Identifying and prioritizing the barriers and facilitators to mHealth adoption among older adults: an expert-driven best–worst method approach to inform healthcare
Informatics for Health and Social Care, vol.51, no.1, pp.49-61, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 51 Issue: 1
- Publication Date: 2026
- Doi Number: 10.1080/17538157.2025.2585333
- Journal Name: Informatics for Health and Social Care
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, CINAHL, Library, Information Science & Technology Abstracts (LISTA), MEDLINE, Psycinfo
- Page Numbers: pp.49-61
- Keywords: barriers and facilitators, Mobile health, multi-criteria decision making, older adults, technology adoption
- Karadeniz Technical University Affiliated: Yes
Abstract
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.