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| Name | Class |
|---|---|
| ETH Zurich | OTHER |
| National University of Singapore | OTHER |
| Nanyang Technological University | OTHER |
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Non-communicable diseases (NCDs), such as cardiovascular disease, diabetes, or cancer, and common mental disorders (CMDs), such as depression or anxiety, represent the primary causes of death and disability worldwide, causing major health and financial burdens. Lifestyle behaviours, including physical activity, diet, stress and emotional regulation, tobacco smoking, alcohol consumption, and sleep are important modifiable risk factors associated with the prevention and management of both NCDs and CMDs.
LvL UP is a mHealth intervention aimed at preventing NCDs and CMDs in adults from multi-ethnic Southeast Asian populations. Building upon leading evidence- and theory-based frameworks in the areas of mental health and behaviour change, a multidisciplinary team of researchers developed LvL UP as a holistic intervention centred around three core pillars: Move More, Eat Well, Stress Less.
This study is aimed at (i) evaluating the effectiveness and cost-effectiveness of LvL UP, an mHealth lifestyle intervention for the prevention of NCDs and CMDs, and (ii) establishing the optimal blended approach in LvL UP that balances effective personalized lifestyle support with scalability.
Participants will:
Participants will be randomly allocated to groups 1 or 2 (LvL UP or comparison) following a 2:1 ratio favoring the LvL UP group. At month 1 (decision point), participants from the LvL UP group will be classified as responders or non-responders based on pre-specified criteria. Non-responder participants will be re-randomized with equal probability (1:1) to one of the two second-line conditions: (i) continuing with the initial intervention (LvL UP) or (ii) additional MI support sessions (LvL UP + MI). The LvL UP pilot study results (NCT06360029) will be used to inform the tailoring variable(s) for the trial (i.e., to define response / non-response at month 1). Engagement and app evaluation variables (e.g., number of app components completed over the first 4 weeks, net promoter score), preliminary intervention effects (e.g., initial positive response), or a combination of the two will be used.
Participants will also take part in a process evaluation informed by the UK Medical Research Council guidelines to explore implementation (process, fidelity, dose, adaptations, reach), mechanisms of action (participant experience and response to intervention, mediators, unexpected pathways and consequences), and contextual factors that may affect implementation and intervention outcomes. Methods will entail qualitative and quantitative approaches, including surveys, interviews, web-based and app-based analytic data, and direct observation. In addition, other measures have been added to the research protocol to address the following exploratory aims (all relevant measures are described in the outcomes section):
A. To identify the most cost-effective intervention condition from the societal perspective (e.g., self-reported sickness absence).
B. To explore time-varying and baseline moderators on intervention outcomes (e.g., sociodemographic variables, personality).
C. To investigate behavior maintenance by assessing the intervention outcomes at six months of follow-up (i.e., 12 months after baseline).
D. To investigate which smartphone sensor data might be effective in predicting user state of receptivity to LvL UP notifications (receptivity: the likelihood of engaging with a LvL UP notification, operationalized as higher response rate and decreased response time).
E. To investigate which signals (e.g., audio signals, usage patterns) are most useful for predicting the presence of subclinical depression, anxiety, stress and/or other mental health conditions.
F. To validate a 7-day modified Food Frequency Questionnaire based on My Healthy Plate recommendations by Singapore Health Promotion Board (HPB).
G. To develop and evaluate a supervised non-responder / dropout prediction model using retrospective user app engagement data from the trial.
H. To explore views on climate and climate change and assess their perceived impact (including impact on health behaviours).
