MOVE@NUS digital intervention cohort: protocol of a pilot study to promote healthy movement in university students

Abstract

Introduction

Due to academic pressures and irregular schedules, university students often face challenges in maintaining healthy movement behaviours (including sleep, physical activity, and screen time), which are interrelated and influence both physical and mental health. Smartwatch- and smartphone-based ecological momentary assessments (EMAs) and ecological momentary interventions (EMIs) offer real-time, context-aware strategies to promote movement behaviours. This pilot study aims to assess the feasibility and preliminary effectiveness of a hybrid approach that combines continuous digital monitoring of movement behaviours with sequentially embedded randomised controlled trials (RCTs) evaluating EMIs.

Methods/Design

MOVE@NUS pilot study employed a five-month hybrid design that combines continuous passive monitoring (primarily via Apple Watches, supplemented by iPhones) with three embedded RCTs targeting sleep (RCT-1), physical activity (RCT-2), and screen time (RCT-3). For each RCT, participants are randomised on a 1:1:1 schedule (control, intervention 1, intervention 2). Eligible participants are first-year undergraduates at the National University of Singapore, aged 18–25 years, who own or regularly use an iPhone and an Apple Watch. EMIs, delivered via the study app, comprise standard health messages or personalised reminders based on HealthKit data or participants’ self-reported behaviours and preferences. Self-reported measures include eight EMA bursts (three-day periods every two weeks) and online questionnaires at baseline, midway (2.5 months), and endpoint (5 months). All EMIs and EMAs are text-based and can be completed in under two minutes. Feasibility outcomes include recruitment, engagement, and user experience assessed through quantitative surveys and semi-structured interviews. Preliminary effectiveness will be explored separately for each RCT, comparing movement behaviours between intervention and control groups.

Discussion

Findings from this study will inform the development of scalable and longer-term digital intervention cohorts for promoting healthier lifestyles among university students. Furthermore, as university students soon transition to the workforce, insights gained will inform scalable digital health interventions for broader populations.

Trial registration

ClinicalTrials.gov ID NCT06597890 First Posted: 19 September 2024.

Supplementary Information

The online version contains supplementary material available at 10.1186/s44167-026-00097-z.

Background

The advancement of digital technologies is a cornerstone in the future of preventive health interventions, with growing applications in monitoring and promoting healthy behaviours in everyday life [1–4]. Digital interventions have been widely explored for mental health care, where they offer accessible and scalable solutions that support its well-being [5, 6], and studies on psychotic disorders reported high levels of user satisfaction and engagement, highlighting their potential for adoption [1, 6]. Beyond mental health, digital interventions increasingly target movement behaviours such as physical activity, which is closely linked to overall health outcomes [2, 7, 8]. These findings underscore the growing need to explore how digital platforms can optimise behavioural change interventions [5]. Smartphones, widely adopted among university students, offer a promising platform for real-time, personalised health interventions [9, 10]. In Singapore, smartphone penetration among individuals aged 15–24 reached 100% by 2021 [11–13], alongside rapid growth in wearable device use, with penetration estimates ranging from 35 to 55% in the general population [14–16]. Given their ubiquity in students' daily lives, smartphones and wearables provide unique opportunities for real-time feedback and behavioural modification strategies [5]. With the integration of health monitoring features into mainstream mobile operating systems, smartphones and wearable devices equipped with these capabilities may serve to enable continuous monitoring of physiological and behavioural data, including step counts, heart rate, and GPS-based movement patterns [1, 9, 17–20]. One study found that 49% of iOS (iPhone Operating System) users in primary care had conditions suitable for telemonitoring, highlighting the broad healthcare applications of these technologies [20]. Additionally, the iOS HealthKit has demonstrated validity and reliability in measuring gait speed and step length in adults [21]. Given the high prevalence of iPhone ownership among university students in Singapore, leveraging these technologies for preventive health interventions may improve engagement and effectiveness, particularly in promoting healthier behaviours among university students [15, 22–24].

Smartphone-based ecological momentary assessments have transformed behavioural research by addressing key limitations of traditional assessment methods, such as recall bias and infrequent data collection. EMAs capture self-reported mood, behaviour, and contextual factors in real time [1, 18], and allow for frequent and naturalistic data collection, providing insights into the dynamic relationships between health behaviours over time [1, 9]. For example, EMAs can prompt participants multiple times a day to report their physical activity and sedentary behaviour while also capturing contextual factors such as mood, social environment, and location [25–29]. Ecological momentary interventions extend the concept of EMAs by offering real-time, context-sensitive and personalised interventions in daily life [1, 19]. EMIs can take various forms, including voice calls, images, text messages, and educational content [2]. Tailoring can be a one-time event, determined by the user’s baseline characteristics before the start of the EMIs program, or can be dynamic and be modified as the user’s status or needs change over time [18, 30].

