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The aim was to determine how jump load affects knee complaints in elite men's volleyball. We collected data from four men's premier league volleyball teams through three seasons in a prospective cohort study (65 players, 102 player‐seasons). Vert inertial measurement devices captured the jump load (jump frequency and jump height) from 21 088 daily player sessions, and knee complaints were reported in 3568 weekly OSTRC‐O questionnaires. Mixed complementary log–log regression models described the probability of (i) experiencing symptoms if players were currently asymptomatic, (ii) worsening symptoms if players had symptoms, and (iii) recovery from knee complaints. Based on our causal assumptions, weekly jump load was modeled as the independent variable, adjusted for age (years), weight (kg), position on volleyball team, and past jump load. No certain evidence of an association was found between weekly jump load and probability of (i) knee complaints (p from 0.10 to 0.32 for three restricted cubic splines of load), (ii) worsening symptoms if the player already had symptoms (p from 0.11 to 0.97), (iii) recovery (p from 0.36 to 0.63). The probability of knee complaints was highest for above‐average weekly jump load (~1.2% for an outside hitter with mean age and height) compared with low loads (~1%) and very high loads (→ ~ 0%). The association between jump load and knee complaints risk remains unclear. Small differences in risk across observed jump load levels were observed. It would likely require substantially increased sample sizes to detect this association with certainty. [ABSTRACT FROM AUTHOR]

Quantifying the pre-season workload of professional Rugby Union players, in relation to their respective positions not only provides crucial insights into their physical demands and training needs but also underscores the significance of the acute:chronic workload ratio (ACWR) in assessing workload. However, given the diversity in ACWR calculation methods, their applicability requires further exploration. As a result, this study aims to analyze the workload depending on the player's positions and to compare three ACWR calculation methods. Fifty-seven players were categorized into five groups based on their playing positions: tight five (T5), third-row (3R), number nine (N9), center, and third line defense (3L). The coupled and uncoupled rolling averages (RA), as well as the exponentially weighted moving average ACWR method, were employed to compute measures derived from GPS data. Changes throughout the pre-season were assessed using the one-way and two-way analysis of variance. The results revealed that N9 covered significantly greater distances and exhibited higher player load compared to T5 and 3L [p 2.5 m s−2), accelerations (>2.5 m s−2), acceleration distance (>2 m s−2), high-speed running (>15 km h−1), very high-speed running (>21 km h−1, VSHR), sprint running (>25 km h−1, SR) distance. When using coupled RA ACWR method, centers exposed significantly greater values to T5 (p

The relationship between recent (acute) training load relative to long-term (chronic) training load may be associated with sports injury risk. We explored the potential for modelling acute and chronic loads separately to address current statistical methodology limitations. We also determined whether there was any evidence of an interaction in the association between acute and chronic training loads and injury risk in football. A men's Qatar Stars League football cohort (1 465 players, 1 977 injuries), where training load was defined as the number of minutes of activity, and a Norwegian elite U-19 football cohort (81 players, 60 injuries), where training load was defined as the session rating of perceived exertion (sRPE). Mixed logistic regression was run with training load on the current day (acute load) and cumulative past training load estimated by distributed lag non-linear models (chronic load) as independent variables. Injury was the outcome. An interaction between acute and chronic training load was modelled. In both football populations, we observed that the risk of injury on the current day for different values of acute training load was highest for players with low chronic load, followed by high and then medium chronic load. The slopes varied substantially between different levels of chronic training load, indicating an interaction. Modelling acute and chronic loads separately in regression models is a suitable statistical approach for analysing the association between relative training load and injury risk in injury prevention research. Sports scientists should also consider the potential for interactions between acute and chronic load. [ABSTRACT FROM AUTHOR]

The relationship between recent (acute) training load relative to long-term (chronic) training load may be associated with sports injury risk. We explored the potential for modelling acute and chronic loads separately to address current statistical methodology limitations. We also determined whether there was any evidence of an interaction in the association between acute and chronic training loads and injury risk in football. A men’s Qatar Stars League football cohort (1 465 players, 1 977 injuries), where training load was defined as the number of minutes of activity, and a Norwegian elite U-19 football cohort (81 players, 60 injuries), where training load was defined as the session rating of perceived exertion (sRPE). Mixed logistic regression was run with training load on the current day (acute load) and cumulative past training load estimated by distributed lag non-linear models (chronic load) as independent variables. Injury was the outcome. An interaction between acute and chronic training load was modelled. In both football populations, we observed that the risk of injury on the current day for different values of acute training load was highest for players with low chronic load, followed by high and then medium chronic load. The slopes varied substantially between different levels of chronic training load, indicating an interaction. Modelling acute and chronic loads separately in regression models is a suitable statistical approach for analysing the association between relative training load and injury risk in injury prevention research. Sports scientists should also consider the potential for interactions between acute and chronic load.

