Search Results (4)

Background/Objectives: Orthosomnia has become a concern in the field of sleep medicine. The purpose of this cross-sectional study was to estimate the prevalence of orthosomnia in the general population. Methods: We collected data from 523 participants via the Generalized Anxiety Disorder Scale, Anxiety and Preoccupation about Sleep Questionnaire, and Athens Insomnia Scale. Additionally, we gathered information about participants’ use of commercial sleep-tracking wearable devices. Results: We developed a four-criteria algorithm to identify cases of orthosomnia: ownership of a wearable sleep-tracking device, AIS score ≥ 6, GAD-7 score ≤ 14, and APSQ score ≥ 40 or APSQ score ≥ 35 or APSQ score ≥ 30, for conservative, moderate, and lenient prevalence estimates, respectively. One hundred seventy-six (35.8%) (95% CI 34.6–40.1%) participants regularly used sleep-tracking devices. The prevalence rates of algorithm-identified orthosomnia in the study sample were: 16 participants (3.0%, 95% CI 1.6–4.5%), 45 participants (8.6%, 95% CI 6.2–11.0%), 73 participants (14.0%, 95% CI 10.9–16.9%) for the for conservative, moderate, and lenient prevalence estimates, respectively. Individuals with orthosomnia were not significantly different in terms of age and sex. The cases consistently had higher AIS scores than non-cases across all APSQ cutoffs, indicating more severe insomnia symptoms, with significant differences observed at each cutoff point. Conclusions: This study offers initial insights into the prevalence of orthosomnia within our sample at a specific time. The findings reveal notable rates of orthosomnia among individuals using sleep-tracking devices; however, we must acknowledge the limitations inherent in a cross-sectional design. Full article

More and more people quantify their sleep using wearables and are becoming obsessed in their pursuit of optimal sleep (“orthosomnia”). However, it is criticized that many of these wearables are giving inaccurate feedback and can even lead to negative daytime consequences. Acknowledging these facts, we here optimize our previously suggested sleep classification procedure in a new sample of 136 self-reported poor sleepers to minimize erroneous classification during ambulatory sleep sensing. Firstly, we introduce an advanced interbeat-interval (IBI) quality control using a random forest method to account for wearable recordings in naturalistic and more noisy settings. We further aim to improve sleep classification by opting for a loss function model instead of the overall epoch-by-epoch accuracy to avoid model biases towards the majority class (i.e., “light sleep”). Using these implementations, we compare the classification performance between the optimized (loss function model) and the accuracy model. We use signals derived from PSG, one-channel ECG, and two consumer wearables: the ECG breast belt Polar® H10 (H10) and the Polar® Verity Sense (VS), an optical Photoplethysmography (PPG) heart-rate sensor. The results reveal a high overall accuracy for the loss function in ECG (86.3 %, $\kappa$ = 0.79), as well as the H10 (84.4%, $\kappa$ = 0.76), and VS (84.2%, $\kappa$ = 0.75) sensors, with improvements in deep sleep and wake. In addition, the new optimized model displays moderate to high correlations and agreement with PSG on primary sleep parameters, while measures of reliability, expressed in intra-class correlations, suggest excellent reliability for most sleep parameters. Finally, it is demonstrated that the new model is still classifying sleep accurately in 4-classes in users taking heart-affecting and/or psychoactive medication, which can be considered a prerequisite in older individuals with or without common disorders. Further improving and validating automatic sleep stage classification algorithms based on signals from affordable wearables may resolve existing scepticism and open the door for such approaches in clinical practice. Full article

Despite sleep health being critically important for athlete performance and well-being, sleep health in marathoners is understudied. This foundational study explored relations between sleep health, individual characteristics, lifestyle factors, and marathon completion time. Data were obtained from the 2016 London Marathon participants. Participants completed the Athlete Sleep Screening Questionnaire (ASSQ) along with a brief survey capturing individual characteristics and lifestyle factors. Sleep health focused on the ASSQ sleep difficulty score (SDS) and its components. Linear regression computed relations among sleep, individual, lifestyle, and marathon variables. The analytic sample (N = 943) was mostly male (64.5%) and young adults (66.5%). A total of 23.5% of the sample reported sleep difficulties (SDS ≥ 8) at a severity warranting follow-up with a trained sleep provider. Middle-aged adults generally reported significantly worse sleep health characteristics, relative to young adults, except young adults reported significantly longer sleep onset latency (SOL). Sleep tracker users reported worse sleep satisfaction. Pre-bedtime electronic device use was associated with longer SOL and longer marathon completion time, while increasing SOL was also associated with longer marathon completion. Our results suggest a deleterious influence of pre-bedtime electronic device use and sleep tracker use on sleep health in marathoners. Orthosomnia may be a relevant factor in the relationship between sleep tracking and sleep health for marathoners. Full article

Technological advances in sleep monitoring have seen an explosion of devices used to gather important sleep metrics. These devices range from instrumented ‘smart pyjamas’ through to at-home polysomnography devices. Alongside these developments in sleep technologies, there have been concomitant increases in sleep monitoring in athletic populations, both in the research and in practical settings. The increase in sleep monitoring in sport is likely due to the increased knowledge of the importance of sleep in the recovery process and performance of an athlete, as well as the well-reported challenges that athletes can face with their sleep. This narrative review will discuss: (1) the importance of sleep to athletes; (2) the various wearable tools and technologies being used to monitor sleep in the sport setting; (3) the role that sleep tracking devices may play in gathering information about sleep; (4) the reliability and validity of sleep tracking devices; (5) the limitations and cautions associated with sleep trackers; and, (6) the use of sleep trackers to guide behaviour change in athletes. We also provide some practical recommendations for practitioners working with athletes to ensure that the selection of such devices and technology will meet the goals and requirements of the athlete. Full article