University students, often struggling with academic stress, irregular routines, and social commitments, represent a particularly vulnerable group for targeted health interventions [24, 31]. Sleep, physical activity, and screen time are interrelated and critically influence students' physical and mental well-being [31–36]. Insufficient physical activity, in particular, contributes to the development of non-communicable diseases such as obesity and cardiovascular disease [8, 34]. Poor sleep, commonly observed in this group, has been linked to impaired cognitive function, reduced academic performance, and heightened risks of mental health issues and chronic diseases [35]. Excessive screen time has been associated with reduced physical activity, poor sleep quality, and increased mental health challenges [32, 33]. Despite the potential of EMIs to address these health behaviours, there is a lack of research systematically evaluating real-time, tailored EMIs for university students. Two studies examined text message-based EMIs to reduce alcohol consumption, with mixed effectiveness: one study found reduced drinking among women only, and the other was effective in one college but not another [23, 37]. Another study investigating a theory-based online intervention targeting multiple health behaviours (including alcohol consumption, physical activity, fruit and vegetable intake, and smoking) among new university students reported significant reductions in smoking but minimal impact on other behaviours [24]. These findings highlight both the potential and limitations of EMIs in promoting health behaviours in this population, underscoring the need for further research [27].

Traditional RCTs, while methodologically rigorous, often involve complex, multi-component strategies that are not evaluated for effectiveness individually [38, 39]. Instead, MOVE@NUS pilot study leverages a hybrid design combining continuous smartphone and smartwatch-based monitoring of movement behaviours, with several small, independent EMIs evaluated through sequential embedded RCTs [9, 23, 29, 37, 40, 41]. The study design is an innovative application in this student population in behavioural science, building upon the principles of prior multi-component intervention studies [42]. The primary aim of this pilot is to evaluate the feasibility of implementing this hybrid design among university students, with a focus on recruitment, retention, engagement, data completeness, intervention delivery processes, and participant experience. The secondary aim is to explore the preliminary effectiveness of behavioural change interventions to inform intervention fine-tuning and technical improvements for the main MOVE@NUS intervention cohort. The findings will provide critical methodological insights into the delivery of real-time, personalised digital interventions and serve as foundational evidence to inform the development of a future full-scale program tailored to university students [23, 37].

Methods

Study design

The MOVE@NUS pilot study follows a five-month hybrid study design, combining continuous digital monitoring with three sequentially embedded RCTs, separately targeting sleep (RCT-1), physical activity (RCT-2), or screen time (RCT-3) [40, 42]. Each RCT randomises first-year university students (1:1:1) into two intervention groups and a control group and is preceded and followed by at least two weeks of digital monitoring (Fig. 1). Movement behaviours are tracked using Apple Watches, with supplementary data from iPhones, all retrieved via Apple HealthKit. EMAs, administered via smartphones every two weeks, collect self-reported data on physical activity, sleep, screen time, well-being, and cognition. Both EMIs and EMAs are text-based, with each session taking less than two minutes to complete. Online questionnaires are also administered at baseline, midway (2.5 months), and endpoint (5 months) to collect data on demographics, lifestyles, and movement behaviours. This study adheres to the Standard Protocol Items Recommendations for Interventional Trials (SPIRIT) and Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and online TeleHealth (CONSORT-EHEALTH) for designing and reporting on digital intervention randomised trial protocols [43, 44].

Fig. 1.

Study design of MOVE@NUS pilot study: assessments, data collection, and interventions. Note The study begins with online assessments, which are also administered at the midway (2.5 months), and endpoint (5 months). Ecological Momentary Assessments (EMAs) are delivered in bursts throughout the study; each burst lasts 3 days. The intervention period comprises three sequential, embedded RCTs (each lasting 2 weeks): RCT-1 (Sleep), RCT-2 (Physical Activity), and RCT-3 (Screen Time), with continuous passive monitoring via Apple Watch and iPhone. Participants are re-randomised ('R') 1:1:1 to intervention or control groups at the start of each new RCT phase, and optional interviews are conducted to gather participant experience.

