脑卒中数字疗法：从筛查到干预的进展与挑战

doi:10.12114/j.issn.1007-9572.2025.0147

摘要/Abstract

摘要： 脑卒中是我国居民死亡和残疾的主要原因之一，给患者和社会带来沉重负担。数字疗法作为一种新兴治疗手段，在脑卒中全病程管理中展现出巨大潜力。本文综述了数字疗法在脑卒中筛查、预防、急性期管理、康复和长期随访等方面的研究进展，分析了数字疗法临床应用的优势和面临的挑战，并对未来发展方向进行展望。当前研究显示，数字疗法可显著提高脑卒中风险筛查的覆盖率和准确性，在康复训练中可提供个性化、高强度的干预方案，并通过远程监测改善患者长期管理效果。然而，数字疗法在标准化评估体系、循证医学证据、老年患者适应性、数据安全和医保政策等方面仍面临诸多挑战。未来需加强高质量临床研究，完善标准化评价体系，推动多学科协作，并制定合理的医保政策，促进数字疗法在脑卒中防治中的规范应用与发展。

关键词: 脑卒中, 数字疗法, 筛查, 干预, 康复, 循证医学

Abstract:

Stroke is one of the main causes of death and disability among residents in China, which imposes a heavy burden on patients and society. Digital therapy, as an emerging treatment method, has shown great potential in the management of the entire stroke course. This article reviewed the research progress of digital therapy in stroke screening, prevention, acute phase management, rehabilitation, and long-term follow-up. It analyzed the advantages and challenges of clinical application of digital therapy and looked forward to future development directions. Current research showed that digital therapy could significantly improve the coverage and accuracy of stroke risk screening, provided personalized and high-intensity intervention plans in rehabilitation training, and improved long-term patient management outcomes through remote monitoring. However, digital therapy still faces many challenges in standardized evaluation systems, evidence-based medicine, elderly patient adaptability, data security, and medical insurance policies. In the future, it is necessary to strengthen high-quality clinical research, improve standardized evaluation systems, promote interdisciplinary collaboration, and formulate reasonable medical insurance policies to promote the standardized application and development of digital therapies in stroke prevention and treatment.

Key words: Stroke, Digital therapy, Screening, Intervention, Rehabilitation, Evidence-based medicine

余浩,廖鹏,罗力. 脑卒中数字疗法：从筛查到干预的进展与挑战[J]. 中国全科医学, 2026, 29(02): 240-246. DOI: 10.12114/j.issn.1007-9572.2025.0147.
YU Hao,LIAO Peng,LUO Li. Digital Therapy for Stroke: Progress and Challenges from Screening to Intervention[J]. Chinese General Practice, 2026, 29(02): 240-246. DOI: 10.12114/j.issn.1007-9572.2025.0147.

