Application effect of mobile medical technology in health management of tuberculosis patients: A Meta-analysis

doi:10.19982/j.issn.1000-6621.20250227

Abstract

Abstract:

Objective: To systematically evaluate the application effect of mobile medical technology in tuberculosis patient health management, to provide reference for clinical practice. Methods: A systematic search of Web of Science, Embase, Cochrane Library, PubMed, CINAHL, CBM, CNKI, WanFang Data and VIP database was conducted for randomised controlled trials about mobile medical technology’s usage on tuberculosis patient health management. The retrieval time limit was from database establishment to May 30, 2025. Two researchers independently screened literature,extracted data and evaluated quality. Meta-analysis was performed using RevMan 5.4 and Stata 16.0 software. Results: A total of 10 articles and 5615 patients were included. Meta-analysis showed that after applying mobile medical technology for the health management of tuberculosis patients, treatment compliance rate (RR=1.20,95%CI:1.12-1.29,P<0.001), treatment success rate (RR=1.09,95%CI:1.03-1.16,P=0.006), and sputum examination rate (RR=1.14,95%CI:1.06-1.22,P<0.001) of the observation group were all statistically significantly higher than those of the conventional group. Adverse outcomes occurrence in the observation group was significantly lower than that of the conventional group (RR=0.75,95%CI:0.59-0.96,P=0.020). Subgroup analysis showed that compared with information reminders and electronic pill boxes, patients who received intervention through WeChat program (RR=1.28,95%CI:1.17-1.40, P<0.001) and mobile face-to-face video (RR=1.29,95%CI:1.13-1.46,P<0.001) had significantly higher treatment compliance rates; compared with hospital-community and hospital-family intervention models, patients took hospital-community-family intervention model had significantly higher treatment compliance rate (RR=1.24,95%CI:1.16-1.33,P<0.001); compared with an ≤3 months intervention, patients getting intervention >3 months had significantly higher treatment compliance rate (RR=1.23,95%CI:1.19-1.27,P<0.001). Conclusion: The implementation of mobile medical technology can improve treatment compliance rate, treatment success rate and sputum examination rate of tuberculosis patients, and reduce incidence of adverse outcomes. Mobile medical technology with strong real-time interaction such as WeChat and mobile video can be used, together with adopting hospital-community-family linkage mode, extending intervention time, constructing intervention plan according to factors influencing usage of mobile medical technology, to further improve the intervention effect.

Key words: Tuberculosis, Computer communication networks, Mate-analysis, Nursing care

CLC Number:

Gao Dan, Yao Liwei, Huang Jinpeng, Liu Xiaoxia, Zhang Yue, Ling Lin. Application effect of mobile medical technology in health management of tuberculosis patients: A Meta-analysis[J]. Chinese Journal of Antituberculosis, 2025, 47(11): 1433-1441. doi: 10.19982/j.issn.1000-6621.20250227