Participants and recruitment

Participants must meet the following inclusion criteria: (1) between 18 and 25 years old, (2) enrolled in the first year of an undergraduate degree at the National University of Singapore (NUS), (3) own or regularly use an iPhone with a data plan, (4) are willing to download and engage with the study-specific smartphone app, and (5) own an Apple Watch (Series 6/SE or newer) and are willing to wear it continuously throughout the study duration, including during sleeping. Participants will be excluded if they are pregnant or plan to travel overseas for more than six weeks at a time during the upcoming semester. Recruitment will occur through multiple channels. The University Health Centre will distribute recruitment emails to all eligible students via their university email addresses. Supplementary recruitment efforts will include promotion through social media platforms commonly used by Singaporean students (Telegram, WhatsApp, and WeChat) with specific targeting of NUS freshman groups, as well as strategic placement of informational posters across campus. Interested students will be directed to complete a brief questionnaire to confirm their eligibility. Eligible participants will be invited to attend an on-site enrolment session, during which written informed consent will be obtained (Online Appendix 1). For participants under the age of 21, separate written consent from a legal guardian will also be required (Online Appendix 2). Access to HealthKit data will be requested through the study-specific smartphone app, which will require them to provide consent for their data to be accessed. All assessments and intervention content will be delivered via each participant’s own smartphone and smartwatch.

Interventions

MOVE@NUS: digital health behaviour app

The MOVE@NUS study app, developed and tested by our team, is designed specifically for this study using the Cogniss platform (Fig. 2) [45]. The app integrates with iOS HealthKit to collect health data and deliver EMAs and EMIs. Unlike most smartphone applications that focus on either research prototypes or direct consumer markets, the app is designed specifically for this study to support personalised health monitoring and behaviour change, aiming for a high level of personalisation [45].

Fig. 2.

MOVE@NUS pilot study app interface and pop-up notifications. Note Screenshots illustrating key features of the MOVE@NUS app (left to right, top to bottom): home screen, HealthKit data sync permission, sleep guidelines, stair climbing self-report, stair climbing feedback based on self-report, screen time reduction goal, pop-up notification to reduce screen time, screen break strategies, pop-up notification reminding users to apply their chosen strategy, and screen time upload from iOS setting

The MOVE@NUS app is made available exclusively to enrolled participants. During on-site enrolment, participants will access the platform by scanning a QR code, which directs them to the registration page. They will sign up using a designated randomised username and password, after which a consent page for HealthKit data sync will be automatically generated. Upon providing consent, they will gain access to study features. The app is designed to keep participants logged in to prevent disruptions; however, in cases of accidental logout, they can request their credentials from the study team, who will provide account details within two working days.

Ecological momentary interventions in the MOVE@NUS pilot study

Multiple EMI formats are used, including evidence-based standard health messages, and personalised reminders derived from HealthKit data, self-reported behaviours, and stated preferences (Table 1). Interventions are selected a priori based on existing scientific evidence to serve as test cases for evaluating the technical and behavioural feasibility of delivering real-time, personalised interventions, rather than definitively establishing intervention effectiveness. To minimise notification fatigue and maintain participant engagement, the notification system is programmed with built-in limits, delivering no more than seven notifications per day during any intervention phase, regardless of group assignment.

Table 1.

Summaries of ecological momentary interventions implemented in MOVE@NUS pilot study

RCT	Group	Content description	Message frequency
Sleep	Sleep Duration Nudge	Feedback on the past 7-day sleep duration with guideline-based recommendations	Weekly reports + Daily nudge (late evening)
	Sleep Hygiene Choice	Evidence-based sleep hygiene strategies with participant selection	Weekly strategy selection + Daily nudge (late morning)
	Control Group	No intervention	–
Physical Activity	Stair Climbing Nudge	Encouragement and contextual nudges based on real-time flights climbed to take stairs instead of elevators or escalators	Daily report (late morning) + Daily nudges (early afternoon, late afternoon)
	VPA (Vigorous Physical Activity) Choice	Brief, intense physical activity options with daily selection and nudges	Daily strategy selection (late morning) + Daily nudges (early afternoon, late afternoon)
	Control Group	No intervention	-
Screen Time	Screen Time Break Nudge	Break reminders with alternative activity suggestions	Daily report (late morning) + Daily nudges (6 total: noon to night, every ~ 2 h)
	Screen Time Goal-Setting	Personalized daily screen time reduction goal setting and monitoring	Daily report (late morning) + Daily nudge (afternoon)
	Control Group	No intervention	–

Following group allocation and one day before the commencement of each intervention phase, participants will receive an instructional WhatsApp message about the upcoming intervention. They will not be informed of their specific intervention assignment to maintain allocation concealment. Throughout the study duration, a dedicated project email and WhatsApp account remain available for participant inquiries and to send periodic reminders about app engagement to ensure successful data synchronisation and intervention delivery.

