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The environmental conditions during the Tokyo Olympic and Paralympic Games are expected to be challenging, which increases the risk for participating athletes to develop heat-related illnesses and experience performance loss. To allow safe and optimal exercise performance of Dutch elite athletes, the Thermo Tokyo study aimed to determine thermoregulatory responses and performance loss among elite athletes during exercise in the heat, and to identify personal, sports-related, and environmental factors that contribute to the magnitude of these outcomes. For this purpose, Dutch Olympic and Paralympic athletes performed two personalized incremental exercise tests in simulated control (15°C, relative humidity (RH) 50%) and Tokyo (32°C, RH 75%) conditions, during which exercise performance and (thermo)physiological parameters were obtained. Thereafter, athletes were invited for an additional visit to conduct anthropometric, dual-energy X-ray absorptiometry (DXA), and 3D scan measurements. Collected data also served as input for a thermophysiological computer simulation model to estimate the impact of a wider range of environmental conditions on thermoregulatory responses. Findings of this study can be used to inform elite athletes and their coaches on how heat impacts their individual (thermo)physiological responses and, based on these data, advise which personalized countermeasures (i.e. heat acclimation, cooling interventions, rehydration plan) can be taken to allow safe and maximal performance in the challenging environmental conditions of the Tokyo 2020 Olympic and Paralympic Games., (© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.)

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.

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.

Objectives: Sleep is essential for recovery and performance in elite athletes. While it is generally assumed that exercise benefits sleep, high training load may jeopardize sleep and hence limit adequate recovery. To examine this, the current study assessed objective sleep quantity and sleep stage distributions in elite athletes and calculated their association with perceived training load., Design: Mixed-methods., Methods: Perceived training load, actigraphy and one-channel EEG recordings were collected among 98 elite athletes during 7 consecutive days of regular training., Results: Actigraphy revealed total sleep durations of 7:50±1:08h, sleep onset latencies of 13±15min, wake after sleep onset of 33±17min and sleep efficiencies of 88±5%. Distribution of sleep stages indicated 51±9% light sleep, 21±8% deep sleep, and 27±7% REM sleep. On average, perceived training load was 5.40±2.50 (scale 1-10), showing large daily variability. Mixed-effects models revealed no alteration in sleep quantity or sleep stage distributions as a function of day-to-day variation in preceding training load (all p's>.05)., Conclusions: Results indicate healthy sleep durations, but elevated wake after sleep onset, suggesting a potential need for sleep optimization. Large proportions of deep sleep potentially reflect an elevated recovery need. With sleep quantity and sleep stage distributions remaining irresponsive to variations in perceived training load, it is questionable whether athletes' current sleep provides sufficient recovery after strenuous exercise., (Copyright © 2017 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.)

Studies monitoring vitamin D status in athletes are seldom conducted for a period of 12 months or longer, thereby lacking insight into seasonal fluctuations. The objective of the current study was to identify seasonal changes in total 25-hydroxyvitamin D (25(OH)D) concentration throughout the year. Fifty-two, mainly Caucasian athletes with a sufficient 25(OH)D concentration (>75 nmol/L) in June were included in this study. Serum 25(OH)D concentration was measured every three months (June, September, December, March, June). In addition, vitamin D intake and sun exposure were assessed by questionnaires at the same time points. Highest total 25(OH)D concentrations were found at the end of summer (113 ± 26 nmol/L), whereas lowest concentrations were observed at the end of winter (78 ± 30 nmol/L). Although all athletes had a sufficient 25(OH)D concentration at the start of the study, nearly 20% of the athletes were deficient (<50 nmol/L) in late winter.

