Influencing factors of patients' adoption intention toward online medical services: a meta-analysis_Chinese Journal of Evidence-Based Medicine

Authors：

CAO Xuefei , NING Zhipeng ,  HOU Yanhong

China Pharmaceutical University, Nanjing 211198, P. R. China;

Corresponding author：

HOU Yanhong, Email: 1020092084@cpu.edu.cn

Keywords：

Online medical service; Adoption intention; Influencing factor; Meta-analysis; Systematic review; Cross-sectional study

DOI：

10.7507/1672-2531.202110144

Video：

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Objective To systematically review the influencing factors of patients’ adoption intention toward online medical services. Methods CNKI, VIP, WanFang Data, Web of Science, PubMed and EMbase databases were electronically searched to collect cross-sectional studies on the influencing factors of patients’ adoption intention toward online medical services from inception to August 2021. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies; then, meta-analysis was performed by using Stata 16.0 software. Results A total of 28 cross-sectional studies involving 10 200 patients were included. The results of meta-analysis showed that perceived usefulness, perceived ease of use, and performance expectations were highly positively correlated with adoption intention. Effort expectation, social influence, convenience, self-efficacy, perceived value, individual innovation and perceived behavior control were moderately positively correlated factors. Perceived health threat was a low positive correlation factor of adoption intention. Resistance to change and perception of risk were moderately negatively correlated. Technology anxiety was a low negative correlation factor of adoption intention. There was a highly positive correlation between adoption intention and use behavior. Conclusion Current evidence shows that there is a highly positive correlation between adoption intention and use behavior, influencing factors of patients’ adoption intention include perceived usefulness, perceived ease of use, perceived value, etc.

Citation： CAO Xuefei, NING Zhipeng, HOU Yanhong. Influencing factors of patients' adoption intention toward online medical services: a meta-analysis. Chinese Journal of Evidence-Based Medicine, 2022, 22(3): 316-323. doi: 10.7507/1672-2531.202110144 Copy