Detailed intervention protocols by RCT

RCT-1: Sleep.

• Intervention Group 1—Sleep Duration Nudge: Feedback is personalised using objective sleep data and aligned with established sleep duration guidelines (7–9 h/day) for young adults, providing individualised recommendations [46, 47]. Participants receive weekly feedback reports based on their previous 7-day sleep duration patterns from HealthKit data, accompanied by daily reminder messages [47].

• Intervention Group 2—Sleep Hygiene Choice: Sleep hygiene interventions improve sleep quality in university students [48]. Through the app, participants select weekly from a list of evidence-based sleep hygiene strategies (e.g., reducing screen use before bedtime, no coffee after 3 PM) [48]. Daily reminders are tailored to strategies actively selected by each participant, ensuring relevance and personal commitment to the chosen approaches.

• Control Group: Participants in this group continue standard app usage and data collection without receiving any intervention messages.

RCT-2: Physical Activity.

• Intervention Group 1—Stair Climbing Nudge: Stair climbing is a practical and effective physical activity leveraging existing environmental opportunities for physical activity enhancement [49, 50]. Participants receive daily encouragement and contextual reminders to choose stairs over elevators or escalators, with feedback based on HealthKit data on actual flights of stairs climbed.

• Intervention Group 2—VPA (Vigorous Physical Activity) Choice: Short bursts of activity improve cardiovascular health [50, 51]. Participants receive daily prompts to select from a comprehensive menu of brief, vigorous-intensity physical activities (e.g., brisk walking sessions between classes, squats while waiting), designed to be integrated into typical university student routines and environments.

• Control Group: Participants in this group continue standard app usage and data collection without receiving any intervention messages.

RCT-3: Screen Time.

• Intervention Group 1—Screen Time Break Nudge: Breaking prolonged screen use can reduce digital fatigue and promote healthier habits [52]. Participants receive contextually-aware reminders to take breaks from screen use, with suggestions for brief alternative activities (e.g., stretching, brief walks) [52]. The system queries the current smartphone usage status before providing tailored break suggestions, ensuring intervention relevance.

• Intervention Group 2—Screen Time Goal-Setting: Personalised goal-setting interventions improve adherence to behaviour change [53]. Participants set personalised daily screen time reduction goals through the app interface [53]. The intervention utilises iOS screen time data to provide daily personalised goal-setting support and progress feedback.

• Control Group: Participants in this group continue standard app usage and data collection without receiving any intervention messages.

Quality assurance and technical validation

To ensure the technical reliability of data collection and the accuracy of EMI delivery, a formal Quality Assurance (QA) procedure is implemented both before and throughout study deployment.

1. Technical Reliability Testing.

   • Alpha Testing: The research team conducts internal testing to evaluate system stability and detect functional errors. Based on identified issues, iterative refinements are made to visual design, page layout, assessment scheduling, and the wording of in-app messages and notifications.
   • Beta Testing: Because this project is itself a pilot study, a small independent pilot group (N = 5) tests the app over a predefined period to assess usability, navigational flow, and system reliability. All system errors identified during this phase are logged and corrected before full launch.
2. Intervention Delivery Fidelity.

   • Timing Accuracy: EMI notifications are verified to arrive within ± 10 min of the programmed schedule.
   • Content Accuracy: Dynamic and personalised contents of EMIs are tested to ensure they parse and display correctly according to the predefined triggers.

Clarification of technical constraint

Because some personalised EMIs rely on participants manually checking and entering their Apple Health data, the interventions function as adaptive, rather than a fully automated real-time Just-in-Time Adaptive Intervention (JITAI) [3]. Participants review their behaviour metrics (e.g., flights climbed, sleep duration) and input these values into the study app, which then generates tailored messages. The intervention retains key adaptive features, including behaviour-based tailoring and preference-based personalisation, ensuring that EMIs remain timely, relevant, and user-centred within current technical constraints.

Patient and participant involvement

The design of the MOVE@NUS pilot study is informed by feedback from participants in a prior micro-longitudinal digital health study among university students [15]. During that study, participants provided formative feedback on strategies for improving physical activity, sleep, and reducing screen time through structured multiple-choice options and open-ended responses at multiple time points. Their responses informed the development of intervention content, message preferences, and the tone, language, and scheduling of the EMI messages. During the pilot, online questionnaires measure demographics, lifestyles, and movement behaviours, and participants will contribute behaviour data through both passive tracking and EMAs. After completing the study, they will be invited to provide further feedback via quantitative surveys and optional semi-structured interviews. These insights will guide refinement of intervention content, delivery parameters, and app features for the MOVE@NUS main trial.

