Factors Influencing Health Care Technology Acceptance in Older Adults Based on the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology: Meta-Analysis (Preprint)

BACKGROUND The technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT) are widely used to examine health care technology acceptance among older adults. However, existing literature exhibits considerable heterogeneity, making it difficult to determine consistent predictors of acceptance and behavior. OBJECTIVE We aimed to (1) determine the influence of perceived usefulness (PU), perceived ease of use (PEOU), and social influence (SI) on the behavioral intention (BI) to use health care technology among older adults and (2) assess the moderating effects of age, gender, geographic region, type of health care technology, and presence of visual demonstrations. METHODS A systematic search was conducted across Google Scholar, Web of Science, Scopus, IEEE Xplore, and ProQuest databases on March 15, 2024, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Of the 1167 initially identified studies, 41 studies (11,574 participants; mean age 67.58, SD 4.76 years; and female:male ratio=2.00) met the inclusion criteria. The studies comprised 12 mobile health, 12 online or telemedicine, 9 wearable, and 8 home or institution hardware investigations, with 23 studies from Asia, 7 from Europe, 7 from African-Islamic regions, and 4 from the United States. Studies were eligible if they used the TAM or UTAUT, examined health care technology adoption among older adults, and reported zero-order correlations. Two independent reviewers screened studies, extracted data, and assessed methodological quality using the Newcastle-Ottawa Scale, evaluating selection, comparability, and outcome assessment with 34% (14/41) of studies rated as good quality and 66% (27/41) as satisfactory. RESULTS Random-effects meta-analysis revealed significant positive correlations for PU-BI (r=0.607, 95% CI 0.543-0.665; P<.001), PEOU-BI (r=0.525, 95% CI 0.462-0.583; P<.001), and SI-BI (r=0.551, 95% CI 0.468-0.624; P<.001). High heterogeneity was observed across studies (I²=95.9%, 93.6%, and 95.3% for PU-BI, PEOU-BI, and SI-BI, respectively). Moderator analyses revealed significant differences based on geographic region for PEOU-BI (Q=8.27; P=.04), with strongest effects in Europe (r=0.628) and weakest in African-Islamic regions (r=0.480). Technology type significantly moderated PU-BI (Q=8.08; P=.04) and SI-BI (Q=14.75; P=.002), with home or institutional hardware showing the strongest effects (PU-BI: r=0.736; SI-BI: r=0.690). Visual demonstrations significantly enhanced PU-BI (r=0.706 vs r=0.554; Q=4.24; P=.04) and SI-BI relationships (r=0.670 vs r=0.492; Q=4.38; P=.04). Age and gender showed no significant moderating effects. CONCLUSIONS The findings indicate that PU, PEOU, and SI significantly impact the acceptance of health care technology among older adults, with heterogeneity influenced by geographic region, type of technology, and presence of visual demonstrations. This suggests that tailored strategies for different types of technology and the use of visual demonstrations are important for enhancing adoption rates. Limitations include varying definitions of older adults across studies and the use of correlation coefficients rather than controlled effect sizes. Results should therefore be interpreted within specific contexts and populations.