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5.	Ajzen I, Fishbein M. Understanding attitudes and predicting social behavior. NJ: Prentice-Hall Englewood Cliffs, 1980.
6.	Ajzen I. From intentions to actions: a theory of planned behavior. Berlin: Springer, 1985.
7.	Ajzen I. Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. J Appl Soc Psychol, 2002, 32: 665-683.
8.	Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q, 1989, 13(3): 319-340.
9.	Kim J, Park HA. Development of a health information technology acceptance model using consumers' health behavior intention. J Med Internet Res, 2012, 14(5): e133.
10.	Venkatesh V, Morris MG, Davis GB, et al. User acceptance of information technology: toward a unified view. MIS Q, 2003, 27(3): 425-478.
11.	Venkatesh V, Thong JYL. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q, 2012, 36(1): 157-178.
12.	Tan BC. The role of perceived consumer effectiveness on value-attitude-behaviour model in green buying behavior context. Aust J Basic Appl Sci, 2011, 5(12): 157-166.
13.	Williams MD, Rana NP, Dwivedi YK. The unified theory of acceptance and use of technology (UTAUT): a literature review. J Enterp Inf Manag, 2015, 28(3): 443-448.
14.	Ajzen I, Fishbein M. Understanding attitudes and predicting social behavior. New Jersey: Prentice-Hall, 1980.
15.	Peterson RA, Brown SP. On the use of beta coefficients in meta-analysis. J Appl Psychol, 2005, 90(1): 175-181.
16.	Wilson DB. Practical meta-analysis effect size calculator. Available at: https://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php.
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18.	Alaiad A, Alsharo M, Alnsour Y. The determinants of m-health adoption in developing countries: an empirical investigation. Appl Clin Inform, 2019, 10(5): 820-840.
19.	Apolinário-Hagen J, Menzel M, Hennemann S, et al. Acceptance of mobile health apps for disease management among people with multiple sclerosis: web-based survey study. JMIR Form Res, 2018, 2(2): e11977.
20.	Breil B, Kremer L, Hennemann S, et al. Acceptance of mHealth apps for self-management among people with hypertension. Stud Health Technol Inform, 2019, 267: 282-288.
21.	Deng Z, Hong Z, Ren C, et al. What predicts patients' adoption intention toward mHealth services in China: empirical study. JMIR Mhealth Uhealth, 2018, 6(8): e172.
22.	Deng Z, Mo X, Liu S. Comparison of the middle-aged and older users' adoption of mobile health services in China. Int J Med Inform, 2014, 83(3): 210-224.
23.	Hoque R, Sorwar G. Understanding factors influencing the adoption of mHealth by the elderly: an extension of the UTAUT model. Int J Med Inform, 2017, 101: 75-84.
24.	Li D, Hu Y, Pfaff H, et al. Determinants of patients' intention to use the online inquiry services provided by internet hospitals: empirical evidence from China. J Med Internet Res, 2020, 22(10): e22716.
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26.	Salgado T, Tavares J, Oliveira T. Drivers of mobile health acceptance and use from the patient perspective: survey study and quantitative model development. JMIR Mhealth Uhealth, 2020, 8(7): e17588.
27.	Tang Y, Yang YT, Shao YF. Acceptance of online medical websites: an empirical study in China. Int J Environ Res Public Health, 2019, 16(6): 943.
28.	Wang H, Liang L, Du C, et al. Implementation of online hospitals and factors influencing the adoption of mobile medical services in china: cross-sectional survey study. JMIR Mhealth Uhealth, 2021, 9(2): e25960.
29.	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.
30.	Zhang X, Liu S, Wang L, et al. Mobile health service adoption in China Integration of theory of planned behavior, protection motivation theory and personal health differences. Online Inf Rev, 2019, 44(1): 1-23.
31.	Zhang Y, Liu C, Luo S, et al. Factors influencing patients' intentions to use diabetes management apps based on an extended unified theory of acceptance and use of technology model: web-based survey. J Med Internet Res, 2019, 21(8): e15023.
32.	Zhu Z, Liu Y, Che X, et al. Moderating factors influencing adoption of a mobile chronic disease management system in China. Inform Health Soc Care, 2018, 43(1): 22-41.
33.	卞毛毛, 宋健, 王文娟, 等. 糖尿病应用程序使用意向量表的编制. 中华全科医学, 2020, 18(9): 1577-1580.
34.	邓子豪, 陈志锋, 曾淼坤, 等. 武汉市患者移动医疗服务使用意愿及影响因素研究. 中国卫生统计, 2020, 37(2): 206-209.
35.	窦凯丽. 慢病患者移动技术接受度模型的设计与实践. 杭州: 浙江大学, 2018.
36.	赖玉珊. 移动中医健康服务用户采纳意愿问卷研制及初步应用. 广州: 广州中医药大学, 2016.
37.	李冰雪. 高血压病患者移动医疗管理平台使用意愿模型的构建及验证. 南京: 南京中医药大学, 2020.
38.	刘连英. 成都市门诊患者互联网诊疗服务使用影响因素研究. 成都: 成都中医药大学, 2019.
39.	聂丽, 张凯丽. 慢性病患者移动医疗服务使用意愿影响因素分析. 中国卫生事业管理, 2021, 38(6): 468-472.
40.	彭前. 基于UTAUT理论的在线健康社区用户接受与使用行为影响因素研究. 广州: 暨南大学, 2020.
41.	王尧. 病患医疗服务模式选择行为研究. 成都: 西南财经大学, 2016.
42.	燕菊, 刘婷, 苗秀欣, 等. 痛风患者移动医疗使用意愿调查及影响因素分析. 中华现代护理杂志, 2021, 27(8): 1006-1011.
43.	杨佳. 移动医疗APP患者持续使用行为分析—基于信任的分析视角. 中医药管理杂志, 2018, 26(18): 52-55.
44.	钟姝雅. 基于UTAUT理论的移动医疗APP用户接受行为影响因素研究. 杭州: 杭州师范大学, 2017.
45.	周腾. 患者群体对移动医疗平台使用意愿的影响因素研究. 广州: 南方医科大学, 2019.
46.	Dou K, Yu P, Deng N, et al. Patients' acceptance of smartphone health technology for chronic disease management: a theoretical model and empirical test. JMIR Mhealth Uhealth, 2017, 5(12): e177.
47.	Dehzad F, Hilhorst C, de Bie C, et al. Adopting health apps, what’s hindering doctors and patients. Health, 2014, 6(16): 2204-2217.
48.	Kim HW, Kankanhalli A. Investigating user resistance to information systems implementation: a status quo bias perspective. MIS Q, 2009, 33(3): 567-582.

