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Background: Thigh-worn accelerometers have established reliability and validity for measurement of free-living physical activity-related behaviors. However, comparisons of methods for measuring sleep and time in bed using the thigh-worn accelerometer are rare. The authors compared the thigh-worn accelerometer algorithm that estimates time in bed with the output of a sleep diary (time in bed and time asleep). Methods: Participants (N = 5,498), from the 1970 British Cohort Study, wore an activPAL device on their thigh continuously for 7 days and completed a sleep diary. Bland–Altman plots and Pearson correlation coefficients were used to examine associations between the algorithm derived and diary time in bed and asleep. Results: The algorithm estimated acceptable levels of agreement with time in bed when compared with diary time in bed (mean bias of −11.4 min; limits of agreement −264.6 to 241.8). The algorithm-derived time in bed overestimated diary sleep time (mean bias of 55.2 min; limits of agreement −204.5 to 314.8 min). Algorithm and sleep diary are reasonably correlated (ρ = .48, 95% confidence interval [.45, .52] for women and ρ = .51, 95% confidence interval [.47, .55] for men) and provide broadly comparable estimates of time in bed but not for sleep time. Conclusions: The algorithm showed acceptable estimates of time in bed compared with diary at the group level. However, about half of the participants were outside of the ±30 min difference of a clinically relevant limit at an individual level.

Purpose: Little is known about the effect of sleep restriction (SR) on different domains of athletes’ physical performance. Therefore, the aim of this randomized, counterbalanced, and crossover study was to evaluate the effect of acute SR on sport-specific technical and athletic performance in male junior tennis players. Methods: Tennis players (N = 12; age 15.4 ± 2.6 y) were randomly allocated to either a sleep-restriction condition (SR, n = 6), where they experienced acute sleep restriction the night before the test session (≤5 h of sleep), or to a control condition (CON, n = 6), where they followed their habitual sleep–wake routines. Testing procedures included 20 left and right serves, 15 forehand and backhand crosscourt shots, and a repeated-sprint-ability test (RSA). The accuracy of serves and shots was considered for further analysis. One week later, players of SR joined CON, and players of CON experienced SR, and all test procedures were repeated. Results: Significant decrease in the accuracy of right (−17.5%, P = .010, effect size [ES] = 1.0, moderate) and left serve (−14.1%, P = .014, ES = 1.2, large), crosscourt backhand (−23.9%, P = .003, ES ≥ 2.0, very large), and forehand shot (−15.6%, P = .014, ES = 1.1, moderate) were observed in SR compared to CON, while RSA was similar in both conditions. Conclusion: Coaches and athletes at the team and individual level should be aware that 1 night of SR affects sport-specific but not athletic performance in tennis players.

Purpose: To compare the cardiorespiratory responses of a traditional session of high-intensity interval training session with that of a session of similar duration and average load, but with decreasing workload within each bout in cyclists and runners. Methods: A total of 15 cyclists (maximal oxygen uptake [$\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$] 62 [6] mL·kg⁻¹·min⁻¹) and 15 runners ($\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$ 58 [4] mL·kg⁻¹·min⁻¹) performed both sessions at the maximal common tolerable load on different days. The sessions consisted of four 4-minute intervals interspersed with 3 minutes of active recovery. Power output was held constant for each bout within the traditional day, whereas power started 40 W (2 km·h⁻¹) higher and finished 40 W (2 km·h⁻¹) lower than average within each bout of the decremental session. Results: Average oxygen uptake during the high-intensity intervals was higher in the decremental session in cycling (89 [4]% vs 86 [5]% of $\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$, P = .002) but not in running (91 [4]% vs 90 [4]% of $\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$, P = .38), as was the time spent >90% of $\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$ and the time spent >90% of peak heart rate. Average heart rate (P < .001), pulmonary ventilation (P < .001), and blood lactate concentration (P < .001) were higher during the decremental sessions in both cycling and running. Conclusions: Higher levels of physiological perturbations were achieved during decremental sessions in both cycling and running. These differences were, however, more prominent in cycling, thus making cycling a more attractive modality for testing the effects of a training intervention.

