Relationship Between External and Internal Load Measures in Youth Beach Handball

2021 ◽  
pp. 1-7
Author(s):  
Alice Iannaccone ◽  
Andrea Fusco ◽  
Antanas Skarbalius ◽  
Audinga Kniubaite ◽  
Cristina Cortis ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Perceived Exertion ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Rating Of Perceived Exertion ◽  
Internal Load ◽  
Independent Variable ◽  
The Relationship ◽  
And Training

Purpose: Assessing the relationship between external load (EL) and internal load (IL) in youth male beach handball players. Methods: A total of 11 field players from the Lithuanian U17 beach handball team were monitored across 14 training sessions and 7 matches. The following EL variables were assessed by means of inertial movement units: PlayerLoad™, accelerations, decelerations, changes of direction, and jumps and total of inertial movements. IL was assessed objectively and subjectively using the summated heart rate zones and training load calculated via session rating of perceived exertion, respectively. Spearman correlations (ρ) were used to assess the relationship between EL and IL. The interindividual variability was investigated using linear mixed models with random intercepts with IL as dependent variable, PlayerLoad™ as the independent variable, and players as random effect. Results: The lowest significant (P < .05) relationship was for high jumps with objective (ρ = .56) and subjective (ρ = .49) IL. The strongest relationship was for PlayerLoad™ with objective (ρ = .9) and subjective (ρ = .84) IL. From the linear mixed model, the estimated SD of the random intercepts was 19.78 arbitrary units (95% confidence interval, 11.75–33.31); SE = 5.26, and R2 = .47 for the objective IL and 6.03 arbitrary units (95% confidence interval, 0.00–7330.6); SE = 21.87; and R2 = .71 for the subjective IL. Conclusions: Objective and subjective IL measures can be used as a monitoring tool when EL monitoring is not possible. Coaches can predict IL based on a given EL by using the equations proposed in this study.

scholarly journals Usefulness of Linear Mixed-Effects Models to Assess the Relationship between Objective and Subjective Internal Load in Team Sports

2021 ◽  
Vol 18 (2) ◽  
pp. 392
Author(s):  
Alice Iannaccone ◽  
Daniele Conte ◽  
Cristina Cortis ◽  
Andrea Fusco
Keyword(s):  
Heart Rate ◽  
Perceived Exertion ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Team Sports ◽  
Rating Of Perceived Exertion ◽  
Subjective Measures ◽  
Internal Load ◽  
Random Slopes ◽  
The Relationship

Internal load can be objectively measured by heart rate-based models, such as Edwards’ summated heart rate zones, or subjectively by session rating of perceived exertion. The relationship between internal loads assessed via heart rate-based models and session rating of perceived exertion is usually studied through simple correlations, although the Linear Mixed Model could represent a more appropriate statistical procedure to deal with intrasubject variability. This study aimed to compare conventional correlations and the Linear Mixed Model to assess the relationships between objective and subjective measures of internal load in team sports. Thirteen male youth beach handball players (15.9 ± 0.3 years) were monitored (14 training sessions; 7 official matches). Correlation coefficients were used to correlate the objective and subjective internal load. The Linear Mixed Model was used to model the relationship between objective and subjective measures of internal load data by considering each player individual response as random effect. Random intercepts were used and then random slopes were added. The likelihood-ratio test was used to compare statistical models. The correlation coefficient for the overall relationship between the objective and subjective internal data was very large (r = 0.74; ρ = 0.78). The Linear Mixed Model using both random slopes and random intercepts better explained (p < 0.001) the relationship between internal load measures. Researchers are encouraged to apply the Linear Mixed Models rather than correlation to analyze internal load relationships in team sports since it allows for the consideration of the individuality of players.

