"auto","created_at":"2025-04-09 17:32:08","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":268380,"visible":true,"origin":"","legend":"supplementary information 2: days without PEM","description":"","filename":"SUPPPLEMENTARYFIGUREdayswithoutpem.tif","url":"https://assets-eu.researchsquare.com/files/rs-5951040/v1/fcb4756781c616239040cc4c.tif"},{"id":80251756,"identity":"dbb34b6b-4369-4533-ad4b-5986ac725425","added_by":"auto","created_at":"2025-04-09 17:24:08","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":79989,"visible":true,"origin":"","legend":"supplementary information 1: details of the intervention","description":"","filename":"SUPPLEMENTARYfiguredataflow.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5951040/v1/9c7405b108cbe9a25c0bd001.jpg"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"A Digital Platform with Activity Tracking for Energy Management Support in Long COVID: A Randomised Controlled Trial","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eFollowing the outbreak of the Sars-Cov-2 (COVID-19) virus in March 2020, a significant proportion of those who became infected experienced persistent symptoms lasting several months commonly known as long COVID (LC). Symptoms of LC include pain, lethargy, exercise intolerance, myalgia, cognitive impairment, and fatigue \u003csup\u003e1,2\u003c/sup\u003e. Symptoms could be disabling even if initial effects were mild, and could occur immediately after the infection or some weeks later \u003csup\u003e3\u003c/sup\u003e. These accounts appear similar to post-exertional malaise (PEM; described as an intolerance to mental and physical exertion, triggering an aggravation of symptoms typically lasting\u0026thinsp;\u0026gt;\u0026thinsp;14 hours up to several days \u003csup\u003e4\u003c/sup\u003e) reported by people with conditions such as myalgic encephalitis/chronic fatigue syndrome (ME/CFS).\u003c/p\u003e \u003cp\u003eLC is associated with highly variable symptom loads, fluctuating between periods of relative alleviation and exacerbation, often preceded by exertion \u003csup\u003e5\u003c/sup\u003e. Our Public Patient Involvement (PPI) group and others \u003csup\u003e6\u003c/sup\u003e describe 'push-crash' cycles where a perception of getting better leads to a push to return to normal activities precipitating a subsequent crash with severe worsening of symptoms, similar to PEM. Early work confirmed that 50% of people with LC meet the technical definition of PEM lasting\u0026thinsp;\u0026gt;\u0026thinsp;14 hours, while nearly all report some PEM-like symptoms lasting up to 14 hours \u003csup\u003e6,7\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, the cause of persistent symptoms is not well understood, and a clear pathophysiological mechanism remains elusive. This is particularly true for those whose initial symptoms were mild and for whom there are no health records since most did not attend hospital \u003csup\u003e5\u003c/sup\u003e or receive any COVID-19 treatment that might explain prolonged rehabilitation (e.g. mechanical ventilation). Without a clear underlying mechanism, treatment options have focussed on symptom management to help people with LC manage activities of daily living (ADL). Building on the similarities in symptoms between LC and ME/CFS, the most common symptom management strategy used in LC support is energy management, the only support strategy currently recommended for ME/CFS \u003csup\u003e8\u003c/sup\u003e. Energy management uses techniques such as planning, feedback, and activity diaries to help individuals manage their \u0026lsquo;energy envelope\u0026rsquo; to limit the debilitating consequences of their condition by reducing or avoiding periods of symptom exacerbation \u003csup\u003e9\u0026ndash;11\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEnergy management is suitable for self-management and is associated with higher adherence and greater patient-reported benefit \u003csup\u003e12\u003c/sup\u003e. However, despite multiple underlying theories \u003csup\u003e9,13\u003c/sup\u003e, concrete steps for symptom self-management are rare. Energy management also requires participants to recall and compare two poorly described constructs; 'energy availability' and 'energy use', which our PPI group and evidence from the ME/CFS literature find challenging, especially when dealing with impaired cognition and memory \u003csup\u003e14\u003c/sup\u003e. Additionally, the dynamic nature of energy management requires recalling activities that might have triggered previous bouts of PEM-like symptoms, even if they occurred weeks or months earlier. Despite its widespread use, evidence to support the efficacy of energy management in preventing bouts of PEM is extremely limited \u003csup\u003e11,15\u003c/sup\u003e. Furthermore, there are no contemporary trials that leverage mobile and wearable technology, which could address many of the challenges associated with managing and tracking activity to manage LC symptoms \u003csup\u003e11,15\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe aim of this trial was to determine if activity tracking combined with just-in-time messages helped people with LC implement energy management and reduce incidents of symptom exacerbation. We hypothesised \u003cem\u003ea priori\u003c/em\u003e that a just-in-time intervention to assist energy management (intervention group) would reduce frequency or severity of PEM compared to usual care (control).\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Design\u003c/h2\u003e\n \u003cp\u003eThe study was designed as a pragmatic single-centre randomised controlled trial. The intervention was a remotely delivered, just-in-time energy management support programme designed to reduce the frequency or severity of PEM experienced by individuals experiencing LC. The trial ran between January 2022 and September 2023. The study was approved by the University of the West of Scotland Institutional Ethics Board (approval number 16638) and the trial was registered with ICTRN (ISRCTN16033549; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.isrctn.com/ISRCTN16033549\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eInclusion criteria\u003c/h2\u003e\n \u003cp\u003eEligible participants had to be adults (\u0026ge;\u0026thinsp;18yrs) reporting persistent symptoms following a COVID-19 infection, which interfered with daily activities (in line with NICE guidelines 2021). Many of those eligible to participate in the trial had contracted COVID-19 before home-based testing kits were available and before polymerase chain reaction (PCR) testing was made publicly available. Consequently, we accepted self-reported persistent symptoms of \u0026ge;\u0026thinsp;8 weeks following an infection consistent with COVID-19. Participants had to recover at home (e.g. access to their GP but no ongoing clinical care related to their COVID-19 infection) and have access to a smartphone with Android version 6 or iOS version 10 or higher.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eExclusion Criteria\u003c/h3\u003e\n\u003cp\u003eParticipants were excluded if they (1) had insufficient English language to understand messages, (2) had no smartphone access, (3) were participating in another LC intervention, (4) had impaired cognitive function which compromises comprehension of study information or messaging, (5) were receiving therapies known to cause symptom exacerbations (e.g. chemotherapy) or aimed at treating LC, (6) or (6) were receiving ongoing care for LC via primary or secondary care services.\u003c/p\u003e\n\u003ch3\u003eRecruitment\u003c/h3\u003e\n\u003cp\u003eAt the time of recruitment, the UK was engaged in COVID-19 related lockdowns. Individuals expressing interest to trial information distributed via social media were contacted via telephone or video conferencing for a briefing which included screening for inclusion and exclusion criteria, a verbal overview of the study and an opportunity to ask questions. Participants were provided with an information sheet and re-contacted at least 7-days later to provide a further opportunity to ask questions and, if willing, enrol in the trial. Participants provided written informed consent and were allocated an enrolment number. Recruitment was facilitated by our partner organisation, Long COVID Scotland, and involved promotion of the trial via online social groups, social media, print media, a study website and meetings with Long COVID Scotland members. The trial targeted people who had not been hospitalised following their COVID-19 infection, therefore there was no data linkage or recruitment via primary or secondary care.