The two aims of this study were: (i) to analyse the correlations between weekly acute workload (wAW), chronic workload, acute/chronic workload rate (wACWR), training monotony and strain and the weekly (w) reports of delayed onset muscle soreness, wFatigue, wStress, wSleep quality, and the Hooper indicator (wHI); (ii) to analyse the relationships between early, mid and end preparation season (PS) and the full PS. Ten elite young wrestlers participated in this study. The subjects who were included in this research were wrestlers participating in competitions organized by the National Turkish Wrestling Federation. The subjects were monitored for 32 weeks and were divided into three time periods: early PS, W1 to W11; mid PS, W12 to W22; and end PS, W23 to W32. Very large correlations were found for wAW and wACWR with wFatigue and wHI during end PS. Also, the same workload parameters were significantly correlated with wStress (r = 0.66; P = 0.03), wSleep (r = 0.78; P = 0.004), and wHI (r = 0.72; P = 0.01) during mid PS. The results of this study offer new perspectives for specialists regarding the perceived level of load and the variations of wellbeing during a PS at the level of elite young wrestlers. [ABSTRACT FROM AUTHOR]

The acute:chronic workload ratio (ACWR) is a metric that can be used to monitor training loads during sport. Over the last decade researchers have investigated how this metric relates to injury, yet little consideration has been given to how this metric interacts with performance. Two prospective longitudinal studies were implemented investigating internal and external ACWRs and match outcome in junior tennis players. Forty-two and 24 players were recruited to participate in the internal and external load studies, respectively. Internal load was measured using session rate of perceive exertion, while external load was defined as total swing counts. The main dependent variable was tennis match performance which was extracted from the universal tennis rating website. The ACWR for internal and external load were the primary independent variables. Acute load was defined as the total load for one week, while a 4-week rolling average represented chronic load. There were no significant associations between internal (p-value = .23) or external (p-value = .81) ACWR and tennis match performance as assessed by multivariate regressions. The ACWRs in these datasets were close to 1.00, thus a balanced training load was undertaken by these athletes upon entering match play but was not related to match success.

The two aims of this study were: (i) to analyse the correlations between weekly acute workload (wAW), chronic workload, acute/chronic workload rate (wACWR), training monotony and strain and the weekly (w) reports of delayed onset muscle soreness, wFatigue, wStress, wSleep quality, and the Hooper indicator (wHI); (ii) to analyse the relationships between early, mid and end preparation season (PS) and the full PS. Ten elite young wrestlers participated in this study. The subjects who were included in this research were wrestlers participating in competitions organized by the National Turkish Wrestling Federation. The subjects were monitored for 32 weeks and were divided into three time periods: early PS, W1 to W11; mid PS, W12 to W22; and end PS, W23 to W32. Very large correlations were found for wAW and wACWR with wFatigue and wHI during end PS. Also, the same workload parameters were significantly correlated with wStress (r = 0.66; P = 0.03), wSleep (r = 0.78; P = 0.004), and wHI (r = 0.72; P = 0.01) during mid PS. The results of this study offer new perspectives for specialists regarding the perceived level of load and the variations of wellbeing during a PS at the level of elite young wrestlers.

Abstract Background The purpose of the present study was to explore the feasibility of collecting GPS data and the usability of GPS data to evaluate associations between the training load and onset of running-related knee injuries (RRKIs). Methods Participants of the INSPIRE-trial, a randomized-controlled trial on running injury prevention, were asked to participate in this study. At baseline, demographic variables were collected. Follow-up questionnaires assessed information on RRKIs. Participants with a new reported RRKI and uninjured participants were sent a GPS export request. Weekly GPS-based training distances were used to calculate Acute:Chronic Workload Ratios (ACWRs). Results A total of 240 participants (62.7%) tracked their running training sessions with the use of a GPS-enabled device or platform and were willing to share their GPS data. From the participants (N = 144) who received a GPS export request, 50.0% successfully shared their data. The majority (69.4%) of the shared GPS data were usable for analyses (N = 50). GPS data were used to present weekly ACWRs of participants with and without an RRKI eight weeks prior to RRKI onset or running event. Conclusions It seems feasible to collect GPS data from GPS-enabled devices and platforms used by recreational runners. The results indicate that GPS data is usable to calculate weekly ACWRs to evaluate associations between training load and onset of RRKIs in recreational runners. Therefore, GPS-based ACWR measures can be used for future studies to evaluate associations between training load and onset of RRIs.

