Patient Acceptance of Health Information Technologies: Comparative Insights From Wearables, TelE-Medicine, and mHealth
Author(s)
Jorge Brantes Ferreira, PhD1, André Luiz Parreiras, PhD2, José Eduardo de Moura Castro Zacour, Master2, Tharcisio Alexandrino Caldeira, PhD2, Marcus Fabio Rodrigues Peixoto, PhD2.
1Associate Professor of Business Administration, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil, 2Business Administration, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil.
1Associate Professor of Business Administration, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil, 2Business Administration, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil.
OBJECTIVES: The adoption of health information technologies (HITs) by patients remains uneven across different technological modalities. This study aimed to comparatively analyze the determinants of patient acceptance of three categories of HITs (wearable devices, telemedicine, and mobile health apps) through an integrative model grounded in the Technology Acceptance Model (TAM), enriched with the constructs of trust and self-efficacy. The research aimed to identify both commonalities and divergences in the adoption processes across these technologies, to inform targeted strategies to enhance patient engagement.
METHODS: Data were compiled from three independent but methodologically harmonized survey studies focused respectively on wearable devices (n=376), telemedicine services (n=360), and mHealth applications (n=467). All participants had prior direct experience with the respective technologies, increasing the validity of the findings. Structural equation modeling with multigroup analysis was employed to assess the relationships between trust, self-efficacy, perceived usefulness, perceived ease of use, attitude, and behavioral intention, and to test for moderation effects of the type of HIT.
RESULTS: All models demonstrated a good fit and explained a substantial variance in behavioral intention to use the technologies. Trust and self-efficacy positively influenced usefulness and ease of use across all technologies, though the strength of these effects varied. Trust was more influential in telemedicine, while self-efficacy played a stronger role in mHealth. Usefulness consistently emerged as the principal driver of attitudes, which in turn predicted intention. Telemedicine showed the highest readiness for adoption, while wearables exhibited greater variability, likely reflecting differences in perceived integration into daily life. Technology type moderated key pathways, indicating non-uniform adoption processes across modalities.
CONCLUSIONS: This research offers theoretical and managerial insights by elucidating both universal and technology-specific drivers of HIT acceptance. The findings can guide healthcare providers, technology developers, and policymakers in tailoring interventions to improve the adoption and sustained use of various digital health innovations.
METHODS: Data were compiled from three independent but methodologically harmonized survey studies focused respectively on wearable devices (n=376), telemedicine services (n=360), and mHealth applications (n=467). All participants had prior direct experience with the respective technologies, increasing the validity of the findings. Structural equation modeling with multigroup analysis was employed to assess the relationships between trust, self-efficacy, perceived usefulness, perceived ease of use, attitude, and behavioral intention, and to test for moderation effects of the type of HIT.
RESULTS: All models demonstrated a good fit and explained a substantial variance in behavioral intention to use the technologies. Trust and self-efficacy positively influenced usefulness and ease of use across all technologies, though the strength of these effects varied. Trust was more influential in telemedicine, while self-efficacy played a stronger role in mHealth. Usefulness consistently emerged as the principal driver of attitudes, which in turn predicted intention. Telemedicine showed the highest readiness for adoption, while wearables exhibited greater variability, likely reflecting differences in perceived integration into daily life. Technology type moderated key pathways, indicating non-uniform adoption processes across modalities.
CONCLUSIONS: This research offers theoretical and managerial insights by elucidating both universal and technology-specific drivers of HIT acceptance. The findings can guide healthcare providers, technology developers, and policymakers in tailoring interventions to improve the adoption and sustained use of various digital health innovations.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
MT34
Topic
Medical Technologies
Topic Subcategory
Digital Health
Disease
No Additional Disease & Conditions/Specialized Treatment Areas