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eRandomisation and masking\u003c/h2\u003e\n \u003cp\u003eA secure third-party service (studyrandomizer.com) was used to randomise 250 participants 1:1 into two evenly distributed study arms (intervention or control) with each arm consisting of 125 participants (50% of the total sample; equal allocation). Randomisation into the respective arms was conducted using a permuted block algorithm, with a fixed block size of 125 participants. Participants randomised to the intervention group received usual care with just-in-time messaging support, and the control group received usual care only (Fig.\u0026nbsp;1). NS-H generated the sequence, enrolled participants, and assigned them to the trial groups. NS-H was the senior post-doctoral trial manager for the rest of the trial. Blinding was impossible due to the requirements of the experimental group. Both groups were required to download an app. The intervention group downloaded the Pace Me app which included energy management support and data collection instruments, whereas the control group\u0026rsquo;s app only contained data collection instruments. Participants created an account, logged in, provided additional e-consent (ensuring participants understood their interaction with the app), and undertook baseline assessments. Screengrabs of checkbox completion and digital signatures were captured and securely stored separate to other study data. Participants self-reported gender at their initial meeting at enrolment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e***INSERT FIG 1 NEAR HERE***\u003c/p\u003e\n\u003ch3\u003eProcedures\u003c/h3\u003e\n\u003cp\u003eFollowing randomisation, a study start date approximately one week later was agreed. This provided time for an activity tracker (Fitbit Charge 5, Fitbit Inc., San Francisco, CA, USA) to be posted to intervention group participants. Subsequently, an online meeting was scheduled to assist participants in downloading the Pace Me app, registering an account and completing the e-consent, and turning off all notifications from Fitbit that would encourage additional exercise. Intervention participants entered their corresponding activity tracker number to activate the intervention features of the app (including real time tracking of energy management), while control participants only had access to in app questionnaires (Fig.\u0026nbsp;2). All participants completed questionnaires at the online meeting, to ensure baseline data capture before starting the intervention or control part of the experiment. All participants received automated alerts to remind them when they were due to complete specific questionnaires and reminders if they remained uncompleted a week after the intended completion time.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003cp\u003e***INSERT FIG 2 NEAR HERE***\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eIntervention Design\u003c/h2\u003e\n \u003cp\u003eThe Pace Me app aimed to support activity pacing in individuals with long COVID. The intervention design was informed by the Behaviour Change Wheel (BCW) \u003csup\u003e16\u003c/sup\u003e, evidence from literature \u003csup\u003e11,17\u003c/sup\u003e, and PPI feedback. The final design used goal setting, action planning, self-monitoring, feedback, prompts, and personalisation to assist users in managing energy levels. The intervention integrated heart rate and step count feedback using Fitbit Charge 5, logging of PEM, logging of symptoms, and support messaging when patients exceeded their energy allowance (see supplementary information 1 for full details of the intervention).\u003c/p\u003e\n \u003cp\u003eThe intervention used a personalised activity allowance, initially set to no more than 30 minutes of activity above 60% of their age-predicted heart rate maximum. This allowance was iteratively adjusted in response to participants\u0026rsquo; activity and PEM reports. If participants reported PEM and had exceeded the allowance, the allowance remained unchanged (i.e. the PEM may be due to poor pacing). If participants reported PEM but had remained within their allowance for the preceding 3 days, the allowance was reduced in a stepwise fashion (i.e. activity allowance may be too large to prevent PEM). Conversely, the allowance was increased if participants did not report PEM for three consecutive weeks. For participants with unstable heart rates (e.g. autonomic issues), step counts were used to set activity allowance. These limits were not intended as targets but as a guide for energy management.\u003c/p\u003e\n \u003cp\u003eThe Pace Me app featured a home screen that displayed real-time tracking showing how much of that day\u0026rsquo;s energy allowance participants had used. Visualisations and emojis showed when participants were most at risk of exceeding their limits and provided feedback to help participants understand their pacing patterns. Participants received personalised notifications as their activity approached specific thresholds, capped at three per day to avoid overwhelming users. Notifications were triggered when participants reached 50%, 75%, and 100% of their activity allowance. They included an alert message and support tips sourced from individuals with experience in pacing strategies for managing energy (Fig.\u0026nbsp;3).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003cp\u003e***INSERT FG 3 NEAR HERE***\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eOutcomes\u003c/h2\u003e\n \u003cp\u003eThe primary outcome was frequency and severity of PEM assessed using the DePaul Symptom Questionnaire - PEM (DSQ-PEM) \u003csup\u003e4\u003c/sup\u003e (LC-COS Post-exertion symptoms), pre- and post-intervention. Questions 1\u0026ndash;5 were measured on a five-point Likert scale with a \u0026lsquo;frequency\u0026rsquo; domain (0\u0026thinsp;=\u0026thinsp;none of the time, 1\u0026thinsp;=\u0026thinsp;a little of the time, 2\u0026thinsp;=\u0026thinsp;about half the time, 3\u0026thinsp;=\u0026thinsp;most of the time, and 4\u0026thinsp;=\u0026thinsp;all of the time) and a \u0026lsquo;severity\u0026rsquo; domain. (0\u0026thinsp;=\u0026thinsp;symptom not present, 1\u0026thinsp;=\u0026thinsp;mild, 2\u0026thinsp;=\u0026thinsp;moderate, 3\u0026thinsp;=\u0026thinsp;severe, and 4\u0026thinsp;=\u0026thinsp;very severe). Questions 6\u0026ndash;8 and 10 were dichotomous yes/no responses, and question 9 asked \u0026lsquo;if you feel worse after activities, how long does this last?\u0026rsquo; with six options: \u0026le;1 h, 2\u0026ndash;3 h, 4\u0026ndash;10 h, 11\u0026ndash;13 h, 14\u0026ndash;23 h, or \u0026ge;\u0026thinsp;24 h.\u003c/p\u003e\n \u003cp\u003eSecondary outcomes were 12-Item Short Form Health Survey (SF-12), EuroQol 5-Dimension Health Questionnaire (EQ5D), Pain Visual Analogue Scale (VAS), Patient Health Questionnaire-4 (PHQ4), Fatigue Severity Scale (7-item version; FSS-7), Medical Research Council Breathlessness Questionnaire (MRC BQ), Symbol Digit Modalities Test (SDMT) total correct (out of 60), total time for correct answers only, average time per correct answer.