A digital platform with activity tracking for energy management support in long COVID: a randomised controlled trial

Domain: neuroscience
Published: 2026-02-02
Editorial outcome: major_revision

Abstract
In a 6-month pragmatic randomised controlled trial (RCT; ISRCTN16033549), we compared a just-in-time intervention to support energy management in adults with long COVID (LC) to standard care. Participants received 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 energy management when they reached 50%, 75%, and 100% of their daily ‘activity allowance’. The primary outcome was post-exertional malaise (PEM) measured by the De Paul Symptom Questionnaire-PEM (DSQ-PEM). Of 369 participants assessed for eligibility, 250 participants were randomised 1:1, and 77 controls and 84 intervention participants were included in the final per-protocol analysis. There was no time by group interaction for the DSQ-PEM. The intervention group value was 48 (95% CI 44-53) at baseline and 46 (95% CI 41-51) post-intervention (arbitrary units). The control group value was 47 (95% CI 42-52) at baseline and 44 (95% CI 39-49) at follow-up (interaction effect p=0.614, η²p=0.002; trivial). No individual question exhibited an interaction effect (p > 0.05). Although the intervention had minimal effect compared to control, the substantial recovery rates previously reported in LC, coupled with our wide inclusion criteria may have masked intervention effects. Therefore, future studies should consider this energy management framework in conditions without such recovery rates, such as CFS.

Introduction
Following the UK 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 fatigue1,2. Symptoms can be disabling even if initial effects were mild, and can occur immediately after infection or some weeks later3. 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 >14h up to several days4) reported by people with conditions, such as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS).

LC is associated with highly variable symptom loads, fluctuating between periods of relative alleviation and exacerbation, often preceded by exertion5. Our public patient involvement (PPI) group and others6 describe ‘push-crash’ cycles where a perception of getting better leads to a push to return to normal activities precipitating a subsequent crash with worsening of symptoms, similar to PEM. Early work confirmed that 50% of people with LC meet the technical definition of PEM lasting >14h, while nearly all report some PEM-like symptoms lasting up to 14h6,7.

The cause of persistent symptoms in LC is poorly understood, and a pathophysiological mechanism remains elusive. This is particularly true for those whose initial symptoms were mild and for non-hospitalised individuals5, who did not receive any treatment that might explain prolonged rehabilitation (e.g. mechanical ventilation). Without a clear underlying mechanism, treatment options have focused on symptom management to help people with LC manage activities of daily living. Building on the symptom similarities between LC and ME/CFS, the most common symptom management strategy in LC is energy management, the only strategy currently recommended for ME/CFS8. 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 exacerbation9–11.

Energy management is suitable for self-management and is associated with higher adherence and greater patient-reported benefit12. However, despite multiple underlying theories9,13, 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 memory14. 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 (in ME/CFS), evidence to support the efficacy of energy management in preventing bouts of PEM is extremely limited11,15. Encouragingly, Parker and colleagues16 provided some preliminary evidence that a structured energy management protocol resulted in a significant reduction in the number of PEM episodes in people with LC. However, this intervention seems analogous to graded exercise therapy (a gradual increase in physical activity over time), rather than energy management per se, which is highly contentious with the LC and ME/CFS support groups.

To our knowledge, there are no contemporary efficacy trials that leverage mobile and wearable technology, which could address many of the challenges associated with energy management11,15. A protocol for a feasibility study including heart rate variability (HRV) biofeedback in people with LC was published in 202217 (the registration for the present study was in 2021), with the feasibility study published in 202418. However, only 13 participants completed the study, limiting confidence in results. On the other hand, a commercially available platform (Visible), with a reported $1m start-up fund and over 100,000 users is delivering biofeedback-informed energy management support. In the company’s recent preprint, within-person changes in HRV and heart rate emerged as predictors of symptom change, with higher heart rate and lower HRV conferring risk for ‘crashes’, ‘fatigue’, and ‘brain fog’19. However, key limitations, including measurement validity, sampling bias, and data accessibility render this platform unsuitable for research. However, we argue there are several limitations with this platform that render it unsuitable for research purposes. Firstly, the concepts of ‘crashes’, ‘fatigue’, and ‘brain fog’ are quantified without the use of validated instruments, but a 1–3 Likert scale ratings system. This means that heart rate and HRV may predict 1, 2, or 3, but whether 1, 2, or 3 is a valid representation of ‘crash’ (i.e. PEM) is uncertain. Secondly, as this is a consumer-focussed application, it is behind a paywall, which creates bias in ‘recruitment’ (they are arguably consumers rather than participants). Thirdly, HRV and heart rate data were collapsed into large time epochs, which is necessary for data analysis due to the size of the data, but this does not permit in-the-moment warnings of upcoming PEM (or ‘crash’). Finally, it also suffers from ascertainment bias in that participants self-declare their impairment and as such, the dataset contains people with ME/CFS, LC, any other co-morbidities, anyone who feels tired often, and anyone who downloads the app for interest. Anybody can contribute to the Visible dataset. Therefore, a platform that warns participants of potential PEM, informed by minute-by-minute biofeedback, using validated instruments, without a subscription cost, is non-existent to date. Therefore, 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).

Results
Of 369 participants assessed for eligibility, 119 were deemed ineligible or withdrew prior to randomisation. In total, 250 participants were randomised (125 per group), but 14 controls withdrew upon group allocation, and 11 of the intervention group discontinued intervention. Thirty-four control and 30 intervention participants were lost to follow-up. Therefore, 84 intervention participants and 77 control participants completed the 6-month follow-up and post-intervention data collection for the primary endpoint and were included in final per-protocol analysis (see Supplementary Information for age and gender distributions, and for employment status at baseline). Throughout the study, 33 participants were switched to step count monitoring. In the final per-protocol analysis, this applied to 23 participants. 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 1 and 2. The mean and standard deviation (SD) (±) number of 100% messages received per intervention group participant in month 1, 2, 3, 4, 5, and 6 was 13 ± 10, 10 ± 10, 8 ± 9, 7 ± 9, 6 ± 10, and 6 ± 10, respectively. The mean number of days participant exceeded their 100% energy allowance over the full 6 months was 51 ± 50 days.

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) at baseline and 46 (95% CI 41–51) post-intervention. The control group value was 47 (95% CI 42–52) at baseline and 44 (95% CI 39–49) at follow-up (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.

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.

To confirm our per-protocol analysis above, we conducted intention-to-treat analysis on the primary outcome variable. For the DSQ-PEM sum score (visualised in Fig. 1), the mixed-effects model revealed no effect of time (p=0.097), group (p=0.195), or interaction effect (p=0.621). Questions 1–5 responses are visualised in Fig. 2. For DSQ-PEM Q1 severity, the mixed-effects model revealed no effect of time (p=0.414), group (p=0.926), or interaction effect (p=0.053). For DSQ-PEM Q1 frequency, the mixed-effects model revealed no effect of time (p=0.052), group (p=0.813), or interaction effect (p=0.423). For DSQ-PEM Q2 severity, the mixed-effects model revealed no effect of time (p=0.454), group (p=0.147), or interaction effect (p=0.792). For DSQ-PEM Q2 frequency, the mixed-effects model revealed no effect of time (p=0.154), group (p=0.099), or interaction effect (p=0.639). For DSQ-PEM Q3 severity, the mixed-effects model revealed no effect of time (p=0.309), or interaction effect (p=0.489), but there was an effect of group (p=0.016), favouring intervention. Tukey’s post hoc test revealed a difference between groups at month 2 only (p=0.015), favouring intervention. For DSQ-PEM Q3 frequency, the mixed-effects model revealed no effect of time (p=0.107), or interaction effect (p=0.584), but there was an effect of group (p=0.038), favouring intervention. Tukey’s post hoc test revealed no differences between groups at any specific month. For DSQ-PEM Q4 severity, the mixed-effects model revealed no effect of time (p=0.496), group (p=0.580), or interaction effect (p=0.309). For DSQ-PEM Q4 frequency, the mixed-effects model revealed no effect of time (p=0.391), group (p=0.650), or interaction effect (p=0.961). For DSQ-PEM Q5 severity, the mixed-effects model revealed no effect of time (p=0.174), group (p=0.182), or interaction effect (p=0.739). For DSQ-PEM Q5 frequency, the mixed-effects model revealed no effect of time (p=0.591), group (p=0.455), or interaction effect (p=0.618).

To confirm the tests of mean differences above (i.e. ANOVA) were robust, we determine the number of individuals considered positive for PEM (i.e. discrete yes/no). A score of ≥2 for both frequency and severity on a specific item (Q1–5) suggests PEM is both present and clinically relevant4. Therefore, participants could be either positive or negative for PEM. When groups were combined, McNemar’s Test revealed the number of individuals classified as PEM positive decreased from baseline to follow-up (p=0.004). 111 participants were considered PEM positive at baseline and follow-up, 31 participants were PEM positive at baseline but not at follow-up (improvement), 12 participants were PEM negative before but positive after (worsening), and seven participants were PEM negative at both time points. In the intervention group, McNemar’s Test revealed no change in PEM status from baseline to post-intervention (p=0.394). Fifty-nine participants were considered PEM positive at baseline and post-intervention, 13 participants were PEM positive at baseline but not at post-intervention (improvement), nine participants were PEM negative before but positive after (worsening), and three participants were PEM negative at both time points. In the control group, the number of individuals classified as PEM positive decreased from baseline to follow-up (p=0.001). Fifty-two participants were considered PEM positive at baseline and follow-up, 18 participants were PEM positive at baseline but not at follow-up (improvement), three participants were PEM negative before but positive after (worsening), and four participants were PEM negative at both time points. We subsequently used Chi squared test for between group effects, which exhibited no differences at baseline (p=0.411) or follow-up (p=0.155).

To confirm our results were relevant for both genders, we disaggregated the results. In females only, repeated measures ANOVA revealed an effect of time (p=0.021, η²p=0.041; small), but not group (p=0.825, η²p=0.000; trivial), or interaction effect (p=0.358, η²p=0.007; trivial) for the primary outcome variable (sum of the DSQ-PEM). The intervention group value was 50 (95% CI 45–55) at baseline and 43 (95% CI 38–49) post-intervention. The control group value was 49 (95% CI 43–54) at baseline and 46 (95% CI 40–52) at follow-up. In males only, repeated measures ANOVA revealed no effect of time (p=0.275, η²p=0.038; small), or group (p=0.057, η²p=0.112; moderate), but there was an interaction effect (p=0.045, η²p=0.123; moderate) for the primary outcome variable. The intervention group value was 43 (95% CI 33–53) at baseline and 56 (95% CI 46–65) post-intervention. The control group value was 41 (95% CI 30–52) at baseline and 37 (95% CI 27–46) at follow-up.