Data collection

Data sources

1. Apple Watch and iPhone: The smartwatch- and smartphone-based measures provide objective, high-quality data on physical activity and sleep with minimal participant burden, retrieved via Apple HealthKit. Although iOS screen time is not part of HealthKit, participants will also upload screenshots of their system-recorded screen time from iOS Settings, enabling passive monitoring of screen time.

2. EMAs: EMAs capture self-reported data on physical activity, sleep, wellbeing, cognition, and screen time. These measures complement the HealthKit data and provide additional context for participant behaviours and experiences. EMAs are administered in eight bursts, each lasting three days and occurring every two weeks. Participants receive one prompt per day in the mid-afternoon to report on daily behaviours and experiences (Fig. 1). This schedule balances data comprehensiveness with participant burden, and previous studies have shown acceptable retention rates (~ 67–100%) [9]. This structured approach ensures real-time data collection while minimising participant fatigue.

3. Questionnaires: Self-reported questionnaires will be administered via a REDCap (Research Electronic Data Capture) survey on participants' smartphones at baseline, midway, and the study endpoint [54, 55]. The questionnaires measure demographics, lifestyles, and movement behaviours (Supplementary Table 1). Completion of the full questionnaire takes approximately 30 min. Participants who do not complete the questionnaires will receive a reminder email after one week, and if necessary, a second reminder will be sent one week later.

Feasibility

Feasibility of this study will be assessed through:

1. Recruitment Metrics: eligibility rates, enrolment rates, and non-response rates.

2. Retention and Engagement: questionnaire follow-up completion rates, EMA burst response rates, and HealthKit data completeness (number of days tracked).

3. User Experience: participants' experiences with the MOVE@NUS pilot study and specific EMIs received will be evaluated through a mixed-methods approach, combining quantitative survey responses with qualitative feedback. Short online surveys on participants' experiences will be conducted at baseline, after each of the three RCTs, and at the endpoint of the study. Additionally, semi-structured interviews will be conducted to gain a deeper understanding of participants' perspectives. To structure participant experience, the study will adopt a micro–macro framework which integrates elements from established models, including the Technology Acceptance Model (TAM), COM-B (Capability, Opportunity, Motivation, Behaviour), MARS (Motivation, Ability, Role Perception, Situational Factors), and social/digital determinants of health [56–59].

This framework supports systematic data collection across two levels:

• Micro level: immediate, direct experiences including intervention-specific feedback, user experience with the application, and overall study experience.

• Macro level: broader contextual factors influencing participants' engagement.

Preliminary effectiveness

Preliminary effectiveness will be assessed for health behaviours, including sleep, physical activity, and screen time. (Table 2). Analyses will include only participants with available data from both pre-intervention and intervention phases.

1. Sleep (RCT-1): Sleep duration is derived from HealthKit, which records time in bed as a proxy for sleep duration. This measure is chosen because sleep duration has an important influence on many biological processes, such as inflammation, glucose regulation, appetite, and energy expenditure, as well as psychological processes such as memory consolidation and attention [60].

2. Physical activity (RCT-2): Step counts and flights climbed are derived from HealthKit. Step counts are a widely accepted and interpretable metric of overall physical activity [61], while stair climbing captures short bouts of vigorous physical activity, which have been shown to improve cardiometabolic health in everyday settings [49]. In Singapore, it has been shown that smartphone- and wearable-based interventions demonstrated feasibility for promoting both step counts and stair climbing, suggesting their potential as physical activity targets within digital health strategies [62]. These outcomes are chosen to capture both volume and intensity of physical activity within the naturalistic campus environment.

3. Screen time (RCT-3): Total smartphone screen time is collected from weekly uploads of iOS Settings screenshots through EMA bursts. Prolonged screen time not only displaces time for physical activity [63, 64] but also disrupts sleep through blue-light exposure and heightened psychological engagement [65, 66], potentially altering cognitive processing [67] and increasing overweight/obesity rates [68].

Table 2.