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eTo determine sample size, our primary outcome variable was the DSQ-PEM. Using previous work, a minimum clinically relevant difference can be estimated as a change of 13 points on a 100-point scale \u003csup\u003e18\u003c/sup\u003e. Assuming a standard deviation (SD) of 25 \u003csup\u003e18\u003c/sup\u003e, this resulted in a pairwise effect size of d\u0026thinsp;=\u0026thinsp;0.5 (Cohen\u0026rsquo;s f\u0026thinsp;=\u0026thinsp;0.25). We calculated our desired sample size for a two-way mixed-model (within- and between-subjects) analysis of variance (ANOVA). Using the WebPower package in R Studio, and the wp.rmanova function, with two groups, two time points, a medium effect size (\u003cem\u003ef\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.25), assuming sphericity, an alpha of 0.05, desired statistical power of 0.9, testing for an interaction effect, the total n was 170 (85 per group). Consequently, we aimed to recruit 125 participants per group to allow for 30% drop-out. A post-hoc power calculation resulted in observed power of 0.88 with our sample size of 161, an effect size of \u003cem\u003ef\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.25, and an alpha level of 0.05. All analyses were conducted using Jamovi version 2.3.21. Data were tested for for normal distribution and homogeneity of variance to confirm parametric assumptions were met. Data are presented in text and tables as means and 95% confidence intervals (CI) unless otherwise stated. Because of randomisation, we did not undertake analysis of baseline equivalence, since the null hypothesis must be true and any differences due to chance \u003csup\u003e19\u003c/sup\u003e. Only participants who completed follow-up testing were included in analysis (i.e. per protocol analysis). The effect of the energy management intervention on main and secondary outcomes was examined using two-way mixed-model ANOVA with condition (intervention or control) as the between-subjects factor and time (pre- and post-intervention) as a within subjects factor. Alpha level is reported as exact p values and not described dichotomously as \u0026apos;significant\u0026apos; or otherwise as recommended by the American Statistical Association \u003csup\u003e20\u003c/sup\u003e. We expressed effect sizes from the ANOVA as partial eta-squared (\u0026eta;\u0026sup2;p), with values of 0.01, 0.06, and 0.14 interpreted as small, moderate, and large, respectively \u003csup\u003e21\u003c/sup\u003e. For categorical data, (DSQ-PEM questions 6\u0026ndash;10) we used McNemar\u0026rsquo;s Test for paired samples (pre- to post- intervention), or Chi squared test for between group effects (intervention vs. control).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eRole of the funding source\u003c/h2\u003e\n \u003cp\u003eThe funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eOf 368 participants assessed for eligibility, 55 were deemed ineligible, therefore 313 participants were recruited but 63 withdrew prior to randomisation. In total, 250 participants were randomised (125 per group), but 36 control and eight intervention participants were lost to follow-up. At the analysis stage, 12 control and 24 intervention participants were excluded from analysis due to missing data. Therefore, 84 intervention participants and 77 control participants completed the six-month follow-up and post-intervention data collection for the primary endpoint. Some participants completed the primary endpoint but not secondary outcome questionnaires, despite reminders. Therefore, the sample size for each outcome variable is reported in tables.\u003c/p\u003e \u003cp\u003eUsing the sum of the DSQ-PEM questions 1\u0026ndash;5 expressed on a 100-point scale (as per our power calculation), the intervention group value was 48 (95% CI 44\u0026ndash;53) pre-intervention and 46 (95% CI 41\u0026ndash;51) post-intervention. The control group value was 47 (95% CI 42\u0026ndash;52) pre-intervention and 44 (95% CI 39\u0026ndash;49) post-intervention (interaction effect p\u0026thinsp;=\u0026thinsp;0.614, η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.002; trivial). Analysis of individual questions are reported in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There was a between group effect for question 1 frequency response, but no time or interaction effects were observed. For questions 6\u0026ndash;8, and 10, the proportion of subjects who reported yes or no did not differ by time except for question 7 whereby the intervention group exhibited a higher proportion of yes responses pre-intervention compared to post-intervention. For question 9, there was a within-group effect for time, whereby participants felt worse after activities but there were no between group differences. There were no other observed effects.\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\u003eDePaul Symptom Questionnaire - Post-exertional Malaise (DSQ-PEM) question responses for intervention and control groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePost\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eRepeated measures analysis of variance (ANOVA)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003ep value time (η\u0026sup2;p)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep value group (η\u0026sup2;p)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep value interaction (η\u0026sup2;p)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eDSQ-PEM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eQ1 severity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1 (1.9\u0026ndash;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2 (1.9\u0026ndash;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.072 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.020; small effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c7\" namest=\"c6\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.354 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.005; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.058 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.022; small effect)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;77)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3 (1.9\u0026ndash;2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8 (1.5\u0026ndash;2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eQ1 frequency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 (2.7\u0026ndash;3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8 (2.5\u0026ndash;3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.063 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.022; small effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c7\" namest=\"c6\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.039 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.027; small effect)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.704 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.001; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;77)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6 (2.4\u0026ndash;2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4 (2.1\u0026ndash;2.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eQ2 severity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1 (1.8\u0026ndash;2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0 (1.7\u0026ndash;2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.380 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.005; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c7\" namest=\"c6\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.716 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.001; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.656 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.001; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;77)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2 (1.9\u0026ndash;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0 (1.7\u0026ndash;2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eQ2 frequency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.9 (2.6\u0026ndash;3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7 (2.4-3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.694 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.001; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c7\" namest=\"c6\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.711 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.001; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.316 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.006; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;77)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7 (2.4-3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8 (2.5\u0026ndash;3.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eQ3 severity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6 (1.3-2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9 (1.5\u0026ndash;2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.380 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.005; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c7\" namest=\"c6\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.333 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.006; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.623 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.002; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;77)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1 (1.8\u0026ndash;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0 (1.6\u0026ndash;2.