A personalized mobile app for physical activity: An experimental mixed-methods study

Huong Ly Tong; Juan C Quiroz; Ahmet Baki Kocaballi; Kiran Ijaz; Enrico Coiera; Clara K Chow; Liliana Laranjo

doi:10.1177/20552076221115017

A personalized mobile app for physical activity: An experimental mixed-methods study

Digit Health. 2022 Jul 21:8:20552076221115017. doi: 10.1177/20552076221115017. eCollection 2022 Jan-Dec.

Authors

Huong Ly Tong¹, Juan C Quiroz², Ahmet Baki Kocaballi³, Kiran Ijaz⁴, Enrico Coiera⁴, Clara K Chow¹, Liliana Laranjo¹

Affiliations

¹ Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia.
² Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia.
³ School of Computer Science, University of Technology Sydney, Sydney, Australia.
⁴ Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.

Abstract

Objectives: To investigate the feasibility of the be.well app and its personalization approach which regularly considers users' preferences, amongst university students.

Methods: We conducted a mixed-methods, pre-post experiment, where participants used the app for 2 months. Eligibility criteria included: age 18-34 years; owning an iPhone with Internet access; and fluency in English. Usability was assessed by a validated questionnaire; engagement metrics were reported. Changes in physical activity were assessed by comparing the difference in daily step count between baseline and 2 months. Interviews were conducted to assess acceptability; thematic analysis was conducted.

Results: Twenty-three participants were enrolled in the study (mean age = 21.9 years, 71.4% women). The mean usability score was 5.6 ± 0.8 out of 7. The median daily engagement time was 2 minutes. Eighteen out of 23 participants used the app in the last month of the study. Qualitative data revealed that people liked the personalized activity suggestion feature as it was actionable and promoted user autonomy. Some users also expressed privacy concerns if they had to provide a lot of personal data to receive highly personalized features. Daily step count increased after 2 months of the intervention (median difference = 1953 steps/day, p-value <.001, 95% CI 782 to 3112).

Conclusions: Incorporating users' preferences in personalized advice provided by a physical activity app was considered feasible and acceptable, with preliminary support for its positive effects on daily step count. Future randomized studies with longer follow up are warranted to determine the effectiveness of personalized mobile apps in promoting physical activity.

Keywords: Mobile applications [MeSH]; digital technology [MeSH]; exercise [MeSH]; health behavior [MeSH]; personalization; physical activity; tailoring.