Community-based educational interventions for prevention of type II diabetes: a global systematic review and meta-analysis

Tayebeh Shirvani, Zeinab Javadivala, Somayeh Azimi, Abdolreza Shaghaghi, Zahra Fathifar, H D R Devender Bhalla, Mohammadhiwa Abdekhoda, Haidar Nadrian, Tayebeh Shirvani, Zeinab Javadivala, Somayeh Azimi, Abdolreza Shaghaghi, Zahra Fathifar, H D R Devender Bhalla, Mohammadhiwa Abdekhoda, Haidar Nadrian

Abstract

Purpose: Our objective was to estimate the change in community-based education interventions throughout the world that may effectuate in risk parameters of type II diabetes (T2D), including the diabetes incidence rate, fasting blood glucose, hemoglobin A1C, body mass index, waist circumference, and systolic and diastolic blood pressure.

Methods: A comprehensive search for globally eligible studies was conducted on PubMed, Embase, ProQuest, CINAHL nursing & allied health source, Cochrane Library, Google Scholar, conference proceedings, and reference lists. Data were extracted using JBI standardized data extraction tool. The primary outcome variables were diabetes incidence rate, fasting blood sugar (FBS), hemoglobin A1c (HbAlc), body mass index (BMI), waist circumference (WC), systolic/diastolic blood pressure (s/d BP). Random-effects meta-analysis and sub-group analyses were conducted.

Results: Nineteen interventional studies were included in the review, and ten studies were pooled in the meta-analysis (n = 16,106, mean age = 41.5 years). The incidence rate of T2D was reported in three trials, within which the risk of developing T2D was reduced by 54.0% in favor of community-based educational interventions, (RR = 0.54, 95% CI = 0.38-0.75; p < 0.001). In eleven (n = 11,587) and six (n = 6416) studies, the pooled mean differences were - 0.33 (95% CI: - 0.45 to - 0.20, p < 0.0001) and - 0.15 (95% CI: - 0.28 to - 0.03, p < 0.0001) for FBS and HbA1c levels, respectively. Positive significant effects were observed on reducing BMI [pooled mean difference = - 0.47 (95% CI: - 0.66 to - 0.28), I2 = 95.7%, p < 0.0001] and WC [pooled mean difference = - 0.66 (95% CI: - 0.89 to - 0.43), I2 = 97.3%, p < 0.0001]. The use of theoretical frameworks was found to provide a 48.0% change in fasting blood sugar.

Conclusions: Based on a comprehensive data collection of about 16,106 participants and reasonable analyses, we conclude that educational interventions may reduce diabetes incidence by 54.0%, particularly through reductions in fasting blood glucose, body mass index, and waist circumference. The diabetes risk parameters may favorably improve irrespective of the duration of intervention, at as low as 6 months. The application of theoretical frameworks while designing educational interventions is also encouraged.

Systematic review registration: PROSPERO CRD42018115877.

Keywords: Behavior change; Community-based; Diabetes; Educational intervention; Epidemiology; Prevention.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Flowchart describing the pathway to search of community-based studies that had education as an intervention for the prevention of diabetes
Fig. 2
Fig. 2
Meta-analysis results showing the effect of diabetes interventions on diabetes incidence (a), HBA1c level (b), body mass index (c), waist circumstance (d), and diastolic (e) and systolic (f) blood pressure
Fig. 3
Fig. 3
Subgroup meta-analysis results showing the effect of diabetes interventions on fasting blood glucose levels based on the duration of follow-up (a), gender (b), age (c), and using theoretical framework for designing their interventions (d)
Fig. 4
Fig. 4
Funnel plots showing the effects of heterogeneity and publication bias in studies that addressed the prevention of diabetes through interventions for effecting change in the fasting blood glucose (a) and hemoglobin A1c (b) levels