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eQ3 frequency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5 (2.2\u0026ndash;2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4 (2.2\u0026ndash;2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.460 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.003; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c7\" namest=\"c6\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.890 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.000; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.856 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.000; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;77)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5 (2.2\u0026ndash;2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5 (2.1\u0026ndash;2.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eQ4 severity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1 (1.8\u0026ndash;2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9 (1.6\u0026ndash;2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.311 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.006; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c7\" namest=\"c6\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.317 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.002; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.269 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.008; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;77)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2 (1.8\u0026ndash;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0 (1.7\u0026ndash;2.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eQ4 frequency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.9 (2.6\u0026ndash;3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7 (2.4-3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.681 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.001; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c7\" namest=\"c6\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.322 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.006; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.164 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.012; small effect)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;77)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6 (2.2\u0026ndash;2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7 (2.4-3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eQ5 severity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0 (1.7\u0026ndash;2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9 (1.6\u0026ndash;2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.080 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.019; small effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c7\" namest=\"c6\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.861 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.000; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.262 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.008; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;77)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1 (1.8\u0026ndash;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8 (1.5\u0026ndash;2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eQ5 frequency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6 (2.3\u0026ndash;2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4 (2.0-2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.552 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.002; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c7\" namest=\"c6\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.411 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.004; trivial effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.368 (η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.005; trivial effect).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;77)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3 (2.0-2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3 (2.0-2.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMcNemar\u0026rsquo;s test\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eChi squared test\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCount (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eCount (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eWithin group p value (\u003c/b\u003e\u003cb\u003eX\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eBetween group p value (\u003c/b\u003e\u003cb\u003eX\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eQ6 response\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (pre n\u0026thinsp;=\u0026thinsp;84, post n\u0026thinsp;=\u0026thinsp;83)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 yes (11%)\u003c/p\u003e \u003cp\u003e75 no (89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 yes (14%)\u003c/p\u003e \u003cp\u003e71 no (86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.439 (0.600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePre; p\u0026thinsp;=\u0026thinsp;1.000 (0.0.00)\u003c/p\u003e \u003cp\u003ePost; p\u0026thinsp;=\u0026thinsp;0.787 (0.073)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (pre n\u0026thinsp;=\u0026thinsp;77, post n\u0026thinsp;=\u0026thinsp;77)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 yes (10%)\u003c/p\u003e \u003cp\u003e69 no (90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 yes (13%)\u003c/p\u003e \u003cp\u003e67 no (87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.251 (1.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eQ7 response\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (pre n\u0026thinsp;=\u0026thinsp;84, post n\u0026thinsp;=\u0026thinsp;83)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 yes (98%)\u003c/p\u003e \u003cp\u003e2 no (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 yes (87%)\u003c/p\u003e \u003cp\u003e11 no (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.007 (7.36)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePre; p\u0026thinsp;=\u0026thinsp;0.266 (1.24)\u003c/p\u003e \u003cp\u003ePost; p\u0026thinsp;=\u0026thinsp;0.596 (0.280)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (pre n\u0026thinsp;=\u0026thinsp;77, post n\u0026thinsp;=\u0026thinsp;76)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 yes (95%)\u003c/p\u003e \u003cp\u003e4 no (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 yes (89%)\u003c/p\u003e \u003cp\u003e8 no (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.206 (1.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eQ8 response\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (pre n\u0026thinsp;=\u0026thinsp;84, post n\u0026thinsp;=\u0026thinsp;84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 yes (90%)\u003c/p\u003e \u003cp\u003e13 no (1 0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 yes (77%)\u003c/p\u003e \u003cp\u003e19 no (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.157 (2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePre; p\u0026thinsp;=\u0026thinsp;0.465 (0.533)\u003c/p\u003e \u003cp\u003ePost; p\u0026thinsp;=\u0026thinsp;0.258 (1.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (pre n\u0026thinsp;=\u0026thinsp;77, post n\u0026thinsp;=\u0026thinsp;77)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 yes (90%)\u003c/p\u003e \u003cp\u003e8 no (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 yes (84%)\u003c/p\u003e \u003cp\u003e12 no (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.206 (1.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ 9 response\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (pre n\u0026thinsp;=\u0026thinsp;84, post n\u0026thinsp;=\u0026thinsp;82)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 reported\u0026thinsp;\u0026le;\u0026thinsp;1 hr (1%)\u003c/p\u003e \u003cp\u003e5 reported 2\u0026ndash;3 hr (6%)\u003c/p\u003e \u003cp\u003e21reported 4\u0026ndash;10 hr (25%)\u003c/p\u003e \u003cp\u003e2 reported 11\u0026ndash;13 hr (2%)\u003c/p\u003e \u003cp\u003e7 reported 14\u0026ndash;23 hr (8%\u003c/p\u003e \u003cp\u003e48reported\u0026thinsp;\u0026ge;\u0026thinsp;24 hr (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 reported\u0026thinsp;\u0026le;\u0026thinsp;1 hr (2%)\u003c/p\u003e \u003cp\u003e8 reported 2\u0026ndash;3 hr (10%)\u003c/p\u003e \u003cp\u003e15 reported 4\u0026ndash;10 hr (18%)\u003c/p\u003e \u003cp\u003e1 reported 11\u0026ndash;13 hr (1%)\u003c/p\u003e \u003cp\u003e15 reported 14\u0026ndash;23 hr (18%)\u003c/p\u003e \u003cp\u003e41 reported\u0026thinsp;\u0026ge;\u0026thinsp;24 hr (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (74.