{"title":"影响全球电子健康素养的因素:系统回顾和荟萃分析。","authors":"Zhong Hua, Song Yuqing, Liu Qianwen, Chen Hong","doi":"10.2196/50313","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>eHealth literacy has increasingly emerged as a critical determinant of health, highlighting the importance of identifying its influencing factors; however, these factors remain unclear. Numerous studies have explored this concept across various populations, presenting an opportunity for a systematic review and synthesis of the existing evidence to better understand eHealth literacy and its key determinants.</p><p><strong>Objective: </strong>This study aimed to provide a systematic review of factors influencing eHealth literacy and to examine their impact across different populations.</p><p><strong>Methods: </strong>We conducted a comprehensive search of papers from PubMed, CNKI, Embase, Web of Science, Cochrane Library, CINAHL, and MEDLINE databases from inception to April 11, 2023. We included all those studies that reported the eHealth literacy status measured with the eHealth Literacy Scale (eHEALS). Methodological validity was assessed with the standardized Joanna Briggs Institute (JBI) critical appraisal tool prepared for cross-sectional studies. Meta-analytic techniques were used to calculate the pooled standardized β coefficient with 95% CIs, while heterogeneity was assessed using I2, the Q test, and τ2. Meta-regressions were used to explore the effect of potential moderators, including participants' characteristics, internet use measured by time or frequency, and country development status. Predictors of eHealth literacy were integrated according to the Literacy and Health Conceptual Framework and the Technology Acceptance Model (TAM).</p><p><strong>Results: </strong>In total, 17 studies met the inclusion criteria for the meta-analysis. Key factors influencing higher eHealth literacy were identified and classified into 3 themes: (1) actions (internet usage: β=0.14, 95% CI 0.102-0.182, I2=80.4%), (2) determinants (age: β=-0.042, 95% CI -0.071 to -0.020, I2=80.3%; ethnicity: β=-2.613, 95% CI -4.114 to -1.112, I2=80.2%; income: β=0.206, 95% CI 0.059-0.354, I2=64.6%; employment status: β=-1.629, 95% CI -2.323 to -0.953, I2=99.7%; education: β=0.154, 95% CI 0.101-0.208, I2=58.2%; perceived usefulness: β=0.832, 95% CI 0.131-1.522, I2=68.3%; and self-efficacy: β=0.239, 95% CI 0.129-0.349, I2=0.0%), and (3) health status factor (disease: β=-0.177, 95% CI -0.298 to -0.055, I2=26.9%).</p><p><strong>Conclusions: </strong>This systematic review, guided by the Literacy and Health Conceptual Framework model, identified key factors influencing eHealth literacy across 3 dimensions: actions (internet usage), determinants (age, ethnicity, income, employment status, education, perceived usefulness, and self-efficacy), and health status (disease). These findings provide valuable guidance for designing interventions to enhance eHealth literacy.</p><p><strong>Trial registration: </strong>PROSPERO CRD42022383384; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022383384.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e50313"},"PeriodicalIF":6.0000,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11933766/pdf/","citationCount":"0","resultStr":"{\"title\":\"Factors Influencing eHealth Literacy Worldwide: Systematic Review and Meta-Analysis.\",\"authors\":\"Zhong Hua, Song Yuqing, Liu Qianwen, Chen Hong\",\"doi\":\"10.2196/50313\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>eHealth literacy has increasingly emerged as a critical determinant of health, highlighting the importance of identifying its influencing factors; however, these factors remain unclear. 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Meta-analytic techniques were used to calculate the pooled standardized β coefficient with 95% CIs, while heterogeneity was assessed using I2, the Q test, and τ2. Meta-regressions were used to explore the effect of potential moderators, including participants' characteristics, internet use measured by time or frequency, and country development status. Predictors of eHealth literacy were integrated according to the Literacy and Health Conceptual Framework and the Technology Acceptance Model (TAM).</p><p><strong>Results: </strong>In total, 17 studies met the inclusion criteria for the meta-analysis. 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引用次数: 0