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参考文献 41

[1]	WANG W Z, JIANG B, SUN H X, et al. Prevalence, incidence, and mortality of stroke in China: results from a nationwide population-based survey of 480 687 adults[J]. Circulation, 2017, 135(8): 759-771. DOI: 10.1161/CIRCULATIONAHA.116.025250.
[2]	LI Z X, JIANG Y, LI H, et al. China's response to the rising stroke burden[J]. BMJ, 2019, 364: l879. DOI: 10.1136/bmj.l879.
[3]	PODLESNY N J, NIEDERMANN F, DEETJEN U, et al. Digital health ecosystems, hybrid care pathways and data ethics in healthcare [R]. Chicago: McKinsey & Company, 2020.
[4]	KARIO K, HARADA N, OKURA A. Digital therapeutics in hypertension: evidence and perspectives[J]. Hypertension, 2022, 79(10): 2148-2158. DOI: 10.1161/HYPERTENSIONAHA.122.19414.
[5]	WANG C, LEE C, SHIN H. Digital therapeutics from bench to bedside[J]. NPJ Digit Med, 2023, 6(1): 38. DOI: 10.1038/s41746-023-00777-z.
[6]	PHAN P, MITRAGOTRI S, ZHAO Z M. Digital therapeutics in the clinic[J]. Bioeng Transl Med, 2023, 8(4): e10536. DOI: 10.1002/btm2.10536.
[7]	SVERDLOV O, VAN DAM J, HANNESDOTTIR K, et al. Digital therapeutics: an integral component of digital innovation in drug development[J]. Clin Pharmacol Ther, 2018, 104(1): 72-80. DOI: 10.1002/cpt.1036.
[8]	GBD 2021 Stroke Risk Factor Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021[J]. Lancet Neurol, 2024, 23(10): 973-1003. DOI: 10.1016/S1474-4422(24)00369-7.
[9]	CHEN J, JIN W, ZHANG X X, et al. Telerehabilitation approaches for stroke patients: systematic review and meta-analysis of randomized controlled trials[J]. J Stroke Cerebrovasc Dis, 2015, 24(12): 2660-2668. DOI: 10.1016/j.jstrokecerebrovasdis.2015.09.014.
[10]	DENG N, CHEN J, LIU Y Y, et al. Using mobile health technology to deliver a community-based closed-loop management system for chronic obstructive pulmonary disease patients in remote areas of China: development and prospective observational study[J]. JMIR Mhealth Uhealth, 2020, 8(11): e15978. DOI: 10.2196/15978.
[11]	FEIGIN V L, NORRVING B, MENSAH G A. Primary prevention of cardiovascular disease through population-wide motivational strategies: insights from using smartphones in stroke prevention[J]. BMJ Glob Health, 2017, 2(2): e000306. DOI: 10.1136/bmjgh-2017-000306.
[12]	CHANG K V, HUNG C Y, KAO C W, et al. Development and validation of the standard Chinese version of the CARE item set(CARE-C)for stroke patients[J]. Medicine(Baltimore), 2015, 94(42): e1828. DOI: 10.1097/MD.0000000000001828.
[13]	PEREZ M V, MAHAFFEY K W, HEDLIN H, et al. Large-scale assessment of a smartwatch to identify atrial fibrillation[J]. N Engl J Med, 2019, 381(20): 1909-1917. DOI: 10.1056/NEJMoa1901183.
[14]	CHEN Y, HUANG Q F, SHENG C S, et al. Detection rate and treatment gap for atrial fibrillation identified through screening in community health centers in China(AF-CATCH): a prospective multicenter study[J]. PLoS Med, 2020, 17(7): e1003146. DOI: 10.1371/journal.pmed.1003146.
[15]	ZHANG H, ZHANG J, LI H B, et al. Validation of single centre pre-mobile atrial fibrillation apps for continuous monitoring of atrial fibrillation in a real-world setting: pilot cohort study[J]. J Med Internet Res, 2019, 21(12): e14909. DOI: 10.2196/14909.
[16]	STEINHUBL S R, WAALEN J, EDWARDS A M, et al. Effect of a home-based wearable continuous ECG monitoring patch on detection of undiagnosed atrial fibrillation: the mSToPS randomized clinical trial[J]. JAMA, 2018, 320(2): 146-155. DOI: 10.1001/jama.2018.8102.
[17]	BARBOSA I O F, DE OLIVEIRA B C, SANTOS C K M, et al. Smartphone-based applications for atrial fibrillation detection: a systematic review and meta-analysis of diagnostic test accuracy[J]. Telemed J E Health, 2025, 31(6): 687-700. DOI: 10.1089/tmj.2024.0579.
[18]	CHUNG H W, TAI C J, CHANG P L, et al. The effectiveness of a traditional Chinese medicine-based mobile health app for individuals with prediabetes: randomized controlled trial[J]. JMIR Mhealth Uhealth, 2023, 11: e41099. DOI: 10.2196/41099.
[19]	JEONG Y J, KIM H S, KANG H G. Development and evaluation of a mobile application to prevent recurrent stroke by enhancing self-management on health outcomes for stroke survivors[J]. Comput Inform Nurs, 2024, 42(1): 53-62. DOI: 10.1097/CIN.0000000000001050.
[20]	KAMOEN O, MAQUEDA V, YPERZEELE L, et al. Stroke coach: a pilot study of a personal digital coaching program for patients after ischemic stroke[J]. Acta Neurol Belg, 2020, 120(1): 91-97. DOI: 10.1007/s13760-019-01218-z.
[21]	CHENG C, CHAU C L. Gamification-based intervention for enhancing team effectiveness and coping flexibility: randomized controlled trial[J]. Front Psychiatry, 2022, 13: 941252. DOI: 10.3389/fpsyt.2022.941252.