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePre; p\u0026thinsp;=\u0026thinsp;0.861 (1.91)\u003c/p\u003e \u003cp\u003ePost; p\u0026thinsp;=\u0026thinsp;0.667 (3.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (pre n\u0026thinsp;=\u0026thinsp;77, post n\u0026thinsp;=\u0026thinsp;76)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 reported\u0026thinsp;\u0026le;\u0026thinsp;1 hr (3%)\u003c/p\u003e \u003cp\u003e5 reported 2\u0026ndash;3 hr (6%)\u003c/p\u003e \u003cp\u003e13 reported 4\u0026ndash;10 hr (17%)\u003c/p\u003e \u003cp\u003e2 reported 11\u0026ndash;13 hr (3%)\u003c/p\u003e \u003cp\u003e7 reported 14\u0026ndash;23 hr (9%\u003c/p\u003e \u003cp\u003e48 reported\u0026thinsp;\u0026ge;\u0026thinsp;24 hr (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 reported\u0026thinsp;\u0026le;\u0026thinsp;1 hr (3%)\u003c/p\u003e \u003cp\u003e5 reported 2\u0026ndash;3 hr (7%)\u003c/p\u003e \u003cp\u003e10 reported 4\u0026ndash;10 hr (13%)\u003c/p\u003e \u003cp\u003e2 reported 11\u0026ndash;13 hr (3%)\u003c/p\u003e \u003cp\u003e10 reported 14\u0026ndash;23 hr (13%\u003c/p\u003e \u003cp\u003e47 reported\u0026thinsp;\u0026ge;\u0026thinsp;24 hr (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (69.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ10 response\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (pre n\u0026thinsp;=\u0026thinsp;82, post n\u0026thinsp;=\u0026thinsp;80)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 yes (91%)\u003c/p\u003e \u003cp\u003e7 no (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 yes (93%)\u003c/p\u003e \u003cp\u003e6 no (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.198 (4.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePre; p\u0026thinsp;=\u0026thinsp;0.712 (0.136)\u003c/p\u003e \u003cp\u003ePost; p\u0026thinsp;=\u0026thinsp;0.453 (1.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (pre n\u0026thinsp;=\u0026thinsp;77, post n\u0026thinsp;=\u0026thinsp;77)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 yes (90%)\u003c/p\u003e \u003cp\u003e7 no (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 yes (86%)\u003c/p\u003e \u003cp\u003e10 no (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.317 (1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e***INSERT Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e NEAR HERE***\u003c/h2\u003e \u003cp\u003eSecondary outcomes are reported in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. No interaction effects were evident, suggesting the energy management intervention was not superior to control. Some effects for time were evident, suggesting an improvement in secondary outcomes from baseline to post-intervention follow-up in both groups.\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\u003e\u003cem\u003eSf12\u0026thinsp;=\u0026thinsp;12-Item Short Form Health Survey, EQ5D: EuroQol 5-Dimension Health Questionnaire, Pain VAS: Pain Visual Analogue Scale, PHQ4: Patient Health Questionnaire-4, FSS (7 item): Fatigue Severity Scale (7-item version), MRC BQ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eMedical Research Council Breathlessness Questionnaire, SEMCD\u0026thinsp;=\u0026thinsp;Self-Efficacy to Manage Chronic Disease, SDMT\u0026thinsp;=\u0026thinsp;Symbol Digit Modalities Test, TC\u0026thinsp;=\u0026thinsp;total correct (out of 60), TTCA\u0026thinsp;=\u0026thinsp;total time for correct answers only, TPCA\u0026thinsp;=\u0026thinsp;average time per correct answer.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePost\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRepeated measures analysis of variance (ANOVA)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value time (η\u0026sup2;p)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep value group (η\u0026sup2;p)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep value interaction (η\u0026sup2;p)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eHRQoL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSF12 - Physical heath\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;53)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (54\u0026ndash;54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (54\u0026ndash;55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.754 (0.001; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.969 (0.000; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.349 (0.009; trivial)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;50)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (54\u0026ndash;54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (54\u0026ndash;54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSF12 - Mental Health\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;53)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (59\u0026ndash;59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (59\u0026ndash;59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.003 (0.086; moderate)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.241 (0.014; small)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.531 (0.004; trivial)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;50)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (59\u0026ndash;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (59\u0026ndash;60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eEQ5D Anxiety\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4 (1.2\u0026ndash;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2 (1.0-1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.075 (0.020; small)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.583 (0.002; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.957 (0.000; trivial)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;73)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3 (1.2\u0026ndash;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2 (0.9\u0026ndash;1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eEQ5D Mobility\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3 (1.1\u0026ndash;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5 (1.2\u0026ndash;1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.416 (0.004; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.906 (0.000; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.543 (0.002; trivial)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;73)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5 (1.3\u0026ndash;1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5 (1.2\u0026ndash;1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eEQ5D Pain\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9 (1.7\u0026ndash;2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8 (1.6-2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.721 (0.001; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.933 (0.000; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.569 (0.002; trivial)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;73)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8 (1.6-2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8 (1.6\u0026ndash;2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eEQ5D Self-care\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (0.8\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (0.8\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.431 (0.004; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.803 (0.000; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.180 (0.012; small)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;73)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (0.8\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9 (0.7\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eEQ5D Usual activities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5 (2.3\u0026ndash;2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3 (2.1\u0026ndash;2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.003 (0.056; small)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.530 (0.003; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.657 (0.001; trivial)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;73)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6 (2.4\u0026ndash;2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4 (2.1\u0026ndash;2.