As suggested by a reviewer, we also analysed results without those who were switched to steps rather than heart rate. Repeated measures ANOVA revealed no effect of time (p=0.213, η²p=0.011; small), group (p=0.998, η²p=0.000; trivial), or interaction effect (p=0.776, η²p=0.001; trivial) for the primary outcome variable.

Methods
Study design
The study was designed as a pragmatic single-centre RCT. 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; recruitment ran between November 2021 and February 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://doi.org/10.1186/ISRCTN16033549). Informed consent was obtained from all participants.

Participants
Inclusion criteria. Eligible participants had to be adults (≥18 years) reporting persistent symptoms following a COVID-19 infection which interfered with daily activities (in line with NICE guidelines 2021) and experiencing PEM. 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. Participant 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) could not wear a FitBit at home or work, (7) had another fatigue-related condition (e.g. CFS), or (8) were receiving ongoing care for LC via primary or secondary care services.

Recruitment. Individuals expressed interest to trial information on social media by sharing their email address, or contacting our PPI partner (LC Scotland), or contacting us directly via email. We emailed interested parties an information sheet and asked them to review the material and respond after 48 h. If there was no response, we followed up with a reminder email asking if they were still interested in participating. Those who confirmed their interest were scheduled for an online meeting or a phone call, during which we reviewed inclusion and exclusion criteria, provided a brief overview of the study, and offered time for questions about the trial. Participants chose whether they wished to enrol in the study, and we obtained their verbal consent to proceed with randomisation during that meeting. Recruitment was assisted by our partner organisation, LC Scotland, and involved promotion of the trial via social media groups, print media, a study website, and meetings with LC 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 at the first meeting (Fig. 3) after enrolment using a permuted block algorithm, with a fixed block size. Participants randomised to the intervention group received usual care with just-in-time messaging support, and the control group received usual care only. NS-H was the senior post-doctoral trial manager and generated the sequence, enroled participants, and assigned them to the trial groups. Participant blinding was impossible due to the requirements of the intervention group.

Procedures
Following randomisation at meeting 1, participants were contacted via email after the meeting to arrange meeting 2 for onboarding, approximately 1 week later depending on participant availability. This provided time for an activity tracker (Fitbit Charge 5, Fitbit Inc., San Francisco, CA, USA) to be posted to intervention group participants. At the second meeting, all participants downloaded the study app (‘Pace Me’), while only the intervention group also downloaded the FitBit app. The intervention group’s version of the study app included energy management support and data collection instruments, while the control group’s version included only the data collection instruments. NS-H guided participants through the app onboarding process, which involved creating an account on the study app, logged in, and provided additional electronic consent to ensure they understood their interaction with the app. Intervention participants were assisted to disable FitBit app notifications. Screenshots of completed checkboxes and digital signatures were captured and securely stored separately from other study data. Gender was self-reported at the initial enrolment meeting and was not a factor in the study design. 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. 4). At meeting 2, participants were encouraged to fill in the questionnaires as soon after the meeting as possible. We checked the database one week later and contacted them if they had not completed the questionnaires 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. Participants were contacted at the end of the study to complete all questionnaires before returning their Fitbit or being provided with the energy management platform as a thank you for participation.

Intervention design
The Pace Me app aimed to support energy management in individuals with LC. The intervention design was informed by the Behaviour Change Wheel35, evidence from literature11,36, and PPI feedback. The final design used goal setting, action planning, self-monitoring, feedback, prompts, and personalisation to assist users in managing energy. The intervention integrated heart rate (and step count if needed) feedback using Fitbit Charge 5, logging of PEM, logging of symptoms, and support messaging when patients exceeded their energy allowance (see Supplementary Information for full details of the intervention implementation, and for a data pipeline visualisation).

The intervention used a personalised activity allowance, initially set to no more than 30 min 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 have been due to poor energy management). 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 who requested to be changed from heart rate, step counts were used to set activity allowance. These limits were not intended as targets but as a guide for energy management. During the demonstration of the app and pacing feature, participants were informed that if they did not receive a notification indicating they had reached 50% of their daily activity allowance, they were on track. Pacing notifications were only sent to prompt users to ‘slow down’. This understanding was evident in the emails we received, in which some participants apologised for overexerting themselves when they received a 100% notification. Within the app, there was a ‘request a callback’ function, and it was explained this was how participants could report adverse effects.

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 app notifications as their activity exceeded specific thresholds, capped at three notifications 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. 5). These images were created using the free version of Canva (Browser version; Sydney, Australia).

Outcomes
The primary outcome was frequency and severity of PEM assessed using the De Paul Symptom Questionnaire-PEM (DSQ-PEM)4, 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 the 12-Item Short Form Health Survey, the EuroQol 5-Dimension Health Questionnaire, Pain Visual Analogue Scale, Patient Health Questionnaire-4, Fatigue Severity Scale (7-item version), Medical Research Council Breathlessness Questionnaire, Symbol Digit Modalities Test total correct (out of 60), total time for correct answers only, and 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 scale37. Assuming a SD of 2537, this resulted in an effect size of 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 (version 2024.04.2+764), and the wp.rmanova function, with two groups, two time points, f=0.25, assuming sphericity, α=0.05, 1−β=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.

