Self-Monitoring Utilization Patterns Among Individuals in an Incentivized Program for Healthy Behaviors
Open accessAbstract
BACKGROUND: The 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. OBJECTIVE: This 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. METHODS: We 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. RESULTS: Thirty-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). CONCLUSIONS: This 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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Participants using devices with automatic data entry engaged with the platform 4 times longer than those who manually entered data.
“Participants using devices with automatic data entry engaged with the platform 4 times longer than those who manually entered data [94].”
From Digital Health Interventions to Prevent Type 2 Diabetes Mellitus: Systematic Review (2025) · cites this paper as [94]
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- Authors
- Kim, J. Y., Wineinger, N. E., Taitel, M., Radin, J. M., Akinbosoye, O., Jiang, J., Nikzad, N., Orr, G., Topol, E., & Steinhubl, S.
- Affiliations
- Seoul National University (KR)
- Walgreens (United States) (US)
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2016-11-17
- Volume
- 18
- Issue
- 11
- Pages
- e292
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC5133453
- OpenAlex
- W2549774070
- Cited by
- 37
- References
- 33
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 1 · 2022: 4 · 2021: 4 · 2020: 5 · 2019: 5 · 2018: 9 · 2017: 5
- FWCI
- 11.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (UL1TR001114)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Keywords
- self-monitoring, activity monitoring, wearable devices, health engagement, digital health interventions
- MeSH terms
- Adolescent, Adult, Aged, Female, Humans, Male, Middle Aged, Motivation, Self-Assessment, Health Behavior, Telemedicine, Young Adult
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