Machine-based Algorithm for Increased Physical Activity and Sustained User Engagement

NCT ID: NCT06388317

Last Updated: 2025-04-25

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 1249 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2023-01-01
• Study Completion Date: 2025-03-24

Brief Summary

A cohort analytic study (i.e., observational study following a cohort over time to compare outcomes between those exposed and unexposed to an intervention) was conducted over a 17-week period (March 7, 2022 to June 30, 2022). The study consisted of a five-week baseline period (March 7, 2022 to April 10, 2022). During the baseline, Telus Wellbeing users earned FI for meeting personalized but static daily step goals. A 12-week intervention period followed (April 11, 2022 to June 30, 2022). During the intervention period, users were randomized by a senior Telus Wellbeing software engineer into either control (i.e., continuing with their personalized static goal) or intervention groups (i.e., testing the new ML-driven 'Smart Mode' feature offering personalized and adaptive daily step goals) using a random number generator in Python (version 3.9; i.e., non-pi package random binomial). The randomization ratio was about 1:2 in favour of the intervention group. Both groups were informed they would be testing a new 'Smart Mode' feature at intervention onset, however, only those allocated to the intervention group received ML-driven goals. Notably, the study sample consisted of more highly engaged users (e.g., registered on the app at least five weeks prior to the intervention, took initiative to synchronize their activity trackers with the app). The Strengthening the reporting of observational studies in epidemiology (STROBE) statement flow chart and checklist for cohort studies is provided.

Detailed Description

We partnered with Telus Health Inc. to begin to examine the effectiveness of a supervised ML algorithm integrated into their Telus Wellbeing corporate wellness app (iOS and Android versions), formerly known as "Sprout-At-Work". As of April 29, 2025, Telus Wellbeing was the employee wellness solution for more than 50 companies across 70 countries (i.e., approximately 90% of users registered within USA). At the time they reported over 400,000 registered users and about 100,000 monthly active users (MAUs; i.e., users who engage with the platform at least once a month). Download and use of the app is voluntary and of no financial cost to employees. The multi-component app is rooted in the core components of the three-layer Behaviour Change Wheel (BCW), a comprehensive framework for designing and evaluating behaviour change interventions.

Telus Wellbeing operationalizes the COM-B model, for example, by providing educational content and PA self-monitoring opportunities to build knowledge and skills (i.e., capability), offering features like peer-to-peer sharing and team challenges to foster social support (i.e., opportunity), and using FI (i.e., motivation) to drive goal achievement (see Supplementary File A for more details). Telus Wellbeing also addresses structural barriers (e.g., built environment), systemic challenges (e.g., socioeconomic disparities), and contextual factors (e.g., social norms) through the integration of promising BCTs. For example, by offering offline functionality (i.e., environment restructuring [12.1]), subsidies for wellness programs (i.e., material incentive [6.1]), and connecting users to local health services (i.e., social support [3.1]), Telus Wellbeing supports diverse user needs (Supplementary File A).

After downloading Telus Wellbeing, users can open the app and navigate through the landing and home page. On the home page, the number of steps completed that day and the users daily step goal are shown. Participants can click on two icons at the top of the home page. If the users click on the left icon, the history page is displayed. The history page allows participants to track their performance over the past week by showing their daily steps and daily goals on a color-coded bar graph. The green bar indicates the accomplishment of achieving the step goal on the corresponding day, and the red bar indicates failure to achieve the step goal on the corresponding day. The built-in health chip in the iPhone and Android devices collects the step data, and the accuracy of step counts collected by the iPhone and Android health chip has been validated in a number of studies to have comparable accuracy to an ActiGraph. The push notification for the app is also activated (if activated by the user), and the standard iOS and Android push notification is used. The push notification is visible in the landing page and in the recent notifications tab on the phone.

Eligible participants will start a five-week run-in period after downloading the app. The purpose of the run-in period is to collect baseline daily steps, and assess if the participant is able to comply with the requirements needed to regularly use the Telus Wellbeing app. During the run-in period, all participants in the control and the intervention groups will receive a traditional personalized daily step goal based on their historical step data. The machine learning algorithm will not be used to compute step goals for participants in the intervention group during the run-in period. Dynamically increasing step goals will be used in the run-in period to engage participants in using the app regularly. In addition, all participants will receive a push notification at 8:00 AM that provides the day's step goal, and if the participant accomplishes the goal before 8:00 PM, then another push notification will be sent to congratulate that participant on reaching their step goal for the day. The identical goals between the 2 groups during the run-in period is used to establish a reference level of initial physical activity, which will be used in the statistical analyses to compare the difference in daily steps between run-in and 12 weeks for the 2 groups. Data collected during the run-in period will be used by the machine learning algorithm to compute step goals for the intervention period. This is a valid approach because run-in data will be indicative of the preference of different participants. All participants will have been placed into one of two groups. The allocation of app users to groups will be implemented by Telus Health after the run-in period. After the five-week run-in period, participants in the control group will be provided with a personalized static daily step goal hrough the Telus Wellbeing app. Participants will receive a push notification at 8:00 AM every day that provides that day's step goal (10,000 steps), and if the participant achieves the goal before 8:00 PM, then another push notification will be sent to congratulate the participant on reaching their step goal (of 10,000 steps) for the day.

