Tackling the lack of diversity in health research

Peer reviewedPostprin

Energy expenditure during common sitting and standing tasks: examining the 1.5 MET definition of sedentary behaviour

Background: Sedentary behavior is defined as any waking behavior characterized by an energy expenditure of 1.5 METS or less while in a sitting or reclining posture. This study examines this definition by assessing the energy cost (METs) of common sitting, standing and walking tasks. Methods: Fifty one adults spent 10 min during each activity in a variety of sitting tasks (watching TV, Playing on the Wii, Playing on the PlayStation Portable (PSP) and typing) and non-sedentary tasks (standing still, walking at 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, and 1.6 mph). Activities were completed on the same day in a random order following an assessment of resting metabolic rate (RMR). A portable gas analyzer was used to measure oxygen uptake, and data were converted to units of energy expenditure (METs). Results: Average of standardized MET values for screen-based sitting tasks were: 1.33 (SD: 0.24) METS (TV), 1.41 (SD: 0.28) (PSP), and 1.45 (SD: 0.32) (Typing). The more active, yet still seated, games on the Wii yielded an average of 2.06 (SD: 0.5) METS. Standing still yielded an average of 1.59 (SD: 0.37) METs. Walking MET values increased incrementally with speed from 2.17 to 2.99 (SD: 0.5 - 0.69) METs. Conclusions: The suggested 1.5 MET threshold for sedentary behaviors seems reasonable however some sitting based activities may be classified as non-sedentary. The effect of this on the definition of sedentary behavior and associations with metabolic health needs further investigation

How different data sources and definitions of neighbourhood influence the association between food outlet availability and body mass index: a cross-sectional study.

Inconsistencies in methodologies continue to inhibit understanding of the impact of the environment on body mass index (BMI). To estimate the effect of these differences, we assessed the impact of using different definitions of neighbourhood and data sets on associations between food outlet availability within the environment and BMI. Previous research has not extended this to show any differences in the strength of associations between food outlet availability and BMI across both different definitions of neighbourhood and data sets. Descriptive statistics showed differences in the number of food outlets, particularly other food retail outlets between different data sets and definitions of neighbourhood. Despite these differences, our key finding was that across both different definitions of neighbourhood and data sets, there was very little difference in size of associations between food outlets and BMI. Researchers should consider and transparently report the impact of methodological choices such as the definition of neighbourhood and acknowledge any differences in associations between the food environment and BMI

Examining the validity and utility of two secondary sources of food environment data against street audits in England

Background: Secondary data containing the locations of food outlets is increasingly used in nutrition and obesity research and policy. However, evidence evaluating these data is limited. This study validates two sources of secondary food environment data: Ordnance Survey Points of Interest data (POI) and food hygiene data from the Food Standards Agency (FSA), against street audits in England and appraises the utility of these data. Methods: Audits were conducted across 52 Lower Super Output Areas in England. All streets within each Lower Super Output Area were covered to identify the name and street address of all food outlets therein. Audit-identified outlets were matched to outlets in the POI and FSA data to identify true positives (TP: outlets in both the audits and the POI/FSA data), false positives (FP: outlets in the POI/FSA data only) and false negatives (FN: outlets in the audits only). Agreement was assessed using positive predictive values (PPV: TP/(TP+FP)) and sensitivities (TP/(TP+FN)). Variations in sensitivities and PPVs across environment and outlet types were assessed using multi-level logistic regression. Proprietary classifications within the POI data were additionally used to classify outlets, and agreement between audit-derived and POI-derived classifications was assessed. Results: Street audits identified 1172 outlets, compared to 1100 and 1082 for POI and FSA respectively. PPVs were statistically significantly higher for FSA (0.91, CI: 0.89-0.93) than for POI (0.86, CI: 0.84-0.88). However, sensitivity values were not different between the two datasets. Sensitivity and PPVs varied across outlet types for both datasets. Without accounting for this, POI had statistically significantly better PPVs in rural and affluent areas. After accounting for variability across outlet types, FSA had statistically significantly better sensitivity in rural areas and worse sensitivity in rural middle affluence areas (relative to deprived). Audit-derived and POI-derived classifications exhibited substantial agreement (p < 0.001; Kappa = 0.66, CI: 0.63 - 0.70). Conclusions: POI and FSA data have good agreement with street audits; although both datasets had geographic biases which may need to be accounted for in analyses. Use of POI proprietary classifications is an accurate method for classifying outlets, providing time savings compared to manual classification of outlets

Childhood body size and pubertal timing in relation to adult mammographic density phenotype

Background: An earlier age at onset of breast development and longer time between pubertal stages has been implicated in breast cancer risk. It is not clear whether associations of breast cancer risk with puberty or predictors of onset of puberty, such as weight and height, are mediated via mammographic density, an important risk factor for breast cancer. Methods: We investigated whether childhood body size and pubertal timing and tempo, collected by questionnaire, are associated with percentage and absolute area mammographic density at ages 47-73 years in 1105 women recruited to a prospective study. Results: After controlling for adult adiposity, weight at ages 7 and 11 years was strongly significantly inversely associated with percentage and absolute dense area (p trend < 0.001), and positively associated with absolute nondense area. Greater height at age 7, but not age 11, was associated with lower percentage density (p trend = 0.016). Later age at menarche and age at when regular periods were established was associated with increased density, but additional adjustment for childhood weight attenuated the association. A longer interval between thelarche and menarche, and between thelarche and regular periods, was associated with increased dense area, even after adjusting for childhood weight (p trend = 0.013 and 0.028, respectively), and was independent of age at pubertal onset. Conclusions: Greater prepubertal weight and earlier pubertal onset are associated with lower adult breast density, but age at pubertal onset does not appear to have an independent effect on adult density after controlling for childhood adiposity. A possible effect of pubertal tempo on density needs further investigation

Adverse responses and physical activity: secondary analysis of the PREPARE trial

Physical activity has been hypothesized to cause adverse metabolic responses in a minority of participants. We use secondary analysis of a randomized controlled trial to investigate rates of adverse metabolic responses in a population at high risk of type 2 diabetes. Methods: We investigated data from the PREPARE trial; individuals with impaired glucose tolerance were randomized to the following: control (advice leaflet); intervention 1, a 3-h group-based structured education program aimed at promoting physical activity; or intervention 2, a 3-h structured education program with personalized pedometer use. Intervention 2, but not intervention 1, resulted in increased physical activity at 3, 6, and 12 months. An adverse response was defined as a change of ≥0.8 mmolIL-1 for fasting glucose, ≥1.3 mmolIL -1 for 2-h glucose, ≥0.42 mmolIL-1 for triglycerides, and j0.12 mmolIL-1 or less for HDL-cholesterol. Each group included 29 participants. Data were collected between 2006 and 2008 and analyzed in 2013. Results: In total, 12 (41%) participants in intervention 2 had an adverse response; rates in intervention 1 and the control group were 23 (79%) and 22 (76%), respectively. The odds of an adverse response were reduced in intervention 2 compared with control (OR, 0.22; 95% CI, 0.07-0.69). For the combined cohort, those who had increased physical activity at each time point had reduced odds of an adverse response compared with those who did not (OR, 0.30; 95% CI, 0.10-0.93). Conclusion: Although some individuals experienced an adverse metabolic response after a successful physical activity intervention, rates were higher under control conditions. This study does not support the hypothesis that increased physical activity per se increases the risk of an adverse metabolic response. Copyright © 2014 by the American College of Sports Medicine