This study aimed to evaluate the relationship between injury risk, acute load (AL), acute chronic workload ratio (ACWR) and a new proposed ACWR. Design: a retrospective cohort study of the year 2018 was conducted on Argentine first-division soccer players. Participants: Data from 35 players (age = 26.7 ± 4.71 years; height = 176.28 ± 6.09 cm; mass = 74.2 ± 5.27 kg) were recorded; 12 players' data were analyzed for 1 year, and 23 players' data were analyzed for 6 months. Interventions: The mean difference of ACWR (MD = 0.22), high-density interval (HDI 95% = (0.07, 0.36)) and AL (MD = 449.23, HDI 95% = (146.41, 751.2)) between groups turned out to be statistically significant. The effect size between groups comparing ACWR and AL was identical (ES = 0.64). Results: The probability of suffering an injury conditioned by ACWR or random ACWR was similar for all estimated quantiles, and the differences between them were not statistically significant. Conclusions: The ACWR ratio, using internal load monitoring, is no better than a synthetic ACWR created from a random denominator to predict the probability of injury. ACWR should not be used in isolation to analyze the causality between load and injury. [ABSTRACT FROM AUTHOR]

The acute:chronic workload ratio (ACWR) is a metric that can be used to monitor training loads during sport. Over the last decade researchers have investigated how this metric relates to injury, yet little consideration has been given to how this metric interacts with performance. Two prospective longitudinal studies were implemented investigating internal and external ACWRs and match outcome in junior tennis players. Forty-two and 24 players were recruited to participate in the internal and external load studies, respectively. Internal load was measured using session rate of perceive exertion, while external load was defined as total swing counts. The main dependent variable was tennis match performance which was extracted from the universal tennis rating website. The ACWR for internal and external load were the primary independent variables. Acute load was defined as the total load for one week, while a 4-week rolling average represented chronic load. There were no significant associations between internal (p-value = .23) or external (p-value = .81) ACWR and tennis match performance as assessed by multivariate regressions. The ACWRs in these datasets were close to 1.00, thus a balanced training load was undertaken by these athletes upon entering match play but was not related to match success. [ABSTRACT FROM AUTHOR]

Abstract Background The interpretation of the load variations across a period seems important to control the weekly progression or variation of the load, or to identify within- micro and mesocycle variations. Thus, the aim of this study was to describe the in-season variations of training monotony, training strain, and acute: chronic workload ratio (ACWR) through session rating of perceived exertion (s-RPE), total distance and high-speed running (HSR) according to playing positions in an elite soccer team. Methods Seventeen professional players from an European First League team participated in this study. They were divided four central defenders (CD), three wide defenders (WD), four central midfielders (CM), three wide midfielders (WM) and three strikers (ST). The players were monitored daily over a 41-week period of competition where 52 matches occurred during the 2015–2016 in-season. Through the collection of s-RPE, total distance and HSR, training monotony, training strain and ACWR were calculated for each measure, respectively. Data were analysed across ten mesocycles (M: 1–10). Results The main results showed significant differences (p

Injuries in professional soccer are a significant concern for teams, and they are caused amongst others by high training load. This cohort study describes the relationship between workload parameters and the occurrence of non-contact injuries, during weeks with high and low workload in professional soccer players throughout the season. Twenty-one professional soccer players aged 28.3 ± 3.9 yrs. who competed in the Iranian Persian Gulf Pro League participated in this 48-week study. The external load was monitored using global positioning system (GPS, GPSPORTS Systems Pty Ltd) and the type of injury was documented daily by the team's medical staff. Odds ratio (OR) and relative risk (RR) were calculated for non-contact injuries for high- and low-load weeks according to acute (AW), chronic (CW), acute to chronic workload ratio (ACWR), and AW variation (Δ-Acute) values. By using Poisson distribution, the interval between previous and new injuries were estimated. Overall, 12 non-contact injuries occurred during high load and 9 during low load weeks. Based on the variables ACWR and Δ-AW, there was a significantly increased risk of sustaining non-contact injuries (p < 0.05) during high-load weeks for ACWR (OR: 4.67), and Δ-AW (OR: 4.07). Finally, the expected time between injuries was significantly shorter in high load weeks for ACWR [1.25 vs. 3.33, rate ratio time (RRT)] and Δ-AW (1.33 vs. 3.45, RRT) respectively, compared to low load weeks. The risk of sustaining injuries was significantly larger during high workload weeks for ACWR, and Δ-AW compared with low workload weeks. The observed high OR in high load weeks indicate that there is a significant relationship between workload and occurrence of non-contact injuries. The predicted time to new injuries is shorter in high load weeks compared to low load weeks. Therefore, the frequency of injuries is higher during high load weeks for ACWR and Δ-AW. ACWR and Δ-AW appear to be good indicators for estimating the injury risk, and the time interval between injuries. [ABSTRACT FROM AUTHOR]