References

    1. Chisari G, C.E. Borzì AM, Chisari CG. Amniotic membrane use in type 2 diabetes patients with chronic ulcers: microbiological evaluation and therapeutic benefits. Acta Med Mediterr. 2017;33:431–435.
    1. Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract. 2010;87(1):4–14. doi: 10.1016/j.diabres.2009.10.007.
    1. Esteghamati A, et al. Diabetes in Iran: prospective analysis from first nationwide diabetes report of National Program for Prevention and Control of Diabetes (NPPCD-2016) Sci Rep. 2017;7(1):13461. doi: 10.1038/s41598-017-13379-z.
    1. Organization, W.H. Antimicrobial resistance: global report on surveillance: World Health Organization; 2014.
    1. Saaristo T, et al. Lifestyle intervention for prevention of type 2 diabetes in primary health care: one-year follow-up of the Finnish National Diabetes Prevention Program (FIN-D2D) Diabetes Care. 2010;33(10):2146–2151. doi: 10.2337/dc10-0410.
    1. Craig P, et al. Developing and evaluating complex interventions: the new Medical Research Council guidance. 2013.
    1. Chisanga C. Diabetes in young people in Africa: diabetes education, an important key element to managing diabetes in Zambia, in 13th European Diabetes and Endocrinology Congress, A. Diabetes Association of Zambia, Editor 2018. J Diabetes Metab Ireland. 2018. Available at: . Accessed 7 Mar 2021.
    1. Peyrot M, R.R Patient-reported outcomes in adults with type 2 diabetes using mealtime inhaled technosphere insulin and basal insulin versus premixed insulin. Diabetes Technol Ther. 2011;13(12):1201–1206. doi: 10.1089/dia.2011.0037.
    1. Baquedano IR, S.M. Martins TA, Zanetti ML. Self-care of patients with diabetes mellitus cared for at an emergency service in Mexico. Rev Lat Am Enfermagem. 2010;18(6):1195–1202. doi: 10.1590/S0104-11692010000600021.
    1. Rankin P, et al. Effectiveness of a volunteer-delivered lifestyle modification program for reducing cardiovascular disease risk factors. Am J Cardiol. 2012;109(1):82–86. doi: 10.1016/j.amjcard.2011.07.069.
    1. Werfalli M, et al. Effectiveness of community-based peer-led diabetes self-management programmes (COMP-DSMP) for improving clinical outcomes and quality of life of adults with diabetes in primary care settings in low and middle-income countries (LMIC): a systematic review and meta-analysis. BMJ Open. 2015;5(7).
    1. Nissinen A, Berrios X, Puska P. Community-based noncommunicable disease interventions: lessons from developed countries for developing ones. Bull World Health Organ. 2001;79(10):963–970.
    1. Shirinzadeh M, et al. The effect of community-based programs on diabetes prevention in low-and middle-income countries: a systematic review and meta-analysis. Glob Health. 2019;15(1):1–13. doi: 10.1186/s12992-019-0451-4.
    1. Renders CM, et al. Interventions to improve the management of diabetes in primary care, outpatient, and community settings. Diabetes Care. 2001;24(10):1821–1833. doi: 10.2337/diacare.24.10.1821.
    1. Weiss CH. How can theory-based evaluation make greater headway? Eval Rev. 1997;21(4):501–524. doi: 10.1177/0193841X9702100405.
    1. Michie S, Prestwich A. Are interventions theory-based? Development of a theory coding scheme. Health Psychol. 2010;29(1):1. doi: 10.1037/a0016939.
    1. Karimy M, A.M. Zareban I, Taher M, Abedi A. Determinants of adherence to self-care behavior among women with type 2 diabetes: an explanation based on health belief model. Med J Islam Repub Iran. 2016;30:368.
    1. Galaviz KI, et al. Global diabetes prevention interventions: a systematic review and network meta-analysis of the real-world impact on incidence, weight, and glucose. Diabetes Care. 2018;41(7):1526–1534. doi: 10.2337/dc17-2222.
    1. Ali MK, Echouffo-Tcheugui JB, Williamson DF. How effective were lifestyle interventions in real-world settings that were modeled on the Diabetes Prevention Program? Health Aff. 2012;31(1):67–75. doi: 10.1377/hlthaff.2011.1009.
