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

Transl Behav Med. 2017 Jun;7(2):277-285. doi: 10.1007/s13142-017-0508-y.


The use of mobile health applications (apps) especially in the area of lifestyle behaviors has increased, thus providing unprecedented opportunities to develop health programs that can engage people in real-time and in the real-world. Yet, relatively little is known about which factors relate to the engagement of commercially available apps for health behaviors. This exploratory study examined behavioral engagement with a weight loss app, Lose It! and characterized higher versus lower engaged groups. Cross-sectional, anonymized data from Lose It! were analyzed (n = 12,427,196). This dataset was randomly split into 24 subsamples and three were used for this study (total n = 1,011,008). Classification and regression tree methods were used to identify subgroups of user engagement with one subsample, and descriptive analyses were conducted to examine other group characteristics associated with engagement. Data mining validation methods were conducted with two separate subsamples. On average, users engaged with the app for 29 days. Six unique subgroups were identified, and engagement for each subgroup varied, ranging from 3.5 to 172 days. Highly engaged subgroups were primarily distinguished by the customization of diet and exercise. Those less engaged were distinguished by weigh-ins and the customization of diet. Results were replicated in further analyses. Commercially-developed apps can reach large segments of the population, and data from these apps can provide insights into important app features that may aid in user engagement. Getting users to engage with a mobile health app is critical to the success of apps and interventions that are focused on health behavior change.

Keywords: Big data; Classification and regression tree; Data mining; Mobile health application; Mobile health technology; Smartphone app; User engagement.

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Cross-Sectional Studies
  • Data Mining / methods
  • Female
  • Health Behavior
  • Humans
  • Male
  • Middle Aged
  • Mobile Applications*
  • Patient Participation*
  • Self Report
  • Smartphone
  • Telemedicine* / methods
  • Weight Reduction Programs* / methods
  • Young Adult