1. 周洁. 公立医院加速互联网化的机遇与瓶颈. 新民周刊, 2020, (30): 84-87.
2. 国务院办公厅. 关于促进“互联网+医疗健康”发展的意见. 当代农村财经, 2018, (6): 42-45.
3. 国家卫健委, 国家中医药管理局. 关于印发互联网诊疗管理办法(试行)等3个文件的通知. 中华人民共和国国务院公报, 2019, (2): 49-58.
4. 梁馨月, 李莹. 互联网诊疗模式的研究现状与展望. 经营与管理, 2020, 432(6): 24-27.
5. Ajzen I, Fishbein M. Understanding attitudes and predicting social behavior. NJ: Prentice-Hall Englewood Cliffs, 1980.
6. Ajzen I. From intentions to actions: a theory of planned behavior. Berlin: Springer, 1985.
7. Ajzen I. Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. J Appl Soc Psychol, 2002, 32: 665-683.
8. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q, 1989, 13(3): 319-340.
9. Kim J, Park HA. Development of a health information technology acceptance model using consumers' health behavior intention. J Med Internet Res, 2012, 14(5): e133.
10. Venkatesh V, Morris MG, Davis GB, et al. User acceptance of information technology: toward a unified view. MIS Q, 2003, 27(3): 425-478.
11. Venkatesh V, Thong JYL. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q, 2012, 36(1): 157-178.
12. Tan BC. The role of perceived consumer effectiveness on value-attitude-behaviour model in green buying behavior context. Aust J Basic Appl Sci, 2011, 5(12): 157-166.
13. Williams MD, Rana NP, Dwivedi YK. The unified theory of acceptance and use of technology (UTAUT): a literature review. J Enterp Inf Manag, 2015, 28(3): 443-448.
14. Ajzen I, Fishbein M. Understanding attitudes and predicting social behavior. New Jersey: Prentice-Hall, 1980.
15. Peterson RA, Brown SP. On the use of beta coefficients in meta-analysis. J Appl Psychol, 2005, 90(1): 175-181.
16. Wilson DB. Practical meta-analysis effect size calculator. Available at: https://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php.
17. Cohen J. Statistical power analysis for the behavioral sciences. Mahwah: Lawrence Erlbaum Associates, 1988: 79-81.
18. Alaiad A, Alsharo M, Alnsour Y. The determinants of m-health adoption in developing countries: an empirical investigation. Appl Clin Inform, 2019, 10(5): 820-840.
19. Apolinário-Hagen J, Menzel M, Hennemann S, et al. Acceptance of mobile health apps for disease management among people with multiple sclerosis: web-based survey study. JMIR Form Res, 2018, 2(2): e11977.
20. Breil B, Kremer L, Hennemann S, et al. Acceptance of mHealth apps for self-management among people with hypertension. Stud Health Technol Inform, 2019, 267: 282-288.
21. Deng Z, Hong Z, Ren C, et al. What predicts patients' adoption intention toward mHealth services in China: empirical study. JMIR Mhealth Uhealth, 2018, 6(8): e172.
22. Deng Z, Mo X, Liu S. Comparison of the middle-aged and older users' adoption of mobile health services in China. Int J Med Inform, 2014, 83(3): 210-224.
23. Hoque R, Sorwar G. Understanding factors influencing the adoption of mHealth by the elderly: an extension of the UTAUT model. Int J Med Inform, 2017, 101: 75-84.
24. Li D, Hu Y, Pfaff H, et al. Determinants of patients' intention to use the online inquiry services provided by internet hospitals: empirical evidence from China. J Med Internet Res, 2020, 22(10): e22716.
