Mining health app data to find more and less successful weight loss subgroups

Katrina J. Serrano, Mandi Yu, Kisha I. Coa, Linda M. Collins, Audie A. Atienza

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Article numbere154
JournalJournal of medical Internet research
Volume18
Issue number6
DOIs
StatePublished - Jun 2016

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Weight Loss
Health
Data Mining
Weights and Measures
Cost of Illness
Cost Savings
Health Care Costs
Life Style
Body Weight
Regression Analysis
Research Personnel
Diet

All Science Journal Classification (ASJC) codes

  • Health Informatics

Cite this

Serrano, Katrina J. ; Yu, Mandi ; Coa, Kisha I. ; Collins, Linda M. ; Atienza, Audie A. / Mining health app data to find more and less successful weight loss subgroups. In: Journal of medical Internet research. 2016 ; Vol. 18, No. 6.
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Mining health app data to find more and less successful weight loss subgroups. / Serrano, Katrina J.; Yu, Mandi; Coa, Kisha I.; Collins, Linda M.; Atienza, Audie A.

In: Journal of medical Internet research, Vol. 18, No. 6, e154, 06.2016.

Research output: Contribution to journalArticle

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T1 - Mining health app data to find more and less successful weight loss subgroups

AU - Serrano, Katrina J.

AU - Yu, Mandi

AU - Coa, Kisha I.

AU - Collins, Linda M.

AU - Atienza, Audie A.

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AB - 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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