Journal of Shanghai Jiao Tong University (Medical Science) ›› 2022, Vol. 42 ›› Issue (6): 797-804.doi: 10.3969/j.issn.1674-8115.2022.06.015
• Public health • Previous Articles
LI Guodong1(), YAN Shaohua1(), ZHANG Qiuxia1, LEI Li1, ZHANG Xinlu1, LIANG Hongbin1, LU Junyan2, XIAO Min2, LUO Wei1, PU Jun3, XIU Jiancheng1()
Received:
2022-01-27
Accepted:
2022-06-20
Online:
2022-06-28
Published:
2022-08-19
Contact:
XIU Jiancheng
E-mail:48379807@qq.com;xiujch@163.com
Supported by:
CLC Number:
LI Guodong, YAN Shaohua, ZHANG Qiuxia, LEI Li, ZHANG Xinlu, LIANG Hongbin, LU Junyan, XIAO Min, LUO Wei, PU Jun, XIU Jiancheng. 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.
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URL: https://xuebao.shsmu.edu.cn/EN/10.3969/j.issn.1674-8115.2022.06.015
Item | Regular management group (n=2 987) | “Co-prevention and co-management” model group (n=2 876) | P value |
---|---|---|---|
Age/year | 68.88 ± 7.88 | 69.53 ± 8.01 | 0.002 |
Male/n(%) | 1 154 (38.63) | 1 086 (37.76) | 0.509 |
BMI/(kg·m-2) | 24.43 ± 3.47 | 24.28 ± 3.69 | 0.123 |
Waist circumstance/cm | 86.28 ± 9.18 | 85.45 ± 9.75 | 0.001 |
Smoking/n(%) | 0.665 | ||
Never | 2 483 (83.15) | 2 393 (83.23) | |
Current smoker | 317 (10.62) | 317 (11.03) | |
Ever smoker | 186 (6.23) | 165 (5.74) | |
Drinking/n(%) | 0.014 | ||
Never | 2 576 (86.24) | 2 557 (88.91) | |
Sometimes | 279 (9.34) | 212 (7.37) | |
Often | 27 (0.90) | 17 (0.59) | |
Everyday | 105 (3.52) | 90 (3.13) | |
Exercise/n(%) | 0.000 | ||
Never | 671 (22.46) | 974 (33.87) | |
Sometimes | 280 (9.37) | 320 (11.13) | |
At least once a week | 56 (1.87) | 47 (1.63) | |
Everyday | 1 980 (66.29) | 1 535 (53.37) | |
History of hypertension/n(%) | 2 330 (78.00) | 2 276 (79.14) | 0.305 |
History of diabetes/n(%) | 747 (25.01) | 756 (26.29) | 0.275 |
CKD/n(%) | 516 (17.50) | 635 (22.63) | 0.000 |
Heart rate/bpm | 73.44 ± 11.86 | 72.64 ± 11.90 | 0.010 |
SBP/mmHg | 142.75 ± 19.34 | 145.30 ± 19.16 | 0.000 |
DBP/mmHg | 82.32 ± 11.42 | 83.65 ± 11.44 | 0.000 |
FBG/(mmol·L-1) | 4.80 (4.20, 5.70) | 4.71 (4.16, 5.62) | 0.055 |
Scr/(μmol·L-1) | 77.88 (66.28, 93.06) | 80.71 (68.30, 96.19) | 0.000 |
eGFR/[mL·(min·1.73m2)-1] | 76.34 (64.80, 88.63) | 72.71 (61.25, 85.98) | 0.000 |
TC/(mmol·L-1) | 5.22 (4.50, 5.95) | 5.22 (4.55, 5.95) | 0.527 |
TAG/(mmol·L-1) | 1.47 (1.06, 2.11) | 1.48 (1.06, 2.20) | 0.354 |
LDL-C/(mmol·L-1) | 3.33 (2.73, 3.95) | 3.36 (2.79, 3.96) | 0.337 |
HDL-C/(mmol·L-1) | 1.32 (1.10, 1.58) | 1.31 (1.09, 1.58) | 0.261 |
Hypertensive treatment/n(%) | 1 582 (53.05) | 1 515 (52.79) | 0.860 |
Diabetes treatment/n(%) | 611 (20.49) | 518 (18.05) | 0.020 |
Statin treatment/n(%) | 9 (0.30) | 5 (0.17) | 0.425 |
