Exploratory Analysis of the Digital Health Gender Paradox in Consumer-Facing Digital Health Technology Adoption: Cross-Sectional Survey Study.

Abstract

Background: Digital health technologies have become integral to modern health care, yet consumer adoption remains uneven. These technologies span a range of consumer-facing tools, including remote care services, mobile apps, wearable devices, conversational agents, and digital medication services, with uptake varying considerably across tool types and by gender. An earlier analysis of this dataset revealed a gender paradox: women reported higher digital literacy compared to men yet demonstrated lower adoption and curiosity toward digital health tools. The Capability, Opportunity, Motivation - Behavior (COM-B) model proposes that behavior occurs when individuals have the capability (skills and confidence), opportunity (access, affordability, and contextual support), and motivation (trust, perceived usefulness, and willingness) to act.

Objective: This study aimed to examine associations between digital literacy, self-reported COM-B domains, gender, and adoption across digital health technologies and to explore whether these associations differ by gender.

Methods: This cross-sectional quantitative study with an integrated qualitative component enrolled 416 adults and used multivariable logistic regression to examine associations between COM-B domains and adoption across five consumer-facing digital health technologies. Exploratory gender-by-predictor interaction and gender-stratified analyses examined potential gender-specific patterns, while self-reported reasons for nonuse were mapped to COM-B domains using directed content analysis.

Results: Women had lower odds of chatbot/virtual assistant use (adjusted odds ratio [aOR] 0.32, 95% CI 0.18-0.59), marginally lower odds of e-pharmacy use (aOR 0.48, 95% CI 0.22-1.01), and higher odds of mobile health (mHealth) app use (aOR 2.59, 95% CI 1.28-5.25). Of 20 exploratory interaction tests, chatbot digital literacy-by-gender (P=.021) and capability-by-gender (P=.044) interactions were nominally significant. In directed barrier analysis, the gender distribution differed significantly for mHealth apps (P=.009), whereas the wearable result showed a trend (P=.079); motivation-related barriers were most frequent for most technologies.

Conclusions: Behavioral determinants of digital health technology adoption varied by technology type. Women used several technologies less often than men despite reporting higher digital literacy, capability, and motivation, and opportunity emerged as the most consistent factor across the models examined. Exploratory analyses suggested these patterns may differ by gender for selected technologies but did not support a single gender-differentiated pathway. These findings are exploratory and require confirmation in larger studies.