Background: Compared with normoxia, repeated short (5–10 s) sprints (>10 efforts) with incomplete recovery (≤30 s) in hypoxia likely cause substantial performance reduction accompanied by larger metabolic disturbances and magnitude of neuromuscular fatigue. However, the effects of hypoxia on performance of repeated long (30 s) “all-out” efforts with near complete recovery (4.5 min) and resulting metabolic and neuromuscular adjustments remain unclear. Purpose: The intention was to compare acute performance, metabolic, and neuromuscular responses across repeated Wingates between hypoxia and normoxia. Methods: On separate visits, 6 male participants performed 4 × 30-second Wingate efforts with 4.5-minute recovery in either hypoxia (fraction of inspired oxygen: 0.145) or normoxia. Responses to exercise (muscle and arterial oxygenation trends, heart rate, and blood lactate concentration) and the integrity of neuromuscular function in the knee extensors were assessed for each exercise bout. Results: Mean (P = .80) and peak (P = .92) power outputs, muscle oxygenation (P = .88), blood lactate concentration (P = .72), and perceptual responses (all Ps > .05) were not different between conditions. Arterial oxygen saturation was significantly lower, and heart rate higher, in hypoxia versus normoxia (P < .001). Maximal voluntary contraction force and peripheral fatigue indices (peak twitch force and doublets at low and high frequencies) decreased across efforts (all Ps < .001) irrespective of conditions (all Ps > .05). Conclusion: Despite heightened arterial hypoxemia and cardiovascular solicitation, hypoxic exposure during 4 repeated 30-second Wingate efforts had no effect on performance and accompanying metabolic and neuromuscular adjustments.

Purpose: The aim of this study was to investigate changes in the power profile of U23 professional cyclists during a competitive season based on maximal mean power output (MMP) and derived critical power (CP) and work capacity above CP (W′) obtained during training and racing. Methods: A total of 13 highly trained U23 professional cyclists (age = 21.1 [1.2] y, maximum oxygen consumption = 73.8 [1.9] mL·kg^–1·min^–1) participated in this study. The cycling season was split into pre-season and in-season. In-season was divided into early-, mid-, and late-season periods. During pre-season, a CP test was completed to derive CP_test and W′_test. In addition, 2-, 5-, and 12-minute MMP during in-season were used to derive CP_field and W′_field. Results: There were no significant differences in absolute 2-, 5-, and 12-minute MMP, CP_field, and W′_field between in-season periods. Due to changes in body mass, relative 12-minute MMP was higher in late-season compared with early-season (P = .025), whereas relative CP_field was higher in mid- and late-season (P = .031 and P = .038, respectively) compared with early-season. There was a strong correlation (r = .77–.83) between CP_test and CP_field in early- and mid-season but not late-season. Bland–Altman plots and standard error of estimates showed good agreement between CP_test and in-season CP_field but not between W′_test and W′_field. Conclusion: These findings reveal that the power profile remains unchanged throughout the in-season, except for relative 12-minute MMP and CP_field in late-season. One pre-season and one in-season CP test are recommended to evaluate in-season CP_field and W′_field.

Purpose: To quantify and describe relationships between subjective and external measures of training load in professional youth soccer players. Methods: Data from differential ratings of perceived exertion (dRPE) and 7 measures of external training load were collected from 20 professional youth soccer players over a 46-week season. Relationships were described by repeated-measures correlation, principal component analysis, and factor analysis with oblimin rotation. Results: Significant positive (.44 ≤ r ≤ .99; P < .001) within-individual correlations were obtained across dRPE and all external training load measures. Correlation magnitudes were found to decrease when training load variables were expressed per minute. Principal component analysis provided 2 components, which described 83.3% of variance. The first component, which described 72.9% of variance, was heavily loaded by all measures of training load, while the second component, which described 10.4% of the variance, appeared to have a split between objective and subjective measures of volume and intensity. Exploratory factor analysis identified 4 theoretical factors, with correlations between factors ranging from .5 to .8. These factors could be theoretically described as objective volume, subjective volume, objective running, and objective high-intensity measures. Removing dRPE measures from the analysis altered the structure of the model, providing a 3-factor solution. Conclusions: The dRPE measures are significantly correlated with a range of external training load measures and with each other. More in-depth analysis showed that dRPE measures were highly related to each other, suggesting that, in this population, they would provide practitioners with similar information. Further analysis provided characteristic groupings of variables.

