Pilot Sequential Multiple Assignment Randomised Trial of LvL UP: an Adaptive Holistic mHealth Coaching Intervention Integrating Physical Activity, Diet, and Mental Health

Pilot Sequential Multiple Assignment Randomised Trial of LvL UP: an Adaptive Holistic mHealth Coaching Intervention Integrating Physical Activity, Diet, and Mental Health | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pilot Sequential Multiple Assignment Randomised Trial of LvL UP: an Adaptive Holistic mHealth Coaching Intervention Integrating Physical Activity, Diet, and Mental Health Shenglin Zheng, Oscar Castro, Jacqueline Louise Mair, Ahmad I. Jabir, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7372529/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Jan, 2026 Read the published version in International Journal of Behavioral Nutrition and Physical Activity → Version 1 posted 10 You are reading this latest preprint version Abstract Background Mobile Health (mHealth) interventions are promising for addressing due burden of noncommunicable diseases and common mental disorders but often focus on single domains and lack adaptability. LvL UP contributes novel evidence by operationalising a holistic mHealth coaching intervention that integrates physical activity, diet, and emotional regulation, with adaptive human support. Methods The eight-week trial (April–July 2024) recruited adults in Singapore aged 21–59 at risk of chronic conditions. Participants were randomised 2:1 to the intervention (LvL UP app with a peer supporter–LvL UP Buddy) or comparison (control app with educational resources). After four weeks, non-responders (completed < 6 digital coaching sessions or rated session usefulness < 4/5) were re-randomised 1:1 to continue or receive three additional motivational interviewing (MI)-informed sessions with a human coach; responders remained on their original allocation. Primary outcomes included feasibility indicators: recruitment, LvL UP Buddy enrolment, non-responder rate, retention, data completion, and engagement. Secondary outcomes measured changes from baseline to eight weeks in mental well-being, psychological distress, physical activity, sleep duration, and fruit and vegetable intake. Six progression criteria were prespecified to guide advancement to a full SMART trial. Results Of the 458 individuals screened, 394 were eligible, and 123 were enrolled (82 interventions; 41 controls). Most intervention participants (95.1%) were paired with a LvL UP Buddy. Thirty-eight participants (46.3%) were non-responders; of those receiving MI sessions, 52.6% (10/19) completed all three. Eight-week retention was high (91.5% intervention; 92.7% control), with 12.2% missing data. Positive trends were observed in mental well-being (2.12, 95% CI [-0.58, 4.82]), psychological distress (-0.94 [-2.08, 0.20]), and sleep duration (0.49 hours/week [0.17, 0.82]). The study met five of six prespecified progression criteria: recruiting ≥ 60 participants within six weeks, achieving ≥ 75% retention, maintaining ≤ 20% missing data, obtaining a 40–60% non-responder rate, and showing a positive change in ≥ 1 health-related outcome. The digital coaching session adherence fell below the target (39.5% vs 70%). Conclusions LvL UP was feasible for delivery and evaluation using a SMART design. The results provide strong operational guidance and a solid foundation for the refinement and implementation of a fully powered trial. Registry: ClinicalTrials.gov, TRN: NCT06360029, Registration date: 7 April 2024 SMART Adaptive Interventions Digital Health mHealth Prevention Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Healthy ageing, maintaining physical and mental health across the lifespan, is an urgent global priority as populations age rapidly. 1 , 2 This concept extends beyond managing disease in older adults to preventing noncommunicable diseases (NCDs) and common health disorders (CMDs) in younger and middle-aged populations. 3 Achieving healthy ageing requires scalable, accessible solutions that promote behaviour change across diverse populations. 4 , 5 , 6 Mobile health (mHealth) interventions offer a promising approach to deliver such solutions at scale. 7 Evidence suggests that mHealth interventions can promote health behaviours and outcomes, especially in settings with limited access to conventional healthcare. 4 , 5 , 6 , 8 However, many mHealth interventions target a single behaviour, which limits their ability to address the complex interconnections between NCDs and CMDs. Depression or anxiety, are also associated with physical inactivity or poor dietary habits, creating a cycle that increases NCD risk and worsens CMDs 9 , 10 , 11 Recognising these relationships, holistic mHealth interventions that simultaneously target physical activity, diet, and mental health have shown benefits for weight management, lifestyle improvement, and mental well-being. 12 , 13 , 14 , 15 While holistic mHealth interventions show promise, augmenting them with human support may further improve adherence and outcomes. 8 , 16 , 17 However, widespread human support is costly and difficult to scale. 8 , 16 Conversational agents offer a lower-cost and scalable alternative, although their capacity to fully replicate human support remains unclear. 18 , 19 , 20 Adaptive intervention strategies, tailoring support intensity based on individual needs, can optimise both effectiveness and resource use. 21 , 22 For instance, participants can begin with digital support and escalate to human-delivered coaching only when needed. Building on these principles, we developed LvL UP 3.0, an adaptive, holistic mHealth coaching intervention for the prevention of NCDs and CMDs. It includes a smartphone application (LvL UP app) featuring an automated conversational agent and self-management tools, plus peer support from a LvL UP Buddy. Participants who do not meet pre-specified response criteria escalate to receive additional human coaching. The intervention was developed and refined between 2021 and 2023, informed by systematic reviews, 8 , 15 , 23 market analyses, 24 , 25 user-centred studies, 26 , 27 and feasibility studies. 28 , 29 , 30 To evaluate LvL UP, we use a sequential multiple assignment randomised trial (SMART) design, which allows participants to progress through multiple stages with more than one randomisation point. 22 , 31 While SMART designs have been used in treatment-focused trials, their application in preventive mHealth interventions is limited. 32 , 33 Existing SMARTs have focused on stage-specific comparisons instead of viewing the intervention as a continuous process, and often lack control arms. 32 Following the Medical Research Council’s guidance on complex interventions, 34 we conducted this pilot trial to assess the feasibility of using a SMART design to evaluate LvL UP in terms of recruitment, non-responder rate, retention, data completion rate, and engagement. We also explored preliminary changes in mental health and behavioural outcomes, comparing adaptive strategies with the control arm. Trial findings were evaluated against pre-specified progression criteria to guide decisions on advancing to a fully powered trial. Methods Trial design and procedure This eight-week SMART pilot trial ran in Singapore from April 15, 2024, to 1 August 1, 2024. Baseline assessments were conducted in person at the Saw Swee Hock School of Public Health, National University of Singapore (NUS). The four-week mid-intervention survey was completed online, and follow-up assessments at eight weeks were completed either online or in person at NUS. Ethical approval was obtained from the Institutional Review Boards of NUS (NUS-IRB-2023-421), ETH Zurich (EK-2024-N-13-A), and Nanyang Technological University (NTU-IRB-2024-305). All participants provided written informed consent. The published protocol is available in Supplement 1. The trial was prospectively registered on ClinicalTrials.gov (NCT06360029) on 7 April 2024. Reporting follows the CONSORT extension for randomised pilot and feasibility trials (Supplement 2). 35 Participants were randomised in a 2:1 ratio to the intervention or the control arm. After four weeks, non-responders (defined as completing fewer than six digital coaching sessions or rating session usefulness below four out of five) in the intervention arm were re-randomised (1:1) to either continue the intervention (Group B) or receive additional human support (Group C). “Responders” continued without change (Group A), whereas the control participants remained on the control app (Group D). Two adaptive intervention strategies were embedded (Fig. 1 ): Strategy #1 (Group A and Group B) used only the LvL UP app with a LvL UP Buddy for eight weeks, regardless of response status; Strategy #2 (Group A and Group C) added additional human support for non-responders in the second stage. Participants received up to SGD 250 in grocery vouchers: SGD 90 (baseline), SGD 20 (four-week survey), SGD 110 (follow-up), and SGD 30 (interview). Participants Participants were recruited via social media (Telegram, Facebook, Instagram), university email lists, community outreach through a recruitment agency, and referrals. Interested individuals completed an online screening survey and were invited for baseline assessments if eligible. Eligible criteria were as follows: aged 21 to 59 years; Singapore citizens or permanent residents; living in Singapore during the study; English proficiency; smartphone ownership (Apple iOS ≥ 12.4 or Android ≥ 8.0) with internet access; and at risk of NCDs or CMDs. Risk was assessed using a composite score: Patient Health Questionnaire-4 scores ≥ 3 36 or at least two of the following: (1) insufficient physical activity (< 150 minutes of moderate-to-vigorous physical activity/week); (2) unhealthy diet ( 1 serving of sugary beverages/fast food daily); (3) family history of diagnosed physical or mental health conditions; or (4) BMI ≥ 23 kg/m². 37 Exclusion criteria included a diagnosis of chronic disease, pregnancy, the use of medications affecting blood pressure or glucose metabolism, or participation as a LvL UP Buddy. Interventions First stage of intervention (weeks 0–4): LvL UP app with LvL UP Buddy Participants in the intervention arm downloaded the LvL UP app (version 3.0), which promotes physical activity, diet and emotional regulation through four core components (Fig. 2 ): Digital coaching sessions: 30 brief coaching sessions (5–8 minutes each) covering health literacy and psychoeducation via a rule-based conversational agent. Life Hacks: 48 actionable, trackable health tips for habit formation. Breeze: A breathing training game with biofeedback for stress reduction. Self-regulation tools: “MakanMemo” food diary, Journal, and “StepLah” step tracker for behaviour monitoring and self-reflection. Participants were encouraged to complete 12 digital coaching sessions and use at least one additional component daily. Engagement was supported through “My Tasks”, in-app virtual rewards, notifications, WhatsApp reminders, and weekly emails. Intervention participants were asked to nominate a LvL UP Buddy for peer support. 38 Buddies received weekly WhatsApp prompts to encourage participants, share healthy tips, and co-engage in healthy activities. Buddies were reimbursed up to SGD 70 for completing four-and weight-week surveys, as well as for participating in interviews. Second stage of intervention (weeks 5–8): MI-informed coaching for non-responders Non-responders received three weekly WhatsApp-based sessions with trained human coaches (RRM and RK), both with psychology backgrounds and MI training. Each session (30–40 minutes) followed core MI strategies (open-ended questions, reflections, affirmations, and summaries), and was structured around engagement, focusing, evoking and planning. 39 Coaches documented strategies used and received weekly feedback from a certified MI practitioner (KG). Control arm Participants in the control arm (Group D) received public health information on physical activity, diet, and mental health via WhatsApp (Supplement 3), and used a control app with an identical interface but no active intervention components. The control app only collected mood data using the International Positive and Negative Affect Schedule Short-Form every 2–3 days. 40 Outcomes Feasibility Feasibility was assessed across six indicators: Recruitment: channel-specific enrolment-to-eligibility ratios and proportion of participants recruited within six-weeks. LvL UP Buddy uptake: proportion of intervention participants successfully paired with a LvL UP Buddies. Non-responder rate: proportion of non-responders vs responders at week five. Retention: proportion of participants completing eight-week follow-up. Data completion: percentage of missing data for each outcome at follow-up. Intervention engagement: number of active app days (days with at least one completed task in the app), app component usage, and MI session attendance. Additional implementation outcomes included usability and user satisfaction. Usability was assessed using the System Usability Scale (SUS; scores 0–100, > 70 acceptable, 50–70 marginally acceptable, or < 50 unacceptable). 41 User satisfaction was assessed with the Net Promoter Score (NPS; -100 to + 100 ), calculated as the percentage of promoters (scores 9–10) minus detractors (scores 0–6). 42 Both measures were collected in the four-week online survey. Mental health and lifestyle outcomes Mental well-being was measured using the Warwick-Edinburgh Mental Well-being Scale (WEMWBS-14), (14 items, scores 14–70), with higher scores indicating better mental well-being. 43 Psychological distress was measured by Kessler Psychological Distress Scale (K6, 6 items, scores 0–24), where higher scores reflect greater distress. 44 MVPA (mins/week) was measured using the International Physical Activity Questionnaire–Long Form. 45 Diet assessed daily intake of vegetable and fruit using a modified Food Frequency Questionnaire. 46 Sleep duration (hours/day) was measured using one item from the Pittsburgh Sleep Quality Index. 47 Pre-specified progression criteria Progression criteria were developed in collaboration with the study’s Scientific Advisory Board. Advancement to a full trial was supported if the following were met: 1) ≥ 50% of target recruitment within six weeks; 2) 40–60% non-responder rate; 3) ≥ 75% retention; 4) ≤ 20% missing data for mental well-being outcome; 5) ≥ 70% intervention participants completing 12 digital coaching sessions; 6) positive directional change in at least one health-related outcome. If any criterion was not met, the protocol would be revised prior to the definitive trial. Assessment of harms Participants were encouraged to report adverse events via an open-ended question in the follow-up assessment. No serious harm was anticipated. Sample size and randomisation A precision-based approach was used for sample size estimation. 48 Assuming a 50% non-responder rate, 5% Type I error, and 30% margin of error, a minimum of 97 participants was required. The final sample size was set at 120 to account for an 18% attrition. 