Technical and Tactical Training Load in Professional Volleyball Players

2019 ◽  
Vol 14 (10) ◽  
pp. 1338-1343
Author(s):  
Thiago S. Duarte ◽  
Danilo L. Alves ◽  
Danilo R. Coimbra ◽  
Bernardo Miloski ◽  
João C. Bouzas Marins ◽  
...  
Keyword(s):  
Perceived Exertion ◽  
Training Load ◽  
Technical Training ◽  
Rating Of Perceived Exertion ◽  
Different Types ◽  
Volleyball Players ◽  
Tactical Training ◽  
Training Impulse ◽  
The Relationship ◽  
And Training

Purpose: To analyze the technical and tactical training load in professional volleyball players, using subjective internal training load (session rating of perceived exertion  [SRPE]) and objective internal training load (training impulse of the heart rate [HR]) and the relationship between them. Methods: The sample was composed of 15 male professional volleyball players. They were monitored during 37 training sessions that included both technical (n = 23) and tactical (n = 14) training. Technical and training load was calculated using SRPE and training impulse of the HR. Results: Significant correlations were found between the methods in tactical (r = .616) and technical training (r = −.414). Furthermore, it was noted that technical training occurs up to 80% of HRmax (zone 3) and tactical training between 70% and 90% of HRmax (zones 3–4). Conclusions: The training impulse of the HR method has proved to be effective for training-load control during tactical training. However, it was limited compared with technical training. Thus, the use of SRPE is presented as a more reliable method in the different types of technical training in volleyball.

scholarly journals Influence of Faster and Slower Recovery-Profile Classifications, Self-Reported Sleep, Acute Training Load, and Phase of the Microcycle on Perceived Recovery in Futsal Players

2020 ◽  
Vol 15 (5) ◽  
pp. 648-653 ◽  
Author(s):  
Carolina F. Wilke ◽  
Samuel P. Wanner ◽  
Weslley H.M. Santos ◽  
Eduardo M. Penna ◽  
Guilherme P. Ramos ◽  
...  
Keyword(s):  
Sleep Quality ◽  
Perceived Exertion ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Total Quality ◽  
Training Impulse

Purpose: To determine whether daily perceived recovery is explained from a multifactorial single-session classification of recovery (ie, faster vs slower) or other circumstantial factors (ie, previous training load, self-reported sleep, or phase of the microcycle). Methods: Nineteen elite male futsal players were initially allocated to a recovery-classification group (faster recovery, slower physiological, or slower perceptual) based on previous research using a multifactorial cluster-analysis technique. During 4 ensuing weeks of preseason, training loads were monitored via player load, training impulse, and session rating of perceived exertion. Before each day’s training, players reported their perception of recovery (Total Quality of Recovery scale [TQR]) and the number of hours and perceived quality of sleep the night prior. A hierarchical linear mixed model was used to analyze the effect of the different recovery profiles, training load, sleep, and phase of the microcycle (ie, start, middle, end) on daily TQR. Results: The recovery classification of players (P = .20), training load (training impulse, P = .32; player load, P = .23; session rating of perceived exertion, P = .46), and self-reported hours slept the night before (P = .45) did not significantly influence TQR. However, perceived sleep quality (P < .01) and phase of the microcycle (P < .01) were significantly associated with TQR (r2 = .41). Conclusions: Neither recovery classification nor prior training load influenced perceived recovery during the preseason. However, higher TQR was evident with better self-reported sleep quality, whereas lower values were associated with phases of the training week.

Determining the Relationship Between Internal Load Markers and Noncontact Injuries in Young Elite Soccer Players

2019 ◽  
Vol 14 (4) ◽  
pp. 421-425 ◽  
Author(s):  
Javier Raya-González ◽  
Fabio Yuzo Nakamura ◽  
Daniel Castillo ◽  
Javier Yanci ◽  
Maurizio Fanchini
Keyword(s):  
Confidence Interval ◽  
Odds Ratio ◽  
Perceived Exertion ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Load Ratio ◽  
Rating Of Perceived Exertion ◽  
Soccer Players ◽  
Internal Load ◽  
Injury Occurrence

