Abstract
Wrist worn raw-data accelerometers are used increasingly in large scale population research. We examined whether sleep parameters can be estimated from these data in the absence of sleep diaries, which are common in sleep actigraphy. Our heuristic algorithm uses the variance in estimated z-axis angle and makes basic assumptions about sleep interruptions. Detected sleep period time window (SPT-window), was compared against sleep diary in 3752 participants (range=60-82years) and polysomnography in sleep clinic patients (N=28) and in healthy good sleepers (N=22). The SPT-window derived from the algorithm was 12.9 and 3.2 minutes longer compared with sleep diary in men and women, respectively. Average c-statistic to detect the SPT-window compared to polysomnography was 0.86 and 0.83 in clinic and healthy sleepers, respectively. We demonstrated the accuracy of our algorithm to detect the SPT-window. The value of this algorithm lies in studies such as UK Biobank where a sleep diary was not used.