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The purpose of this study is to evaluate the effectiveness of social network in improving drug compliance and risk factors control rate of stroke high-risk population after discharge.
Stroke is the leading cause of death among residents in China, with the characteristics of high morbidity, high mortality, high disability rate, high recurrence rate and so on, which brings huge economic burden to the patients' families and society. Strengthening the comprehensive management of the high-risk population of stroke, improving the medication compliance of patients and the control rate of stroke risk factors play a key role in reducing stroke recurrence.
This study is a multicenter, prospective, randomized, single-blind study, which aims to use the tool of WeChat Mini Programs to realize the post-hospital follow-up management of the high-risk population of stroke. The follow-up time is 12 months. The main measurement result was the change of patients' medication compliance after comprehensive management.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| conventional care group | Other | Patients are managed according to the conventional methods after enrollment. |
|
| Social network-based intervention group | Experimental | Patients are managed according to to the integrated digital platform |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| conventional care | Behavioral | Patients in conventional care group will receive standardized education based on ASA/AHA 2021 guidelines prior to discharge,2 delivered verbally by a certified Brain-Heart Health Manager (BHHM) and supplemented with an expert-reviewed booklet. Content will cover medication adherence, risk factor control, stroke recognition, emergency response, and follow-up plans. A contact number will be provided for post-discharge support. A baseline archive will document demographics, lifestyle, and cardiovascular risk factors. |
| Measure | Description | Time Frame |
|---|---|---|
| Good Medication Adherence to all guideline-recommended vascular prevention medications at 12 months post-discharge | Adherence is assessed using self-reported data, with participants asked to indicate the number of days they missed taking a dose for each medication class during the preceding 30 days. This evaluation is conducted separately for each of the five evidence-based secondary prevention drug classes: antihypertensives, hypoglycemics, lipid-lowering agents, anticoagulants, and antiplatelets. Good adherence for each class is defined as taking the prescribed medication on more than 24 days out of the previous 30 days (corresponding to an adherence rate >80%). To meet the primary endpoint, a participant must achieve this >80% adherence threshold simultaneously across all five medication classes at the 12-month follow-up. Any self-directed cessation or adjustment of the regimen without medical consultation is categorized as non-adherence. Conversely, patients who cease or adjust their medications according to medical advice will be considered adherent. For those who adjust their medications base | 12 months post-discharge |
| Measure | Description | Time Frame |
|---|---|---|
| Proportion of good medication adherence to stroke prevention drugs post-discharge | Assessed using the Morisky-8 Medication Adherence Scale [MMAS-8]. Good adherence is defined as an MMAS-8 score greater than 6.The lowest score is 0, and the highest score is 8. A higher score indicates better adherence. | 1 month, 3 months, 6 months and 12 months post-discharge |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Zhang lingjuan | Changhai Hospital Affiliated to Naval Medical University | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Changhai Hospital | Shanghai | Shanghai Municipality | 200433 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| Background | Xu, A.D. Building a bridge between guidelines and practice to standardize secondary prevention of ischemic stroke/transient ischemic attack in China [J].Stroke,20105(6):429-430. https://doi.org/10.3969/j.issn.1673-5765.2010.06.002 | ||
| 29734470 | Background | Bridgwood B, Lager KE, Mistri AK, Khunti K, Wilson AD, Modi P. Interventions for improving modifiable risk factor control in the secondary prevention of stroke. Cochrane Database Syst Rev. 2018 May 7;5(5):CD009103. doi: 10.1002/14651858.CD009103.pub3. | |
| 28122885 |
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Individual, de-identified participant data used in these analyses will be shared by request from any qualified investigator following approval of a protocol and signed data access agreement via both the trial steering committee.
Data sharing will be available from 12 months after the publication of the main results.
1.The data sharing will be only for the purposes of health and medical research and within the constraints of the consent under which the data were originally gathered. 2.The Custodian of the Collection will not consider any Proposals for data sharing that unblind, or potentially unblind, randomised comparisons in active / ongoing trials. 3.Requesters should be employees of a recognised academic institution, health service organisation, commercial research organisation or from the pharmaceutical industry. Requesters must have experience in medical research. 4.Requesters must be able to demonstrate through their peer review publications in the area of interest their ability to carry out the proposed use of the requested dataset from a Collection. 5.The Requesters must not have a conflict of interest that may potentially influence their interpretation of any analyses.
