Identifying major impact factors affecting the continuance intention of mHealth: a systematic review and multi-subgroup meta-analysis

• Published in: NPJ digital medicine Vol. 5; no. 1; pp. 145 - 13
• Main Authors: Wang, Tong, Wang, Wei, Liang, Jun, Nuo, Mingfu, Wen, Qinglian, Wei, Wei, Han, Hongbin, Lei, Jianbo
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.09.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 692/700/228; 706/689/477/2811; Biomedicine; Biotechnology; Digital technology; Impact factors; Medical technology; Medicine; Medicine & Public Health; Meta-analysis; Review; Review Article; Telemedicine
• ISSN: 2398-6352; 2398-6352
• DOI: 10.1038/s41746-022-00692-9

The mobile health (mHealth) industry is an enormous global market; however, the dropout or continuance of mHealth is a major challenge that is affecting its positive outcomes. To date, the results of studies on the impact factors have been inconsistent. Consequently, research on the pooled effects of impact factors on the continuance intention of mHealth is limited. Therefore, this study aims to systematically analyze quantitative studies on the continuance intention of mHealth and explore the pooled effect of each direct and indirect impact factor. Until October 2021, eight literature databases were searched. Fifty-eight peer-reviewed studies on the impact factors and effects on continuance intention of mHealth were included. Out of the 19 direct impact factors of continuance intention, 15 are significant, with attitude (β = 0.450; 95% CI: 0.135, 0.683), satisfaction (β = 0.406; 95% CI: 0.292, 0.509), health empowerment (β = 0.359; 95% CI: 0.204, 0.497), perceived usefulness (β = 0.343; 95% CI: 0.280, 0.403), and perceived quality of health life (β = 0.315, 95% CI: 0.211, 0.412) having the largest pooled effect coefficients on continuance intention. There is high heterogeneity between the studies; thus, we conducted a subgroup analysis to explore the moderating effect of different characteristics on the impact effects. The geographic region, user type, mHealth type, user age, and publication year significantly moderate influential relationships, such as trust and continuance intention. Thus, mHealth developers should develop personalized continuous use promotion strategies based on user characteristics.