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.
Patrick C. Maughan, Niall G. MacFarlane, and Paul A. Swinton
Thimo Wiewelhove, Constantin Thase, Marcel Glahn, Anthony Hessel, Christoph Schneider, Laura Hottenrott, Tim Meyer, Michael Kellmann, Mark Pfeiffer, and Alexander Ferrauti
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.
Oliver W.A. Wilson, Kelsey E. Holland, Lucas D. Elliott, Michele Duffey, and Melissa Bopp
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.
Bryndan W. Lindsey, Ali Boolani, Justin J. Merrigan, Nelson Cortes, Shane V. Caswell, and Joel R. Martin
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.
Brandon J. Shad, Janice L. Thompson, James Mckendry, Andrew M. Holwerda, Yasir S. Elhassan, Leigh Breen, Luc J.C. van Loon, and Gareth A. Wallis
The impact of resistance exercise frequency on muscle protein synthesis rates remains unknown. The aim of this study was to compare daily myofibrillar protein synthesis rates over a 7-day period of low-frequency (LF) versus high-frequency (HF) resistance exercise training. Nine young men (21 ± 2 years) completed a 7-day period of habitual physical activity (BASAL). This was followed by a 7-day exercise period of volume-matched, LF (10 × 10 repetitions at 70% one-repetition maximum, once per week) or HF (2 × 10 repetitions at ∼70% one-repetition maximum, five times per week) resistance exercise training. The participants had one leg randomly allocated to LF and the other to HF. Skeletal muscle biopsies and daily saliva samples were collected to determine myofibrillar protein synthesis rates using 2H2O, with intracellular signaling determined using Western blotting. The myofibrillar protein synthesis rates did not differ between the LF (1.46 ± 0.26%/day) and HF (1.48 ± 0.33%/day) conditions over the 7-day exercise training period (p > .05). There were no significant differences between the LF and HF conditions over the first 2 days (1.45 ± 0.41%/day vs. 1.25 ± 0.46%/day) or last 5 days (1.47 ± 0.30%/day vs. 1.50 ± 0.41%/day) of the exercise training period (p > .05). Daily myofibrillar protein synthesis rates were not different from BASAL at any time point during LF or HF (p > .05). The phosphorylation status and total protein content of selected proteins implicated in skeletal muscle ribosomal biogenesis were not different between conditions (p > .05). Under the conditions of the present study, resistance exercise training frequency did not modulate daily myofibrillar protein synthesis rates in young men.
Stephanie G. Kerrigan, Evan M. Forman, Dave Williams, Mitesh Patel, Caitlin Loyka, Fengqing Zhang, Ross D. Crosby, and Meghan L. Butryn
Background: Financial incentives and feedback on behavior offer promise for promoting physical activity. However, evidence for the effect of each of these techniques is inadequate. The present study evaluated the effects of daily versus weekly feedback and incentives contingent on reaching a daily walking goal versus noncontingent incentives in a 2 × 2 trial. Methods: Participants (N = 57) had a body mass index >25 kg/m2 and were insufficiently active. Participants received a daily walking goal that adapted weekly. Results: Participants receiving daily feedback increased daily steps (P = .03) more than those receiving weekly feedback. Participants receiving contingent incentives did not significantly increase steps (P = .12) more than those receiving noncontingent incentives. A trend-level effect (P = .09) suggested that there may be an interaction such that the combination of daily feedback and contingent incentives is most effective. Conclusions: Results indicate that feedback is an important component of remotely delivered PA interventions and that evaluating each component of low-intensity interventions may help to improve efficacy. Moreover, results indicate that possible synergistic effects of feedback and rewards should be investigated further to help optimize interventions.
