User Experience and Extended Technology Acceptance Model in Commercial Health Care App Usage Among Patients With Cancer: Mixed Methods Study

Abstract

Background: The shift in medical care toward prediction and prevention has led to the emergence of digital health care as a valuable tool for managing health issues. Aiding long-term follow-up care for cancer survivors and contributing to improved survival rates. However, potential barriers to mobile health usage, including age-related disparities and challenges in user retention for commercial health apps, highlight the need to assess the impact of patients' abilities and health status on the adoption of these interventions.

Objective: This study aims to investigate the app adherence and user experience of commercial health care apps among cancer survivors using an extended technology acceptance model (TAM).

Methods: The study enrolled 264 cancer survivors. We collected survey results from May to August 2022 and app usage records from the app companies. The survey questions were created based on the TAM.

Results: We categorized 264 participants into 3 clusters based on their app usage behavior: short use (n=77), medium use (n=101), and long use (n=86). The mean usage days were 9 (SD 11) days, 58 (SD 20) days, and 84 (SD 176) days, respectively. Analysis revealed significant differences in perceived usefulness (P=.01), interface satisfaction (P<.01), equity (P<.01), and utility (P=.01) among the clusters. Structural equation modeling indicated that perceived ease-of-use significantly influenced perceived usefulness (β=0.387, P<.01), and both perceived usefulness and attitude significantly affected behavioral intention and actual usage.

Conclusions: This study showed the importance of positive user experience and clinician recommendations in facilitating the effective usage of digital health care tools among cancer survivors and contributing to the evolving landscape of medical care.

Keywords: behavioral intervention; cancer; cancer survivors; clinician; digital health care; disparities; health care app; health status; mHealth; medical care; mixed-method study; structural equation modeling; technology acceptance model; user experience.