A New Approach for Assessing Sleep Duration and Postures from Ambulatory Accelerometry

Interest in the effects of sleeping behavior on health and performance is continuously increasing-both in research and with the general public. Ecologically valid investigations of this research topic necessitate the measurement of sleep within people's natural living contexts. We present evidence that a new approach for ambulatory accelerometry data offers a convenient, reliable, and valid measurement of both people's sleeping duration and quality in their natural environment. Ninety-two participants (14-83 years) wore acceleration sensors on the sternum and right thigh while spending the night in their natural environment and following their normal routine. Physical activity, body posture, and change in body posture during the night were classified using a newly developed classification algorithm based on angular changes of body axes. The duration of supine posture and objective indicators of sleep quality showed convergent validity with self-reports of sleep duration and quality as well as external validity regarding expected age differences. The algorithms for classifying sleep postures and posture changes very reliably distinguished postures with 99.7% accuracy. We conclude that the new algorithm based on body posture classification using ambulatory accelerometry data offers a feasible and ecologically valid approach to monitor sleeping behavior in sizable and heterogeneous samples at home.