Whole-body photobiomodulation improves post-exercise recovery but does not affect performance or physiological response during maximal anaerobic cycling.

This study aims to examine the effects of acute whole-body photobiomodulation (wbPBM), applied pre-exercise, on bouts of anaerobic cycling (Wingate) performances. Forty-eight healthy, active males and females participated in this single-blind, randomized, crossover study. Participants visited the laboratory three times to complete repeat (4 ×) Wingate testing, with one week between each visit. All participants completed baseline testing during their first visit and randomly received either the wbPBM or placebo condition before testing on the second visit, followed by the opposite condition on the third visit. There were no significant condition × time interactions for any variable (peak power, average power, power decrement, lactate, heart rate, ratings of perceived exertion, heart rate variability (HRV), root-mean square of differences between R-R intervals (rMSSD), power in the high-frequency range (HF) average, power in the low-frequency range (LF) average, total power, LF/HF, or power in the very-low-frequency range average). A main condition effect was only noted for heart rate, where peak heart rate was significantly higher for wbPBM (145, 141-148 bpm) than placebo (143, 139-146 bpm; p = 0.006) and baseline testing (143, 140-146; p = 0.049) throughout the entire testing session (i.e., collapsed across all timepoints). Furthermore, HRV (rMSSD) the following morning after testing was significantly higher for the wbPBM session compared to placebo (p = 0.043). There were no differences in perceived recovery (p = 0.713) or stress (p = 0.978) scores between wbPBM and placebo. Implementing 20 min of wbPBM immediately prior to maximal bouts of anaerobic cycling did not improve performance (i.e., power output) or physiological responses (e.g., lactate). However, wbPBM elicited the ability to work at a higher heart rate throughout testing and seemed to enhance recovery through improved HRV the following morning.

Evaluations of Commercial Sleep Technologies for Objective Monitoring During Routine Sleeping Conditions.

PURPOSE: The commercial market is saturated with technologies that claim to collect proficient, free-living sleep measurements despite a severe lack of independent third-party evaluations. Therefore, the present study evaluated the accuracy of various commercial sleep technologies during in-home sleeping conditions. MATERIALS AND METHODS: Data collection spanned 98 separate nights of ad libitum sleep from five healthy adults. Prior to bedtime, participants utilized nine popular sleep devices while concurrently wearing a previously validated electroencephalography (EEG)-based device. Data collected from the commercial devices were extracted for later comparison against EEG to determine degrees of accuracy. Sleep and wake summary outcomes as well as sleep staging metrics were evaluated, where available, for each device. RESULTS: Total sleep time (TST), total wake time (TWT), and sleep efficiency (SE) were measured with greater accuracy (lower percent errors) and limited bias by Fitbit Ionic [mean absolute percent error, bias (95% confidence interval); TST: 9.90%, 0.25 (-0.11, 0.61); TWT: 25.64%, -0.17 (-0.28, -0.06); SE: 3.49%, 0.65 (-0.82, 2.12)] and Oura smart ring [TST: 7.39%, 0.19 (0.04, 0.35); TWT: 36.29%, -0.18 (-0.31, -0.04); SE: 5.42%, 1.66 (0.17, 3.15)], whereas all other devices demonstrated a propensity to over or underestimate at least one if not all of the aforementioned sleep metrics. No commercial sleep technology appeared to accurately quantify sleep stages. CONCLUSION: Generally speaking, commercial sleep technologies displayed lower error and bias values when quantifying sleep/wake states as compared to sleep staging durations. Still, these findings revealed that there is a remarkably high degree of variability in the accuracy of commercial sleep technologies, which further emphasizes that continuous evaluations of newly developed sleep technologies are vital. End-users may then be able to determine more accurately which sleep device is most suited for their desired application(s).