All analyses were conducted using Jamovi version 2.3.21. Data were tested 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 chance38. Only participants who completed follow-up testing were included in the primary analysis (i.e. per protocol analysis). The effect of the intervention on primary and secondary outcomes was examined using two-way mixed-model ANOVAs with condition (intervention or control) as the between-subjects factor and time (pre- and post-intervention) as a within subjects factor. To confirm our assumptions were robust, we conducted sensitivity analysis in the form of intention to treat analysis for all participants who were randomised. In the case of missing data, mixed-effects models were used to account for the repeated measures structure of the data and to provide robust handling of missing data. Unlike non-parametric methods, which require complete case analysis and often assume homogeneity of variance across groups, mixed-effects models offer greater flexibility by allowing the inclusion of data under the assumption that data are missing at random. The linear mixed-effects model was specified for DSQ-PEM sum score (primary outcome variable), and DSQ-PEM questions 1–5, with time (seven monthly time points) and group (intervention and control) as fixed effects and subject ID included as a random intercept to account for individual variability. No outcome distributions deviated from normality, so no data transformations were applied. In the case of main effects, location of differences was further investigated using the Tukey post-hoc test. Alpha level is reported as exact p values and not described dichotomously as ‘significant’ or otherwise as recommended by the American Statistical Association39. 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, respectively40. For categorical data, (DSQ-PEM questions 6-10, and DSQ-PEM screening as positive or negative) we used McNemar’s Test for paired samples (pre- to post-intervention), or Chi squared test for between group effects (intervention vs. control).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This is a thoughtful well-written effort to test a mobile device for energy self-management in a pragmatic trial in people with\nLong COVID. Intervention guidance is based on self-report PEM in relation to objective heart rate and step counts. This\nguidance yielded Just-in-time messages to increase subject awareness of opportunities to adjust activity levels to reduce\nand better manage self-report PEM.\nThe authors did not seek to determine at post-intervention if messages were actually perceived by subjects as “just in time,”\nan important element of usability. Given no significant improvement in the active intervention group despite fairly granular\nmanagement feedback to subjects, post-intervention feedback from participants might have helped to identify potentially\ncorrectable issues with the protocol.\nAlso, data about psychological and affective states (motivation, mood, stress levels) which can influence physical activity,\nsedentary behavior and PEM were not collected. For instance, patients with ME/CFS and Long COVID may disregard\nsymptom-based signals (e.g., increased headache) of upcoming crashes, a manifestation of severe PEM. Ignoring such\nsignals may be driven by self-defeating motivation to stay as active as possible in the moment and wishful thinking about\navoiding a subsequent crash. Identifying these affective states during an intervention may increase patient awareness of\nhow they can and do undermine energy self-management.\nNo adherence data were presented. Although “guidance” rather than direct instructions to modify activity levels was\nprovided, it is unclear how often the guidance offered was actually followed. For instance, was a guidance message that\nactivity level was too high followed by a reduction in steps? If such guidance was ignored, this also suggests the importance\nof collaborative sessions with study subjects regarding study design.\nAlthough Energy Management is the central construct and intervention goal, it is not clear how energy was measured. No\ndefinition was given. The authors noted that Energy Management requires participants to recall and compare two poorly\ndescribed constructs; 'energy availability' and 'energy use', especially problematic when dealing with impaired patients. It\nseems that energy was used as more of an appealing intervention label rather than an operationalized construct. This issue\nshould be addressed.\nThere may be no published peer review data on the use of digital technology for energy management in CFS, but the\ncommercial company Visible has collected apparently enormous amounts of mobile HR/HRV and activity monitor data in\nLong COVID and CFS patients in order to generate pacing suggestions via their wearable app. It might inform the authors’\nthinking about their mobile app trial to communicate with this company. A recently posted paper apparently based on Visible\ntechnology data on Research Square (Aitken et al; DOI: 10.21203/rs.3.rs-5423422/v1 ) provides predictive models of HR\nand HRV in relation to symptom exacerbations in ME/CFS and Long COVID using data from mobile device technology.\nDetail\nLine 65; myalgic encephalitis—misspelled. Should be: Encephalomyelitis\nLine 103. The trial ran between January 2022 and September 2023. But the paper later states: At the time of recruitment, the\nUK was engaged in COVID-19 related lockdowns. Please clarify. Lockdowns ended in 2020.\nWhat are the noteworthy results?\nThis is an initial controlled trial of mobile digital technology consisting of age-predicted heart rate maximum, step counts and\nPEM reporting to help guide and manage activity levels that maximize physical function and moderate risk of triggering\nPEM.\n- Will the work be of significance to the field and related fields? How does it compare to the established literature? If the work\nis not original, please provide relevant references.\nThe trial does break new ground in the sense that no previously published trials have used this algorithmic approach in\nLong COVID or ME/CFS. The intervention combines objective measures and symptom self-report to improve illness\nmanagement all housed in a wearable device. Although there is no published scientific literature on this, a popular\ncommercial device, i.e., Visible, takes a very similar approach to Long COVID and ME/CFS self-management. A reference to\nthis product would indicate a more thorough review of such devices.\n- Does the work support the conclusions and claims, or is additional evidence needed?\nThe data does support its conclusions which are appropriately modest given null findings.\n- Are there any flaws in the data analysis, interpretation and conclusions? - Do these prohibit publication or require revision?\nRevision is required to more thoroughly consider the methodological limitations of the study as indicated above.\n- Is the methodology sound? Does the work meet the expected standards in your field?\nThe methodology is consistent with the standards of a pragmatic RCT.\n- Is there enough detail provided in the methods for the work to be reproduced?\nYes. The methodology contains sufficient detail to be reproduced. Given the technicals of a device- based intervention some\nconsultation would probably be necessary.", "reviewer_2": "General comments\nAs a reviewer, I had the privilege of reviewing the paper \"A Digital Platform with Activity Tracking for Energy Management\nSupport in Long COVID: A Randomized Controlled Trial\".\nThe present study examines the effects of a 6-month just-in-time intervention to support energy management in Long COVID\n(LC) patients. The utilisation of activity trackers and the \"Pace Me\" app facilitated the quantification of energy levels, with\nparticipants instructed to maintain heart rate below 60% of maximum capacity for no more than 30 minutes per day. In cases\nof instances of permanent overruns, permanent symptoms or underruns, it was customary to make individual adjustments to\nthe aforementioned limit. The objective of the intervention was to reduce the severity of post-exertional malaise (PEM). The\nstudy is a randomised controlled trial and includes 250 participants with LC. The results indicate that there is no difference in\nthe severity of PEM between the intervention and standard care groups.\nIn general, the title is clear and communicates effectively that this is a randomised controlled trial. The paper is well\nstructured and clearly formulated.\nThe summary offers a comprehensive overview of the study, including the rationale and specific aims. The introduction is\nclearly structured, addressing the scientific background and leading to the rationale for the study, with the specific aims\nclearly stated. The randomisation process and subsequent statistical analysis are both clear and satisfactory.\nThe study would have been enhanced by the inclusion of a parameter for daily mental workload, in addition to heart rate, to\ndetermine energy management limits. For instance, simple questions regarding mental exertion could have been evaluated\nusing a Likert scale during the first week. This could be particularly beneficial given that PEM is characterised as physical and\nmental stress intolerance.\nHowever, there are some issues, that should be addressed before publication of the manuscript.\nSpecific comments\nSummary:\nLines 39-42:\nPlease note that the number of participants analysed for the intervention group does not match. Firstly, the six participants\nwho withdrew from the intervention were subsequently omitted from the listing. Secondly, the number of analysed\nintervention participants should have been n=87. A comparison of the data with Figure 1 and the results is required, with any\nnecessary corrections to the listing.\nIntervention: 125 participants - 8 (lost to follow-up) - 24 participants (missing data) – 6 participants (discontinued intervention)\n= 87 participants\nLines 47-48:\nThe sentence \"Digitally supported energy management in people with LC had no effect on PEM compared to standard\ncare.\" is overly generalised and could be interpreted as applying to all LC patients and all digital methods of supporting\nenergy management. Please clarify whether this result applies to your cohort, or rather, your digital support for energy\nmanagement, the Pace Me app.\nIntroduction:\nThe section is well structured and presented.\nMethods:\nRandomisation and masking:\nLine 154 Figure 1:\n1. As outlined in the summary above, Figure 1 shows that of the 125 participants assigned to the intervention, eight were lost\nto follow-up and six discontinued the intervention, with the remaining 84 participants being evaluated. However, the correct\nnumber of intervention participants should be n=87.\n2. It would be beneficial to ascertain the current status of the three participants not included in the listing. Could you kindly\nexplain why these were not included in the statistical analysis?\nProcedures:\nLines 164-167:\nAs you previously stated, the baseline questionnaires were completed during the online meeting, while the remaining\nquestionnaires were completed via the app. Could you please confirm whether this is correct?\nPlease can you confirm whether the questionnaires administered during the online meeting received active or passive\nsupport? It is important to note that active support during completion could potentially compromise the quality of the data, as\nthe follow-up questionnaires had to be completed in a different format via the app. Could you please explain why the\nquestionnaires were not also collected during a final online meeting? This would result in a standardised questionnaire\nsurvey. If this is not the case, please provide clearer wording in the text to make it more understandable.\nIntervention Design:\nLines 187-188:\n1. Please add the number of participants with unstable heart rate.\n2. It would be important to know whether the participants with an unstable heart rate exceeded the limits of energy\nmanagement more or less frequently than the other participants. The same applies to days with PEM or increased\nsymptoms. Treacy et al. (2017) observed that wrist-worn activity trackers lose accuracy at speeds below 4.2 km/h. It has\nbeen demonstrated that the significance of the actual steps is underestimated as the pace slows down\n(https://doi.org/10.1093/ptj/pzx010). It is reasonable to assume that LC patients move around more slowly than healthy\npeople in everyday life due to the restrictions caused by the various symptoms they experience, such as fatigue and pain.\nThis suggests that these participants may have taken more steps than the activity tracker indicated. In this instance, it would\nbe necessary to examine whether the transgression of the limits was perhaps not well recognised.\n3. It would be interesting to know how you go about dealing with activities, such as cycling, that are not step countable.\n4. In the event that this assumption were to be valid, it would be necessary to remove these participants from the analysis, as\nenergy management in this form would be unfeasible for them.\nLines 193-196:\n1. It is imperative to ascertain whether the participants who received the notification of 50% of the activity allowance during\nthe afternoon or early evening were subsequently issued with further notifications, possibly even the 100% of the activity\nallowance. It is important to note that an inactive day could also have the opposite effect by specifying the percentages (50,\n75, 100). For instance, the participants were encouraged to engage in slightly more physical activity.