Mobile health technology (WeChat) for the hierarchical management of community hypertension: protocol for a cluster randomized controlled trial

References

• Lim SS, Vos T, Flaxman AD, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380(9859):2224–2260. doi:10.1016/S0140-6736(12)61766-823245609
   PubMed Web of Science ®Google Scholar
• National Health Commission of the People’s Republic of China. Report on the Status of Nutrition and Chronic Diseases of Chinese Residents (2015). Beijing: People's Medical Publishing House; 2015.
   Google Scholar
• Chen WW, Gao RL, Liu LS, et al. Summary of Cardiovascular disease report in China 2017. Chin Circ J. 2017;6:521–530.
   Google Scholar
• Lu Y, Lu J, Wang X, 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). Lancet. 2017;390(10112):2549–2558. doi:10.1016/S0140-6736(17)32478-929102084
   PubMed Web of Science ®Google Scholar
• Wang Z, Chen Z, Zhang L, et al. Status of hypertension in China: results from the China Hypertension survey, 2012–2015. Circulation. 2018;137(22):2344–2356. doi:10.1161/CIRCULATIONAHA.117.03238029449338
   PubMed Web of Science ®Google Scholar
• Zhang LL. Effect of Community-based Self-management Program among Hypertension Patient. China: Chinese Center for Disease Control and Prevention; 2010.
   Google Scholar
• World Health Organization. Towards the development of an mHealth strategy: a literature review. Available from: http://www.who.int/goe/mobile_health/mHealthReview_Aug09.pdf. Accessed 7 22, 2019.
   Google Scholar
• Burke LE, Ma J, Azar KM, et al. Current science on consumer use of mobile health for cardiovascular disease prevention. Circulation. 2015;132(12):1157–1213. doi:10.1161/CIR.000000000000023226271892
   PubMed Web of Science ®Google Scholar
• Demiris G, Afrin LB, Speedie S, et al. Patient-centered applications: use of information technology to promote disease management and wellness. A white paper by the AMIA knowledge in motion working group. J Am Med Inform Assoc. 2008;15(1):8–13. doi:10.1197/jamia.M249217947617
   PubMed Web of Science ®Google Scholar
• Istepanian RSH, Jovanov E, Zhang YT. Guest editorial introduction to the special section on M-Health: beyond seamless mobility and global wireless health-care connectivity. IEEE Trans Inf Technol Biomed. 2004;8(4):405–414.15615031
   PubMedGoogle Scholar
• World Health Organization. Global observatory for eHealth series-Volume 3: mHealth: new horizons for health through mobile technologies. Available from: www.who.int/goe/publications/ehealth_series_vol3/en/. Accessed 7 22, 2019.
   Google Scholar
• Sun DL, Li RL. The application of mobile medicine in the prevention and control of chronic diseases. Chin J Tissue Eng Res. 2016;20(40):6074–6080.
   Google Scholar
• Wang H, Liu F. The construction and development of mobile medical treatment. Comput Syst Appl. 2015;24(12):18–24.
   Google Scholar
• Muralidharan S, Mohan V, Anjana RM, et al. Mobile health technology (mDiab) for the prevention of type 2 diabetes: protocol for a randomized controlled trial. JMIR Res Protoc. 2017;6(12):e242. doi:10.2196/resprot.864429233806
   PubMedGoogle Scholar
• Green BB, Cook AJ, Ralston JD, et al. Effectiveness of home blood pressure monitoring, web communication, and pharmacist care on hypertension control: a randomized controlled trial. JAMA. 2008;299(24):2857–2867. doi:10.1001/jama.299.24.285718577730
   PubMed Web of Science ®Google Scholar
• Vargas G, Cajita MI, Whitehouse E, Han H-R. Use of short messaging service for hypertension management: a systematic review. J Cardiovasc Nurs. 2017;32(3):260–270. doi:10.1097/JCN.000000000000033627111819
   PubMed Web of Science ®Google Scholar
• Logan AG, Irvine MJ, McIsaac WJ, et al. Effect of home blood pressure telemonitoring with self-care support on uncontrolled systolic hypertension in diabetics. Hypertension. 2012;60(1):51–57. doi:10.1161/HYPERTENSIONAHA.111.18840922615116
   PubMed Web of Science ®Google Scholar
• Hou J, Ndasauka Y, Pan X, et al. Weibo or WeChat? Assessing preference for social networking sites and role of personality traits and psychological factors. Front Psychol. 2018;9:545. doi:10.3389/fpsyg.2018.0054529755384
   PubMed Web of Science ®Google Scholar
• CNNIC (2015). China Internet Network Information Center the Thirty-Sixth Statistics Report of China Internet Development. Available from: http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/201507/P020150723549500667087.pdf
   Google Scholar
• Zhu D, He Y, Tian F. Social life within HERE: how SNS empowers different generations in China Beijing: Social Sciences Academic Press (China); 2018.
   Google Scholar
• Statista. Number of monthly active WeChat users from 1st quarter 2012 to 1st quarter 2019 (in millions). Available from: http://www.statista.com/statistics/255778/number-of-active-wechat-messenger-accounts/. Accessed 7 22, 2019.
   Google Scholar
• Tong H. Research on WeChat according to communication study and its influence. Chongqing Social Sci. 2013;9:61–66.