After the five-week run-in period, participants in the intervention group will receive adaptively personalized step goals through the Telus Wellbeing app. The daily step goals will be computed using machine learning on the complete history (past steps and goals) of the user. Machine learning will be applied every day to reduce variance in future steps and goals. Participants will receive a push notification at 8:00 AM every day that provides today's step goal, and if the participant accomplishes the goal before 8:00 PM, then another push notification will be sent to congratulate the participant on reaching their step goal for that day. Machine learning will adaptively compute personalized step goals that are predicted to maximize future physical activity for each participant based on all their past steps' data and goals of each participant. Machine learning is applied to each participant individually, and it consists of two main steps. The first step is to use all of the participant's data to construct a quantitative model that predicts how many steps the participant will take in the future, given a prescribed set of step goals, and an important aspect of the model is a component that describes how achieving goals in the present can increase the likelihood of achieving goals in the future. The second step is to use this quantitative model to select a sequence of step goals that maximizes the predicted future number of steps. To make the process of updating step goals adaptive, machine learning is applied each day (using all the users' past data) to generate step goals for the coming day. Moreover, the step goals computed by machine learning for the coming day are not constant but increase or decrease based on the model prediction. The Telus Wellbeing app will automatically track the participants' step counts each day and will provide goals regardless of their level of engagement within the app over the 12-week study period. As the data is being analyzed retrospectively, no "end of study" letter will be provided to participants. However, upon registering for the studies procedures, all users provided written informed consent for their data to be collected and analyzed.

Conditions

Health Behavior

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: TREATMENT
• Blinding Strategy: SINGLE

Study Groups

Adaptive Step Goal (Intervention)

The 'Smart Mode' feature used a propriety ML algorithm to generate 16 user clusters based on daily step count patterns from approximately 100,000 global Telus Wellbeing users over the previous two years (March 1, 2020 to March 1, 2022; characteristics of these users unknown). The algorithm then compared time series data from participants' five-week baseline against the repository of 16 user cluster patterns to determine to which cluster they would be assigned. Once assigned, a difference in proportions was calculated (ratio of previous week's average daily step count over the step count goal for that week vs. ratio of average daily step count from two weeks prior over step count goal for that week \[e.g., 8000 steps/7500 steps=1.067 vs. 8000 steps/9500 steps=0.8421\]). Chi-square testing was used to assess statistical significance of the difference (p\<0.05). This was done to determine whether the next calculated goal would go up, down, or stay the same.

Group Type: EXPERIMENTAL

Adaptive Step Goal (Intervention)

Intervention Type: BEHAVIORAL

Receiving automated personalized daily step goals,

Static Step Goal (Control)

Over the course of the 12-week intervention period controls continued with their static daily step goal, equivalent to their baseline period weekly average daily step count. Control participants received an out-of-app push notification at 8:00 a.m. each Monday during the intervention period reminding them of their static daily step goal. If the participant achieved their daily step goal before 8:00 p.m. on any day a push notification was sent to congratulate them on reaching their goal for the day. No notifications were sent past 8:00 p.m. Participants achieving daily step goals were rewarded with FI in the form of points (i.e., "SproutBucks"). They could redeem "SproutBucks" either "on-platform" (e.g., gift cards to StarbucksTM, BestBuyTM, iTunesTM, VisaTM) or "off-platform" (e.g., employer specific rewards like vouchers for fitness membership discounts). The value of "SproutBucks" was unique to companies, ranging from $0.00 to $1.00 USD per daily goal achieved.

Group Type: ACTIVE_COMPARATOR

Static Step Goal (Control)

Intervention Type: BEHAVIORAL

Receiving personalized static daily step goals.

Interventions

Adaptive Step Goal (Intervention)

Receiving automated personalized daily step goals,

Intervention Type: BEHAVIORAL

Static Step Goal (Control)

Receiving personalized static daily step goals.

Intervention Type: BEHAVIORAL

Eligibility Criteria

Inclusion Criteria

• Adults aged 18 years or older
• User intent to become physically active in the next 12 weeks
• Own a smartphone device
• Willing to install and use the Telus Wellbeing app (which requires Internet connection) every day for 12 weeks
• Ability to speak and read English

Exclusion Criteria

• Known medical conditions or physical problems that require special attention in a physical activity program
• Planning an international trip during the next 3 months, which could interfere with daily server uploads of mobile phone data
• Pregnant or gave birth during the past 6 months
• Current participation in lifestyle modification programs or research studies that may confound study results

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Western University, Canada

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Western University

London, Ontario, Canada

Countries

Canada

References

Michie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci. 2011 Apr 23;6:42. doi: 10.1186/1748-5908-6-42.

Reference Type: BACKGROUND

PMID: 21513547 (View on PubMed)

Mitchell MS, Orstad SL, Biswas A, Oh PI, Jay M, Pakosh MT, Faulkner G. Financial incentives for physical activity in adults: systematic review and meta-analysis. Br J Sports Med. 2020 Nov;54(21):1259-1268. doi: 10.1136/bjsports-2019-100633. Epub 2019 May 15.

Reference Type: BACKGROUND

PMID: 31092399 (View on PubMed)

Armijo-Olivo S, Stiles CR, Hagen NA, Biondo PD, Cummings GG. Assessment of study quality for systematic reviews: a comparison of the Cochrane Collaboration Risk of Bias Tool and the Effective Public Health Practice Project Quality Assessment Tool: methodological research. J Eval Clin Pract. 2012 Feb;18(1):12-8. doi: 10.1111/j.1365-2753.2010.01516.x. Epub 2010 Aug 4.

Reference Type: BACKGROUND

PMID: 20698919 (View on PubMed)

Other Identifiers

123632

Identifier Type: -

Identifier Source: org_study_id