Motionlogger watchware

In the present study, we used the actigraph Micro Motionlogger Watch (Ambulatory Monitoring, Inc., Ardsley, NY, USA). The hardware consists of a piezoelectric accelerometer with a sensitivity ≥ 0.01 g. The sampling frequency was 10 Hz while filters were set to 2–3 Hz. We initialized the actigraphs, through the Motionlogger Watchware software (Ambulatory Monitoring, Inc., Ardsley, NY, USA), to collect data, in zero crossing mode, in 1-min epochs. The primary actigraphic output, i.e., motor activity counts, can be converted into a dichotomous variable, i.e., sleep/wakefulness, according to the algorithm, previously validated against polysomnography, by Cole and colleagues [22 ,23 (link)].

The actigraph model Micro Motionlogger Watch (Ambulatory Monitoring, Inc., Ardsley, NY, USA) was used. The hardware was composed of a triaxal accelerometer, with a sensitivity of 0.01 g, filters at 2–3 Hz, and a sampling frequency of 32 Hz. The actigraphs were initialized in zero crossing mode through the Motionlogger Watchware software (Ambulatory Monitoring, Inc., Ardsley, NY, USA) in order to acquire motor activity data in 1-min epochs. Motor activity data can also be transformed into dichotomous information about sleep and wake according to validated algorithms [12 ,13 (link)].

The actigraph model Micro Motionlogger Watch (Ambulatory Monitoring, Inc., Ardsley, NY, USA) was used in the current study. The device was equipped with a triaxial accelerometer with a sensitivity of 0.01 g, while the filters and sampling frequency were set at 2–3 Hz and 32 Hz, respectively. Actigraphs were initialized through the Motionlogger Watchware software (Ambulatory Monitoring, Inc., Ardsley, NY, USA) in zero crossing mode to collect data in 1 min epochs, while actigraphic records were analyzed through the software Action W2 (Ambulatory Monitoring, Inc., Ardsley, NY, USA) using the algorithms validated by Cole and Kripke [19 ] and Cole et al. [20 (link)].