Combining accelerometry (ACC) with heart rate (HR) monitoring is thought to improve activity energy expenditure (AEE) estimations compared with ACC alone to evaluate the validity of ACC and HR used alone or combined. The purpose of this study was to estimate AEE in free-living conditions compared with doubly labeled water (DLW). Ten-day free-living AEE was measured by a DLW protocol in 35 18- to 55-yr-old men (11 lean active; 12 lean sedentary; 12 overweight sedentary) wearing an Actiheart (combining ACC and HR) and a RT3 accelerometer. AEE was estimated using group or individual calibration of the HR/AEE relationship, based on an exercise-tolerance test. In a subset (n = 21), AEE changes (ΔAEE) were measured after 1 mo of detraining (active subjects) or an 8-wk training (sedentary subjects). Actiheart-combined ACC/HR estimates were more accurate than estimates from HR or ACC alone. Accuracy of the Actiheart group-calibrated ACC/HR estimates was modest [intraclass correlation coefficient (ICC) = 0.62], with no bias but high root mean square error (RMSE) and limits of agreement (LOA). The mean bias of the estimates was reduced by one-third, like RMSE and LOA, by individual calibration (ICC = 0.81). Contrasting with group-calibrated estimates, the Actiheart individual-calibrated ACC/HR estimates explained 40% of the variance of the DLW-ΔAEE (ICC = 0.63). This study supports a good level of agreement between the Actiheart ACC/HR estimates and DLW-measured AEE in lean and overweight men with varying fitness levels. Individual calibration of the HR/AEE relationship is necessary for AEE estimations at an individual level rather than at group scale and for ΔAEE evaluation.

"Objective" methods to monitor physical activity and sedentary patterns in free-living conditions are necessary to further our understanding of their impacts on health. In recent years, many software solutions capable of automatically identifying activity types from portable accelerometry data have been developed, with promising results in controlled conditions, but virtually no reports on field tests. An automatic classification algorithm initially developed using laboratory-acquired data (59 subjects engaging in a set of 24 standardized activities) to discriminate between 8 activity classes (lying, slouching, sitting, standing, walking, running, and cycling) was applied to data collected in the field. Twenty volunteers equipped with a hip-worn triaxial accelerometer performed at their own pace an activity set that included, among others, activities such as walking the streets, running, cycling, and taking the bus. Performances of the laboratory-calibrated classification algorithm were compared with those of an alternative version of the same model including field-collected data in the learning set. Despite good results in laboratory conditions, the performances of the laboratory-calibrated algorithm (assessed by confusion matrices) decreased for several activities when applied to free-living data. Recalibrating the algorithm with data closer to real-life conditions and from an independent group of subjects proved useful, especially for the detection of sedentary behaviors while in transports, thereby improving the detection of overall sitting (sensitivity: laboratory model = 24.9%; recalibrated model = 95.7%). Automatic identification methods should be developed using data acquired in free-living conditions rather than data from standardized laboratory activity sets only, and their limits carefully tested before they are used in field studies.

BackgroundAccording to the social ecological model of health-related behaviors, it is now well accepted that environmental factors influence habitual physical activity. Most previous studies on physical activity determinants have assumed spatial homogeneity across the study area, i.e. that the association between the environment and physical activity is the same whatever the location. The main novelty of our study was to explore geographical variation in the relationships between active commuting (walking and cycling to/from work) and residential environmental characteristics.Methods4,164 adults from the ongoing Nutrinet-Santé web-cohort, residing in and around Paris, France, were studied using a geographically weighted Poisson regression (GWPR) model. Objective environmental variables, including both the built and the socio-economic characteristics around the place of residence of individuals, were assessed by GIS-based measures. Perceived environmental factors (index including safety, aesthetics, and pollution) were reported by questionnaires.ResultsOur results show that the influence of the overall neighborhood environment appeared to be more pronounced in the suburban southern part of the study area (Val-de-Marne) compared to Paris inner city, whereas more complex patterns were found elsewhere. Active commuting was positively associated with the built environment only in the southern and northeastern parts of the study area, whereas positive associations with the socio-economic environment were found only in some specific locations in the southern and northern parts of the study area. Similar local variations were observed for the perceived environmental variables.ConclusionsThese results suggest that: (i) when applied to active commuting, the social ecological conceptual framework should be locally nuanced, and (ii) local rather than global targeting of public health policies might be more efficient in promoting active commuting.

Background:Obesity in youth remains a major public health issue. Yet no effective long-term preventive strategy exists. We previously showed that a school-based socio-ecological approach targeting behavior and social/environmental influences on physical activity (PA) prevented 4-year excessive weight gain in 12-year olds. In this study, we investigated if this efficacy persists 30 months after intervention cessation.Methods and Findings:The program targeted students, family, school and the living environment to promote/support PA and prevent sedentary behavior (SB). A total of 732 students from eight randomized middle schools completed the 4-year trial. At the 30-month post-trial follow-up, body mass index (BMI), fat mass index (FMI), leisure PA (LPA), home/school/workplace active commuting, TV/video time (TVT), and attitudes toward PA were measured in 531 adolescents. The beneficial effects of the intervention on the excess BMI increase (+0.01 vs +0.34 kg m−2 in the intervention and control groups, respectively) and on the overweight incidence in initially non-overweight students (4.3% vs 8.6% odds ratio=0.48 (95% confidence interval: 0.23–1.01)) were maintained at the post-trial follow-up. LPA was not maintained at the level achieved during the trial. However, we still observed a prevention of the age-related decrease of the adolescents' percentage reporting regular LPA (−14.4% vs −26.5%) and a higher intention to exercise in the intervention group. The intervention promoted lower TVT (−14.0 vs +13.6 min per day) and higher active commuting changes (+11.7% vs −4.8%). Trends in higher BMI reduction in students with high initial TVT and in the least wealthy group were noted. TVT changes throughout the follow-up predicted excess BMI and FMI changes.Conclusions:Long-term multilevel approach targeting PA and SB prevents excessive weight gain up to 30 months after intervention cessation. The efficacy may be higher in the most sedentary and least wealthy adolescents. Healthy PA-related behavior inducing long-lasting weight effects can be promoted in youth providing that an ecological approach is introduced in the prevention strategy.