\n1. Could you please clarify whether the participants were given any guidelines regarding energy management? For\ninstance, were they encouraged to reach 75% as often as possible? If so, please add this to the text.\nResults:\nIn general, the paper would benefit from improved characterisation of the participants. These could be included in the\nbaseline characteristics of the supplementary information. This would include, for example, information on self-reported pre-\nexisting medical conditions and the percentage of participants who were working, on sick leave, or unemployed during the\nstudy period. LC patients are a very heterogeneous group. During medical consultations with LC patients, it has been noted\nthat symptoms may have been present prior to the onset of the condition, or may be explained by factors such as sleep\napnoea or other pre-existing conditions. For those affected who are still in work, energy management and maintaining limits\nare significantly more difficult than for those on sick leave or unemployed, as work may involve fixed hours and non-self-\ndetermined workloads and activities. A more thorough characterisation could reveal points for discussion that could explain\nthe lack of effect of the intervention.\nInformation regarding the percentage of participants who reached their individual limit per day (50%, 75%, 100%) would be\nadvantageous.\nLines 246-252:\n1. Please note that the number of participants analysed for the intervention group does not match. Firstly, the six participants\nwho withdrew from the intervention were subsequently omitted from the listing. Secondly, the number of analysed\nintervention participants should have been n=87. A comparison of the data with Figure 1 and the summary is required, with\nany necessary corrections to the listing. However, the correct number of intervention participants should be n=87.\n2. It would be beneficial to ascertain the current status of the three participants not included in the listing. Could you kindly\nexplain why these were not included in the statistical analysis?\nLine 271 table 2:\n1. Please add an \"l\" to the \"SF12 - Physical Health\" field. Please ensure that you write consistently in rows 1 and 2 of the\ntable for SF12.\n2. Please check the Physical and Mental Health baseline and post-scores of the SF-12 for accuracy. It appears to be an\nanomalous finding that the mean values and the CI values for baseline and post-scores are identical for SF12 - Physical\nHealth, with all participants having a score of 54 (CI 54-54). The same applies to SF12 – Mental Health. In this instance, the\nbaseline and post-scores are also identical. However, a significant difference over time was reported, which appears\ninconsistent with the values presented.\nDiscussion:\nIn general, the discussion was well-written and structured. However, in some places, it did become somewhat protracted.\nLines 276-278:\nPlease clarify whether this result applies to your cohort, or rather, your digital support for energy management.\nThe second paragraph:\n1. This paragraph provides an overview of the recovery process for individuals affected by LC. This is also mentioned in the\nsummary in lines 48-49. Could you please clarify whether there are any participants in your group who no longer have\nsymptoms, or whose PEM screening is no longer positive?\n2. The third section of the paper addresses the limitations of the study. It should also be noted that the participants' mental\nexertion was not taken into account in the energy management strategy. Managing mental exertion is a very central aspect\nof energy management. The definition of PEM (line 63) states that PEM refers to intolerance for physical and mental\nexertion. Assessing mental function using a monthly questionnaire via the app is not sufficient in this case. In future\nresearch, consideration should be given to the inclusion of mental exertion in energy management assessments.\nPlease could you also add this (2.) to the limitations in paragraph three.", "reviewer_3": "I have reviewed the manuscript entitled \"A Digital Platform with Activity Tracking 1 for Energy Management Support in Long\nCOVID: A Randomised Controlled Trial\" with interest and in accordance with the journal's guidelines. This trials provides\nrelevant insights into energy management for people with long COVID. However, I have identified several concerns with the\nmethodology and analyses, which I believe merit further examination.\nMain Issues\nThere are some inconsistencies in the flowchart figures that should be addressed to ensure transparency and accuracy in\nthe reporting of patient flow. For instance, while 55 patients are reported as excluded, only 48 have a corresponding\nexclusion reason documented. Similarly, within the intervention group, there appears to be a discrepancy in the number of\nanalyzed patients. Specifically, out of 125 patients who received the allocated intervention, 8 were lost to follow-up, 6\ndiscontinued the intervention, and 24 were excluded from analysis due to incomplete post-intervention data. This should\nresult in 87 patients remaining, yet the flowchart indicates a total of only 84.\nThe main analysis was conducted on participants who completed follow-up testing. However, no formal definition of the\nanalysis population was provided (e.g. safety, intention-to-treat [ITT] or per-protocol [PP]). The authors should justify the\npopulation used for the primary analysis. Furthermore, repeating the analyses in an ITT population (i.e. all randomised\nparticipants), as is standard practice in clinical trials, would strengthen the validity of the findings.\nA two-way mixed-model ANOVA was used in the item-level analysis (Table 1). However, this method may not be the most\nappropriate for item-level analysis involving Likert-type items scored from 0 to 4. This is mainly because ANOVA treats item\nscores as continuous with equal spacing between values, and the assumptions of normality and sphericity are difficult to\njustify with 5-point scales. Ordinal logistic regression with repeated measures or Generalised Estimating Equations for\nordinal data would be a more appropriate method.\nIn conclusion, the authors claim that there were no adverse incidents and that the intervention was safe. However, they\nshould explain in the methodology section how adverse and unintended events were defined (e.g., severity, timing) and\nassessed (e.g., spontaneous reporting, checklists, clinician assessment).\nA baseline table summarising the demographic and clinical profile of the subjects included in the analysis, organised by\nstudy group, must be included as Table 1. This is standard practice when reporting on clinical trials and would help readers\nto understand the type of subjects included in the trial. The supplemental table showing the age and sex distribution is\ninsufficient.\nThe authors used the CONSORT flowchart to describe subject inclusion and exclusion process. However, to ensure\nadherence to recognised standards for the transparent and complete clinical trial reporting, the authors should also include\nthe 2025 CONSORT checklist as supplementary material.\nMinor Issues\nThe summary should include the main study objective, as articulated at the end of the introduction, to provide readers with\nimmediate context.\nPost-hoc power calculations are generally discouraged, as they do not provide additional insight once results are known.\nThe authors should cite the exact R package (e.g. WebPower) used to estimate sample size, and also reference the R\nversion and Jamovi platform usedThe data sharing statement does not include any information on how to get in touch with\nthe authors for a reasonable request.\nThe manuscript would benefit from the inclusion of a clear longitudinal plot showing each group's response to the DSQ-PEM\nquestionnaire at both assessment points. Graphing the data will allow readers to visualize how activity tracking plus just-in-\ntime messaging (intervention group) affected each outcome compared to standard care (control group). For instance, the\nnumerical outcomes can be presented in the form of a graph that illustrates the evolution of each group's mean score at\nbaseline and post. In the context of categorical outcomes, the utilization of a Sankey plot is a recommended approach for the\ngraphical representation of alterations occurring at two timepoints.\nThe data sharing statement should include clear instructions on how to contact the corresponding author for reasonable data\naccess requests", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": "REVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\n1. This is a thoughtful well-written effort to test a mobile device for energy self-management\nin a pragmatic trial in people with Long COVID. Intervention guidance is based on self-\nreport PEM in relation to objective heart rate and step counts. This guidance yielded\nJust-in-time messages to increase subject awareness of opportunities to adjust activity\nlevels to reduce and better manage self-report PEM.\nWe thank R1 for their positive comments upon our manuscript.\n2. The authors did not seek to determine at post-intervention if messages were actually\nperceived by subjects as “just in time,” an important element of usability. Given no\nsignificant improvement in the active intervention group despite fairly granular\nmanagement feedback to subjects, post-intervention feedback from participants might\nhave helped to identify potentially correctable issues with the protocol.\nThank you for your comment. We did seek post-intervention feedback from participants\nand these findings are reported elsewhere (https://pubmed.ncbi.nlm.nih.gov/39529893/).\nHowever, this feedback was centred around patient reported outcomes and how the\nintervention helped participants manage symptoms of LC. Although we did not focus on\nthe timing of the messages per se, feedback from participants suggested that the\nPaceMe app was beneficial for (1) PEM management, (2) Support, (3) Validation, and (4)\nControl and Agency. This suggests that participants valued the intervention, even if it\nwas not efficacious for reducing DSQ-PEM over six months and highlights the value of a\ndigital health intervention as a vital component of rehabilitation.\n3. Also, data about psychological and affective states (motivation, mood, stress levels)\nwhich can influence physical activity, sedentary behavior and PEM were not collected.\nFor instance, patients with ME/CFS and Long COVID may disregard symptom-based\nsignals (e.g., increased headache) of upcoming crashes, a manifestation of severe PEM.\nIgnoring such signals may be driven by self-defeating motivation to stay as active as\npossible in the moment and wishful thinking about avoiding a subsequent crash.\nIdentifying these affective states during an intervention may increase patient awareness\nof how they can and do undermine energy self-management.\nThank you for raising this, and we fully agree with your point. Unfortunately,\nunderstanding the impact of affective states was not a focus of this study and these data\nwere not collected. At the time of developing and rolling out this pragmatic trial, the\noverarching goal was to provide people with LC with a potential solution that would help\nthem manage their energy levels and prevent PEM. It required a careful balance of\ngathering enough data to answer essential research questions but not too much that\nwould impose undue burden on participants, especially considering the nature of the\ntopic. We involved PPI groups throughout the planning and execution of the trial to\nensure the needs of the target group were met. But we acknowledge that not all potential\nmediators or moderators were evaluated. We have added a statement about this to our\nlimitations.\n“Thirdly, we did not assess participants’ psychological or affective states (e.g., motivation,\nmood, stress), which may influence physical activity behaviour, pacing decisions, and\nsusceptibility to post-exertional malaise.”\n4. No adherence data were presented. Although “guidance” rather than direct instructions\nto modify activity levels was provided, it is unclear how often the guidance offered was\nactually followed. For instance, was a guidance message that activity level was too high\nfollowed by a reduction in steps? If such guidance was ignored, this also suggests the\nimportance of collaborative sessions with study subjects regarding study design.\nThank you for raising this, and we fully agree with your point. Unfortunately, we feel this\nis outside the scope of the present manuscript. Our primary aim was to measure\neffectiveness of the intervention, regardless of engagement/adherence/compliance etc.\nAdherence/compliance data would be indicative of measuring behavioural change, which\nis of course interesting, but was not our primary aim. We may explore this suggestion in\na future manuscript but consider it exploratory analysis rather than answering our\nprimary research question. The complication is also that individuals could receive four\nmessages per day (25%, 50%, 75%, 100% energy allowance used), and we would need\nto set a proximity to the message being sent to the window of behaviour change (i.e. HR\nreduced for 1, 2, 3, 4, 5, n?? hours). There are many decisions which we would need to\nrationalise before we even consider writing the script to pull down this data.\nHowever, we have included the number of 100% received on average in the results\nsection, which may be indicative of energy allowance exceeded, which may indicate\nadherence.