Quantifying the pre-season workload of professional Rugby Union players, in relation to their respective positions not only provides crucial insights into their physical demands and training needs but also underscores the significance of the acute:chronic workload ratio (ACWR) in assessing workload. However, given the diversity in ACWR calculation methods, their applicability requires further exploration. As a result, this study aims to analyze the workload depending on the player's positions and to compare three ACWR calculation methods. Fifty-seven players were categorized into five groups based on their playing positions: tight five (T5), third-row (3R), number nine (N9), center, and third line defense (3L). The coupled and uncoupled rolling averages (RA), as well as the exponentially weighted moving average ACWR method, were employed to compute measures derived from GPS data. Changes throughout the pre-season were assessed using the one-way and two-way analysis of variance. The results revealed that N9 covered significantly greater distances and exhibited higher player load compared to T5 and 3L [p < 0.05, effect size (ES) = 0.16-0.68]. Additionally, 3L players displayed the highest workload across various measures, including counts of accelerations and decelerations (>2.5 m s -2 ), accelerations (>2.5 m s -2 ), acceleration distance (>2 m s -2 ), high-speed running (>15 km h -1 ), very high-speed running (>21 km h -1 , VSHR), sprint running (>25 km h -1 , SR) distance. When using coupled RA ACWR method, centers exposed significantly greater values to T5 (p < 0.05, ES = 0.8) and 3R (p < 0.05, ES = 0.83). Moreover, centers exhibited greater (p < 0.05, ES = 0.67-0.91) uncoupled RA ACWR values for VHSR and SR than T5 and 3R. When comparing the three ACWR methods, although significant differences emerged in some specific cases, the ES were all small (0-0.56). In light of these findings, training should be customized to the characteristics of players in different playing positions and the three ACWR calculation methods can be considered as equally effective approaches., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Con el objetivo de analizar posibles relaciones entre las modalidades de ratio de carga aguda:crónica (tanto por promedios de carga consecutivos, ACWR, como en su ponderación exponencial dinámica, EWMA), en relación con la lesionabilidad en el fútbol femenino, se realizó un estudio cuasiexperimental sin intervención de las ratios obtenidas a partir de las 212 sesiones de entrenamiento y partidos realizados durante una temporada en un equipo de fútbol femenino amateur (N = 17). Las variables utilizadas para el cálculo de las ratios de carga aguda y carga crónica fueron la percepción subjetiva del esfuerzo (RPE) con relación a la carga interna, el tiempo de exposición de cada jugadora durante las sesiones como carga externa (CAR) y la especificidad (ESP) en relación a la programación del entrenamiento. El análisis estadístico mostró diferencias significativas respecto a la variable lesión en las ratios RPE EWMA (4:16, 7:28, 7:21), ESP EWMA (4:16, 7:28, 7:21), CAR EWMA (4:16, 7:28, 7:21) y CAR ACWR (4:16, 7:28) (p

Injuries in professional soccer are a significant concern for teams, and they are caused amongst others by high training load. This cohort study describes the relationship between workload parameters and the occurrence of non-contact injuries, during weeks with high and low workload in professional soccer players throughout the season. Twenty-one professional soccer players aged 28.3 ± 3.9 yrs. who competed in the Iranian Persian Gulf Pro League participated in this 48-week study. The external load was monitored using global positioning system (GPS, GPSPORTS Systems Pty Ltd) and the type of injury was documented daily by the team's medical staff. Odds ratio (OR) and relative risk (RR) were calculated for non-contact injuries for high- and low-load weeks according to acute (AW), chronic (CW), acute to chronic workload ratio (ACWR), and AW variation (Δ-Acute) values. By using Poisson distribution, the interval between previous and new injuries were estimated. Overall, 12 non-contact injuries occurred during high load and 9 during low load weeks. Based on the variables ACWR and Δ-AW, there was a significantly increased risk of sustaining non-contact injuries (p < 0.05) during high-load weeks for ACWR (OR: 4.67), and Δ-AW (OR: 4.07). Finally, the expected time between injuries was significantly shorter in high load weeks for ACWR [1.25 vs. 3.33, rate ratio time (RRT)] and Δ-AW (1.33 vs. 3.45, RRT) respectively, compared to low load weeks. The risk of sustaining injuries was significantly larger during high workload weeks for ACWR, and Δ-AW compared with low workload weeks. The observed high OR in high load weeks indicate that there is a significant relationship between workload and occurrence of non-contact injuries. The predicted time to new injuries is shorter in high load weeks compared to low load weeks. Therefore, the frequency of injuries is higher during high load weeks for ACWR and Δ-AW. ACWR and Δ-AW appear to be good indicators for estimating the injury risk, and the time interval between injuries.

Background: The purpose of the present study was to explore the feasibility of collecting GPS data and the usability of GPS data to evaluate associations between the training load and onset of running-related knee injuries (RRKIs). Methods: Participants of the INSPIRE-trial, a randomized-controlled trial on running injury prevention, were asked to participate in this study. At baseline, demographic variables were collected. Follow-up questionnaires assessed information on RRKIs. Participants with a new reported RRKI and uninjured participants were sent a GPS export request. Weekly GPS-based training distances were used to calculate Acute:Chronic Workload Ratios (ACWRs). Results: A total of 240 participants (62.7%) tracked their running training sessions with the use of a GPS-enabled device or platform and were willing to share their GPS data. From the participants (N = 144) who received a GPS export request, 50.0% successfully shared their data. The majority (69.4%) of the shared GPS data were usable for analyses (N = 50). GPS data were used to present weekly ACWRs of participants with and without an RRKI eight weeks prior to RRKI onset or running event. Conclusions: It seems feasible to collect GPS data from GPS-enabled devices and platforms used by recreational runners. The results indicate that GPS data is usable to calculate weekly ACWRs to evaluate associations between training load and onset of RRKIs in recreational runners. Therefore, GPS-based ACWR measures can be used for future studies to evaluate associations between training load and onset of RRIs. [ABSTRACT FROM AUTHOR]

