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Related Experiment Video

Updated: May 24, 2026

An Application for Pairing with Wearable Devices to Monitor Personal Health Status
06:58

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Published on: February 3, 2022

Personalizing Mobile Applications for Health Behavioral Change According to Age and Gender.

Laetitia Gosetto1,2, Gilles Falquet3, Christian Lovis1,2

  • 1Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary

Mobile health (mHealth) technologies can aid behavior change for chronic diseases, but user diversity requires personalized approaches. This study found age and gender significantly impact preferences for mHealth behavior change mechanisms.

Keywords:
agebehavior changegendermHealthpersonalization

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Last Updated: May 24, 2026

An Application for Pairing with Wearable Devices to Monitor Personal Health Status
06:58

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

Published on: February 3, 2022

Area of Science:

  • Digital Health
  • Behavioral Science
  • Human-Computer Interaction

Background:

  • Mobile health (mHealth) technologies are crucial for chronic disease prevention and management.
  • One-size-fits-all mHealth solutions are limited by user diversity.
  • Understanding user preferences for behavior change mechanisms is key to effective mHealth design.

Purpose of the Study:

  • To investigate how age and gender influence preferences for 14 behavior change mechanisms in mHealth.
  • To identify specific mHealth features that resonate differently across demographic groups.

Main Methods:

  • Cross-sectional online survey with 214 participants (mean age 29.4 years).
  • Logistic regression analysis to determine the effects of age and gender on mechanism preferences.
  • Evaluation of preferences for 14 distinct behavior change techniques.

Main Results:

  • Self-monitoring, progression, challenge, and quests were universally preferred mHealth mechanisms.
  • Older adults (>35 years) showed lower preference for rewards but higher preference for prompts and cues.
  • Women preferred avatars and showed lower attraction to social comparison and competition compared to men.

Conclusions:

  • Personalized mHealth designs are essential for optimizing user engagement and effectiveness.
  • Tailoring behavior change mechanisms based on age and gender can enhance chronic disease management.
  • Future mHealth interventions should consider demographic variations in user preferences for greater impact.