老年人对网络直播运动指导使用意愿的调查研究：基于整合技术接受模型与感知风险理论

doi:10.12114/j.issn.1007-9572.2023.0474

摘要/Abstract

摘要： 背景既往关于老年人"互联网+健康管理"的研究多集中于其对移动健康应用的使用意愿，较少涉及老年人网络直播运动指导。目的基于整合技术接受（UTAUT）模型与感知风险理论，调查老年人对网络直播运动指导的使用意愿。方法 2022年4—8月，采用方便抽样法选择在北京市丰台区方庄社区卫生服务中心就诊的老年人作为研究对象，采用自拟调查问卷、面对面方式进行调查。问卷主要内容是老年人对网络直播运动指导的使用意愿。结果本研究共发放问卷390份，回收有效问卷376份，有效回收率为96.41%（376/390）。在376名研究对象中，男161人、女215人。改良UTAUT模型拟合指数优于合格标准，拟合度良好。结构方程模型检验结果显示，绩效期望（β=0.424）、努力期望（β=0.325）、社会影响（β=0.596）、促进条件（β=0.204）对老年人网络直播运动指导使用意愿有正向影响，感知风险（β=-0.634）对老年人网络直播运动指导使用意愿有负向影响（P<0.001）。结论基于改良UTAUT模型与感知风险理论，绩效期望、努力期望、社会影响、促进条件均正向影响老年人对网络直播运动指导的使用意愿，感知风险负向影响老年人对网络直播运动指导的使用意愿。

关键词: 运动活动, 老年人, 互联网干预, 使用意愿, 网络直播运动指导, 整合技术接受模型, 感知风险理论

Abstract:

Background

Previous studies on "internet+health management" for the elderly mainly focused on the willingness to use mobile health applications, with less attention to the webcast exercise guidance.

Objective

Based on the unified theory of acceptance and use of technology (UTAUT) model and the perceived risk theory, we investigated the older people's willingness to use webcast exercise guidance.

Methods

From April to August 2022, a convenience sampling method was used to select older people visiting the Fangzhuang Community Health Service Center in Fengtai District, Beijing, as the subjects, and a self-designed questionnaire was conducted face-to-face. The questionnaire mainly assessed older adults' intention to use webcast exercise guidance.

Results

A total of 390 questionnaires were distributed, and 376 valid questionnaires were collected, with a valid recovery rate of 96.4% (376/390). Of the 376 participants, 161 were men and 215 were women. The fit index of the modified UTAUT model was better than the acceptable standards, indicating a good fit. The structural equation model test results showed that, performance expectancy (β=0.424), effort expectancy (β=0.325), social influence (β=0.596), and facilitating conditions (β=0.204) had a positive impact on the elderly's intention to use webcast exercise guidance, respectively, while perceived risk (β=-0.634) had a negative impact (P<0.001) .

Conclusion

Based on the UTAUT model and the perceived risk theory, performance experience, effort experience, social influence, and facilitating condition all positively affect the elderly's willingness to use webcast exercise guidance, while perceived risk negatively affects the Unified theory of acceptance and use of technology; Perceived risk theoryelderly's willingness to use webcast exercise guidance.

Key words: Motor activity, Aged, Internet-based intervention, Intention to use, Webcast exercise guidance

中图分类号:

R 87 R 339.34 R 197.32

相彤,黄亚芳,吴浩. 老年人对网络直播运动指导使用意愿的调查研究：基于整合技术接受模型与感知风险理论[J]. 中国全科医学, 2026, 29(13): 1720-1725. DOI: 10.12114/j.issn.1007-9572.2023.0474.
XIANG Tong,HUANG Yafang,WU Hao. Investigation Study on Older People's Willingness to Use Webcast Exercise Guidance: Based on the UTAUT Model and the Perceived Risk Theory[J]. Chinese General Practice, 2026, 29(13): 1720-1725. DOI: 10.12114/j.issn.1007-9572.2023.0474.