Measures collected in the MOVE@NUS pilot study via Apple Watch, iPhone, and Ecological momentary assessments

Outcome measures	Description
Apple Watch and iPhone Monitoring
Steps	Number of Steps
Stair Climbed	Number of flights of stairs climbed
Active Energy Burned	Calories burned to maintain active bodily activities
Basal Energy Burned	Calories burned to maintain basic bodily functions
Distance Cycling	Distance covered through cycling activities
Distance Walking/Running	Distance covered through walking or running
Time Spent in Bed	Time period lying in bed but still awake
Time Spent Asleep	Total duration spent asleep
Heartbeats per Minute	Average heart rate, measured in beats per minute
Resting Heart Rate	Average heart rate during rest
Walking Heart Rate Average	Average heart rate during walking
Heart Rate Variability (HRV)	Intervals between successive heartbeats
Screen Time Screenshots	Uploaded screen time screenshots from iOS settings
Ecological Momentary Assessments
Sleep Duration	Self-reported in-bed time and wake-up time
Sleep Quality	Self-reported sleep quality
Stair Climbing	Frequency of stair climbing in the last day
Vigorous Physical Activity	Frequency of vigorous activity in the last day
Recreational Screen Time	Time spent on smartphones for recreational use
Wellbeing	Self-reported ratings of mood, energy, and life satisfaction over the past two weeks will be assessed at the end of each intervention or monitoring period, using a 5-point scale. This is based on the five-item World Health Organisation Wellbeing Index (WHO-5) [69]
Cognition	Self-reported ratings of memory, sharpness, and attention over the past seven days will be assessed at the end of each intervention or monitoring period using a 5-point scale. This is derived from the PROMIS (Patient-Reported Outcomes Measurement Information System) Cognitive Function – Abilities scale. [70, 71]

Randomisation

Sequence allocation

The randomisation sequence will be generated using a secure, computer-based random number generator, with no manual input to avoid bias. For each RCT, all enrolled participants will be independently randomised based on a 1:1:1 ratio using the same method. This approach helps distribute potential carryover effects across conditions and minimise systematic bias from previous intervention assignments. While this approach cannot eliminate carryover effects, it aims to balance their influence across the study population.

Allocation concealment mechanism

The allocation sequence will be generated and managed by a researcher who is not involved in participant recruitment or intervention delivery to ensure allocation concealment. Once a participant is deemed eligible and consents to the study, their assignment will be determined automatically based on the generated randomisation sequence at each RCT phase. To maintain allocation concealment, participants will not be informed about the existence of other intervention arms or their specific group allocation. Since all interventions are delivered through smartphones, participants will not have face-to-face contact with each other, minimising the risk of unintentional disclosure of group assignments.

Blinding

The study will employ an Assessor-Blind design with Partial Participant Masking (Masked to Comparative Arms). Due to the nature of the behavioural intervention (daily personalised nudges in intervention groups vs. no intervention in control groups), full participant blinding is not feasible as participants will inevitably be aware of the intervention they are receiving. However, to minimise bias, the following measures will be implemented: (1) Assessor Blinding: Outcome assessors and data analysts will remain blinded to group allocation until the final analysis is complete. (2) Partial Participant Masking: Participants will be masked to the specific study hypotheses and the exact nature of the opposing arm. They will be informed that the study compares different variations of health monitoring, without explicitly knowing which arm they belong to at each RCT phase. All app messages and interactions for the intervention groups are standardised using pre-defined templates to ensure consistency across participants.

Data storing and management

Two trained researchers will oversee data collection, storage, and analysis. These researchers will oversee the monitoring of all incoming data, ensuring that the data is complete and accurately recorded. To promote retention, participants will receive reimbursements, along with WhatsApp reminders and in-app notifications before EMA bursts or interventions.

Data will be securely managed on an encrypted server, with anonymised IDs, automated data imports, and range checks to ensure quality. For participants who discontinue or deviate from the intervention protocols, outcome data from HealthKit will continue to be passively collected, provided participants do not revoke data-sharing permissions. No additional EMA data will be collected for participants who discontinue, but existing data up to the point of withdrawal will be included in the analysis.

Participant reimbursement

To enhance adherence and encourage continued participation throughout the study, participants will be eligible to receive reimbursements totalling up to S$90, distributed at midpoint and endpoint of the study (Supplementary Table 2). These reimbursements are intended to acknowledge participants' time and effort and motivate their continued engagement.

Statistical analysis

Categorical variables will be reported as frequencies and percentages, while continuous variables will be summarised using means and standard deviations. Descriptive statistics will be used to assess feasibility outcomes. A day is considered valid for passive data if the wearable device records ≥ 10 h of wear time [72]. Periods of non-wear are identified using automated detection of prolonged zero-data intervals. For sleep duration analyses, only valid days that also contained recorded nocturnal data (8:00 PM–8:00 AM) will be included to ensure reliable estimation [73].