摘要
背景:电子卫生素养日益成为健康的关键决定因素,突出了确定其影响因素的重要性;然而,这些因素仍不清楚。许多研究在不同人群中探索了这一概念,为系统审查和综合现有证据提供了机会,以更好地了解电子卫生素养及其关键决定因素。目的:本研究旨在对影响电子健康素养的因素进行系统回顾,并检查其在不同人群中的影响。方法:全面检索PubMed、CNKI、Embase、Web of Science、Cochrane Library、CINAHL和MEDLINE数据库自成立至2023年4月11日的论文。我们纳入了所有报告了用电子健康素养量表(eHEALS)测量的电子健康素养状况的研究。方法效度评估采用标准化的乔安娜布里格斯研究所(JBI)关键评估工具准备的横断面研究。采用荟萃分析技术计算95% ci的合并标准化β系数,并使用I2、Q检验和τ2评估异质性。meta回归用于探索潜在调节因素的影响,包括参与者的特征、以时间或频率衡量的互联网使用情况以及国家发展状况。根据素养与健康概念框架和技术接受模型(TAM)对电子健康素养的预测因子进行整合。结果:总共有17项研究符合meta分析的纳入标准。确定了影响提高电子健康素养的关键因素并将其分为3个主题:(1)行动(互联网使用:β=0.14, 95% CI 0.102-0.182, I2=80.4%),(2)决定因素(年龄:β=-0.042, 95% CI -0.071至-0.020,I2=80.3%;种族:β=-2.613, 95% CI为-4.114 ~ -1.112,I2=80.2%;收入:β=0.206, 95% CI 0.059 ~ 0.354, I2=64.6%;就业状况:β=-1.629, 95% CI为-2.323 ~ -0.953,I2=99.7%;教育程度:β=0.154, 95% CI 0.101 ~ 0.208, I2=58.2%;感知有用性:β=0.832, 95% CI 0.131 ~ 1.522, I2=68.3%;自我效能:β=0.239, 95% CI 0.129 ~ 0.349, I2=0.0%),(3)健康状况因素(疾病:β=-0.177, 95% CI -0.298 ~ -0.055, I2=26.9%)。结论:本系统综述在扫盲与健康概念框架模型的指导下,从三个维度确定了影响电子健康素养的关键因素:行动(互联网使用)、决定因素(年龄、种族、收入、就业状况、教育、感知有用性和自我效能)和健康状况(疾病)。这些发现为设计干预措施以提高电子卫生素养提供了有价值的指导。试验注册:PROSPERO CRD42022383384;https://www.crd.york.ac.uk/PROSPERO/view/CRD42022383384。
Factors Influencing eHealth Literacy Worldwide: Systematic Review and Meta-Analysis.
Background: eHealth literacy has increasingly emerged as a critical determinant of health, highlighting the importance of identifying its influencing factors; however, these factors remain unclear. Numerous studies have explored this concept across various populations, presenting an opportunity for a systematic review and synthesis of the existing evidence to better understand eHealth literacy and its key determinants.
Objective: This study aimed to provide a systematic review of factors influencing eHealth literacy and to examine their impact across different populations.
Methods: We conducted a comprehensive search of papers from PubMed, CNKI, Embase, Web of Science, Cochrane Library, CINAHL, and MEDLINE databases from inception to April 11, 2023. We included all those studies that reported the eHealth literacy status measured with the eHealth Literacy Scale (eHEALS). Methodological validity was assessed with the standardized Joanna Briggs Institute (JBI) critical appraisal tool prepared for cross-sectional studies. Meta-analytic techniques were used to calculate the pooled standardized β coefficient with 95% CIs, while heterogeneity was assessed using I2, the Q test, and τ2. Meta-regressions were used to explore the effect of potential moderators, including participants' characteristics, internet use measured by time or frequency, and country development status. Predictors of eHealth literacy were integrated according to the Literacy and Health Conceptual Framework and the Technology Acceptance Model (TAM).
Results: In total, 17 studies met the inclusion criteria for the meta-analysis. Key factors influencing higher eHealth literacy were identified and classified into 3 themes: (1) actions (internet usage: β=0.14, 95% CI 0.102-0.182, I2=80.4%), (2) determinants (age: β=-0.042, 95% CI -0.071 to -0.020, I2=80.3%; ethnicity: β=-2.613, 95% CI -4.114 to -1.112, I2=80.2%; income: β=0.206, 95% CI 0.059-0.354, I2=64.6%; employment status: β=-1.629, 95% CI -2.323 to -0.953, I2=99.7%; education: β=0.154, 95% CI 0.101-0.208, I2=58.2%; perceived usefulness: β=0.832, 95% CI 0.131-1.522, I2=68.3%; and self-efficacy: β=0.239, 95% CI 0.129-0.349, I2=0.0%), and (3) health status factor (disease: β=-0.177, 95% CI -0.298 to -0.055, I2=26.9%).
Conclusions: This systematic review, guided by the Literacy and Health Conceptual Framework model, identified key factors influencing eHealth literacy across 3 dimensions: actions (internet usage), determinants (age, ethnicity, income, employment status, education, perceived usefulness, and self-efficacy), and health status (disease). These findings provide valuable guidance for designing interventions to enhance eHealth literacy.
期刊介绍:
The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades.
As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor.
Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.