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Characterizing user engagement with health app data: a data mining approach.

Katrina J Serrano1, Kisha I Coa2, Mandi Yu3

  • 1National Cancer Institute, National Institutes of Health, Rockville, MD, USA. katrina.serrano@nih.gov.

Translational Behavioral Medicine
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Summary

Engagement with commercial health apps like Lose It! is crucial for behavior change. Highly engaged users customized diet and exercise, while less engaged users focused on weigh-ins and diet customization.

Keywords:
Big dataClassification and regression treeData miningMobile health applicationMobile health technologySmartphone appUser engagement

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Area of Science:

  • Digital Health
  • Behavioral Science
  • Health Informatics

Background:

  • Mobile health (mHealth) apps are increasingly used for lifestyle behavior modification.
  • Understanding factors driving user engagement in commercial health apps is limited.

Purpose of the Study:

  • To explore behavioral engagement with a popular weight loss app, Lose It!.
  • To identify characteristics differentiating highly engaged from less engaged user groups.

Main Methods:

  • Analysis of anonymized, cross-sectional data from over 1 million Lose It! users.
  • Utilized classification and regression tree methods for subgroup identification.
  • Descriptive analyses and data mining validation were performed on separate subsamples.

Main Results:

  • Average user engagement was 29 days, with identified subgroups ranging from 3.5 to 172 days.
  • High engagement correlated with customization of both diet and exercise.
  • Lower engagement was associated with weigh-ins and diet customization only.

Conclusions:

  • Commercial mHealth apps offer significant reach for public health interventions.
  • App features, particularly customization of diet and exercise, are key drivers of sustained user engagement.
  • Enhancing user engagement is critical for the success of mHealth interventions aimed at behavior change.