Background: Understanding environmental correlates of physical activity can inform

The ActiReg w (PreMed AS, Oslo, Norway) system is unique in using combined recordings of body position and motion alone or combined with heart rate (HR) to calculate energy expenditure (EE) and express physical activity (PA). The ActiReg w has two pairs of position and motion sensors connected by cables to a battery-operated storage unit fixed to a waist belt. Each pair of sensors was attached by medical tape to the chest and to the front of the right thigh respectively. The collected data were transferred to a personal computer and processed by a dedicated program ActiCalc w . Calculation models for EE with and without HR are presented. The models were based on literature values for the energy costs of different activities and therefore require no calibration experiments. The ActiReg w system was validated against doubly labelled water (DLW) and indirect calorimetry. The DLW validation demonstrated that neither EE calculated from ActiReg w data alone (EE AR ) nor from combined ActiReg w and HR data (EE AR -HR ) were statistically different from DLW results. The EE AR procedure causes some underestimation of EE . 11 MJ corresponding to a PA level . 2·0. This underestimation is reduced by the EE AR -HR procedure. The objective recording of the time spent in different body positions and at different levels of PA may be useful in studies of PA in different groups and in studies of whether recommendations for PA are being met. The comparative ease of data collection and calculation should make ActiReg w a useful instrument to measure habitual PA level and EE.

BackgroundIncreasing empirical evidence supports associations between neighborhood environments and physical activity. However, since most studies were conducted in a single country, particularly western countries, the generalizability of associations in an international setting is not well understood. The current study examined whether associations between perceived attributes of neighborhood environments and physical activity differed by country.MethodsPopulation representative samples from 11 countries on five continents were surveyed using comparable methodologies and measurement instruments. Neighborhood environment × country interactions were tested in logistic regression models with meeting physical activity recommendations as the outcome, adjusted for demographic characteristics. Country-specific associations were reported.ResultsSignificant neighborhood environment attribute × country interactions implied some differences across countries in the association of each neighborhood attribute with meeting physical activity recommendations. Across the 11 countries, land-use mix and sidewalks had the most consistent associations with physical activity. Access to public transit, bicycle facilities, and low-cost recreation facilities had some associations with physical activity, but with less consistency across countries. There was little evidence supporting the associations of residential density and crime-related safety with physical activity in most countries.ConclusionThere is evidence of generalizability for the associations of land use mix, and presence of sidewalks with physical activity. Associations of other neighborhood characteristics with physical activity tended to differ by country. Future studies should include objective measures of neighborhood environments, compare psychometric properties of reports across countries, and use better specified models to further understand the similarities and differences in associations across countries.

Objective: The validation of dietary assessment methods is critical in the evaluation of the relation between dietary intake and health. The aim of this study was to assess the validity of a food frequency questionnaire by comparing energy intake with energy expenditure measured with the doubly labelled water method. Design: Total energy expenditure was measured with the doubly labelled water (DLW) method during a 10 day period. Furthermore, the subjects filled in the food frequency questionnaire about 18 -35 days after the DLW phase of the study was completed. Subjects: Twenty-one healthy, non-pregnant females volunteered to participate in the study; only 17 subjects completed the study. Results: The group energy intake was on average 10% lower than the energy expenditure, but the difference was not statistically significant. However, there was a wide range in reporting accuracy: seven subjects were identified as acceptable reporters, eight as under-reporters and two were identified as over-reporters. The width of the 95% confidence limits of agreement in a Bland and Altman plot for energy intake and energy expenditure varied from 7 5 to 3 MJ. Conclusion: The data showed that there was substantial variability in the accuracy of the food frequency questionnaire at the individual level. Furthermore, the results showed that the questionnaire was more accurate for groups than individuals.

BackgroundNeighborhood environment studies of physical activity (PA) have been mainly single-country focused. The International Prevalence Study (IPS) presented a rare opportunity to examine neighborhood features across countries. The purpose of this analysis was to: 1) detect international neighborhood typologies based on participants’ response patterns to an environment survey and 2) to estimate associations between neighborhood environment patterns and PA.MethodsA Latent Class Analysis (LCA) was conducted on pooled IPS adults (N=11,541) aged 18 to 64 years old (mean=37.5 ±12.8 yrs; 55.6% women) from 11 countries including Belgium, Brazil, Canada, Colombia, Hong Kong, Japan, Lithuania, New Zealand, Norway, Sweden, and the U.S. This subset used the Physical Activity Neighborhood Environment Survey (PANES) that briefly assessed 7 attributes within 10–15 minutes walk of participants’ residences, including residential density, access to shops/services, recreational facilities, public transit facilities, presence of sidewalks and bike paths, and personal safety. LCA derived meaningful subgroups from participants’ response patterns to PANES items, and participants were assigned to neighborhood types. The validated short-form International Physical Activity Questionnaire (IPAQ) measured likelihood of meeting the 150 minutes/week PA guideline. To validate derived classes, meeting the guideline either by walking or total PA was regressed on neighborhood types using a weighted generalized linear regression model, adjusting for gender, age and country.ResultsA 5-subgroup solution fitted the dataset and was interpretable. Neighborhood types were labeled, “Overall Activity Supportive (52% of sample)”, “High Walkable and Unsafe with Few Recreation Facilities (16%)”, “Safe with Active Transport Facilities (12%)”, “Transit and Shops Dense with Few Amenities (15%)”, and “Safe but Activity Unsupportive (5%)”. Country representation differed by type (e.g., U.S. disproportionally represented “Safe but Activity Unsupportive”). Compared to the Safe but Activity Unsupportive, two types showed greater odds of meeting PA guideline for walking outcome (High Walkable and Unsafe with Few Recreation Facilities, OR= 2.26 (95% CI 1.18-4.31); Overall Activity Supportive, OR= 1.90 (95% CI 1.13-3.21). Significant but smaller odds ratios were also found for total PA.ConclusionsMeaningful neighborhood patterns generalized across countries and explained practical differences in PA. These observational results support WHO/UN recommendations for programs and policies targeted to improve features of the neighborhood environment for PA.