[22]	SIT J W, CHAIR S Y, CHAN YIP C W, et al. Effect of health empowerment intervention for stroke self-management on behaviour and health in stroke rehabilitation patients[J]. Hong Kong Med J, 2018, 24 Suppl 2(1): 12-15.
[23]	CARCEL C, CLANCY L, HARRIS K, et al. Randomised controlled decentralised feasibility trial of a fixed low-dose combination antihypertensive drug strategy to attenuate cognitive decline in high-risk adults[J]. BMJ Open, 2024, 14(8): e080862. DOI: 10.1136/bmjopen-2023-080862.
[24]	PRON G, IERACI L, KAULBACK K, et al. Internet-based device-assisted remote monitoring of cardiovascular implantable electronic devices: an evidence-based analysis[J]. Ont Health Technol Assess Ser, 2012, 12(1): 1-86.
[25]	DEVORE A D, GRANGER B B, FONAROW G C, et al. Care optimization through patient and hospital engagement clinical trial for heart failure: rationale and design of CONNECT-HF[J]. Am Heart J, 2020, 220: 41-50. DOI: 10.1016/j.ahj.2019.09.012.
[26]	RAYMOND L, CASTONGUAY A, DOYON O, et al. Nurse practitioners' involvement and experience with AI-based health technologies: a systematic review[J]. Appl Nurs Res, 2022, 66: 151604. DOI: 10.1016/j.apnr.2022.151604.
[27]	LI Z, HAN X G, SHENG J, et al. Virtual reality for improving balance in patients after stroke: a systematic review and meta-analysis[J]. Clin Rehabil, 2016, 30(5): 432-440. DOI: 10.1177/0269215515593611.
[28]	INOUE D, CHO B, MORI M, et al. Preliminary study on the clinical application of augmented reality neuronavigation[J]. J Neurol Surg A Cent Eur Neurosurg, 2013, 74(2): 71-76. DOI: 10.1055/s-0032-1333415.
[29]	DODAKIAN L, MCKENZIE A L, LE V, et al. A home-based telerehabilitation program for patients with stroke[J]. Neurorehabil Neural Repair, 2017, 31(10/11): 923-933. DOI: 10.1177/1545968317733818.
[30]	BANIHANI J, CHOUKOU M A. A home-based hand rehabilitation platform for hemiplegic patients after stroke: a feasibility study[J]. Heliyon, 2024, 10(16): e35565. DOI: 10.1016/j.heliyon.2024.e35565.
[31]	BANIQUED P D E, STANYER E C, AWAIS M, et al. Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review[J]. J Neuroeng Rehabil, 2021, 18(1): 15. DOI: 10.1186/s12984-021-00820-8.
[32]	ZHU Y, WANG C, LI J, et al. Effect of different modalities of artificial intelligence rehabilitation techniques on patients with upper limb dysfunction after stroke-a network meta-analysis of randomized controlled trials[J]. Front Neurol, 2023, 14: 1125172. DOI: 10.3389/fneur.2023.1125172.
[33]	TAN J, GONG E, GALLIS J A, et al. Primary care-based digital health-enabled stroke management intervention: long-term follow-up of a cluster randomized clinical trial[J]. JAMA Netw Open, 2024, 7(12): e2449561. DOI: 10.1001/jamanetworkopen.2024.49561.
[34]	LIU L, GUO J Y, JIAO X T, et al. The effect of digital therapeutics intervention on improving hypertension management in adults: a meta-analysis of randomized controlled trial[J]. Hypertens Res, 2025, 48(2): 456-469. DOI: 10.1038/s41440-024-01892-4.
[35]	GANDAPUR Y, KIANOUSH S, KELLI H M, et al. The role of mHealth for improving medication adherence in patients with cardiovascular disease: a systematic review[J]. Eur Heart J Qual Care Clin Outcomes, 2016, 2(4): 237-244. DOI: 10.1093/ehjqcco/qcw018.
[36]	GENG G L, HE W, DING L, et al. Impact of transitional care for discharged elderly stroke patients in China: an application of the integrated behavioral model[J]. Top Stroke Rehabil, 2019, 26(8): 621-629. DOI: 10.1080/10749357.2019.1647650.
[37]	BELL A D, MACCALLUM C, MARGOLESE S, et al. Clinical practice guidelines for Cannabis and cannabinoid-based medicines in the management of chronic pain and co-occurring conditions[J]. Cannabis Cannabinoid Res, 2024, 9(2): 669-687. DOI: 10.1089/can.2021.0156.
[38]	MURTHI S, MARTINI N, FALCONER N, et al. Evaluating EHR-integrated digital technologies for medication-related outcomes and health equity in hospitalised adults: a scoping review[J]. J Med Syst, 2024, 48(1): 79. DOI: 10.1007/s10916-024-02097-5.
[39]	SAVICKAS V, STEWART A J, REES-ROBERTS M, et al. Opportunistic screening for atrial fibrillation by clinical pharmacists in UK general practice during the influenza vaccination season: a cross-sectional feasibility study[J]. PLoS Med, 2020, 17(7): e1003197. DOI: 10.1371/journal.pmed.1003197.
[40]	ZHANG H X, WANG T, ZHANG Z X, et al. The current status of stroke-related smartphone applications available to adopt in China: a systematic review study[J]. Medicine(Baltimore), 2020, 99(27): e20656. DOI: 10.1097/MD.0000000000020656.
[41]	CHEN C Y, LIU A T, ZHANG Z, et al. Digital therapeutics in hypertension: How to make sustainable lifestyle changes[J]. J Clin Hypertens(Greenwich), 2024, 26(10): 1125-1132. DOI: 10.1111/jch.14894.

