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Objective: To compare physical activity (PA) patterns in pregnant woman with and without gestational diabetes (GDM) and to assess the effects of an exercise intervention on change in PA patterns, blood glucose levels and pregnancy outcomes in GDM women.Methods: For the first objective, PA patterns were studied in 795 pregnant women with and without GDM. For the second objective, the Women in India with Gestational Diabetes Strategy-Model of Care (WINGS-MOC) intervention were evaluated in 151 women out of 189 with GDM. PA was assessed using a validated questionnaire and a pedometer. Changes in PA patterns, glycemic parameters and neonatal outcomes were evaluated.Results: Overall, only 10% of pregnant women performed recommended levels of PA. Women with GDM were significantly more sedentary compared to those without GDM (86.2 vs. 61.2%, p<0.001). After the MOC was implemented in women with GDM, there was a significant improvement in PA and a decrease in sedentary behaviour amongst women (before MOC, moderate activity: 15.2%, sedentary: 84.8% vs. after MOC-moderate: 26.5%, sedentary: 73.5%; p<0.001), and an increase in their daily step count from 2206/day to 2476/day (p<0.001). Fasting 1 and 2-h postprandial glucose values significantly decreased (p<0.001 for all). Sedentary behaviour was associated with a fourfold higher risk (p=0.02), and recreational walking with 70% decreased risk, of adverse neonatal outcomes (p=0.04) after adjusting for potential confounders.Conclusions: PA levels are inadequate amongst this group of pregnant women studied i.e. those with and without GDM. However, a low-cost, culturally appropriate MOC can bring about significant improvements in PA in women with GDM. These changes are associated with improved glycemic control and reduction in adverse neonatal outcomes. [ABSTRACT FROM AUTHOR]

Abstract: Objective:: Simple risk scores for identifying people with undiagnosed diabetes have been developed, mostly in Caucasian groups. This may not be suitable for Asian Indians, therefore this study was undertaken to develop and validate a simple diabetes risk score in an urban Asian Indian population with a high prevalence of diabetes. We also tested whether this score was applicable to South Asian migrants living in a different cultural context. Research design and methods:: A population based Cohort of 10,003 participants aged ≥20 years was divided into two equal halves (Cohorts 1 and 2), after excluding people with known diabetes. Cohort 1 (n =4993) was used to derive the risk score. Validation of the score was performed in the other half of the survey population (Cohort 2) (n =5010). The validation was also done in a separate survey population in Chennai, India (Cohort 3) (n =2002) (diagnosis of diabetes was based on OGTT) and in the South Asian Cohort of the 1999 Health Survey for England (n =676) (fasting glucose value≥7mmol/l and HbA1c≥6.5% were used for diagnosis). A logistic regression model was used to compute the β coefficients for risk factors. The risk score value was derived from a receiver operating characteristic curve. Results:: The significant risk factors included in the risk score were age, BMI, waist circumference, family history of diabetes and sedentary physical activity. A risk score value of >21 gave a sensitivity, specificity, positive predictive value and negative predictive value of 76.6%, 59.9%, 9.4% and 97.9% in Cohort 1, 72.4%, 59%, 8.3% and 97.6% in Cohort 2 and 73.7%, 61.0%, 12.2% and 96.9% in Cohort 3, respectively. The higher distribution of risk factors in the UK Cohort means that at the same cut point the score was much more sensitive but also less specific. (sensitivity 92.2%, specificity 25.7%, positive predictive value of 21.6% and negative predictive value of 93.7%). Conclusions:: A diabetes risk score involving simple non-biochemical measurements was developed and validated in a native Asian Indian population. This easily applicable simple score could play an important role as the first step in the process of identifying individuals with an increased likelihood of having prevalent but undiagnosed diabetes. The different distribution of risk factors with the migrant Asian Indians living in England and the different relationship between sensitivity and specificity for the same score demonstrate that risk scores and cut-points developed and tested even within one ethnic group cannot be generalized to individuals of the same ethnic group living in a different cultural setting where the distribution of risk factors for diabetes is different. [Copyright &y& Elsevier]