Identifying and prioritizing the barriers and facilitators to mHealth adoption among older adults: an expert-driven best–worst method approach to inform healthcare

Abstract

This study identifies and prioritizes barriers and facilitators shaping olderadults’ adoption of mobile health (mHealth) technologies, including appsand wearables. Using the Best–Worst Method (BWM), an expert-driven multi-criteria decision-making approach, we evaluate a structured frameworkspanning five domains: Technological Proficiency and Confidence, Physicaland Cognitive Limitations, Perceived Relevance and Need, Usability andDesign, and Economic Factors. A multidisciplinary panel of clinicians, geron-tology and rehabilitation specialists, public-health researchers, and HCI/tech-nology practitioners assessed the relative importance of each domain and itssub-criteria. Results indicate that personal barriers dominate: Physical andCognitive Limitations and Technological Proficiency/Confidence rank high-est, with lack of familiarity with technology and limited technical skillsemerging as pivotal obstacles. By contrast, Economic Factors and Usability/Design, while relevant, are comparatively less decisive in determininguptake. The findings translate into practical guidance for health systemsand developers, emphasizing staged digital-literacy supports, age-inclusiveinterface requirements (clear text, high contrast, large touch targets, forgiv-ing flows), and lightweight clinician cueing integrated into routine care. Theproposed framework offers a replicable, decision-oriented basis to prioritizeinterventions, inform procurement and design, and monitor implementation,with the overarching aim of improving mHealth use, self-management, andquality of life among older adults.