数字工具类型	样本量（名）	灵敏度（%）	特异度（%）	阳性预测值（%）
心电手表^[13]	419 297	92.6	98.2	84.0
PPG手环^[15]	8 635	91.2	94.8	78.5
单导联心电记录仪^[16]	5 214	96.2	94.0	76.4
智能手机App^[17]	2 100	84.9	91.5	64.8

数字工具类型	样本量（名）	灵敏度（%）	特异度（%）	阳性预测值（%）
心电手表^[13]	419 297	92.6	98.2	84.0
PPG手环^[15]	8 635	91.2	94.8	78.5
单导联心电记录仪^[16]	5 214	96.2	94.0	76.4
智能手机App^[17]	2 100	84.9	91.5	64.8

干预类型	样本量（名）	干预时长（月）	主要结局指标	干预效果
移动健康管理平台^[18]	2 876	24	脑卒中发生率	降低28.6%
游戏化行为干预^[21]	1 234	6	健康行为改变	步行量增加1 850步/d
远程监测预警系统^[24]	1 876	12	风险事件预警	准确率82.6%
综合干预平台^[20]	2 105	18	脑卒中复发率	降低17.5%

干预类型	样本量（名）	干预时长（月）	主要结局指标	干预效果
移动健康管理平台^[18]	2 876	24	脑卒中发生率	降低28.6%
游戏化行为干预^[21]	1 234	6	健康行为改变	步行量增加1 850步/d
远程监测预警系统^[24]	1 876	12	风险事件预警	准确率82.6%
综合干预平台^[20]	2 105	18	脑卒中复发率	降低17.5%

技术类型	主要功能	适用阶段	适用功能障碍	优势	局限性
VR康复系统	沉浸式训练	恢复期/缓慢恢复期	运动功能/认知功能	高参与度、可量化	设备成本高、适应性要求高
AR康复系统	视觉引导和反馈	恢复期	步态/平衡功能	实时反馈、易于接受	技术要求高、场地限制
远程康复系统	居家监督训练	恢复期/缓慢恢复期	综合功能障碍	便利、长期坚持	缺乏直接指导、技术依赖性
AI个性化系统	动态调整方案	全康复阶段	多种功能障碍	精准化、效率高	算法复杂、需大数据支持
机器人辅助系统	高强度精准训练	急性期/恢复期	肢体运动功能	高强度、精准	成本极高、操作复杂