\n“The mean and standard deviation (±) number of 100% messages received per\nparticipant in month 1, 2, 3, 4, 5, and 6 was 13 ± 10, 10 ± 10, 8 ± 9, 7 ± 9, 6 ± 10, and 6 ±\n10, respectively. The mean number of days those participants exceeded their 100%\nenergy allowance over the full six months was 51± 50 days.”\n5. Although Energy Management is the central construct and intervention goal, it is not\nclear how energy was measured. No definition was given. The authors noted that Energy\nManagement requires participants to recall and compare two poorly described\nconstructs; 'energy availability' and 'energy use', especially problematic when dealing\nwith impaired patients. It seems that energy was used as more of an appealing\nintervention label rather than an operationalized construct. This issue should be\naddressed.\nWe thank R1 for this observation and apologise for the ambiguity in prose. Energy\nmanagement is indeed a difficult construct to define. We imagine this is the reason some\nhave resorted to metaphors such as the spoon theory (https://www.rcot.co.uk/latest-\nnews/spoon-theory). We have provided the definition in the introduction: “Energy\nmanagement uses techniques such as planning, feedback, and activity diaries to help\nindividuals manage their ‘energy envelope’ to limit the debilitating consequences of their\ncondition by reducing or avoiding periods of symptom exacerbation.”\nLikewise, ‘energy availability’ and ‘energy used’ are vague constructs, which we argue\nare immeasurable in the current context. Of course, the SI unit of energy is the joule (J),\nsometimes expressed in kcal for the consumer. It would be possible for us to calculate\nenergy expenditure from heart rate\n(https://journals.sagepub.com/doi/abs/10.1260/174795407782233146), but this would be\nmoving one degree away from the unit we measured.\nOur initial limit was set based on guidance from Physios for ME (one of the authors on\nthis manuscript). The details of the algorithm are outlined in the methods of the\nmanuscript: “The intervention used a personalised activity allowance, initially set to no\nmore than 30 minutes of activity above 60% of their age-predicted heart rate maximum.\nThis allowance was iteratively adjusted in response to participants’ activity and PEM\nreports. If participants reported PEM and had exceeded the allowance, the allowance\nremained unchanged (i.e. the PEM may be due to poor pacing). If participants reported\nPEM but had remained within their allowance for the preceding 3 days, the allowance\nwas reduced in a stepwise fashion (i.e. activity allowance may be too large to prevent\nPEM). Conversely, the allowance was increased if participants did not report PEM for\nthree consecutive weeks.” This threshold is considered the starting point that Physios for\nME use to aid people with ME/CFS integrate HR pacing into their lives. At the time of this\nproject inception, there were no data on pacing in LC, so we used the limits commonly\nused in ME/CFS.\nTo operationalise energy management, we used HR thresholds as described above, in\nan attempt to reduce cognitive burden of manually tracking activities and PEM. We have\nincluded the following image in supplementary material to outline the data pipeline. We\ncan move this into the manuscript if you feel this would explain the process better to the\nreader.\n6. There may be no published peer review data on the use of digital technology for energy\nmanagement in CFS, but the commercial company Visible has collected apparently\nenormous amounts of mobile HR/HRV and activity monitor data in Long COVID and CFS\npatients in order to generate pacing suggestions via their wearable app. It might inform\nthe authors’ thinking about their mobile app trial to communicate with this company. A\nrecently posted paper apparently based on Visible technology data on Research Square\n(Aitken et al; DOI: 10.21203/rs.3.rs-5423422/v1) provides predictive models of HR\nand HRV in relation to symptom exacerbations in ME/CFS and Long COVID using data from\nmobile device technology.\nWe thank R1 for their suggestion. We have added the following paragraph to the\nintroduction. We hope this expands upon the current state of energy management\nknowledge in long COVID.\nDespite its widespread use in ME/CFS, evidence to support the efficacy of energy management in\npreventing bouts of PEM is extremely limited 11,15. Encouragingly, Parker and colleagues 16\nprovided preliminary evidence that a structured energy management protocol significantly\nreduced the number of PEM episodes in people with LC. However, upon closer inspection, this\nintervention seems analogous to graded exercise therapy (a gradual increase in physical activity\nover time), rather than energy management per se, which is highly contentious with the LC and\nME/CFS support groups. To our knowledge, no contemporary efficacy trials have leveraged\nmobile and wearable technology to address the challenges of tracking activity to manage LC\nsymptoms 11,15. A protocol for a feasibility study including heart rate variability biofeedback in\npeople with LC was published in 2022 17, with the feasibility study published in 2024 18.\nHowever, only 13 participants completed the study, limiting confidence in results. On the other\nhand, a commercially available platform (Visible), has already launched biofeedback-informed\nenergy management support, backed by a reported $1m in start-up funding and over 100,000\nusers. According to the company’s recent preprint, within-person changes in HRV and heart rate\nemerged as predictors of symptom change, with higher heart rate and lower HRV conferring risk\nfor ‘crashes’, ‘fatigue’,￼￼. However, we argue there are several limitations with this platform\nthat render it unsuitable for research purposes. Firstly, the concepts of ‘crashes’, ‘fatigue’, and\n‘brain fog’ are quantified without the use of validated instruments, but a 1-3 rating system. This\nmeans that heart rate and HRV may predict 1, 2, or 3, but whether 1, 2, or 3 is a valid\nrepresentation of ‘crash’ (i.e. PEM) is uncertain. Secondly, as this is a consumer-focussed\napplication, it is behind a paywall which creates bias in ‘recruitment’ (they are arguably\nconsumers rather than participants). Thirdly, the HRV and heart rate data were collapsed into large\ntime epochs, which is necessary for data analysis due to the size of the dataset, but this does not\npermit in-the-moment warnings of upcoming PEM (or ‘crash’). Finally, it suffers from\nascertainment bias in that participants self-declare their impairment and as such, the dataset\ncontains people with ME/CFS, Long COVID, and any other co-morbidities, anyone feels tired\noften, and anyone who downloads it for interest. Anyone can contribute to the Visible dataset.\nCurrently, there is no platform that warns participants of potential PEM, informed by minute-by-\nminute biofeedback, using validated instruments, without a subscription fee.\n7. Detail\nLine 65; myalgic encephalitis—misspelled. Should be: Encephalomyelitis\nCorrected as suggested.\n8. Line 103. The trial ran between January 2022 and September 2023. But the paper later\nstates: At the time of recruitment, the UK was engaged in COVID-19 related lockdowns.\nPlease clarify. Lockdowns ended in 2020.\nWe thank Reviewer 1 for this observation. The statement has been removed, and the\nreference to recruitment during lockdown has been corrected. The revised text now\nreads: 'The trial ran between January 2022 and September 2023, with recruitment taking\nplace from November 2021 to the end of February 2023.'\"\n9. What are the noteworthy results?\nThis is an initial controlled trial of mobile digital technology consisting of age-predicted\nheart rate maximum, step counts and PEM reporting to help guide and manage activity\nlevels that maximize physical function and moderate risk of triggering PEM.\nWe thank R1 for their positive comments upon our manuscript.\n10. Will the work be of significance to the field and related fields? How does it compare to\nthe established literature? If the work is not original, please provide relevant references.\nThe trial does break new ground in the sense that no previously published trials have\nused this algorithmic approach in Long COVID or ME/CFS. The intervention combines\nobjective measures and symptom self-report to improve illness management all housed\nin a wearable device. Although there is no published scientific literature on this, a popular\ncommercial device, i.e., Visible, takes a very similar approach to Long COVID and\nME/CFS self-management. A reference to this product would indicate a more thorough\nreview of such devices.\nWe thank R1 for their positive comments upon our manuscript and have referred to the\nVisible app in the introduction as requested.\n11. Does the work support the conclusions and claims, or is additional evidence needed?\nThe data does support its conclusions which are appropriately modest given null\nfindings.\nThank you.\n12. Are there any flaws in the data analysis, interpretation and conclusions? - Do these\nprohibit publication or require revision?\nRevision is required to more thoroughly consider the methodological limitations of the\nstudy as indicated above.\nWe thank R1 for these comments and hope we have addressed your comments\nsatisfactorily.\n13. Is the methodology sound? Does the work meet the expected standards in your field?\nThe methodology is consistent with the standards of a pragmatic RCT.\nThank you.\n14. Is there enough detail provided in the methods for the work to be reproduced?\nYes. The methodology contains sufficient detail to be reproduced. Given the technicals of\na device- based intervention some consultation would probably be necessary.\nThank you. We have included large supplementary material to outline the technical\naspects of the intervention, but including this within the manuscript would exceed the\nword limit of Nature Comms.\n15. Reviewer #2 (Remarks to the Author):\nGeneral comments\nAs a reviewer, I had the privilege of reviewing the paper \"A Digital Platform with Activity\nTracking for Energy Management Support in Long COVID: A Randomized Controlled\nTrial\".\nWe thank R2 for their positive comment about our manuscript.\n16. The present study examines the effects of a 6-month just-in-time intervention to support\nenergy management in Long COVID (LC) patients. The utilisation of activity trackers and\nthe \"Pace Me\" app facilitated the quantification of energy levels, with participants\ninstructed to maintain heart rate below 60% of maximum capacity for no more than 30\nminutes per day. In cases of instances of permanent overruns, permanent symptoms or\nunderruns, it was customary to make individual adjustments to the aforementioned limit.\nThe objective of the intervention was to reduce the severity of post-exertional malaise\n(PEM). The study is a randomised controlled trial and includes 250 participants with LC.\nThe results indicate that there is no difference in the severity of PEM between the\nintervention and standard care groups. In general, the title is clear and communicates\neffectively that this is a randomised controlled trial. The paper is well structured and\nclearly formulated.\nWe thank R2 for their positive comments about our manuscript, and it is reassuring that\nR2 has grasped the overall methodology of the project, as it is challenging to\ncommunicate.\n17. The summary offers a comprehensive overview of the study, including the rationale and\nspecific aims. The introduction is clearly structured, addressing the scientific background\nand leading to the rationale for the study, with the specific aims clearly stated. The\nrandomisation process and subsequent statistical analysis are both clear and\nsatisfactory.\nWe thank R2 for their positive comments about our manuscript.\n18. The study would have been enhanced by the inclusion of a parameter for daily mental\nworkload, in addition to heart rate, to determine energy management limits. For instance,\nsimple questions regarding mental exertion could have been evaluated using a Likert\nscale during the first week. This could be particularly beneficial given that PEM is\ncharacterised as physical and mental stress intolerance.\nWe thank R2 for their comment, but these data were not collected at the time. We agree\nit would be useful to determine cognitive load, as well as heart rate, but this was outside\nthe scope of the project. Our aim was to assess HR supported activity management and\nwe agree that adding mental workload would have been useful but there is no passive\nway of such data collection, thus requiring daily updates from users which in turn\ninvolves daily prompts, reminders, and notifications, all adding to participant burden. We\nhave included some text in the limitations to address this:\n“…participants' mental exertion was not considered in the energy management strategy.\nManaging mental exertion is of course a central aspect of energy management. In future\nresearch, consideration should be given to the inclusion of mental exertion in energy\nmanagement assessments, although this could increase participant burden so a passive\nmeasure of mental exertion would be beneficial.”\n19. However, there are some issues, that should be addressed before publication of the\nmanuscript.\nSpecific comments\nSummary:\nLines 39-42:\nPlease note that the number of participants analysed for the intervention group does not\nmatch. Firstly, the six participants who withdrew from the intervention were subsequently\nomitted from the listing. Secondly, the number of analysed intervention participants\nshould have been n=87. A comparison of the data with Figure 1 and the results is\nrequired, with any necessary corrections to the listing.