King Abdulaziz University, Saudi Arabia.
Health Informatics J. 2021 Jan-Mar;27(1):1460458220976737. doi: 10.1177/1460458220976737.
Due to rapid advancements in the field of information and communication technologies, mobile health (mHealth) has become a significant topic in the delivery of healthcare. Despite the perceived advantages and the large number of mHealth initiatives, the success of mHealth ultimately relies on whether these initiatives are used; their benefits will be diminished should people not use them. Previous literature has found that the adoption of mHealth by users is not yet widespread, and little research has been conducted on this problem. Therefore, this study identifies the antecedents of the intention to use mHealth and proposes a general model that might prove beneficial in explaining the acceptance of mHealth. The authors performed a quantitative meta-analysis of 49 journal papers published over the past 10 years and systematically reviewed the evidence regarding the most commonly identified factors that may affect the acceptance of mHealth. The findings indicate that the proposed model includes the seven most commonly used relationships in the selected articles. More specifically, the model assumes that perceived usefulness positively affects perceived ease of use and user behavioral intention to use mHealth is commonly influenced by five factors: perceived usefulness, perceived ease of use, attitude toward behavior, subjective norms, and facilitating conditions. The results of this work provide important insights into the predictors of mHealth acceptance for future researchers and practitioners.
由于信息和通信技术领域的快速发展，移动健康 (mHealth) 已成为医疗保健服务的重要议题。尽管人们认为 mHealth 具有优势，并且已经开展了大量的 mHealth 计划，但 mHealth 的成功最终取决于这些计划是否被使用；如果人们不使用它们，它们的好处将会减少。先前的文献发现，用户对 mHealth 的采用尚未普及，并且对此问题的研究很少。因此，本研究确定了使用 mHealth 的意图的前因，并提出了一个通用模型，该模型可能有助于解释对 mHealth 的接受。作者对过去 10 年发表的 49 篇期刊论文进行了定量元分析，并系统地回顾了可能影响 mHealth 接受度的最常见因素的证据。研究结果表明，所提出的模型包含了所选文章中使用最多的七个关系。更具体地说，该模型假设感知有用性对感知易用性有积极影响，用户使用 mHealth 的行为意图通常受到五个因素的影响：感知有用性、感知易用性、对行为的态度、主观规范和促进条件。这项工作的结果为未来的研究人员和从业者提供了有关 mHealth 接受度预测因素的重要见解。