    1. Mudaliar U, et al. Cardiometabolic risk factor changes observed in diabetes prevention programs in US settings: a systematic review and meta-analysis. PLoS Med. 2016;13(7):e1002095. doi: 10.1371/journal.pmed.1002095.
    1. Balagopal P, et al. A community-based diabetes prevention and management education program in a rural village in India. Diabetes Care. 2008;31(6):1097–1104. doi: 10.2337/dc07-1680.
    1. Ibrahim N, et al. Effects of a community-based healthy lifestyle intervention program (co-HELP) among adults with prediabetes in a developing country: a quasi-experimental study. PLoS One. 2016;11(12):e0167123. doi: 10.1371/journal.pone.0167123.
    1. Rowan CP, et al. Community-based culturally preferred physical activity intervention targeting populations at high risk for type 2 diabetes: results and implications. Can J Diabetes. 2016;40(6):561–569. doi: 10.1016/j.jcjd.2016.05.011.
    1. Penn L, Ryan V, White M. Feasibility, acceptability and outcomes at a 12-month follow-up of a novel community-based intervention to prevent type 2 diabetes in adults at high risk: mixed methods pilot study. BMJ Open. 2013;3(11):e003585. doi: 10.1136/bmjopen-2013-003585.
    1. Katula JA, et al. One-year results of a community-based translation of the Diabetes Prevention Program: Healthy-Living Partnerships to Prevent Diabetes (HELP PD) Project. Diabetes Care. 2011;34(7):1451–1457. doi: 10.2337/dc10-2115.
    1. Ackermann RT, et al. Long-term effects of a community-based lifestyle intervention to prevent type 2 diabetes: the DEPLOY extension pilot study. Chronic Illn. 2011;7(4):279–290. doi: 10.1177/1742395311407532.
    1. Ockene IS, et al. Outcomes of a Latino community-based intervention for the prevention of diabetes: the Lawrence Latino Diabetes Prevention Project. Am J Public Health. 2012;102(2):336–342. doi: 10.2105/AJPH.2011.300357.
    1. Daniel M, et al. Effectiveness of community-directed diabetes prevention and control in a rural Aboriginal population in British Columbia, Canada. Soc Sci Med. 1999;48(6):815–832. doi: 10.1016/S0277-9536(98)00403-1.
    1. Raman A, et al. Insulin resistance is improved in overweight African American boys but not in girls following a one-year multidisciplinary community intervention program. J Pediatr Endocrinol Metab. 2010;23(1-2):109–120. doi: 10.1515/JPEM.2010.23.1-2.109.
    1. Balagopal P, et al. A community-based participatory diabetes prevention and management intervention in rural India using community health workers. Diabetes Educ. 2012;38(6):822–834. doi: 10.1177/0145721712459890.
    1. Ramachandran A, Snehalatha C, Mary S, Mukesh B, Bhaskar AD, Vijay V. The Indian Diabetes Prevention Programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1) Diabetologia. 2006;49(2):289–297. doi: 10.1007/s00125-005-0097-z.
    1. Harati H, et al. Reduction in incidence of type 2 diabetes by lifestyle intervention in a middle eastern community. Am J Prev Med. 2010;38(6):628–636. e1. doi: 10.1016/j.amepre.2010.03.003.
    1. Group, D.P.P.R 10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. Lancet. 2009;374(9702):1677–1686. doi: 10.1016/S0140-6736(09)61457-4.
    1. Davies MJ, et al. A community based primary prevention programme for type 2 diabetes integrating identification and lifestyle intervention for prevention: the Let’s Prevent Diabetes cluster randomised controlled trial. Prev Med. 2016;84:48–56. doi: 10.1016/j.ypmed.2015.12.012.
    1. Yin Z, et al. Cultural adaptation of an evidence-based lifestyle intervention for diabetes prevention in Chinese women at risk for diabetes: results of a randomized trial. Int Health. 2018;10(5):391–400. doi: 10.1093/inthealth/ihx072.
    1. Pedley CF, et al. The 24-month metabolic benefits of the healthy living partnerships to prevent diabetes: a community-based translational study. Diabetes Metab Syndr Clin Res Rev. 2018;12(3):215–220. doi: 10.1016/j.dsx.2017.09.011.