Tab 1 Comparison of general data, laboratory examination indexes and treatment between the two groups
Item | Regular management group (n=2 987) | “Co-prevention and co-management” model group (n=2 876) | P value |
---|---|---|---|
Age/year | 68.88 ± 7.88 | 69.53 ± 8.01 | 0.002 |
Male/n(%) | 1 154 (38.63) | 1 086 (37.76) | 0.509 |
BMI/(kg·m-2) | 24.43 ± 3.47 | 24.28 ± 3.69 | 0.123 |
Waist circumstance/cm | 86.28 ± 9.18 | 85.45 ± 9.75 | 0.001 |
Smoking/n(%) | 0.665 | ||
Never | 2 483 (83.15) | 2 393 (83.23) | |
Current smoker | 317 (10.62) | 317 (11.03) | |
Ever smoker | 186 (6.23) | 165 (5.74) | |
Drinking/n(%) | 0.014 | ||
Never | 2 576 (86.24) | 2 557 (88.91) | |
Sometimes | 279 (9.34) | 212 (7.37) | |
Often | 27 (0.90) | 17 (0.59) | |
Everyday | 105 (3.52) | 90 (3.13) | |
Exercise/n(%) | 0.000 | ||
Never | 671 (22.46) | 974 (33.87) | |
Sometimes | 280 (9.37) | 320 (11.13) | |
At least once a week | 56 (1.87) | 47 (1.63) | |
Everyday | 1 980 (66.29) | 1 535 (53.37) | |
History of hypertension/n(%) | 2 330 (78.00) | 2 276 (79.14) | 0.305 |
History of diabetes/n(%) | 747 (25.01) | 756 (26.29) | 0.275 |
CKD/n(%) | 516 (17.50) | 635 (22.63) | 0.000 |
Heart rate/bpm | 73.44 ± 11.86 | 72.64 ± 11.90 | 0.010 |
SBP/mmHg | 142.75 ± 19.34 | 145.30 ± 19.16 | 0.000 |
DBP/mmHg | 82.32 ± 11.42 | 83.65 ± 11.44 | 0.000 |
FBG/(mmol·L-1) | 4.80 (4.20, 5.70) | 4.71 (4.16, 5.62) | 0.055 |
Scr/(μmol·L-1) | 77.88 (66.28, 93.06) | 80.71 (68.30, 96.19) | 0.000 |
eGFR/[mL·(min·1.73m2)-1] | 76.34 (64.80, 88.63) | 72.71 (61.25, 85.98) | 0.000 |
TC/(mmol·L-1) | 5.22 (4.50, 5.95) | 5.22 (4.55, 5.95) | 0.527 |
TAG/(mmol·L-1) | 1.47 (1.06, 2.11) | 1.48 (1.06, 2.20) | 0.354 |
LDL-C/(mmol·L-1) | 3.33 (2.73, 3.95) | 3.36 (2.79, 3.96) | 0.337 |
HDL-C/(mmol·L-1) | 1.32 (1.10, 1.58) | 1.31 (1.09, 1.58) | 0.261 |
Hypertensive treatment/n(%) | 1 582 (53.05) | 1 515 (52.79) | 0.860 |
Diabetes treatment/n(%) | 611 (20.49) | 518 (18.05) | 0.020 |
Statin treatment/n(%) | 9 (0.30) | 5 (0.17) | 0.425 |
Variable | Model 1 | Model 2 | Model 3 | ||||||
---|---|---|---|---|---|---|---|---|---|
β | 95% CI | P value | β | 95% CI | P value | β | 95% CI | P value | |
Change in SBP | -3.198 | -4.106‒ -2.290 | 0.000 | -2.159 | -2.952‒-1.366 | 0.000 | -2.083 | -2.893‒-1.273 | 0.000 |
Change in DBP | -0.557 | -1.127‒0.012 | 0.055 | 0.209 | -0.273‒0.690 | 0.396 | 0.163 | -0.329‒0.655 | 0.517 |
Change in BMI | 0.001 | -0.075‒0.078 | 0.978 | 0.005 | -0.080‒0.070 | 0.889 | -0.008 | -0.084‒0.068 | 0.837 |
Change in FBG | 0.051 | -0.052‒0.153 | 0.332 | 0.050 | -0.043‒0.144 | 0.291 | 0.079 | -0.014‒0.173 | 0.095 |
Change in TC | 0.112 | 0.069‒0.155 | 0.000 | 0.116 | 0.075‒0.157 | 0.000 | 0.114 | 0.073‒0.155 | 0.000 |