Purpose: To examine the association and predictive ability of internal load markers with regard to noncontact injuries in young elite soccer players. Methods: Twenty-two soccer players (18.6 [0.6] y) who competed in the Spanish U19 League participated in the study. During a full season, noncontact injuries were recorded and, using session rating of perceived exertion, internal weekly load (sum of load of all training sessions and matches for each week) and acute:chronic workload ratio (typically, acute = current week and chronic = rolling 4-wk average) were calculated. A generalized estimating equation analysis was used to examine the association of weekly and acute:chronic load-ratio markers with a noncontact injury in the subsequent week. Load variables were also analyzed for predictive ability with receiver operating characteristic curve and area under the curve. Results: No association was found for weekly load (odds ratio = 1.00; 90% confidence interval, 0.99–1.00) and acute:chronic load ratio (odds ratio = 0.16; 90% confidence interval, 0.01–1.84) with respect to injury occurrence. In addition, the analyzed load markers showed poor ability to predict injury occurrence (area under the curve < .50). Conclusions: The results of this study suggest that internal load markers are not associated with noncontact injuries in young soccer players and present poor predictive capacity with regard to the latter.

scholarly journals Factors Influencing the Association Between Coach and Athlete Rating of Exertion: a Systematic Review and Meta-analysis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Darren Paul ◽  
Paul Read ◽  
Abdulaziz Farooq ◽  
Luke Jones
Keyword(s):  
Systematic Review ◽  
Perceived Exertion ◽  
Meta Analysis ◽  
Random Effect ◽  
Positive Association ◽  
Rating Of Perceived Exertion ◽  
Original Research ◽  
Eligibility Criteria ◽  
High Association ◽  
The Relationship

Abstract Background Subjective monitoring of rate of perceived exertion is common practice in many sports. Typically, the information is used to understand the training load and at times modify forthcoming sessions. Identifying the relationship between the athlete and coach’s interpretation of training would likely further benefit understanding load management. The aim of this systematic review was to evaluate the relationship between coaches’ rating of intended exertion (RIE) and/or rating of observed exertion (ROE) and athletes’ reported rating of perceived exertion (RPE). Methods The review was undertaken in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We conducted a search of Medline, Google Scholar, Science Direct, SPORTDiscus, and Web of Science databases. We assessed the correlation between coach-reported RIE and/or ROE and RPE. Assessment for risk of bias was undertaken using the Quality Appraisal for Reliability Studies (QAREL) checklist. Inclusion criteria were (1) male and/or female individuals, (2) individual and/or team sport active participants, and (3) original research article published in the English language. Results Data from 19 articles were found to meet the eligibility criteria. A random effect meta-analysis based on 11 studies demonstrated a positive association of player vs. coach rating of RIE (r = 0.62 [95% CI 0.5 to 0.7], p < 0.001). The pooled correlation from 7 studies of player vs. coach rating on ROE was r = 0.64 95% CI (0.5 to 0.7), p < 0.001. Conclusion There was a moderate to high association between coach RIE and/or ROE and athlete-reported RPE and this association seems to be influenced by many factors. The suggestions we present in this review are based on imploring practitioners to consider a multi-modal approach and the implications of monitoring when using RPE. Trial Registration CRD42020193387

scholarly journals “How Am I Going, Coach?”—The Effect of Augmented Feedback During Small-Sided Games on Locomotor, Physiological, and Perceptual Responses

2020 ◽  
Vol 15 (5) ◽  
pp. 677-684
Author(s):  
Jonathon J.S. Weakley ◽  
Dale B. Read ◽  
Hugh H.K. Fullagar ◽  
Carlos Ramirez-Lopez ◽  
Ben Jones ◽  
...  
Keyword(s):  
Heart Rate ◽  
High Speed ◽  
Perceived Exertion ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Low Frequency ◽  
Rating Of Perceived Exertion ◽  
Sprint Speed ◽  
Rugby Players ◽  
Differential Rating

