2型糖尿病患者使用E-health管理疾病意愿与影响机制

doi:10.19723/j.issn.1671-167X.2025.03.016

Abstract

Abstract:

Objective: To comprehensively investigate how the willingness of patients with type 2 diabetes mellitus to use E-health technologies, such as the application (APP) or Wechat mini-programs and the underlying influencing factors works for its mechanisms. Methods: Based on the extended technology acceptance model, a cross-sectional questionnaire survey was conducted among 559 patients with type 2 diabetes from six provinces or municipalities in the eastern, central, and western regions of China from November to December 2024. The survey aimed to investigate the patients' willingness to use APP or Wechat mini-programs and the influencing factors. Correlation analysis and structural equation modeling methods were used to analyze the influencing factors of patients ' willingness to use and to explore the mechanisms. Results: A total of 559 patients were surveyed, with an average willingness score of 10.68 (out of a total score of 15). Age and education level were found to be influencing factors of willingness to use, younger age and higher education (high school/college) were significantly associated with stronger willingness to use (P < 0.05). Spearman correlation analysis revealed that perceived usefulness, perceived ease of use, disease perception, social support, self-efficacy, and external cues were positively correlated with willingness to use, while perceived barriers were negatively correlated (all P < 0.05). Structural equation modeling demonstrated direct effects of perceived usefulness (β=0.375, P < 0.001), disease perception (β=-0.240, P < 0.001), self-efficacy (β=0.313, P=0.019), social support (β=-0.336, P=0.042), and external cues (β=0.609, P < 0.001) on willingness to use. Perceived ease of use indirectly influenced willingness through perceived usefulness (total effect=0.374). Self-efficacy affected usage intention partially mediated by disease perception, external cues influenced intention through perceived usefulness, and perceived barriers impacted intention via perceived usefulness, with external cues exhibiting the strongest total effect (β=0.672). All these effects were statistically significant (P < 0.05). Conclusion: Patients with type 2 diabetes mellitus have a high willingness to use E-health technologies like APP or Wechat mini-programs, particularly younger individuals and those with high school/college education. Increasing perceived usefulness, self-efficacy, and external promotion can enhance willingness to use. However, higher disease perception and higher social support are associated with decreased willingness to use. Perceived ease of use and self-efficacy can also affect willingness to use through multiple mechanisms.

Key words: Type 2 diabetes mellitus, E-health, Willingness to use, Structural equation model, Technology acceptance model

CLC Number:

R193.3

Ziyan CHEN, Xiaoyue ZHANG, Yiwu GU, Chun CHANG. Type 2 diabetes patients use E-health to manage disease willingness and influence mechanisms[J].Journal of Peking University (Health Sciences), 2025, 57(3): 522-528.