Figures/Tables 10

纳入文献	发表年份	国家	观察组患者例数/对照组患者例数	对照组干预措施	观察组干预措施		干预模式	干预时长 (月)	结局指标	移动医疗使用影响因素
纳入文献	发表年份	国家	观察组患者例数/对照组患者例数	对照组干预措施	干预形式	干预方法	干预模式	干预时长 (月)	结局指标	移动医疗使用影响因素
Bao等^[8]	2022	中国	53/59	常规护理	微信平台	基于健康行为改变理论进行知识教育、自我调节、社会支持	医院-家庭	3	a	整合行为理论、有用性、易用性
Gashu等^[9]	2021	埃塞俄比亚	152/154	社区支持者观察	信息提醒	每日用药、每周取药图文提醒	医院-社区-家庭	4	a、b	多模态提醒系统、消除阅读障碍
Iribarren等^[10]	2022	阿根廷	21/21	常规护理	手机程序	每日服药自我报告、不良反应跟踪、治疗进度日历、论坛	医院-家庭	6	b	技术可行性、易用性、多模态监测、系统性支持
Kumwichar 等^[11]	2022	泰国	57/57	面对面督导治疗	手机视频	记录并上传服药视频,监测服药情况	医院-社区	2	c、f、g	网络覆盖率、医护执行力、政策支持
Liu等^[12]	2023	中国	1388/1298	常规护理	电子药盒	每月服药依从性数据审查、分级管理	医院-家庭	6	c、g	情境适配、技术可行性
Qin等^[13]	2025	中国	371/370	标准护理	微信小程序	用药提醒、随访提醒、医患实时沟通平台、个性化健康教育推送、未服药警报系统	医院-社区-家庭	6	c、f、g	健康信念模型、社会认知理论、实时交互与数据驱动管理
Santra等^[14]	2021	印度	110/110	标准督导	信息提醒	每日短信提醒、每周电话沟通:健康教育、服药提醒	医院-社区	3	d	技术接受度、文化适配性和成本可持续性
张小其等^[15]	2022	中国	108/108	定期入户访视	手机视频	上传服药视频,监测服药情况	医院-社区-家庭	6	b、d、h	实时互动反馈、便利性、数据隐私、安全与伦理
张婷等^[16]	2017	中国	74/74	常规护理	微信公众号	每周2~3次推送结核病防治知识(文字/图片/语音/视频)、每日2个时段在线答疑	医院	6	f、h、i	可行性、内容全面性、互动支持、定期推送、固定管理时段
李亚敏等^[17]	2023	中国	515/515	常规护理	微信、电子药盒	微信签到、电子药盒自动上传服药数据	医院-社区-家庭	8	e、f、i	技术接受度与满意度

