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Exploring the State-of-Receptivity for mHealth Interventions.

Florian Künzler1, Varun Mishra2, Jan-Niklas Kramer3

  • 1ETH Zürich.

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Summary
This summary is machine-generated.

Understanding user receptivity is key for effective mobile health interventions. This study identified personal and contextual factors influencing engagement with Just-In-Time Adaptive Interventions (JITAI), improving intervention design and effectiveness.

Keywords:
EngagementInterruptionInterventionMobile HealthReceptivity

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Area of Science:

  • Mobile Health (mHealth)
  • Human-Computer Interaction
  • Behavioral Science

Background:

  • Just-In-Time Adaptive Interventions (JITAI) show promise for personalized mHealth support.
  • User receptivity is critical for JITAI success, but factors influencing it in mHealth contexts are underexplored.
  • Previous research focused on generic notifications, not specific mHealth interventions.

Purpose of the Study:

  • To investigate factors influencing user receptivity to mHealth interventions.
  • To identify participant-specific and contextual predictors of receptivity.
  • To explore the relationship between receptivity, intervention effectiveness, and future engagement.

Main Methods:

  • A 6-week study involving 189 participants using a chatbot-based digital coach (Ally) for physical activity.
  • Development of metrics to measure user receptivity to interventions.
  • Machine learning models were built to predict user receptivity.

Main Results:

  • Participant characteristics (age, personality, device type) significantly correlated with overall receptivity.
  • Contextual factors (time, battery, activity, location) significantly predicted in-the-moment receptivity.
  • Machine learning models achieved up to a 77% increase in F1 score for receptivity detection.

Conclusions:

  • Personal and contextual factors significantly impact user receptivity to mHealth interventions.
  • Higher receptivity may enhance intervention effectiveness, creating a positive feedback loop for engagement.
  • Accurate detection of receptivity can optimize JITAI delivery and improve user outcomes.