Journal of Shanghai Jiao Tong University (Medical Science) >
Evaluation of the application effect of “Internet+”-based “co-prevention and co-management” health management model for cardio-cerebrovascular diseases on the improvement of blood pressure in target surveillance population in community
Received date: 2022-01-27
Accepted date: 2022-06-20
Online published: 2022-08-19
Supported by
National Key R&D Program of China(2018YFC1312803);National Natural Science Foundation of China(81974266)
·To evaluate the effect of “Internet+”-based “co-prevention and co-management” health management model for cardio-cerebrovascular diseases on the improvement of blood pressure in target surveillance population in community.
·Target surveillance population in community who participated in the annual physical examination provided by the National Basic Public Health Service project at least twice in Xintang Town, Zengcheng District, Guangzhou from January 2020 and July 2021 were enrolled, and divided into regular management group (n=2 987) and “co-prevention and co-management” model group (n=2 876) based on whether they had received “Internet+”-based “co-prevention and co-management” health management model for cardio-cerebrovascular diseases or not. The regular management group received the regular management mode which included an annual physical examination. In addition to regular treatment, the “co-prevention and co-management” model group received “Internet+ health education”, and their villages were provided with wearable electrocardiogram monitoring equipment. The baseline levels (before the intervention) of the two groups were compared, and the differences of blood pressure changes between the two groups before and after the interventions were observed. Covariance analysis was used to analyze whether the effect of different intervention measures on blood pressure was affected by its baseline blood pressure level. Multi-variable linear regression model was used to explore the association between the “Internet+”-based “co-prevention and co-management” health management model for cardio-cerebrovascular diseases and the control of blood pressure and other cardio-cerebrovascular disease risk factors.
·Compared with the regular management group, the baseline levels of systolic blood pressure (SBP) and diastolic blood pressure (DBP) in the “co-prevention and co-management” model group were higher (both P=0.000). After the median intervention time of 227 days, the changes of SBP and DBP in the “co-prevention and co-management” model group before and after the interventions were -0.28 mmHg (95% CI -0.94?0.37, P=0.398) and -0.68 mmHg (95% CI -1.09?-0.27, P=0.001), respectively; the changes in the regular treatment group were 2.92 mmHg (95% CI 2.29?3.54, P=0.000) and -0.12 mmHg (95% CI -0.51?0.28, P=0.554), respectively; the differences of SBP and DBP before and after the intervention between the two groups were 3.20 mmHg (95% CI 2.29?4.11, P=0.000) and 0.56 mmHg (95% CI -0.01?1.13, P=0.055), respectively. Covariance analysis showed that after adjusting for SBP before the intervention, compared with the regular treatment group, the SBP in the “co-prevention and co-management” model group was reduced by 2.06 mmHg (P=0.000). In the multi-variable linear regression model, after adjusting the confounding factors, the “Internet+”-based “co-prevention and co-management” health management model for cardio-cerebrovascular diseases was associated with lower SBP (P=0.000).
·The application of “Internet+”-based “co-prevention and co-management” health management model for cardio-cerebrovascular diseases among target surveillance population in community can help improving the control of systolic blood pressure.
Guodong LI , Shaohua YAN , Qiuxia ZHANG , Li LEI , Xinlu ZHANG , Hongbin LIANG , Junyan LU , Min XIAO , Wei LUO , Jun PU , Jiancheng XIU . Evaluation of the application effect of “Internet+”-based “co-prevention and co-management” health management model for cardio-cerebrovascular diseases on the improvement of blood pressure in target surveillance population in community[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2022 , 42(6) : 797 -804 . DOI: 10.3969/j.issn.1674-8115.2022.06.015
1 | GBD Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990?2019: a systematic analysis for the Global Burden of Disease Study 2019[J]. Lancet, 2020, 396(10258): 1204-1222. |
2 | ROTH G A, MENSAH G A, JOHNSON C O, et al. Global burden of cardiovascular diseases and risk factors, 1990-2019: update from the GBD 2019 study[J]. J Am Coll Cardiol, 2020, 76(25): 2982-3021. |
