Exercise patterns and behaviour in Asian Indians: Data from the baseline survey of the Diabetes Community Lifestyle Improvement Program (D-CLIP)

Anjana, Ranjit Mohan; Ranjani, Harish; Unnikrishnan, Ranjit; Weber, Mary Beth; Mohan, Viswanathan; Narayan, K.M. Venkat

doi:10.1016/j.diabres.2014.09.053

Cited by 20 publications

(17 citation statements)

References 26 publications

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“…This trend was consistent across studies from southeast Asia, the western Pacific, Africa, and the eastern Mediterranean. The notable exceptions were found in populations from urban areas: residents of Aleppo, 60 pre-diabetics in southern India, 61 and residents of multiple Indian cities. 43 , 46 In these settings, low-income and less educated groups had the highest prevalence of inactivity.…”

Section: Resultsmentioning

confidence: 91%

“…Three studies were low quality, 18 were moderate (including the WHO surveys), and eight were of high quality. Nine studies 37 , 38 , 48 , 52 , 53 , 54 , 55 , 57 , 61 reported outcomes based on WHO recommendations and results from the International and Global Physical Activity Questionnaires, 62 , 63 , 64 five studies used definitions derived from other sources, 32 , 41 , 44 , 58 , 60 and 15 did not refer to any pre-existing definition. 25 , 27 , 31 , 36 , 42 , 43 , 45 , 46 , 47 , 49 , 50 , 51 , 56 , 59 , 65 Measures of sedentary behaviour (not technically a non-communicable disease behavioural risk factor) were reported in 19 studies; high or sufficient levels of activity were reported in ten studies.…”

Section: Resultsmentioning

confidence: 99%

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Socioeconomic status and non-communicable disease behavioural risk factors in low-income and lower-middle-income countries: a systematic review

Allen

Williams

Townsend

et al. 2017

The Lancet Global Health

560

381

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SummaryBackgroundNon-communicable diseases are the leading global cause of death and disproportionately afflict those living in low-income and lower-middle-income countries (LLMICs). The association between socioeconomic status and non-communicable disease behavioural risk factors is well established in high-income countries, but it is not clear how behavioural risk factors are distributed within LLMICs. We aimed to systematically review evidence on the association between socioeconomic status and harmful use of alcohol, tobacco use, unhealthy diets, and physical inactivity within LLMICs.MethodsWe searched 13 electronic databases, including Embase and MEDLINE, grey literature, and reference lists for primary research published between Jan 1, 1990, and June 30, 2015. We included studies from LLMICs presenting data on multiple measures of socioeconomic status and tobacco use, alcohol use, diet, and physical activity. No age or language restrictions were applied. We excluded studies that did not allow comparison between more or less advantaged groups. We used a piloted version of the Cochrane Effective Practice and Organisation of Care Group data collection checklist to extract relevant data at the household and individual level from the included full text studies including study type, methods, outcomes, and results. Due to high heterogeneity, we used a narrative approach for data synthesis. We used descriptive statistics to assess whether the prevalence of each risk factor varied significantly between members of different socioeconomic groups. The study protocol is registered with PROSPERO, number CRD42015026604.FindingsAfter reviewing 4242 records, 75 studies met our inclusion criteria, representing 2 135 314 individuals older than 10 years from 39 LLMICs. Low socioeconomic groups were found to have a significantly higher prevalence of tobacco and alcohol use than did high socioeconomic groups. These groups also consumed less fruit, vegetables, fish, and fibre than those of high socioeconomic status. High socioeconomic groups were found to be less physically active and consume more fats, salt, and processed food than individuals of low socioeconomic status. While the included studies presented clear patterns for tobacco use and physical activity, heterogeneity between dietary outcome measures and a paucity of evidence around harmful alcohol use limit the certainty of these findings.InterpretationDespite significant heterogeneity in exposure and outcome measures, clear evidence shows that the burden of behavioural risk factors is affected by socioeconomic position within LLMICs. Governments seeking to meet Sustainable Development Goal (SDG) 3.4—reducing premature non-communicable disease mortality by a third by 2030—should leverage their development budgets to address the poverty-health nexus in these settings. Our findings also have significance for health workers serving these populations and policy makers tasked with preventing and controlling the rise of non-communicable diseases.FundingWHO.

