Objective Sleep Measurement

From an athletic standpoint the importance of sleep cannot be over emphasised. It plays an essential role in maintenance, restoration and repair processes within the body as well as regulating circadian rhythm and often emotional well-being. Insufficient sleep can have a detrimental impact upon athletic performance, recovery and training progression. Cardiovascular performance and maximal exercise capabilities are significantly diminished following sleep deprivation as a result of a reduction in time to exhaustion and minute ventilation (Azboy & Kaygisiz, 2009). A lack of sleep can elicit hormonal imbalances which in turn affect substrate availability. Evidently, glucose regulation is significantly influenced by circadian rhythm and thus sleep cycles. Sleep disturbances may be associated with inefficient glycogen storage – given glucose/glycogen serves as the most important energy source during exercise, this can have a negative impact on performance (Cummiskey et al. 2013). Furthermore, increases in cortisol production and concurrent decreases in insulin-like growth factor stimulated by lack of sleep can significantly impair the growth and repair of muscle tissue (Dattilo et al. 2011). It is abundantly clear that athletes across all sports require sufficient sleep to optimise their performance. Cross sectional data suggests that elite athletes often exhibit poorer quality of sleep in comparison with non-athletes and sleep disturbances are more common prior to an athletic event (Leeder et al. 2012). Taking this into consideration it is clear that the monitoring of sleep is a crucial element of an effective athlete management strategy.

At present, sleep analysis in professional sport is largely subjective; team management often rely on recall and feedback to monitor the sleeping patterns of their athletes. Quantifying sleep quality based on numerical questions has become common practice amongst many organisations. Although subjective measures are useful to an extent, they are often open to interpretation and don’t deliver the definitive answers and clarity objective measurement can provide. Comprehensive objective measures of sleep do exist. Polysomnography is considered the ‘gold standard’ of sleep measurement, however, it is expensive, invasive and not easily reproducible on a large scale (Van de Water et al. 2011). Over the last two decades actigraphy (a practice that involves wearing a small unit to measure gross motor activity) has emerged as a non-invasive and simple method of monitoring sleep cycles. Its reliability and accuracy in comparison to polysomnography has made it a popular alternative. Although actigraphs are cost effective, user friendly and easily attainable their ability to accurately estimate sleep time and sleep onset latency has been questioned (Tyron, 2004) Interestingly, recent research concluded that a salivary biomarker of physical fatigue could be utilised to distinguish between a sleep deprived and rested individual (Michaels et al, 2013). Although the evidence underpinning the validation of a biological objective measure of sleep quality appears promising, integrating the results of experimental data into a practical domain may be a lengthy process. The creation of a definitive, yet practical objective measurement of sleep would be revolutionary for athletic management – a simple test reproducible on a large scale could provide critical information and feedback for sport scientist and managers alike. In the professional sporting domain, where the margin for error and the ultimate difference between success and failure is infinitesimal, any competitive edge cannot be overlooked.

Although a practical, accurate and definitive measure of sleep is yet to be productised, simple steps could be taken to improve monitoring practices. At present, Edge10 offers a comprehensive wellness package that incorporates subjective measurements of sleep. We believe combining this data with an objective measurement could be hugely beneficial. With a built in alert system based on biological variance and customisable thresholds, the amalgamation of subjective and objective data could provide a more in-depth analysis of sleeping patterns. Provided any objective data can be exported to file format, this information could be integrated into the current system and analysed. As a market leader at the forefront of athletic management strategy Edge10 is constantly striving for improvement in line with scientific progression. We believe this is an exciting opportunity to expand upon our current wellness package – if anyone has any questions about this feel free to get in touch!

Get the edge

Get in touch to try out Edge10 for yourself and see how it can improve results for yourself, team or club.

Request a demo