\nIntervention: 125 participants - 8 (lost to follow-up) - 24 participants (missing data) – 6\nparticipants (discontinued intervention) = 87 participants\nWe apologise for this oversight. We made an error in the text/figure. The number of\nparticipants analysed (n=84) was correct, and we have amended the text in the abstract,\nresults, and figure 1 (CONSORT).\n20. Lines 47-48:\nThe sentence \"Digitally supported energy management in people with LC had no effect\non PEM compared to standard care.\" is overly generalised and could be interpreted as\napplying to all LC patients and all digital methods of supporting energy management.\nPlease clarify whether this result applies to your cohort, or rather, your digital support for\nenergy management, the Pace Me app.\nWe have amended this to:\n“In this study, energy management supported with the Pace Me app had no effect on PEM in\npeople with LC compared to standard care.”\nWe hope this improves the clarity of the abstract.\n21. Introduction:\nThe section is well structured and presented.\nWe thank R2 for their positive comments about our introduction.\n22. Methods:\nRandomisation and masking:\nLine 154 Figure 1:\nAs outlined in the summary above, Figure 1 shows that of the 125 participants assigned\nto the intervention, eight were lost to follow-up and six discontinued the intervention, with\nthe remaining 84 participants being evaluated. However, the correct number of\nintervention participants should be n=87.\nWe apologise for this oversight. We made an error in the text/figure. The number of\nparticipants analysed (n=84) was correct, and we have amended the text in the abstract,\nresults, and figure 1 (CONSORT).\n23. It would be beneficial to ascertain the current status of the three participants not included\nin the listing. Could you kindly explain why these were not included in the statistical\nanalysis?\nWe apologise for this oversight. We made an error in the text/figure. The number of\nparticipants analysed (n=84) was correct, and we have amended the text in the abstract,\nresults, and figure 1 (CONSORT).\n24. Procedures:\nLines 164-167:\nAs you previously stated, the baseline questionnaires were completed during the online\nmeeting, while the remaining questionnaires were completed via the app. Could you\nplease confirm whether this is correct?\nWe have now corrected this,’ \"During the meeting, participants were encouraged to\ncomplete all baseline questionnaires afterward to avoid adding to their burden, as the\nmeeting itself lasted an hour.\"\n25. Please can you confirm whether the questionnaires administered during the online\nmeeting received active or passive support? It is important to note that active support\nduring completion could potentially compromise the quality of the data, as the follow-up\nquestionnaires had to be completed in a different format via the app. Could you please\nexplain why the questionnaires were not also collected during a final online meeting?\nThis would result in a standardised questionnaire survey. If this is not the case, please\nprovide clearer wording in the text to make it more understandable.\nWe have now included a section that reads:\n“At meeting 2, participants were encouraged to fill in the questionnaires as soon after the meeting\nas possible. We checked the database one week later and contacted them if they had not\ncompleted the questionnaires to ensure baseline data capture before starting the intervention or\ncontrol part of the experiment.\"\n\"Participants were contacted at the end of the study to complete all questionnaires\nbefore returning their Fitbit or being provided with the energy management platform as a\nthank you for participation”\nIntervention Design:\nLines 187-188:\n1. Please add the number of participants with unstable heart rate.\nWe have now included a section that reads: \" Throughout the study, thirty-three\nparticipants reported unstable heart rate data and were switched to step count\nmonitoring. In the final per-protocol analysis, this applied to 23 participants. These limits\nwere not intended as targets but as a guide for energy management.”\n26. It would be important to know whether the participants with an unstable heart rate\nexceeded the limits of energy management more or less frequently than the other\nparticipants. The same applies to days with PEM or increased symptoms. Treacy et al.\n(2017) observed that wrist-worn activity trackers lose accuracy at speeds below 4.2\nkm/h. It has been demonstrated that the significance of the actual steps is\nunderestimated as the pace slows down (https://doi.org/10.1093/ptj/pzx010). It is\nreasonable to assume that LC patients move around more slowly than healthy people in\neveryday life due to the restrictions caused by the various symptoms they experience,\nsuch as fatigue and pain. This suggests that these participants may have taken more\nsteps than the activity tracker indicated. In this instance, it would be necessary to\nexamine whether the transgression of the limits was perhaps not well recognised\nIt would be interesting to know how you go about dealing with activities, such as cycling,\nthat are not step countable.\nIn the event that this assumption were to be valid, it would be necessary to remove these\nparticipants from the analysis, as energy management in this form would be unfeasible\nfor them.\nThank you for your thoughtful comments regarding the limitations of wrist-worn activity\ntrackers in detecting slow and non-ambulatory movement, and the implications for\nenergy management in individuals with Long COVID.\nWe would like to clarify that our intervention was primarily based on heart rate, not step\ncount. Specifically, participants received messaging triggered by exceeding a\npersonalised heart rate allowance, which was defined as no more than 30 minutes of\nactivity above 60% of their age-predicted heart rate maximum. For participants with\nunstable heart rates, step counts were used to set activity allowances as an alternative.\nDuring the study N=23 (per-protocol) participants were moved to step-based thresholds\non request. For those step thresholds, we acknowledge that step-based thresholds may\nunderestimate activity in slow movers, as noted in Treacy et al. (2017).\nBy basing the primary activity threshold on heart rate rather than steps, we aimed to\nensure that slow walking and non-ambulatory activities (e.g., cycling) were still captured,\nas these activities typically result in an elevated heart rate even when step counts are\nlow or absent. For cycling and other non-step activities, the Fitbit Charge 5 records heart\nrate continuously, allowing us to monitor exertion regardless of step count. Previous\nstudies using the Fitbit Charge HR (an earlier model with similar sensor technology to\nthe Charge 5) generally demonstrated strong to very strong correlations with the\nreference standard (polar chest strap) in free living conditions, with mean absolute\npercentage errors typically within ±10% which is considered acceptable for consumer-\ngrade devices (Fuller D, Colwell E, Low J, Orychock K, Tobin MA, Simango B, Buote R,\nVan Heerden D, Luan H, Cullen K, Slade L, Taylor NGA. Reliability and Validity of\nCommercially Available Wearable Devices for Measuring Steps, Energy Expenditure,\nand Heart Rate: Systematic Review. JMIR Mhealth Uhealth. 2020 Sep 8;8(9):e18694.\ndoi: 10.2196/18694). However, the device consistently underestimated HR, although the\ndegree of underestimation increases at higher activity intensities. For example, in adults,\nthe underestimation was greater during moderate-to-vigorous activity, but group-level\nerrors still fell within 10%. We have added the following to the limitations section:\n“While Fitbit devices have demonstrated reasonable validity for step counting and heart\nrate tracking in healthy adults, their accuracy diminishes at slower walking speeds\nTreacy et al. (2017) and higher intensity activity (Fuller et al 2020), respectively.\nTherefore, some episodes of overexertion may not have been adequately detected.”\n27. Lines 193-196:\nIt is imperative to ascertain whether the participants who received the notification of 50%\nof the activity allowance during the afternoon or early evening were subsequently issued\nwith further notifications, possibly even the 100% of the activity allowance. It is important\nto note that an inactive day could also have the opposite effect by specifying the\npercentages (50, 75, 100). For instance, the participants were encouraged to engage in\nslightly more physical activity.\nWe thank the reviewer for this suggestion. We can confirm that participants who received\nthe 50% notification may be subsequently issued with further notifications, possibly even\nthe 100% notification if they exceeded their allowance.\nWe can clarify that participants were never encouraged to engage in activity that would\nraise heart rate (see the next response to reviewer also). They were only ever alerted\nwhen they were at risk of doing too much activity. We have included this section: \"During\nthe demonstration of the app and pacing feature, participants were informed that if they\ndid not receive a notification indicating they had reached 50% of their daily activity\nallowance, they were on track. Pacing notifications were only sent to prompt users to\nslow down. This understanding was evident in the emails we received, in which\nparticipants apologised for overexerting themselves when they received a 100%\nnotification.\" Moreover, we have included the number of 100% messages received by the\nintervention group in the methods section:\n“The mean and standard deviation (±) number of 100% messages received per\nparticipant in month 1, 2, 3, 4, 5, and 6 was 13 ± 10, 10 ± 10, 8 ± 9, 7 ± 9, 6 ± 10, and 6 ±\n10, respectively. The mean number of days those participants exceeded their 100%\nenergy allowance over the full six months was 51± 50 days.”\n28. Could you please clarify whether the participants were given any guidelines regarding\nenergy management? For instance, were they encouraged to reach 75% as often as\npossible? If so, please add this to the text.\nWe thank the reviewer for this suggestion. We have included this section: \"During the\ndemonstration of the app and pacing feature, participants were informed that if they did\nnot receive a notification indicating they had reached 50% of their daily activity\nallowance, they were on track. Pacing notifications were only sent to prompt users to\nslow down. This understanding was evident in the emails we received, in which\nparticipants apologised for overexerting themselves when they received a 100%\nnotification.\"\n29. Results:\nIn general, the paper would benefit from improved characterisation of the participants.\nThese could be included in the baseline characteristics of the supplementary information.\nThis would include, for example, information on self-reported pre-existing medical\nconditions and the percentage of participants who were working, on sick leave, or\nunemployed during the study period. LC patients are a very heterogeneous group.\nDuring medical consultations with LC patients, it has been noted that symptoms may\nhave been present prior to the onset of the condition, or may be explained by factors\nsuch as sleep apnoea or other pre-existing conditions. For those affected who are still in\nwork, energy management and maintaining limits are significantly more difficult than for\nthose on sick leave or unemployed, as work may involve fixed hours and non-self-\ndetermined workloads and activities. A more thorough characterisation could reveal\npoints for discussion that could explain the lack of effect of the intervention.\nWe thank the reviewer for this feedback. While we did not collect any pre-existing\nmedical data, but participants with other pre-existing fatigue-related conditions were\nexcluded from the study. We have updated the inclusion and exclusion criteria section of\nthe manuscript to make this clearer. We have included a supplementary table to show\nthe gender and age distribution between groups, and have included a supplementary\nfigure to visualise the change to employment caused by LC at baseline.\n30. Information regarding the percentage of participants who reached their individual limit\nper day (50%, 75%, 100%) would be advantageous.\nWe have included the number of 100% messages received by the intervention group by\nmonth in the methods section:\n“The mean and standard deviation (±) number of 100% messages received per\nparticipant in month 1, 2, 3, 4, 5, and 6 was 13 ± 10, 10 ± 10, 8 ± 9, 7 ± 9, 6 ± 10, and 6 ±\n10, respectively. The mean number of days those participants exceeded their 100%\nenergy allowance over the full six months was 51± 50 days.”\n31. Lines 246-252:\nPlease note that the number of participants analysed for the intervention group does not\nmatch. Firstly, the six participants who withdrew from the intervention were subsequently\nomitted from the listing. Secondly, the number of analysed intervention participants\nshould have been n=87. A comparison of the data with Figure 1 and the summary is\nrequired, with any necessary corrections to the listing. However, the correct number of\nintervention participants should be n=87.\nWe apologise for this oversight. We made an error in the missing data text/figure. The\nnumber of participants analysed (n=84) was correct, and we have amended the text in\nthe abstract, results, and figure 1 (CONSORT).\n32. It would be beneficial to ascertain the current status of the three participants not included\nin the listing. Could you kindly explain why these were not included in the statistical\nanalysis?