The primary focus of this article is to highlight that an exponentially weighted model should be used to calculate an athlete's workload and that physiological and biomechanical workloads should be considered as two distinct factors in calculating an athlete's total workload. Current methods used to assess an athlete's workload, such as the Olympic Weightlifting and Training Impulse (TRIMP) methods, use a version of the traditional "Volume x Intensity" equation to calculate workload. This is done by calculating intensity, through either internal (Heart Rate, Rate of Perceived Exertion (RPE), Blood Lactate, etc.) or external (Weight used, Distance Travelled, etc.) metrics, and volume, through either duration or repetitions, depending on the activity at hand. While useful for monitoring athlete workloads, these methods use a linear model to calculate accumulated workload, with two efforts at 50% maximal intensity being considered the same as one effort at 100% of maximal intensity, something which has been shown anecdotally and through research to be untrue. This paper suggests that the workload calculations should be modified from "Volume x Intensity" to "Volume x IntensityX" to account for the fatigue cost of an exercise increasing exponentially as exercise intensity increases. The paper also details how and why this method, or traditional methods, can be modified so that an athletes' total workload is the sum of their physiological and biomechanical workloads, instead of one all-encompassing metric, and how this can better aid coaches to track their athletes and identify overload or underload related risk factors. [ABSTRACT FROM AUTHOR]

The study aim was 2-fold (i) to describe and compare the in-season variations of acute: chronic workload ratio (ACWR) coupled, ACWR uncoupled, and exponentially weighted moving average (EWMA) through session-rated perceived exertion (s-RPE), total distance (TD), high-speed running distance (HSRD), and sprint distance across different periods of a professional soccer season (early, mid, and end-season) between starters and non-starters; (ii) to analyze the relationship the aforementioned measures across different periods of the season for starters and non-starters. Twenty elite soccer players (mean ± SD age, 29.4 ± 4.4 y; height, 1.8 ± 0.1 m; and body mass, 74.8 ± 2.3 kg). They were divided into starter and non-starter groups and were evaluated for 20 weeks. ACWR had general changes throughout the season. At the beginning and end of the mid-season, the highest ACWR was observed in three parameters: s-RPE, TD, and HSRD. ACWR and EWMA through sprint distance were higher at the beginning of the early-season than at any other time of the season. The ACWR coupled of s-RPE shows a significant higher value for non-starters than starters (p = 0.015; g = −1.01 [−1.98, −0.09]) and the ACWR coupled of TD shows a significant higher value for starters than non-starters in early-season (p < 0.01; g = 3.01 [1.78, 4.46]) and shows a significant higher value for non-starters than starters in mid-season (p < 0.01; g = −2.52 [−3.83, −1.39]), and end-season (p < 0.01; g = −2.57 [−3.89, −1.43]). While the EWMA of TD shows a significant higher value for starters than non-starters in early-season (p < 0.01; g = 2.25 [1.17, 3.49]) and mid-season (p < 0.01; g = 2.42 [1.31, 3.71]), and shows a significant higher value for non-starters than starters in end-season (p < 0.01; g = −2.23 [−3.47, −1.16]). Additionally, we found some correlations between external and internal load measures during three periods of the in-season. The study's main finding was that the indexes of ACWR and EWMA were useful to detect differences between period and between playing status with the exception for the sprint variable. In addition, the necessary work for non-starter players' improvement is not done during training, and these players lose their readiness as the season progresses. Consequently, these players perform poorly during the match. Therefore, coaches and their staff should consider devising new activities to keep non-starter players physically fit. This deficit must be accounted for in training because they compete in fewer matches and have less burden than starters. [ABSTRACT FROM AUTHOR]

The study aim was 2-fold (i) to describe and compare the in-season variations of acute: chronic workload ratio (ACWR) coupled, ACWR uncoupled, and exponentially weighted moving average (EWMA) through session-rated perceived exertion (s-RPE), total distance (TD), high-speed running distance (HSRD), and sprint distance across different periods of a professional soccer season (early, mid, and end-season) between starters and non-starters; (ii) to analyze the relationship the aforementioned measures across different periods of the season for starters and non-starters. Twenty elite soccer players (mean ± SD age, 29.4 ± 4.4 y; height, 1.8 ± 0.1 m; and body mass, 74.8 ± 2.3 kg). They were divided into starter and non-starter groups and were evaluated for 20 weeks. ACWR had general changes throughout the season. At the beginning and end of the mid-season, the highest ACWR was observed in three parameters: s-RPE, TD, and HSRD. ACWR and EWMA through sprint distance were higher at the beginning of the early-season than at any other time of the season.The ACWR coupled of s-RPE shows a significant higher value for non-starters than starters (p = 0.015; g = −1.01 [−1.98, −0.09]) and the ACWR coupled of TD shows a significant higher value for starters than non-starters in early-season (p

