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Understanding the effects of built environment on physical activity is important for promoting healthy lifestyles in cities. Yet, very few studies have used objective continuous location data and measures of physical activity while addressing biases to causal inference. In addition, the effect of the environment on body postures during trips, albeit of physiological importance, is rarely addressed. Using mixed models for compositional data on sensor-derived physical activity information, we estimated the effects of greenery, destination density, public transport time efficiency, and average area education among 692 home-work journeys made by 121 healthy adults (80 men, 41 women). Higher levels of greenery, public transport time efficiency, and average area education along the shortest network path between home and workplaces were found to reduce contemporaneous sedentary postures and increase physical activity. These relationships suggest that decision makers should consider greening cities and improving public transport efficiency as an effective way to reduce the prevalence of sedentary behaviors in our societies.

International audience; Here we propose a new machine learning algorithm for classification of human activities by means of accelerometer and gyroscope signals. Based on a novel hierarchical system of logistic regression classifiers and a relatively small set of features extracted from the filtered signals, the proposed algorithm outperformed previous work on the DaLiAc (Daily Life Activity) and mHealth datasets. The algorithm also represents a significant improvement in terms of computational costs and requires no feature selection and hyper-parameter tuning. The algorithm still showed a robust performance with only two (ankle and wrist) out of the four devices (chest, wrist, hip and ankle) placed on the body (96.8% vs. 97.3% mean accuracy for the DaLiAc dataset). The present work shows that low-complexity models can compete with heavy, inefficient models in classification of advanced activities when designed with a careful upstream inspection of the data.

Previous studies about the effects of physical activity and sedentary behaviors on health rarely recorded the exact body postures and movements, although they might be of metabolic relevance. Moreover, few studies treated the time budget of behaviors as compositions and little was done to characterize the distribution of durations of behavior sequences in relation with health. Data from the RECORD (Residential Environment and CORonary heart Disease) study of two combined VitaMove accelerometers worn at the trunk and upper leg for a week by 154 male and female adults (age = 50.6 &plusmn, 9.6 years, BMI = 25.8 &plusmn, 3.9 kg/m2) were analyzed. Using both iso-temporal substitution and compositional analysis, we examined associations between five physical behaviors (lying, sitting, standing, low physical activity, moderate-to-vigorous activity) and seven health outcomes (fasting serum glucose, low- and high-density lipoprotein, and triglycerides levels, body mass index, and waist circumference). After adjustment for confounding variables, total standing time was positively associated with better lipid profile, and lying during the day with adiposity. No significant association was observed between breaking up moderate-to-vigorous physical activity and health. This study highlights the importance of refined categories of postures in research on physical activity and health, as well as the necessity for new tools to characterize the distribution of behavior sequence durations, considering both bouts and micro-sequences.