Purpose: To investigate whether providing global positioning system feedback to players between bouts of small-sided games (SSGs) can alter locomotor, physiological, and perceptual responses. Methods: Using a reverse counterbalanced design, 20 male university rugby players received either feedback or no feedback during “off-side” touch rugby SSGs. Eight 5v5, 6 × 4-minute SSGs were played over 4 d. Teams were assigned to a feedback or no-feedback condition (control) each day, with feedback provided during the 2-min between-bouts rest interval. Locomotor, heart rate, and differential rating of perceived exertion of breathlessness and leg-muscle exertion were measured and analyzed using a linear mixed model. Outcomes were reported using effect sizes (ES) and 90% confidence intervals (CI), and then interpreted via magnitude-based decisions. Results: Very likely trivial to unclear differences at all time points were observed in heart rate and differential rating of perceived exertion measures. Possibly to very likely trivial effects were observed between conditions, including total distance (ES = 0.15; 90 CI, −0.03 to 0.34), high-speed distance (ES = −0.07; 90 CI, −0.27 to 0.13), and maximal sprint speed (ES = 0.11; 90% CI, −0.11 to 0.34). All within-bout comparisons showed very likely to unclear differences, apart from possible increases in low-speed distance in bout 2 (ES = 0.23; 90% CI, 0.01 to 0.46) and maximal sprint speed in bout 4 (ES = 0.21; 90% CI, −0.04 to 0.45). Conclusions: In this study, verbal feedback did not alter locomotor, physiological, or perceptual responses in rugby players during SSGs. This may be due to contextual factors (eg, opposition) or the type (ie, distance) or low frequency of feedback provided.

scholarly journals The Difference in Neuromuscular Fatigue and Workload During Competition and Training in Elite Cricketers

2019 ◽  
Vol 14 (4) ◽  
pp. 439-444 ◽  
Author(s):  
Kieran Cooke ◽  
Tom Outram ◽  
Raph Brandon ◽  
Mark Waldron ◽  
Will Vickery ◽  
...  
Keyword(s):  
Perceived Exertion ◽  
Neuromuscular Function ◽  
Peak Force ◽  
Rating Of Perceived Exertion ◽  
Jump Height ◽  
Countermovement Jump ◽  
The Difference ◽  
The Relationship ◽  
External Loads ◽  
And Training

Purpose: First, to assess changes in neuromuscular function via alterations in countermovement-jump strategy after training and 2 forms of competition and second, to compare the relationship between workloads and fatigue in seam bowlers and nonseam bowlers. Methods: Twenty-two professional cricketers’ neuromuscular function was assessed at baseline, immediately post and +24 h posttraining, and after multiday and 1-day cricket events. In addition, perceptual (rating of perceived exertion [RPE] and soreness) measures and external loads (PlayerLoad™, number of sprints, total distance, and overs) were monitored across all formats. Results: Seam bowlers covered more distance, completed more sprints, and had a higher RPE in training (P < .05), without any difference in soreness compared with nonseam bowlers. Compared with seam bowlers, the nonseam bowlers’ peak force decreased post-24 h compared with baseline only in 1-d cricket (95% CI, 2.1–110.0 N; P < .04). There were no pre–post training or match differences in jump height or alterations in jump strategy (P > .05). Seam bowlers increased their peak jumping force from baseline to immediately posttraining or game (95% CI, 28.8–132.4 N; P < .01) but decreased between postcricket to +24 h (95% CI, 48.89–148.0 N; P < .001). Conclusion: Seam bowlers were more accustomed to high workloads than nonseamers and thus more fatigue resistant. Changes in jump height or strategy do not appear to be effective methods of assessing fatigue in professional crickets. More common metrics such as peak force are more sensitive.

