Assessment of physical activity using accelerometry, an activity diary, the heart rate method and the Indian migration study questionnaire in south Indian adults.

OBJECTIVE: To validate questionnaire-based physical activity level (PAL) against accelerometry and a 24 h physical activity diary (24 h AD) as reference methods (Protocol 2), after validating these reference methods against the heart rate-oxygen consumption (HRVO2) method (Protocol 1). DESIGN: Cross-sectional study. SETTING: Two villages in Andhra Pradesh state and Bangalore city, South India. SUBJECTS: Ninety-four participants (fifty males, forty-four females) for Protocol 2; thirteen males for Protocol 1. RESULTS: In Protocol 2, mean PAL derived from the questionnaire (1.72 (sd 0.20)) was comparable to that from the 24 h AD (1.78 (sd 0.20)) but significantly higher than the mean PAL derived from accelerometry (1.36 (sd 0.20); P < 0.001). Mean bias of PAL from the questionnaire was larger against the accelerometer (0.36) than against the 24 h AD (-0.06), but with large limits of agreement against both. Correlations of PAL from the questionnaire with that of the accelerometer (r = 0.28; P = 0.01) and the 24 h AD (r = 0.30; P = 0.006) were modest. In Protocol 1, mean PAL from the 24 h AD (1.65 (sd 0.18)) was comparable, while that from the accelerometer (1.51 (sd 0.23)) was significantly lower (P < 0.001), than mean PAL obtained from the HRVO2 method (1.69 (sd 0.21)). CONCLUSIONS: The questionnaire showed acceptable validity with the reference methods in a group with a wide range of physical activity levels. The accelerometer underestimated PAL in comparison with the HRVO2 method.

Sociodemographic patterning of non-communicable disease risk factors in rural India: a cross sectional study.

OBJECTIVES: To investigate the sociodemographic patterning of non-communicable disease risk factors in rural India. DESIGN: Cross sectional study. SETTING: About 1600 villages from 18 states in India. Most were from four large states due to a convenience sampling strategy. PARTICIPANTS: 1983 (31% women) people aged 20-69 years (49% response rate). MAIN OUTCOME MEASURES: Prevalence of tobacco use, alcohol use, low fruit and vegetable intake, low physical activity, obesity, central adiposity, hypertension, dyslipidaemia, diabetes, and underweight. RESULTS: Prevalence of most risk factors increased with age. Tobacco and alcohol use, low intake of fruit and vegetables, and underweight were more common in lower socioeconomic positions; whereas obesity, dyslipidaemia, and diabetes (men only) and hypertension (women only) were more prevalent in higher socioeconomic positions. For example, 37% (95% CI 30% to 44%) of men smoked tobacco in the lowest socioeconomic group compared with 15% (12% to 17%) in the highest, while 35% (30% to 40%) of women in the highest socioeconomic group were obese compared with 13% (7% to 19%) in the lowest. The age standardised prevalence of some risk factors was: tobacco use (40% (37% to 42%) men, 4% (3% to 6%) women); low fruit and vegetable intake (69% (66% to 71%) men, 75% (71% to 78%) women); obesity (19% (17% to 21%) men, 28% (24% to 31%) women); dyslipidaemia (33% (31% to 36%) men, 35% (31% to 38%) women); hypertension (20% (18% to 22%) men, 22% (19% to 25%) women); diabetes (6% (5% to 7%) men, 5% (4% to 7%) women); and underweight (21% (19% to 23%) men, 18% (15% to 21%) women). Risk factors were generally more prevalent in south Indians compared with north Indians. For example, the prevalence of dyslipidaemia was 21% (17% to 33%) in north Indian men compared with 33% (29% to 38%) in south Indian men, while the prevalence of obesity was 13% (9% to 17%) in north Indian women compared with 24% (19% to 30%) in south Indian women. CONCLUSIONS: The prevalence of most risk factors was generally high across a range of sociodemographic groups in this sample of rural villagers in India; in particular, the prevalence of tobacco use in men and obesity in women was striking. However, given the limitations of the study (convenience sampling design and low response rate), cautious interpretation of the results is warranted. These data highlight the need for careful monitoring and control of non-communicable disease risk factors in rural areas of India.

Refinement and validation of an FFQ developed to estimate macro- and micronutrient intakes in a south Indian population.

OBJECTIVE: Potential error sources in nutrient estimation with the FFQ include inaccurate or biased recall and overestimation or underestimation of intake due to too many or too few items on the FFQ, respectively. Here we report the refinement of an FFQ that overestimated nutrient intake and its validation against multiple 24 h recalls. STUDY DESIGN: Data on 2527 participants in south India (Trivandrum) were available for the original FFQ (OFFQ) that overestimated nutrient intake (132 food items). After excluding participants with implausible energy intake estimates (<2.72 MJ/d (<650 kcal/d), >15.69 MJ/d (>3750 kcal/d)) we ran stepwise regression analyses with selected nutrients as the outcomes and food intake (servings/d) as predictor variables (n 1867). From these results and expert consultation we refined the FFQ (RFFQ), and validated it by comparing intakes obtained with it and the mean of two 24 h recalls among 100 participants. RESULTS: The OFFQ overestimated usual daily nutrient intake before and after exclusions [for energy: 13.39 (sd 5.46) MJ (3201 (sd 1305) kcal) and 10.96 (sd 2.65) MJ (2619 (sd 634) kcal), respectively]. In stepwise analyses, fifty-seven food items explained 90 % of the variance in nutrients; we retained thirteen food items because participants consumed them at least twice monthly and twelve food items that local nutritionists recommended. Mean energy intake estimated from the RFFQ (eighty-two food items) was 7.94 (sd 2.05) MJ (1897 (sd 489) kcal). The de-attenuated correlations between mean 24 h recall and RFFQ intakes ranged from 0.25 (vitamin A) to 0.82 (fat). CONCLUSION: We refined an FFQ that overestimated nutrient intake by shortening and redesigning, and validated it by comparisons with 24 h dietary recall data.