Using the smart health management services and devices among China’s adults and the influencing factors: A mixed-methods study^☆

Abstract

Background: As the emerging of structural imbalance characterized by surging demand and insufficient high-quality supply in China’s health management system, smart health management services become a novel measure to address this gap. Smart health management services refer to the health monitoring, assessment and intervention with support of information communication and artificial intelligence technologies.

Objective: To systematically analyze the current status, needs, and influencing factors of the using of smart health management services and devices among China’s adults, thereby providing evidence support and suggestions for its development.

Methods: A mixed-methods design was employed, combining quantitative and qualitative research methods. In the quantitative section, participants aged 18 years and above were selected by a stratified cluster random sampling method, their intentions and behaviors in utilizing smart health management services were analyzed by structural equation model (SEM).

In the qualitative research section, 13 interviewees were selected for semi-structured, one-on-one interviews, the findings were analyzed by grounded theory coding. Quantitative and qualitative findings were integrated using an explanatory sequential mixed-methods framework.

Results: A total of 2786 adults participated the questionnaire survey with a response rate of 96.07 %. Of them, 13 participants agreed to attend the semi-structured, one-on-one interviews. The main findings are as follows: (1) 37.7 % of the adult participants used smart health management devices. The use rate presents decline with increasing age, and lower use among older adults. (2)The demand for smart health management systems shows a diversified trend, and significant differences between age groups. Overall, participants believe that certain basic functions of the smart health systems, such as health monitoring, are needed, and they hope that it can answer questions raised by users. Qualitative study further revealed that participants' needs for smart health systems are in line with Maslow's Hierarchy of Needs, which includes needs at various levels from "basic life safety and health security" to "active learning and self-actualization." Young participants prefer the support function for basic preventive activities and optimization of lifestyle; older participants then are more concerned about whether the system has practical functions for disease management. (3)The average using willingness was moderately high (62.68 ± 20.65). In the SEM, behavioral attitude emerged as the strongest predictor of willingness of use(β=0.568, P < 0.001), followed by subjective norms (β = 0.103, P < 0.001) and media motivation (β = 0.094, P < 0.001). Electronic health literacy exerted significant indirect effects on both willingness (β = 0.045, P < 0.001) and behavior (β=0.051, P < 0.001) via media motivation, while perceived behavioral control influenced them indirectly (β = 0.014 and 0.016, both P < 0.001). Living in urban areas positively affected both willingness(β = 0.056, P < 0.001) and behavior (β = 0.125, P < 0.001). Health insurance coverage significantly promoted willingness (β = 0.039, P < 0.001). (4)Qualitative findings revealed multiple barriers to using, including high costs, product quality concerns, discomfort during using, and security issues. Attitudes toward smart health management devices were polarized, positive or negative evaluations stemmed directly from experience, perceived benefits, and device intelligence level, whereas neutral users tended to discontinue use due to a lack of perceived value. In addition, personal beliefs and cultural values strongly influenced individuals’ acceptance.

Conclusion: The study identified a distinct pattern of “high wish and low use” among adults regarding smart health management services. Both using behavior and demand pattern exhibited clear age-specific differences and were shaped by a number of factors. To bridge the gap between willingness to use and actual using behavior, future efforts should focus on age-appropriate design, precision implementation, and collaboration with primary care facilities, thereby enhancing adults’ capacity for actively managing their health.

Keywords

Smart health management services / Adults / Theory of planned behavior / Explanatory sequence hybrid method / Willingness and behavior

Declarations

Not applicable.

Authors' contributions

Conceptualization, Z.Xin.; Methodology, Z.Xin.; Data curation, Z.Xin., W.Y., Z.Xu., C.P,; Formal analysis, Z.Xin.; Funding acquisition, not applicable; Project administration, not applicable; Resources, not applicable; Supervision, S.X.; Validation, S.X.; Writing—original draft, Z.Xin.; Writing—review and editing, S.X. All authors have read and agreed to the published version of the manuscript.

Ethical approval and consent to participate

The study received approval from Ethics Committee of the Shandong Provincial Hospital (Approval No SWYX:2023-198).