\nWe apologise for this oversight. We made an error in the missing data text/figure. The\nnumber of participants analysed (n=84) was correct, and we have amended the text in\nthe abstract, results, and figure 1 (CONSORT).\n33. Line 271 table 2:\n1. Please add an \"l\" to the \"SF12 - Physical Health\" field. Please ensure that you write\nconsistently in rows 1 and 2 of the table for SF12.\nCorrected as suggested. We have added the ‘l’ to health and capitalised the H for\nconsistency.\n34. Please check the Physical and Mental Health baseline and post-scores of the SF-12 for\naccuracy. It appears to be an anomalous finding that the mean values and the CI values\nfor baseline and post-scores are identical for SF12 - Physical Health, with all participants\nhaving a score of 54 (CI 54-54). The same applies to SF12 – Mental Health. In this\ninstance, the baseline and post-scores are also identical. However, a significant\ndifference over time was reported, which appears inconsistent with the values presented.\nThank you for this observation. We have previously checked and rechecked these\nstatistics because we thought it implausible. However, it is worth noting that the SF-12\nconversion (for comparison with the SF-36) has some log functions which reduces the\nvariability.\nBelow are the results for the SF-12 physical (pulled straight from Jamovi).\nWithin Subjects Effects\nSum of Squares df Mean Square F p\n35. 36.\ntime 0.0159 1 0.0159 0.0986 0.754\n37. 38.\ntime ✻ group 0.1424 1 0.1424 0.8843 0.349\nBelow are the results for the SF-12 mental health (pulled straight from Jamovi).\nWithin Subjects Effects\nSum of Squares df Mean Square F p\ntime 2.950 1 2.950 9.552 0.003\ntime ✻ group (2) 0.122 1 0.122 0.396 0.531\nWe made a minor error in rounding in table 2, which has now been corrected.\nControl pre: 59 (59-60), control post: 59 (59-60). Intervention pre: 59 (59-59),\nintervention post: 59 (59-59).\nTO\nControl pre : 59 (59-59), control post: 59 (59-60). Intervention pre: 59 (59-59),\nintervention post: 59 (59-60).\nStill, we are surprised with the p value.\n38. Discussion:\nIn general, the discussion was well-written and structured. However, in some places, it\ndid become somewhat protracted.\nLines 276-278:\nPlease clarify whether this result applies to your cohort, or rather, your digital support for\nenergy management.\nApologies, we have amended this to state ‘the present study was…’\n39. The second paragraph:\nThis paragraph provides an overview of the recovery process for individuals affected by\nLC. This is also mentioned in the summary in lines 48-49. Could you please clarify\nwhether there are any participants in your group who no longer have symptoms, or\nwhose PEM screening is no longer positive?\nWe thank the reviewer for this comment and have assumed they mean the DSQ-PEM\nscreening criteria of ≥2 for both frequency and severity on a specific item, which suggests\nPEM is both present and clinically relevant. We have included the following analyses in the\nresults section:\nIn terms of screening for PEM, a score of ≥2 for both frequency and severity on a specific\nitem (Q1-5) suggests PEM is both present and clinically relevant. Therefore, participants\ncould be either positive or negative for PEM. When groups were combined, McNemar’s Test\nrevealed the number of individuals classified as PEM positive decreased from baseline to\nmonth 6 (p=0.004). 111 participants were considered PEM positive at baseline and month 6,\n31 participants were PEM positive at baseline but not at follow-up (improvement), 12\nparticipants were PEM negative before but positive after (worsening), and 7 participants\nwere PEM negative at both time points. In the intervention group, McNemar’s Test revealed\nno change in PEM status from baseline to month 6 (p=0.394). 59 participants were\nconsidered PEM positive at baseline and month 6, 13 participants were PEM positive at\nbaseline but not at follow-up (improvement), 9 participants were PEM negative before but\npositive after (worsening), and 3 participants were PEM negative at both time points. In the\ncontrol group, the number of individuals classified as PEM positive decreased from baseline\nto month 6 (p=0.001). 52 participants were considered PEM positive at baseline and month\n6, 18 participants were PEM positive at baseline but not at follow-up (improvement), 3\nparticipants were PEM negative before but positive after (worsening), and 4 participants\nwere PEM negative at both time points. Indeed, 19 improved and five worsened. We\nsubsequently used Chi squared test for between group effects which exhibited no\ndifferences at baseline (p=0.411) or month 6 (p=0.155).\n40. The third section of the paper addresses the limitations of the study. It should also be\nnoted that the participants' mental exertion was not taken into account in the energy\nmanagement strategy. Managing mental exertion is a very central aspect of energy\nmanagement. The definition of PEM (line 63) states that PEM refers to intolerance for\nphysical and mental exertion. Assessing mental function using a monthly questionnaire\nvia the app is not sufficient in this case. In future research, consideration should be\ngiven to the inclusion of mental exertion in energy management assessments.\nPlease could you also add this (2.) to the limitations in paragraph three.\nWe thank R2 for this suggestion and have added the following to the discussion:\n“Secondly, participants' mental exertion was not considered in the energy management strategy.\nManaging mental exertion is of course a central aspect of energy management. In future research,\nconsideration should be given to the inclusion of mental exertion in energy management\nassessments, although this could increase participant burden so a passive measure of mental\nexertion would be very beneficial.”\n41. Reviewer #3 (Remarks to the Author):\nI have reviewed the manuscript entitled \"A Digital Platform with Activity Tracking 1 for\nEnergy Management Support in Long COVID: A Randomised Controlled Trial\" with\ninterest and in accordance with the journal's guidelines. This trials provides relevant\ninsights into energy management for people with long COVID. However, I have\nidentified several concerns with the methodology and analyses, which I believe merit\nfurther examination.\nWe thank R3 for their positive comments about our manuscript.\n42. Main Issues\nThere are some inconsistencies in the flowchart figures that should be addressed to\nensure transparency and accuracy in the reporting of patient flow. For instance, while 55\npatients are reported as excluded, only 48 have a corresponding exclusion reason\ndocumented. Similarly, within the intervention group, there appears to be a discrepancy\nin the number of analyzed patients. Specifically, out of 125 patients who received the\nallocated intervention, 8 were lost to follow-up, 6 discontinued the intervention, and 24\nwere excluded from analysis due to incomplete post-intervention data. This should result\nin 87 patients remaining, yet the flowchart indicates a total of only 84.\nWe apologise for this oversight. We made an error in the missing data text/figure. The\nnumber of participants analysed (n=84) was correct, and we have amended the text in\nthe abstract, results, and figure 1 (CONSORT).\n43. The main analysis was conducted on participants who completed follow-up testing.\nHowever, no formal definition of the analysis population was provided (e.g. safety,\nintention-to-treat [ITT] or per-protocol [PP]). The authors should justify the\npopulation used for the primary analysis. Furthermore, repeating the analyses in an ITT\npopulation (i.e. all randomised participants), as is standard practice in clinical trials,\nwould strengthen the validity of the findings.\nWe thank the reviewer for this suggestion. In the initial submission, we did convey that a\nper protocol analysis would be conducted. However, we agree with the reviewer that\nrepeating the analyses in an ITT population (i.e. all randomised participants), has\nstrengthened the validity of the findings.\nAlong with published guidance (https://www.bmj.com/content/342/bmj.d40), we have\nadded ITT analysis to confirm our findings were robust. The statistical analysis section\nand results section now reflects this. Broadly speaking, the results from the ITT support\nthe PP analysis, with two p values moving from just over/under 0.05 to just under/over\n0.05. However, no wholesale differences were observed, so we are confident in our\nconclusions. Thank you again for this suggestion. We feel the change had added\nrobustness to the manuscript.\n44. A two-way mixed-model ANOVA was used in the item-level analysis (Table 1). However,\nthis method may not be the most appropriate for item-level analysis involving Likert-type\nitems scored from 0 to 4. This is mainly because ANOVA treats item scores as\ncontinuous with equal spacing between values, and the assumptions of normality and\nsphericity are difficult to justify with 5-point scales. Ordinal logistic regression with\nrepeated measures or Generalised Estimating Equations for ordinal data would be a\nmore appropriate method.\nWe appreciate R3’s concern regarding the treatment of the 5-point Likert scale as\ncontinuous data. While Likert-type items are sometimes considered ordinal in nature,\nthere is substantial scholarly support for treating aggregated or multi-point Likert scales\nas continuous variables. Our methodological decision aligns with established\npsychometric practices and empirical evidence. Indeed, the initial development and\ntesting of the DSQ-PEM treated questions 1-5 as continuous data\n(https://pmc.ncbi.nlm.nih.gov/articles/PMC4788471/), and thus reported means and\nSDs. The typical criticism of this approach is that difference between responses as not\nconsidered equal. However, for the DSQ-PEM frequency responses, 0 is none of the\ntime, 4 is all of the time, and 2 is ‘about half of the time’, indicating that 4 is indeed\ndouble 2, and 0 is a true zero.\nIndeed, several other authors have treated the DSQ-PEM as continuous data and\nanalysed these data using parametric tests such as ANOVA\n(https://www.mdpi.com/2035-8377/15/1/1; https://www.nature.com/articles/s41598-023-\n28955-9; https://pmc.ncbi.nlm.nih.gov/articles/PMC9383197/).\nAdditionally, when multiple items are summed or averaged to form a scale—particularly\nwith five or more categories—researchers often treat the resulting variable as\napproximately continuous. This is predicated on the assumption that measurement error\nand individual item nonlinearity are mitigated through aggregation\n(https://link.springer.com/article/10.1007/s10459-010-9222-y). Empirical and simulation-\nbased studies have demonstrated that parametric statistical methods, including linear\nregression and ANOVA, yield robust results when applied to Likert-type data with five or\nmore response categories. For instance, Norman\n(https://link.springer.com/article/10.1007/s10459-010-9222-y) argues that the central\nlimit theorem supports the use of parametric techniques, particularly as the number of\nitems and respondents increases. Similarly, Carifio and Perla\n(https://pubmed.ncbi.nlm.nih.gov/19120943/) contend that treating Likert scales as\ninterval-level data is both methodologically and practically justifiable, especially when\nthe scales are symmetrical and balanced (as per the DSQ-PEM).\nMoreover, the psychometric literature supports the notion that the precision and\ninformational value of Likert scales improve with more response options. Harpe\n(https://www.sciencedirect.com/science/article/abs/pii/S1877129715200196)\nemphasises that Likert scales with five or more points (as per the DSQ-PEM) approach\ninterval-level measurement properties, thus allowing for their treatment as continuous\nvariables in many applied settings. In practice, many prominent psychological and social\nscience instruments (e.g., Beck Depression Inventory, Rosenberg Self-Esteem Scale)\nemploy Likert-style formats and are analysed using parametric methods, further\nsupporting the practical viability of this approach.\nIn the present study, the 0–4 scale was used consistently across multiple items, with\nresponses averaged to generate the composite score as our primary outcome. The\nDSQ-PEM exhibited high consistency (Test-retest: Pearson’s correlation coefficients =\n0.85 or higher. Internal consistency: Cronbach’s alpha = 0.88\n[https://pmc.ncbi.nlm.nih.gov/articles/PMC4871625/] and 0.95\n[https://pmc.ncbi.nlm.nih.gov/articles/PMC4830389/]), suggesting the items formed a\ncoherent and reliable construct. Treating the resulting scale as continuous allowed us to\nleverage the benefits of parametric modelling, including increased statistical power and\ninterpretability.\nWhile we acknowledge the theoretical distinction between ordinal and interval\nmeasurement levels, we assert that our analytic approach is consistent with best\npractices in applied quantitative research, and other researchers using the DSQ-PEM.\n45. In conclusion, the authors claim that there were no adverse incidents and that the\nintervention was safe. However, they should explain in the methodology section how\nadverse and unintended events were defined (e.g., severity, timing) and assessed (e.g.,\nspontaneous reporting, checklists, clinician assessment).\nWe thank the reviewer for this comment and have added the following to the\nmanuscript:\nWithin the app, there was a ‘request a call back’ function, and it was explained this was\nhow participants could report adverse effects.