图/表 5

参考文献 44

[1]	CHEN Y, LIU W. Utilization and out-of-pocket expenses of primary care among the multimorbid elderly in China: a two-part model with nationally representative data[J]. Front Public Health, 2022, 10: 1057595. DOI: 10.3389/fpubh.2022.1057595.
[2]	Look AHEAD Research Group, WING R R. Long-term effects of a lifestyle intervention on weight and cardiovascular risk factors in individuals with type 2 diabetes mellitus: four-year results of the Look AHEAD Trial[J]. Arch Intern Med, 2010, 170(17): 1566-1575. DOI: 10.1001/archinternmed.2010.334.
[3]	PEDERSEN L R, OLSEN R H, ANHOLM C, et al. Effects of 1 year of exercise training versus combined exercise training and weight loss on body composition, low-grade inflammation and lipids in overweight patients with coronary artery disease: a randomized trial[J]. Cardiovasc Diabetol, 2019, 18(1): 127.
[4]	GLASGOW R E, EDWARDS L L, WHITESIDES H, et al. Reach and effectiveness of DVD and in-person diabetes self-management education[J]. Chronic Illn, 2009, 5(4): 243-249.
[5]	THOOLEN B, DE RIDDER D, BENSING J, et al. Who participates in diabetes self-management interventions? Issues of recruitment and retainment[J]. Diabetes Educ, 2007, 33(3): 465-474.
[6]	TATE D F, JACKVONY E H, WING R R. Effects of Internet behavioral counseling on weight loss in adults at risk for type 2 diabetes: a randomized trial[J]. JAMA, 2003, 289(14): 1833-1836. DOI: 10.1001/jama.289.14.1833.
[7]	WANNER M, MARTIN-DIENER E, BRAUN-FAHRLÄNDER C, et al. Effectiveness of active-online, an individually tailored physical activity intervention, in a real-life setting: randomized controlled trial[J]. J Med Internet Res, 2009, 11(3): e23.
[8]	STRECHER V J, MCCLURE J B, ALEXANDER G L, et al. Web-based smoking-cessation programs: results of a randomized trial[J]. Am J Prev Med, 2008, 34(5): 373-381.
[9]	STRECHER V J, MCCLURE J, ALEXANDER G, et al. The role of engagement in a tailored web-based smoking cessation program: randomized controlled trial[J]. J Med Internet Res, 2008, 10(5): e36. DOI: 10.2196/jmir.1002.
[10]	CRANE S J, GANESH R, POST J A, et al. Telemedicine consultations and follow-up of patients with COVID-19[J]. Mayo Clin Proc, 2020, 95(9S): S33-34.
[11]	沈晓丹，韦靖，梁芳，等. 移动健康教育平台在心脏瓣膜置换术后患者居家护理中的应用[J]. 护理实践与研究, 2022, 19(24): 3738-3742. DOI: 10.3969/j.issn.1672-9676.2022.24.022.
[12]	杨新星，宗瑜瑾，朱霆，等. 互联网直播在医学超声教学与会议中的应用[J]. 中国数字医学, 2018, 13(3): 60-62.
[13]	周裕琼. 数字弱势群体的崛起:老年人微信采纳与使用影响因素研究[J]. 新闻与传播研究, 2018, 25(7): 66-86.
[14]	李嘉兴. 生命周期视角下移动社交网络老年用户使用行为过程研究[D]. 长春: 吉林大学, 2019.
[15]	YU T K, CHAO C M. Assessing older adults' information technology ability: the development of a multiple item scale[J]. International Journal of Human Computer Interaction, 2014, 30(4-6): 435-445. DOI: 10.1080/10447318.2014.880141.
[16]	ALSSWEY A, AL-SAMARRAIE H. Elderly users'acceptance of mHealth user interface(Ul)design-based culture: the moderator role of age[J]. Journal on Multimodal User Interfaces, 2020, 14(1): 49-59. DOI: 10.1007/s12193-019-00307-w.
[17]	CIMPERMAN M, MAKOVEC BRENČIČ M, TRKMAN P. Analyzing older users' home telehealth services acceptance behavior-applying an Extended UTAUT model[J]. Int J Med Inform, 2016, 90: 22-31. DOI: 10.1016/j.ijmedinf.2016.03.002.
[18]	HSU C W, PENG C C. What drives older adults' use of mobile registration apps in Taiwan? An investigation using the extended UTAUT model[J]. Inform Health Soc Care, 2022, 47(3): 258-273. DOI: 10.1080/17538157.2021.1990299.
[19]	黄齐娴，李晓宛，袁睿洁，等. 数字鸿沟与数字反哺:基于UTAUT模型下互联网医疗代际使用差异的研究[J]. 中国集体经济, 2022(10): 82-85.
[20]	CUNNINGHAM S M. The major dimensions of perceived risk[M]. Boston: Harvard University Press, 1967.