The study was powered to compare the baseline to 6 months change in mental well-being between LvL UP (A+B) and the comparison condition and between LvL UP + adaptive MI (A+C) versus the comparison condition (primary objective). For both comparisons, we assume the effect size (Cohen's d) for well-being to be 0.3 and the responder rate to stage 1 intervention (LvL UP) to be 50%. The effect size was estimated by aggregating data from five meta-analyses on mHealth interventions and their reported impact on participant's mental well-being. To obtain a marginal power of at least 80% with a two-tailed Type I error rate of 5% for each outcome, 458 participants will be required; we further buffer for 73% retention following a recent meta-analysis estimate for digital health interventions lasting more than 8 weeks, rounding the number for a final sample size of 650. Therefore, 217 participants will be randomized to the comparison condition, and 433 participants will be randomized to start with stage 1 intervention (LvL UP), of which we estimate about 217 participants (50% non-responders) will be re-randomized to either continue with LvL UP or receive MI at stage 2 (LvL UP + adaptive MI). Participants randomized to group 1 (LvL UP) are asked to nominate a LvL UP Buddy, which will take part in the process evaluation. The total sample size is thus 1,073 (650 as main participants and 423 as LvL UP Buddies). The above sample size calculations might be refined based on the LvL UP pilot study results.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| LvL UP | Experimental | Downloading and using the LvL UP app |
|
| LvL UP + MI | Experimental | Downloading and using the LvL UP app + taking part in motivational interviewing support sessions |
|
| Comparison | Active Comparator | Receiving healthy lifestyle and mental well-being resources from Singapore's Health Promotion Board |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| LvL UP | Behavioral | The LvL UP app includes four lifestyle intervention components centred around three core pillars, Move More (physical activity), Eat Well (healthy nutrition), and Stress Less (mental well-being), as follows: (i) conversational agent-delivered health literacy coaching sessions, (ii) daily "Life Hacks" (healthy habit suggestions), (iii) therapeutic Tools including step-based activity tracker, food diary, and journal and (iv) gamified slow-paced breathing training (Breeze). These components are delivered using an innovative engagement approach that combines storytelling, MI, feedback on progress, just-in-time adaptive notifications and gamification. As part of the LvL UP app onboarding, participants are asked to nominate a 'LvL UP Buddy' (e.g., a friend, family member, or spouse) to provide additional support. Buddies are expected to complete different tasks, such as sending messages of encouragement or engaging in intervention-related activities together with the participant. |
| Measure | Description | Time Frame |
|---|---|---|
| Mental well-being (Warwick-Edinburgh Mental Well-being Scale) | Assessed using the Warwick-Edinburgh Mental Well-being Scale (WEMWBS-14). The total score ranges from 14 to 70, with higher values indicating higher mental well-being. | Baseline, month 1, month 3, month 6, month 9, month 12 |
| Measure | Description | Time Frame |
|---|---|---|
| Subjective well-being (World Health Organization Well-Being Index) | Assessed using the World Health Organization Well-Being Index (WHO-5). The total score ranges from 0 to 25, with higher values indicating higher well-being. | Baseline, month 1, month 3, month 6, month 9, month 12 |
| Mental health (depression - Patient Health Questionnaire-9) |
| Measure | Description | Time Frame |
|---|---|---|
| LvL UP app: Access to smartphone sensors (physical activity) | Sensor: Accelerometer. Data type: Categorical (sitting, standing, walking, in a vehicle, running or bicycling) . | Continuously throughout the study period (i.e., assessed every day during 12 months) |
| LvL UP app: Access to smartphone sensors (WiFi connection status) |
Inclusion Criteria:
(i) aged 21 to 59 years,
(ii) Singapore citizens, permanent residents, or foreigners residing in Singapore on long-term passes with at least 1 year of validity remaining,
(iii) planning to reside in Singapore for the duration of the study (eligible participants can undertake sporadic trips overseas, as long as these do not conflict with in-person study visits),
(iv) proficient in English (the LvL UP app is currently only available in English),
(v) owners of a smartphone (minimum requirements: iOS version 12.4 and Android version 8) with internet access,
(vi) able to provide informed consent, and
(vii) identified as 'at risk' of developing non-communicable diseases and/or common mental disorders.
Exclusion Criteria:
(i) diagnosed with one of the following chronic diseases: heart disease (e.g., heart attack and stroke), cancer, chronic respiratory diseases (e.g., chronic obstructed pulmonary disease and asthma), diabetes (type 1 or type 2), or chronic kidney disease.
(ii) diagnosed with one of the following mental disorders: major depressive disorders (depression), bipolar, eating disorders, post-traumatic stress disorder (PTSD), anxiety disorders, severe personality disorder, substance use disorders, or other mental health conditions diagnosed by a mental health professional.
(iii) pregnant, (iv) currently taking medications prescribed for high blood pressure or high blood glucose.
(v) past or current LvL UP users, including participants from the LvL UP pilot trial (NCT06360029) or HAPPY trial (Harnessing Human Potential and Improving Health Span in Women and their Children Trial).
(vi) already participating or planning to participate in the LvL UP trial as a 'LvL UP Buddy'.
(vii) enrolled or planning to enroll in another lifestyle intervention research study before or during the study period.
