Zhao Jing, Li Bei, Sun Jianwei, Zeng Xu, Zheng Jing
Department of Biomedical Informatics, School of Life Sciences, Central South University, Changsha, China.
Shenzhen Health Development Research and Data Management Center, Shenzhen, China.
Front Digit Health. 2025 Aug 1;7:1543428. doi: 10.3389/fdgth.2025.1543428. eCollection 2025.
Chronic diseases are a significant public health concern. Internet diagnosis and treatment services can effectively monitor chronic diseases and are vital for alleviating the healthcare system burden caused by these conditions. Distinguishing itself from prior investigations, this study focuses on the critical cohort of chronic disease patients and, building upon the UTAUT2 framework, introduces additional constructs such as trust and medical habits. It systematically examines the pivotal determinants influencing the acceptance and utilization of Internet diagnosis and treatment services among chronic disease patients in Shenzhen, China.
This study centers on the population of chronic disease patients in Shenzhen, China, by developing a theoretical model to elucidate their behavioral intentions toward utilizing Internet diagnosis and treatment services. Employing empirical methods, the research identifies the key determinants that influence patients' acceptance and adoption of these services. Furthermore, based on the interactive mechanisms among these factors, targeted policy recommendations are advanced to enhance service utilization rates and optimize the quality of Internet diagnosis and treatment services.
Guided by the theoretical framework, and informed by expert consultations and a preliminary survey, the questionnaire was meticulously designed and refined. Employing a five-point Likert scale, the survey investigated the usage patterns of Internet diagnosis and treatment services among chronic disease patients in Shenzhen, China, as well as the factors influencing their behavioral intention. Utilizing convenience sampling, a total of 823 valid responses were collected. Subsequent data analysis was conducted using SPSS 26.0 and AMOS 28.0 software, encompassing descriptive statistics and structural equation modeling. Furthermore, the Bootstrap method was employed to rigorously assess the mediating effects within the model.
The empirical findings reveal that: (1) Model validation indicates that performance expectancy ( = 0.151, = 0.002), effort expectancy ( = 0.105, = 0.022), social influence ( = 0.206, < 0.001), price value ( = 0.138, = 0.002), trust ( = 0.124, = 0.003), and electronic health literacy ( = 0.184, < 0.001) exert significant positive effects on the behavioral intention to use Internet diagnosis and treatment services. Conversely, perceived risk negatively influences behavioral intention ( = 0.094, = 0.008), whereas the effect of medical habits on behavioral intention is not statistically significant ( > 0.05). (2) Performance expectancy partially mediates the relationships between effort expectancy, trust, electronic health literacy, and behavioral intention, while effort expectancy partially mediates the relationship between electronic health literacy and behavioral intention.
Performance expectancy, effort expectancy, social influence, price value, trust, perceived risk, and electronic health literacy constitute the principal determinants shaping the behavioral intention of chronic disease patients to adopt Internet diagnosis and treatment services. Drawing on these findings, this study advances targeted policy recommendations aimed at optimizing user experience and fostering the sustainable, high-quality development of Internet diagnosis and treatment services within chronic disease management.
慢性病是一个重大的公共卫生问题。互联网诊断和治疗服务可以有效地监测慢性病，对于减轻这些疾病给医疗系统带来的负担至关重要。本研究与以往调查不同，聚焦于慢性病患者这一关键群体，并在UTAUT2框架的基础上，引入了信任和医疗习惯等额外的构念。它系统地考察了影响中国深圳慢性病患者接受和使用互联网诊断和治疗服务的关键决定因素。
本研究以中国深圳的慢性病患者群体为中心，通过构建一个理论模型来阐明他们使用互联网诊断和治疗服务的行为意图。采用实证方法，该研究确定了影响患者接受和采用这些服务的关键决定因素。此外，基于这些因素之间的互动机制，提出了有针对性的政策建议，以提高服务利用率并优化互联网诊断和治疗服务的质量。
在理论框架的指导下，经专家咨询和初步调查，精心设计并完善了问卷。采用五点李克特量表，该调查研究了中国深圳慢性病患者对互联网诊断和治疗服务的使用模式以及影响其行为意图的因素。采用便利抽样，共收集到823份有效回复。随后使用SPSS 26.0和AMOS 28.0软件进行数据分析，包括描述性统计和结构方程建模。此外，采用Bootstrap方法严格评估模型中的中介效应。
实证结果表明：（1）模型验证表明，绩效期望（β = 0.151，p = 0.002）、努力期望（β = 0.105，p = 0.022）、社会影响（β = 0.206，p < 0.001）、价格价值（β = 0.138，p = 0.002）、信任（β = 0.124，p = 0.003）和电子健康素养（β = 清0文中未给出具体数值，推测为清0.184，p < 0.001）对使用互联网诊断和治疗服务的行为意图有显著的正向影响。相反，感知风险对行为意图有负面影响（β = 0.094，p = 0.008），而医疗习惯对行为意图的影响在统计学上不显著（p > 0.05）。（2）绩效期望部分中介了努力期望、信任、电子健康素养与行为意图之间的关系，而努力期望部分中介了电子健康素养与行为意图之间的关系。
绩效期望、努力期望、社会影响、价格价值、信任、感知风险和电子健康素养构成了影响慢性病患者采用互联网诊断和治疗服务行为意图的主要决定因素。基于这些发现，本研究提出了有针对性的政策建议，旨在优化用户体验并促进慢性病管理中互联网诊断和治疗服务的可持续、高质量发展。
