Adnan Ahmer, Irvine Rebecca Eilish, Williams Allison, Harris Matthew, Antonacci Grazia
Faculty of Medicine, Imperial College London, London, United Kingdom.
National Institute of Health Research (NIHR) Applied Research Collaboration (ARC) Northwest London, Department of Primary Care and Public Health, Imperial College London, London, United Kingdom.
J Med Internet Res. 2025 May 7;27:e66432. doi: 10.2196/66432.
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.
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.
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.
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.
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.
PROSPERO CRD42024532974; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024532974.
移动健康应用程序（MHA）在现代医疗保健服务中使用得越来越频繁。技术接受模型（TAM）是预测医疗保健技术接受度使用最广泛的框架。自该模型于1989年问世以来，技术取得了代际进步，并且该模型的扩展版本也已实施。
本系统评价旨在重新审视TAM模型，以确定其在预测现代MHA接受度方面的有效性，回顾相关的核心和扩展结构及其之间的关系。
在本系统评价中，于2024年3月8日对MEDLINE、Embase、Global Health、APA PsycINFO、CINAHL和Scopus数据库进行了检索，无时间限制，以查找评估基于TAM的框架用于MHA接受度的研究。符合数据提取条件的研究必须是经过同行评审的英文初级研究文章，使用TAM作为主要技术接受度评估框架，评估具有健康相关效用的MHA，并报告应用程序使用数据。数据被提取并分组为5个扩展的TAM结构主题。使用乔安娜·布里格斯研究所（JBI）工具进行质量评估。对于横断面研究方法（9/14，64%），使用JBI分析性横断面研究检查表。对于非横断面研究（5/14，36%），使用与特定研究设计最相关的JBI检查表。对于混合方法研究（1/14，7%），除了使用最适合定量设计的JBI检查表外，还应用JBI定性研究检查表。随后根据PRISMA（系统评价和Meta分析的首选报告项目）方法进行叙述性综合分析。
共识别出2790条记录，纳入了14项研究。此外，10项研究验证了TAM及其扩展版本在评估MHA方面的有效性。核心TAM结构（感知有用性、感知易用性和行为意向）之间的关系得到了验证。扩展的TAM结构被分为5个主题：健康风险、应用因素、社会因素、数字素养和信任。数字素养、信任和应用因素扩展结构主题具有显著的预测能力。应用因素对MHA接受度的预测能力最强。感知有用性以及与社会因素、设计美学和个性化相关的扩展结构对社会经济背景较差的人群影响更大。
TAM是评估MHA接受度的有效框架。虽然原始的TAM结构具有显著的预测能力，但纳入特定于社会和临床背景的扩展TAM结构可以增强模型的预测能力。本评价的结果可用于优化MHA的用户参与度并最大限度地减少医疗保健不平等。我们的研究结果还强调了随着技术和社会环境的演变，调整TAM和其他可接受性框架的必要性。
PROSPERO CRD42024532974；https://www.crd.york.ac.uk/PROSPERO/view/CRD42024532974 。