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| Name | Affiliation | Role |
|---|---|---|
| Falk Mueller-Riemenschneider, Professor | National University of Singapore | Principal Investigator |
| Tobias Kowatsch, Professor | ETH Zurich | Principal Investigator |
| Konstantina Griva, Professor | Nanyang Technological University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Saw Swee Hock School of Public Health | Singapore | 117549 | Singapore | |||
| Singapore ETH Center |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 37234382 | Background | Castro O, Mair JL, Salamanca-Sanabria A, Alattas A, Keller R, Zheng S, Jabir A, Lin X, Frese BF, Lim CS, Santhanam P, van Dam RM, Car J, Lee J, Tai ES, Fleisch E, von Wangenheim F, Tudor Car L, Muller-Riemenschneider F, Kowatsch T. Development of "LvL UP 1.0": a smartphone-based, conversational agent-delivered holistic lifestyle intervention for the prevention of non-communicable diseases and common mental disorders. Front Digit Health. 2023 May 10;5:1039171. doi: 10.3389/fdgth.2023.1039171. eCollection 2023. | |
| 38412023 |
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| ID | Term |
|---|---|
| D015438 | Health Behavior |
| D000073296 | Noncommunicable Diseases |
| D040242 | Risk Reduction Behavior |
| D009043 | Motor Activity |
| D000092862 | Psychological Well-Being |
| ID | Term |
|---|---|
| D001519 | Behavior |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
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| LvL UP + MI | Behavioral | After 4 weeks, participants categorized as non-responders from the LvL UP group will be re-randomized into second-stage conditions: (i) continuing with the initial intervention (LvL UP) or (ii) additional human-delivered motivational interviewing (MI) support sessions (LvL UP + MI). The MI-informed sessions for non-responders will consist of four sessions delivered via WhatsApp, lasting between 30 and 40 minutes. The content of support will include various MI-based strategies such as use of ask-offer-ask framework and strategic use of communication skills (open-ended questions, reflections, affirmations and summaries) as per four MI processes:
|
|
| Comparison | Behavioral | Participants randomised to the comparison condition will receive a study pack including physical activity, diet and mental well-being content extracted from existing Health Promotion Board (HPB) resources. Established in 2001, the HPB (https://hpb.gov.sg/) is a government organisation under the Ministry of Health committed to promoting healthy living in Singapore. HPB's organises health promotion and disease prevention programmes covering various health domains. HPB-developed resources were selected as the comparator because they are the 'go-to', nation-wide health resources in Singapore which cover LvL UP's domains. |
|
Assessed using the Patient Health Questionnaire-9. The total score ranges from 0 to 27. Scores of 0-4, 5-9, 10-14, 15-19, 20-27 are the ranges for none, mild, moderate, moderately severe and severe depression, respectively. |
| Baseline, month 1, month 3, month 6, month 9, month 12 |
| Mental health (stress - Kessler Psychological Distress Scale) | Assessed using the Kessler Psychological Distress Scale (K6). The total score ranges from 10 to 50, with higher values indicating higher levels of psychological distress. | Baseline, month 1, month 3, month 6, month 9, month 12 |
| Health-related quality of life (European Quality of Life 5 Dimensions 5 Level Version) | Assessed using the European Quality of Life 5 Dimensions 5 Level Version (EQ-5D-5L). The EQ-5D-5L descriptive system of 5 health dimensions (Mobility, Self-care, Usual activities, Pain / discomfort, Anxiety / depression) includes 5 response categories of no problem, slight problems, moderate problems, severe problems, and extreme problems. Health states are scored to give the EQ-5D-5L index using a scoring algorithm from a value set derived from valuation tasks undertaken with general population samples. | Baseline, month 3, month 6, month 9, month 12 |
| Health behaviours (physical activity - International Physical Activity Questionnaire) | Assessed using the International Physical Activity Questionnaire (IPAQ-long). Results can be reported in categories (low activity levels, moderate activity levels or high activity levels) or as a continuous variable (MET minutes a week). | Baseline, month 1, month 3, month 6, month 9, month 12 |
| Health behaviours (steps - smartphone-based) | Smartphone-based steps | Throughout the study period (i.e., assessed every day during 12 months) |
| Health behaviours (sleep - Pittsburgh Sleep Quality Index) | Assessed using the Pittsburgh Sleep Quality Index (PSQI). The total score ranges from 0 to 27, with higher scores indicating more acute sleep disturbances. | Baseline, month 6, month 12 |