The general consensus among sport and exercise genetics researchers is that genetic tests have no role to play in talent identification or the individualised prescription of training to maximise performance. Despite the lack of evidence, recent years have witnessed the rise of an emerging market of direct-to-consumer marketing (DTC) tests that claim to be able to identify children's athletic talents. Targeted consumers include mainly coaches and parents. There is concern among the scientific community that the current level of knowledge is being misrepresented for commercial purposes. There remains a lack of universally accepted guidelines and legislation for DTC testing in relation to all forms of genetic testing and not just for talent identification. There is concern over the lack of clarity of information over which specific genes or variants are being tested and the almost universal lack of appropriate genetic counselling for the interpretation of the genetic data to consumers. Furthermore independent studies have identified issues relating to quality control by DTC laboratories with different results being reported from samples from the same individual. Consequently, in the current state of knowledge, no child or young athlete should be exposed to DTC genetic testing to define or alter training or for talent identification aimed at selecting gifted children or adolescents. Large scale collaborative projects, may help to develop a stronger scientific foundation on these issues in the future., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/)

Background: It has been demonstrated that protein ingestion before sleep increases muscle protein synthesis rates during overnight recovery from an exercise bout. However, it remains to be established whether dietary protein ingestion before sleep can effectively augment the muscle adaptive response to resistance-type exercise training., Objective: Here we assessed the impact of dietary protein supplementation before sleep on muscle mass and strength gains during resistance-type exercise training., Methods: Forty-four young men (22 ± 1 y) were randomly assigned to a progressive, 12-wk resistance exercise training program. One group consumed a protein supplement containing 27.5 g of protein, 15 g of carbohydrate, and 0.1 g of fat every night before sleep. The other group received a noncaloric placebo. Muscle hypertrophy was assessed on a whole-body (dual-energy X-ray absorptiometry), limb (computed tomography scan), and muscle fiber (muscle biopsy specimen) level before and after exercise training. Strength was assessed regularly by 1-repetition maximum strength testing., Results: Muscle strength increased after resistance exercise training to a significantly greater extent in the protein-supplemented (PRO) group than in the placebo-supplemented (PLA) group (+164 ± 11 kg and +130 ± 9 kg, respectively; P < 0.001). In addition, quadriceps muscle cross-sectional area increased in both groups over time (P < 0.001), with a greater increase in the PRO group than in the PLA group (+8.4 ± 1.1 cm(2) vs. +4.8 ± 0.8 cm(2), respectively; P < 0.05). Both type I and type II muscle fiber size increased after exercise training (P < 0.001), with a greater increase in type II muscle fiber size in the PRO group (+2319 ± 368 μm(2)) than in the PLA group (+1017 ± 353 μm(2); P < 0.05)., Conclusion: Protein ingestion before sleep represents an effective dietary strategy to augment muscle mass and strength gains during resistance exercise training in young men. This trial was registered at clinicaltrials.gov as NCT02222415., (© 2015 American Society for Nutrition.)

The ability to transport and use haemin as an iron source is frequently observed in clinical isolates of Shigella spp. and pathogenic Escherichia coli. We found that many of these haem-utilizing E. coli strains contain a gene that hybridizes at high stringency to the S. dysenteriae type 1 haem receptor gene, shuA. These shuA-positive strains belong to multiple phylogenetic groups and include clinical isolates from enteric, urinary tract and systemic infections. The distribution of shuA in these strains suggests horizontal transfer of the haem transport locus. Some haem-utilizing pathogenic E. coli strains did not hybridize with shuA, so at least one other haem transport system is present in this group. We also characterized the chromosomal region containing shuA in S. dysenteriae. The shuA gene is present in a discrete locus, designated the haem transport locus, containing eight open reading frames. Several of the proteins encoded in this locus participate with ShuA in haem transport, as a Salmonella typhimurium strain containing the entire haem transport locus used haem much more efficiently than the same strain containing only shuA. The haem transport locus is not present in E. coli K-12 strains, but the sequences flanking the haem transport locus in S. dysenteriae matched those at the 78.7 minute region of E. coli K-12. The junctions and flanking sequences in the shuA-positive pathogenic E. coli strains tested were nearly identical to those in S. dysenteriae, indicating that, in these strains, the haem transport locus has an organization similar to that in S. dysenteriae, and it is located in the same relative position on the chromosome.