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.
Alexander T. Latinjak, Eduardo Morelló-Tomás, and Lucia Figal-Gómez
The aim of this article is to present an exploratory interview framework called #SportPsychMapping that can serve as guidance to practitioners in exploring the psychological reality of individuals and collectives. To meet their aim, in this article, the authors address (a) the context in which the exploratory interview framework was developed, (b) the theoretical structure used to select topics and questions, (c) the structure of the interview, (d) the topics and questions in the central section of the interview, (e) the summary section of the interview, and (f) different ways the exploratory interview framework has been applied. The hallmarks of #SportPsychMapping are the structure that includes an opening, central, and summary section; the central section, in which external variables, biopsychological states and traits, and psychological skills are explored; and the summary section, where an individual map is created with key concepts and phrases that reflect the interviewee’s main responses.
Jeanne Barcelona, Erin Centeio, Paige Arvidson, and Kowsar Hijazi
Purpose: This exploratory study evaluated how youth healthy eating (HE) and physical activity (PA) behaviors could be influenced by a whole-of-school program, which was transformed to a virtual setting at the onset of the COVID-19 pandemic. The authors investigated how students experienced programming and the role of students’ perceptions of parental support in their self-reported engagement in HE and PA. Methods: PA and HE curricula were provided across 15 schools over 12 weeks. Students (N = 879, M age = 12.12 years, 63% female) completed a survey evaluating the value and perceptions around programmatic aspects as well as their self-reported engagement in HE and PA. Results: Multiple regression analyses revealed positive relationships between parental support for PA and student engagement, as well as positive relationships between students’ self-efficacy and HE behaviors. Conclusion: Findings indicate that students utilized virtual HE and PA programming and that parent support helped to facilitate engagement in PA and HE behaviors beyond the school setting.
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.