References 31

[1]	付亮, 任坦坦, 张培泽, 等. 世界卫生组织《结核病整合指南模块4:耐药结核病治疗2022年更新版》解读. 中国防痨杂志, 2023, 45(4):336-348. doi:10.19982/j.issn.1000-6621.20220523.
[2]	World Health Organization. Global tuberculosis report 2024. Geneva: World Health Organization, 2024.
[3]	Fuady A, Arifin B, Yunita F, et al. Stigma towards people with tuberculosis: a cross-cultural adaptation and validation of a scale in Indonesia. BMC Psychol, 2023, 11(1):112. doi:10.1186/s40359-023-01161-y. pmid: 37055814
[4]	聂菲菲, 矫晓克, 雷国华, 等. 结核病健康管理护理工作室的构建及运行效果评价. 中国护理管理, 2025, 25(3):450-454. doi:10.3969/j.issn.1672-1756.2025.03.024.
[5]	Aisyah DN, Ahmad RA, Artama WT, et al. Knowledge, Attitudes, and Behaviors on Utilizing Mobile Health Technology for TB in Indonesia: A Qualitative Pilot Study. Front Public Health, 2020, 8:531514. doi:10.3389/fpubh.2020.531514.
[6]	张秀, 李月琳, 章小童. “健康中国2030”规划框架下我国健康信息政策内容分析. 情报理论与实践, 2020, 43(9):24-31. doi:10.16353/j.cnki.1000-7490.2020.09.005.
[7]	Cumpston M, Li T, Page MJ, et al. Updated guidance for trusted systematic reviews: a new edition of the Cochrane Handbook for Systematic Reviews of Interventions. Cochrane Database Syst Rev, 2019, 10(10):ED000142. doi:10.1002/14651858.ED000142.
[8]	Bao Y, Wang C, Xu H, et al. Effects of an mHealth Intervention for Pulmonary Tuberculosis Self-management Based on the Integrated Theory of Health Behavior Change: Randomized Controlled Trial. JMIR Public Health Surveill, 2022, 8(7):e34277. doi:10.2196/34277.
[9]	Gashu KD, Gelaye KA, Lester R, et al. Effect of a phone reminder system on patient-centered tuberculosis treatment adherence among adults in Northwest Ethiopia: a randomised controlled trial. BMJ Health Care Inform, 2021, 28(1):e100268. doi:10.1136/bmjhci-2020-100268.
[10]	Iribarren SJ, Milligan H, Chirico C, et al. Patient-centered mobile tuberculosis treatment support tools (TB-TSTs) to improve treatment adherence: A pilot randomized controlled trial exploring feasibility, acceptability and refinement needs. Lancet Reg Health Am, 2022, 13:100291. doi:10.1016/j.lana.2022.100291.
[11]	Kumwichar P, Chongsuvivatwong V, Prappre T. Tuberculosis Treatment Compliance Under Smartphone-Based Video-Observed Therapy Versus Community-Based Directly Observed Therapy: Protocol for a Cluster Randomized Controlled Trial. JMIR Res Protoc, 2022, 11(7):e38796. doi:10.2196/38796.
[12]	Liu X, Thompson J, Dong H, et al. Digital adherence technologies to improve tuberculosis treatment outcomes in China: a cluster-randomised superiority trial. Lancet Glob Health, 2023, 11(5): e693-e703. doi:10.1016/S2214-109X(23)00068-2.
[13]	Qin N, Chen J, Chen L, et al. Effectiveness of a mobile health application on treatment outcomes among patients with tuberculosis in Shanghai, China: a multicentre randomised controlled trial. BMJ Glob Health, 2025, 10(6):e018022. doi:10.1136/bmjgh-2024-018022.
[14]	Santra S, Garg S, Basu S, et al. The effect of a mhealth intervention on anti-tuberculosis medication adherence in Delhi, India: A quasi-experimental study. Indian J Public Health, 2021, 65(1):34-38. doi:10.4103/ijph.IJPH87920. pmid: 33753687
[15]	张小其, 李敏璐, 刘靖元. 互联网+视频服药APP督导方式在肺结核患者治疗管理中的应用效果. 新发传染病电子杂志, 2022, 7(1):66-69. doi:10.19871/j.cnki.xfcrbzz.2022.01.015.
[16]	张婷, 席明霞, 谢和宾, 等. 应用微信对初治肺结核患者实施健康教育的效果观察. 当代护士(上旬刊), 2017(9):154-157. doi:10.3969/j.issn.1006-6411.2017.09.083.
[17]	李亚敏, 高志东, 李艳圆, 等. 微信和电子药盒在北京市肺结核患者督导服药管理中的应用效果评价. 中国防痨杂志, 2023, 45(9):872-879. doi:10.19982/j.issn.1000-6621.20230115.
[18]	吴哲渊, 肖文静, 秦楠, 等. 中国结核病数字服药依从性技术应用指南. 中国防痨杂志, 2025, 47(4):385-397. doi:10.19982/j.issn.1000-6621.20250042.
[19]	李晓芬, 彭建明, 刘志东, 等. 采用《结核病防治手机一体化管理系统》进行微信转诊和随访的效果评价. 中国防痨杂志, 2020, 42(8):858-862. doi:10.3969/j.issn.1000-6621.2020.08.015.
[20]	Chen EC, Owaisi R, Goldschmidt L, et al. Patient perceptions of video directly observed therapy for tuberculosis: a systematic review. J Clin Tuberc Other Mycobact Dis, 2023, 35:100406. doi:10.1016/j.jctube.2023.100406.
[21]	高丹, 王玲华, 黄金鹏, 等. 基于“互联网+”医院-社区-家庭联动模式结核病患者随访方案的构建. 中国防痨杂志, 2024, 46(2):228-238. doi:10.19982/j.issn.1000-6621.20230344.
[22]	Wang X, Fu Q, Zhou M, et al. How Integrated Digital Tools Can Improve Tuberculosis Medication Adherence: A Longitudinal Study in China. Telemed J E Health, 2024, 30(2):490-498. doi:10.1089/tmj.2023.0084.
[23]	Planas R, Yuguero O. Technological prescription: evaluation of the effectiveness of mobile applications to improve depression and anxiety. Systematic review. Inform Health Soc Care, 2021, 46(3):273-290. doi:10.1080/17538157.2021.
[24]	Musiimenta A, Tumuhimbise W, Atukunda EC, et al. Mobile Health Technologies May Be Acceptable Tools for Providing Social Support to Tuberculosis Patients in Rural Uganda: A Parallel Mixed-Method Study. Tuberc Res Treat, 2020,2020:7401045. doi:10.1155/2020/7401045.
[25]	Cattamanchi A, Crowder R, Kityamuwesi A, et al. Digital adherence technology for tuberculosis treatment supervision: A stepped-wedge cluster-randomized trial in Uganda. PLoS Med, 2021, 18(5):e1003628. doi:10.1371/journal.pmed.1003628.
[26]	Sekandi JN, Onuoha NA, Buregyeya E, et al. Using a Mobile Health Intervention (DOT Selfie) With Transfer of Social Bundle Incentives to Increase Treatment Adherence in Tuberculosis Patients in Uganda: Protocol for a Randomized Controlled Trial. JMIR Res Protoc, 2021, 10(1):e18029. doi:10.2196/18029.
[27]	Alipanah N, Jarlsberg L, Miller C, et al. Adherence interventions and outcomes of tuberculosis treatment: A systematic review and meta-analysis of trials and observational studies. PLoS Med, 2018, 15(7):e1002595. doi:10.1371/journal.pmed.1002595.
[28]	Manyazewal T, Woldeamanuel Y, Getinet T, et al. Patient-reported usability and satisfaction with electronic medication event reminder and monitor device for tuberculosis: a multicentre, randomised controlled trial. EClinicalMedicine, 2023, 56:101820. doi:10.1016/j.eclinm.2022.101820.
[29]	Tumuhimbise W, Atwine D, Kaggwa F, et al. Acceptability and feasibility of a mobile health application for enhancing public private mix for TB care among healthcare Workers in Southwestern Uganda. BMC Digit Health, 2023, 1(1):9. doi:10.1186/s44247-023-00009-0. pmid: 38014370
[30]	Lee J, Lee MA. Validation and usability study of the framework for a user needs-centered mHealth app selection. Int J Med Inform, 2022, 167:104877. doi:10.1016/j.ijmedinf.2022.104877.
[31]	Naik N, Hameed BMZ, Shetty DK, et al. Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility?. Front Surg, 2022, 9:862322. doi:10.3389/fsurg.2022.862322.