3 | ZHOU M G, WANG H D, ZENG X Y, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017[J]. Lancet, 2019, 394(10204): 1145-1158. |
4 | 中国心血管健康与疾病报告编写组. 中国心血管健康与疾病报告2020概要[J]. 中国循环杂志, 2021, 36(6): 521-545. |
4 | Writing Committee of the Report on Cardiovascular Health and Diseases in China. Report on cardiovascular health and diseases burden in China: an updated summary of 2020[J]. Chin Circ J, 2021, 36(6): 521-545. |
5 | PIEPOLI M F, HOES A W, AGEWALL S, et al. 2016 European Guidelines on cardiovascular disease prevention in clinical practice: the Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts) Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR)[J]. Eur Heart J, 2016, 37(29): 2315-2381. |
6 | YANG X L, LI J X, HU D S, et al. Predicting the 10-year risks of atherosclerotic cardiovascular disease in Chinese population: the China-PAR project (prediction for ASCVD risk in China)[J]. Circulation, 2016, 134(19): 1430-1440. |
7 | LU J P, LU Y, WANG X C, et al. Prevalence, awareness, treatment, and control of hypertension in China: data from 1.7 million adults in a population-based screening study (China PEACE Million Persons Project)[J]. Lancet, 2017, 390(10112): 2549-2558. |
8 | YUSUF S, JOSEPH P, RANGARAJAN S, et al. Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study[J]. Lancet, 2020, 395(10226): 795-808. |
9 | 刘子言, 肖月, 赵琨, 等. 国家基本公共卫生服务项目实施进展与成效[J]. 中国公共卫生, 2019, 35(6): 657-664. |
9 | LIU Z Y, XIAO Y, ZHAO K, et al. Implementation progress and effect of National Essential Public Health Services Program in China[J]. Chin J Public Health, 2019, 35(6): 657-664. |
10 | YEOH E K, WONG M C S, WONG E L Y, et al. Benefits and limitations of implementing Chronic Care Model (CCM) in primary care programs: a systematic review[J]. Int J Cardiol, 2018, 258: 279-288. |
11 | TURNER B J, HOLLENBEAK C S, LIANG Y Y, et al. A randomized trial of peer coach and office staff support to reduce coronary heart disease risk in African-Americans with uncontrolled hypertension[J]. J Gen Intern Med, 2012, 27(10): 1258-1264. |
12 | FRANEK J. Self-management support interventions for persons with chronic disease: an evidence-based analysis[J]. Ont Health Technol Assess Ser, 2013, 13(9): 1-60. |
13 | 陆勇, 季正明. 社区卫生定向服务模式在社区慢性病管理中的应用[J]. 中国慢性病预防与控制, 2004, 12(2): 73-75. |
13 | LU Y, JI Z M. Application of community health oriented service model in community chronic disease management[J]. Chin J Prev Contr Chron Non-commun Dis, 2004, 12(2): 73-75. |
14 | 严立群, 方波. “知己”健康管理模式在慢性病管理中的应用[J]. 中医药管理杂志, 2018, 26(1): 168-170. |
14 | YAN L Q, FANG B. Application of “confidant” health management mode in chronic disease management[J]. J Tradit Chin Med Manage, 2018, 26(1): 168-170. |
15 | LEI L, TANG Y Z, ZHANG Q X, et al. The association between the frequency of annual health checks participation and the control of cardiovascular risk factors[J]. Front Cardiovasc Med, 2022, 9: 860503. |
16 | 中国高血压防治指南修订委员会高血压联盟, 中华医学会心血管病学分会, 中国医师协会高血压专业委员会, 等. 中国高血压防治指南(2018年修订版)[J]. 中国心血管杂志, 2019, 24(1): 24-56. |
16 | Writing Group of 2018 Chinese Guidelines for the Management of Hypertension, Chinese Hypertension League, Chinese Society of Cardiology, Chinese Medical Doctor Association Hypertension Committee, et al. 2018 Chinese guidelines for the management of hypertension[J]. Chin J Cardiovasc Med, 2019, 24(1): 24-56. |
17 | NELSON R G, GRAMS M E, BALLEW S H, et al. Development of risk prediction equations for incident chronic kidney disease[J]. JAMA, 2019, 322(21): 2104-2114. |
18 | STEVENS P E, LEVIN A, KIDNEY DISEASE: IMPROVING GLOBAL OUTCOMES CHRONIC KIDNEY DISEASE GUIDELINE DEVELOPMENT WORK GROUP MEMBERS. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline[J]. Ann Intern Med, 2013, 158(11): 825-830. |
19 | LEVEY A S, BOSCH J P, LEWIS J B, et al. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group[J]. Ann Intern Med, 1999, 130(6): 461-470. |
20 | KARIO K, NOMURA A, HARADA N, et al. Efficacy of a digital therapeutics system in the management of essential hypertension: the HERB-DH1 pivotal trial[J]. Eur Heart J, 2021, 42(40): 4111-4122. |
21 | MCMANUS R J, LITTLE P, STUART B, et al. Home and Online Management and Evaluation of Blood Pressure (HOME BP) using a digital intervention in poorly controlled hypertension: randomised controlled trial[J]. BMJ, 2021, 372: m4858. |
22 | LISóN J F, PALOMAR G, MENSORIO M S, et al. Impact of a web-based exercise and nutritional education intervention in patients who are obese with hypertension: randomized wait-list controlled trial[J]. J Med Internet Res, 2020, 22(4): e14196. |
/
〈 |
|
〉 |