Figures/Tables 5

Table 1

Table 2

Table 3

Table 4

Table 5

References 18

1	中华医学会糖尿病学分会. 中国2型糖尿病防治指南(2020年版)[J]. 中华糖尿病杂志, 2021, 13 (4): 315- 409.
2	Magliano DJ , Boyko EJ , IDF diabetes atlas 10th edition scientific committee . IDF diabetes atlas[M]. Brussels: International Diabetes Federation, 2021: 34- 36.
3	嵇加佳, 刘林, 楼青青, 等. 2型糖尿病患者自我管理行为及血糖控制现状的研究[J]. 中华护理杂志, 2014, 49 (5): 617- 620.
4	Heidel A , Hagist C . Potential benefits and risks resulting from the introduction of health apps and wearables into the german statutory health care system: Scoping review[J]. JMIR Mhealth Uhealth, 2020, 8 (9): e16444. doi: 10.2196/16444
5	赵亚珍, 王晓云. 移动健康技术在糖尿病病人自我管理中应用的研究进展[J]. 护理研究, 2023, 37 (13): 2395- 2398.
6	Gong E , Baptista S , Russell A , et al. My diabetes coach, a mobile app-based interactive conversational agent to support type 2 diabetes self-management: Randomized effectiveness-implementation trial[J]. J Med Internet Res, 2020, 22 (11): e20322. doi: 10.2196/20322
7	Zhang X , Zhang L , Lin Y , et al. Effects of E-health-based interventions on glycemic control for patients with type 2 diabetes: A Bayesian network meta-analysis[J]. Front Endocrinol (Lausanne), 2023, 14, 1068254. doi: 10.3389/fendo.2023.1068254
8	Eberle C , Stichling S . Clinical improvements by telemedicine interventions managing type 1 and type 2 diabetes: systematic meta-review[J]. J Med Internet Res, 2021, 23 (2): e23244. doi: 10.2196/23244
9	Zhang Y , Li X , Luo S , et al. Use, perspectives, and attitudes regarding diabetes management mobile apps among diabetes patients and diabetologists in China: National web-based survey[J]. JMIR Mhealth Uhealth, 2019, 7 (2): e12658. doi: 10.2196/12658
10	汪洋, 王觅也, 周有莲, 等. 我国互联网糖尿病健康信息资源的合理应用及挑战[J]. 中国全科医学, 2021, 24 (16): 2098- 2102.
11	Holmen H , Torbjørnsen A , Wahl AK , et al. A mobile health intervention for self-management and lifestyle change for persons with type 2 diabetes, Part 2: One-year results from the norwegian randomized controlled trial RENEWING HEALTH[J]. JMIR Mhealth Uhealth, 2014, 2 (4): e57. doi: 10.2196/mhealth.3882
12	Zhang L , He X , Shen Y , et al. Effectiveness of smartphone APP-based interactive management on glycemic control in Chinese patients with poorly controlled diabetes: Randomized controlled trial[J]. J Med Internet Res, 2019, 21 (12): e15401. doi: 10.2196/15401
13	王淇, 吴浩, 魏学娟, 等. 方庄社区卫生服务中心应用移动终端APP进行糖尿病管理的效果评价研究[J]. 中国全科医学, 2020, 23 (7): 844- 848.
14	万巧琴, 尚少梅, 来小彬, 等. 2型糖尿病患者自我管理行为量表的信、效度研究[J]. 中国实用护理杂志, 2008, 24 (7): 26- 27.
15	Breil B , Salewski C , Apolinário-Hagen J . Comparing the accep-tance of mobile hypertension apps for disease management among patients versus clinical use among physicians: Cross-sectional survey[J]. JMIR Cardio, 2022, 6 (1): e31617.
16	崔洪成. 移动健身App使用意愿研究: 基于技术准备度与技术接受模型(TRAM)[J]. 中国体育科技, 2022, 58 (6): 104- 113.
17	AlBar AM , Hoque MR . Patient Acceptance of E-health services in Saudi Arabia: An integrative perspective[J]. Telemed 1 E Health, 2019, 25 (9): 847- 852.
18	卫艳利, 曹晓强, 蔡雁, 等. 基于技术接受模型慢性病远程监护服务使用意愿的因素[J]. 中国全科医学, 2018, 21 (8): 958- 964.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

Variables	Patients, n (%)	Score, ${\bar x}$±s	F/t	P
Gender			1.775	0.077
Male	244 (43.65)	10.87±2.23
Female	315 (56.35)	10.53±2.30
Age/years			5.283	< 0.001^***
18-	60 (10.73)	11.53±2.09
40-	81 (14.49)	11.11±2.07
50-	132 (23.61)	10.42±2.13
60-	173 (30.95)	10.75±2.39
70-	113 (20.21)	10.11±2.31
Domicile			-1.730	0.084
Urban	311 (55.64)	10.53±2.23
Rural	248 (44.36)	10.86±2.32
Area			1.154	0.283
East	177 (31.66)	10.64±2.50
Midland	217 (38.82)	10.53±2.35
West	165 (29.52)	10.91±1.87
Education			4.843	0.028^*
Primary school and below	107 (19.14)	10.09±2.13
Junior high school	187 (33.45)	10.67±2.28
High school or technical school	130 (23.26)	11.00±2.36
Junior college	54 (9.66)	10.94±2.41
University and higher	81 (14.49)	10.75±2.11
Marital status			0.407	0.685
Married	495 (88.55)	10.66±2.23
Single/separated or divorced/widow	64 (11.45)	10.80±2.56
Income per month/yuan			0.180	0.671
≤1 499	146 (26.12)	10.75±2.45
1 500-2 999	160 (28.62)	10.59±2.27
3 000-5 999	160 (28.62)	10.49±2.15
≥6 000	93 (16.64)	10.82±2.20
Course of disease/years			3.627	< 0.001^***
≤5	328 (58.68)	10.97±2.23
>5	231 (41.32)	10.26±2.27
Complication			1.429	0.158
Yes	50 (8.94)	10.26±2.15
No	509 (91.06)	10.72±2.28
Diabetes comorbidities
Yes	242 (43.29)	10.60±2.42	0.652	0.515
No	317 (56.71)	10.73±2.15
Family history of diabetes
Yes	210 (37.57)	10.83±2.38	-1.245	0.214
No	349 (62.43)	10.58±2.20
Total	559 (100.00)	10.68±2.27

