You can’t do this over six weeks or six months. “You can’t easily give somebody an EEG to take home and have next to the bed. “There has been practically no possibility of getting detailed sleep structures in a normal life setting over a long period of time,” says Till Roenneberg of LMU Munich in Germany. According to the researchers, the findings represent a major breakthrough in sleep research because, for the first time, it will now be possible to objectively capture the real-life sleep habits and sleep quality of large numbers of people. But now researchers reporting in Current Biology on December 28 have found a way to capture detailed information on human sleep cycles over long periods of time while individuals slumber at home in their usual way. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.To measure a person’s sleep, researchers have always relied on costly and time-consuming approaches that could only be used in a sleep lab. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society. With its good performance across daytime and night-time, it enables analyses of sleep-wake patterns in long recordings performed to assess circadian and sleep regularity and is therefore an excellent objective alternative to sleep logs in field settings. These results demonstrate the validity of the Munich Actimetry Sleep Detection Algorithm in faithfully estimating sleep-wake patterns in field studies. The Munich Actimetry Sleep Detection Algorithm overestimated sleep onset (~21 min) and underestimated wake after sleep onset (~26 min), while not performing systematically differently from polysomnography in other sleep parameters. Compared with polysomnography, the Munich Actimetry Sleep Detection Algorithm reached a median sensitivity of 92% (85%-100%) but low specificity of 33% (10%-98%), owing to the low frequency of wake episodes in the night-time polysomnographic recordings.

Mean onset and offset times were highly correlated (r =.

Compared with sleep logs, the Munich Actimetry Sleep Detection Algorithm classified sleep with a median sensitivity of 80% (interquartile range = 75%-86%) and specificity of 91% (87%-92%). Epoch-by-epoch analyses were conducted and comparisons of sleep measures carried out via Bland-Altman plots and correlations. Polysomnographic validation was performed on a clinical sample of 23 individuals undergoing one night of polysomnography. Sleep-log validation was performed on two field samples collected over 54 and 34 days (median) in 34 adolescents and 28 young adults. The Munich Actimetry Sleep Detection Algorithm was validated against sleep logs and polysomnography. The Munich Actimetry Sleep Detection Algorithm uses a moving 24-h threshold and correlation procedure estimating relatively consolidated periods of sleep and wake. Here, we evaluated the performance of our Munich Actimetry Sleep Detection Algorithm. Periods of sleep and wakefulness can be estimated from wrist-locomotor activity recordings via algorithms that identify periods of relative activity and inactivity. Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep-wake patterns from activity recordings

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Actimeter sleep