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eEQ5D Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38 (0.31\u0026ndash;0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38 (0.30\u0026ndash;0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.456 (0.004; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.324 (0.006; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.178 (0.012; small)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;73)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43 (0.37\u0026ndash;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39 (0.31\u0026ndash;0.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePain\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eVAS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;78)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (41\u0026ndash;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (40\u0026ndash;52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.721 (0.001; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.933 (0.000; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.569 (0.002; trivial)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;71)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (40\u0026ndash;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (38\u0026ndash;51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnxiety and Depression\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ePHQ4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;74)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.3 (4.7-6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7 (3.9\u0026ndash;5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.002 (0.065; small)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.686 (0.001; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.764 (0.001; trivial)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;74)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.2 (4.6\u0026ndash;5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5 (3.6\u0026ndash;5.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFatigue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eFSS \u0026minus;\u0026thinsp;7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;76)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.3 (6.1\u0026ndash;6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0 (5.8\u0026ndash;6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001 (0.118; large)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.693 (0.001; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.808 (0.000; trivial)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;71)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.4 (6.3\u0026ndash;6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0 (5.8\u0026ndash;6.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBreathlessness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eMRC BQ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6 (1.4\u0026ndash;1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4 (1.3\u0026ndash;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.037 (0.028; small)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.723 (0.001; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.369 (0.005; trivial)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;74)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6 (1.4\u0026ndash;1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5 (1.3\u0026ndash;1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSelf-Management\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSEMCD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;81)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (25\u0026ndash;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (28\u0026ndash;33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.431 (0.004; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.803 (0.000; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.180 (0.012; small)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;72)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (24\u0026ndash;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (27\u0026ndash;33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCognitive Function\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSDMT TC (/60)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;82)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (57\u0026ndash;59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (59\u0026ndash;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.154 (0.013; small)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.355 (0.005; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.107 (0.016; small)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;77)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (57\u0026ndash;59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (58\u0026ndash;59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSDMT TTCA (s)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;82)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133 (126\u0026ndash;141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132 (113\u0026ndash;151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.250 (0.008; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.476 (0.003; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.119 (0.015; small)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;77)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146 (129\u0026ndash;163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127 (120\u0026ndash;134)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSDMT TPCA (s)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntervention (n\u0026thinsp;=\u0026thinsp;82)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3 (2.2\u0026ndash;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2 (1.9\u0026ndash;2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.154 (0.013; small)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.355 (0.005; trivial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.107 (0.016; small)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl (n\u0026thinsp;=\u0026thinsp;77)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5 (2.2\u0026ndash;2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2 (2.0-2.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e***INSERT Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e NEAR HERE***\u003c/h2\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis is the first study of its kind to examine digital technology to support energy management in conditions with PEM. We rigorously tested\u0026nbsp;guided energy management in this randomised controlled trial (RCT), the first since the landmark PACE Trial, published over a decade ago\u0026nbsp;\u003csup\u003e22\u003c/sup\u003e. The main finding of this study was that a just-in-time intervention in conjunction with a wearable activity tracker to support energy management in people with LC was not more effective than usual care. There were no intervention effects on the primary outcome, DSQ-PEM score, for questions 1-5 (or the sum of 1-5). Both groups experienced an improvement in secondary outcomes, including mental health, severity of problems in usual activities, anxiety and depression, fatigue, and breathlessness at follow up. This indicates regression to the mean, whereby both groups improved over time.\u0026nbsp;As a result, we must reject our hypothesis that a just-in-time intervention to assist energy management would reduce frequency or severity of PEM compared to controls. Nevertheless, the intervention group reported fewer ‘yes’ responses to question 7 ‘do you experience a worsening of your fatigue/energy related illness after engaging in minimal physical effort?’ over time, while no differences were observed for the control group. Additionally, responses to question 9 whereby participants indicated whether they felt worse after activity indicated an improvement over time in the intervention group. This suggests that the intervention group perceived less PEM symptoms due to physical effort over the course of the intervention. However, given these findings are not corroborated by other items from the DSQ-PEM these findings should be interpreted with caution.