Personalizing mobile applications for health based on user profiles: A preference matrix from a scoping review

Area of Science:

• Digital Health
• Behavioral Science
• Human-Computer Interaction

Background:

• Unhealthy behaviors are major contributors to global mortality and morbidity.
• Mobile health (mHealth) applications offer a promising avenue for promoting healthier lifestyles.
• Personalized mHealth interventions can significantly enhance user engagement and intervention effectiveness.

Purpose of the Study:

• To guide the personalization of mHealth interventions for health behavior change.
• To define optimal mechanisms for specific user profiles.
• To synthesize existing research on personalization in behavior change apps.

Main Methods:

• A scoping review of online databases was conducted.
• Articles published between 2008 and 2024 on personalization, behavior change apps, and mHealth mechanisms were identified.
• Extracted mechanisms and user profiles were categorized and analyzed for relationships.

Main Results:

• 18 articles were retained from 1806 identified, detailing 4 user profiles (including personality and gamer types) and 21 mechanisms (behavioral change techniques, gamification, app features).
• Limited preference relations (51% coverage) were found between user profiles and mechanisms, with social mechanisms and self-efficacy theory highlighted.
• The Big Five and Hexad scale user profiles accounted for a significant portion of identified relations.

Conclusions:

• Personalization of mHealth interventions requires understanding the interplay between user profiles and specific mechanisms.
• Social mechanisms and self-efficacy-based techniques are crucial for persuasive system design in mHealth.
• Future research should focus on completing the relation matrix and developing reliable personality assessments for tailored mHealth strategies.