    1. Sranacharoenpong K, Praditsorn P, Churak P. Developing a diabetes prevention education program for community health care workers in Thailand: translation of the knowledge to at-risk people. J Public Health. 2018;26(5):515–522. doi: 10.1007/s10389-018-0897-5.
    1. Soltero EG, et al. ¡ Viva Maryvale!: a multilevel, multisector model to community-based diabetes prevention. Am J Prev Med. 2019;56(1):58–65. doi: 10.1016/j.amepre.2018.07.034.
    1. Soltero EG, et al. Effects of a community-based diabetes prevention program for Latino youth with obesity: a randomized controlled trial. Obesity. 2018;26(12):1856–1865. doi: 10.1002/oby.22300.
    1. Yang Q, et al. Racial/ethnic differences in association of fasting glucose–associated genomic loci with fasting glucose, HOMA-B, and impaired fasting glucose in the US adult population. Diabetes Care. 2010;33(11):2370–2377. doi: 10.2337/dc10-0898.
    1. Aidenloo NS, et al. Optimal glycemic and hemoglobin a1c thresholds for diagnosing diabetes based on prevalence of retinopathy in an Iranian population. Iran Red Crescent Med J. 2016;18(8).
    1. Lim W, Ma S, Heng D, Tai ES, Khoo CM, Loh TP. Screening for diabetes with HbA1c: test performance of HbA1c compared to fasting plasma glucose among Chinese, Malay and Indian community residents in Singapore. Sci Rep. 2018;8(1):12419. doi: 10.1038/s41598-018-29998-z.
    1. Little RR, Roberts WL. A review of variant hemoglobins interfering with hemoglobin A1c measurement. 2009. p. SAGE Publications.
    1. Coban E, Ozdogan M, Timuragaoglu A. Effect of iron deficiency anemia on the levels of hemoglobin A1c in nondiabetic patients. Acta Haematol. 2004;112(3):126–128. doi: 10.1159/000079722.
    1. Kamel E, McNeill G, Van Wijk M. Change in intra-abdominal adipose tissue volume during weight loss in obese men and women: correlation between magnetic resonance imaging and anthropometric measurements. Int J Obes. 2000;24(5):607. doi: 10.1038/sj.ijo.0801204.
    1. Seo D-C, Choe S, Torabi MR. Is waist circumference ≥ 102/88 cm better than body mass index ≥ 30 to predict hypertension and diabetes development regardless of gender, age group, and race/ethnicity? Meta-analysis. Prev Med. 2017;97:100–108. doi: 10.1016/j.ypmed.2017.01.012.
    1. Khaylis A, et al. A review of efficacious technology-based weight-loss interventions: five key components. Telemed E Health. 2010;16(9):931–938. doi: 10.1089/tmj.2010.0065.
    1. Nafradi L, et al. Intentional and unintentional medication non-adherence in hypertension: the role of health literacy, empowerment and medication beliefs. J Pub Health Res. 2016;5(3):762–768.
    1. Nations M, et al. Balking blood pressure “control” by older persons of Bambuí, Minas Gerais State, Brazil: an ethno-epidemiological inquiry. Cadernos Saúde Públ. 2011;27(3):378–389. doi: 10.1590/S0102-311X2011001500008.
    1. Najafipour F, M.M, Yavari A, Nadrian H, Aliasgarzadeh A, Abbasi NM, Niafar M, Gharamaleki JH, Sadra V. Effect of regular exercise training on changes in HbA1c, BMI and VO2max among patients with type 2 diabetes mellitus: an 8-year trial. BMJ Open Diabetes Res Care. 2017;5(1).
    1. Pals RA, S.T. Velasco ER, Grabowski D. The role of theories in interventions targeting preteens with type 1 diabetes: a critical literature review. Child Care Health Dev. 2020;46(2):155–174. doi: 10.1111/cch.12730.
    1. Dalgetty R, M.C. Dombrowski SU. Examining the theory-effectiveness hypothesis: a systematic review of systematic reviews. Br J Health Psychol. 2019;24(2):334–356. doi: 10.1111/bjhp.12356.
    1. Al-Maskari F, El-Sadig M, Nagelkerke N. Assessment of the direct medical costs of diabetes mellitus and its complications in the United Arab Emirates. BMC Public Health. 2010;10(1):679. doi: 10.1186/1471-2458-10-679.
    1. Schueller SM, et al. Purple: a modular system for developing and deploying behavioral intervention technologies. J Med Internet Res. 2014;16(7):e181. doi: 10.2196/jmir.3376.

Source: PubMed

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