| Health behaviours (smoking and alcohol consumption - ad hoc questionnaire) | Assessed using a short quantity-frequency survey on smoking and alcohol consumption used in previous epidemiological studies | Baseline, month 6, month 12 |
| Anthropometry (body weight) | During study visits research staff will assess participants' body weight in kilograms. Weight and height will be combined to report Body Mass Index (BMI) in kg/m2. | Baseline, month 6 |
| Anthropometry (body height) | During study visits research staff will assess participants' body height in meters. Weight and height will be combined to report Body Mass Index (BMI) in kg/m2. | Baseline, month 6 |
| Anthropometry (waist circumference) | During study visits research staff will assess participants' waist circumference in meters. | Baseline, month 6 |
| Anthropometry (hip circumference) | During study visits research staff will assess participants' hip circumference in meters. | Baseline, month 6 |
| Anthropometry (body composition) | To assess body composition we will use a Tanita MC-780 scale using bioelectrical impedance to register resistance, reactance, lean body mass (kg), and the percentage of body fat. | Baseline, month 6 |
| Anthropometry (self-reported height and weight) | Self-reported height (kilograms) and weight (meters) via online survey. Weight and height will be combined to report Body Mass Index (BMI) in kg/m2. | month 1, month 3, month 9, month 12 |
| Resting blood pressure | During study visits research staff will assess diastolic and systolic blood pressure using an automated blood pressure monitor (Dinamap - Carescape V100, GE Pacific). | Baseline, month 6 |
| Health behaviours (diet quality 1 - diet screener) | Assessed using a 37-item diet screener developed and validated in Singapore | Baseline, month 1, month 3, month 6, month 9, month 12 |
| Health behaviours (diet quality 2 - ad hoc Food Frequency Questionnaire) | Modified 7-item Food Frequency Questionnaire (ad hoc) assessing diet quality | Baseline, month 1, month 3, month 6, month 9, month 12 |
| Blood metabolic profile (concentration of albumin, creatinine, DHEA-S, HbA1c, hsCRP, TC, HDL, TG, LDL-calculated, random glucose, and full blood count) | During study visits research staff will collect a non-fasting venous blood sample to assess the following blood markers: albumin, creatinine, DHEA-S, HbA1c, hsCRP, lipid panel (TC, HDL, TG and LDL-calculated), random glucose, and full blood count. | Baseline, month 6 |
| Blood metabolic profile (food intake and health status) | Immediately before the blood drawing procedure participants will be asked about their last food intake and whether they are feeling sick (or have been sick in the past few days), as these have an impact in the blood test results. | Baseline, month 6 |
| Self-reported health status | Assessed using the following item: "Would you say your health in general is..." (Excellent / Very good / Fair / Poor) | Baseline, month 3, month 6, month 9, month 12 |
| Change in health status | Assessed using the following item: "Compared with 3 months ago, would you say your health is now..." (Better / Worse / About the same) | Baseline, month 3, month 6, month 9, month 12 |
Sensor: WiFi. Data type: Categorical (connected/disconnected) and String (name of network). |
| Continuously throughout the study period (i.e., assessed every day during 12 months) |
| LvL UP app: Access to smartphone sensors (WiFi networks in range) | Sensor: WiFi. Data type: String (name and device IDs of near-by WiFi Access Points). | Continuously throughout the study period (i.e., assessed every day during 12 months) |
| LvL UP app: Access to smartphone sensors (bluetooth scan) | Sensor: Bluetooth. Data type: String (name and device IDs of near-by devices). | Continuously throughout the study period (i.e., assessed every day during 12 months) |
| LvL UP app: Access to smartphone sensors (battery % level and charging status) | Sensor: Battery Status. Data type: Float representing battery level and Categorical (charging/discharging/none). | Continuously throughout the study period (i.e., assessed every day during 12 months) |
| LvL UP app: Access to smartphone sensors (screen on/off) | Sensor: Screen Events. Data type: Binary (on/off). | Continuously throughout the study period (i.e., assessed every day during 12 months) |
| LvL UP app: Access to smartphone sensors (call and SMS metadata) | Sensor: Call/SMS logs. Data type: Strings (containing hash of number), time, duration of call / length of text. | Continuously throughout the study period (i.e., assessed every day during 12 months) |
| LvL UP app: Access to smartphone sensors (app usage & app name from notifications and action) | Sensor: App Usage. Data type: String/Categorical (social, entertainment, communication, etc). | Continuously throughout the study period (i.e., assessed every day during 12 months) |