25. Miao R, Wu Q, Wang Z, et al. Factors that influence users' adoption intention of mobile health: a structural equation modeling approach. Int J Prod Res, 2017, 55(19): 5801-5815.
26. Salgado T, Tavares J, Oliveira T. Drivers of mobile health acceptance and use from the patient perspective: survey study and quantitative model development. JMIR Mhealth Uhealth, 2020, 8(7): e17588.
27. Tang Y, Yang YT, Shao YF. Acceptance of online medical websites: an empirical study in China. Int J Environ Res Public Health, 2019, 16(6): 943.
28. Wang H, Liang L, Du C, et al. Implementation of online hospitals and factors influencing the adoption of mobile medical services in china: cross-sectional survey study. JMIR Mhealth Uhealth, 2021, 9(2): e25960.
29. 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.
30. Zhang X, Liu S, Wang L, et al. Mobile health service adoption in China Integration of theory of planned behavior, protection motivation theory and personal health differences. Online Inf Rev, 2019, 44(1): 1-23.
31. Zhang Y, Liu C, Luo S, et al. Factors influencing patients' intentions to use diabetes management apps based on an extended unified theory of acceptance and use of technology model: web-based survey. J Med Internet Res, 2019, 21(8): e15023.
32. Zhu Z, Liu Y, Che X, et al. Moderating factors influencing adoption of a mobile chronic disease management system in China. Inform Health Soc Care, 2018, 43(1): 22-41.
33. 卞毛毛, 宋健, 王文娟, 等. 糖尿病应用程序使用意向量表的编制. 中华全科医学, 2020, 18(9): 1577-1580.
34. 邓子豪, 陈志锋, 曾淼坤, 等. 武汉市患者移动医疗服务使用意愿及影响因素研究. 中国卫生统计, 2020, 37(2): 206-209.
35. 窦凯丽. 慢病患者移动技术接受度模型的设计与实践. 杭州: 浙江大学, 2018.
36. 赖玉珊. 移动中医健康服务用户采纳意愿问卷研制及初步应用. 广州: 广州中医药大学, 2016.
37. 李冰雪. 高血压病患者移动医疗管理平台使用意愿模型的构建及验证. 南京: 南京中医药大学, 2020.
38. 刘连英. 成都市门诊患者互联网诊疗服务使用影响因素研究. 成都: 成都中医药大学, 2019.
39. 聂丽, 张凯丽. 慢性病患者移动医疗服务使用意愿影响因素分析. 中国卫生事业管理, 2021, 38(6): 468-472.
40. 彭前. 基于UTAUT理论的在线健康社区用户接受与使用行为影响因素研究. 广州: 暨南大学, 2020.
41. 王尧. 病患医疗服务模式选择行为研究. 成都: 西南财经大学, 2016.
42. 燕菊, 刘婷, 苗秀欣, 等. 痛风患者移动医疗使用意愿调查及影响因素分析. 中华现代护理杂志, 2021, 27(8): 1006-1011.
43. 杨佳. 移动医疗APP患者持续使用行为分析—基于信任的分析视角. 中医药管理杂志, 2018, 26(18): 52-55.
44. 钟姝雅. 基于UTAUT理论的移动医疗APP用户接受行为影响因素研究. 杭州: 杭州师范大学, 2017.
45. 周腾. 患者群体对移动医疗平台使用意愿的影响因素研究. 广州: 南方医科大学, 2019.
46. Dou K, Yu P, Deng N, et al. Patients' acceptance of smartphone health technology for chronic disease management: a theoretical model and empirical test. JMIR Mhealth Uhealth, 2017, 5(12): e177.
47. Dehzad F, Hilhorst C, de Bie C, et al. Adopting health apps, what’s hindering doctors and patients. Health, 2014, 6(16): 2204-2217.
48. Kim HW, Kankanhalli A. Investigating user resistance to information systems implementation: a status quo bias perspective. MIS Q, 2009, 33(3): 567-582.

Chinese Journal of Evidence-Based Medicine

Influencing factors of patients' adoption intention toward online medical services: a meta-analysis

Abstract Full text Figures/Tables Video References Cited by

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