\u003c/p\u003e\n\u003cp\u003eSeveral factors may explain the lack of intervention efficacy. At the time of study development, LC was a relatively new condition, with most individuals having been initially infected during the first wave of COVID-19. There was an emerging view that LC symptoms were very similar to other post-viral conditions such as ME/CFS, such that they were almost indistinguishable as separate conditions\u0026nbsp;\u003csup\u003e23\u003c/sup\u003e. Consequently, the intervention was developed under the assumption that LC would resemble ME/CFS and that individuals would experience prolonged and potentially persistent symptoms. This assumption holds for a subset of LC patients. For instance, members of our PPI group have been managing PEM and other symptom exacerbations since their initial infection in 2020. However, new evidence suggests that most individuals recover from LC, albeit very slowly, over a period of several months. For example, in a separate study, we tracked around 250 individuals for 9 months after their initial COVID-19 infection, generating early prospective data on the transition from acute infection to LC\u0026nbsp;\u003csup\u003e24\u003c/sup\u003e. In that study, most experienced symptoms for four to seven months, with around 3% experiencing longer-lasting symptoms at follow up. Similarly, Oliveira et al.\u0026nbsp;\u003csup\u003e25\u003c/sup\u003e tracked 34 \u0026nbsp;LC patients for 12 months, finding that many initially met the case definition for ME/CFS, but most showed symptom improvement by the 12-month follow-up. Jason et al.\u0026nbsp;\u003csup\u003e26\u003c/sup\u003e also found that over five months, most LC patients reported improvements in symptoms, particularly sleep and incidence of PEM. A consequence of these findings is that, for most individuals (excluding a small proportion who may develop a more persistent post-viral condition), LC differs from conditions like ME/CFS, where recovery can take years or may not occur at all. Consequently, the effectiveness of this type of activity-tracking just-in-time intervention is less certain for individuals with ME/CFS, who are unlikely to experience significant recovery or symptom reduction within the timeframe of such trials.\u003c/p\u003e\n\u003cp\u003eThere are some limitations of this trial that should be noted. At the time of recruitment, there was no agreed case definition for LC. Moreover, several of our participants had LC from \u003cem\u003epresumed\u003c/em\u003e COVID-19 infection early in the pandemic, before home testing kits were available. We excluded hospitalised individuals who may have undergone PCR confirmation early in the pandemic. Consequently, definitive diagnosis of the participants’ condition was not possible. While ascertainment bias, for this reason, is both common and well-established in LC research\u0026nbsp;\u003csup\u003e27\u003c/sup\u003e, it remains a limitation that should be considered. Nevertheless, robust randomisation and allocation concealment should prevent systematic effect in the resulting data. A second limitation, although necessary, was that we provided activity trackers to the intervention group but not the control group. The reason we believed this necessary was provision of a wearable would be in itself a form of intervention. As such, it is possible that similar frequency and severity of PEM existed between the two groups despite differences in physical activity, but due to our design we cannot examine this. Future work may wish to attempt to control for this limitation. Similarly, it is possible that some control participants used their own activity tracker or app to modify and manage their activity. For example, part way through the trial a commercial app aimed at energy management was launched and some control participants may have decided to use that app, introducing contamination bias. Thirdly, while two-arm RCTs are excellent for determining efficacy of an intervention, they are less helpful in providing data for optimisation. This optimisation is crucial for enhancing effectiveness, which is often only achieved through repeated testing and refinement. Our qualitative work indicated participants viewed the Pace Me platform positively, gaining a sense of insight, validation, and control over their condition\u0026nbsp;\u003csup\u003e28\u003c/sup\u003e. Therefore, although our framework was not successful at reducing PEM in people with LC in 2022 and 2023, we suggest this should be developed and refined for deployment in different settings or population, much like the model of drug repurposing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite the limitations described above, the present study had a significant number of strengths. Firstly, we tested a novel method of energy management utilising technology, meaning this study is transformative compared with previous work which relied on journalling and activity planning. This project provided a user-friendly platform that simplified complex tasks of tracking activity, comparing PEM events, and influencing daily activity decisions for the end user. We hope that despite the null findings herein, this energy management framework can be employed for other PEM-experiencing conditions such as lupus, ME/CFS, multiple sclerosis (MS), rheumatoid arthritis, etc. Advances in digital technologies have opened unprecedented opportunities to deliver effective and scalable behaviour change interventions and just-in-time adaptive interventions (JITAIs) have existed for almost a decade\u0026nbsp;\u003csup\u003e29\u003c/sup\u003e, but until now, had not been applied to conditions with PEM. Therefore, implementing just-in-time energy management frameworks in these conditions could have significant impact given the scalability and inclusivity of remote support\u0026nbsp;\u003csup\u003e30\u003c/sup\u003e. This step-wise change in how energy management can be delivered could result in positive individual, societal, and economic impacts.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study found a lack of treatment effect for those who received a just-in-time energy management intervention. Importantly, there were no adverse incidents in the intervention group, addressing a potential concern raised by our PPI group regarding the use of activity trackers in this population. This equivalence, along with the absence of adverse incidents, demonstrates that the intervention was safe and feasible. Future work should explore efficacy of similar apps in patients with conditions that are not expected to recover such as ME/CFS.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS ACCORDING TO THE CREDIT TAXONOMY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualisation: Nicholas F Sculthorpe; Methodology: Nilihan EM Sanal-Hayes, Lawrence D Hayes, Jacqueline L Mair, Joanne Ingram, Jane Ormerod, David Carless, Natlie Hilliard, Marie Mclaughlin, Rachel Meach, Nicholas F Sculthorpe; Formal analysis and investigation: Nilihan EM Sanal-Hayes, Nicholas F Sculthorpe; Investigation: Nilihan EM Sanal-Hayes, Nicholas F Sculthorpe; Resources: Nilihan EM Sanal-Hayes, Lawrence D Hayes, Jacqueline L Mair, Joanne Ingram, Jane Ormerod, David Carless, Natlie Hilliard, Marie Mclaughlin, Rachel Meach, Nicholas F Sculthorpe; Writing - original draft preparation: Nilihan EM Sanal-Hayes, Lawrence D Hayes, Jacqueline L Mair, Antonio Dello Iacono, Joanne Ingram, Nicholas F Sculthorpe; Writing - review and editing: Nilihan EM Sanal-Hayes, Jacqueline L Mair, Lawrence D Hayes, Antonio Dello Iacono, Joanne Ingram, Jane Ormerod, David Carless, Natlie Hilliard, Marie Mclaughlin, Rachel Meach, Nicholas F Sculthorpe; Visualization: Nicholas F Sculthorpe; Supervision: Nicholas F Sculthorpe; Project administration: Nilihan EM Sanal-Hayes, Lawrence D Hayes, Jacqueline L Mair, Antonio Dello Iacono, Joanne Ingram, Jane Ormerod, David Carless, Natlie Hilliard, Marie Mclaughlin, Rachel Meach, Nicholas F Sculthorpe; Funding acquisition: Lawrence D Hayes, Jacqueline L Mair, Joanne Ingram, Jane Ormerod, David Carless, Natlie Hilliard, Nicholas F Sculthorpe\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDECLARATION OF INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA SHARING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collected for this study, including individual anonymised participant data and a data dictionary defining each field in the set will be made available to others upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003cbr\u003e\u003c/strong\u003eThis study was funded by the National Institute for Health and Care Research (NIHR) (Research Award COV-LT2-0010). The views expressed in this publication are those of the authors and not necessarily those of NIHR or the Department of Health and Social Care.