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Data-Driven User-Type Clustering of a Physical Activity Promotion App: Usage Data Analysis Study.

Christina Kranzinger1, Verena Venek1, Harald Rieser1

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Summary
This summary is machine-generated.

Data-driven methods identified distinct user groups for the Fit-mit-ILSE app, revealing patterns in physical activity engagement among older adults. Younger women in Salzburg showed the highest app usage, informing targeted interventions.

Keywords:
Jenks natural breaks algorithmPartitioning Around Medoids algorithmactive and assisted livingapp usagecluster analysisphysical activity promotionusage groups

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Area of Science:

  • Gerontology
  • Digital Health
  • Human-Computer Interaction

Background:

  • Physical inactivity is a major global health risk, exacerbated by increasing sedentary lifestyles and age-related decline in physical activity.
  • The Fit-mit-ILSE research prototype aims to enhance physical fitness and technology adoption in adults aged 55 and older.
  • It integrates active and assisted living technologies with smart services via the ILSE app.

Purpose of the Study:

  • To develop and present data-driven methods for clustering health and fitness app user types.
  • To identify distinct usage patterns within the ILSE app's 'Fit at home' function.
  • To move beyond expert-defined thresholds for user segmentation in active and assisted living contexts.

Main Methods:

  • Collected ILSE app log data from 165 participants across two field trial phases.
  • Applied Jenks natural breaks algorithm for data-driven derivation of cluster thresholds, replacing expert-defined ones.
  • Utilized Partitioning Around Medoids for multidimensional clustering based on detailed app usage patterns.

Main Results:

  • Clustering revealed 4 user groups based on 'Fit at home' function usage; 38.2% used it weekly to bi-weekly.
  • Low usage was more prevalent in men, older adults, and specific regions (Vienna vs. Salzburg).
  • Younger women in Salzburg exhibited the highest average app usage, differing across gender, age, and region.

Conclusions:

  • Data-driven clustering offers objective thresholds for analyzing app usage data.
  • These methods complement expert-based definitions and identify specific user groups for targeted interventions.
  • Findings highlight the potential for personalized engagement strategies in digital health for older adults.