The purpose of the present study was to evaluate the performance of a low-cost commercial smartwatch, the Xiaomi Mi Band (MB), in extracting physical activity and sleep-related measures and show its potential use in addressing questions that require large-scale real-time data and/or intercultural data including low-income countries. We evaluated physical activity and sleep-related measures and discussed the potential application of such devices for large-scale step and sleep data acquisition. To that end, we conducted two separate studies. In Study 1, we evaluated the performance of MB by comparing it to the GT3X (ActiGraph, wGT3X-BT), a scientific actigraph used in research, as well as subjective sleep reports. In Study 2, we distributed the MB across four countries (Austria, Germany, Cuba, and Ukraine) and investigated physical activity and sleep among these countries. The results of Study 1 indicated that MB step counts correlated highly with the scientific GT3X device, but did display biases. In addition, the MB-derived wake-up and total-sleep-times showed high agreement with subjective reports, but partly deviated from GT3X predictions. Study 2 revealed similar MB step counts across countries, but significant later wake-up and bedtimes for Ukraine than the other countries. We hope that our studies will stimulate future large-scale sensor-based physical activity and sleep research studies, including various cultures.

The COVID-19 pandemic and lockdowns worldwide forced children and adolescents to change and adapt their lives to an unprecedented situation. Using an online survey, we investigated whether they showed changes in sleep quality and other related factors due to this event. Between February 21st, 2021 and April 19th, 2021, a total of 2,290 Austrian children and adolescents (6–18 years) reported their sleep habits and quality of sleep as well as physical activity, daylight exposure and usage of media devices during and, retrospectively, before the pandemic. Results showed an overall delay of sleep and wake times. Almost twice as many respondents reported having sleeping problems during the pandemic as compared to before, with insomnia, nightmares and daytime sleepiness being the most prevalent problems. Furthermore, sleeping problems and poor quality of sleep correlated positively with COVID-19 related anxiety. Lastly, results showed a change from regular to irregular bedtimes during COVID-19, higher napping rates, a strong to very strong decrease in physical activity and daylight exposure, as well as a high to very high increase in media consumption. We conclude that the increase in sleeping problems in children and adolescent during COVID-19 is concerning. Thus, health promoting measures and programs should be implemented and enforced.

Objective:The aim was to assess the psychosocial burden, risk-perception and attitudes regarding the coronavirus pandemic among the Austrian population after the second infection wave in Austria.MethodsA self-designed questionnaire was available online from 17th January to 19th February 2021. Knowledge, attitudes, fears, and psychosocial burdens were collected in a comprehensive convenience sample of 3,848 adults from the Austrian general population.Results67.2% reported their greatest fear was that a close relative could be infected; the fear of dying from COVID-19 oneself, however, was mentioned least frequently (15.2%). Isolation from family and friends (78%), homeschooling for parents (68.4%), and economic consequences (67.7%) were perceived as most stressful factors during the pandemic. Personal risk for COVID-19-associated (ICU) hospitalization was overestimated 3- to 97-fold depending on age group. Depending on the media mainly consumed, the sample could be divided into two subsamples whose estimates were remarkably opposite to each other, with regular public media users overestimating hospitalization risk substantially more.ConclusionThe results show a high degree of psychosocial burden in the Austrian population and emphasize the need for more objective risk communication in order to counteract individually perceived risk and consequently anxiety. Altogether data call for a stronger focus and immediate action for supporting mental well-being and general health in the aftermath of the coronavirus pandemic.

ObjectivesThe negative psychosocial effects of the COVID-19 pandemic are becoming increasingly apparent. Children and adolescents in particular, were affected and torn away from their daily life routines. The aim of our survey is to evaluate the psychosocial burden and impairments of children and adolescents in Austria during the COVID-19 pandemic by using cross-sectional analysis.SettingAn Austrian-wide online survey was conducted from 21 February to 19 April 2021 for children and adolescents. The questionnaire was distributed widely using the national press agency and public media.ParticipantsUsing an online questionnaire, 5,483 children and adolescents between 6 and 18 years of age were sampled.Outcome measureQuantitative responses to questions regarding the children’s feelings, worries, and needs concerning the COVID-19 pandemic were measured. Furthermore, the children were sampled for subjective risk perception as well as their sleep quality.ResultsMost children reported a high degree of fear due to the pandemic, especially female (48.1%) participants being under more emotional strain than their male (35.9%) counterparts. Associated with this, we found a strong overestimation of COVID-19-associated hospitalization likelihood (>100-fold) across all age groups. In addition, an alarming lack of positive perspective during the ongoing pandemic is evident across all age groups, including the youngest participants aged 6–10 years. Feelings of anger and annoyance (58.2%), loneliness (46%), and sadness (42.7%) are reported much more frequently than before the pandemic. On the other hand, only 15.6% reported feeling well (or even better; 2%) since the COVID-19 pandemic. Last but not least, our study shows an alarming 37% of children and adolescents who now report poorer sleep quality than before the pandemic.ConclusionThe results of this survey indicate the high burden and emotional strain for children and adolescents during the pandemic. Personal contact with friends and family is mentioned as the most protective factor for their mental health. The study results underscore the need for immediate action to limit the collateral damage that has already occurred on a psychosocial and developmental level among younger generations worldwide.

Sleep staging based on polysomnography (PSG) performed by human experts is the de facto “gold standard” for the objective measurement of sleep. PSG and manual sleep staging is, however, personnel-intensive and time-consuming and it is thus impractical to monitor a person’s sleep architecture over extended periods. Here, we present a novel, low-cost, automatized, deep learning alternative to PSG sleep staging that provides a reliable epoch-by-epoch four-class sleep staging approach (Wake, Light [N1 + N2], Deep, REM) based solely on inter-beat-interval (IBI) data. Having trained a multi-resolution convolutional neural network (MCNN) on the IBIs of 8898 full-night manually sleep-staged recordings, we tested the MCNN on sleep classification using the IBIs of two low-cost (<EUR 100) consumer wearables: an optical heart rate sensor (VS) and a breast belt (H10), both produced by POLAR®. The overall classification accuracy reached levels comparable to expert inter-rater reliability for both devices (VS: 81%, κ = 0.69; H10: 80.3%, κ = 0.69). In addition, we used the H10 and recorded daily ECG data from 49 participants with sleep complaints over the course of a digital CBT-I-based sleep training program implemented in the App NUKKUAA™. As proof of principle, we classified the IBIs extracted from H10 using the MCNN over the course of the training program and captured sleep-related changes. At the end of the program, participants reported significant improvements in subjective sleep quality and sleep onset latency. Similarly, objective sleep onset latency showed a trend toward improvement. Weekly sleep onset latency, wake time during sleep, and total sleep time also correlated significantly with the subjective reports. The combination of state-of-the-art machine learning with suitable wearables allows continuous and accurate monitoring of sleep in naturalistic settings with profound implications for answering basic and clinical research questions.