49 Randomisation sequences were generated in Stata by the first author (SZ). Simple randomisation was used at baseline, and block randomisation (block size = 4) was applied for re-randomisation at week five. To blind the assessors, participants received only sealed packages with study IDs on the basis of arrival order after all baseline assessments. The staff distributing the packages were also blinded to group allocation and did not participate in data collection. Data analysis Of the 123 enrolled, one was excluded for age ineligibility, leaving 122 for analysis. Baseline characteristics are presented as frequencies (percentages) for categorical variables and as means ± standard deviations (SDs) or medians (Interquartile ranges, Q1–Q3) for continuous variables. Preliminary effectiveness analyses followed the intention-to-treat principle. Generalised estimating equations (GEE) with robust variance estimation and an exchangeable correlation structure were used to estimate changes from baseline across two adaptive intervention strategies versus the control group. To account for the complex allocation design, a weight-and-replicate method was applied: observations in Group A were replicated and inverse probability weights were assigned: 1.5 for Group A, 3 for Groups B, C, and D. 50 The interaction terms between condition and time were used to test for differential changes in outcomes. Results Recruitment and allocation From March 29 to May 10, 2024, 458 individuals were screened; 394 (86.0%) were eligible, and 123 were enrolled. Participants were primarily recruited from Telegram (75/123, 61.0%), word-of-mouth (17/123, 13.8%), community outreach (18/123, 14.6%), and email (11/123, 8.9%). Although smaller in scale, email had the highest conversion rate (11/17, 64.7%), followed by Telegram (75/177, 42.4%) and word-of-mouth (17/58, 29.3%). At baseline, 82 participants were randomised to the intervention and 41 to the control arm (Fig. 3 ). Most intervention participants (78/82, 95.1%) paired with a LvL UP Buddy. At week five, 38 non-responders (46.3%) were re-randomised to either continue (Group B; n = 19) or receive additional human support (Group C; n = 19). Responders continued without change (Group A; n = 42). Control participants remined in Group D throughout. Retention and data completion At eight-week follow-up, retention was 91.5% (75/82, intervention) and 92.7% (38/41, control). Of the seven intervention arm dropouts, one was excluded for being underage, four were from Group B, and two were from Group C. The overall dropout rate was 8.1%. Missing data rates were 8.1% (10/123) for questionnaire-based outcomes and 12.2% (15/123) for physical assessments. Among LvL UP Buddies, 70.5% (55/78) completed both four- and eight-week surveys. Baseline characteristics Table 1 summarises the baseline demographics. The mean age of participants was 35.3 years (SD = 10.3), with 62.6% female. Most participants were Chinese (92%), employed (75.4%), and college-educated (62.6%). Over half (57.1%) reported household monthly income above SGD 6,000. Regarding digital health experience, 51.6% already used health-related digital tools, and 59.0% wore fitness trackers. Table 1 Participant characteristics at baseline. Total (n = 122) Group A (n = 42) Group B (n = 20) Group C (n = 19) Group D (n = 41) Age (Mean+-SD) 35.31 ± 10.26 38.02 ± 11.23 31.00 ± 8.71 35.14 ± 12.16 34.73 ± 8.34 Sex Female 76 (62.3) 25 (59.5) 11 (55.0) 17 (89.5) 23 (56.1) Male 46 (37.7) 17 (40.5) 9 (45.0) 2 (10.5) 18 (43.9) Ethnicity Chinese 112 (91.8) 38 (90.5) 18 (90.0) 19 (100.0) 37 (90.2) Indian 7 (5.7) 1 (2.4) 2 (10.0) 0 (0.0) 4 (9.8) Malay 1 (0.8) 1 (2.4) 0 (0.0) 0 (0.0) 0 (0.0) Others 2 (1.6) 2 (4.8) 0 (0.0) 0 (0.0) 0 (0.0) Education Secondary/Post secondary 45 (36.9) 20 (47.6) 6 (30.0) 7 (36.8) 12 (29.3) Bachelor 55 (45.1) 14 (33.3) 9 (45.0) 9 (47.4) 23 (56.1) Postgraduate 22 (18.0) 8 (19.1) 5 (25.0) 3 (15.8) 6 (14.6) Employment Students/Not employed 30 (24.6) 10 (23.8) 9 (45.0) 4 (21.1) 7 (17.1) Employed 92 (75.4) 32 (76.2) 11 (55.0) 15 (79.0) 34 (82.9) Marital status Currently married 38 (31.1) 16 (38.1) 4 (20.0) 4 (21.1) 14 (34.2) Single/Separated/Divorced 84 (68.9) 26 (61.9) 16 (80.0) 15 (79.0) 27 (65.9) Monthly household income < SGD6000 48 (42.9) 20 (51.3) 2 (11.1) 8 (53.3) 18 (45.0) SGD6000-9999 33 (29.5) 12 (30.8) 7 (38.9) 2 (13.3) 12 (30.0) SGD10000 and above 31 (27.7) 7 (18.0) 9 (50.0) 5 (33.3) 10 (25.0) Wearable use Not currently using 50 (41.0) 17 (40.5) 10 (50.0) 4 (21.1) 19 (46.3) Currently using 72 (59.0) 25 (59.5) 10 (50.0) 15 (79.0) 22 (53.7) Health Program Use Not currently using 59 (48.4) 16 (38.1) 14 (70.0) 8 (42.1) 21 (51.2) Currently using 63 (51.6) 26 (61.9) 6 (30.0) 11 (57.9) 20 (48.8) Group A : Intervention arm responders who used the LvL UP app with a LvL UP Buddy for 8 weeks. Group B : Intervention arm non-responders who used the LvL UP app with a LvL UP Buddy for 8 weeks. Group C : Intervention arm non-responders who used the LvL UP app with a LvL UP Buddy during the first stage (weeks 1–4) and received additional human coaching sessions during the second stage (weeks 5–8) of the intervention. Group D : Control arm participants. Of the 123 enrolled participants, one was excluded for being underage, leaving a total sample of 122 in the table. Baseline differences were observed across intervention subgroups, reflecting self-selection into responders and non-responder trajectories. Group A tended to be older. Group C had more female participants (Table 1 ), as well as lower baseline mental well-being and higher psychological distress than Group B (Table 2 ). Table 2 Mental and behavioural outcomes at baseline and eight-week follow-up assessments. Outcomes Group A Group B Group C Group D Baseline (n = 42) Eight-week (n = 42) Baseline (n = 20) Eight-week (n = 16) Baseline (n = 19) Eight-week (n = 17) Baseline (n = 41) Eight-week (n = 38) Mental well-being (score) 47.55 ± 8.13 50.19 ± 7.91 47.70 ± 8.45 48.94 ± 10.46 45.37 ± 7.60 44.94 ± 5.54 47.63 ± 8.26 48.29 ± 7.53 Psychological distress (score) 6.05 ± 4.39 4.79 ± 3.57 6.35 ± 3.88 5.88 ± 4.53 7.05 ± 3.98 6.47 ± 3.50 4.85 ± 3.91 4.66 ± 3.95 Sleep duration (hours/day) 6.63 ± 1.27 6.93 ± 1.14 6.45 ± 0.81 6.66 ± 0.77 6.42 ± 1.07 6.51 1.06 6.71 ± 1.09 6.53 ± 0.85 MVPA (minutes/week) 60.00 (0, 120.00) 97.50 (0, 340.00) 37.50 (0, 295.00) 60.00 (0, 290.00) 150.00 (0, 360.00) 60.00 (0, 280.00) 20.00 (0, 180.00) 75.00 (0, 345.00) Fruit intake (servings/day) 0.53 (0.23, 2.00) 0.56 (0.31,1.44) 0.25 (0.08,0.94) 1.09 (0.30, 4.75) 0.80 (0.37, 4.50) 0.86 (0.53, 4.68) 0.86 (0.43,2.03) 0.73 (0.36, 2.58) Vegetable intake (servings/day) 4.82 (0.86, 5.86) 3.92 (0.86, 5.50) 0.80 (0.40, 5.00) 0.68 (0.34, 2.07) 4.86 (0.86, 5.36) 2.64 (0.86, 4.94) 3.00 (0.86, 5.21) 2.22 (0.71, 5.14) Group A : Intervention arm responders who used the LvL UP app with a LvL UP Buddy for 8 weeks. Group B : Intervention arm non-responders who used the LvL UP app with a LvL UP Buddy for 8 weeks. Group C : Intervention arm non-responders who used the LvL UP app with a LvL UP Buddy during the first stage (weeks 1–4) and received three additional motivational interviewing-informed sessions with human coaches during the second stage (weeks 5–8) of the intervention. Group D : Control arm participants. MVPA: moderate-to-vigorous intensity physical activity Data presented in the table as: mean ± SD, or median (Q1, Q3). Intervention engagement Figure 4 shows that daily active use dropped approximately 60% during the first 10 days, then stabilised. By day 28 (four weeks), 40% of participants remained active; by day 56 (eight weeks), 30%. On average, participants used the LvL UP app on 24 of 56 days (SD = 17). For digital coaching, 39.5% (32/81) completed all 12 sessions as intended, and 70.4% (57/81) completed at least six. Digital coaching was the most frequently used feature (median 11, IQR 4–13), followed by Life Hacks (median 7, IQR 1–30), MakanMemo (median 7, IQR 1–20), Breeze (median 2, IQR 0–11), and Journal (median 1, IQR 0–4). Technical issues affected data collection for the StepLah tracker. Among non-responders in Group C, 78.9% (15/19) completed at least one MI-informed coaching session, and 52.6% (10/19) completed all three. App usability and satisfaction The average SUS score for the LvL UP app (n = 70) was marginally acceptable at 60.50 (SD = 16.90). The NPS was + 15.6, with 46.7% promoters and 31.1% detractors. Change from baseline GEE analyses showed favourable trends for Strategy #1 (Groups A + B) compared with the control arm (Group D) in mental well-being and psychological distress (Table 3 ). Both intervention strategies were associated with improvements in sleep duration. Due to the pilot nature of the trial, no adjustments were made for baseline covariates. Table 3 Differences in change from baseline of mental health and behavioural outcomes: two adaptive intervention strategies vs control arm. Outcomes Strategy #1 vs control Strategy #2 vs control Coefficient (95% CI) Coefficient (95% CI) Mental well-being (score) 2.12 (-0.58, 4.82) 0.65 (-1.83, 3.14) Psychological distress (score) -0.94 (-2.08, 0.20) -0.72 (-1.84, 0.40) Sleep duration (hours/day) 0.49 (0.17, 0.82) 0.40 (0.03, 0.76) MVPA (minutes/week) 44.50 (-42.9, 131.93) -16.14 (-121.19, 88.92) Fruit intake (servings/day) 0.23 (-1.04, 1.50) -0.22 (-1.48, 1.05) Vegetable intake (servings/day) 0.01 (-1.63, 1.66) 0.02 (-1.74, 1.79) Strategy #1: participants received LvL UP app, including a LvL UP Buddy, regardless of their response status (Group A + B); Strategy #2: participants received LvL UP app, including a LvL UP Buddy at first stage of intervention, and received three additional motivational interviewing-informed sessions with human coaches if they were non-responders after 4 weeks. (Group A + C) Control: Group D MVPA: moderate-to-vigorous physical activity Progression criteria for proceeding to a definitive trial As summarised in Table 4 , five of the six progression criteria were met; only digital coaching session adherence fell below the target (39.5% vs target of 70%). No adverse events were reported. DISCUSSION This pilot trial showed the feasibility of delivering and evaluating LvL UP using a SMART design. Key feasibility indicators, including rapid recruitment, high buddy uptake, strong retention, low rates of missing data, and an acceptable non-responder rate, support operational viability. Positive trends were observed in mental health and sleep duration, particularly with Strategy #1. Five of the six pre-specified progression criteria were achieved, except for digital coaching adherence. These findings provide valuable guidance for future definitive trials and contribute to the emerging evidence base on adaptive, holistic mHealth interventions. Recruitment, often a major barrier in mHealth interventions, was completed within six weeks. 51 , 52 , 53 Broad inclusion criteria allowed over 85% of screened individuals to qualify, while an automated portal streamlined screening and scheduling and reduced administrative burden. 54 Telegram reached research-motivated individuals and generated over 80 sign-ups within six hours. Consistent with prior findings, targeted email outreach, though smaller in scale, had the highest conversion rates. 55 Finally, a tiered reimbursement structure may have further incentivised timely enrolment. These lessons have informed a hybrid recruitment strategy for the definitive trial. For participant retention, the rate exceeded 90% with low rates of missing data, which is higher than that of other mHealth interventions. 49 , 51 , 56 , 57 , 58 This is noteworthy given the complexity of the intervention. Structured retention strategies may have driven this performance, including flexible survey completion windows, multi-channel reminders, direct communication, and standardised protocols (e.g., staff rehearsal, manuals, and real-time data monitoring and validation). 59 , 56 These practices highlight how structured, participant-centred logistics can improve short-term retention and data completion rates. In SMART designs, non-responder rates between 20% and 80% are considered optimal. 58 Our observed rate of 46.3% was consistent with the sample size assumption. The non-responder definition, based on digital coaching session completion and usefulness rating, was practical but may have misclassified participants who engaged passively or showed improvements despite low app usage. For the definitive trial, we will incorporate interim assessments of mental well-being to refine the response classification. App engagement followed a typical mHealth pattern: early attrition and a subsequent plateau at 30–40% daily active use. 60 , 61 Despite engagement strategies, digital coaching adherence was below the target. Minimal onboarding guidance (e.g., reliance on printed handouts) and technical issues may have impaired the navigation of multiple app components and created additional usability friction. 23 Such challenges are common in early-stage mHealth interventions and are associated with disengagement. These findings highlight the need to improve the user experience. 23 , 27 , 62 , 63 In contrast, MI-informed session adherence exceeded that of digital coaching, aligning with the literature showing greater user engagement with human-delivered support. 64 , 65 , 66 However, the fixed three-week schedule may have limited flexibility. The definitive trial will expand to six sessions over six months to allow greater personalisation and sustained engagement. Exploratory analyses showed improvements in mental health and sleep outcomes among intervention participants. These estimates were consistent with prior studies on the synergistic effects of holistic mHealth interventions. 15 Interestingly, Strategy #1 showed stronger trends than Strategy #2, possibly due to differences in gender composition between Groups B and C, or insufficient intensity of MI-informed sessions. 67 , 68 , 69 As this was a pilot trial, these findings are preliminary and should be interpreted with caution. LvL UP contributes to the evolving digital health landscape by operationalising a holistic mHealth coaching model within an adaptive SMART framework. A key strength was the end-to-end implementation of the SMART design, including real-time escalation from digital to human-delivered support. The use of a control app that mirrored the intervention interface helped minimise expectation bias and strengthen internal validity. Additionally, the application of a validated, precision-based sample size calculation ensured efficient use of resources while providing a strong foundation for a future definitive trial. Beyond informing the next phase, these findings have broader implications for developing scalable, person-centred mHealth interventions aligned with public health goals for preventive care. Limitations This trial has several limitations. The sample was relatively young, highly educated, and primarily recruited from a Telegram research group, which may limit generalisability. While ethically approved, the substantial compensation may have inflated short-term enrolment and retention. Lastly, allocation concealment via sealed opaque envelopes introduces a risk of mis-ordering or unintentional unblinding; future trials will adopt a centralised digital randomisation to mitigate this. Conclusions This pilot trial demonstrated the feasibility of implementing LvL UP 3.0, a holistic mHealth lifestyle coaching intervention with adaptive support, within a SMART design. While improvements are needed to improve digital engagement, the findings provide strong operational guidance and a foundation for a larger-scale trial to evaluate long-term effectiveness and cost-effectiveness. Declarations Ethics approval and consent to participate Ethical approval was obtained from the Institutional Review Boards of NUS (NUS-IRB-2023-421), ETH Zurich (EK-2024-N-13-A), and Nanyang Technological University (NTU-IRB-2024-305). Consent for publication Not applicable Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available due to privacy or other restrictions but are available from the corresponding author on reasonable request. Competing interests J.L.M., F.v.W., E.F., and T.K. are affiliated with the Centre for Digital Health Interventions, funded in part by the Swiss health insurer CSS, the Swiss digital health investor MTIP, and the Austrian healthcare provider Mavie Next. E.F. is, and T.K. was, co-founder of Pathmate Technologies, a university spin-off company. However, Pathmate Technologies, CSS, MTIP, and Mavie Next were not involved in the design, interpretation, or analysis of the study, nor in the writing of the manuscript. