Can menstrual health apps selected based on users' needs change health-related factors? A double-blind randomized controlled trial

J Am Med Inform Assoc. 2019 Jul 1;26(7):655-666. doi: 10.1093/jamia/ocz019.

Abstract

Objective: Most healthcare providers are reluctant to use health apps for healthcare because there is no rigorous way of choosing the best app for their patient or consumer. Accordingly, we developed a new method of app selection that fully considers target users' needs. This study verified whether health apps selected based on target users' needs can influence health-related factors.

Materials and methods: We conducted a randomized control trial of women with dysmenorrhea and premenstrual syndrome using App A (the best app selected using the new method) and App B (the app with the highest number of users worldwide). The intervention was performed over 4 months to include at least 3 menstrual cycles.

Results: Sixty-one app users completed the 16-week intervention. While users rated both apps as higher in quality than previously used menstrual apps, only App A users showed significant improvements in overall satisfaction, app outcome expectancy, the number of days with records, app social influence, intent to recommend, and the possibility of behavioral or cognitive changes in their symptom management. The number of menus used increased over time. While the app self-efficacy and the number of relief methods did not significantly differ between groups, they still showed an increase in App A users.

Conclusions: When a menstrual app reflected users' needs, they recorded their symptoms more often and reported higher app quality, satisfaction, and intention to recommend. This study can not only benefit the selection of menstrual apps, but also confirm that mobile health apps can improve health-related factors.

Keywords: dysmenorrhea; mobile applications; needs assessment; premenstrual syndrome; randomized controlled trial.

Publication types

  • Randomized Controlled Trial
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Double-Blind Method
  • Dysmenorrhea* / physiopathology
  • Female
  • Humans
  • Menstrual Cycle*
  • Mobile Applications*
  • Patient Satisfaction
  • Premenstrual Syndrome* / physiopathology
  • Principal Component Analysis