\n46. A baseline table summarising the demographic and clinical profile of the subjects\nincluded in the analysis, organised by study group, must be included as Table 1. This is\nstandard practice when reporting on clinical trials and would help readers to understand\nthe type of subjects included in the trial. The supplemental table showing the age and\nsex distribution is insufficient.\nWe thank the reviewer for this feedback but argue this is not a typical clinical trial. We did\nnot collect any pre-existing medical data, participants with other pre-existing fatigue-\nrelated conditions were excluded from the study. We have ensured the inclusion and\nexclusion criteria section of the manuscript is clearer. Additionally, we have also added a\nsection on self-reported occupation status at baseline to supplementary material.\n47. The authors used the CONSORT flowchart to describe subject inclusion and exclusion\nprocess. However, to ensure adherence to recognised standards for the transparent and\ncomplete clinical trial reporting, the authors should also include the 2025 CONSORT\nchecklist as supplementary material.\nAdded as requested.\n48. Minor Issues\nThe summary should include the main study objective, as articulated at the end of the\nintroduction, to provide readers with immediate context.\nWe thank the reviewer for this suggestion and have added the following to the start of\nthe discussion: “The aim of this trial was to determine if activity tracking combined with\njust-in-time messages helped people with LC implement energy management and\nreduce incidents of symptom exacerbation. We hypothesised a priori that a just-in-time\nintervention to assist energy management (intervention group) would reduce frequency\nor severity of PEM compared to usual care (control). The main finding of the present\nstudy was that …”\n49. Post-hoc power calculations are generally discouraged, as they do not provide\nadditional insight once results are known.\nWe have now removed this section.\n50. The authors should cite the exact R package (e.g. WebPower) used to estimate sample\nsize, and also reference the R version and Jamovi platform used. The data sharing\nstatement does not include any information on how to get in touch with the authors for a\nreasonable request.\nWe thank R3 for this suggestion and have now added the following:\n“Using the WebPower package in R Studio (version 2024.04.2+764), and the wp.rmanova\nfunction, with two groups, two time points, f=0.25, assuming sphericity, α=0.05, 1-β=0.9,\ntesting for an interaction effect, the total n was 170 (85 per group). Consequently, we aimed to recruit\n125 participants per group to allow for 30% drop-out. All analyses were conducted using Jamovi\nversion 2.3.21.”\nAdditionally, the data sharing agreement has been updated:\n“Data collected for this study, including individual anonymised participant data and a data\ndictionary defining each field in the set will be made available to others upon reasonable request\nto the corresponding author (l.hayes4@lancaster.ac.uk).”\n51. The manuscript would benefit from the inclusion of a clear longitudinal plot showing\neach group's response to the DSQ-PEM questionnaire at both assessment points.\nGraphing the data will allow readers to visualize how activity tracking plus just-in-time\nmessaging (intervention group) affected each outcome compared to standard care\n(control group). For instance, the numerical outcomes can be presented in the form of a\ngraph that illustrates the evolution of each group's mean score at baseline and post. In\nthe context of categorical outcomes, the utilization of a Sankey plot is a recommended\napproach for the graphical representation of alterations occurring at two timepoints.\nWe thank the reviewer for this suggestion and have added figures 4 and 5 which reflect the\nITT analysis (monthly data points). However, we would like to keep the tables as we believe\nthis is the most appropriate for open science and reproducibility of RCTs. Moreover, any\nresearchers seeking to meta-analyse data from the results of this manuscript will find it easier\nto use data from the table, rather than a figure.\n52. The data sharing statement should include clear instructions on how to contact the\ncorresponding author for reasonable data access requests\nThe data sharing agreement has been updated:\n“Data collected for this study, including individual anonymised participant data and a data\ndictionary defining each field in the set will be made available to others upon reasonable request\nto the corresponding author (l.hayse4@lancaster.ac.uk)\n53. Reviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nThank you for reviewing our manuscript."}, {"round": 2, "reviewer_comments": {"reviewer_2": "I would like to express my sincere gratitude for your comprehensive response to the extensive comments.\nIn point 26 of the \"Response to Referees Letter\", a detailed explanation was provided, describing that the primary activity threshold was determined on the basis of heart rate.\nPlease accept my apologies for any inconvenience caused; it may be the case that I did not articulate my comment with\nsufficient precision.\nThe question regarding cycling should pertain to patients with unstable heart rates who were included in the analysis.\nThe heart rate value could not be used as a benchmark in this case.\nIt is my understanding that such activity cannot be recorded and taken into account due to the unstable heart rate.\nConsequently, the exclusion of these 23 patients from the analysis may be warranted, as their inclusion could potentially\ninfluence the results, for instance, if their cycling habits significantly impacted the outcomes.", "reviewer_3": "We would like to thank the authors for their detailed responses to the points we flagged in our review. Most of the answers\nare satisfactory.\nThe intention-to-treat analysis was reported in the Results section, under the subtitle: 'Sensitivity Analysis'. In randomized\ntrials, the purpose of an ITT analysis is to estimate the effect of the intervention while maintaining the benefits of\nrandomization, regardless of adherence to the protocol or any deviations from it. Sensitivity analyses, on the other hand, are\nadditional analyses carried out to check the robustness of the main results to different assumptions. Therefore, the subtitle\n'Sensitivity Analysis' must be removed or substituted with 'Intention-to-Treat Analysis'.\nWe appreciate the extended and detailed justification of the DSQ-PEM analysis, and we agree with the authors on most\npoints. However, when reviewing the cited papers as examples of studies that have treated the DSQ-PEM as continuous\ndata and analysed it using parametric tests such as ANOVA, we did not find the same approach if we understanded\ncorrectly. Jason et al. (https://www.mdpi.com/2035-8377/15/1/1) and Nepotchatky et al.\n(https://www.nature.com/articles/s41598-023-28955-9) appear to report DSQ results as a 100-point composite score. In\ncontrast, Twomey et al. (https://pmc.ncbi.nlm.nih.gov/articles/PMC9383197/) reported DSQ items using the step scoring\nmethod, where a frequency and severity score of 2,2 on any items 1–5 is considered indicative of PEM.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": "Reviewer #2 (Remarks to the Author):\nI would like to express my sincere gratitude for your comprehensive response to the extensive\ncomments. In point 26 of the \"Response to Referees Letter\", a detailed explanation was\nprovided, describing that the primary activity threshold was determined on the basis of heart\nrate.\nPlease accept my apologies for any inconvenience caused; it may be the case that I did not\narticulate my comment with sufficient precision. The question regarding cycling should pertain\nto patients with unstable heart rates who were included in the analysis. The heart rate value\ncould not be used as a benchmark in this case. It is my understanding that such activity cannot\nbe recorded and taken into account due to the unstable heart rate. Consequently, the exclusion\nof these 23 patients from the analysis may be warranted, as their inclusion could potentially\ninfluence the results, for instance, if their cycling habits significantly impacted the outcomes.\nWe thank the reviewer for their suggestion, and we politely disagree these participants should be\nremoved for reasons of equality, bias, and ecological validity, we have included sensitivity analysis\nwhere these participants were removed as follows:\n“As suggested by a reviewer, we also analysed results without those who were switched to steps\nrather than heart rate. Repeated measures ANOVA revealed no effect of time (p=0.213, η²p=0.011;\nsmall), group (p=0.998, η²p=0.000; trivial), or interaction effect (p=0.776, η²p=0.001; trivial) for the\nprimary outcome variable.”\nReviewer #3 (Remarks to the Author):\nWe would like to thank the authors for their detailed responses to the points we flagged in our\nreview. Most of the answers are satisfactory.\nThe intention-to-treat analysis was reported in the Results section, under the subtitle:\n'Sensitivity Analysis'. In randomized trials, the purpose of an ITT analysis is to estimate the\neffect of the intervention while maintaining the benefits of randomization, regardless of\nadherence to the protocol or any deviations from it. Sensitivity analyses, on the other hand, are\nadditional analyses carried out to check the robustness of the main results to different\nassumptions. Therefore, the subtitle 'Sensitivity Analysis' must be removed or substituted with\n'Intention-to-Treat Analysis'.\nWe thank the reviewer for this clarity and have deleted this title.\nWe appreciate the extended and detailed justification of the DSQ-PEM analysis, and we agree\nwith the authors on most points. However, when reviewing the cited papers as examples of\nstudies that have treated the DSQ-PEM as continuous data and analysed it using parametric\ntests such as ANOVA, we did not find the same approach if we understanded correctly. Jason et\nal. (https://www.mdpi.com/2035-8377/15/1/1) and Nepotchatky et al.\n(https://www.nature.com/articles/s41598-023-28955-9) appear to report DSQ results as a 100-\npoint composite score. In contrast, Twomey et al. (https://pmc.ncbi.nlm.nih.gov/articles/PMC9383197/)\nreported DSQ items using the step scoring method, where a frequency and severity score of 2,2 on any items 1–5 is considered indicative of\nPEM.\nWe thank the reviewer for their comment. We apologise for being unclear. In our current manuscript,\nwe have also reported DSQ-PEM results as a 100-point composite score (as per Jason et al and\nNepotchatky et al). For example, in our results, we communicate “Using the sum of the DSQ-PEM\nquestions 1-5 expressed on a 100-point scale (as per our power calculation), the intervention group\nvalue was 48 (95% CI 44-53) at baseline and 46 (95% CI 41-51) post-intervention. The control group\nvalue was 47 (95% CI 42-52) at baseline and 44 (95% CI 39-49) at follow-up (interaction effect\np=0.614, η²p=0.002; trivial).”\nAdditionally, in the last round of revisions reviewer 2 requested “This paragraph provides an\noverview of the recovery process for individuals affected by LC. This is also mentioned in the\nsummary in lines 48-49. Could you please clarify whether there are any participants in your group\nwho no longer have symptoms, or whose PEM screening is no longer positive?” We interpreted this\nto mean, presentation of data using the step scoring method, as per Twomey et al.. This is a binary\nyes/no for presentation of PEM as you have correctly identified. To address reviewer 2’s previous\ncomment, we have also presented results in this way as follows:\n“To confirm the tests of mean differences above (i.e. ANOVA) were robust, we determine the number\nof individuals considered positive for PEM (i.e. discrete yes/no). A score of ≥2 for both frequency and\nseverity on a specific item (Q1-5) suggests PEM is both present and clinically relevant 4. Therefore,\nparticipants could be either positive or negative for PEM. When groups were combined, McNemar’s\nTest revealed the number of individuals classified as PEM positive decreased from baseline to follow-\nup (p=0.004). 111 participants were considered PEM positive at baseline and follow-up, 31\nparticipants were PEM positive at baseline but not at follow-up (improvement), 12 participants were\nPEM negative before but positive after (worsening), and seven participants were PEM negative at\nboth time points. In the intervention group, McNemar’s Test revealed no change in PEM status from\nbaseline to post-intervention (p=0.394). 59 participants were considered PEM positive at baseline and\npost-intervention, 13 participants were PEM positive at baseline but not at post-intervention\n(improvement), nine participants were PEM negative before but positive after (worsening), and three\nparticipants were PEM negative at both time points. In the control group, the number of individuals\nclassified as PEM positive decreased from baseline to follow-up (p=0.001). 52 participants were\nconsidered PEM positive at baseline and follow-up, 18 participants were PEM positive at baseline but\nnot at follow-up (improvement), three participants were PEM negative before but positive after\n(worsening), and four participants were PEM negative at both time points. We subsequently used Chi\nsquared test for between group effects which exhibited no differences at baseline (p=0.411) or follow-\nup (p=0.155).”\nPlease let us know if our writing style is unclear or confusing in any way and we can alter the\nmanuscript for clarity.\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nThank you for reviewing the manuscript."}]

Decision letter