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.

Objective: Light exposure techniques have been recommended to combat sleep issues caused by disruption to circadian regularity in the athletic population, although studies are lacking. Methods: A total of 17 professional male Australian Football athletes (age ± SD: 22 ± 3 years) wore a wrist actigraph to measure sleep parameters, and a wearable light sensor to measure melanopic equivalent daylight illuminance (mEDI, in lux) for 14 days. Participants completed three sleep questionnaires at the end of the data collection period and completed well-being surveys 6 times. The Sleep Regularity Index (SRI) for each player was also calculated from actigraphy data. Light exposure data were organised into three different timeframes: morning (wake time + 2 hours), daytime (end of morning to 6 pm), and evening (2 hours leading up to bedtime) for analysis. Repeated measures correlation was conducted for objective sleep measures and mEDI values per timeframe. Pearson’s correlation was conducted on subjective sleep measures and well-being measures against mEDI values per timeframe. Results: Higher morning light was associated with significantly (p < 0.001) greater total sleep time (r = 0.31). Higher daytime light exposure was associated with higher subjective sleep quality (r = 0.48, p < 0.05). Higher evening light exposure was associated with higher Athlete Sleep Screening Questionnaire (ASSQ) global scores (r = 0.52, p < 0.05). There were no other significant correlations between light exposure and sleep or well-being measures (p > 0.05). Conclusion: Higher morning and daylight exposure levels were associated with various positive objective and subjective sleep measures in professional team sport athletes, supporting the need for education on optimising light exposure to improve circadian function, sleep, and health.

Ambient temperature and thermoregulation influence sleep quality. This study investigated the effects of a temperature-controlled mattress cover on sleep and perceptual outcomes in healthy adults. In a randomised, counterbalanced, crossover design, 34 healthy adults (20 F, 14 M; age, 30 ± 5 y) used a temperature-controlled mattress cover for 14 nights, following ≥3 nights of familiarisation. The temperature feature was on for 7 nights (POD) and off for 7 nights (CON). Sleep was assessed via wrist actigraphy, while heart rate (HR), heart rate variability (HRV), and respiratory rate (RR) were recorded by embedded sensors in the mattress cover. Participants completed daily and weekly questionnaires evaluating sleep quality, thermal comfort, and thermal sensation. Linear mixed models showed significant main effects of condition favouring POD over CON for all daily perceived outcomes (all p < 0.05). A large, significant improvement in perceived sleep quality was observed (p = 0.001, d = 0.92). No significant differences were found in objective sleep metrics or biometric measures (all p ≥ 0.05). A temperature-controlled mattress cover was associated with improved subjective sleep quality and thermal-related perceptions despite minimal changes in objective or biometric outcomes, which may in part reflect expectancy, or placebo effects. Further research is needed to explore whether these perceptual benefits lead to physiological improvements over time.

Light exposure has been measured previously using wrist-worn devices, but they may not reflect retinal light accurately. Few studies have evaluated the validity and reliability of light sensors placed near the eyes. This study aims to compare a wearable ‘light button’ to a factory-calibrated digital light meter (DLM) and evaluate the light button’s inter-device reliability. Two light buttons and a DLM were tested side-by-side indoors and outdoors for 8 h. Lux values were compared using typical error of estimate (TEE), mean differences, 95% levels of agreement, Pearson correlation coefficient ( r ) and coefficient of variation (CV%). Inter-device reliability was assessed using TEE, mean differences and intraclass correlation coefficients (ICCs). Indoor validity analysis resulted in a TEE of 71.0 lx, mean difference was 24.7 lx, a strong correlation ( r = 0.96) and CV of 11.5%. Outdoors, TEE was 45.3 lx, mean difference was 82.3 lx, with a strong correlation ( r = 1.00) and CV of 1.8%. Inter-device reliability indoors and outdoors had TEEs of 10.7 lx and 33.5 lx, mean differences of 67.1 lx and 45.3 lx and strong ICCs of 0.90 and 0.98, respectively. The light buttons are valid and reliable for monitoring light exposure indoors and outdoors. Further validation is required in free-living conditions.

Sleep regularity is an important but under-measured dimension of sleep health. Objective indices from actigraphy or wearables are robust but resource-intensive. The Sleep Regularity Questionnaire (SRQ) offers a brief subjective tool, but its validity against objective and diary-based indices in healthy adults is unclear. In Part 1, 31 adults wore a smart ring continuously for 21 nights. Device-derived regularity metrics included the Sleep Regularity Index (SRI), interdaily stability (IS), social jetlag (SJL), composite phase deviation (CPD), and the standard deviation of sleep onset and wake time. In Part 2, 52 adults completed a one-week sleep diary, from which variability in sleep timing, total sleep time (TST), SJL and nightly perceived sleep quality were derived. All participants completed the SRQ and Brief Pittsburgh Sleep Quality Index (B-PSQI). In Part 1, associations between SRQ scores and device-derived SRI, IS, SJL, CPD and timing variability were small (absolute r ≤ 0.36). Higher SRQ Global and Sleep Continuity scores were moderately associated with better B-PSQI global scores (r –0.37 to –0.44). In Part 2, SRQ Global and Circadian Regularity showed small-to-moderate associations with higher diary-rated sleep quality and lower bedtime variability (r ≈ 0.40 and –0.32 to –0.34), while correlations with other diary metrics and B-PSQI were weak (absolute r ≤ 0.25). The SRQ shows modest convergent validity with diary-based timing variability and perceived sleep quality, but only weak correspondence with smart ring-based sleep regularity indices. It is likely to complement, rather than replace, objective monitoring in healthy adults with relatively regular sleep–wake patterns.

Background: Emerging research indicates that light exposure may influence sleep quality. Identifying key light-exposure behaviours associated with poor sleep quality in athletes may allow practitioners to efficiently screen for sleep difficulties and prioritise athletes for further assessment. Translating these findings into a practical screening tool could enhance willingness of high-performance professionals to monitor sleep and light exposure in athletes. Hypothesis: Key predictor variables identified by feature reduction techniques will lead to higher predictive accuracy in determining which light behaviours are associated with poor sleep quality in athletes. Study Design: Cross-sectional study. Level of Evidence: Level 3. Methods: 121 athletes from varying competitive levels completed questionnaires, including the Light Exposure Behaviour Assessment (LEBA) and Pittsburgh Sleep Quality Index (PSQI). Poor sleep quality was defined using the PSQI cut-off >5. Least absolute shrinkage and selection operator (LASSO) regression identified light exposure variables from the LEBA questionnaire most strongly associated with good and poor sleep quality in athletes. Three models were compared: a full-variable model (23 items), a factor-specific model (Factor 3: screen/device use), and a feature-reduced model (LASSO-selected items). Results: Phone use before bed, checking phone/watch during the night, were identified as variables of greatest association with poor sleep quality and used for reduced feature set modelling. On an independent test set, the feature-reduced model achieved area under the curve (AUC) 0.83, sensitivity 0.70, and specificity 0.92. Conclusions: Our findings report that phone-related behaviours before and in bed are associated with a higher likelihood of poor sleep quality. These behaviours, combined with the developed nomogram, provide a preliminary, low-burden screening tool to identify athletes who may be experiencing sleep difficulties. The high specificity indicates that athletes flagged by the tool are likely to have genuine poor sleep quality, warranting further assessment to identify underlying causes and appropriate interventions. Clinical Relevance: Education and interventions focused on light exposure factors were identified as most influencing sleep quality in a multifaceted athletic population and could be prioritised to optimise sleep quality. The developed sleep quality nomogram may be useful as a decision-making tool to improve sleep monitoring practice among practitioners.