步骤	检索式
#1	(″Tuberculosis″[Mesh]) OR (“Tuberculosis*”OR“Phthisis”OR“Pulmonary Tuberculosis”OR“Extrapulmonary Tuberculosis”)
#2	(″Health Information Management″[Mesh]) OR (“Health Management*”OR“Medication Management”OR “Follow-up Mana-gement”OR“Adherence”OR“Self-management”OR“Hospital Management”OR“Continuing Care”OR“Follow-up”OR“Community Management”OR“Home Management”OR“Family Management”OR“Health Education”OR“Remote Care”)
#3	(″Telemedicine″[Mesh]) OR (“mHealth”OR“Mobile Health”OR“Internet of Things”OR“Intelligent Medical Devices”OR“Telemedicine”OR“Remote Health”OR“eHealth”OR“Applications”OR“App”OR“Internet”OR“WeChat”OR“Short Messages”OR“Wearable Devices”)
#4	#1 AND #2 AND #3

亚组	文献数量 (篇)	RR (95%CI)值	异质性检验			双尾检验
亚组	文献数量 (篇)	RR (95%CI)值	χ²值	P值	I²值(%)	Z值	P值
干预方式
微信程序	2	1.28(1.17~1.40)	0.65	0.420	0	5.430	<0.001
信息提醒	2	1.13(1.04~1.22)	3.58	0.060	72	2.930	0.003
手机视频	2	1.29(1.13~1.46)	0.63	0.430	0	3.770	<0.001
电子药盒	1	1.22(1.17~1.27)	-	-	-	10.210	<0.001
干预模式
医院-社区	2	1.13(1.03~1.25)	5.41	0.020	82	2.610	0.009
医院-家庭	2	1.22(1.18~1.27)	1.03	0.310	3	10.410	<0.001
医院-社区-家庭	3	1.24(1.16~1.33)	0.46	0.800	0	6.370	<0.001
干预时长(月)
≤3	3	1.19(1.07~1.31)	12.74	0.002	84	3.330	<0.001
>3	4	1.23(1.19~1.27)	0.69	0.880	0	11.990	<0.001