Using a randomized controlled trial and a real-life stressor, we could show that exercise appears to be a useful preventive strategy to buffer the effects of stress on the autonomic nervous system, which might result into detrimental health outcomes.

There are various applications of physical activity monitoring for medical purposes, such as therapeutic rehabilitation, fitness enhancement or the use of physical activity as context information for evaluation of other vital data. Physical activity can be estimated using acceleration sensor-systems fixed on a person's body. By means of pattern recognition methods, it is possible to identify with certain accuracy which movement is being performed. This work presents a comparison of different methods for recognition of daily-life activities, which will serve as basis for the development of an online activity monitoring system.

No abstract

Empirical evidence shows that physical behavior positively impacts human health. Recently, researchers have started to differentiate between physical activity and sedentary behavior showing independent effects on somatic health. However, whether this differentiation is also relevant for mood dimensions is largely unknown. For investigating the dynamic relationships between sedentary behavior and mood dimensions in daily life, ambulatory assessment (AA) has become the state‐of‐the‐art methodology. To investigate whether sedentary behaviors influence mood dimensions, we conducted an AA study in the everyday life of 92 university employees over 5 days. We continuously measured sedentary behavior via accelerometers and assessed mood repeatedly 10 times each day on smartphone diaries. To optimize our sampling strategy, we used a sophisticated sedentary‐triggered algorithm. We employed multilevel modeling to analyze the within‐subject effects of sedentary behavior on mood. Sedentary time (15‐minute intervals prior to each e‐diary assessment) and sedentary bouts (30‐minute intervals of uninterrupted sedentary behavior) negatively influenced valence and energetic arousal (all Ps < 0.015). In particular, the more participants were sedentary in their everyday life, the less they felt well and energized. Exploratory analyses of the temporal course of these effects supported our findings. Sedentary behavior can be seen as a general risk factor because it impacts both somatic and mental health. Most importantly, physical activity and sedentary behavior showed independent effects on mood dimensions. Accordingly, future studies should consider the two sides of the physical behavior coin: How should physical activity be promoted? and How can sedentary behavior be reduced?

Reliable signals are the basic prerequisite for most mobile ECG monitoring applications. Especially when signals are analyzed automatically, capable motion artifact detection algorithms are of great importance. This article presents different artifact detection algorithms for ECG systems with dry electrodes. The algorithms are based on the measurement of additional parameters that are correlated with the artifacts. We describe a mobile measurement system and the procedure used for the evaluation of these algorithms. The algorithms are assessed based upon their effect on QRS detection. The best algorithm improved sensitivity (Se) from 98.7% to 99.8% and positive predictive value (+P) from 98.3% to 99.9%, while 15% of the signal was marked as artifact. This corresponds to a decrease in false positive and false negative detected beats by 89.9%. Different metrics to evaluate the performance of an artifact detection algorithm are presented.