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This project was conducted as part of the Future Health Technologies programme (Campus for Research Excellence and Technological Enterprise), which is jointly funded by the National Research Foundation, Prime Minister’s Office, Singapore and ETH Zurich, Switzerland. The funders of the study had no role in conceptualization, study design, data collection, data analysis, data interpretation, decision to publish, or writing of the report. Authors’ contributions J.L.M., O.C., S.Z., and A.I.J. conceptualised the trial under the supervision of F.M.R., T.K., K.G., E.S.T., and R.M.v.D. O.S., S.Z., J.L.M., and A.I.J. designed the study with guidance from X.Y. and B.C. J.L.M., O.C., S.Z., A.I.J., S.Y.X.T., F.v.W., E.F., T.K., E.S.T., F.M.R., and R.M.v.D. contributed to the development of the LvL UP intervention. A.I.J., A.S., and S.N. maintained the LvL UP and control apps during the trial. X.Y. and B.C. performed the sample size calculations. S.Z. organised motivational interviewing training, screened participants (with assistance from A.I.J.), and generated the randomisation sequences. R.R.M. and R.K.W.S. delivered the MI-informed sessions under the supervision of K.G. S.Z., S.Y.X.T., A.I.J., O.C., and J.L.M. recruited participants, prepared study documents, and monitored data collection. A.I.J. acquired and cleaned the dataset. S.Z. conducted the statistical analyses under the guidance of X.Y. and B.C. and drafted the manuscript with input from all co-authors. F.v.W., E.F., T.K., F.M.R., ES.T., and R.M.v.D. secured project funding. All authors reviewed and approved the final manuscript. Acknowledgements We would like to thank the Singapore Population Health Studies team at the Saw Swee Hock School of Public Health, National University of Singapore, for their invaluable efforts in data collection and study coordination. 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Social Science & Medicine . 2004;58(12):2585-2600. doi:10.1016/j.socscimed.2003.09.008 Additional Declarations Competing interest reported. J.L.M., F.v.W., E.F., and T.K. are affiliated with the Centre for Digital Health Interventions, funded in part by the Swiss health insurer CSS, the Swiss digital health investor MTIP, and the Austrian healthcare provider Mavie Next. E.F. is, and T.K. was, co-founder of Pathmate Technologies, a university spin-off company. However, Pathmate Technologies, CSS, MTIP, and Mavie Next were not involved in the design, interpretation, or analysis of the study, nor in the writing of the manuscript. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Supplementary Files Supplement1.Studyprotocol.pdf Supplement2.CONSORT2010checklistforpilotortrials.doc Supplement3Publichealthinformation.pdf Cite Share Download PDF Status: Published Journal Publication published 03 Jan, 2026 Read the published version in International Journal of Behavioral Nutrition and Physical Activity → Version 1 posted Editorial decision: Revision requested 26 Oct, 2025 Reviews received at journal 24 Oct, 2025 Reviewers agreed at journal 19 Oct, 2025 Reviews received at journal 15 Sep, 2025 Reviewers agreed at journal 28 Aug, 2025 Reviewers agreed at journal 26 Aug, 2025 Reviewers invited by journal 26 Aug, 2025 Editor assigned by journal 20 Aug, 2025 Submission checks completed at journal 20 Aug, 2025 First submitted to journal 14 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Müller-Riemenschneider","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIie3OsQrCMBCA4QuCXQpZz8F3OAgUxMFXaRDsEtBJHBwCASfBte8hdK4IunRwFHTQN6gvoEa7iEOqm2D+6W74uAPw+X6wQDfMc+DV3qgnYc4q0tJfE8o/JsHKEFMwFIe5KGHSlZpvd24SShOzDDrZsYgQikRqVCMn6YE0OctuFO1VBGy2tiSM3Vf4+UGARKpEya6W8KKGYPUYESpCpi2BJK8hZ0PSEtwPxhhvEjFD5RT2sf4aL5bwtL8sy2m3veDbk9s8il+HJoRUT94KPrji8/l8/9QduI5BQsUmz3cAAAAASUVORK5CYII=","orcid":"","institution":"National University of Singapore and National University Health System","correspondingAuthor":true,"prefix":"","firstName":"Falk","middleName":"","lastName":"Müller-Riemenschneider","suffix":""}],"badges":[],"createdAt":"2025-08-14 10:15:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7372529/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7372529/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12966-025-01869-7","type":"published","date":"2026-01-03T15:57:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90473911,"identity":"436431db-52d4-47b1-a022-e2ded489ef91","added_by":"auto","created_at":"2025-09-03 06:40:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":401224,"visible":true,"origin":"","legend":"\u003cp\u003eSequential Multiple Assignment Randomised Trial (SMART) design with a control arm for LvL UP.\u003c/p\u003e\n\u003cp\u003eNote: R stands for randomisation; Adaptive MI refers to three weekly motivational interviewing-informed sessions with human coaches; Non-responders were defined as completing fewer than 6 digital coaching sessions or rating session usefulness below 4 out of 5.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7372529/v1/85b2fd737650472b6e23d40f.png"},{"id":90473921,"identity":"0801bc84-3892-4f04-9d08-cf39618f4d7e","added_by":"auto","created_at":"2025-09-03 06:40:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2167295,"visible":true,"origin":"","legend":"\u003cp\u003eScreenshots of the LvL UP app (3.0).\u003c/p\u003e\n\u003cp\u003eB1-B2: Digital coaching sessions delivered by the rule-based conversational agent. F1-F3: Self-regulation tools. Screenshots capture from LvL UP app version 3.0.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7372529/v1/668c1229b636125605ca167f.png"},{"id":90473922,"identity":"f59fa069-31d8-4afd-9fbf-1b86165e3241","added_by":"auto","created_at":"2025-09-03 06:40:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":852019,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant flow chart.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7372529/v1/89addbad2a9ca0f498876347.png"},{"id":90473927,"identity":"a5994c55-c0d5-4af1-8a28-e6b467da72c6","added_by":"auto","created_at":"2025-09-03 06:40:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":168273,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of participants using the app at least once per day during the intervention period.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7372529/v1/d1e28e892245acf3af797364.png"},{"id":99545576,"identity":"6b6f0000-3da7-4e8e-a1e5-5648161edf7a","added_by":"auto","created_at":"2026-01-05 16:08:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5697965,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7372529/v1/0ab1c245-521b-4146-aa0e-7fba83c3d81e.pdf"},{"id":90473916,"identity":"575063ca-7309-49d5-9ce9-089db592a07a","added_by":"auto","created_at":"2025-09-03 06:40:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":714751,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement1.Studyprotocol.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7372529/v1/03d1427dc7d78ecf235cb7c4.pdf"},{"id":90474509,"identity":"fc0658d2-d6b8-44ad-8346-7591eb18fd03","added_by":"auto","created_at":"2025-09-03 06:48:20","extension":"doc","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":235008,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement2.CONSORT2010checklistforpilotortrials.doc","url":"https://assets-eu.researchsquare.com/files/rs-7372529/v1/01fd2590a0cbc3e86bfb0e37.doc"},{"id":90473938,"identity":"4976741e-be8f-400d-bd1d-7ecf7ddecdd4","added_by":"auto","created_at":"2025-09-03 06:40:22","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":35826267,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement3Publichealthinformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7372529/v1/220f27bf8fa9f3a7d92ce1ee.pdf"}],"financialInterests":"Competing interest reported. J.L.M., F.v.W., E.F., and T.K. are affiliated with the Centre for Digital Health Interventions, funded in part by the Swiss health insurer CSS, the Swiss digital health investor MTIP, and the Austrian healthcare provider Mavie Next. E.F. is, and T.K. was, co-founder of Pathmate Technologies, a university spin-off company. However, Pathmate Technologies, CSS, MTIP, and Mavie Next were not involved in the design, interpretation, or analysis of the study, nor in the writing of the manuscript. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.","formattedTitle":"Pilot Sequential Multiple Assignment Randomised Trial of LvL UP: an Adaptive Holistic mHealth Coaching Intervention Integrating Physical Activity, Diet, and Mental Health","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHealthy ageing, maintaining physical and mental health across the lifespan, is an urgent global priority as populations age rapidly.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e This concept extends beyond managing disease in older adults to preventing noncommunicable diseases (NCDs) and common health disorders (CMDs) in younger and middle-aged populations.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Achieving healthy ageing requires scalable, accessible solutions that promote behaviour change across diverse populations.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eMobile health (mHealth) interventions offer a promising approach to deliver such solutions at scale.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Evidence suggests that mHealth interventions can promote health behaviours and outcomes, especially in settings with limited access to conventional healthcare.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e However, many mHealth interventions target a single behaviour, which limits their ability to address the complex interconnections between NCDs and CMDs. Depression or anxiety, are also associated with physical inactivity or poor dietary habits, creating a cycle that increases NCD risk and worsens CMDs\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Recognising these relationships, holistic mHealth interventions that simultaneously target physical activity, diet, and mental health have shown benefits for weight management, lifestyle improvement, and mental well-being.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eWhile holistic mHealth interventions show promise, augmenting them with human support may further improve adherence and outcomes.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e However, widespread human support is costly and difficult to scale.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Conversational agents offer a lower-cost and scalable alternative, although their capacity to fully replicate human support remains unclear.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Adaptive intervention strategies, tailoring support intensity based on individual needs, can optimise both effectiveness and resource use.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e For instance, participants can begin with digital support and escalate to human-delivered coaching only when needed.\u003c/p\u003e\u003cp\u003eBuilding on these principles, we developed LvL UP 3.0, an adaptive, holistic mHealth coaching intervention for the prevention of NCDs and CMDs. It includes a smartphone application (LvL UP app) featuring an automated conversational agent and self-management tools, plus peer support from a LvL UP Buddy. Participants who do not meet pre-specified response criteria escalate to receive additional human coaching. The intervention was developed and refined between 2021 and 2023, informed by systematic reviews,\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e market analyses,\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e user-centred studies,\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and feasibility studies.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eTo evaluate LvL UP, we use a sequential multiple assignment randomised trial (SMART) design, which allows participants to progress through multiple stages with more than one randomisation point.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e While SMART designs have been used in treatment-focused trials, their application in preventive mHealth interventions is limited.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Existing SMARTs have focused on stage-specific comparisons instead of viewing the intervention as a continuous process, and often lack control arms.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Following the Medical Research Council’s guidance on complex interventions,\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e we conducted this pilot trial to assess the feasibility of using a SMART design to evaluate LvL UP in terms of recruitment, non-responder rate, retention, data completion rate, and engagement. We also explored preliminary changes in mental health and behavioural outcomes, comparing adaptive strategies with the control arm. Trial findings were evaluated against pre-specified progression criteria to guide decisions on advancing to a fully powered trial.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eTrial design and procedure\u003c/p\u003e\u003cp\u003eThis eight-week SMART pilot trial ran in Singapore from April 15, 2024, to 1 August 1, 2024. Baseline assessments were conducted in person at the Saw Swee Hock School of Public Health, National University of Singapore (NUS). The four-week mid-intervention survey was completed online, and follow-up assessments at eight weeks were completed either online or in person at NUS.\u003c/p\u003e\u003cp\u003eEthical approval was obtained from the Institutional Review Boards of NUS (NUS-IRB-2023-421), ETH Zurich (EK-2024-N-13-A), and Nanyang Technological University (NTU-IRB-2024-305). All participants provided written informed consent. The published protocol is available in Supplement 1.\u003c/p\u003e\u003cp\u003eThe trial was prospectively registered on ClinicalTrials.gov (NCT06360029) on 7 April 2024. Reporting follows the CONSORT extension for randomised pilot and feasibility trials (Supplement 2).\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eParticipants were randomised in a 2:1 ratio to the intervention or the control arm. After four weeks, non-responders (defined as completing fewer than six digital coaching sessions or rating session usefulness below four out of five) in the intervention arm were re-randomised (1:1) to either continue the intervention (Group B) or receive additional human support (Group C). “Responders” continued without change (Group A), whereas the control participants remained on the control app (Group D).\u003c/p\u003e\u003cp\u003eTwo adaptive intervention strategies were embedded (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): Strategy #1 (Group A and Group B) used only the LvL UP app with a LvL UP Buddy for eight weeks, regardless of response status; Strategy #2 (Group A and Group C) added additional human support for non-responders in the second stage.