Copyright of SPORMETRE: The Journal of Physical Education & Sport Sciences / Beden Eğitimi ve Spor Bilimleri Dergisi is the property of SPORMETRE: The Journal of Physical Education & Sport Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: The interpretation of the load variations across a period seems important to control the weekly progression or variation of the load, or to identify within- micro and mesocycle variations. Thus, the aim of this study was to describe the in-season variations of training monotony, training strain, and acute: chronic workload ratio (ACWR) through session rating of perceived exertion (s-RPE), total distance and high-speed running (HSR) according to playing positions in an elite soccer team. Methods: Seventeen professional players from an European First League team participated in this study. They were divided four central defenders (CD), three wide defenders (WD), four central midfielders (CM), three wide midfielders (WM) and three strikers (ST). The players were monitored daily over a 41-week period of competition where 52 matches occurred during the 2015–2016 in-season. Through the collection of s-RPE, total distance and HSR, training monotony, training strain and ACWR were calculated for each measure, respectively. Data were analysed across ten mesocycles (M: 1–10). Results: The main results showed significant differences (p < 0.05) for TMs-RPE between CD vs. ST (moderate effect) in M2; between CD vs. CM (moderate effect) for TS of s-RPE; between CD vs. ST moderate effect) in M6 for ACWR of s-RPE. In addition, there was significant difference between CM vs. ST (moderate effect) in M2 for TS of TD; between WD vs. ST (moderate effect) in M3 for ACWR of TD. Moreover, there were significant differences for TM of HSR between CD vs. WD (very large effect); CD vs. WD (moderate effect) in M4 for TS of HSR. Conclusions: The present study presents new insights to coaches and technical staff about the variation profiling of TM, TS, and ACWR calculated with internal and external load measures, between player positions during 10 mesocycles. [ABSTRACT FROM AUTHOR]

This study was conducted to determine if the acute: chronic workload ratio (ACWR) is related to the incidence of non-contact injuries. The purpose is to compare the external load of injured and non-injured soccer players with the same characteristics, such as position and age. The present analysis considers both the four and the two weeks preceding an injury. Physical characteristics were recorded and analyzed through global positioning systems (GPS) evaluation over one season of 24 competitive microcycles, 144 training sessions, and 32 matches in a total of 35 professional soccer players from the Greek Super League 1 and Super League 2. The loads calculated were total distance (TD), 15–20 km/h, 20–25 km/h, 25–30 km/h, accelerations (ACC) > 2.5 m/s2, and decelerations (DEC) > 2.5 m/s2). Nine injured athletes exceeded the critical threshold of an ACWR > 1.3 several times compared with non-injured athletes that did not reach this level. The present study showed that ACWR is related to a subsequent occurrence of injury but that the threshold of an ACWR can vary. This seems to be mainly influenced by assessing the load of the last two weeks compared with that of the four weeks before the injury.

This study aimed to evaluate the relationship between injury risk, acute load (AL), acute chronic workload ratio (ACWR) and a new proposed ACWR. Design: a retrospective cohort study of the year 2018 was conducted on Argentine first-division soccer players. Participants: Data from 35 players (age = 26.7 ± 4.71 years; height = 176.28 ± 6.09 cm; mass = 74.2 ± 5.27 kg) were recorded; 12 players’ data were analyzed for 1 year, and 23 players’ data were analyzed for 6 months. Interventions: The mean difference of ACWR (MD = 0.22), high-density interval (HDI 95% = (0.07, 0.36)) and AL (MD = 449.23, HDI 95% = (146.41, 751.2)) between groups turned out to be statistically significant. The effect size between groups comparing ACWR and AL was identical (ES = 0.64). Results: The probability of suffering an injury conditioned by ACWR or random ACWR was similar for all estimated quantiles, and the differences between them were not statistically significant. Conclusions: The ACWR ratio, using internal load monitoring, is no better than a synthetic ACWR created from a random denominator to predict the probability of injury. ACWR should not be used in isolation to analyze the causality between load and injury.

