ArticleJournal of medical Internet research2016
Self-Monitoring Utilization Patterns Among Individuals in an Incentivized Program for Healthy Behaviors.
Article in Journal of medical Internet research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Digital Health Interventions to Prevent Type 2 Diabetes Mellitus: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Smartphone-Based Health Program for Improving Physical Activity and Tackling Obesity for Young Adults: A Systematic Review and Meta-Analysis.International journal of environmental research and public health · 2019Pooled it
- Lifestyle modification intervention among pregnant women with hypertension based on the self-determination theory using M-Health.Frontiers in public health · 2025Trial
- The effect of the Smart Health Continuous Feedback For Elderly Exercise (SHe CoFFEE) program on mobility: a randomized controlled pilot study.Frontiers in public health · 2024Trial
- Predicting Outcomes from Engagement With Specific Components of an Internet-Based Physical Activity Intervention With Financial Incentives: Process Analysis of a Cluster Randomized Controlled Trial.Journal of medical Internet research · 2019Trial
- Predictors and Outcomes of Digital Weighing and Activity Tracking Lapses Among Young Adults During Weight Gain Prevention.Obesity (Silver Spring, Md.) · 2021Article
- Mobile Health Apps on COVID-19 Launched in the Early Days of the Pandemic: Content Analysis and Review.JMIR mHealth and uHealth · 2020Review
- The Demographic Representativeness and Health Outcomes of Digital Health Station Users: Longitudinal Study.Journal of medical Internet research · 2020Article
- Measuring Engagement in eHealth and mHealth Behavior Change Interventions: Viewpoint of Methodologies.Journal of medical Internet research · 2018Article
- Interaction and Engagement with an Anxiety Management App: Analysis Using Large-Scale Behavioral Data.JMIR mental health · 2018Article
- Predicting which type of push notification content motivates users to engage in a self-monitoring app.Preventive medicine reports · 2018Article
- Technologies for Innovative Monitoring to Reduce Blood Pressure and Change Lifestyle Using Mobile Phones in Adult and Elderly Populations (TIM Study): Protocol for a Randomized Controlled Trial.JMIR research protocols · 2018Article
- A Smartphone App to Promote an Active Lifestyle in Lower-Educated Working Young Adults: Development, Usability, Acceptability, and Feasibility Study.JMIR mHealth and uHealth · 2018Article
- Engagement, compliance and retention with a gamified online social networking physical activity intervention.Translational behavioral medicine · 2017Article
- A Review of Technology-Assisted Interventions for Diabetes Prevention.Current diabetes reports · 2017Review
- Weight loss in a digital app-based diabetes prevention program powered by artificial intelligence.Digital healthArticle
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundThe advent of digital technology has enabled individuals to track meaningful biometric data about themselves. This novel capability has spurred nontraditional health care organizations to develop systems that aid users in managing their health. One of the most prolific systems is Walgreens Balance Rewards for healthy choices (BRhc) program, an incentivized, Web-based self-monitoring program.
objectiveThis study was performed to evaluate health data self-tracking characteristics of individuals enrolled in the Walgreens' BRhc program, including the impact of manual versus automatic data entries through a supported device or apps.
methodsWe obtained activity tracking data from a total of 455,341 BRhc users during 2014. Upon identifying users with sufficient follow-up data, we explored temporal trends in user participation.
resultsThirty-four percent of users quit participating after a single entry of an activity. Among users who tracked at least two activities on different dates, the median length of participating was 8 weeks, with an average of 5.8 activities entered per week. Furthermore, users who participated for at least twenty weeks (28.3% of users; 33,078/116,621) consistently entered 8 to 9 activities per week. The majority of users (77%; 243,774/315,744) recorded activities through manual data entry alone. However, individuals who entered activities automatically through supported devices or apps participated roughly four times longer than their manual activity-entering counterparts (average 20 and 5 weeks, respectively; P<.001).
conclusionsThis study provides insights into the utilization patterns of individuals participating in an incentivized, Web-based self-monitoring program. Our results suggest automated health tracking could significantly improve long-term health engagement.
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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.