To evaluate preliminary effectiveness, longitudinal outcomes related to physical activity, sleep, and screen time will be analysed using Linear Mixed Models (LMMs). These models account for repeated measures by incorporating random effects (e.g., individual participant variability) to model within-subject correlations. Fixed effects will include intervention phases and time. Crucially, to account for potential carryover effects inherent in the sequential design, although there is at least 2 weeks between each RCT phase, the analysis of subsequent RCTs (RCT-2 and RCT-3) will explicitly model sequential confounding. The participant’s allocation status in prior RCTs (e.g., RCT-1 intervention vs. control) will be included as a fixed covariate. This ensures that the estimated effects of the current intervention are isolated from the residual influence of previous intervention modules. Regarding missing data, LMMs handle missingness under the assumption of Missing at Random (MAR). However, acknowledging that attrition in digital health interventions may yield data that are Missing Not at Random (MNAR), we will assess patterns of missingness across groups. If descriptive data suggest differential non-response linked to intervention allocation, sensitivity analyses will be conducted to evaluate the robustness of the primary results against MNAR assumptions. Additionally, generalised estimating equations (GEE) may be considered for categorical outcome variables to assess intervention effects over time, as this approach is robust to model misspecifications. Final analytical decisions will be made after a thorough assessment of the collected data. All statistical analyses will be conducted using R version 4.2.2, with two-sided tests at a 5% significance level.

Sample size

Given the exploratory nature of this pilot study, sample size calculations are not applicable, as the study will also assess recruitment feasibility within the constraints of strict eligibility requirements and a complex embedded RCT design. Therefore, the success thresholds of the feasibility indicators are also not defined.

Data monitoring

Data monitoring committee (DMC)

A formal DMC is not established for this pilot study due to the low-risk nature of the interventions, which involve digital monitoring and non-invasive EMIs. Study oversight will be conducted by the research team to ensure unbiased monitoring and reporting.

Interim analyses and stopping guidelines

No formal interim analyses or stopping guidelines are planned for this pilot study. However, the research team will routinely review participant data and gather feedback to identify and address operational issues every two weeks. Participants will receive support via email and a private WhatsApp account for questions and concerns. A designated researcher will respond to inquiries within two working days. Any decisions to modify or terminate the study will be made by the principal investigator.

Harms

Adverse events or unintended effects from interventions or procedures are not anticipated. Any participant-reported issues, whether solicited or spontaneous, will be documented and addressed promptly. Serious adverse events will be escalated to the ethics committee for review.

Auditing

No independent auditing is planned for this study. The research team will conduct regular internal checks to ensure protocol compliance, data integrity, and ethical conduct throughout the trial.

Discussion

By adopting a hybrid digital cohort design with embedded RCTs, the MOVE@NUS pilot study will assess the feasibility of continuous digital monitoring and real-time delivery of personalised EMIs, and examine the preliminary effectiveness in promoting physical activity, improving sleep, and reducing screen time. Key feasibility outcomes include recruitment and retention rates, compliance with EMA and EMI prompts, technical reliability of Apple Watch data integration via iOS HealthKit, and the utility of passively collected movement data for tailoring interventions. Findings will inform the design of larger, longer-term digital intervention cohorts and guide the development of scalable digital strategies to promote healthier behaviours in university students and beyond.

Previous studies on EMIs have predominantly focused on mental health, with a smaller number addressing smoking cessation, substance abuse, diet, weight loss, and physical activity [3, 19, 23, 24, 37, 74–76]. While these studies have demonstrated the potential of EMIs in promoting behaviour change through real-time interventions, significant knowledge gaps remain. First, few studies have leveraged the combination of self-reported data and passive monitoring data (from smartwatches and smartphones) to create truly personalised interventions for this population [9]. Second, there is limited research on the simultaneous real-time monitoring of physical activity, sleep, and screen time, which are interconnected and significantly impact student well-being. Current research often relies on retrospective self-reports or isolated measurements that fail to capture the dynamic nature and interdependence of these behaviours throughout the day. Third, while some studies have explored the potential of EMIs in promoting behaviour change, their specific impact on these key behaviours remains unexplored in university students, who face unique challenges during their transition to independent living and will soon transition to working adults.

