Graphical abstract

Commercial sleep devices and mobile-phone applications for scoring sleep are gaining ground. In order to provide reliable information about the quantity and/or quality of sleep, their performance needs to be assessed against the current gold standard, i.e., polysomnography (PSG; measuring brain, eye, and muscle activity). Here, we assessed some commercially available sleep trackers, namely an activity tracker; Mi band (Xiaomi, Beijing, China), a scientific actigraph: Motionwatch 8 (CamNTech, Cambridge, UK), and a much-used mobile phone application: Sleep Cycle (Northcube, Gothenburg, Sweden). We recorded 27 nights in healthy sleepers using PSG and these devices and compared the results. Surprisingly, all devices had poor agreement with the PSG gold standard. Sleep parameter comparisons revealed that, specifically, the Mi band and the Sleep Cycle application had difficulties in detecting wake periods which negatively affected their total sleep time and sleep-efficiency estimations. However, all 3 devices were good in detecting the most basic parameter, the actual time in bed. In summary, our results suggest that, to date, the available sleep trackers do not provide meaningful sleep analysis but may be interesting for simply tracking time in bed. A much closer interaction with the scientific field seems necessary if reliable information shall be derived from such devices in the future.

Commercial sleep devices and mobile-phone applications for scoring sleep are gaining ground. In order to provide reliable information about the quantity and/or quality of sleep, their performance needs to be assessed against the current gold-standard, i.e. polysomnography (PSG; measuring brain, eye and muscle activity). We here assessed some commercially available sleep trackers, namely; a commercial activity tracker: Mi band (Xiaomi, BJ, CHN), a scientific actigraph: Motionwatch 8 (CamNTech, CB, UK), and a much used sleep application: Sleep Cycle (Northcube, GOT, SE). We recorded 27 nights in healthy sleepers using PSG and these devices. Surprisingly, all devices had very poor agreement with the gold standard. Sleep parameter comparisons revealed that specifically the Mi band and the sleep cycle application had difficulties in detecting wake periods which negatively affected the total sleep time and sleep efficiency estimations. However, all 3 devices were good in detecting the most basic parameter, the actual time in bed. In summary, our results suggest that, to-date; available sleep trackers do not provide meaningful sleep analysis but may be interesting for simply tracking times in bed. A much closer interaction with the scientific field seems necessary if reliable information shall be derived from such devices in the future.

During the past years, the prevalence of sleep problems has been increasing steadily in industrial societies and represents a major social and socioeconomic burden. The situation in Austria was last evaluated in 2007 by Zeitlhofer and colleagues in a representative sample of 1000 participants. In the current study, we sought to evaluate the sleep behaviour of the Austrian population in an ongoing online survey, in which we have collected data from 986 participants (66% women, mean age 40.9 ± 16.4 years) between March 2018 and May 2019. Sleep duration was appropriate in 52% of the respondents (i.e. 7-9 h per night). However, we found an alarmingly high number of self-reported sleep problems (46%), and only 31% of the participants classified themselves as "good sleepers" using a validated self-report questionnaire (Pittsburgh Sleep Quality Index, PSQI). Furthermore, many participants reported suffering from sleep problems for a very long time (86% > 6 months; 37% > 5 years) suggesting that currently available treatment options are either ineffective or not employed. Possible reasons for sleep problems could include irregular sleep-wake cycles, increased perceived stress levels, and the use of electronic devices just before sleep.

Sleep and memory studies often focus on overnight rather than long‐term memory changes, traditionally associating overnight memory change (OMC) with sleep architecture and sleep patterns such as spindles. In addition, (para‐)sympathetic innervation has been associated with OMC after a daytime nap using heart rate variability (HRV). In this study we investigated overnight and long‐term performance changes for procedural memory and evaluated associations with sleep architecture, spindle activity (SpA) and HRV measures (R‐R interval [RRI], standard deviation of R‐R intervals [SDNN], as well as spectral power for low [LF] and high frequencies [HF]). All participants (N = 20, Mage = 23.40 ± 2.78 years) were trained on a mirror‐tracing task and completed a control (normal vision) and learning (mirrored vision) condition. Performance was evaluated after training (R1), after a full‐night sleep (R2) and 7 days thereafter (R3). Overnight changes (R2‐R1) indicated significantly higher accuracy after sleep, whereas a significant long‐term (R3‐R2) improvement was only observed for tracing speed. Sleep architecture measures were not associated with OMC after correcting for multiple comparisons. However, individual SpA change from the control to the learning night indicated that only “SpA enhancers” exhibited overnight improvements for accuracy and long‐term improvements for speed. HRV analyses revealed that lower SDNN and LF power was associated with better OMC for the procedural speed measure. Altogether, this study indicates that overnight improvement for procedural memory is specific for spindle enhancers, and is associated with HRV during sleep following procedural learning.