Copyright of Apunts: Educació Física i Esports is the property of Institut Nacional d'Educacio Fisica de Catalunya and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: This study’s aim was twofold: (i) to compare starters and non-starters on a professional soccer team in terms of variations in training intensity indexes across a season, calculated through total distance, sprint distance, accelerations (Acc), and decelerations (Dec) and (ii) to analyse the relationship between the intensity indexes for each playing status. Methods: Nineteen players (age, 29.4 ± 4.4 years; height, 1.8 ± 0.1 m; body mass, 74.8 ± 2.3 kg) were divided into starters and non-starters and followed for 43 weeks using global positioning systems. Results: Training intensity measures (acute:chronic workload ratio [ACWR], coupled and uncoupled) were higher during the latter stage of the season. Total distance peaked during the mid-season, whereas the highest value for exponentially weighted moving average (EWMA) was recorded later in the season. Interestingly, the EMWA of total distance showed little variation during the season for players of both playing statuses. The EWMA of total distance showed a significant higher value for starters than non-starters (p = 0.036; g = 1.27 [0.31, 2.32]). The interruption in games between week 34 and week 35 due to COVID-19 moved some measures into the injury risk zone — namely, the ACWR coupled of sprint distance and Dec; the ACWR uncoupled of total distance, sprint distance, Acc, Dec; and the EWMA of sprint distance, Acc and Dec. Conclusions: The highest training intensity measures were reported late in the season and were similar between starters and non-starters. Across the season, only one difference between starters and non-starters occurred, revealing that training intensity was properly managed throughout the season regardless of the status of the players.

Background: The aims of this study were to describe the variations of training monotony (TM), training strain (TS), and acute:chronic workload ratio (ACWR) through Hooper Index categories (fatigue, stress, DOMS, and sleep quality) and to compare those variations between player status and player positions. Methods: Seventeen male professional soccer players participated in this study. Considering player status, participants were divided in nine starters and eight non-starters. Additionally, participants were divided by playing positions: three wide defenders, four central defenders, three wide midfielders, four central midfielders, and three strikers. They were followed during 40-week in-season period. TM, TS, and ACWR were calculated for each HI category, respectively. Data were grouped in 10 mesocycles for further analysis. Results: Results showed variations across the mesocycles. In general, starters showed higher values for TM, TS, and ACWR calculations than non-starters, although there were some exceptions. Regarding player positions, significant differences were found in stress between wide defenders vs central midfielders for TM (p = 0.033, ES = 5.16), central defenders vs wide defenders for ACWR (p = 0.044, ES = 4.95), and in sleep between wide defenders and strikers for TM (p = 0.015, ES = 5.80). Conclusions: This study revealed that an analysis of players’ well-being parameters according to player status and positions can provide clear information to the coaches and their staff to complement the tasks of training monitoring.

ObjectiveTo examine the relationships between the occurrence and severity of injuries using three workload ratios (ACWR, EWMA, REDI) in elite female soccer players and international male and female pentathletes.Materials and MethodsFemale soccer players in the U16 to U18 national French teams (n = 24) and international athletes (n = 12, 4 women and 8 men) in the French modern pentathlon team were monitored throughout an entire season. The Acute Chronic Workload Ratio (ACWR), the Exponentially Weighted Moving Averages (EWMA), and the Robust Exponential Decreasing Index (REDI) were calculated for internal load by the ROE method in soccer and external load in pentathlon. The occurrence and severity of injuries (determined according to time-loss) were quantified in the sweet spot zone [0.8; 1.3] and compared to the other zones of load variation: [0; 0.8], [1.3; 1.5], [1.5; +8], using the three ratios.ResultsOver the study period, a total of sixty-six injuries (2.75 per athlete) were reported in the soccer players and twelve in pentathletes (1 per athlete). The cumulative severity of all injuries was 788 days lost in soccer and 36 in pentathlon: respectively, 11.9 days lost per injury in soccer player and 3.0 per pentathlete. The mean values across the three methods in soccer showed a higher number of injuries detected in the [0; 0.8] workload ratio zone: 22.3 ± 6.4. They were 17.3 ± 3.5 in the sweet spot ([0.8–1.3] zone) and 17.6 ± 5.5 in the [1.5; +8] zone. In comparison to the [1.5; +8] zone, soccer players reported a higher number of days lost to injuries in the presumed sweet spot and in the [0–0.8] zone: 204.7 ± 28.7 and 275.0 ± 120.7 days, respectively. In pentathletes, ten of the twelve injuries (83.3%) occurred in the presumed sweet spot. REDI was the only method capable of tracking workloads over all-time series.ConclusionIn the present cohort of elite soccer players and pentathletes, acute chronic workload calculations showed an association with injury occurrence and severity but did not provide evidence supporting existence of a sweet spot diminishing injury risk.