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot | Yes | No | No | Study Protocol | May 22, 2024 | Jan 24, 2026 |
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| Social network-based intervention | Behavioral | Participants in Social network-based intervention group are onboard to the integrated digital platform. BHHMs facilitate the activation of the digital interface via a unique QR code, assist in the creation of a comprehensive electronic health record (EHR), and guide participants through an interactive tutorial to ensure technical proficiency in data entry and communication features. |
|
| Risk factor control, including blood glucose(mmol/L), blood pressure(mmHg), lipid profile(mmol/L), body mass index (BMI) (kg/m ^ 2), waist circumference(cm), hip circumference(cm), and smoking status post-discharge. | The method of measurement is as follows: Blood lipids(mmol/L): fasting blood sampling measurement Blood glucose(mmol/L): fasting fingertip blood glucose Blood pressure(mmHg): using a sphygmomanometer to measure BMI (kg/m ^ 2): weight(kg) / height (m ^ 2) Waist circumference(cm), Hip circumference(cm), Smoking: Patient self-report | 1 month, 3 months, 6 months and 12 months post-discharge |
| Health-related quality of life (HRQoL) | Assessed using the EuroQol Five-Dimension Five-Level Scale (EQ-5D-5L),including visual analog scale (score range of 0-100, higher score indicates better health status) and utility score (score range of[ -0.391,1], higher score indicates better quality of life for patients) | 1 month, 3 months, 6 months and 12 months post-discharge |
| Anxiety symptom severity | assessed using the 7-item Generalized Anxiety Disorder Scale (GAD-7),the score range is 0-21 points, with higher scores indicating greater levels of anxiety. | 1 month, 3 months, 6 months and 12 months post-discharge |
| Depressive symptom severity | Assessed using the 9-item Patient Health Questionnaire (PHQ-9),the score range is 0-27 points, with higher scores indicating more severe depression. | 1 month, 3 months, 6 months and 12 months post-discharge |
| Stroke prevention knowledge scores | Assessed using the Stroke Prevention Knowledge Questionnaire,the score range is 0-36 points, with higher scores indicating better mastery of stroke related knowledge. | 1 month, 3 months, 6 months and 12 months post-discharge |
| Personal motivation for stroke prevention | Assessed using the Stroke Attitude Questionnaire,the total score is the sum of 16 items, and the higher the score, the better the cognitive attitude. | 1 month, 3 months, 6 months and 12 months post-discharge |
| Perceived social support | Assessed using the Perceived Social Support Scale (PSSS),there are a total of 12 items, with each item assigned a score of 1-7, for a total score of 12-84. The higher the score, the higher the individual's level of social support. | 1 month, 3 months, 6 months and 12 months post-discharge |
| Stroke prevention-related health behavior scores | Assessed using the Stroke Prevention Health Behavior Scale,the scale consists of 25 items and uses the Likert 4-point scoring method. The higher the total score, the higher the level of healthy behavior. | 1 month, 3 months, 6 months and 12 months post-discharge |
| Self-efficacy for chronic disease management | Assessed using the Chronic Disease Self-Efficacy Scale,the scale consists of 6 items, with each item rated on a scale of 1-10. The average score is taken, and the higher the average score, the stronger the self-efficacy. | 1 month, 3 months, 6 months and 12 months post-discharge |
| Intentions regarding prehospital delay in stroke emergency care | Assessed using the Prehospital Delay Behavior Intention Scale for Stroke,the scale consists of 27 items and uses the Likert 5-point scoring method, with a score range of 27-135 points. The higher the score, the stronger the intention and likelihood of delaying medical treatment. | 1 month, 3 months, 6 months and 12 months post-discharge |
| Incidence of major adverse cerebrovascular and cardiovascular events (MACCE) | Including stroke, acute coronary syndrome, and vascular death | 1month, 3months, 6months and 12-months post-discharge |
| Background |
| Benjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R, de Ferranti SD, Floyd J, Fornage M, Gillespie C, Isasi CR, Jimenez MC, Jordan LC, Judd SE, Lackland D, Lichtman JH, Lisabeth L, Liu S, Longenecker CT, Mackey RH, Matsushita K, Mozaffarian D, Mussolino ME, Nasir K, Neumar RW, Palaniappan L, Pandey DK, Thiagarajan RR, Reeves MJ, Ritchey M, Rodriguez CJ, Roth GA, Rosamond WD, Sasson C, Towfighi A, Tsao CW, Turner MB, Virani SS, Voeks JH, Willey JZ, Wilkins JT, Wu JH, Alger HM, Wong SS, Muntner P; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart Disease and Stroke Statistics-2017 Update: A Report From the American Heart Association. Circulation. 2017 Mar 7;135(10):e146-e603. doi: 10.1161/CIR.0000000000000485. Epub 2017 Jan 25. No abstract available. |
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| 32657676 | Background | Ranganai E, Matizirofa L. An analysis of recent stroke cases in South Africa: Trend, seasonality and predictors. S Afr Med J. 2020 Jan 29;110(2):92-99. doi: 10.7196/SAMJ.2020.v110i2.013891. |
| 20075360 | Background | Glader EL, Sjolander M, Eriksson M, Lundberg M. Persistent use of secondary preventive drugs declines rapidly during the first 2 years after stroke. Stroke. 2010 Feb;41(2):397-401. doi: 10.1161/STROKEAHA.109.566950. Epub 2010 Jan 14. |
| Background | He, S. (2017). Development of a medication reminder app and its study on improving medication adherence [Master's thesis/Doctoral dissertation]. Anhui Medical University. |
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| Background | EuroQol Research Foundation. EQ-5D-5L User Guide, Version 4.0[EB/OL]. (2025-08). |
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| 36827597 | Result | Li DM, Lu XY, Yang PF, Zheng J, Hu HH, Zhou Y, Zhang LJ, Liu JM. Coordinated Patient Care via Mobile Phone-Based Telemedicine in Secondary Stroke Prevention: A Propensity Score-Matched Cohort Study. J Nurs Care Qual. 2023 Jul-Sep 01;38(3):E42-E49. doi: 10.1097/NCQ.0000000000000693. Epub 2023 Feb 24. |
| Prot_000.pdf |
| SAP | No | Yes | No | Statistical Analysis Plan | Dec 26, 2025 | Jan 24, 2026 | SAP_001.pdf |
| ID | Term |
|---|---|
| D020521 | Stroke |
| D002561 | Cerebrovascular Disorders |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |
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