\u003c/p\u003e\u003cp\u003eParticipants received up to SGD 250 in grocery vouchers: SGD 90 (baseline), SGD 20 (four-week survey), SGD 110 (follow-up), and SGD 30 (interview).\u003c/p\u003e\u003cp\u003eParticipants\u003c/p\u003e\u003cp\u003eParticipants were recruited via social media (Telegram, Facebook, Instagram), university email lists, community outreach through a recruitment agency, and referrals. Interested individuals completed an online screening survey and were invited for baseline assessments if eligible.\u003c/p\u003e\u003cp\u003eEligible criteria were as follows: aged 21 to 59 years; Singapore citizens or permanent residents; living in Singapore during the study; English proficiency; smartphone ownership (Apple iOS ≥ 12.4 or Android ≥ 8.0) with internet access; and at risk of NCDs or CMDs. Risk was assessed using a composite score: Patient Health Questionnaire-4 scores ≥ 3\u003csup\u003e36\u003c/sup\u003e or at least two of the following: (1) insufficient physical activity (\u0026lt; 150 minutes of moderate-to-vigorous physical activity/week); (2) unhealthy diet (\u0026lt; 2 servings of fruit/vegetables or \u0026gt; 1 serving of sugary beverages/fast food daily); (3) family history of diagnosed physical or mental health conditions; or (4) BMI ≥ 23 kg/m².\u003csup\u003e37\u003c/sup\u003e Exclusion criteria included a diagnosis of chronic disease, pregnancy, the use of medications affecting blood pressure or glucose metabolism, or participation as a LvL UP Buddy.\u003c/p\u003e\u003cp\u003eInterventions\u003c/p\u003e\n\u003ch3\u003eFirst stage of intervention (weeks 0–4): LvL UP app with LvL UP Buddy\u003c/h3\u003e\n\u003cp\u003eParticipants in the intervention arm downloaded the LvL UP app (version 3.0), which promotes physical activity, diet and emotional regulation through four core components (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDigital coaching sessions: 30 brief coaching sessions (5\u0026ndash;8 minutes each) covering health literacy and psychoeducation via a rule-based conversational agent.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eLife Hacks: 48 actionable, trackable health tips for habit formation.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eBreeze: A breathing training game with biofeedback for stress reduction.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eSelf-regulation tools: \u0026ldquo;MakanMemo\u0026rdquo; food diary, Journal, and \u0026ldquo;StepLah\u0026rdquo; step tracker for behaviour monitoring and self-reflection.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eParticipants were encouraged to complete 12 digital coaching sessions and use at least one additional component daily. Engagement was supported through \u0026ldquo;My Tasks\u0026rdquo;, in-app virtual rewards, notifications, WhatsApp reminders, and weekly emails.\u003c/p\u003e\u003cp\u003eIntervention participants were asked to nominate a LvL UP Buddy for peer support.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e Buddies received weekly WhatsApp prompts to encourage participants, share healthy tips, and co-engage in healthy activities. Buddies were reimbursed up to SGD 70 for completing four-and weight-week surveys, as well as for participating in interviews.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSecond stage of intervention (weeks 5\u0026ndash;8): MI-informed coaching for non-responders\u003c/h2\u003e\u003cp\u003eNon-responders received three weekly WhatsApp-based sessions with trained human coaches (RRM and RK), both with psychology backgrounds and MI training. Each session (30\u0026ndash;40 minutes) followed core MI strategies (open-ended questions, reflections, affirmations, and summaries), and was structured around engagement, focusing, evoking and planning.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e Coaches documented strategies used and received weekly feedback from a certified MI practitioner (KG).\u003c/p\u003e\u003cp\u003eControl arm\u003c/p\u003e\u003cp\u003eParticipants in the control arm (Group D) received public health information on physical activity, diet, and mental health via WhatsApp (Supplement 3), and used a control app with an identical interface but no active intervention components. The control app only collected mood data using the International Positive and Negative Affect Schedule Short-Form every 2\u0026ndash;3 days.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eOutcomes\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eFeasibility\u003c/h3\u003e\n\u003cp\u003eFeasibility was assessed across six indicators:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eRecruitment: channel-specific enrolment-to-eligibility ratios and proportion of participants recruited within six-weeks.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eLvL UP Buddy uptake: proportion of intervention participants successfully paired with a LvL UP Buddies.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eNon-responder rate: proportion of non-responders vs responders at week five.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eRetention: proportion of participants completing eight-week follow-up.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eData completion: percentage of missing data for each outcome at follow-up.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIntervention engagement: number of active app days (days with at least one completed task in the app), app component usage, and MI session attendance.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eAdditional implementation outcomes included usability and user satisfaction. Usability was assessed using the System Usability Scale (SUS; scores 0\u0026ndash;100, \u0026gt;\u0026thinsp;70 acceptable, 50\u0026ndash;70 marginally acceptable, or \u0026lt;\u0026thinsp;50 unacceptable).\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e User satisfaction was assessed with the Net Promoter Score (NPS; -100 to +\u0026thinsp;100 ), calculated as the percentage of promoters (scores 9\u0026ndash;10) minus detractors (scores 0\u0026ndash;6).\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e Both measures were collected in the four-week online survey.\u003c/p\u003e\n\u003ch3\u003eMental health and lifestyle outcomes\u003c/h3\u003e\n\u003cp\u003eMental well-being was measured using the Warwick-Edinburgh Mental Well-being Scale (WEMWBS-14), (14 items, scores 14\u0026ndash;70), with higher scores indicating better mental well-being.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e Psychological distress was measured by Kessler Psychological Distress Scale (K6, 6 items, scores 0\u0026ndash;24), where higher scores reflect greater distress.\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eMVPA (mins/week) was measured using the International Physical Activity Questionnaire\u0026ndash;Long Form.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e Diet assessed daily intake of vegetable and fruit using a modified Food Frequency Questionnaire.\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e Sleep duration (hours/day) was measured using one item from the Pittsburgh Sleep Quality Index.\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003ePre-specified progression criteria\u003c/p\u003e\u003cp\u003eProgression criteria were developed in collaboration with the study\u0026rsquo;s Scientific Advisory Board. Advancement to a full trial was supported if the following were met: 1)\u0026thinsp;\u0026ge;\u0026thinsp;50% of target recruitment within six weeks; 2) 40\u0026ndash;60% non-responder rate; 3)\u0026thinsp;\u0026ge;\u0026thinsp;75% retention; 4)\u0026thinsp;\u0026le;\u0026thinsp;20% missing data for mental well-being outcome; 5)\u0026thinsp;\u0026ge;\u0026thinsp;70% intervention participants completing 12 digital coaching sessions; 6) positive directional change in at least one health-related outcome. If any criterion was not met, the protocol would be revised prior to the definitive trial.\u003c/p\u003e\u003cp\u003eAssessment of harms\u003c/p\u003e\u003cp\u003eParticipants were encouraged to report adverse events via an open-ended question in the follow-up assessment. No serious harm was anticipated.\u003c/p\u003e\u003cp\u003eSample size and randomisation\u003c/p\u003e\u003cp\u003eA precision-based approach was used for sample size estimation.\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e Assuming a 50% non-responder rate, 5% Type I error, and 30% margin of error, a minimum of 97 participants was required. The final sample size was set at 120 to account for an 18% attrition.\u003csup\u003e49\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eRandomisation sequences were generated in Stata by the first author (SZ). Simple randomisation was used at baseline, and block randomisation (block size\u0026thinsp;=\u0026thinsp;4) was applied for re-randomisation at week five. To blind the assessors, participants received only sealed packages with study IDs on the basis of arrival order after all baseline assessments. The staff distributing the packages were also blinded to group allocation and did not participate in data collection.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eOf the 123 enrolled, one was excluded for age ineligibility, leaving 122 for analysis. Baseline characteristics are presented as frequencies (percentages) for categorical variables and as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SDs) or medians (Interquartile ranges, Q1\u0026ndash;Q3) for continuous variables.\u003c/p\u003e\u003cp\u003ePreliminary effectiveness analyses followed the intention-to-treat principle. Generalised estimating equations (GEE) with robust variance estimation and an exchangeable correlation structure were used to estimate changes from baseline across two adaptive intervention strategies versus the control group. To account for the complex allocation design, a weight-and-replicate method was applied: observations in Group A were replicated and inverse probability weights were assigned: 1.5 for Group A, 3 for Groups B, C, and D.\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e The interaction terms between condition and time were used to test for differential changes in outcomes.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eRecruitment and allocation\u003c/p\u003e\u003cp\u003eFrom March 29 to May 10, 2024, 458 individuals were screened; 394 (86.0%) were eligible, and 123 were enrolled. Participants were primarily recruited from Telegram (75/123, 61.0%), word-of-mouth (17/123, 13.8%), community outreach (18/123, 14.6%), and email (11/123, 8.9%). Although smaller in scale, email had the highest conversion rate (11/17, 64.7%), followed by Telegram (75/177, 42.4%) and word-of-mouth (17/58, 29.3%).\u003c/p\u003e\u003cp\u003eAt baseline, 82 participants were randomised to the intervention and 41 to the control arm (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Most intervention participants (78/82, 95.1%) paired with a LvL UP Buddy. At week five, 38 non-responders (46.3%) were re-randomised to either continue (Group B; n\u0026thinsp;=\u0026thinsp;19) or receive additional human support (Group C; n\u0026thinsp;=\u0026thinsp;19). Responders continued without change (Group A; n\u0026thinsp;=\u0026thinsp;42). Control participants remined in Group D throughout.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRetention and data completion\u003c/p\u003e\u003cp\u003eAt eight-week follow-up, retention was 91.5% (75/82, intervention) and 92.7% (38/41, control). Of the seven intervention arm dropouts, one was excluded for being underage, four were from Group B, and two were from Group C. The overall dropout rate was 8.1%. Missing data rates were 8.1% (10/123) for questionnaire-based outcomes and 12.2% (15/123) for physical assessments. Among LvL UP Buddies, 70.5% (55/78) completed both four- and eight-week surveys.\u003c/p\u003e\u003cp\u003eBaseline characteristics\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarises the baseline demographics. The mean age of participants was 35.3 years (SD\u0026thinsp;=\u0026thinsp;10.3), with 62.6% female. Most participants were Chinese (92%), employed (75.4%), and college-educated (62.6%). Over half (57.1%) reported household monthly income above SGD 6,000. Regarding digital health experience, 51.6% already used health-related digital tools, and 59.0% wore fitness trackers.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParticipant characteristics at baseline.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;122)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGroup A\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGroup B\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGroup C\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGroup D\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (Mean+-SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.31\u0026thinsp;\u0026plusmn;\u0026thinsp;10.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.02\u0026thinsp;\u0026plusmn;\u0026thinsp;11.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.00\u0026thinsp;\u0026plusmn;\u0026thinsp;8.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35.14\u0026thinsp;\u0026plusmn;\u0026thinsp;12.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e34.73\u0026thinsp;\u0026plusmn;\u0026thinsp;8.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76 (62.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (59.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (55.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17 (89.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23 (56.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46 (37.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (40.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (45.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18 (43.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChinese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e112 (91.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (90.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (90.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37 (90.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4 (9.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary/Post secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45 (36.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (47.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7 (36.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12 (29.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBachelor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (45.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (45.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9 (47.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23 (56.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostgraduate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (18.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (19.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6 (14.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudents/Not employed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30 (24.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (45.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7 (17.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92 (75.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (76.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (55.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15 (79.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e34 (82.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrently married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38 (31.