[21]	吴桂林. 健康类微信公众号用户使用意愿影响因素研究[D]. 广州: 广东外语外贸大学, 2021.
[22]	刘萌萌. 在线健康社区用户信息采纳行为意向影响因素研究[D]. 武汉: 华中科技大学, 2019.
[23]	SINGH S, SINGH N, KALINIĆ Z, et al. Assessing determinants influencing continued use of live streaming services: an extended perceived value theory of streaming addiction[J]. Expert Systems with Applications, 2021, 168: 114241.
[24]	MITZNER T L, BORON J B, FAUSSET C B, et al. Older adults talk technology: technology usage and attitudes[J]. Comput Human Behav, 2010, 26(6): 1710-1721.
[25]	STEELE R, LO A, SECOMBE C, et al. Elderly persons' perception and acceptance of using wireless sensor networks to assist healthcare[J]. Int J Med Inform, 2009, 78(12): 788-801.
[26]	SUN Y, WANG N, GUO X, et al. Understanding the acceptance of mobile health services: a comparison and integration of alternative models[J]. Journal of Electronic Commerce Research, 2013, 14(2): 183-200.
[27]	HOQUE R, SORWAR G. Understanding factors influencing the adoption of mHealth by the elderly: an extension of the UTAUT model[J]. Int J Med Inform, 2017, 101: 75-84.
[28]	DWIVEDI Y K, SHAREEF M A, SIMINTIRAS A C, et al. A generalised adoption model for services: a cross-country comparison of mobile health(m-health)[J]. Government Information Quarterly, 2016, 33(1): 174-187. DOI: 10.1016/j.giq.2015.06.003.
[29]	BEARDEN W O, ETZEL M J. Reference group influence on product and brand purchase decisions[J]. Journal of Consumer Research, 1982, 9(2): 183-194. DOI: 10.1086/208911.
[30]	HSIEH, PI-JUNG. An empirical investigation of patients' acceptance and resistance toward the health cloud: the dual factor perspective[J]. Computers in Human Behavior, 2016, 63: 959-969. DOI: 10.1016/j.chb.2016.06.029.
[31]	WU W, ZHANG B, LI S, et al. Exploring factors of the willingness to accept AI-assisted learning environments: an empirical investigation based on the UTAUT model and perceived risk theory[J]. Front Psychol, 2022, 13: 870777.
[32]	CHAO C M. Factors determining the behavioral intention to use mobile learning: an application and extension of the UTAUT Model[J]. Front Psychol, 2019, 10: 1652. DOI: 10.3389/fpsyg.2019.01652.
[33]	VENKATESH V, THONG J Y L, XU X. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology[J]. MIS Quarterly, 2012, 36(1): 157-158. DOI: 10.2307/41410412.
[34]	STONE R N, GRØNHAUG K. Perceived risk: further considerations for the marketing discipline[J]. European Journal of Marketing, 1993, 27(3): 39-50.
[35]	NUNNALLY J C. Psychometric Theory[M]. 3 Ed. New York: Tata McGraw-hill Education, 1994.
[36]	ANDERSON J C, GERBING D W. Structural equation modeling in practice: a review and recommended two-step approach[J]. Psychological Bulletin, 1988, 27(1): 5-24.
[37]	国家统计局. 中华人民共和国2023年国民经济和社会发展统计公报[EB/OL]. (2024-02-29)[2024-04-12].
[38]	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[D]. 广州: 暨南大学, 2020.
[39]	YU R P. Use of messaging APPs and social network sites among older adults: a mixed-method study[J]. Int J Commun, 2020, 14: 4453-4473.
[40]	DENG Z, MO X, LIU S. Comparison of the middle-aged and older users' adoption of mobile health services in China[J]. Int J Med Inform, 2014, 83(3): 210-224.
[41]	王艺蓉. 患者对糖尿病移动医疗服务使用意愿的影响因素研究——以某大型三甲综合医院糖尿病患者为例[D]. 广州: 南方医科大学, 2017.
[42]	KOTHGASSNER O D, GOREIS A, KAFKA J X, et al. Agency and gender influence older adults' presence-related experiences in an interactive virtual environment[J]. Cyberpsychol Behav Soc Netw, 2018, 21(5): 318-324.
[43]	VENKATESH V, MORRIS M G, DAVIS G B, et al. User acceptance of information technology: toward a unified view[J]. MIS Quarterly, 2003, 27(3): 425-478.
[44]	丁志宏，张现苓. 中国城镇老年人上网状况及其影响因素[J]. 人口研究, 2021, 45(2): 61-74.

