Yang Hyo Jun, Lee Ji-Hyun, Lee Wonjae
Graduate School of Culture Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
J Med Internet Res. 2025 Mar 28;27:e65269. doi: 10.2196/65269.
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.
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.
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.
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.
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.
技术接受模型（TAM）和技术接受与使用统一理论（UTAUT）被广泛用于研究老年人对医疗保健技术的接受情况。然而，现有文献存在相当大的异质性，难以确定一致的接受度和行为预测因素。
我们旨在（1）确定感知有用性（PU）、感知易用性（PEOU）和社会影响（SI）对老年人使用医疗保健技术的行为意向（BI）的影响，以及（2）评估年龄、性别、地理区域、医疗保健技术类型和是否存在视觉演示的调节作用。
2024年3月15日，按照PRISMA（系统评价和Meta分析的首选报告项目）指南，在谷歌学术、科学网、Scopus、IEEE Xplore和ProQuest数据库中进行了系统检索。在最初识别的1167项研究中，41项研究（11574名参与者；平均年龄67.58岁，标准差4.76岁；女性与男性比例为2.00）符合纳入标准。这些研究包括12项移动健康、12项在线或远程医疗、9项可穿戴设备以及8项家庭或机构硬件调查，其中23项研究来自亚洲，7项来自欧洲，7项来自非洲伊斯兰地区，4项来自美国。如果研究使用了TAM或UTAUT，研究了老年人对医疗保健技术的采用情况，并报告了零阶相关性，则该研究符合条件。两名独立评审员筛选研究、提取数据，并使用纽卡斯尔-渥太华量表评估方法学质量，评估选择、可比性和结果评估，34%（14/41）的研究被评为高质量，66%（27/41）为满意。
随机效应Meta分析显示，PU与BI之间存在显著正相关（r = 0.607，95% CI 0.543 - 0.665；P <.001），PEOU与BI之间存在显著正相关（r = 0.525，95% CI 0.462 - 0.583；P <.001），SI与BI之间存在显著正相关（r = 0.551，95% CI 0.468 - 0.624；P <.001）。各研究间观察到高度异质性（PU与BI、PEOU与BI、SI与BI的I²分别为95.9%、93.6%和95.3%）。调节因素分析显示，基于地理区域，PEOU与BI存在显著差异（Q = 8.27；P =.04），在欧洲影响最强（r = 0.628），在非洲伊斯兰地区影响最弱（r = 0.480）。技术类型对PU与BI（Q = 8.08；P =.04）和SI与BI（Q = 14.75；P =.002）有显著调节作用，家庭或机构硬件显示出最强的影响（PU与BI：r = 0.736；SI与BI：r = 0.690）。视觉演示显著增强了PU与BI的关系（r = 0.706对r = 0.554；Q = 4.24；P =.04）以及SI与BI的关系（r = 0.670对r = 0.492；Q = 4.38；P =.04）。年龄和性别未显示出显著的调节作用。
研究结果表明，PU、PEOU和SI显著影响老年人对医疗保健技术的接受度，异质性受地理区域、技术类型和视觉演示的影响。这表明针对不同类型技术的定制策略以及视觉演示的使用对于提高采用率很重要。局限性包括各研究中老年人的定义不同，以及使用的是相关系数而非控制效应量。因此，结果应在特定背景和人群中进行解释。