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (38.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14 (34.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle/Separated/Divorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84 (68.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (61.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (80.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15 (79.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27 (65.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonthly household income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt; SGD6000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48 (42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (51.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (53.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18 (45.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSGD6000-9999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (29.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (38.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (13.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12 (30.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSGD10000 and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (27.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (18.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10 (25.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWearable use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot currently using\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (41.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (40.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19 (46.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrently using\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72 (59.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (59.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15 (79.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22 (53.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth Program Use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot currently using\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59 (48.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (38.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (70.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (42.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21 (51.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrently using\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63 (51.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (61.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (57.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20 (48.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGroup A\u003c/b\u003e: Intervention arm responders who used the LvL UP app with a LvL UP Buddy for 8 weeks.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGroup B\u003c/b\u003e: Intervention arm non-responders who used the LvL UP app with a LvL UP Buddy for 8 weeks.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGroup C\u003c/b\u003e: Intervention arm non-responders who used the LvL UP app with a LvL UP Buddy during the first stage (weeks 1\u0026ndash;4) and received additional human coaching sessions during the second stage (weeks 5\u0026ndash;8) of the intervention.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGroup D\u003c/b\u003e: Control arm participants.\u003c/p\u003e\u003cp\u003eOf the 123 enrolled participants, one was excluded for being underage, leaving a total sample of 122 in the table.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBaseline differences were observed across intervention subgroups, reflecting self-selection into responders and non-responder trajectories. Group A tended to be older. Group C had more female participants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), as well as lower baseline mental well-being and higher psychological distress than Group B (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMental and behavioural outcomes at baseline and eight-week follow-up assessments.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOutcomes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eGroup A\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eGroup B\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eGroup C\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eGroup D\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBaseline\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEight-week\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBaseline\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEight-week\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBaseline\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEight-week\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBaseline\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eEight-week\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMental well-being\u003c/p\u003e\u003cp\u003e(score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47.55\u0026thinsp;\u0026plusmn;\u0026thinsp;8.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.19\u0026thinsp;\u0026plusmn;\u0026thinsp;7.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.70\u0026thinsp;\u0026plusmn;\u0026thinsp;8.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48.94\u0026thinsp;\u0026plusmn;\u0026thinsp;10.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e45.37\u0026thinsp;\u0026plusmn;\u0026thinsp;7.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e44.94\u0026thinsp;\u0026plusmn;\u0026thinsp;5.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e47.63\u0026thinsp;\u0026plusmn;\u0026thinsp;8.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e48.29\u0026thinsp;\u0026plusmn;\u0026thinsp;7.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychological distress\u003c/p\u003e\u003cp\u003e(score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.05\u0026thinsp;\u0026plusmn;\u0026thinsp;4.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.79\u0026thinsp;\u0026plusmn;\u0026thinsp;3.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.35\u0026thinsp;\u0026plusmn;\u0026thinsp;3.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.05\u0026thinsp;\u0026plusmn;\u0026thinsp;3.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.47\u0026thinsp;\u0026plusmn;\u0026thinsp;3.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.85\u0026thinsp;\u0026plusmn;\u0026thinsp;3.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.66\u0026thinsp;\u0026plusmn;\u0026thinsp;3.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep duration\u003c/p\u003e\u003cp\u003e(hours/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.51 1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMVPA\u003c/p\u003e\u003cp\u003e(minutes/week)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60.00\u003c/p\u003e\u003cp\u003e(0, 120.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97.50\u003c/p\u003e\u003cp\u003e(0, 340.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.50\u003c/p\u003e\u003cp\u003e(0, 295.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60.00\u003c/p\u003e\u003cp\u003e(0, 290.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e150.00\u003c/p\u003e\u003cp\u003e(0, 360.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e60.00\u003c/p\u003e\u003cp\u003e(0, 280.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.00\u003c/p\u003e\u003cp\u003e(0, 180.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e75.00\u003c/p\u003e\u003cp\u003e(0, 345.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFruit intake\u003c/p\u003e\u003cp\u003e(servings/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003cp\u003e(0.23, 2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003cp\u003e(0.31,1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003cp\u003e(0.08,0.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003cp\u003e(0.30, 4.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003cp\u003e(0.37, 4.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003cp\u003e(0.53, 4.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003cp\u003e(0.43,2.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003cp\u003e(0.36, 2.58)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetable intake (servings/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.82\u003c/p\u003e\u003cp\u003e(0.86, 5.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.92\u003c/p\u003e\u003cp\u003e(0.86, 5.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003cp\u003e(0.40, 5.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003cp\u003e(0.34, 2.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.86\u003c/p\u003e\u003cp\u003e(0.86, 5.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.64\u003c/p\u003e\u003cp\u003e(0.86, 4.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003cp\u003e(0.86, 5.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.22\u003c/p\u003e\u003cp\u003e(0.71, 5.14)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGroup A\u003c/b\u003e: Intervention arm responders who used the LvL UP app with a LvL UP Buddy for 8 weeks.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGroup B\u003c/b\u003e: Intervention arm non-responders who used the LvL UP app with a LvL UP Buddy for 8 weeks.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGroup C\u003c/b\u003e: Intervention arm non-responders who used the LvL UP app with a LvL UP Buddy during the first stage (weeks 1\u0026ndash;4) and received three additional motivational interviewing-informed sessions with human coaches during the second stage (weeks 5\u0026ndash;8) of the intervention.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGroup D\u003c/b\u003e: Control arm participants.\u003c/p\u003e\u003cp\u003eMVPA: moderate-to-vigorous intensity physical activity\u003c/p\u003e\u003cp\u003eData presented in the table as: mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, or median (Q1, Q3).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIntervention engagement\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that daily active use dropped approximately 60% during the first 10 days, then stabilised. By day 28 (four weeks), 40% of participants remained active; by day 56 (eight weeks), 30%. On average, participants used the LvL UP app on 24 of 56 days (SD\u0026thinsp;=\u0026thinsp;17).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor digital coaching, 39.5% (32/81) completed all 12 sessions as intended, and 70.4% (57/81) completed at least six. Digital coaching was the most frequently used feature (median 11, IQR 4\u0026ndash;13), followed by Life Hacks (median 7, IQR 1\u0026ndash;30), MakanMemo (median 7, IQR 1\u0026ndash;20), Breeze (median 2, IQR 0\u0026ndash;11), and Journal (median 1, IQR 0\u0026ndash;4). Technical issues affected data collection for the StepLah tracker.\u003c/p\u003e\u003cp\u003eAmong non-responders in Group C, 78.9% (15/19) completed at least one MI-informed coaching session, and 52.6% (10/19) completed all three.\u003c/p\u003e\u003cp\u003eApp usability and satisfaction\u003c/p\u003e\u003cp\u003eThe average SUS score for the LvL UP app (n\u0026thinsp;=\u0026thinsp;70) was marginally acceptable at 60.50 (SD\u0026thinsp;=\u0026thinsp;16.90). The NPS was +\u0026thinsp;15.6, with 46.7% promoters and 31.1% detractors.\u003c/p\u003e\u003cp\u003eChange from baseline\u003c/p\u003e\u003cp\u003eGEE analyses showed favourable trends for Strategy #1 (Groups A\u0026thinsp;+\u0026thinsp;B) compared with the control arm (Group D) in mental well-being and psychological distress (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Both intervention strategies were associated with improvements in sleep duration. Due to the pilot nature of the trial, no adjustments were made for baseline covariates.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifferences in change from baseline of mental health and behavioural outcomes: two adaptive intervention strategies vs control arm.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOutcomes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStrategy #1 vs control\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStrategy #2 vs control\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoefficient (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMental well-being (score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.12 (-0.58, 4.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.65 (-1.83, 3.14)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychological distress (score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.94 (-2.08, 0.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.72 (-1.84, 0.40)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep duration (hours/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.49 (0.17, 0.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.40 (0.03, 0.76)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMVPA (minutes/week)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44.50 (-42.9, 131.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-16.14 (-121.19, 88.92)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFruit intake (servings/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.23 (-1.04, 1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.22 (-1.48, 1.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetable intake (servings/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.01 (-1.63, 1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02 (-1.74, 1.79)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eStrategy #1: participants received LvL UP app, including a LvL UP Buddy, regardless of their response status (Group A\u0026thinsp;+\u0026thinsp;B);\u003c/p\u003e\u003cp\u003eStrategy #2: participants received LvL UP app, including a LvL UP Buddy at first stage of intervention, and received three additional motivational interviewing-informed sessions with human coaches if they were non-responders after 4 weeks. (Group A\u0026thinsp;+\u0026thinsp;C)\u003c/p\u003e\u003cp\u003eControl: Group D\u003c/p\u003e\u003cp\u003eMVPA: moderate-to-vigorous physical activity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eProgression criteria for proceeding to a definitive trial\u003c/p\u003e\u003cp\u003eAs summarised in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, five of the six progression criteria were met; only digital coaching session adherence fell below the target (39.5% vs target of 70%). No adverse events were reported.