\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNational Institute for Health and Care Research\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGreenhalgh T, Knight M, A\u0026rsquo;Court C, Buxton M, Husain L. Management of post-acute covid-19 in primary care. \u003cem\u003eBMJ\u003c/em\u003e 2020; \u003cstrong\u003e370\u003c/strong\u003e: m3026.\u003c/li\u003e\n\u003cli\u003eHayes LD, Ingram J, Sculthorpe NF. More Than 100 Persistent Symptoms of SARS-CoV-2 (Long COVID): A Scoping Review. \u003cem\u003eFrontiers in Medicine\u003c/em\u003e 2021; \u003cstrong\u003e8\u003c/strong\u003e. https://www.frontiersin.org/articles/10.3389/fmed.2021.750378 (accessed Feb 15, 2024).\u003c/li\u003e\n\u003cli\u003eSleat D, Wain R, Miller B. Long Covid: Reviewing the Science and Assessing the Risk. 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Silverback Publishing, 2014.\u003c/li\u003e\n\u003cli\u003eMair JL, Salamanca-Sanabria A, Augsburger M, \u003cem\u003eet al.\u003c/em\u003e Effective Behavior Change Techniques in Digital Health Interventions for the Prevention or Management of Noncommunicable Diseases: An Umbrella Review. \u003cem\u003eAnnals of Behavioral Medicine\u003c/em\u003e 2023; \u003cstrong\u003e57\u003c/strong\u003e: 817\u0026ndash;35.\u003c/li\u003e\n\u003cli\u003eJason L, Ohanian D, Brown A, \u003cem\u003eet al.\u003c/em\u003e Differentiating Multiple Sclerosis from Myalgic Encephalomyelitis and Chronic Fatigue Syndrome. \u003cem\u003eInsights Biomed\u003c/em\u003e 2017; \u003cstrong\u003e2\u003c/strong\u003e: 11.\u003c/li\u003e\n\u003cli\u003eMoher D, Hopewell S, Schulz KF, \u003cem\u003eet al.\u003c/em\u003e CONSORT 2010 Explanation and Elaboration: updated guidelines for reporting parallel group randomised trials. \u003cem\u003eBMJ\u003c/em\u003e 2010; \u003cstrong\u003e340\u003c/strong\u003e: c869.\u003c/li\u003e\n\u003cli\u003eHurlbert SH, Levine RA, Utts J. Coup de Gr\u0026acirc;ce for a Tough Old Bull: \u0026ldquo;Statistically Significant\u0026rdquo; Expires. \u003cem\u003eThe American Statistician\u003c/em\u003e 2019; \u003cstrong\u003e73\u003c/strong\u003e: 352\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eLakens D. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. \u003cem\u003eFront Psychol\u003c/em\u003e 2013; \u003cstrong\u003e4\u003c/strong\u003e. DOI:10.3389/fpsyg.2013.00863.\u003c/li\u003e\n\u003cli\u003eWhite P, Chalder T, Sharpe M. The PACE trial: results of a large trial of nonpharmacological treatments. \u003cem\u003eJournal of psychosomatic research\u003c/em\u003e 2011; \u003cstrong\u003e70\u003c/strong\u003e: 622‐.\u003c/li\u003e\n\u003cli\u003eSukocheva OA, Maksoud R, Beeraka NM, \u003cem\u003eet al.\u003c/em\u003e Analysis of post COVID-19 condition and its overlap with myalgic encephalomyelitis/chronic fatigue syndrome. \u003cem\u003eJ Adv Res\u003c/em\u003e 2022; \u003cstrong\u003e40\u003c/strong\u003e: 179\u0026ndash;96.\u003c/li\u003e\n\u003cli\u003eSculthorpe NF, McLaughlin M, Cerexhe L, \u003cem\u003eet al.\u003c/em\u003e Tracking Persistent Symptoms in Scotland (TraPSS): a longitudinal prospective cohort study of COVID-19 recovery after mild acute infection. \u003cem\u003eBMJ Open\u003c/em\u003e 2025; \u003cstrong\u003e15\u003c/strong\u003e: e086646.\u003c/li\u003e\n\u003cli\u003eOliveira CR, Jason LA, Unutmaz D, Bateman L, Vernon SD. 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How methodological pitfalls have created widespread misunderstanding about long COVID. \u003cem\u003eBMJ Evid Based Med\u003c/em\u003e 2024; \u003cstrong\u003e29\u003c/strong\u003e: 142\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eMeach R, Carless D, Sanal-Hayes NEM, \u003cem\u003eet al.\u003c/em\u003e An Adaptive Pacing Intervention for Adults Living With Long COVID: A Narrative Study of Patient Experiences of Using the PaceMe app. \u003cem\u003eJournal of Patient Experience\u003c/em\u003e 2024; \u003cstrong\u003e11\u003c/strong\u003e: 23743735241272158.\u003c/li\u003e\n\u003cli\u003eRabbi M, Pfammatter A, Zhang M, Spring B, Choudhury T. Automated personalized feedback for physical activity and dietary behavior change with mobile phones: a randomized controlled trial on adults. \u003cem\u003eJMIR Mhealth Uhealth\u003c/em\u003e 2015; \u003cstrong\u003e3\u003c/strong\u003e: e42.\u003c/li\u003e\n\u003cli\u003ePerez MV, Mahaffey KW, Hedlin H, \u003cem\u003eet al.\u003c/em\u003e Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. \u003cem\u003eN Engl J Med\u003c/em\u003e 2019; \u003cstrong\u003e381\u003c/strong\u003e: 1909\u0026ndash;17.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Long COVID, post-acute sequelae of SARS-CoV-2 (PASC), symptoms, digital health, just-in-time intervention, energy management, pacing","lastPublishedDoi":"10.21203/rs.3.rs-5951040/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5951040/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePeople with long COVID (LC) report worsening symptoms after activity, like post-exertional malaise (PEM) in chronic fatigue syndrome (CFS). The National Institute for Health and Care Excellence (NICE) recommends \u0026lsquo;energy management\u0026rsquo; for CFS, but at the time of writing, how people with LC would respond to energy management was unknown.\u003c/p\u003e \u003cp\u003eIn a 6-month pragmatic decentralised randomised controlled trial (RCT), we compared a just-in-time intervention to support energy management in adults with LC to standard care. Participants were randomised to receive either the \u0026lsquo;Pace Me\u0026rsquo; app and a wearable activity tracker (intervention) or an app only with data entry screens (control). The intervention group received just-in-time messages on PEM management when they reached 50%, 75%, and 100% of their daily \u0026lsquo;activity allowance\u0026rsquo;. The primary outcome was PEM measured by the DePaul Symptom Questionnaire-Post-Exertional Malaise (DSQ-PEM).\u003c/p\u003e \u003cp\u003eOf 368 participants assessed for eligibility, 250 participants were randomised 1:1, but 36 control and eight intervention participants were lost to follow-up. 12 control and 24 intervention participants were excluded from analysis due to missing data. 84 intervention participants and 77 control participants were analysed. There was no time by group interaction for the DSQ-PEM. The intervention group value was 48 (95% CI 44\u0026ndash;53) pre-intervention and 46 (95% CI 41\u0026ndash;51) post-intervention (arbitrary units). The control group value was 47 (95% CI 42\u0026ndash;52) pre-intervention and 44 (95% CI 39\u0026ndash;49) post-intervention (interaction effect p\u0026thinsp;=\u0026thinsp;0.614, η\u0026sup2;p\u0026thinsp;=\u0026thinsp;0.002; trivial). No individual question exhibited an interaction effect (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eDigitally supported energy management in people with LC had no effect on PEM compared to standard care. Although the intervention had no additional effect compared to control, the substantial recovery rate in LC may have masked intervention effects. Therefore, future studies should consider this energy management framework in conditions without such recovery rates, such as CFS.\u003c/p\u003e","manuscriptTitle":"A Digital Platform with Activity Tracking for Energy Management Support in Long COVID: A Randomised Controlled Trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-09 17:24:03","doi":"10.21203/rs.3.rs-5951040/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f2f61146-9062-4c94-b091-2bb90df72d56","owner":[],"postedDate":"April 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":44008034,"name":"Health sciences/Medical research/Outcomes research"},{"id":44008035,"name":"Health sciences/Signs and symptoms/Fatigue"}],"tags":[],"updatedAt":"2026-02-03T08:05:24+00:00","versionOfRecord":{"articleIdentity":"rs-5951040","link":"https://doi.org/10.1038/s41467-025-64831-y","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2026-02-02 05:00:00","publishedOnDateReadable":"February 2nd, 2026"},"versionCreatedAt":"2025-04-09 17:24:03","video":"","vorDoi":"10.1038/s41467-025-64831-y","vorDoiUrl":"https://doi.org/10.1038/s41467-025-64831-y","workflowStages":[]},"version":"v1","identity":"rs-5951040","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5951040","identity":"rs-5951040","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}