Change in LDL-C | 0.063 | 0.028‒0.098 | 0.000 | 0.069 | 0.036‒0.102 | 0.000 | 0.067 | 0.034‒0.100 | 0.000 |
Tab 2 Linear regression analysis of risk factors control and the “Internet+”-based “co-prevention and co-management” health management model for cardio-cerebrovascular diseases
Variable | Model 1 | Model 2 | Model 3 | ||||||
---|---|---|---|---|---|---|---|---|---|
β | 95% CI | P value | β | 95% CI | P value | β | 95% CI | P value | |
Change in SBP | -3.198 | -4.106‒ -2.290 | 0.000 | -2.159 | -2.952‒-1.366 | 0.000 | -2.083 | -2.893‒-1.273 | 0.000 |
Change in DBP | -0.557 | -1.127‒0.012 | 0.055 | 0.209 | -0.273‒0.690 | 0.396 | 0.163 | -0.329‒0.655 | 0.517 |
Change in BMI | 0.001 | -0.075‒0.078 | 0.978 | 0.005 | -0.080‒0.070 | 0.889 | -0.008 | -0.084‒0.068 | 0.837 |
Change in FBG | 0.051 | -0.052‒0.153 | 0.332 | 0.050 | -0.043‒0.144 | 0.291 | 0.079 | -0.014‒0.173 | 0.095 |
Change in TC | 0.112 | 0.069‒0.155 | 0.000 | 0.116 | 0.075‒0.157 | 0.000 | 0.114 | 0.073‒0.155 | 0.000 |
Change in LDL-C | 0.063 | 0.028‒0.098 | 0.000 | 0.069 | 0.036‒0.102 | 0.000 | 0.067 | 0.034‒0.100 | 0.000 |
Variable | Regular management group (n=2 987) | “Co-prevention and co-management” model group (n=2 876) | Changes between the two groups/ [difference (95% CI)] | P value |
---|---|---|---|---|
Change in BMI/(kg·m-2) | 0.18±1.51 | 0.18±1.47 | 0.00 (-0.08‒0.08) | 0.978 |
Change in FBG/(mmol·L-1) | 0.79±1.86 | 0.84±2.07 | -0.05 (-0.15‒0.05) | 0.332 |
Change in TC/(mmol·L-1) | 0.28±0.83 | 0.39±0.82 | -0.11 (-0.15‒ -0.07) | 0.000 |
Change in LDL-C/(mmol·L-1) | 0.03±0.67 | 0.09±0.66 | -0.06 (-0.10‒ -0.03) | 0.000 |
Tab 3 Changes of other cardio-cerebrovascular diseases risk factors in the two groups before and after the interventions and their variance analysis
Variable | Regular management group (n=2 987) | “Co-prevention and co-management” model group (n=2 876) | Changes between the two groups/ [difference (95% CI)] | P value |
---|---|---|---|---|
Change in BMI/(kg·m-2) | 0.18±1.51 | 0.18±1.47 | 0.00 (-0.08‒0.08) | 0.978 |
Change in FBG/(mmol·L-1) | 0.79±1.86 | 0.84±2.07 | -0.05 (-0.15‒0.05) | 0.332 |
Change in TC/(mmol·L-1) | 0.28±0.83 | 0.39±0.82 | -0.11 (-0.15‒ -0.07) | 0.000 |
Change in LDL-C/(mmol·L-1) | 0.03±0.67 | 0.09±0.66 | -0.06 (-0.10‒ -0.03) | 0.000 |
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. |
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. |
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. |
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. |
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. |
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. |
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