\u003c/p\u003e\u003cp\u003e\u003cimg 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rgHIy523Rat0TGseffTR9Jk/bCGA16xZUx7dhPZXe+vexbk8sAGfda3B482o7Nnd/9NrSawBIgeRqSV7rMD/5n/4L1MYQvWeO//RoFX4kb98LfkjsBB88r/jE78zKDoRXIB/K6vjqs9OHTz39k98LKWHBZgyAP5VFkbKg6jTuZQVQRohfOG3X0/hEAVeBBEYLd7Uh3Jggda5pJOXgzbAOv72+/8xxYt1jGWjfUkP8vZ6+4P/OERURqiPysVnLL/SuHPz6dJnAMpA3A//v/+UwtVPCFTyUr6cTxsKjltt1SDe1H/8O4Pnqp+M6RZuyC+88EKynGFFW7VqVRkyACJU1jUswdy0ESEc84lFA+sxcONWGFYO0mJSJVxxCXv66adTfNEunDTwwyGG44TOMRMQYQghJhLikzd+pKlJRucprZgH6SxcuDD5I6aiSKBO0RpOe8QyRMhbbTVz5sxGW3pGG/pk3rxbDQ/007Fjx8qjAS5dupTanPamfyTkECDRMiziCgfhCxYsKEMGIA3Czp8/X6xevbrYs2dPGTIwjhinXAt8V1zckiVLBscOZSBMY5EyVkE6pId1UCCeyBdIU+kznuPYrAtloY5qC+pOuqZ71q5dO+RhizHAPWL69OmlzwA8sDBO1Xfy4+FN9wU+GWP9wKi/oPbfP/TJJNz+1xP/Ph0jOhGsC26IV5bx9T3y+Rn/OAk+gSCMYkliqxXPHft35bei+NH//R9SenVAJMd8KSsCLwo4wlUW0kaw5Sg+5wvOq1uOViA4BdbzGbd9PH3P2ys9bNx4iMihXNRH5eIziumYfh0Q3E//25tP15TpC/fcVh4NHKvfc/CP/fjRr387WB9jugGhKevZtGnT0me0+iD+onWNm7NECJ+6WXMON2+JSE3iZ86MbOsNE7OgLJcvXy6PBkRLFK2nT59OcbQ9Y8WKFUk0AZORvudQTsQyIDJUJ1lwFAYIjhMnTpRHN2HSozxqK8olC44ZHhqPndDDVj7mECGMiYgsa+pT+ovjaK2LYy4Pp+9l4eM740EwdiTEtbStsagyVoEoP378ePqu60jjKIqhGTNmDEsc0R6URWlSd/KI17mpB/eU+PCDoaDKqkt/sjohYh/3I6MmdhFVgIDRkrycQDQisLA24i/RiyX38i9/k76PFNIhvTpU5YsIqxKO7SA+50W6KUe3kC5WVLWvtmvkVJVruKiv1He4/+qf/W7yqwuWYZ2rlQBjuoUJT0ttdcRZFBB8cgxXr15Nn0oL10pcjhZYz5iIqurDRI8wUVgdrly5Mlg/geBoJRLyuGZkaEzltGtnxKUeiI4cOZKsq/S3thHQpwg9jQMcx+3ACiprHuMLwQOMA6WPyy3EErqdmDt3brpWSA+RzAOb4CFK6eerLnWhPUhf6eDM8NADFf1Cf9Gu8WFY8FBCf4207yYKoyJ2sZCyvA2IPC3JRyfrKNZFjhG92t7w4a//U8+sfKRDenWoypflddWlLsTnvEg35egW0sWyG9s334oAVeUaLuo/bemQa7W1JEfbJ3QeY8SY4cBEriXdTpO+0ATPJ4ICZIUjneiqJoLRgq0DCIW8DAJrK8dxe0M7sAbnljSEQ10RY4YPDyq5VRaiZbUKBAgPJEJjgLGKdZY+JW35y8nqWYUstpwfrbV8MpZiOsOxvEpAYYHF+qctDNQlbsuJFuduoD1IP5YT53E8PLDSYtHdsWPHEMt+hAcy3VfldK/sR3oudtl3iYVPy9v/++vvJ6vdgmyrQjt+fPn/TZZKwf7Tbs5/dsk/K78N7BslPdCvH7RKK89Xe0hbLcW3gvhsz4h7UEmX7QV14AGhG1FKufkps07k5WJbQ6s9xzkIZc6NIFA3LvpUedQdbP+Q+Kc/bNk1w4WJVPvN4r7BVmDJ4MYtJ5g4ERFYPKrotAQ73CXaCNsq4hLjSEEA0T6xTogmiZGIrHNa7mZvZd2HB3MrLAHT1gg+gXWW/o1bV0D7WGl7+oC+aIX6VP1UBwlhRE4U2uwfrno7HzQWVf5OD1ekvXnz5lR+5SfLtkQpAqsVxFE4dYvXQbQcm5HDAzztGa38OYyTVv2l/my1cjER6YnY5cUpLUfrJSpZ/vjkJaW43K23/ePb+JynvbZYe9nvqTBQenVgL63OJR3tUSUNBBplqXr7n3hYSHUuZaqykNYBi2dsFyzXdUUz5UBYcl4dMUr8WGdcqxfU6AuVi+0OdS2xlJ2tKTFt9lIjsmO+cX9zO9ivy0MR59Af9JMxwwHrA8uvWGqxKnXaykB8Lc3h4pJyvnSH0wSrfYL4aUk50im8DoiEvHwSGQgi+SGi2u2hjFCmWCfSlxiJMIHFtsTKjDXNDA/GA+3JeIz9Fh+wgHDamnDannPoC8adzsPFvef0qfpJrhOsgHBeFDeUkReWYjoS3uSF1U/lR8y2u7YQpKQftzCQBsdKWyKpCsYzAkx5RYsj5/FyWmxLb7kZGfRLtPLn8EBGmNobF+9rnM8Y7Jd+mHLjwht65d2AShV/9sPyqL9APCHourXGGjOp+Mt/fsukOxlAQDBhMinrJo+YxIqRW9tM/8HcNRnHtRlfPO4mJrFfRv3XGIwxZqKQL6sC1ty4R9IYY0yzsNg1xkwa8mVVHMu7LOcaY4xpJo3bxmCMqcEk3cZgmk1ctjRmrPC4m5jEfrFl1xhjjDHGNBaLXWOMMcYY01gsdo0xxhhjTGOx2DXGGGOMMY3FYtcYY4wxxjQWi11jjDHGGNNYLHaNMcYYY0xjsdg1xhhjjDGNxWLXGGOMMcY0FotdY4wxxhjTWCx2jTHGGGNMY7HYNcYYY4wxjWXK9RuU3weZMmVK+c0YY4wxxpj+QxK3pdg9dOhQeWSMaRrLly8fvAkY0xSYuzyuzVjjcTcxif3ibQzGGGOMMaaxWOwaY4wxxpjGYrFrjDHGGGMai8WuMcYYY4xpLBa7xhhjjDGmsVjsGmOMMcaYxmKxa4wxxhhjGovFrjHGGGOMaSwWu8YYY4wxprFY7BpjjDHGmMZisWuMMcYYYxqLxa4xxhhjjGksFrvjzJYtW4pTp06VR6Yb3nvvvWL58uXp0xgz+syZM6c4cOBAeWSMaTq/+NXl4uiFg8Xpd14uffqTEYvdJ598MgkOub1795YhY8fhw4dT3oIyjZeAHOu8qTf1H23yNjZmpNS9Ti5evFhMmTJliNu+fXsZemt4FGPEkz/xBHkj3FoxXqKOclFWY0bCsmXLbhnzoPEll1+DMSxeY8O9jkz/840fri92/+2W4u/f/2np05/0xLK7YcOG4tChQ8kdPXr0lgtotHn88cdT3pMR6k39R5vJ3MZmdNi6dWuaPOuKygsXLhTXr19P7umnny59i2L27NnF/v37k//JkyeLVatWDU7ImzZtSv6E79ixI/nBggULimPHjpVHxjQDPfitWbOm9LkJYYx7rhFdExwLROu2bdtSGNca147mcl9Hk5Nvn/nz4t1fvVPMuu33iz/97KbStz/p+TaGpUuXFh988EF5NPDkJ6tvbhnUMnQexndZElnml19cro4WVD45BuK9++67xc6dOwfPefPNN1O40sThlx9HiK+waDnFcs1xDBd8z/POIR+dp7oJ0o2W8VgviHWQP59qh3ZlA50bXVUZSUfhKk8sC36x7Hwn7YjqKfJj9b2IZVM+grwVFs/JoRwqr9KXq6qnGV+OHDmSJtATJ06kCXr9+vVlSH0YG4jdlStXpuP58+ene9DBgwfT5E4YTJ8+PX0CVi8m7VmzZpU+Q6EsTPaI5mjJonwc52XF6sUxYoEwXY86liNfIcsbTmkh+iU+8vhCYkZOZWvlDxyTtsIoL2WMx0J1ieVTfaqoqocZPxjTXFNz584tfW5y5syZdG1wjQDXDNcHY4PxwpjXQyTprFu3rti3b18KG851ZPqbn137cfGDi4eLj3/sE8VXHv566du/9FzsYtm9884703cEDsJv9+7dySr4xBNPDBFJ3BxlFeYzCqaXX345+T/zzDOlTz045+677x5M96677kr+iNBr164NlmPz5s1DjpkcBWV88MEHUxjuxRdfHCKGOX7qqadS2EMPPTQosDiuyjuya9euwfBFixYVr7/+ehlSD/Lm3Oeff770GUqrslF+nYsDLGt5GYl39uzZwXhr164tQ26CH+VmEsTRtrl1+b777kttoXZ766230qeO33777VQ+II1YNto+lrvdGBKKr/JyrHbGVfWFmRjQt0zQCxcurBR4gglXwkqcPn36lmVUJt9Lly6lTyZwuHLlSvpkYsdf4rgKykJeTOR8Jz7nzZw5Mx3j9uzZM0QEcsz1RBhignvbkiVL0jHpkB7iHiQKlRaWMdKnTFjdFKb4Ec5VuXASGsoLh3UuF57c7wgjfax0WP7icYS6KJy8qqyE0KoeZmLCQ6XGi+DauXz5chLCErSC8Y7QHe51ZPqb757blT7nz1hSfGbq59P3fqYnYleWTBwTl54cETg8SUpoPPLII4PiDhGDGFJcPqNgQrD1Ggmhe++9N33GYwSboIyPPfZYeTRgrZZYA44Rc4BgrWs1pM7kE+sswVcXBFw7WpXtww8/HJIX3/HLuf3221MZO9WJcjAR0vet+grRqnY7d+7ckHZ84403Uvn0HRErsG4huKHdGBIvvfRSKm98MCI+6ZqJj6ySWFKrBB6TqgQVDotTLnBbgfBT2hs3bkyib/Xq1YOiuZ3VMsKknm+d0MQPjNE48VMnxDtgZZNYAEQh5RDchxAidaAtquKeP3++/FYUM2bMGHIML7zwQvrUvSc/prwi1oVPyh7DxUjqYfqLXl1Hpj/ghbQ33n+tuO3jdxZfnfvN0re/6emeXW6SsrABllMsvRLC0dqA0ELsThQkdiXyKKvKTR16xXjV+Y477hgiEvmOXw4ikf5U/VuJXk2SiGaJ65z7778/iVygfRGxOkbM3nPPPek7eWDZVXvHCbTdGBKE55ZbJl7y4Jw4Js3EAtGKcERQIWTrwGQbxWM7EKgSyVg/EWhYKjkfqybW2LrEbQmd8o+iNLeacS7XgtLKLavtwJhAHTgvXgtY2pQegqRbrl69Wn67FcpeFT6Sepj+opfXkZl45L+28DdXXkmfX5z1pfTZBHq6jQFrKCJKS9VTp05NAlhLyXKA0IrW1ImCRJOWzeXG4iWwsUCiEUtqK5GKkKXOiN7nnnuu9B0KW04Q7vR3q6d6xKzGA1Ze8ovjQ23NJ+WJ7a1tGu3GkKCvELx5OUiDuIheWx4mHggkLIxMoIjDukThVWXFjFZVgTBkQiYfJmg+p02bdsu5reD8uFUgX/Ktgu0AEp+IA8G5CASlhWMM14Uycw5pInKpL3lQL/xZceklpEtb5Yy0HmZs0baECGNp3rx5aS8u/RxhK9Cjjz5aHg0w0uvITDzYm/ud175VbPvRxuKv/m5XOv7JL04lq+6f/EHvV9jHi56KXUQLwoQbILA9oJVVFOGD2JUIQQDlLzlFEFavvvpq+k68dkKZuPEluW7BWqm8uqVd3nmdqQfiT7DXObYXWwR6BdsBoqAciXiXJRbrqbYzVMF4oC3ZD42VFxgfHC9evDgdA2Hs0a6i3RiKcAOmvVpZos3EA3GkFYJ28NJU3AtKX7OVAbTMrnCuLcZLfEFHglBL84g0jhHNrbZDaC+jID5iAcgjFwY5CFHiSARGMY9o5sWfKiQqc1HSDol/5aEtCsOF9lN70va0Vyy/aFcPM/FYsWJF6lvNP/QxY5RrEEc/62VFxh8PawhhMZzryEx82I/7tT8cMGr99c+/W3z//PfS989N63xv7id6/oIay1oSJ4g7xJCsibj4cpEECv4sXbcTYOwL1VI3S9tx/2kOe0EVdzjih72fiC+VGSdrZCc65R3rTD0Qf4IbDvVSnrRdr6Bt8zrpphdBgCu81X5ctgVQbsQsZUbgt3pQeeCBB5Kg15YFhC3H2jcNpIH4jWXT1oNOY0gQDzEvS3SMT/nIw0wstPwdXdWkySSN5VJxIFoQebhWOPcfjiXOmIwJi3uBuQaZqIkbt8xEWKJlWZ40SYP8dMx5EtutQHCSh8qME6RFmjFM4pJyc21xbtXLevEc2grhwdimPPKvEqbdQP6Un7Soc7RKR9rVw4wP3NPpB8YPaAwC4wLDhLaeaDVA0M8a45xHXN03h3sdmf5g3qcWF3/06dXFb3776+LU5YHrfdHMP06fTWHKdcwOGQz2fKnY9DcSj3opD/HODcr9PDnhIaDi0jc9gnsooltiQftrJ/oyP5a948ePV74o2A/Q7h7XZqxpyrjbcPTLxcVf/kNx/ycfLv7iCyNbIZoIxH7puWXXTEywMrP/VUy0FwSNaQpYwSDuc8VP2yCMMWYisnPp94qXVp5thNDNsdidJGDR1fYKHFsUnn322TLUGNMrWC7mp5q0hIyD+NNlxhhjxg5vYzBmEuJtDKaJNGU52fQXHncTk9gvtuwaY4wxxpjGYrFrjDHGGGMai8WuMcYYY4xpLBa7xhhjjDGmsVjsGmOMMcaYxmKxa4wxxhhjGovFrjHGGGOMaSwWu8YYY4wxprFY7BpjjDHGmMbS8j+oGWOMMcYY069I4vrfBRszCfG/CzZNxP+21YwHHncTE/+7YGOMMcYYMymw2DXGGGOMMY3FYtcYY4wxxjQWi11jjDHGGNNYLHaNMcYYY0xjsdg1xhhjjDGNxWLXGGOMMcY0FotdY4wxxhjTWCx2jTHGGGNMY7HYNcYYY4wxjcVi1xhjjDHGNBaLXWOMMcYY01gsdic5W7ZsKQ4fPlwetea9994rli9fnlyd+MR788030/e6eRhjjDFm4vCLX10ujl44WJx+5+XSpz8Zsdh98sknB0UQbu/evWVIZ3opghBW5B+hbN2UpxeQ56lTp8qjWyEstle7uBOJV199tVi6dGlx6NCh4vHHHy99jWk2Fy9eTK4O69evL6ZMmXKL0zU+Z86cIf7Lli1L/hDPjRw4cGBIPGNakY+/HMYS/tu3by99qlE8uThHeZxOPr7xw/XF7r/dUvz9+z8tffqTnlh2N2zYkEQQ7ujRoxNGwD3//PPF2rVry6PxB+vozp07i927d6e22rp1azruB65du1ZMnTq1PDJmcjBr1qxiyZIlt0z6VXBdX79+fdCdPHmymD17djF//vwyRlHs379/MPzIkSPJDzF97Nix5Ldt27ZBMYL/qlWrBuMZ0wrGjMYQbt26dUPEJ99PnDiRDBbtYIwz5i5cuJDSYbwuWLAghXmcTj6+febPi3d/9U4x67bfL/70s5tK3/6k59sYuJg++OCD8uhWS6bAAvr6668XL774YvLXkner+FpGj+GyCuO3efPm9B1/LMaQW47ztElTyCKrMKVRhazIcoLv7777bhKwfI/pw0cffZQ+77rrrvR5++23p888niANtQvkx5RZx3mZYppqO7mYRkT1z6EteIhRXyltrOZKk7LUhT7ReTiBf2z3/JjyteoX6hTLoDrHspIecZRvbCNjWnH+/Pk0wfNwiujtZBkTxK/zsH316tVk9YUZM2akT0BkI5iN6cSlS5eGjLWNGzeme7ZAiPIw1okrV66kOZyHPFi5cmV6YOPe63E6ufjZtR8XP7h4uPj4xz5RfOXhr5e+/UvPxS4X2J133pm+I0CiJfOJJ54YFCtYXR966KHkR9h9993XNr545ZVXBq2iiC8EC5YTjoGwZ555Jn2PKG3CcVijWZKJxHCEOBd4Fbt27RqMx41BWyU4vvvuuwct3RK1gjpSZ4my5557LtUxjydI+6233krfJVB1TL0R1qTJd8Q+bUC+5E/agnqqTMTRg0Ektn0O7UlZ1FeUF+FIvhzjHnzwwZZCNEKbvvzyy4Pnkaba45FHHkntLs6dOzfk+I033igeeOCB8qh7GC9PPfXUYL5jvcXF9DcIBkQv5PeOHKxd3AtXrFhR+gyABUzLwLq/TJs2LQlquHz5cvpEUCNeolXYmHYcP368/HYTxmG3RJEsEMEep5OL757blT7nz1hSfGbq59P3fqYnYheRJGsZYkkDH2GGSJKYy8VMTp34enpF5CEsZS3tBE+eCBxBGTlfIhIQhIJyRAt1BKEuWNrvxkKIcCRf2orPdvtf77///iT4QG2j47fffjsd6zsimjYB6oYQplzUj3zUJ2q3WG+2KCCA6btWwjsHwRon8scee6xt3woeVpjwBfVXWck7lg1/6qXjs2fPpjExXGgvtVGnsWhMDuIUkcpDUtVDYWTHjh1pKVkWMpCFOC4PI0biVgnSnjdvXhIuWM8kjIcjWszkgfHI+NJ4wRo7HLDkMm6VDo4tDeBxOnnghbQ33n+tuO3jdxZfnfvN0re/6emeXcREtJYhonhKlBDuZA3pNn43IKZkcRYIqw8//LA8qk9chsdaWBfKwDmIRNoLKyXHEqVKU3733HPPoCBD5EZBiZUTMQyIcvzj+YL6IRpjGMcRPcnXFbpAGtqGATqXcreD8+64447yaAD6QQ8tWIgR9rQH32kjjpUu+agd5aJwr0vd8hoDTOSIU4SqrFvt2LNnT7F69ery6Fa0PMzSMCBWlDb5YEXmoVDCGPFsTDviw1QUqN2isSjHOJ0+ffqQMI/TZpH/2sLfXHklfX5x1pfSZxPo6TYGiTGJD6yeCGAtWcu1otv43YC4yS21VcKrE9QNgauyRWtxJ/SLBrIuYtXEcollFr9YZ8qLk6WTsnJMfCxMWDkRw4CIxz+erzSoH2nkYSoDUAfKVWcbgogCFaIYbUfVA0YUzrJmI3D5fu+99yYrMm2E+AXyaFWXutQtr5ncYKmSdYsJvQ4s7XI9dVralSCJ8CIRq1DkK+scQsMWM9MNBw8eTGNwpDDXME7zsexx2hzYm/ud175VbPvRxuKv/m5XOv7JL04lq+6f/MFTZaz+p6diF+HABabN6giVqv0/gvhYc0Wn+O3o9LIXwilaYbVfrluhhFBDsAmEWISwVtsfAJEa4eGgneBevHhxunFJ6C1atChtBSAfCTVZgKvqTv0Qk52sn2wPIR5W6zpQHsolXnrppVo3Vyy1WAAE+SHUVRduqtQFwUu91D/UWW8FV0H/x3pW7cdlbCmc8pKvMe3AMobI7cZCxthbs2ZNeTQA95u4UoUgRiREEYEf+eDHp8Qw+yWHY6EzkxME56ZNm24Zg61g3OnFsxzSYFtDxOO0WbAf92t/OPCOz1///LvF989/L33/3LT2D+v9Rs9fUEOQSLAiVNjiEJeco/VQcbUU3Sl+OxBLiBcmlKpzuDCxYCpd9hnHvbd1IR2EptKRCBWIUUQ1Ybn4xJIbz8VRpnaCmwcAxJ+2LEjYxhe1qDsvnlF3pRt/mYClJ/bkxnyrePbZZ1PZOwlj0N5ppUdd5dcO2oA203k8LOQvFNKPsmQD8alzu3YiLm2petIPOYhxXi4knHFX9SKjMRGEg/YjRtfqN0X5vVEmf7YpRLhv8LNNOh8xErdDIIYRyVyrgp93Ii7LxLxdb0wr4m/j8hCFQSGOQf3GM/c9xh7fOScnH+/s0Y1j0uO0mcz71OLijz69uvjNb39dnLp8LPktmvnH6bMpTLlesTbHwGV52JimIEtvHUE+GUDw112WN6ZfYO7yuDZjTVPG3YajXy4u/vIfivs/+XDxF194ofTtX2K/9Nyya4wxxhhj+oudS79XvLTybCOEbo7FrjHGGGOMaSzexmDMJMTbGEwT8TYGMx543E1MvI3BGGOMMcZMCix2jTHGGGNMY7HYNcYYY4wxjcVi1xhjjDHGNBaLXWOMMcYY01gsdo0xxhhjTGOx2DXGGGOMMY3FYtcYY4wxxjQWi11jjDHGGNNYWv4HNf83EGOai69x00Q8rs144HE3MYn90lLsGmOMMcYY0690FLuHDh0qj4wxTWP58uW2RJjGYQubGQ887iYmsV+8Z9cYY4wxxjQWi11jjDHGGNNYLHaNMcYYY0xjsdg1xhhjjDGNxWLXGGOMMcY0FotdY4wxxhjTWCx2jTHGGGNMY7HYNcYYY4wxjcVi1xhjjDHGNBaLXWOMMcYY01gsdo0xxhhjTGOx2DXGGGOMMY3FYncMOHz4cLF3797yyBhjjDFm4vOLX10ujl44WJx+5+XSpz8ZsdjdsmVLsXz58uK9994rfYrizTffLJ588snyyIwniGwLbWO6Y86cOcWUKVOKixcvlj5FcerUqeQnx3FdSCeeu3379jKkSN9jWETlWLZsWekzwPr164ekYYxgrDBm8vGZj7M4tiPt4jHu5B85cODALWPUNINv/HB9sftvtxR///5PS5/+ZMRi99133y3uvvvu4qWXXip9jDGmf2GyR2RGmPAXLFhQnDx5srh+/Xqxf//+dFwHzp09e3Zx4cKFdC7u6aefTmGIhE2bNg36r1u3blA0ELZkyZLkDxIv+JOm0jBGMDaqYEwfP358cJzhZs2aVYYOBeOI4jAeEbjAmDt27Fjy37Zt2+DDFv6rVq0qjhw5ko5Nc/j2mT8v3v3VO8Ws236/+NPPbip9+5OebGNYvHhxcfTo0SHW3RxZgHF8F5yDHzdyhbPs3wrFJw6fslpiTdb5uFgW4shf8cmPcsSw/EkY63R+HshyrTJUnSt/XE67snKsdGM7CfxivkLHONIH4tIvOPxVRrVhHt8YUyTxuXv37vJogDNnzhRLly4t5s+fn45XrlyZBGwrcRHZsWNHEgdV4uLEiRMpTGzcuDFdrwiIy5cvFzNnzkz+OtfCwrRj8+bNt4xdqBrTrTh//nz5rSgWLlw4eHz16tXBh8AZM2akT+CBjIdA0yx+du3HxQ8uHi4+/rFPFF95+Oulb//SE7F75513pokgCsII/nfddVdx6NCh5OQXeeWVV1LY1q1bixdffHGIAKzi3LlzKf7atWtTXC5yzsVvw4YNxXPPPZfiIeTOnj07mDfxxeuvv15MnTp1MN+dO3eWIQNCERGv80gjinAs2teuXUth5BfPpW60B2HcYKiPaFdW8fLLL6ewZ555pvQZCumRLnGAsuqY9Hbt2pX8OZ9yqCyaqHlSJx5+lIPyGGMGro0oPgWiNBerTPwI0k4gUBEbVUvDWMqicFAeCAv8L126lI4Vn/JZWJgqsLQiPPNxqrHDw5nGX90tBy+88MLgnDlt2rRB4atxT56Ea24xzeG75wZ0xPwZS4rPTP18+t7P9OwFNQY84rHKSoil4rHHHiuPimLFihVJPEZ0Qd13331pW8RHH32UjlsRRevbb79dPPTQQ+lc4MJDjCIsb7/99sHvOeTz+OOPp+/Kl/ITl7ooDLCmILAjKoMudOVBfbXEich/4okn0ndoV1bx1FNPld+qIT3SFYhaHd9xxx0pvVZQP+qpMsd6GzOZYeUD8dnr7QEIBAR01dJwO7AeUx7ECQLm9OnTxaOPPlrs27cv+eVbLczkRQ9UVdZbHpwgbqNhjmq1KoG/RDHoemAMIqbxx6Azb968tDWChzLFl7A2/Q0vpL3x/mvFbR+/s/jq3G+Wvv1Nz8QuIMIOHjxYHg0gERfFmQRoJzi3znL7Bx98kMRpjCvIFysmkwv+UVTmIPo+/PDDJLT5HukkInOoYxXtyjpcaBel1clKS/2oR8y/m3oZ01RY5cCS1S16iazKaetQtN6uXr16yFJxO4iHOOEchAUCA0GBH8KjzjYK03zYKsM+8nZEiy8PXK1WJXjIkijm4YpxLBDT+DMuMeiwnQZDEH7kTzlM/5H/2sLfXHklfX5x1pfSZxPoqdjFEoqQi8tsErlRZFaJySo4l6V2OVlDc9hGgbU0xsUpb6yYHFdtGYgg+hC1VWIckVinzJ3oVNbhELdF8NkO6kc98vxbta0xkwWtyDC5s+QL2pfL3tncasWEj/iUIK1y3Hvy7Q5Xrlwpv926FUJ5aOVFSFhwrkQLZaqzjcI0H1YAEJ16yALGDNsM2H4AcfzWtcDKqpu/k8I2COZ50tG1Mn369NrpmokDe3O/89q3im0/2lj81d/tSsc/+cWpZNX9kz9ov8rcT/RU7AKCkkkjgriLv9aA9Zf9sL3innvuSSK7ndW2CgSt9uFyMXOM6EN4IgjjHl2eWhctWlQetSfWF6tr3LM73LK2QunIkpzv52NPcsyL+lFPb1swZihRpLLkC3xi6WLrFfc1TfoIYMJyUVoF1rH4jkLcB7lmzZq0/CywjLHHPoKwkNUuCgr280aLsZm85A9cwFyAWOXhCEGqVVfGD2OZMQ0IYm2JYXzHLTZaOYjjnPikiR+fulbig5jpH9iP+7U/HDAC/vXPv1t8//z30vfPTet8b+snei52uQByCyh7Stmjq2VzxGTcDztSSA+LprYq4PQ7v/GXC3iJLO6HpZy8ZKawaBV9/vnnk0jVuYjzOhMbUF9uJpzHy2I8AIh2ZR0OpMf2EaWX88gjjwxum9BEzVIU1mDlX3WeMeYmTOL6uTEsZ1jRNMl3AsERtzqQlixmCGmWlBWGhS7+0oKEBfFA9yDFlb8x7WCs6CVJhC9CuEqYMr60TxyXj3PmEB7c4t5g9qMrLr8mYvqPeZ9aXPzRp1cXv/ntr4tTl48lv0Uz/zh9NoUpN54CBx4DAwxclrabDBctkxei1pjJBg84FZe+MX0Nc5fHtRlrmjLuNhz9cnHxl/9Q3P/Jh4u/+EL37y9MNGK/9Nyya4wxxhhj+oudS79XvLTybCOEbo7FrjHGGGOMaSyTVuyyN8lbGIwxxhhjmo0tu8YYY4wxprFY7BpjjDHGmMZisWuMMcYYYxqLxa4xxhhjjGksFrvGGGOMMaaxWOwaY4wxxpjGYrFrjDHGGGMai8WuMcYYY4xpLBa7xhhjjDGmsUy5foPy+yBTpkwpvxljjDHGGNN/SOK2FLuHDh0qj4wxTWP58uWDNwFjmgJzl8e1GWs87iYmsV+8jcEYY4wxxjQWi11jjDHGGNNYLHaNMcYYY0xjsdg1xhhjjDGNxWLXGGOMMcY0FotdY4wxxhjTWCx2jTHGGGNMY7HYNcYYY4wxjcVi1xhjjDHGNBaLXWOMMcYY01gsdo0xxhhjTGOx2DXGGGOMMY3FYjdj+fLlxXvvvVceGWOMMcZMTn7xq8vF0QsHi9PvvFz69CcjFrtbtmxJAvHUqVOlz03efPPNFEYc8eSTTxZ79+4tj+qjtPicCFCH4dRjPEHEt+qrfkT18cOJGWsuXrxYfhvK+vXriylTpiQ3Z86c0ncAzlEY7sCBA2XI0PMixFm2bFl5ZEx9GH8aU/lYrKIqHnOF0ojjFfBrdR2Y5vCNH64vdv/tluLv3/9p6dOfjFjsvvvuu8VDDz1UvPLKK6XPTU6ePJnCiCOef/75Yu3ateVRfe67777i0KFD6dMMj7vuuiu14fz580sfY0w3IDyZ5M+cOVP63EQPkdevX08O4YCIFbNnzy7279+fwrg3rlq1KokF3LFjx5L/tm3biu3bt6f4+BPnyJEj6diYujDulixZMmQsalzlIGIZ04zNnK1bt6axeuHChWLz5s2l78B1QPxZs2aVPqaJfPvMnxfv/uqdYtZtv1/86Wc3lb79SU+2MSxatKh4/fXXb7GwHT16NIVFsPIePny4PBrYNiAnqy2ThvywBENuxcM/xovWY5DFWU7p5FAWxYnlEjENlY+0qRsOf01ysj7LtYPyKF60EJMW6eOn8FbWbMpLvFjXGJdj1U/tE+NwLuEqi9ooPxZV/QKkx7HKrDxjWVSvKhRfLsJxHFfkQ1qkLSHBZyxPbA/1DbTLR+kqjLpozOk4EtOKeZtmIusWEz7iYeXKlWXITXiI3L17d3lUFI8++uig5YuxhdjVecRdunRpcfDgweLq1auDFrUZM2akT0CsIDSM6RbG3cyZM8ujIonSS5culUdDYUwypqs4f/58MW3atCGiFtHMcdU1YJrDz679uPjBxcPFxz/2ieIrD3+99O1ferZnlxv3q6++Wh4NiAH82oGAeOKJJ5K1MVptd+7cmSYN/LAEt4J4OhexLWFD3oA/T6ZQlQ5i5sUXXxzM69q1a2XIAIgmhW3YsKHYtWtX8n/mmWdS3XCEyVJKOMc4wnKBJEh38eLFg3HPnj07WGagLlOnTk1htI/yrUIPFMSNZRQvv/xyCqPMVVD/p556KsUBxFs8VpvSVljv8cfdfffdQ8os6z1hjz/+eKp/nKg594EHHiiPbkL6KiOO+tYRj4wVCQs+1b9qc6Una0WdfDSeSI92RUTHY4lu0qLdlNaDDz7Ysq9Nf4NoQOTKQtbNqgjiQiLh9OnTtywRS4AgJhAVcPny5fSJoGAFzKswZjjwULZp06ZktcXt2bNn8H7ZDYxZHsb00MYn97rhpGX6i++eG9AS82csKT4z9fPpez/TM7G7YMGCJCYE3/FrB4Lu3Llz5dFNEFJvv/12edQaxJ1AXH3wwQfpO6JVwkoCOloHBeKcbRYs70O+vQKBqLA77rhjyHaMKqKgpm5VeeKHmEUQCpYqYztQf4U/8sgjbfOl/JoQ+SRuzBfh2g7aTW2EaMuP1aa0QxTMHOcPB7H96HsEoqDO1CUHEUz9BfXO69ANPDisWLGiPLrZJ3Xy0XiibvRBfvzRRx+l4zfeeCOJZUFdydc0DwQpD0wI3m72ziIKEBgbN24sfVpDHlhxyQMhMW/evOL48ePJyiuLssSGMXVgLtBWGdxwVwgQzdzfWJV44YUX0jhly43GZautEaa/4YW0N95/rbjt43cWX537zdK3v+mZ2JVAYnlZy9fya4UEHdbEuMT97LPPJisb/rIsdkMU0SqLRGtOK3/gXC1Vx/1KrYhL21j+qkAwIZwidYR0XaIo6wVR0DIRq35RyFZB31MW2pA+jA8VEepN/SMjqQPp3X777eXRTXqRz4cffpg+Ecj0r9qiztgw/YuWedmWUHeCRxR0s6cRSxl5YOFFXLBPF5GCH+ns2LGjjGlMZ1iVYlVTKxKMKW376gZEs9LYt29fSpOxyJjED+ux6X/yX1v4mysD72B9cdaX0mcT6JnYBZbmeYJkH1q0orUDayFLwaBlcUSRlo8RvcOx8mFJlBDRVoZu0bmUpVMaiLq4tB0tfxGEWC5sEVG5AB4urcTeSEGwYr1U/bAAd0LjAUto1RYGoN4SkWIkdWglYHuZD+OT/lVb4NpttzHN4Omnn04TPFsP2gkHln5Z5Yh7GrHSaquCwFq7cOHC8mgArMdcM4RhTYPp06fbsmu6It+2wJjCIjtc9EsMjGnGImMSGKMem/0Ne3O/89q3im0/2lj81d/tSsc/+cWpZNX9kz9ovzLcT/RU7LJMjcUPoTmee82w6kqk4lpZmO+9994hezGjdVl+EkP5MlC+TSEXrHFLRwShRLy435Wn5PxFvrrQ1rJ+kyZpt7NWDxe2M8T6dbLsgtoXV7WFARDB1F9Qh2gFJk/tBScsPigoThS3bL3gYUuoTzvl0w33339/y/41zQcR0WrPooQuwjiCSOCNdokGrlmui7lz56Zj0Is/3Dv5JD5cuXKltoXYGBF/MSTuGWfsdbM1hngYrzTmGYuMSWCMemz2N+zH/dofPpe+//XPv1t8//z30vfPTWvW+wI9FbsIBwREHasf8IKQloIRMWxriFsHsJ5gQetWkDDZYJVVOrgq6zAimPTJhzgITgk68oxhOYg3WY+5eTBBca7yQ3S1AitgXAbHAjrchwPam/2opEOabAEZDbTHVWWu08faytBOVJIubaV0EZFxbzB7jtVWbKkgrQjloK/1shl9H8uph4hO+XQDfUWfKS2cX1BrJhIGuauy7CJkmfxZ2o1xJSp4YEY04MeyMscSCuSTv/jDz5ARl3Pq7P01RsSxhmNMtvoJOx6yNM4Yv3yP+9PzXwVhLCptxqjpf+Z9anHxR59eXfzmt78uTl0eWAFYNPOP02dTmHKddbkMBjEW0X4F4cPFKAGJFQ9r73DFzUSlH+qFZRWrqvZnm4kBAr3i0jemr2Hu8rg2Y01Txt2Go18uLv7yH4r7P/lw8RdfeKH07V9iv/TUsjtRwLIXX0bCIjic5WozMrCmY/1utYXBGGOMMRODnUu/V7y08mwjhG5OI8UuPxkVtzHwYlX8WSwzNvDzcWxj8IOGMcYYY8aLRm5jMMa0x9sYTBPxNgYzHnjcTUwav43BGGOMMcYYsNg1xhhjjDGNxWLXGGOMMcY0FotdY4wxxhjTWCx2jTHGGGNMY7HYNcYYY4wxjcVi1xhjjDHGNBaLXWOMMcYY01gsdo0xxhhjTGNp+R/UjDHGGGOM6Vckcf3vgo2ZhPjfBZsm4n/basYDj7uJif9dsDHGGGOMmRRY7BpjjDHGmMZisWuMMcYYYxqLxa4xxhhjjGksFrvGGGOMMaaxWOwaY4wxxpjGYrFrjDHGGGMai8WuMcYYY4xpLBa7xhhjjDGmsVjsGmOMMcaYxmKxa4wxxhhjGovFrjHGGGOMaSxjLnZPnTpVbNmyJX1/7733iuXLl6dPM5TYTmMFffHmm2+WR+MDdT58+HB5ZIwxxphx4wc/KL/0Nz0Tu08++WRyk5nREot6KEAAtyOPx2eT+oT69NvDEcKdMpv+5OLFi8nVYf369cWUKVNucboeSSf6HzhwIPlDPDdCnGXLlpVHxrSGcaIxNGfOnNJ3AMaRwnDt5hLOjXHj+PM4bTB/+ZdFcehQeVCC0GX++ulPS4/+pSdiNwq88bYMNpG77rrrxhg8VMyfP7/0qaZuvH6FelE/6tkvPP7446nMpj+ZNWtWsWTJkjS5txMIsHv37uL69euD7uTJk8Xs2bMHr0e+79+/fzBs1apVg2L62LFjyX/btm3F9u3bU3z8iXPkyJF0bEwrGJuMVY090DgijHF04cKFFMYYXLBgQQprhcYpTuPP43QSsHNn+eUG77wzIHS/852i+OxnS8/+pSdi96233ioWL16cHDfxHKyLWLdwH3zwQel7k7fffnswPF/CZllbYXv37i19by7zK1wTkSxpuGjVRIRzHMPzySuWM4r2VmUQsqjC5s2bB78DeehcXDurZLt2wk9lUn7Rqd0Uj+OdNwbuu+++m/xU7vzculbS2G55P7RLL4blD0IxLD9PbZH3IX6C7zH/vG9iv+FiWpEYJ5Yx9ofaF1QOOUH+8lNZKF+ebzw3pst3zovlju0S+4A4Zmw4f/58muC3bt2aRK8m+U4Qf+3atek74wCxu3LlynSMAF66dGlx8ODB4urVq4OWuBkzZqRPQGRX3U+NyWE88bAlGDuXLl1K369cuZLGGmIYGIOMRcZkN3icNpw/+7OiuHz5pnX3xgNM8a/+FRPWwHGf0xOx+/LLLxf33ntvckePHi19B2CCvvvuu5N1C/fiiy+WITd55ZVXUhiTA+Ga4CUYdO7Zs2eHXKCvv/568cADD6QwLnbCOF/xH3zwwSECCOF37dq1FLZhw4YkBgXigfiEcdNgEoJOZQBZVIE66DuiiDx0LnmyDFRFnXYSzz33XEqLeNzEcFgQIxwTR2lq0uVc5fHEE08MaZ9W5O1K/+A61Y82pS0VtmvXrjJkQEiSP2G02UsvvVSGFCmvZ599NoVBFIQ5WCCIRz6MPYlVnaP04fnnn0+fEeqvcuDuu+++5B/HA44yKW3qoX7GAWGMDfmpvXOot/oOR7pxPFGHRYsWpTD6Vf1De8c+eOaZZ5K/GTuwXCF6odV1LLB20ZcrVqxIx6dPn75laRnxgSCZNm1aEtRwmcnmBghqxpCswsZ0w549e4rVq1eXRwP3lRxEcCuw1Gq7gu5PHqeTgH/5Lwesuxs3DhzfmIebwojFrgQAIgGHuIqTN0KYyVsw0edIGOj8jz76KB0jHjRZAJbjN954ozwqUtwo8ghDuAiWakgjorx0cUpYI5wfe+yx9B3xKjHRqQzt4Gk3loc8KbPaLFKnnQSi/Z577knf77///sE61CEKvjvvvDOl1Ym8XWkb2qhT/RQP7rjjjsG8KC/f1Xf0exSHpKnzaG8eUFrx1FNPpU/iP/TQQ8WHH36YjjmHByGQgK1qp6lTpxbnzp0rj24SxwMgPFnBAOqo7+L2229PdWrXF2qXODFQ1zieqIPCGb9qM7VH1dgxYwP3NSZ/HkCiFa2KHTt2FOvWrRu0prUjbpUg7Xnz5hXHjx9P1jMJDsSzMe2I+2nZZqD7CJZcxqLCcGxpaIVWMnDa8sD48zidBMi6+6//NZak0rMZjFjsIniwgAm+Y6mNIHSGAxO9tgXgsGy1A6FBHMXn3DpIoEhQRLotQ4R0EZQRhJIEWU7ddiINtn4AQqmq3K1gwlZdomW7HVX1gE71Q5gpr9gXPMwQbzSJIlYCsaqdJLgpo7YGaDwweaj80TKCiNc4kwWZtGXZxl9pRGiXvN60X1XcKrAmayxa9I4tTORM+ggAWbfakVvWOoF4VtrkgxUZ65oEB+LZmHZoDOEQofGFsRiGYxvD9OnTy9DWaMsDWxjA43QS8Lu/WxS/93tF8alPlR7NYMRiFxGAi6IAq1gvQBjE5WJctADmIDiwlMX4VUvXORJBVaKj2zJESDffe4t4Hq74jyBUaW8sz3XLQ/04jxsW9WhnPY5U1QM61Q9hprbjU8gKOtowDmkjlaMViFfKCIhXjQe1k1xcRZAfojdabPGjXdkukhOt24L2U36dwEJN+tQlPjyY0QNLlSxhTOh1YGmXlYBowcf6lYtk0l64cGF5NAACBQMCYYgMQJTYYma6gftD1dYFwODBeI7jsx1VVmCP04bCvPVP/smA4K2Yw/qZEYldbVfQxC8HCsPSq/2vElt14VwuqLqwpM92gOHA8nHcNyorXzdliFZNoDzREqw20bJ6pG47SZCrrduJ+VxcaXuIxFVugW9FXg+WsChHu/qpnAhbiG1I/rSVrKLE1d7UXoFVNz6kVLV5OxgPr776annUGygD/aF2AtoPC4mZmLB0i8itsx1BMJbXrFlTHg2AhQzRoJ8bYwwgRubOnZuOAZFMPogQPiUy2FvZTf5m8sHYiT9lt2/fvvTAVQVjk20NgnO1n5xxGfejE4aYjcLY47Sh8OsL3/pWUXzjG0XxzW8OfMevIYxI7OZ7OQV+ElJYHZngsbBxEbWzsOVwLkJIVmNcFAo5XHzs8Yzx64oorHvRQq19ut2UgbxlcQXKQ1voPMJaidO67YRQzOsoYZ6DuEJUEod24JgboM6ra1GkHvE84Nx29SOcMC3r5/ACGkKPMOLE/bG9gPbUkr+cBHgk/uIC7S/rLeOBB6d4PhbcfCzQLrQrwl1+tIP2EudgLdYYwWEFriPEGXM6h3rVtcqbkYGlSvsRo2v1m6IIDiZ//epChAc+vfjDAw7HEgf0L9co40Ow75K4nLNRL4wYUwHzVXypjJ8Iiz8ZJn8c+27jOItwT+dcxd20adOQFQmP0wbz9a8Xxb/4F0XxxS8O/AID9zD8GsKU6xVrcwxcWWjNxALBhdiJ/YNg40bDjcoMkLcJYhRrLyLWDOxRrrssb0y/wNzlcW3Gmr4fd/zTiM99rih+8pObv6mLVRfx+/3v9+3+3dgvI96za8aWqpecsEj2Yh9wk8jbhF9nqGvJNsYYYyYN27cXxZe/PPSfRyBwf/azxryoZstuH8K2hfgSINsF4stTZmC5je0CggeEOi8rThZs2TVNxJZdMx70/bjjXwXzsmwD/lNaJPaLxa4xkxCLXdNELHbNeOBxNzGJ/eJtDMYYY4wxprFY7BpjjDHGmMZisWuMMcYYYxqLxa4xxhhjjGksFrvGGGOMMaaxWOwaY4wxxpjGYrFrjDHGGGMai8WuMcYYY4xpLBa7xhhjjDGmsbT8D2rGGGOMMcb0K5K4/nfBxkxC/O+CTRPxv20144HH3cTE/y7YGGOMMcZMCix2jTHGGGNMY7HYNcYYY4wxjcVi1xhjjDHGNBaLXWPMmLFs2bJi+/bt5VHzuHjxYnopgs9uoV1onyYwknboNyZaXU+dOpXKY7pn/fr1jb4/TWYsdo0xXcNkWiXMxlOwNUks9gom7zlz5pRHph0HDhxI45rPHNqwyn8s6JR30x8gu8H3ANMKi11jTFcw8S5durQ4evTopLDc9TO7d+8uzp8/Xx6ZdrzwwgtpXPNpjGkWFrvGmK44ceJEsWbNmmLdunXFwYMHS98BK+KmTZuSCMZCVsfaRBzi4nILZAzLrTXyx7FsWzdv4um8mJ+WomXdq0ojntvJekS7KC7nRTi3VRjEfOLDhMqYh+WWPcUD/GNZtcQtx7Eg35hOPFdp4senyh3bqxsLcqxjfh5+yqdTO/cSxs6RI0dueYijHBcuXChWrVqVvsewVlAn4qoukRgWLbZVbcn3dnkTj/Iy9gmP/RnTi/monxWmNNuNy9hfrfqk1RjJx1zetrGcedoxDBfPJW6sB/lV3QNULrnYRjEsH4emWVjsGmO6Ys+ePcXcuXOLhQsXFnv37i19B6yI27ZtS9Yxfsj76aefLkOqYSJjciIubsmSJUMmyEuXLg2GYZ3UhE0c8lHY/Pnza+XN5MfkpvPIL59cseoRdvLkyVQ2Ta6ce+zYscFz5deK48ePD8alvTTBqn4KI80oRJioaVfCeJhQfKC8Oo+6KowHj9gPCG3OzaEuCxYsSOKJNKgjx6pjHVQv2pvz1F44xEK7NhF1+oH6EIb4HAvoA8YO8Bkf4ijH7Nmzi/3796fvs2bNKkOqoS6xrxhHsf/jGEPECr6rb2SN53u7vIlHeXU9cC0IHkrx49yYD1Am8lea7cblcMe+xghjTHlRFtomsnnz5sG043VOm1FuhXEubRGhHmqzVveA2H4a84J6q+3oF65V00wsdo0xtZEoYIJcuXJlmmiipaQbmIyZaMTq1avTpAoSsAIhdfny5fR95syZaULtFgQUE6vYuHFjEpcR5Un+TJJXr15Nx+S3devW9B0QmO3KEMuO8Dx9+nT6Tv1iGdauXZvaQdC2tCvQHnELQvw+Y8aMwWP1g0Qr5UIw50gESzBRR/I7c+ZMOq5DrBfpRDHKMQ8onajTD2O9lYD86FPIHx66hbpQJxH7n/aTIJ02bVr6VL8x3rrpi06orzSelA9QpiiM243LkYx96sMYU175WIXY1zHfffv2Dbk/cC5tFO83hGs8V0FczlEb6LrGnzLQVytWrBgMq3pINM3AYtcYUxsmpkcffbQ8GhBnTErDgckGy4yWEaPFBbCQKSyKIVls8M8tgu1gkpXAAE2SceJtBcJSS8m43FLWDsS5RCBloJ5Kh/rXhQeNVvnTD4hZTeCa3COUgbJEaAM9RAwHLGMqU12r2Ej6YTRQm7FaAXxSxuE8xKkOCKqqdiFc/sSJIDg1xmTdHCvajcuRjH3GFm2rc3Ht4CFObcgnxxEeeq9cuVIedYa41C3mz3GknVg2zcFi1xhTC4mCKFA5ljW2W5hktIQoJ2ullknlj5iLYFHEH+osnQPiQpZa0KRaZ7JjktVSqFzdJfYoMimDlnTloiWsFZQVkcFEzTmUJSJrG5a0VtapKLpFlaCoC4KMvlc96lrFRtIPo4G2LEigSoTKGtsNqoP6SU4PaCzhaxxViS7509dql7Gg3bgcydhnbHHtxnNxrfoacawwPvMHMe4P06dPL486Q1zqlucfrdpmcmCxW8GTTz5ZvPnmm+VRs6Bu3VgsaIfly5eXR2YygyjIJw4mZpzGVFxe70S+5zeCKNOkx6SPqO5Ep7wRGnE5dseOHbUFGtbsbpbWtQeSsmPZmzdvXjqmDMOxhEscqk3ysmDJpY1Yiq7awgCUgbJIRNFnnCMrMGJY/UGcTlZnhAhCSNS17I6kH0YD6pw/dHEcx2bcRtMJWdmr4FqRWGsVJ6dT3oyJ/CGmW9qNy27HfgQrOWOsnXDX9hFgzGn88hnHoKzd7YRqfg8gbrw/RWg37md6WCb9umN4MvGLX10ujl44WJx+5+XSpz8ZsdjdsmVLEkNyhw8fLkOGB+dHcZWLM74T/t5775U+ows3PMrQa2i32FYSlU0V2ab/yffuARMGQkUTJcKJyQULWSeLK3HZoycrMU4iEasSE48sbVEMMfkrPhObrGad8palSucyAdexqgJ5UFed2yoPwQROHMqOcNIETX7kG9Ops2St/YQ6R6I3gsii/hKvOaSBhY4ykQbL1sQXcXsIcXLrcQ7xaX+Vqa5gHUk/9Brypg1Ud8Ex/pp76E+taLQTboDVk3lD9cMpHdpU2wW4nmhnIFxxNWbUx53yZm+3rpU4V3ZDu3HZ7diPcB4WY405XHxAgngPYAxp/PJJOygMa3enB+mqewDHcYsGTrAyobZF0JOfGco3fri+2P23W4q/f/+npU9/MuXGU+zAWmCAjj906FB51B6EIIOQGykClMlqw4YNgzf3kRLTHyvI86mnniruu+++0qf3IHYfeOCB4vHHHy99xoZu2xPxjbWo7ngw/QEPVhWXvjF9DXOXx3X/QH8hhsdyfh8Nmjruvn3mz4sfXDxczLrt94udS79X+vYPsV96uo3hrrvuStaFN954o/QZsIwyseJyC2kM05IRT6aKh/+7775b7Ny5M31HTMdl9dw6SrjCIKZP3FaQp+KpHIL0dS55Uzalq7zj+biIyhTDSOP1118vXnzxxeRHuorHpyCezovlUjlkBcflT/TyxxG/DrEeeXvFsDyvWM4YRhrxPOJFYv/Iibp9Z4wxxpje8rNrP05C9+Mf+0TxlYe/Xvr2L6O6ZxcxhnjDKoh78MEHhwjHs2fPDoaxlJGD/913350sxXxHTEcWLVpUvPzyzX0kr7766uCLLOQd05dfFYhplmeJN3Xq1CSwW6Ew4mKVpR6czzIQfk888cQQcSZLN2F8Uobnn3++eOihh1Jc/KssyKSxePHiFI6jLrH8lOPatWuD6VIGwbkqD2G7du0qQ1qT14O2jiKZB5iqvBClWMEJow1jGMQ0QXVABKt/CAd9dtN3xhhjjOkt3z03oBvmz1hSfGbq59P3fqanYhdhy2Z09scAQlS/YQePPfZYsmjC7bffngRbtGR2C0sfMY1z584V999//+B3lusFwhi/HEQXglqCEwHLcTuiMH/rrbeSwJYQf+SRRwbriFgkLS3R8Fln2wL1IY0Yl7rk5Vc5lL7a4Zlnnhkszx133NFWvIu8HqQdRXirvGI8+hRinyLolSbiHYEOH3zwQXr4AcJpp48++igd1+07Y4wx/QtLzJpTzMSBF9LeeP+14raP31l8de43S9/+pidiF+sdy82yYkr8ILIkgECiBzHEd+JyDudGgdQNWEix6HI+AlEXDnmrXLjc4hjpJG7bgXhD4CsfvWADH3744bDSRvTl59UVrYDIVnniD4W3g3pg1e4W2r2q7p248847k/UWSCOOlW76zhhjjDHDJ/+1hb+58kr6/OKsL6XPJtATsYto1ZJzfEqL1jqQoJXoJS7ncP5zzz2X/LpFVr+33357cAsDkHcsFw6LZ69BIJJvzAcH3QjUiKzekW6EMwJX2zLyt+dbQT1kde0G+k3trG0IdaGOEsmkoXExVn1njDHGTGbYm/ud175VbPvRxuKv/m5XOv7JL04lq+6f/MFTZaz+Z1T37LJMHX9L8KWXXhoiSOuA8GHJuxUIZiy6/KSLtjAAv3TwyisDTyftuOeee9L52p/KHtRuBOq9996bLLtVYOEmLb20RR7ae4qwayUuCaPecZ8q9UPYd0IPFLKS8qZrHVQPnU/ecc9uK6gfoh6wsNeFPcBR0MaHpLp9Z4wxxpjhw37cr/3hgLHxr3/+3eL75wd+deFz05q1vWRUxa72eWo5GiElP8RUXKbmJacqEHj61QIJsRy2MiC6omBivyuiUXngql5yIg6iC2socbBwkl5dELScH/OJL6jppS38yUP7cNnXrO0PVaKSl9hUbxz7XWP9WkF92Cer7SF1oR7xPKzl2o7Sjth2nFPX+sw4iFsVcGqHun1nTLfw74WrfiOUB1J+pqbXcD21295DWbr5l8fGtKPV+J5o8Fu7MgKZ8WfepxYXf/Tp1cVvfvvr4tTlgf+IuWjmH6fPpjDi39k1ZjjwQBB/Z5gbH9ZrRL4ZfXiAaMLvQjJueHCsWxfEAP8RKv8nAt2mUxcJXbb48KP9/Lg+P3KvfxiAMOGfC9T996vDhX8QwENppx/l73f68fdOEX78B7Nut4FV9Wmr8Q3E58XfOP6GQy+uFerMP3GoY8DpB5ryO7sbjn65uPjLfyju/+TDxV98YXj/NW8iMWq/s2tMXbDE85KaYKtKXauwMcY0BcRqt0J3OPDfxZj4RyJ0TbPhH0e8tPJsI4RujsWuGRfYthK3MbBlwy+hNQcsmlgt9a999S9C82OBtQh/ufhvUTnGKqUwWUvx088c4h+3AygfnVuXmE9+HukrLN+aEMNabZPQv4blM68/6en8dv+ONuYT66t/9RrhmHwpjyx6+MWyx3YirojliWVVPnk7DbfdzED7aMzQdhzH9o/9Itr1KahfY9/pGhMxj1b9EvuOPClf1TXH+XHc811hEMdHzCsvE+RpGdMLLHbNuMB+YL2cJmeaBf9znqVKLSMxqcVjiSIEFBMoL1MSxnYWlnUjLNcSxuS+Z8+eNEliqdILmIRpKwCTpfIhPP5mcydOnDgxWIZ4niZownD8T32Vn4l5zZo1yZ/yUe9csLJcSxjwGZee2bu/cOHCdP66deuGiIEI/ljlVAb5dYIlbeqDyOY8WRERI7Szyq1fbqE+lF/5ECcKF1D7qp3iMX0l2rWbqYbxMHPmzNRe27ZtS2Mrp1WfAuOPdsYfqoQj14/i5OcL9ZPikGera64TjBFd39RN1wLXBXWIY4Lre968eeWRMb3BYtcYMyog3LQnD8GUH1++fDl9P3PmTPqVFoUxoTIZRsGImALEHnGvXLmSjqtg4lZa06ZNS5+5+GyFJn3KADoPYRBFHC9YIoxBIgC0RHz16tX0WQfqo/NXr17dcl8tImDjxo3l0cADAOUaLogqpUe5JVz4RZpYV+IQN6J2mjt3bvqMxxIy0K7dTDWIP+255Z8yxfasAwJZ45D2vnTpUvoe4brIr7Gc6dOn39LvwwEhS510TVI3jgVl1PWNCI9xjekVFrvGmHFBkzCil0lVy5z5sma3MIErnTipjgSEAdZnpYv1LCJ/3GggUSIRAxIsw6EqPUGaekgAxWknjHIUt1O7mfGBPpVlmH6p6lsEJ8K5F+M637YTQdBLVO/bty+JX2N6jcWuMWZcmTFjRrJuarlUrkqI1QGrMRM5aQxXDOYgCrQMKydrJkv8iAL5jwZVghPr8XDFfDsBS5rRMq04w+mPdu1mxhdWE+gPrhWumSqwwhKH8Z1vZekVjCuufyzArAR4C4MZDSx2jTHjCkvfWHaqhFcnqrYpIHBZgoX4T21GAmIAq1MVbDtAsEO7/agSi91scYggCHbs2FEeDfyGt6xgSlv553t5aY9c+OfpScxQ1/ifF4nDFpTh0K7dzMio6tOxoOqaYx8u218A/2jB1/XNFgVgbObl5ufStEfeWxjMaGCxa4wZVxBqWP+0pIprt+wZkVWIcyXWsFRp6ZzfsB2u9TOCNZJJXOXDSViy35CJGj/2p1KeViAaKVvd+kXYU4vlS/lTd+3tBOqtciA+Yr0REGpfCWHSYx+w0tPeWlle5U+9h2uNbdduZmRU9WldeGlN/aEXDHNIU3EQrxoDVdecxiFx8WcsCuLHa5KxmV8jbGUAb2Ewo4X/qYQxk5Cm/FMJYyLMXR7X/Ql9x0NvP1p2Pe4mJrFfbNk1xhhjzLiBtR+LsLcwmNHCYtcYY4wx4wY/R9fqJTljekHLbQzGGGOMMcb0K5K4lWLXGGOMMcaYJuBtDMYYY4wxprFY7BpjjDHGmIZSFP8/5a8aXI7Y/LUAAAAASUVORK5CYII=\"\u003e\u003c/p\u003e\n\u003cdiv\u003e\n "},{"header":"DISCUSSION","content":"\u003cp\u003eThis pilot trial showed the feasibility of delivering and evaluating LvL UP using a SMART design. Key feasibility indicators, including rapid recruitment, high buddy uptake, strong retention, low rates of missing data, and an acceptable non-responder rate, support operational viability. Positive trends were observed in mental health and sleep duration, particularly with Strategy #1. Five of the six pre-specified progression criteria were achieved, except for digital coaching adherence. These findings provide valuable guidance for future definitive trials and contribute to the emerging evidence base on adaptive, holistic mHealth interventions.\u003c/p\u003e\u003cp\u003eRecruitment, often a major barrier in mHealth interventions, was completed within six weeks.\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e Broad inclusion criteria allowed over 85% of screened individuals to qualify, while an automated portal streamlined screening and scheduling and reduced administrative burden.\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e Telegram reached research-motivated individuals and generated over 80 sign-ups within six hours. Consistent with prior findings, targeted email outreach, though smaller in scale, had the highest conversion rates.\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e Finally, a tiered reimbursement structure may have further incentivised timely enrolment. These lessons have informed a hybrid recruitment strategy for the definitive trial.\u003c/p\u003e\u003cp\u003eFor participant retention, the rate exceeded 90% with low rates of missing data, which is higher than that of other mHealth interventions.\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e This is noteworthy given the complexity of the intervention. Structured retention strategies may have driven this performance, including flexible survey completion windows, multi-channel reminders, direct communication, and standardised protocols (e.g., staff rehearsal, manuals, and real-time data monitoring and validation).\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e These practices highlight how structured, participant-centred logistics can improve short-term retention and data completion rates.\u003c/p\u003e\u003cp\u003eIn SMART designs, non-responder rates between 20% and 80% are considered optimal.\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e Our observed rate of 46.3% was consistent with the sample size assumption. The non-responder definition, based on digital coaching session completion and usefulness rating, was practical but may have misclassified participants who engaged passively or showed improvements despite low app usage. For the definitive trial, we will incorporate interim assessments of mental well-being to refine the response classification.\u003c/p\u003e\u003cp\u003eApp engagement followed a typical mHealth pattern: early attrition and a subsequent plateau at 30\u0026ndash;40% daily active use.\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e Despite engagement strategies, digital coaching adherence was below the target. Minimal onboarding guidance (e.g., reliance on printed handouts) and technical issues may have impaired the navigation of multiple app components and created additional usability friction.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Such challenges are common in early-stage mHealth interventions and are associated with disengagement. These findings highlight the need to improve the user experience.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e In contrast, MI-informed session adherence exceeded that of digital coaching, aligning with the literature showing greater user engagement with human-delivered support.\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e However, the fixed three-week schedule may have limited flexibility. The definitive trial will expand to six sessions over six months to allow greater personalisation and sustained engagement.\u003c/p\u003e\u003cp\u003eExploratory analyses showed improvements in mental health and sleep outcomes among intervention participants. These estimates were consistent with prior studies on the synergistic effects of holistic mHealth interventions.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Interestingly, Strategy #1 showed stronger trends than Strategy #2, possibly due to differences in gender composition between Groups B and C, or insufficient intensity of MI-informed sessions.\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e As this was a pilot trial, these findings are preliminary and should be interpreted with caution.\u003c/p\u003e\u003cp\u003eLvL UP contributes to the evolving digital health landscape by operationalising a holistic mHealth coaching model within an adaptive SMART framework. A key strength was the end-to-end implementation of the SMART design, including real-time escalation from digital to human-delivered support. The use of a control app that mirrored the intervention interface helped minimise expectation bias and strengthen internal validity. Additionally, the application of a validated, precision-based sample size calculation ensured efficient use of resources while providing a strong foundation for a future definitive trial. Beyond informing the next phase, these findings have broader implications for developing scalable, person-centred mHealth interventions aligned with public health goals for preventive care.\u003c/p\u003e\u003cp\u003eLimitations\u003c/p\u003e\u003cp\u003eThis trial has several limitations. The sample was relatively young, highly educated, and primarily recruited from a Telegram research group, which may limit generalisability. While ethically approved, the substantial compensation may have inflated short-term enrolment and retention. Lastly, allocation concealment via sealed opaque envelopes introduces a risk of mis-ordering or unintentional unblinding; future trials will adopt a centralised digital randomisation to mitigate this.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis pilot trial demonstrated the feasibility of implementing LvL UP 3.0, a holistic mHealth lifestyle coaching intervention with adaptive support, within a SMART design. While improvements are needed to improve digital engagement, the findings provide strong operational guidance and a foundation for a larger-scale trial to evaluate long-term effectiveness and cost-effectiveness.