JMIR Publications

ABSTRACT

Background:

Utilizing digital health technologies to aid individuals in managing chronic diseases offers a promising solution to overcome health service barriers such as access and affordability. However, their effectiveness depends on adoption and sustained use, influenced by user preferences.

Objective:

This study quantifies the preferences of individuals with chronic heart disease for features of a mobile health app to self-navigate their disease condition.

Methods:

We conducted an unlabeled online choice survey among adults over 18 with chronic heart disease living in Australia, recruited via an online survey platform. Four app attributes—ease of navigation, monitoring of blood pressure and heart rhythm, health education, and symptom diary maintenance—were systematically chosen through a multi-stage process. This process involved a literature review, stakeholder interviews, and expert panel discussions. Participants chose a preferred mobile app out of three alternatives, app A, app B, or neither. A D-optimal design was developed using Ngene software, informed by Bayesian priors derived from pilot survey data. Latent class model (LCM) analysis was conducted using Nlogit software. We also estimated attribute importance and anticipated adoption rates for three app versions.

Results:

Our sample included 302 participants with a mean age of 50.5 years. LCM identified two classes. Older respondents with education beyond high school, prior experience with mobile health apps, and a positive perception of app usefulness were more likely to be in class 1 (85%) than class 2 (15%). Class 1 membership preferred adopting a mobile app (app A: β coefficient 0.74, 95% uncertainty interval (UI) 0.41 to 1.06; app B: β coefficient 0.53, 95%UI 0.22 to 0.85). Basic training was preferred to advanced training (β coefficient -0.48, 95%UI -0.61 to -0.36). Participants favored apps providing post-monitoring recommendations (β coefficient 1.45, 95%UI 1.26 to 1.64), tailored health education (β coefficient 0.50, 95%UI 0.36 to 0.64), and unrestricted symptom diary entry (β coefficient 0.58, 95%UI 0.41 to 0.76). Class 2 showed no preference for app adoption (app A: β coefficient -1.18, 95%UI -2.36 to 0.006; app B: β coefficient -0.78, 95%UI -1.99 to 0.42) or any specific attribute levels. Vital sign monitoring was the most influential attribute among the four. Scenario analysis revealed an 84% probability of app adoption with basic features, rising to 92% when app features aligned with respondents’ preferences.

Conclusions:

The study's findings suggest that designing preference-informed mobile health apps could significantly enhance adoption rates and engagement among individuals with CHD, potentially leading to improved clinical outcomes. Adoption rates were notably higher when app attributes included easy navigation, vital sign monitoring, feedback provision, personalized health education, and flexible data entry for symptom diary maintenance. Some groups may be less receptive to these features, warranting further research to explore factors influencing app adoption among these populations. Clinical Trial: not applicable