Purpose: To examine the ability of a multivariate model to predict maximal oxygen consumption (VO2max) using performance data from a 5-minute maximal test (5MT). Methods: Forty-six road cyclists (age 38  y, height 177  cm, weight 71.4 [8.6] kg, VO2max 61.13 [9.05] mL/kg/min) completed a graded exercise test to assess VO2max and power output. After a 72-hour rest, they performed a test that included a 5-minute maximal bout. Performance variables in each test were modeled in 2 independent equations, using Bayesian general linear regressions to predict VO2max. Stepwise selection was then used to identify the minimal subset of parameters with the best predictive power for each model. Results: Five-minute relative power output was the best explanatory variable to predict VO2max in the model from the graded exercise test (R 2 95% credibility interval, .81–.88) and when using data from the 5MT (R 2 95% credibility interval, .61–.77). Accordingly, VO2max could be predicted with a 5MT using the equation VO2max = 16.6 + (8.87 × 5-min relative power output). Conclusions: Road cycling VO2max can be predicted in cyclists through a single-variable equation that includes relative power obtained during a 5MT. Coaches, cyclists, and scientists may benefit from the reduction of laboratory assessments performed on athletes due to this finding.
Sebastian Sitko, Rafel Cirer-Sastre, Francisco Corbi, and Isaac López-Laval
Jasmien Dumortier, An Mariman, Jan Boone, Liesbeth Delesie, Els Tobback, Dirk Vogelaers, and Jan G. Bourgois
Purpose: This study aimed to determine the influencing factors of potential differences in sleep architecture between elite (EG) and nonelite (NEG) female artistic gymnasts. Methods: Twelve EG (15.1 [1.5] y old) and 10 NEG (15.3 [1.8] y old) underwent a nocturnal polysomnography after a regular training day (5.8 [0.8] h vs 2.6 [0.7] h), and, on a separate test day, they performed an incremental treadmill test after a rest day in order to determine physical fitness status. A multiple linear regression assessed the predictive value of training and fitness parameters toward the different sleep phases. Total sleep time and sleep efficiency (proportion of time effectively asleep to time in bed), as well as percentage of nonrapid eye movement sleep phase 1 (NREM1) and 2 (NREM2), slow wave sleep (SWS), and rapid eye movement sleep (REM), during a single night were compared between EG and NEG using an independent-samples t test. Results: Peak oxygen uptake influenced NREM1 (β = 1.035, P = .033), while amount of weekly training hours predicted SWS (β = 1.897, P = .032). No differences were documented between EG and NEG in total sleep time and sleep efficiency. SWS was higher in EG (36.9% [11.4%]) compared with NEG (25.9% [8.3%], P = .020), compensated by a lower proportion of NREM2 (38.7% [10.2%] vs 48.4% [6.5%], P = .017), without differences in NREM1 and REM. Conclusions: The proportion of SWS was only predicted by weekly training hours and not by training hours the day of the polysomnography or physical fitness, while NREM1 was linked with fitness level. Sleep efficiency did not differ between EG and NEG, but in EG, more SWS and less NREM2 were identified.
Sam McCormack, Ben Jones, Sean Scantlebury, Neil Collins, Cameron Owen, and Kevin Till
Purpose: To compare the physical qualities between academy and international youth rugby league (RL) players using principal component analysis. Methods: Six hundred fifty-four males (age = 16.7 [1.4] y; height = 178.4 [13.3] cm; body mass = 82.2 [14.5] kg) from 11 English RL academies participated in this study. Participants completed anthropometric, power (countermovement jump), strength (isometric midthigh pull; IMTP), speed (10 and 40 m speed), and aerobic endurance (prone Yo-Yo IR1) assessments. Principal component analysis was conducted on all physical quality measures. A 1-way analysis of variance with effect sizes was performed on 2 principal components (PCs) to identify differences between academy and international backs, forwards, and pivots at under 16 and 18 age groups. Results: Physical quality measures were reduced to 2 PCs explaining 69.4% of variance. The first PC (35.3%) was influenced by maximum and 10-m momentum, absolute IMTP, and body mass. Ten and forty-meter speed, body mass and fat, prone Yo-Yo, IMTP relative, maximum speed, and countermovement jump contributed to PC2 (34.1%). Significant differences (P < .05, effect size = −1.83) were identified between U18 academy and international backs within PC1. Conclusion: Running momentum, absolute IMTP, and body mass contributed to PC1, while numerous qualities influenced PC2. The physical qualities of academy and international youth RL players are similar, excluding U18 backs. Principal component analysis can reduce the dimensionality of a data set and help identify overall differences between playing levels. Findings suggest that RL practitioners should measure multiple physical qualities when assessing physical performance.
Oğuz K. Esentürk and Erkan Yarımkaya
The aim of this study was to evaluate the feasibility and potential efficacy of a WhatsApp-based physical activity for children with autism spectrum disorder (ASD). Fourteen parents and their children with ASD participated in the study. The intervention included parents conducting physical activities with their children with ASD for 4 weeks. Physical activity contents were provided to parents via the WhatsApp group. The data were collected through the Leisure Time Exercise Questionnaire and a feasibility questionnaire adapted from previous studies examining the feasibility of web-based physical activities. Parents reported that WhatsApp-based physical activities were a feasible intervention to increase the physical activity level of their children with ASD and stated that the contents of the physical activity shared in the WhatsApp group were useful. The findings provided preliminary evidence for the use of WhatsApp-based physical activities to increase the physical activity level of children with ASD who stay at home due to the pandemic.
