Mobile Health Fitness Interventions: Impact of Features on Routine Use and Data Sharing Acceptability

Amir Razaghizad; Turney McKee; Isabelle Malhamé; Matthias G Friedrich; Nadia Giannetti; Andrew Coristine; Anders Johnson; Euan A Ashley; Steven G Hershman; Brooke Struck; Sekoul Krastev; Dan Pilat; Abhinav Sharma

doi:10.1016/j.jacadv.2023.100613

Mobile Health Fitness Interventions: Impact of Features on Routine Use and Data Sharing Acceptability

JACC Adv. 2023 Sep 22;2(8):100613. doi: 10.1016/j.jacadv.2023.100613. eCollection 2023 Oct.

Authors

Amir Razaghizad^{1

2

3}, Turney McKee⁴, Isabelle Malhamé^{1

5}, Matthias G Friedrich^{3

6}, Nadia Giannetti^{3

6}, Andrew Coristine³, Anders Johnson⁷, Euan A Ashley⁷, Steven G Hershman⁷, Brooke Struck⁴, Sekoul Krastev⁴, Dan Pilat⁴, Abhinav Sharma^{1

2

3}

Affiliations

¹ Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada.
² DREAM-CV Lab, McGill University Health Centre, McGill University, Montreal, Quebec, Canada.
³ Division of Cardiology, McGill University Health Centre, McGill University, Montreal, Quebec, Canada.
⁴ The Decision Lab, Montreal, Quebec, Canada.
⁵ Division of General Internal Medicine, Department of Medicine, McGill University Health Centre, Montreal, Quebec, Canada.
⁶ Courtois Cardiovascular Signature Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada.
⁷ Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA.

Abstract

Background: Mobile health (mHealth) interventions are increasingly being used for cardiovascular research and physical activity promotion.

Objectives: As a result, the authors aimed to evaluate which features facilitate and impede routine engagement with mobile fitness applications.

Methods: We distributed a pan-Canadian online questionnaire via the behavioral research platform Prolific.co to evaluate what features are associated with the use and routine engagement (ie, daily or weekly use) of mHealth fitness applications and attitudes about data sharing. Binary logistic regression was used to quantify the association between these endpoints and exploratory factors such as the perceived utility of various mHealth application features.

Results: The survey received 694 responses. Most people were women (62%), the median age was 28 years (range: 18-78 years), and most people reported current use of an mHealth fitness application (48%). The perceived importance of personal health (OR: 2.40; 95% CI: 1.34-4.50) was the factor most associated with the current use of an mHealth fitness application. The feature most associated with routine engagement was the ability to track progress toward a goal (OR: 5.10; 95% CI: 2.73-9.61) while the most significant barrier was the absence of goal customization features (OR: 0.44; 95% CI: 0.25-0.81). The acceptance of sharing health data for research was high (56%), and privacy concerns did not significantly affect routine engagement (OR: 0.81; 95% CI: 0.40-1.77). Results were consistent across race and gender.

Conclusions: mHealth interventions have the potential to be scaled across populations. Optimizing applications to improve self-monitoring and personalization could increase routine engagement and, thus, user retention and intervention effectiveness.

Keywords: adherence; data sharing; health technology; mobile health; physical activity; primary prevention; public health; smartphone applications.