Purpose: Despite indications of positive effects of sauna (SAU) interventions, effects on performance recovery are unknown. The aim of the current study was to investigate acute effects of SAU bathing after an intensive training session on recovery of swim performance. Methods: In total, 20 competitive swimmers and triathletes (3 female and 17 male) with a minimum of 2 y of competition experience (national level or higher) participated in the study. Athletes completed an intensive training session followed by either a SAU bathing intervention or a placebo (PLAC) condition in a randomized order. SAU consisted of 3 × 8 min of SAU bathing at 80–85°C, whereas during PLAC, athletes applied a deidentified, pH-balanced massage oil while passively resting in a seated position. Prior to training, swimmers conducted a 4 × 50-m all-out swim test that was repeated on the following morning. Furthermore, subjective ratings of fatigue and recovery were measured. Results: Swimmers performed significantly worse after SAU (4 × 50-m pre–post difference: +1.69 s) than after PLAC (−0.66 s; P = .02), with the most pronounced decrease in the first 50 m (P = .04; +2.7%). Overall performance of 15 athletes deteriorated (+2.6 s). The subjective feeling of stress was significantly higher after SAU than after PLAC (P = .03). Conclusion: Based on published findings, the smallest substantial change in swimming performance is an increase in time of more than 1.2 s; thus, the observed reductions appear relevant for competitive swimmers. According to the current results, coaches and athletes should be careful with postexercise SAU if high-intensity training and/or competitions are scheduled on the following day.

The photoproduction of $\omega$ mesons off protons has been studied at the Bonn ELSA accelerator from threshold to $E_{\gamma }=1700\mathrm{MeV}$. Linearly polarized beams were produced via coherent bremsstrahlung. Large photon asymmetries in excess of 50% were obtained. For the first time the pion asymmetries associated with the $\omega \to {\pi }^0\gamma$ decay were measured and found close to zero. The asymmetries indicate $s$-channel resonance formation on top of $t$-channel exchange processes.

The purpose of this study was to investigate blood-based biomarkers and their regulation with regard to different recovery-stress states. A total of 35 male elite athletes (13 badminton, 22 soccer players) were recruited, and two venous blood samples were taken: one in a ‘recovered’ state (REC) after a minimum of one-day rest from exercise and another one in a ‘non-recovered’ state (NOR) after a habitual loading microcycle. Overall, 23 blood-based biomarkers of different physiologic domains, which address inflammation, muscle damage, and tissue repair, were analyzed by Luminex assays. Across all athletes, only creatine kinase (CK), interleukin (IL-) 6, and IL-17A showed higher concentrations at NOR compared to REC time points. In badminton players, higher levels of CK and IL-17A at NOR were found. In contrast, a higher value for S100 calcium-binding protein A8 (S100A8) at REC was found in badminton players. Similar differences were found for BDNF in soccer players. Soccer players also showed increased levels of CK, and IL-6 at NOR compared to REC state. Several molecular markers were shown to be responsive to differing recovery-stress states, but their suitability as biomarkers in training must be further validated.

Current sport‐scientific studies mostly neglect the assessment of sleep architecture, although the distribution of different sleep stages is considered an essential component influencing an athlete's recovery and performance capabilities. A mobile, self‐applied tool like the SOMNOwatch plus EEG might serve as an economical and time‐friendly alternative to activity‐based devices. However, self‐application of SOMNOwatch plus EEG has not been validated against conventional polysomnography (PSG) yet. For evaluation purposes, 25 participants (15 female, 10 male; M _age = 22.92 ± 2.03 years) slept in a sleep laboratory on two consecutive nights wearing both, conventional PSG and SOMNOwatch plus EEG electrodes. Sleep parameters and sleep stages were compared using paired t‐tests and Bland–Altman plots. No significant differences were found between the recordings for Sleep Onset Latency, stages N1 to N3 as well as Rapid Eye Movement stage. Significant differences (Bias [95%‐confidence interval]) were present between Total Sleep Time (9.95 min [−29.18, 49.08], d = 0.14), Total Wake Time (−13.12 min [−47.25, 23.85], d = −0.28), Wake after Sleep Onset (−11.70 min [−47.25, 23.85], d = −0.34) and Sleep Efficiency (2.18% [−7.98, 12.34], d = 0.02) with small effect sizes. Overall, SOMNOwatch plus EEG can be considered a valid and practical self‐applied method for the examination of sleep. In sport‐scientific research, it is a promising tool to assess sleep architecture in athletes; nonetheless, it cannot replace in‐lab PSG for all clinical or scientific purposes.