Variables	Items, n	Score, ${\bar x}$±s
Perceived usefulness	7	28.23±5.91
Perceived ease of use	3	11.62±2.67
Disease perception	4	15.14±3.15
Perceived barrier	4	12.60±4.10
External clues	4	15.69±3.55
Social support	4	15.48±3.53
Self efficacy	3	11.59±2.67
Willingness to use	3	10.68±2.27

Variables	Perceived usefulness	Perceived ease of use	Disease perception	Perceived barrier	External clues	Social support	Self efficacy	Willingness to use
Perceived usefulness	1.000
Perceived ease of use	0.837^***	1.000
Disease perception	0.615^***	0.637^***	1.000
Perceived barrier	0.121^**	0.091^*	0.335^***	1.000
External clues	0.737^***	0.704^***	0.571^***	0.104^*	1.000
Social support	0.701^***	0.648^***	0.549^***	0.142^***	0.868^***	1.000
Self efficacy	0.738^***	0.814^***	0.635^***	0.094^*	0.772^***	0.726^***	1.000
Willingness to use	0.605^***	0.605^***	0.285^***	-0.224^***	0.593^***	0.531^***	0.594^***	1.000

Path			Estimator	95%CI of estimator	Z	P
Perceived usefulness	→	Willingness to use	0.375	0.250，0.500	5.865	< 0.001^***
Perceived ease of use	→	Willingness to use	0.108	-0.088，0.304	1.078	0.281
Disease perception	→	Willingness to use	-0.240	-0.364，-0.116	-3.792	< 0.001^***
Self efficacy	→	Willingness to use	0.313	0.051，0.575	2.338	0.019^*
Perceived barrier	→	Willingness to use	-0.055	-0.114，0.004	-1.837	0.066
Social support	→	Willingness to use	-0.336	-0.661，-0.012	-2.032	0.042^*
External clues	→	Willingness to use	0.609	0.221，0.997	3.075	0.002^**
Disease perception	→	Perceived usefulness	0.057	-0.014，0.128	1.566	0.117
Perceived ease of use	→	Perceived usefulness	0.708	0.632，0.784	18.245	< 0.001^***
External clues	→	Perceived usefulness	0.169	0.096，0.243	4.517	< 0.001^***
Perceived barrier	→	Perceived usefulness	0.044	0.001，0.088	2.004	0.045^*
Self efficacy	→	Perceived ease of use	0.910	0.885，0.935	71.419	< 0.001^***
Perceived barrier	→	Perceived ease of use	-0.054	-0.105，-0.003	-2.081	0.037^*
Self efficacy	→	Disease perception	0.794	0.729，0.858	24.111	< 0.001^***

Path			Direct effect	Total indirect effect	Total effect
Perceived usefulness	→	Willingness to use	0.375		0.375
Perceived ease of use	→	Willingness to use	0.108	0.266	0.374
Disease perception	→	Willingness to use	-0.240	0.021	-0.219
Self efficacy	→	Willingness to use	0.313	0.166	0.479
Perceived barrier	→	Willingness to use	-0.055	-0.004	-0.059
Social support	→	Willingness to use	-0.336		-0.336
External clues	→	Willingness to use	0.609	0.063	0.672

Type 2 diabetes patients use E-health to manage disease willingness and influence mechanisms

RICH HTML

PDF (PC)

Abstract

Cite this article

share this article

Figures/Tables 5

References 18

Related Articles 4

Metrics

Comments

Recommended 0

[1]	Lei BAO,Xia-xia CAI,Ming-yuan ZHANG,Lei-lei REN. Effect of vitamin D3 on mild cognitive impairment in type 2 diabetic mice and its possible mechanism [J]. Journal of Peking University (Health Sciences), 2023, 55(4): 587-592.
[2]	WANG Xiao-qi,CHEN Mei-jun,YUN Qing-ping,SUN Si-wei,WANG Xi-kai,SHI Yu-hui,JI Ying,GUAN Zhong-jun,CHANG Chun. Impact of health literacy on patient experience of outpatients in China and its mechanism [J]. Journal of Peking University (Health Sciences), 2021, 53(3): 560-565.
[3]	CHEN Ping,LI Ze-ming,GUO Yi,SUN Xin-ying,Edwin B. FISHER. To explore medication adherence of patients with type 2 diabetes mellitus using the latent profile analysis based on the Big Five personality theory [J]. Journal of Peking University (Health Sciences), 2021, 53(3): 530-535.
[4]	Jiang XIE,Fei LI. Association of sleep overlap syndrome with type 2 diabetes in a cross-sectional study [J]. Journal of Peking University(Health Sciences), 2019, 51(2): 252-255.