Dim light, sleep tight, and wake up bright:Sleep optimization in athletes by means of light regulation

Despite an elevated recovery need, research indicates that athletes often exhibit relatively poor sleep. Timing and consolidation of sleep is driven by the circadian system, which requires periodic light–dark exposure for stable entrainment to the 24-hour day, but is often disturbed due to underexposure to light in the morning (e.g. low-level indoor lighting) and overexposure to light in the evening (e.g. environmental and screen-light). This study examined whether combining fixed sleep schedules with light regulation leads to more consolidated sleep. Morning light exposure was increased using light-emitting goggles, whereas evening light exposure was reduced using amber-lens glasses. Using a within-subject crossover design, twenty-six athletes (14 female, 12 male) were randomly assigned to start the intervention with the light-regulation-week or the no light-regulation-week. Sleep was monitored by means of sleep diaries and actigraphy. Due to low protocol adherence regarding the fixed sleep-wake schedules, two datasets were constructed; one including athletes who kept a strict sleep-wake schedule (N = 8), and one that also included athletes with a more lenient sleep-wake schedule (N = 25). In case of a lenient sleep-wake schedule, light regulation improved self-reported sleep onset latency (Δ SOL = 8 min). This effect was stronger (Δ SOL = 17 min) and complemented by enhanced subjective sleep quality in case of a strict sleep-wake schedule. None of the actigraphy-based estimates differed significantly between conditions. To conclude, light regulation may be considered a potentially effective strategy to improve subjective sleep, but less obtrusive methods should be explored to increase protocol compliance.</p

Good night, sleep tight and wake up bright - Sleep optimization in elite athletes by means of light regulation

Sleep is essential for recovery and performance capacity in elite athletes. Still, elite athletes appear to sleep worse compared to gender and aged matched non-exercising controls (Leeder, Glaister, Pizzoferro, Dawson, &amp; Pedlar, 2012). This project aims to investigate the potentially beneficial effect of a carefully timed combination of light exposure (morning) and blue-light blocking (evening) on sleep quantity and sleep quality in a field study among elite athletes

Effects of Natural Between-Days Variation in Sleep on Elite Athletes’ Psychomotor Vigilance and Sport-Specific Measures of Performance

Performance capacity in athletes depends on the ability to recover from past exercise. While evidence suggests that athletic performance decreases following (partial) sleep deprivation and increases following sleep extension, it is unclear to which extent natural variation in sleep impacts performance. Sleep quantity and, for the first time, sleep stages were assessed among 98 elite athletes on three non-consecutive nights within a 7-day monitoring period, along with performance tests that were taken on standardized times each following morning. Performance assessment included psychomotor performance (10-minute psychomotor vigilance task) and sport-specific tests of fine (e.g., accuracy) and gross motor skills (e.g., endurance, power). Mixed-effects models were employed to assess the effect of sleep quantity (total sleep time (TST), sleep onset latency (SOL), wake after sleep onset, sleep efficiency) and sleep stage duration (light, deep, REM) on performance. Average TST was 7:30 ± 1:05 hours, with a mean variation of 57 minutes across days. Longer TSTs were associated with faster reaction times (p = 0.04). Analyses indicated small and inconsistent effects of sleep quantity (TST, SOL) and sleep staging (light sleep) on gross motor performance, and no effects on fine motor skill performance. Results indicate that natural variation in sleep quantity impacts psychomotor vigilance to a greater extent than athletic performance. Small or absent effects can be a consequence of the rather small variation in non-manipulated sleep. It is suggested that one night of compromised sleep may not be immediately problematic, but that more extreme sleep loss or accumulated sleep debt may have more severe consequences

How did you sleep?: Exploring by-proxy sleep assessment in a field study setup

Objectives/Introduction: Sleep is crucial for both mental and physical health, and sleep disorders can pose a severe burden on health‐related quality of life. Importantly, family members may be affected by each other's sleep and wake behavior. Finally, there may be differences in sleep perception between family members. As a tool for future studies, we designed a concept for ‘By‐Proxy Sleep Assessment’, namely, the rating of one's partner's or children's sleep quality. This might provide a useful additional sleep quality measure, especially when assessing people with difficulties reporting their own sleep, such as children and people with intellectual disabilities or dementia. Methods: We used data from the FieldLab study, created to collect data on sleep and sleep related behaviors in five households, using an IoT ecosystem to combine subjective and objective information from connected objects measuring variables such as sleep, physical activity and environmental factors. A chat application enabled communication between researchers and participants and qualitative data was obtained through means of questionnaires and scheduled chatbot messages. Participants were asked to rate subjective sleep quality as well as the sleep quality of their partners and children on a scale of 0 to 10 each morning. Results: A total of 143 nights of partner By‐Proxy Sleep Assessment were collected from three couples and 40 nights of By‐Proxy Sleep Assessment for children from two couples. Subject‐proxy differences were averaged over the study period and varied between −0.38 ± 1.32 (−3 ‐ +2) and 1.23 ± 1.30 (−1 ‐ +3) for partners and −0.11 ± 0.97 (−3 ‐ +2) and 0.61 ± 1.50 (−1 ‐ +4) for children/parents. Preliminary analysis of IoT data and qualitative measurements through the chatbot revealed that both internal (e.g. migraines) and external factors (e.g. room temperature) contribute to discrepancies in sleep quality assessment by a proxy. Conclusions: Experience sampling studies can offer a new perspective on sleep quality and sleep perception. Although the By‐Proxy Sleep Assessment ratings already correlated rather well with the subjects’ own ratings, the IoT data may aid in improving the reliability of this approach