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

This study identifies and prioritizes barriers and facilitators shaping older adults' adoption of mobile health (mHealth) technologies, including apps and wearables. Using the Best-Worst Method (BWM), an expert-driven multi-criteria decision-making approach, we evaluate a structured framework spanning five domains: Technological Proficiency and Confidence, Physical and Cognitive Limitations, Perceived Relevance and Need, Usability and Design, and Economic Factors. A multidisciplinary panel of clinicians, gerontology and rehabilitation specialists, public-health researchers, and HCI/technology practitioners assessed the relative importance of each domain and its sub-criteria. Results indicate that personal barriers dominate: Physical and Cognitive Limitations and Technological Proficiency/Confidence rank highest, with lack of familiarity with technology and limited technical skills emerging as pivotal obstacles. By contrast, Economic Factors and Usability/Design, while relevant, are comparatively less decisive in determining uptake. The findings translate into practical guidance for health systems and developers, emphasizing staged digital-literacy supports, age-inclusive interface requirements (clear text, high contrast, large touch targets, forgiving flows), and lightweight clinician cueing integrated into routine care. The proposed framework offers a replicable, decision-oriented basis to prioritize interventions, inform procurement and design, and monitor implementation, with the overarching aim of improving mHealth use, self-management, and quality of life among older adults.