LAUSR

Multidimensional data, a great number of metrics can be derived to capture and describe the unique aspects of PB. The goal of this paper is to help the end-user of PB monitoring devices (novice to intermediate experience) wade through sometimes excessive technical details of accelerometry to outline best practices in selecting and applying devices to quantify three major behavioral categories of common interest to the research community: physical activity (PA), sedentary behavior (SB) and sleep. The effects of these decisions on the metrics (energy expenditure, activity intensity, body position, activity patterns) can occur in a variety of ways. The device, carrying position (hip, wrist, thigh) and recording parameters (epoch length (EL), frequency, memory capacity, recording frequency and filters) have a large influence on the measured activity. The different backgrounds such as study design (purpose, repeated measurements) and duration (time frame, wear time) as well as data storage and evaluation must be taken into account when determining the parameters. Finally, the evaluation must adjust several levers (raw data, context information, non-wear time, intensity classification, compliance) depending on the target variables. Looking into the future, current developments in statistical analysis are discussed, because the research community has not yet reached a consensus on the most promising approach. There are exciting developments ahead of us in the future. Sleep in particular is increasingly being seen as an influencing factor for health. Together with the technical developments in sensors which will become incrementally smaller, more accurate and in the near future will be integrated directly into our clothes or skin, accelerometry is facing exciting times and lots of data to evaluate.