变量	题项	Cronbach's α系数	聚合效度
变量	题项	Cronbach's α系数	CR	AVE	因子载荷
绩效期望	1	0.92	0.92	0.69	0.79
	2				0.82
	3				0.84
	4				0.86
	5				0.85
努力期望	1	0.86	0.87	0.63	0.75
	2				0.85
	3				0.77
	4				0.89
社会影响	1	0.89	0.89	0.73	0.87
	2				0.80
	3				0.86
促进条件	1	0.81	0.81	0.68	0.87
	2				0.86
感知风险	1	0.90	0.90	0.60	0.77
	2				0.83
	3				0.78
	4				0.82
	5				0.73
	6				0.73
使用意愿	1	0.91	—	—	—

变量	题项	Cronbach's α系数	聚合效度
变量	题项	Cronbach's α系数	CR	AVE	因子载荷
绩效期望	1	0.92	0.92	0.69	0.79
	2				0.82
	3				0.84
	4				0.86
	5				0.85
努力期望	1	0.86	0.87	0.63	0.75
	2				0.85
	3				0.77
	4				0.89
社会影响	1	0.89	0.89	0.73	0.87
	2				0.80
	3				0.86
促进条件	1	0.81	0.81	0.68	0.87
	2				0.86
感知风险	1	0.90	0.90	0.60	0.77
	2				0.83
	3				0.78
	4				0.82
	5				0.73
	6				0.73
使用意愿	1	0.91	—	—	—

项目	χ²值	df	χ²/df	IFI	TLI	CFI	RMSEA	SRMR
本研究	714.48	175	4.08	0.91	0.89	0.91	0.09	0.05
合格标准	—	—	1~5	>0.80	>0.80	>0.80	<0.10	<0.08
理想标准	—	—	1~3	>0.90	>0.90	>0.90	<0.08	—

项目	χ²值	df	χ²/df	IFI	TLI	CFI	RMSEA	SRMR
本研究	714.48	175	4.08	0.91	0.89	0.91	0.09	0.05
合格标准	—	—	1~5	>0.80	>0.80	>0.80	<0.10	<0.08
理想标准	—	—	1~3	>0.90	>0.90	>0.90	<0.08	—

变量→使用意愿	β值（非标准化路径）	β值（标准化路径）	P值（标准化路径）	研究假设结果
绩效期望→使用意愿	0.482	0.424	<0.001	支持
努力期望→使用意愿	0.335	0.325	<0.001	支持
社会影响→使用意愿	0.514	0.596	<0.001	支持
促进条件→使用意愿	0.194	0.204	<0.001	支持
感知风险→使用意愿	-0.631	-0.634	<0.001	支持