Consent for publication

Not applicable.

Availability of data and materials

Not applicable.

Funding

This research was supported by grants from the National Natural Science Foundation of China (72474007) and Beijing Association for Science and Technology (bjkx202412).

Competing interests

All authors declare that there are no competing interests

Acknowledgements

Not applicable.

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.cgpj.2025.100089.

References

[1] Zeng Q, Gao XY, Bai SZ. Theory and practice of smart health management. Chin J Health Manag. 2022; 16(1):3-6. doi: 10.3760/cma.j.cn115624-20211203-00736.

[2] Zhang R, Fu JS. Linking network characteristics of online social networks to individual health: A systematic review of literature. Health Commun. 2021; 36(12):1549-1559. doi: 10.1080/10410236.2020.1773703.

[3] iResearch Consulting Group. China Health Management Industry Research Report 2024 [Report/Online]. (2024-03-08) [Accessed 2025-05-28]. Available from: https://report.iresearch.cn/report_pdf.aspx?id=4320.

[4] Zhang HM, Li BL. Diabetes health management under new media: “hospital-commu-nity-family-self ” model: China Med Sci. 2017; 7(18):244-246.

[5] Jin YA, Zhao MH. Internet use and active aging among China’s adults: an analysis based on the 2016 China longitudinal aging social survey data. Popul J. 2019; 41(6):44-55. doi: 10.16405/j.cnki.1004-129X.2019.06.004.

[6] Chen JX, Li T, Chen BY, et al. Construction of a model for the willingness of middle-aged and elderly people to use wearable devices based on the theory of planned behavior. Chin J Health Educ. 2024; 40(03):277-281. doi: 10.16168/j.cnki.issn.1002-9982.2024.03.015.

[7] Ajzen I. From intentions to actions: A theory of planned behavior. In: Kuhl J, Beckman J, eds. Action Control: From Cognition to Behavior. Heidelberg: Springer; 1985:11-39.

[8] Xu XT, Zhao YX, Zhu QH. Empirical study on the health information needs of elderly users in online health communities. Libr Inf Serv. 2019; 63(10):87-96. doi: 10.13266/j.issn.0252-3116.2019.10.010.

[9] Cao C, Li D, Xu Q. Motivational influences affecting middle-aged and elderly users’ participation willingness in health-related social media. Int J Env Res Public Health. 2022; 19(18):11240. doi: 10.3390/ijerph191811240.

[10] Corcoran J. Ecological factors associated with adolescent sexual activity. Soc Work Health Care. 2000; 30:93-111. doi: 10.1300/J010v30n04_06.

[11] Zhang D, Ling GHT, Misnan SHB. A systematic review of factors influencing the vitality of public open spaces: A novel perspective using the social-ecological model (SEM). Sustainability. 2023; 15:5235. doi: 10.3390/su15065235.

[12] Zhang C, Peng XQ, Jiang YZ, et al. Online medical services utilization evaluated through the lens of socioecological theory and the information-motivation-behavioral skills model: evidence from China. Ann N Y Acad Sci. 2021; 1500(1):82-92. doi: 10.1111/nyas.14609.

[13] Giebel GD, Speckemeier C, Abels C, et al. Problems and barriers related to the use of digital health applications: scoping review. J Med Internet Res. 2023; 25:e43808. doi: 10.2196/43808.

[14] Paek HJ, Hove T. Social cognitive factors and perceived social influences that improve adolescent eHealth literacy. Health Commun. 2012; 27(8):727-737. doi: 10.1080/10410236.2011.616627.

[15] Lustria ML, Smith SA, Hinnant CC. Exploring digital divides: an examination of eHealth technology use in health information seeking, communication, and personal health information management in the USA. Health Inform J. 2011; 17(3):224-243. doi: 10.1177/1460458211414843.

[16] Sun CZ, Zhao YR, Wu CA. Study on factors influencing users’ willingness to use smart home-based elderly care services. J Chang Norm Univ. 2024; 43(03):54-63.