The MOVE@NUS pilot study addresses these gaps through several innovative approaches. One of the unique aspects is the integration of both EMAs and smartwatch- and smartphone-based data to tailor interventions in near real-time. Passive data from smartwatches combined with self-reported EMA data provides a more comprehensive understanding of students' behaviour patterns, contextual factors, and preferences. This hybrid approach enables more engaging, personalised interventions while potentially reducing participant burden [9, 18, 19, 23, 37, 77]. Furthermore, the information collected through EMAs will be immediately utilised to personalise and evaluate multiple embedded interventions, creating a responsive and dynamic system that adapts to individual needs and circumstances, enhancing both engagement and effectiveness. Also, unlike traditional studies evaluating a single broad intervention, MOVE@NUS focuses on developing and refining an approach that facilitates the continuous implementation and optimisation of EMIs [9, 29, 37]. This iterative approach helps identify feasible intervention components that can be optimised over time and subsequently integrated into larger, more comprehensive strategies for improving movement behaviours in university students [23, 37]. Beyond its methodological strengths, smartphone-based interventions can reach large groups of individuals in naturalistic settings, offering a cost-effective solution for health promotion and behaviour change at scale [2, 23].

However, there are several limitations of this study. First, as a limitation of the pilot study, intervention personalisation is based on smartwatch and smartphone data viewed and reported by participants, rather than being automatically synced in real-time [78]. As the platform continues to be finalised and upgraded, the MOVE@NUS main study will address this by fully integrating real-time HealthKit data into personalisation algorithms. Second, the requirement for participants to own specific devices may introduce selection bias and limit the generalisability of findings to students from diverse socioeconomic backgrounds. Third, the impossibility of blinding participants to their group allocation introduces a risk of performance bias and the Hawthorne effect [79, 80]. Since the intervention groups receive daily nudges while the control group receives no intervention, participants in the intervention groups may perceive a greater level of observation. This awareness may unintentionally inflate positive responses in subjective, self-reported outcomes. While objective metrics from Apple HealthKit are less susceptible to this bias, the subjective findings will be interpreted with caution regarding this limitation.

In conclusion, this pilot study represents a significant step forward in the use of real-time, tailored EMIs to promote healthy behaviours among university students. By evaluating the feasibility of a hybrid approach that integrates continuous digital monitoring with personalised interventions, this study will establish the groundwork for the larger long-term MOVE@NUS digital intervention cohort in the future while also contributing to the growing body of evidence on digital health strategies. Future research should focus on enhancing personalisation through advanced analytics and machine learning, evaluating the long-term health impacts and behavioural sustainability of EMIs. Such efforts are essential to ensure the scalability and effectiveness of digital interventions in addressing public health challenges across varying socioeconomic and demographic groups.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

ANOVA

Analysis of variance

EMA

Ecological momentary assessment

EMI

Ecological momentary intervention

GEE

Generalised estimating equations

HRV

Heart rate variability

JITAI

Just-in-time adaptive interventions

LMMs

Linear mixed-effects models

MAR

Missing at random

MNAR

Missing not at random

MVPA

Moderate-to-vigorous intensity physical activities

PRs

Permanent residents

PSQI

Pittsburgh sleep quality index

QA

Quality assurance

REDCap

Research electronic data capture

SP2PAQ

Singapore prospective study physical activity questionnaire

VPA

Vigorous physical activity

WHO

World Health Organisation

WHO-5

Five-item World Health Organisation wellbeing index

Author contributions

MC, SE, and FMR conceptualised and designed the MOVE@NUS study. MC, XHC, and MM were responsible for the operational conduct and implementation of the study. All authors participated in the in-house testing of the developed app and provided feedback for refinement. SE and FMR supervised the study and provided critical revisions to enhance the scientific rigour of the protocol manuscript. MC drafted the initial manuscript, with all authors reviewing and approving the final version for submission.

Funding

This study does not receive dedicated project-specific funding. It will be supported as part of the broader Physical Activity and Nutrition Determinants in Asia (PANDA) Research Program at the Saw Swee Hock School of Public Health, National University of Singapore (Grant Number: A-0006101–00-00).

Data availability

Access to the final trial dataset will be restricted to the principal investigator and designated members of the research team. Data will be stored securely, and any shared data will be anonymised and de-identified to ensure participant confidentiality. Vendor access (e.g., Apple, REDCap) is strictly governed by institutional Data Use Agreements. For additional information, please contact the lead author.

Declarations

Ethics approval and consent to participate

Ethical approval for this study has been obtained from the National University of Singapore Institutional Review Board (NUS-IRB) (Approval Number: NUS-IRB-2023-800). Any important protocol modifications, such as changes to eligibility criteria, outcomes, or analytical methods, will be promptly communicated to the NUS-IRB for review and approval. Relevant changes will also be updated in trial registries and communicated to investigators, trial participants, and other relevant parties as appropriate. Study findings will be disseminated through peer-reviewed journals, academic conferences, and summary reports to participants.

Consent for publication

Not applicable. No individual personal data is presented in this manuscript.

Competing interests

The authors declare no competing interests.