Promoting physical activity in overweight couples: Effectiveness of a dyadic action control trial (DYACTIC)

Background: Health behavior change models identify effective self-regulatory skills for behavioral change, but the social context is usually neglected. This study investigated the effectiveness of a dyadic conceptualization of action control for promoting physical activity. Methods: 121 overweight individuals and their partners were randomly allocated to one of two experimental (dyadic vs. individual action control) and two control conditions. Participants completed questionnaires at baseline (T1) and four weeks later (T2) including measures of action control and 7-day recall physical activity. Findings: Results showed that action control signi+cantly increased from T1 to T2 and was overall higher in the experimental conditions compared to control conditions. In terms of physical activity, no overall intervention effect emerged. However, post hoc analyses revealed higher mean levels of sport activities in the dyadic intervention group compared to all other groups. Discussion: Overall, +ndings provide +rst support for the usefulness of a dyadic action control intervention, and suggest further investigation of objective measures of physical activity and secondary outcomes

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.