   Google Scholar
• Zhu R, Xu X, Zhao Y, et al. Decreasing the use of edible oils in China using WeChat and theories of behavior change: study protocol for a randomized controlled trial. Trials. 2018;19(1):610–631. doi:10.1186/s13063-018-2972-130400934
   PubMed Web of Science ®Google Scholar
• Zhang X, Wen D, Liang J, Lei J. How the public uses social media wechat to obtain health information in China: a survey study. BMC Med Inform Decis Mak. 2017;17(Suppl 2):66. doi:10.1186/s12911-017-0470-028699549
   PubMed Web of Science ®Google Scholar
• Zhang Q, Ding FM, Bao JL, et al. Influence of attribution intervention based on WeChat on self-management level of hypertension patients in community. China Medl Herald. 2017;28:162–165.
   Google Scholar
• Dong Y, Wang P, Dai Z, et al. Increased self-care activities and glycemic control rate in relation to health education via Wechat among diabetes patients: a randomized clinical trial. Medicine (Baltimore). 2018;97(50):e13632. doi:10.1097/MD.000000000001363230558051
   PubMed Web of Science ®Google Scholar
• He C, Wu S, Zhao Y, et al. Social media-promoted weight loss among an occupational population: cohort study using a Wechat mobile phone app-based campaign. J Med Internet Res. 2017;19(10):e357. doi:10.2196/jmir.786129061555
   PubMed Web of Science ®Google Scholar
• Yuexiu yearbook editorial department. Yuexiu Yearbook (2017). China: Guangdong Economic Press; 2017.
   Google Scholar
• Bandura A. Self-efficacy: The Exercise of Control. Richmond: Worth Publishers; 1997.
   Google Scholar
• Hu XL, You GY, Li JP. Study on correlation between self-management and self-efficacy of young and middle-aged hypertension patients in community. Chin Nurs Res. 2012;26(16):1523–1525.
   Google Scholar
• Warsi A, Wang PS, LaValley MP, Avorn J, Solomon DH. Self-management education programs in chronic disease: a systematic review and methodological critique of the literature. Arch Intern Med. 2004;164(8):1641–1649. doi:10.1001/archinte.164.15.164115302634
   PubMedGoogle Scholar
• Revision Committee for Chinese guidelines for the management of hypertension. Chinese guidelines for the management of hypertension revised for 2018. Prev Treat Cardio-Cerebral-Vascular Dis. 2019;19(1):1–45.
   Google Scholar
• Chen J. Analysis and Intervention of Knowledge, Belief, Behavior in Elderly Patients with Primary Hypertension in Haizhu District of Guangzhou. China: Southern Medical University; 2011.
   Google Scholar
• Ding W, Li T, Su Q, Yuan M, Lin A. Integrating factors associated with hypertensive patients’ self-management using structural equation model: a cross-sectional study in Guangdong, China. Patient Prefer Adherence. 2018;12:2169–2178. doi:10.2147/PPA.S18031430410312
   PubMed Web of Science ®Google Scholar
• Zhao QL, Liu X. Reliability and validity of the Hypertension Patients Self-Management Behavior Rating Scale (HPSMBRS). Chin Nurs Manage. 2012;12(11):26–31.
   Google Scholar
• Cheng X. The Predictors of Self-Management Behavior in Patients with Hypertension Based on Health Ecological Model. China: Shandong University; 2014.
   Google Scholar
• Bittar N. Maintaining long-term control of blood pressure: the role of improved compliance. Clin Cardiol. 1995;18(6 Suppl 3):III 12–16. doi:10.1002/clc.4960181504
   Google Scholar
• Morisky DE, Green LW, Levine DM. Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care. 1986;24(1):67. doi:10.1097/00005650-198601000-000073945130
   PubMed Web of Science ®Google Scholar
• Dai J, Fu H, Shen Y. Study on compliance of medication for essential hypertension. Chin J Prev Contr Chron Non-Commun Dis. 2000;8(3):143–145.
   Google Scholar
• Xu W, Wang Q, Liang W. Reliability and validity of Morisky questionnaire in measurement of the compliance with hypertensive medications. Chin J Prev Contr Chron Non-Commun Dis. 2007;15(5):424–426.
   Google Scholar
• Hagströmer M, Oja P, Sjöström M. The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutr. 2006;9(6):755–762.16925881
   PubMed Web of Science ®Google Scholar
• Hu B, Lin LF, Zhuang MQ, et al. Reliability and relative validity of three physical activity questionnaires in Taizhou population of China: the Taizhou longitudinal study. Public Health. 2015;129(9):1211–1217. doi:10.1016/j.puhe.2015.03.02425957853
   PubMed Web of Science ®Google Scholar
• Satterthwaite FE. An approximate distribution of estimates of variance components. Biometrics. 1946;2:110–114. doi:10.2307/300201920287815
   PubMed Web of Science ®Google Scholar
• Leyrat C, Morgan KE, Leurent B, Kahan BC. Cluster randomized trials with a small number of clusters: which analyses should be used? Int J Epidemiol. 2018;47(1):321–331. doi:10.1093/ije/dyx16929025158
   PubMed Web of Science ®Google Scholar
• Bosmans JE, van der Laan DM, Yang Y, et al. The cost-effectiveness of an intervention program to enhance adherence to antihypertensive medication in comparison with usual care in community pharmacies. Front Pharmacol. 2019;10:210. doi:10.3389/fphar.2019.0021030899223
   PubMed Web of Science ®Google Scholar
• Briggs A, Claxton K, Sculpher M. Decision Modelling for Health Economic Evaluation. New York: Oxford University Press; 2006.
   Google Scholar