JMIR Publications

ABSTRACT

Background:

Mobile health applications (MHAs) are increasingly used in modern healthcare provision. The technology acceptance model (TAM) is the most widely used framework for predicting healthcare technology acceptance. Since the advent of this model in 1989, technology has made generational advancements and extensions of this model have been implemented. This systematic review aims to re-examine TAM models to establish their validity for predicting the acceptance of modern MHAs, reviewing relevant core and extended constructs, and relationships between them.

Methods:

In this systematic review, MEDLINE, Embase, Global Health, APA PsycINFO, CINAHL, and Scopus databases were searched on March 8th, 2024, with no time constraints, for studies assessing the use of TAM-based frameworks for MHA acceptance. Data was extracted and grouped into 5 extended TAM construct themes. The Joanna Briggs Institute checklist for analytical cross-sectional studies was used for quality assessment. A subsequent narrative synthesis was conducted in line with PRISMA methodology. This protocol was registered with PROSPERO (CRD42024532974). Findings: 2790 records were identified and 14 were included. 10 studies validated the efficacy of TAM and its extensions for the assessment of MHAs. Relationships between core TAM constructs (perceived usefulness, perceived ease of use, and behavioural intention) were validated. Extended TAM constructs were grouped into five themes: health risk, application factors, social factors, digital literacy, and trust. Digital literacy, trust, and application factor extended construct themes had significant predictive capacity. Application factors had the strongest predictive capabilities. PU and extended constructs related to social factors, design aesthetics, and personalisation, were more influential for those from deprived socioeconomic backgrounds. Interpretation: TAM is an effective framework for evaluating MHA acceptance. Whilst original TAM constructs wield significant predictive capacity, incorporation of social and clinical context-specific extended TAM constructs can enhance the model’s predictive capabilities. This review's findings can be applied to optimise MHAs' user engagement and minimise healthcare inequalities. Our findings also underscore the necessity of adapting TAM and other acceptability frameworks as the technological and social landscape evolves.