BackgroundComprehensive assessment of sedentary behavior (SB) and physical activity (PA), including transport-related activities (TRA), is required to design innovative PA promotion strategies. There are few validated instruments that simultaneously assess the different components of human movement according to their context of practice (e.g. work, transport, leisure). We examined test-retest reliability and validity of the Sedentary, Transportation and Activity Questionnaire (STAQ), a newly developed questionnaire dedicated to assessing context-specific SB, TRA and PA.MethodsNinety six subjects (51 women) kept a contextualized activity-logbook and wore a hip accelerometer (Actigraph GT3X + TM) for a 7-day or 14-day period, at the end of which they completed the STAQ. Activity-energy expenditure was measured in a subgroup of 45 subjects using the double labeled water (DLW) method. Test-retest reliability was assessed using intra-class-coefficients (ICC) in a subgroup of 32 subjects who filled the questionnaire twice one month apart. Accelerometry was annotated using the logbook to obtain total and context-specific objective estimates of SB. Spearman correlations, Bland-Altman plots and ICC were used to analyze validity with logbook, accelerometry and DLW data validity criteria.ResultsTest-retest reliability was fair for total sitting time (ICC = 0.52), good to excellent for work sitting time (ICC = 0.71), transport-related walking (ICC = 0.61) and car use (ICC = 0.67), and leisure screen-related SB (ICC = 0.64-0.79), but poor for total sitting time during leisure and transport-related contexts. For validity, compared to accelerometry, significant correlations were found for STAQ estimates of total (r = 0.54) and context-specific sitting times with stronger correlations for work sitting time (r = 0.88), and screen times (TV/DVD viewing: r = 0.46; other screens: r = 0.42) than for transport (r = 0.35) or leisure-related sitting-times (r = 0.19). Compared to contextualized logbook, STAQ estimates of TRA was higher for car (r = 0.65) than for active transport (r = 0.41). The questionnaire generally overestimated work- and leisure-related SB and sitting times, while it underestimated total and transport-related sitting times.ConclusionsThe STAQ showed acceptable reliability and a good ranking validity for assessment of context-specific SB and TRA. This instrument appears as a useful tool to study SB, TRA and PA in context in adults.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-016-3412-3) contains supplementary material, which is available to authorized users.

BackgroundTobacco smoking, alcohol and obesity are important risk factors for a number of non-communicable diseases. The prevalence of these risk factors differ by socioeconomic group in most populations, but this socially stratified distribution may depend on the social and cultural context. Little information on this topic is currently available in the Caribbean. The aim of this study was to describe the distribution of tobacco smoking, alcohol drinking and obesity by several socioeconomic determinants in the French West Indies (FWI).MethodsWe used data from a cross-sectional health survey conducted in Guadeloupe and Martinique in 2014 in a representative sample of the population aged 15–75 years (n = 4054). All analyses were stratified by gender, and encompassed sample weights, calculated to account for the sampling design and correct for non-response. For each risk factor, we calculated weighted prevalence by income, educational level, occupational class and having hot water at home. Poisson regression models were used to estimate age-adjusted prevalence ratios (PR) and 95% confidence intervals (CI).ResultsCurrent smoking and harmful chronic alcohol use were more common in men than in women (PR = 1.80, 95% CI = 1.55–2.09; PR = 4.53, 95% CI = 3.38–6.09 respectively). On the other hand, the prevalence of obesity was higher in women than in men (PR = 0.67, 95% CI = 0.57–0.79). Higher education, higher occupational class and higher income were associated with lower prevalence of harmful alcohol drinking in men (PR = 0.43, 95% CI = 0.25–0.72; PR = 0.73, 95% CI = 0.53–1.01; PR = 0.72, 95% CI = 0.51–1.03 respectively), but not in women. For tobacco smoking, no variation by socioeconomic status was observed in men whereas the prevalence of current smoking was higher among women with higher occupational class (PR = 1.47, 95% CI = 1.13–1.91) and higher income (PR = 1.50, 95% CI = 1.11–2.03). In women, a lower prevalence of obesity was associated with a higher income (PR = 0.43, 95% CI = 0.33–0.56), a higher occupational class (PR = 0.63, 95% CI = 0.50–0.80), a higher educational level (PR = 0.36, 95% CI = 0.26–0.50) and having hot water at home (PR = 0.65, 95% CI = 0.54–0.80).ConclusionWomen of high socio-economic status were significantly more likely to be smokers, whereas alcohol drinking in men and obesity in women were inversely associated with socioeconomic status.

Physical activity (PA) and the energy expenditure it generates (PAEE) are increasingly shown to have impacts on everybody's health (e.g. development of chronic diseases) and to be key factors in maintaining the physical autonomy of elderlies. The SVELTE project objective was to develop an autonomous actimeter, easily wearable and with several days of autonomy, which could record a subject's physical activity during his/her daily life and estimate the associated energy expenditure. A few prototypes and dedicated algorithms were developed based on laboratory experiments. The identification of physical activity patterns algorithm shows good performances (79% of correct identification), based on a trial in semi-free-living conditions. The assessment of the PAEE computation algorithm is under validation based on a clinical trial.

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