BACKGROUND: Mobile health apps (MHAs) are increasingly used in modern health care provision. The technology acceptance model (TAM) is the most widely used framework for predicting health care technology acceptance. Since the advent of this model in 1989, technology has made generational advancements, and extensions of this model have been implemented.
OBJECTIVE: This systematic review aimed to re-examine TAM models to establish their validity for predicting the acceptance of modern MHAs, reviewing relevant core and extended constructs, and the relationships between them.
METHODS: In this systematic review, MEDLINE, Embase, Global Health, APA PsycINFO, CINAHL, and Scopus databases were searched on March 8, 2024, with no time constraints, for studies assessing the use of TAM-based frameworks for MHA acceptance. Studies eligible for data extraction were required to be peer-reviewed, English-language, primary research articles evaluating MHAs with health-related utility, using TAM as the primary technology acceptance evaluation framework, and reporting app use data. Data were extracted and grouped into 5 extended TAM construct themes. Quality assessment was conducted using the Joanna Briggs Institute (JBI) tools. For cross-sectional methodologies (9/14, 64%), the JBI checklist for analytical cross-sectional studies was used. For non-cross-sectional studies (5/14, 36%), the JBI checklist most relevant to the specific study design was used. For mixed methods studies (1/14, 7%), the JBI checklist for qualitative studies was applied, in addition to the JBI checklist most suited to the quantitative design. A subsequent narrative synthesis was conducted in line with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology.
RESULTS: A total of 2790 records were identified, and 14 were included. Furthermore, 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 behavioral intention) were validated. Extended TAM constructs were grouped into 5 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 MHA acceptance predictive capabilities. Perceived usefulness and extended constructs related to social factors, design aesthetics, and personalization were more influential for those from deprived socioeconomic backgrounds.
CONCLUSIONS: TAM is an effective framework for evaluating MHA acceptance. While original TAM constructs wield significant predictive capacity, the 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 optimize MHAs' user engagement and minimize health care inequalities. Our findings also underscore the necessity of adapting TAM and other acceptability frameworks as the technological and social landscape evolves.
TRIAL REGISTRATION: PROSPERO CRD42024532974; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024532974.