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Institutional Review Boards of NUS (NUS-IRB-2023-421), ETH Zurich (EK-2024-N-13-A), and Nanyang Technological University (NTU-IRB-2024-305).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Consent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Availability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to privacy or other restrictions but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Competing interests\u003c/p\u003e\n\u003cp\u003eJ.L.M., F.v.W., E.F., and T.K. are affiliated with the Centre for Digital Health Interventions, funded in part by the Swiss health insurer CSS, the Swiss digital health investor MTIP, and the Austrian healthcare provider Mavie Next. E.F. is, and T.K. was, co-founder of Pathmate Technologies, a university spin-off company. However, Pathmate Technologies, CSS, MTIP, and Mavie Next were not involved in the design, interpretation, or analysis of the study, nor in the writing of the manuscript. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Funding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis project was conducted as part of the Future Health Technologies programme (Campus for Research Excellence and Technological Enterprise), which is jointly funded by the National Research Foundation, Prime Minister\u0026rsquo;s Office, Singapore and ETH Zurich, Switzerland. The funders of the study had no role in conceptualization, study design, data collection, data analysis, data interpretation, decision to publish, or writing of the report.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eJ.L.M., O.C., S.Z., and A.I.J. conceptualised the trial under the supervision of F.M.R., T.K., K.G., E.S.T., and R.M.v.D. O.S., S.Z., J.L.M., and A.I.J. designed the study with guidance from X.Y. and B.C. J.L.M., O.C., S.Z., A.I.J., S.Y.X.T., F.v.W., E.F., T.K., E.S.T., F.M.R., and R.M.v.D. contributed to the development of the LvL UP intervention. A.I.J., A.S., and S.N. maintained the LvL UP and control apps during the trial. X.Y. and B.C. performed the sample size calculations. S.Z. organised motivational interviewing training, screened participants (with assistance from A.I.J.), and generated the randomisation sequences. R.R.M. and R.K.W.S. delivered the MI-informed sessions under the supervision of K.G. S.Z., S.Y.X.T., A.I.J., O.C., and J.L.M. recruited participants, prepared study documents, and monitored data collection. A.I.J. acquired and cleaned the dataset. S.Z. conducted the statistical analyses under the guidance of X.Y. and B.C. and drafted the manuscript with input from all co-authors. F.v.W., E.F., T.K., F.M.R., ES.T., and R.M.v.D. secured project funding. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Acknowledgements\u003c/p\u003e\n\u003cp\u003eWe would like to thank the Singapore Population Health Studies team at the Saw Swee Hock School of Public Health, National University of Singapore, for their invaluable efforts in data collection and study coordination. We would also like to thank Prabhakaran Santhanam, Chang Siang Lim, Roman Keller, Aishah Alattas, Alicia Salamanca-Sanabia, Bea Franziska Frese, and Xiaowen Lin for contributing to the development of earlier versions of the LvL UP app. We are grateful to all participants of the LvL UP pilot trial and their dedicated support buddies, whose time, engagement, and feedback made this study possible.\u0026nbsp;\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKong D, Fu J, Hong Y, Liu S, Luo Y. The Application and Prospect of Mobile Health (mHealth) in Health Service for Older People Living Alone in Community: A Narrative Review. \u003cem\u003eIranian Journal of Public Health\u003c/em\u003e. 2022;51(4):724-732. doi:https://doi.org/10.18502/ijph.v51i4.9233\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. \u003cem\u003eWorld Report on Ageing and Health\u003c/em\u003e. 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Attrition in Conversational Agent\u0026ndash;Delivered Mental Health Interventions: Systematic Review and Meta-Analysis. \u003cem\u003eJournal of Medical Internet Research\u003c/em\u003e. 2024;26(1):e48168. doi:10.2196/48168\u003c/li\u003e\n\u003cli\u003eNahum-Shani I, Qian M, Almirall D, et al. Experimental design and primary data analysis methods for comparing adaptive interventions. \u003cem\u003ePsychol Methods\u003c/em\u003e. 2012;17(4):457-477. doi:10.1037/a0029372\u003c/li\u003e\n\u003cli\u003eBremer W, Sarker A. Recruitment and retention in mobile application-based intervention studies: a critical synopsis of challenges and opportunities. \u003cem\u003eInformatics for Health and Social Care\u003c/em\u003e. 2023;48(2):139-152. doi:10.1080/17538157.2022.2082297\u003c/li\u003e\n\u003cli\u003ePfammatter AF, Mitsos A, Wang S, Hood SH, Spring B. Evaluating and improving recruitment and retention in an mHealth clinical trial: an example of iterating methods during a trial. \u003cem\u003eMhealth\u003c/em\u003e. 2017;3:49. doi:10.21037/mhealth.2017.09.02\u003c/li\u003e\n\u003cli\u003eLane TS, Armin J, Gordon JS. Online Recruitment Methods for Web-Based and Mobile Health Studies: A Review of the Literature. \u003cem\u003eJournal of Medical Internet Research\u003c/em\u003e. 2015;17(7):e4359. doi:10.2196/jmir.4359\u003c/li\u003e\n\u003cli\u003eFrampton GK, Shepherd J, Pickett K, Griffiths G, Wyatt JC. Digital tools for the recruitment and retention of participants in randomised controlled trials: a systematic map. \u003cem\u003eTrials\u003c/em\u003e. 2020;21(1):1-23. doi:10.1186/s13063-020-04358-3\u003c/li\u003e\n\u003cli\u003eGilfoyle M, Melro C, Koskinas E, Salsberg J. Recruitment of patients, carers and members of the public to advisory boards, groups and panels in public and patient involved health research: a scoping review. \u003cem\u003eBMJ Open\u003c/em\u003e. 2023;13(10):e072918. doi:10.1136/bmjopen-2023-072918\u003c/li\u003e\n\u003cli\u003ePoongothai S, Anjana RM, Aarthy R, et al. Strategies for participant retention in long term clinical trials: A participant \u0026ndash;centric approaches. \u003cem\u003ePerspect Clin Res\u003c/em\u003e. 2023;14(1):3-9. doi:10.4103/picr.picr_161_21\u003c/li\u003e\n\u003cli\u003eAmagai S, Pila S, Kaat AJ, Nowinski CJ, Gershon RC. Challenges in Participant Engagement and Retention Using Mobile Health Apps: Literature Review. \u003cem\u003eJ Med Internet Res\u003c/em\u003e. 2022;24(4):e35120. doi:10.2196/35120\u003c/li\u003e\n\u003cli\u003eKim H, Ionides E, Almirall D. A sample size calculator for SMART pilot studies. \u003cem\u003eSIAM Undergrad Res Online\u003c/em\u003e. 2016;9:229-250. doi:10.1137/15s014058\u003c/li\u003e\n\u003cli\u003eDeniz-Garcia A, Fabelo H, Rodriguez-Almeida AJ, et al. Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges. \u003cem\u003eJ Med Internet Res\u003c/em\u003e. 2023;25:e44030. doi:10.2196/44030\u003c/li\u003e\n\u003cli\u003eEaton C, Vallejo N, McDonald X, et al. User Engagement With mHealth Interventions to Promote Treatment Adherence and Self-Management in People With Chronic Health Conditions: Systematic Review. \u003cem\u003eJ Med Internet Res\u003c/em\u003e. 2024;26:e50508. doi:10.2196/50508\u003c/li\u003e\n\u003cli\u003eFleming T, Bavin L, Lucassen M, Stasiak K, Hopkins S, Merry S. Beyond the Trial: Systematic Review of Real-World Uptake and Engagement With Digital Self-Help Interventions for Depression, Low Mood, or Anxiety. \u003cem\u003eJournal of Medical Internet Research\u003c/em\u003e. 2018;20(6):e9275. doi:10.2196/jmir.9275\u003c/li\u003e\n\u003cli\u003eHyzy M, Bond R, Mulvenna M, et al. System Usability Scale Benchmarking for Digital Health Apps: Meta-analysis. \u003cem\u003eJMIR Mhealth Uhealth\u003c/em\u003e. 2022;10(8):e37290. doi:10.2196/37290\u003c/li\u003e\n\u003cli\u003eMair JL, Hashim J, Thai L, et al. Understanding and overcoming barriers to digital health adoption: a patient and public involvement study. \u003cem\u003eTranslational Behavioral Medicine\u003c/em\u003e. 2025;15(1):ibaf010. doi:10.1093/tbm/ibaf010\u003c/li\u003e\n\u003cli\u003eLoughnane C, Laiti J, O\u0026rsquo;Donovan R, Dunne PJ. Systematic review exploring human, AI, and hybrid health coaching in digital health interventions: trends, engagement, and lifestyle outcomes. \u003cem\u003eFront Digit Health\u003c/em\u003e. 2025;7:1536416. doi:10.3389/fdgth.2025.1536416\u003c/li\u003e\n\u003cli\u003eDamschroder LJ, Buis LR, McCant FA, et al. Effect of Adding Telephone-Based Brief Coaching to an mHealth App (Stay Strong) for Promoting Physical Activity Among Veterans: Randomized Controlled Trial. \u003cem\u003eJournal of Medical Internet Research\u003c/em\u003e. 2020;22(8):e19216. doi:10.2196/19216\u003c/li\u003e\n\u003cli\u003eAlley S, Jennings C, Plotnikoff RC, Vandelanotte C. Web-Based Video-Coaching to Assist an Automated Computer-Tailored Physical Activity Intervention for Inactive Adults: A Randomized Controlled Trial. \u003cem\u003eJ Med Internet Res\u003c/em\u003e. 2016;18(8):e223. doi:10.2196/jmir.5664\u003c/li\u003e\n\u003cli\u003eSavage MJ, Procter EL, Magistro D, et al. 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Gender differences in health: a Canadian study of the psychosocial, structural and behavioural determinants of health. \u003cem\u003eSocial Science \u0026amp; Medicine\u003c/em\u003e. 2004;58(12):2585-2600. doi:10.1016/j.socscimed.2003.09.008\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-behavioral-nutrition-and-physical-activity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbn","sideBox":"Learn more about [International Journal of Behavioral Nutrition and Physical Activity](http://ijbnpa.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ijbn/default.aspx","title":"International Journal of Behavioral Nutrition and Physical Activity","twitterHandle":"@IJBNPA","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"SMART, Adaptive Interventions, Digital Health, mHealth, Prevention","lastPublishedDoi":"10.21203/rs.3.rs-7372529/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7372529/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eMobile Health (mHealth) interventions are promising for addressing due burden of noncommunicable diseases and common mental disorders but often focus on single domains and lack adaptability. LvL UP contributes novel evidence by operationalising a holistic mHealth coaching intervention that integrates physical activity, diet, and emotional regulation, with adaptive human support.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThe eight-week trial (April\u0026ndash;July 2024) recruited adults in Singapore aged 21\u0026ndash;59 at risk of chronic conditions. Participants were randomised 2:1 to the intervention (LvL UP app with a peer supporter\u0026ndash;LvL UP Buddy) or comparison (control app with educational resources). After four weeks, non-responders (completed\u0026thinsp;\u0026lt;\u0026thinsp;6 digital coaching sessions or rated session usefulness\u0026thinsp;\u0026lt;\u0026thinsp;4/5) were re-randomised 1:1 to continue or receive three additional motivational interviewing (MI)-informed sessions with a human coach; responders remained on their original allocation. Primary outcomes included feasibility indicators: recruitment, LvL UP Buddy enrolment, non-responder rate, retention, data completion, and engagement. Secondary outcomes measured changes from baseline to eight weeks in mental well-being, psychological distress, physical activity, sleep duration, and fruit and vegetable intake. Six progression criteria were prespecified to guide advancement to a full SMART trial.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOf the 458 individuals screened, 394 were eligible, and 123 were enrolled (82 interventions; 41 controls). Most intervention participants (95.1%) were paired with a LvL UP Buddy. Thirty-eight participants (46.3%) were non-responders; of those receiving MI sessions, 52.6% (10/19) completed all three. Eight-week retention was high (91.5% intervention; 92.7% control), with 12.2% missing data. Positive trends were observed in mental well-being (2.12, 95% CI [-0.58, 4.82]), psychological distress (-0.94 [-2.08, 0.20]), and sleep duration (0.49 hours/week [0.17, 0.82]). The study met five of six prespecified progression criteria: recruiting\u0026thinsp;\u0026ge;\u0026thinsp;60 participants within six weeks, achieving\u0026thinsp;\u0026ge;\u0026thinsp;75% retention, maintaining\u0026thinsp;\u0026le;\u0026thinsp;20% missing data, obtaining a 40\u0026ndash;60% non-responder rate, and showing a positive change in \u0026ge;\u0026thinsp;1 health-related outcome. The digital coaching session adherence fell below the target (39.5% vs 70%).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eLvL UP was feasible for delivery and evaluation using a SMART design. The results provide strong operational guidance and a solid foundation for the refinement and implementation of a fully powered trial.\u003c/p\u003e\u003ch2\u003eRegistry:\u003c/h2\u003e\u003cp\u003eClinicalTrials.gov, TRN: NCT06360029, Registration date: 7 April 2024\u003c/p\u003e","manuscriptTitle":"Pilot Sequential Multiple Assignment Randomised Trial of LvL UP: an Adaptive Holistic mHealth Coaching Intervention Integrating Physical Activity, Diet, and Mental Health","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 06:40:14","doi":"10.21203/rs.3.rs-7372529/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-26T17:14:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-25T01:18:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78491711336017604011218976934680345199","date":"2025-10-19T14:06:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-15T19:49:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19153034554350876795617198759320910461","date":"2025-08-28T14:20:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206764898600152260878270489161345766778","date":"2025-08-26T22:23:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-26T13:48:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-20T10:32:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-20T10:31:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Behavioral Nutrition and Physical Activity","date":"2025-08-14T09:56:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-behavioral-nutrition-and-physical-activity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbn","sideBox":"Learn more about [International Journal of Behavioral Nutrition and Physical Activity](http://ijbnpa.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ijbn/default.aspx","title":"International Journal of Behavioral Nutrition and Physical Activity","twitterHandle":"@IJBNPA","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"517d520b-96c5-4c67-a3cf-f7f435bcbd15","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T16:06:48+00:00","versionOfRecord":{"articleIdentity":"rs-7372529","link":"https://doi.org/10.1186/s12966-025-01869-7","journal":{"identity":"international-journal-of-behavioral-nutrition-and-physical-activity","isVorOnly":false,"title":"International Journal of Behavioral Nutrition and Physical Activity"},"publishedOn":"2026-01-03 15:57:31","publishedOnDateReadable":"January 3rd, 2026"},"versionCreatedAt":"2025-09-03 06:40:14","video":"","vorDoi":"10.1186/s12966-025-01869-7","vorDoiUrl":"https://doi.org/10.1186/s12966-025-01869-7","workflowStages":[]},"version":"v1","identity":"rs-7372529","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7372529","identity":"rs-7372529","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}