| LvL UP app: Access to smartphone sensors (step count) | Sensor: Pedometer. Data type: Integer. | Continuously throughout the study period (i.e., assessed every day during 12 months) |
| LvL UP app: Access to smartphone sensors (brightness) | Sensor: Screen Brightness. Data type: Integer. | Continuously throughout the study period (i.e., assessed every day during 12 months) |
| LvL UP app: Access to smartphone sensors (breeze audio) | Sensor: Audio Mic. Data type: Audio file during Breeze usage (slow-paced breathing tool). | Continuously throughout the study period (i.e., assessed every day during 12 months) |
| LvL UP app: Access to Apple Healthkit & Google Fit data | Including the following data types: activity, hearing, vital signs, nutrition, mobility, mindfulness and sleep, workouts | Continuously throughout the study period (i.e., assessed every day during 12 months) and up to 1 year before the start of the study |
| Process evaluation: Health literacy questionnaire | Knowledge of physical activity and dietary guidelines (ad hoc) | Baseline, month 6 |
| Process evaluation: Stages of change questionnaire | Readiness to change physical activity, eating, and mental wellness habits | Baseline, month 6 |
| Process evaluation: Exit survey (satisfaction and unexpected consequences) | Exit survey exploring satisfaction with the intervention and unexpected consequences of participating in the trial, to be completed after the intensive phase of the intervention (6 months) or earlier if a participant withdraws. | Month 6 |
| Process evaluation: Self-efficacy | Participants will be asked: "How confident are you that you can make changes to your lifestyle behaviours? (not at all confident / slightly confident / somewhat confident / quite confident / extremely confident) | Month 6 |
| Process evaluation (intervention group participants only): System usability scale | Scale giving a global view of subjective assessments towards the LvL UP app. The total System usability scale score is calculated as a percentage, ranging from 0 to 100. A perfect score of 100% signifies flawless usability and an exceptional user experience. | Month 1 |
| Process evaluation (intervention group participants only): Session alliance inventory | Measures the working alliance between participant and digital (LvL UP app) / MI coach. The total score ranges from 12 to 84, with higher values indicating a more positive rating of working alliance. | Month 1, month 3 |
| Process evaluation (human coaches): Behaviour Change Counselling Index | Behavioral coding system designed to measure treatment fidelity for motivational interviewing | Month 3, month 6 |
| Process evaluation (intervention group participants only): UMars | User's assessment of quality of mHealth apps, including the following domains: Engagement, Functionality, Aesthetics, Information, App quality, App subjective quality. All items are assessed on a 5-point scale (1-inadequate, 2-poor, 3-acceptable, 4-good, and 5-excellent). | Month 6 |
| Process evaluation (intervention group participants only): Interview | Process evaluation interview for a subset of participants exploring mechanisms of action, context, and implementation | Month 9 |
| Process evaluation (intervention group participants only): LvL UP Buddy support survey | Ad hoc survey to explore the extent to which the buddy offered support throughout the intervention (e.g., number of support tasks completed) | Month 3, month 6 |
| Process evaluation (LvL UP Buddy participants only): LvL UP Buddy Survey | Ad hoc survey to explore the relationship between buddies and participants (e.g., family, partner, etc) and the extent to which the buddy offered support throughout the intervention (e.g., number of support tasks completed) | Month 3, month 6 |
| Process evaluation (LvL UP Buddy participants only): LvL UP Buddy WhatsApp Check-in | Ad hoc weekly survey via WhatsApp to check whether LvL UP Buddies have completed their support tasks assigned for the week | Every week for 6 months |
| Process evaluation (Research team): Field Notes | To record any notable information that may impact the trial conduct or outcomes | Continuously throughout the study period (i.e., 12 months) |
| Exploratory aims: Sociodemographic | Age, sex, ethnicity, marital status, current employment status, occupation, completed years of education, accommodation type, and household income | Baseline |
| Exploratory aims: Use of digital health technologies | Assessed using the a questionnaire on past/current use of wearables and participation in digital health programes (ad hoc) | Baseline, month 6, month 9, month 12 |
| Exploratory aims: Personality (Big 5 Personality Questionnaire) | Assessed using the Big 5 Personality Questionnaire - short. The questionnaire measures five traits (extraversion, agreeableness, openness, conscientiousness, and neuroticism), and consists of 20 items structured as simple sentences rated in the Likert scale of 5 points, ranging from 1 (totally disagree) to 5 (totally agree). | Baseline |