Floor A.P. van den Brandt, Inge K. Stoter, Ruby T.A. Otter, and Marije T. Elferink-Gemser
Purpose: In long-track speed skating, drafting is a commonly used phenomenon in training; however, it is not allowed in time-trial races. In speed skating, limited research is available on the physical and psychological impact of drafting. The aim of this study was to determine the influence of “skating alone,” “leading,” or “drafting” on physical intensity (heart rate and blood lactate) and perceived intensity (perceived exertion) of speed skaters. Methods: Twenty-two national-level long-track speed skaters with a mean age of 19.3 (2.6) years skated 5 laps, with similar external intensity in 3 different conditions: skating alone, leading, or drafting. Repeated-measures analysis of variance showed differences between the 3 conditions, heart rate (F 2,36 = 10.546, P < .001), lactate (F 2,36 = 12.711, P < .001), and rating of perceived exertion (F 2,36 = 5.759, P < .01). Results: Heart rate and lactate concentration were significantly lower (P < .001) when drafting compared with leading (heart rate Δ = 7  beats·min–1, 4.0% [4.7%]; lactate Δ = 2.3 [2.3] mmol/L, 28.2% [29.9%]) or skating alone (heart rate Δ = 8 [7.1] beats·min–1, 4.6% [3.9%]; lactate Δ = 2.8 [2.5] mmol/L, 33.6% [23.6%]). Rating of perceived exertion was significantly lower (P < .01) when drafting (Δ = 0.8 [1.0], 16.5% [20.9%]) or leading (Δ = 0.5 [0.9], 7.7% [20.5%]) versus skating alone. Conclusions: With similar external intensity, physical intensity, as well as perceived intensity, is reduced when drafting in comparison with skating alone. A key finding of this study is the psychological effect: Skating alone was shown to be more demanding than leading, whereas leading and drafting were perceived to be similar in terms of perceived exertion. Knowledge about the reduction of internal intensity for a drafting skater compared with leading or skating alone can be used by coaches and trainers to optimize training conditions.
Takeshi Koyama, Akira Rikukawa, Yasuharu Nagano, Shogo Sasaki, Hiroshi Ichikawa, and Norikazu Hirose
Purpose: To evaluate the effect of the number of high-acceleration movements on muscle damage and the rating of perceived exertion (RPE) in basketball games. Methods: Twenty-one male collegiate basketball players (mean age, 20.0 [1.0] y) were included. A triaxial accelerometer was used to measure acceleration in basketball-simulated scrimmages. To detect higher physical load during the actual game, the resultant acceleration was calculated, and 3 thresholds were set: >4G, >6G, and >8G resultant accelerations. The number of the extracted movements was calculated at each acceleration threshold. Plasma creatine kinase (CK) levels (marker of muscle damage) were estimated before and 24 hours after the match, and the session-RPE load was calculated within 30 minutes after the match. Pearson product-moment correlations with 95% confidence intervals were used to determine the relationships between the number of high-acceleration movements and plasma CK and session-RPE load. Results: Significant correlations were observed between the number of high-acceleration movements >8G and CK level (r = .74; 95% confidence interval, 0.44–0.89; P < .0001). Furthermore, the correlation coefficient between acceleration and CK increased with increased acceleration threshold (>4G: r = .65; >6G: r = .69). Contrastingly, the correlation coefficient between acceleration and the session-RPE load decreased with increased acceleration threshold (>4G: r = .72; >6G: r = .52; >8G: r = .43). Conclusions: The session-RPE reflects the total amount of movement, while the high-acceleration movement reflects the momentary large impact load or intensity, and they evaluated different factors. Basketball coaching and conditioning professionals recommended combining acceleration and session-RPE when monitoring the load of athletes.
Jacob Walther, Roy Mulder, Dionne A. Noordhof, Thomas A. Haugen, and Øyvind Sandbakk
Purpose: To quantify peak age and relative performance progression toward peak age in cross-country skiing according to event type, sex, and athlete performance level. Methods: International Ski Federation (FIS) points (performance expressed relative to the best athlete) of athletes born between 1981 and 1991, competing in junior world championships or finishing top 30 in world championships or Olympics, were downloaded from the FIS website. Individual performance trends were derived by fitting a quadratic curve to each athletes FIS point and age data. Results: Peak age was 26.2 (2.3) years in distance and 26.0 (1.7) years in sprint events. The sex difference in peak age in sprint events was ∼0.8 years (small, P = .001), while there was no significant sex difference in peak age in distance events (P = .668). Top performers displayed higher peak ages than other athletes in distance (mean difference, ±95% confidence limits = 1.6 y, ±0.6 y, moderate, P < .001) and sprint events (1.0, ±0.6 y, moderate, P < .001). FIS point improvement over the 5 years preceding peak age did not differ between event types (P = .325), while men improved more than women in both events (8.8, ±5.4%, small, P = .002 and 7.5, ±6.4%, small, P = .002). Performance level had a large effect on improvement in FIS points in both events (P < .001). Conclusion: This study provides novel insights on peak age and relative performance progression among world-class cross-country skiers and can assist practitioners, sport institutions, and federations with goal setting and evaluating strategies for achieving success.