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Section: Resultsmentioning

confidence: 91%

Section: Resultsmentioning

confidence: 99%

Socioeconomic status and non-communicable disease behavioural risk factors in low-income and lower-middle-income countries: a systematic review

Allen

Williams

Townsend

et al. 2017

The Lancet Global Health

560

381

View full text Add to dashboard Cite

show abstract

“…When the K-DPP cohort was compared with participants in previous diabetes prevention trials conducted in Asian Indians, a lower proportion of those enrolled in the Indian Diabetes Prevention Programme-1 [23] had hypertension (≥135/80 mmHg; 34 vs 31%) and high total cholesterol (68 vs 42%). Similarly, compared with participants in the baseline survey of the Diabetes Community Lifestyle Improvement Program trial from urban Chennai in India [14], a greater proportion of K-DPP participants were current smokers (27 vs 13%), had hypertension (22 vs 18%) and had high total cholesterol (68 vs 35%). The K-DPP cohort also differed from those enrolled in diabetes prevention trials conducted in white populations.…”

Section: Discussionmentioning

confidence: 99%

Baseline characteristics of participants in the Kerala Diabetes Prevention Program: a cluster randomized controlled trial of lifestyle intervention in Asian Indians

et al. 2016

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Aims To describe the baseline characteristics of participants in the Kerala Diabetes Prevention Programme. Methods The Kerala Diabetes Prevention Programme is a cluster randomized controlled trial of lifestyle intervention for prevention of Type 2 diabetes mellitus in India. Participants in the study were those aged 30–60 years who had an Indian Diabetes Risk Score ≥60 and who were without Type 2 diabetes on oral glucose tolerance test. Data on demographic, lifestyle, clinical and biochemical characteristics were collected using standardized tools. Results A total of 2586 individuals were screened with the Indian Diabetes Risk Score, of these 1529 people (59.1%) had a score ≥60, of whom 1209 (79.1%) underwent an oral glucose tolerance test. A total of 202 individuals (16.7%) had undiagnosed Type 2 diabetes and were excluded, and the remaining 1007 individuals were enrolled in the trial (control arm, n = 507; intervention arm, n = 500). The mean participant age was 46.0 ± 7.5 years, and 47.0% were women. The mean Indian Diabetes Risk Score was 67.1 ± 8.4. More than two-thirds (69.0%) had prediabetes and 31.0% had normal glucose tolerance. The prevalence of cardiometabolic risk factors was high, including current tobacco use (34.4% in men), current alcohol use (39.3% in men), no leisure time exercise (98.0%), no daily intake of fruit and vegetables (78.8%), family history of diabetes (47.9%), overweight or obesity (68.5%), hypertension (22.3%) and dyslipidemia (85.3%). Conclusions The Kerala Diabetes Prevention Programme recruited participants using a diabetes risk score. A large proportion of the participants had prediabetes and there were high rates of cardiometabolic risk factors. The trial will evaluate the effectiveness of lifestyle intervention in a population selected on the basis of a diabetes risk score. (Clinical Trial registration: Australia and New Zealand Clinical Trials Registry: ACTRN12611000262909.)

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“…Urbanization has resulted in several environmental factors which discourage participation in PA, particularly in the transport and occupational domains. In developing countries, less than a quarter of the population exercises regularly 56 . Wide variations in the prevalence of PA have been reported in various countries.…”

Section: Physical Inactivity -A Risk Factor For Diabetesmentioning

confidence: 99%

Built Environment, Physical Activity and Diabetes

Anjana

2017

Current Science

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Type-2 diabetes, which has emerged as a global epidemic in recent years, is strongly related to lifestyle and economic change. The built environment (BE) influences lifestyle factors such as physical activity and diet. Evidence shows that individuals who live in neighbourhoods with the availability of destinations for physical activity within walking/cycling distance are more likely to engage in the same and thereby improve their health. Walking can be increased in neighbourhoods by providing useable and unencroached pedestrian pathways, undertaking motor-traffic reduction strategies, improved perceived neighbourhood safety, increasing good street connectivity, parks, green space, playgrounds and recreation areas. Thus for the BE to positively influence health outcomes and be made more activity-friendly, requires combined efforts of health professionals and stakeholders in the Government as well as the private sector.

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Exercise patterns and behaviour in Asian Indians: Data from the baseline survey of the Diabetes Community Lifestyle Improvement Program (D-CLIP)

Cited by 20 publications

References 26 publications

Socioeconomic status and non-communicable disease behavioural risk factors in low-income and lower-middle-income countries: a systematic review

Socioeconomic status and non-communicable disease behavioural risk factors in low-income and lower-middle-income countries: a systematic review

Baseline characteristics of participants in the Kerala Diabetes Prevention Program: a cluster randomized controlled trial of lifestyle intervention in Asian Indians

Built Environment, Physical Activity and Diabetes

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