[17] Alanzi T, Alhajri A, Almulhim S, et al. Artificial intelligence and patient autonomy in obesity treatment decisions: an empirical study of the challenges. Cureus. 2023; 15(11):e49725. doi: 10.7759/cureus.49725.

[18] Fishbein M, Cappella JN. The role of theory in developing effective health communications. J Commun. 2006; 56(Suppl 1):S1-S17. doi: 10.1111/j.1460-2466.2006.00280.x.

[19] Hall AK, Bernhardt JM, Dodd V, et al. The digital health divide: evaluating online health information access and use among older adults. Health Educ Behav. 2015; 42(2):202-209. doi: 10.1177/1090198114547815.

[20] Zhang X, Han X, Dang Y, et al. User acceptance of mobile health services from users’ perspectives: the role of self-efficacy and response-efficacy in technology acceptance. Inform Health Soc Care. 2017; 42(2):194-206. doi: 10.1080/17538157.2016.1200053.

[21] Zhang Z, Mao YH, Hu YC. Study on the willingness to use smart elderly care services from the perspective of the digital divide among older adults. Northwest Popul J. 2023; 44(01):104-115. doi: 10.15884/j.cnki.issn.1007-0672.2023.01.008.

[22] China Internet Network Information Center. The 48th Statistical Report on China’s Internet development[Report/Online]. (2025-01-17) [Accessed 2025-09-15]. Available from: https://www.cnnic.net.cn/n4/2022/0401/c88-1132.html.

[23] National Bureau of Statistics of China. The Seventh National Population Census Bulletin (No.1)[EB/OL]. (2021-05-11) [Accessed 2025-05-15]. Available from: http://www.stats.gov.cn/tjsj/tjgb/rkpcgb/qgrkpcgb/202106/t20210628_1818824.html.

[24] Hennink MM, Kaiser BN, Marconi VC. Code saturation versus meaning saturation: how many interviews are enough? Qual Health Res. 2017; 27(4):591-608.

[25] Lee J, Lee EH, Chae D. eHealth literacy instruments: systematic review of measurement properties. J Med Internet Res. 2021; 23(11):e30644. doi: 10.2196/30644.

[26] Norman CD, Skinner HA. eHEALS: the eHealth Literacy Scale. J Med Internet Res. 2006; 8(4):e27. doi: 10.2196/jmir.8.4.e27.

[27] Guo S, Yu X, Sun Y, et al. Adaptation and evaluation of the Chinese version of eHEALS and its usage among senior high school students. Chin J Health Educ. 2013; 29(2):106-108. doi: 10.16168/j.cnki.issn.1002-9982.2013.02.019.

[28] Chen G, Gully SM, Eden D. Validation of a new general self-efficacy scale. Organ Res Methods. 2001; 4(1):62-83.

[29] Xiao F, Yi CX. A study of the reliability and validity of the General Self-Efficacy Scale (NGSES). J Mudanjiang Norm Univ (Philos Soc Sci Ed). 2012; 4:127-129.

[30] Zhang F, Zhu SZ, Deng PJ. Application and evaluation of the Perceived Social Support Scale in studying social support among hospitalized patients in China. Nurs Res. 2018; 32(13):2048-2052.

[31] Corbin J, Strauss A. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. 3rd ed. Thousand Oaks: SAGE Publications; 2008.

[32] Zheng W, Han X, Lv Y. The overall status and group differences of chronic diseases in the Chinese population. Soc Sci J. 2022(3):139-149+209.

[33] Standard on age classification and population naming for the silver-aged group in China Presented at:2024 China Conference on Chronic Diseases in the Elderly; 2024 Oct:11-14.

[34] Guo YT, Yao H. Study on the current situation and influencing factors of smart elderly care life: based on a survey of elderly living patterns in Shanghai. Gerontol Sci Res. 2018; 6(07):69-80.

[35] Cui X, Li JY, Wen XH, et al. Knowledge, attitudes, and practices toward smart elderly care among community-dwelling older adults: A structural equation model study. Gen Nurs. 2023; 21(12):1585-1589.