Mining Health App Data to Find More and Less Successful Weight Loss Subgroups
Open accessAbstract
BACKGROUND: More than half of all smartphone app downloads involve weight, diet, and exercise. If successful, these lifestyle apps may have far-reaching effects for disease prevention and health cost-savings, but few researchers have analyzed data from these apps. OBJECTIVE: The purposes of this study were to analyze data from a commercial health app (Lose It!) in order to identify successful weight loss subgroups via exploratory analyses and to verify the stability of the results. METHODS: Cross-sectional, de-identified data from Lose It! were analyzed. This dataset (n=12,427,196) was randomly split into 24 subsamples, and this study used 3 subsamples (combined n=972,687). Classification and regression tree methods were used to explore groupings of weight loss with one subsample, with descriptive analyses to examine other group characteristics. Data mining validation methods were conducted with 2 additional subsamples. RESULTS: In subsample 1, 14.96% of users lost 5% or more of their starting body weight. Classification and regression tree analysis identified 3 distinct subgroups: "the occasional users" had the lowest proportion (4.87%) of individuals who successfully lost weight; "the basic users" had 37.61% weight loss success; and "the power users" achieved the highest percentage of weight loss success at 72.70%. Behavioral factors delineated the subgroups, though app-related behavioral characteristics further distinguished them. Results were replicated in further analyses with separate subsamples. CONCLUSIONS: This study demonstrates that distinct subgroups can be identified in "messy" commercial app data and the identified subgroups can be replicated in independent samples. Behavioral factors and use of custom app features characterized the subgroups. Targeting and tailoring information to particular subgroups could enhance weight loss success. Future studies should replicate data mining analyses to increase methodology rigor.
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Serrano et al distinguished highly engaged subgroups by the number of customizations made to diet and exercise features of a weight loss app.
“In their study examining engagement with a weight loss app, Serrano et al applied classification and regression tree methods to identify subgroups with unique engagement behaviors [79]. They were able to distinguish highly engaged subgroups by the number of customizations made to the diet and exercise features of the app.”
From A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review (2019) · cites this paper as [79]
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- Authors
- Serrano, K. J., Yu, M., Coa, K. I., Collins, L. M., & Atienza, A. A.
- Affiliations
- National Institutes of Health (US)
- ICF International (United States) (US)
- Pennsylvania State University (US)
- Corresponding author
- Serrano, Katrina J
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2016-06-14
- Volume
- 18
- Issue
- 6
- Pages
- e154
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC4925935
- OpenAlex
- W2424092482
- Cited by
- 53
- References
- 34
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 1 · 2023: 4 · 2022: 1 · 2021: 9 · 2020: 10 · 2019: 9 · 2018: 5 · 2017: 7 · 2016: 1
- FWCI
- 13.64
- 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)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
- Health Sciences › Nursing › Nutrition and Dietetics › Nutrition, Health and Food Behavior
- Keywords
- weight loss, mobile health apps, user subgroups, classification and regression trees, behavioral factors, app usage patterns
- MeSH terms
- Adolescent, Adult, Aged, Body Weight, Cross-Sectional Studies, Diet, Female, Humans, Male, Middle Aged, Random Allocation, Weight Loss, Exercise, Cell Phone, Young Adult, Data Mining, Mobile Applications, Datasets as Topic
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