Effectiveness of daily subjective wellness measurements via mobile applications in predicting perceived exertion and training load

2021 ◽  
pp. 175433712110490
Author(s):  
Montgomery Bertschy ◽  
Jeffrey T Howard ◽  
Sakiko Oyama ◽  
Tianou Zhang ◽  
Kelly Cheever
Keyword(s):  
Mobile Application ◽  
Perceived Exertion ◽  
Training Session ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Student Athlete ◽  
Internal Load ◽  
Point Increase ◽  
Time Distance ◽  
And Training

This study aimed to examine the effectiveness of presession subjective wellness, measured by a mobile application, in predicting the rating of perceived exertion (RPE) and training load for collegiate female soccer players throughout all training sessions of a competitive season. Thirty-three training sessions over 10 weeks involving 23 subjects were analyzed. Before each training session, players self-reported wellness measurements (sleep quality, stress, mood, soreness, fatigue, and sleep duration) through a mobile application questionnaire. Training load (active time, distance, GPS session load, and sprint score) was measured by GPS. RPE was self-reported immediately after training sessions. Generalized estimating equations found that presession wellness scores had a significant effect on postsession RPE. A 1 point increase in the wellness score corresponded with a significant increase in RPE ([Formula: see text] = 0.4[Formula: see text], 95% CI (0.05–0.91), p = 0.029). Wellness component of mood had a significant effect on RPE as noted by a 1 SD increase that corresponded to an increase in RPE ([Formula: see text] = 0.[Formula: see text], 95% CI (0.07–0.30), p = 0.001). Psychosocial component of wellness (mood) played the most influential role in predicting RPE. The results suggest that monitoring student-athlete wellness may be helpful in regulating player internal load, and increasing player mood may also lower internal load.

scholarly journals The generalizability crisis

2020 ◽  
pp. 1-37
Author(s):  
Tal Yarkoni
Keyword(s):  
Quantitative Analysis ◽  
Statistical Inference ◽  
Random Effects ◽  
False Positive ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Statistical Procedures ◽  
Subject Variability ◽  
Replication Crisis

Abstract Most theories and hypotheses in psychology are verbal in nature, yet their evaluation overwhelmingly relies on inferential statistical procedures. The validity of the move from qualitative to quantitative analysis depends on the verbal and statistical expressions of a hypothesis being closely aligned—that is, that the two must refer to roughly the same set of hypothetical observations. Here I argue that many applications of statistical inference in psychology fail to meet this basic condition. Focusing on the most widely used class of model in psychology—the linear mixed model—I explore the consequences of failing to statistically operationalize verbal hypotheses in a way that respects researchers' actual generalization intentions. I demonstrate that whereas the "random effect" formalism is used pervasively in psychology to model inter-subject variability, few researchers accord the same treatment to other variables they clearly intend to generalize over (e.g., stimuli, tasks, or research sites). The under-specification of random effects imposes far stronger constraints on the generalizability of results than most researchers appreciate. Ignoring these constraints can dramatically inflate false positive rates, and often leads researchers to draw sweeping verbal generalizations that lack a meaningful connection to the statistical quantities they are putatively based on. I argue that failure to take the alignment between verbal and statistical expressions seriously lies at the heart of many of psychology's ongoing problems (e.g., the replication crisis), and conclude with a discussion of several potential avenues for improvement.

scholarly journals Response transformations for random effect and variance component models

2020 ◽  
pp. 1471082X2096691
Author(s):  
Amani Almohaimeed ◽  
Jochen Einbeck
Keyword(s):  
Maximum Likelihood ◽  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Statistical Technique ◽  
Response Distribution ◽  
Level Data ◽  
Variance Component Models ◽  
Response Transformation

Random effect models have been popularly used as a mainstream statistical technique over several decades; and the same can be said for response transformation models such as the Box–Cox transformation. The latter aims at ensuring that the assumptions of normality and of homoscedasticity of the response distribution are fulfilled, which are essential conditions for inference based on a linear model or a linear mixed model. However, methodology for response transformation and simultaneous inclusion of random effects has been developed and implemented only scarcely, and is so far restricted to Gaussian random effects. We develop such methodology, thereby not requiring parametric assumptions on the distribution of the random effects. This is achieved by extending the ‘Nonparametric Maximum Likelihood’ towards a ‘Nonparametric profile maximum likelihood’ technique, allowing to deal with overdispersion as well as two-level data scenarios.

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