Purpose: To identify whether the use of active recovery (ACT) the day after high-intensity interval training (HIIT) benefits recovery and to assess whether individual responses to ACT are repeatable. Methods: Eleven well-trained, male intermittent-sport athletes (age: 25.5 ± 1.8 y) completed 4 HIIT sessions, each separated by a 2-week washout period. Of the 4 sessions, 2 were followed by passive recovery (PAS) and 2 by 60 minutes of moderate biking (ACT) 24 hours postexercise in the following sequences: ACT→PAS→ACT→PAS or PAS→ACT→PAS→ACT. Before and after HIIT and after 24 and 48 hours of recovery, maximal voluntary isometric strength (MVIC), countermovement jump height (CMJ), tensiomyographic markers of muscle fatigue (TMG), serum concentration of creatine kinase (CK), muscle soreness (MS), and perceived stress state (PS) were determined. Results: A 3-way repeated-measure analysis of variance with a triple-nested random effects model revealed a significant (P < .05) fatigue-related time effect of HIIT on markers of fatigue (MVIC↓; CMJ↓; TMG↑; CK↑; MS↑; PS↑). No significant (P > .05) main effect of recovery strategy was detected. In 9 subjects, the individual results revealed inconsistent and nonrepeatable responses to ACT, while a consistent and repeatable positive or negative response to ACT was found in 2 individuals. Conclusions: The repeated failure of ACT to limit the severity of fatigue was found both at the group level and with most individuals. However, a small percentage of athletes may be more likely to benefit repeatedly from either ACT or PAS. Therefore, the use of ACT should be individualized.

Background: Investigating the impact of the COVID-19 pandemic on both physical activity (PA) and mental health is important to demonstrate the need for interventions. This study examined the apparent impact of the pandemic on college students’ PA, perceived stress, and depressive symptoms. Methods: From 2015 through 2020, data were collected at the beginning and end of the spring semester at a large Northeastern US university via an online survey assessing student demographics, PA, perceived stress, and depressive symptoms. Mixed ANOVA examined differences in PA and mental health changes over the spring semester between “normal” and COVID-19 circumstances. Two-way ANOVA examined the interaction between circumstance and changes in PA in relation to changes in mental health. Results: Participants (n = 1019) were predominately women and non-Hispanic white. There was a significant decline in PA and an increase in perceived stress under COVID-19, but not normal, circumstances and a significant increase in depressive symptoms under COVID-19, but not normal, circumstances among women. Conclusions: A significant decline in PA and mental health among college students occurred under COVID-19 circumstances, and PA did not appear to protect against deterioration in mental health. Proactive and innovative policies, programs, and practices to promote student health and well-being must be explored immediately.

Background: The COVID-19 pandemic has changed our working environment and divided workers into essential or nonessential statuses. Employment status is a major factor determining the amount of physical activity performed. Our purpose was to understand how employment status affects physical activity and sitting time. Methods: Between April 13 and May 4, 2020, 735 full-time employed individuals responded to a survey investigating daily life and overall health during the COVID-19 pandemic. Participants reported how much physical activity they had performed in the last 7 days. Multiple linear regressions were performed for physical activity and sitting time. Results: Physical activity was not associated with employment status. An interaction effect between hours worked and employment status was found for sitting time. Conclusions: Employment status was not related to physical activity; however, it did affect the amount of time spent sitting, with nonessential employees sitting more and working more hours than essential employees. Because greater amounts of daily total sitting time have been associated with increased risk of all-cause mortality, it is important that increased sitting time be attenuated by greater physical activity.