| Exploratory aims: Work Engagement (Work Productivity and Activity Impairment Questionnaire) | Assessed using the Work Productivity and Activity Impairment Questionnaire (WPAI), a five-item scale. For the three first item the answer is number of hours (e.g., During the last 3 months, how many hours did you miss from work because of problems associated with your health?). For the last two items are scored on a scale ranging from 1 to 10, with 1 being 'my health did not affect my work productivity and ability to do regular daily activities at all', and 10 being 'my health affected my work productivity and ability to do regular daily activities completely'. | Baseline, month 3, month 6, month 9, month 12 |
| Exploratory aims: Healthcare utilization | Healthcare utilization in the past three months | Baseline, month 3, month 6, month 9, month 12 |
| Exploratory aims: Climate change and effects on health behaviours | Ad hoc survey assessing the participants' views on climate change and its perceived impact on health behaviours | Baseline, month 6 |
| LvL UP app: Response time | Timestamp when a message was sent by the app to the participant and the timestamp when an answer was provided by the participant | Continuously throughout the study period (i.e., assessed every day during 12 months) |
| LvL UP app: Duration of app usage | Time measure of how long an individual had the app open | Continuously throughout the study period (i.e., assessed every day during 12 months) |
| LvL UP app: Actual usage of app components | Timestamps combined with any interactions performed by the participant with the intervention components (coaching sessions, life hacks, Breeze, journaling) | Continuously throughout the study period (i.e., assessed every day during 12 months) |
| LvL UP app: Coaching topic / sub-topic use | Which coaching topics were used by the participant | Continuously throughout the study period (i.e., assessed every day during 12 months) |
| LvL UP app: Conversational turns | The number of responses of a participant divided by the number of conversational turns offered by the app's chatbot | Continuously throughout the study period (i.e., assessed every day during 12 months) |
| LvL UP app: Breeze (slow-paced breathing tool) usage | Audio-recordings of short voice commands and breathing when using "Breeze" (slow-paced breathing tool) | Continuously throughout the study period (i.e., assessed every day during 12 months) |
| LvL UP app: Access to smartphone sensors (location - obfuscated) | Sensor: GPS. Data type: Float. | Continuously throughout the study period (i.e., assessed every day during 12 months) |
| LvL UP app: Access to smartphone sensors (location types) | Sensor: Apple SensorKit. Data type: Categorical (work, home, gym). | Continuously throughout the study period (i.e., assessed every day during 12 months) |
| LvL UP app: Access to smartphone sensors (raw gyroscope) | Sensor: Gyroscope. Data type: Float. | Continuously throughout the study period (i.e., assessed every day during 12 months) |
| LvL UP app: Access to smartphone sensors (raw accelerometer) | Sensor: Accelerometer. Data type: Float. | Continuously throughout the study period (i.e., assessed every day during 12 months) |
| Process evaluation (intervention group participants only): Net promoter score | 1-item rating via in-app pop-up: "How likely is it that you would recommend LvL UP to a friend?" rating between 0 (not at all likely) and 10 (extremely likely). | Month 1, month 3, month 6 |
| Process evaluation (intervention group participants only): Self-Report Behavioral Automaticity Index | An ad hoc survey to explore whether intervention-related behaviors (physical activity, healthy eating, and practicing breathing exercises) have become automatic throughout the course of the study | Baseline, month 1, month 3, month 6, month 12 |
| Singapore |
| 138602 |
| Singapore |
| Background |
| Jabir AI, Lin X, Martinengo L, Sharp G, Theng YL, Tudor Car L. Attrition in Conversational Agent-Delivered Mental Health Interventions: Systematic Review and Meta-Analysis. J Med Internet Res. 2024 Feb 27;26:e48168. doi: 10.2196/48168. |
| 25791983 | Background | Moore GF, Audrey S, Barker M, Bond L, Bonell C, Hardeman W, Moore L, O'Cathain A, Tinati T, Wight D, Baird J. Process evaluation of complex interventions: Medical Research Council guidance. BMJ. 2015 Mar 19;350:h1258. doi: 10.1136/bmj.h1258. |
| 32529788 | Background | Yan X, Ghosh P, Chakraborty B. Sample size calculation based on precision for pilot sequential multiple assignment randomized trial (SMART). Biom J. 2021 Feb;63(2):247-271. doi: 10.1002/bimj.201900364. Epub 2020 Jun 11. |
| D010549 | Personal Satisfaction |