Observational studyJournal of medical Internet research2020
Examining Responsiveness to an Incentive-Based Mobile Health App: Longitudinal Observational Study.
Observational study in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Exploring Perceived Utility of a Clinic-Integrated HIV Prevention App for Men Who Have Sex With Men in Malaysia: A Qualitative Think-Aloud Study.Health science reports · 2026Article
- Features of Mobile Health Apps for Tobacco Cessation That Appeal to Black Adults Who Use Tobacco Products: Focus Group Study.JMIR mHealth and uHealth · 2026Article
- Boosting Physical Activity Among Individuals With Low Engagement Through Double-Point Incentives in a Community-Based mHealth Intervention: Retrospective Observational Study.JMIR mHealth and uHealth · 2025Observational
- Tracked Physical Activity Levels Before and After a Change in Incentive Strategy Among UK Adults Using a Rewards App: Retrospective Quasi-Experimental Study.JMIR formative research · 2024Article
- Enhancing the implementation and integration of mHealth interventions in resource-limited settings: a scoping review.Implementation science : IS · 2024Article
- Enhancing the Implementation and Integration of mHealth Interventions in Resource-Limited Settings: A Scoping Review.Research square · 2024Article
- Unveiling Consumer Preferences and Intentions for Cocreated Features of a Combined Diet and Physical Activity App: Cross-Sectional Study in 4 European Countries.JMIR human factors · 2023Article
- Reduction of Financial Health Incentives and Changes in Physical Activity.JAMA network open · 2023Article
- Engagement With mHealth COVID-19 Digital Biomarker Measurements in a Longitudinal Cohort Study: Mixed Methods Evaluation.Journal of medical Internet research · 2023Article
- Factors impacting the use of the NZ COVID Tracer application in New Zealand".Smart health (Amsterdam, Netherlands) · 2022Review
- Active Use and Engagement in an mHealth Initiative Among Young Men With Obesity: Mixed Methods Study.JMIR formative research · 2022Article
- Is it worth it? Cost-effectiveness analysis of a commercial physical activity app.BMC public health · 2021Article
- Engagement in Digital Mental Health Interventions: Can Monetary Incentives Help?Frontiers in psychology · 2021Article
- Adding team-based financial incentives to the Carrot Rewards physical activity app increases daily step count on a population scale: a 24-week matched case control study.The international journal of behavioral nutrition and physical activity · 2020Article
- Understanding the user: Patients' perception, needs, and concerns of health apps for chronic constipation.Digital healthArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe Carrot Rewards app was developed as part of a public-private partnership to reward Canadians with loyalty points for downloading the app, referring friends, completing educational health quizzes, and health-related behaviors with long-term objectives of increasing health knowledge and encouraging healthy behaviors. During the first 3 months after program rollout in British Columbia, a number of program design elements were adjusted, creating observed differences between groups of users with respect to the potential impact of program features on user engagement levels.
objectiveThis study examines the impact of reducing reward size over time and explored the influence of other program features such as quiz timing, health intervention content, and type of reward program on user engagement with a mobile health (mHealth) app.
methodsParticipants in this longitudinal, nonexperimental observational study included British Columbia citizens who downloaded the app between March and July 2016. A regression methodology was used to examine the impact of changes to several program design features on quiz offer acceptance and engagement with this mHealth app.
resultsOur results, based on the longitudinal app use of 54,917 users (mean age 35, SD 13.2 years; 65.03% [35,647/54,917] female), indicated that the key drivers of the likelihood of continued user engagement, in order of greatest to least impact, were (1) type of rewards earned by users (eg, movies [+355%; P<.001], air travel [+210%; P<.001], and grocery [+140%; P<.001] relative to gas), (2) time delay between early offers (-64%; P<.001), (3) the content of the health intervention (eg, healthy eating [-10%; P<.001] vs exercise [+20%, P<.001] relative to health risk assessments), and (4) changes in the number of points offered. Our results demonstrate that reducing the number of points associated with a particular quiz by 10% only led to a 1% decrease in the likelihood of offer response (P<.001) and that each of the other design features had larger impacts on participant retention than did changes in the number of points.
conclusionsThe results of this study demonstrate that this program, built around the principles of behavioral economics in the form of the ongoing awarding of a small number of reward points instantly following the completion of health interventions, was able to drive significantly higher engagement levels than those demonstrated in previous literature exploring the intersection of mHealth apps and financial incentives. Previous studies have demonstrated the presence of incentive matters to user engagement; however, our results indicate that the number of points offered for these reward point-based health interventions is less important than other program design features such as the type of reward points being offered, the timing of intervention and reward offers, and the content of the health interventions in driving continued engagement by users.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.