Matías Henríquez, Aitor Iturricastillo, Arturo González-Olguín, Felipe Herrera, Sonny Riquelme, and Raul Reina
This study compared physical performance in a group of international cerebral palsy football players during two formats of small-sided games (SSGs) and performance in a simulated game (SG) according to players’ sport classes (FT1, FT2, and FT3). Internal load (heart rate and rating of perceived exertion) and external load (total distance, distance covered at different velocities, maximum speed reached, acceleration, and deceleration) were obtained with global positioning system devices during two formats of SSGs (2-a-side/SSG2 and 4-a-side/SSG4) and an SG (7-a-side). SSG2 demands faster actions compared with SSG4/SG, and significant differences and large effect sizes were found in the distance covered in Speed Zones 5 (16.0−17.9 km/hr) and 6 (>18.0 km/hr; p < .05;
John Bellettiere, Fatima Tuz-Zahra, Jordan A. Carlson, Nicola D. Ridgers, Sandy Liles, Mikael Anne Greenwood-Hickman, Rod L. Walker, Andrea Z. LaCroix, Marta M. Jankowska, Dori E. Rosenberg, and Loki Natarajan
Little is known about how sedentary behavior (SB) metrics derived from hip- and thigh-worn accelerometers agree for older adults. Thigh-worn activPAL (AP) micro monitors were concurrently worn with hip-worn ActiGraph (AG) GT3X+ accelerometers (with SB measured using the 100 counts per minute [cpm] cut point; AG100cpm) by 953 older adults (age 77 ± 6.6, 54% women) for 4–7 days. Device agreement for sedentary time and five SB pattern metrics was assessed using mean error and correlations. Logistic regression tested associations with four health outcomes using standardized (i.e., z scores) and unstandardized SB metrics. Mean errors (AP − AG100cpm) and 95% limits of agreement were: sedentary time −54.7 [−223.4, 113.9] min/day; time in 30+ min bouts 77.6 [−74.8, 230.1] min/day; mean bout duration 5.9 [0.5, 11.4] min; usual bout duration 15.2 [0.4, 30] min; breaks in sedentary time −35.4 [−63.1, −7.6] breaks/day; and alpha −.5 [−.6, −.4]. Respective Pearson correlations were: .66, .78, .73, .79, .51, and .40. Concordance correlations were: .57, .67, .40, .50, .14, and .02. The statistical significance and direction of associations were identical for AG100cpm and AP metrics in 46 of 48 tests, though significant differences in the magnitude of odds ratios were observed among 13 of 24 tests for unstandardized and five of 24 for standardized SB metrics. Caution is needed when interpreting SB metrics and associations with health from AG100cpm due to the tendency for it to overestimate breaks in sedentary time relative to AP. However, high correlations between AP and AG100cpm measures and similar standardized associations with health outcomes suggest that studies using AG100cpm are useful, though not ideal, for studying SB in older adults.
Hyeonho Yu, Pamela H. Kulinna, and Shannon C. Mulhearn
Background: Environmental provisions can boost students’ discretionary participation in physical activity (PA) during lunchtime at school. This study investigated the effectiveness of providing PA equipment as an environmental intervention on middle school students’ PA levels and stakeholders’ perceptions of the effectiveness of equipment provisions during school lunch recess. Methods: A baseline–intervention research design was used in this study with a first baseline phase followed by an intervention phase (ie, equipment provision phase). A total of 514 students at 2 middle schools (school 1 and school 2) in a rural area of the western United States were observed directly using the System for Observing Play and Leisure Activity in Youth instrument. Interviews were conducted with stakeholders. Paired-sample t tests and visual analysis were conducted to explore differences in PA levels by gender, and common comparison (with trustworthiness measures) was used with the interview data. Results: The overall percentage of moderate to vigorous PA levels was increased in both schools (ranging from 8.0% to 24.0%). In school 2, there was a significant difference in seventh- and eighth-grade students’ moderate to vigorous PA levels from the baseline. Three major themes were identified: (1) unmotivated, (2) unequipped, and (3) unquestionable changes (with students becoming more active). Conclusions: Environmental supports (access, equipment, and supervision) significantly and positively influenced middle school students’ lunchtime PA levels.