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Objective: To examine the relationships between the occurrence and severity of injuries using three workload ratios (ACWR, EWMA, REDI) in elite female soccer players and international male and female pentathletes. Materials and Methods: Female soccer players in the U16 to U18 national French teams (n = 24) and international athletes (n = 12, 4 women and 8 men) in the French modern pentathlon team were monitored throughout an entire season. The Acute Chronic Workload Ratio (ACWR), the Exponentially Weighted Moving Averages (EWMA), and the Robust Exponential Decreasing Index (REDI) were calculated for internal load by the ROE method in soccer and external load in pentathlon. The occurrence and severity of injuries (determined according to time-loss) were quantified in the sweet spot zone [0.8; 1.3] and compared to the other zones of load variation: [0; 0.8], [1.3; 1.5], [1.5; +8], using the three ratios. Results: Over the study period, a total of sixty-six injuries (2.75 per athlete) were reported in the soccer players and twelve in pentathletes (1 per athlete). The cumulative severity of all injuries was 788 days lost in soccer and 36 in pentathlon: respectively, 11.9 days lost per injury in soccer player and 3.0 per pentathlete. The mean values across the three methods in soccer showed a higher number of injuries detected in the [0; 0.8] workload ratio zone: 22.3 ± 6.4. They were 17.3 ± 3.5 in the sweet spot ([0.8–1.3] zone) and 17.6 ± 5.5 in the [1.5; +8] zone. In comparison to the [1.5; +8] zone, soccer players reported a higher number of days lost to injuries in the presumed sweet spot and in the [0–0.8] zone: 204.7 ± 28.7 and 275.0 ± 120.7 days, respectively. In pentathletes, ten of the twelve injuries (83.3%) occurred in the presumed sweet spot. REDI was the only method capable of tracking workloads over all-time series. Conclusion: In the present cohort of elite soccer players and pentathletes, acute chronic workload calculations showed an association with injury occurrence and severity but did not provide evidence supporting existence of a sweet spot diminishing injury risk. [ABSTRACT FROM AUTHOR]

Objectives: Methodological concerns relating to acute-to-chronic workload ratios (ACWR) have been raised. This study aimed to assess the relationship between an alternative predictor variable named 'differential load', representing the smoothed week-to-week rate change in load, and injury risk in first class county cricket (FCCC) fast bowlers.Design: Prospective cohort study.Methods: Bowling loads and injuries were recorded for 49 professional male fast bowlers from six FCCC teams. A range of differential loads and ACWRs were calculated and subjected to a variable selection procedure.Results: Exponentially-weighted 7-day differential load, 9:21-day ACWR, 42-day chronic load, and 9-day acute load were the best-fitting predictor variables in their respective categories. From these, a generalized linear mixed-effects model combining 7-day differential load, 42-day chronic load, and 9-day acute load provided the best model fit. A two-standard deviation (2SD) increase in 7-day differential load (22 overs) was associated with a substantial increase in injury risk (risk ratio [RR]=2.47, 90% CI: 1.27-4.80, most likely harmful), and a 2SD increase in 42-day chronic load (17.5 overs/week) was associated with a most likely harmful increase in injury risk (RR=6.77, 90% CI: 2.15-21.33). For 9-day acute load, very low values (≤1 over/week) were associated with a most likely higher risk of injury versus moderate (17.5 overs/week; RR: 15.50, 90% CI: 6.19-38.79) and very high 9-day acute loads (45.5 overs/week; RR: 133.33, 90% CI: 25.26-703.81).Conclusions: Differential loads may be used to identify potentially harmful spikes in load, whilst mitigating methodological issues associated with ACWRs. [ABSTRACT FROM AUTHOR]

The purpose of the present study was to examine the influence of workload prior to injury on injury (tissue type and severity) in professional soccer players. Twenty-eight days of retrospective training data prior to non-contact injuries (n=264) were collated from 192 professional soccer players. Each injury tissue type (muscle, tendon and ligament) and severity (days missed) were categorised by medical staff. Training data were recorded using global positioning system (GPS) devices for total distance (TD), high speed distance (HSD,>5.5 m/s−1), and sprint distance (SPR,>7.0 m/s−1). Accumulated 1, 2, 3, 4-weekly loads and acute:chronic workload ratios (ACWR) (coupled, uncoupled and exponentially weighted moving average (EWMA) approaches) were calculated. Workload variables and injury tissue type were compared using a one-way ANOVA. The association between workload variables and injury severity were examined using a bivariate correlation. There were no differences in accumulated weekly loads and ACWR calculations between muscle, ligament, and tendon injuries (P>0.05). Correlations between each workload variable and injury severity highlighted no significant associations (P>0.05). The present findings suggest that the ability of accumulated weekly workload or ACWR methods to differentiate between injury type and injury severity are limited using the present variables. [ABSTRACT FROM AUTHOR]

Background: The main purpose of the current study was to compare the within-season variations of workload, training duration, acute/chronic workload ratio (ACWR), training monotony ™, and training strain (TS) through session rating perceived exertion (s-RPE) between starters and non-starters. Methods: Seventeen under-17 European male soccer players (age, 16.2 ± 0.3 y, height, 1.8 ± 0.1 m; body mass, 66.5 ± 4.0 kg) divided in two groups: nine starters and eight non-starters, were evaluated over 50 weeks throughout the season. Results: In general, there were load variations for all players during the full-season. RPE tended to decrease during in-season and RPE, training duration and s-RPE did not present significant differences between starters and non-starters. TM and TS presented lower values for starters in mesocycle (M) 4 and M11 compared to non-starters. TS presented lower values for starters in M4 and M11 compared to non-starters, while in M10 a higher value was found for starters when compared to non-starters. ACWR showed differences between starters and non-starters in two of the mesocycles. Conclusions: This study showed that some mesocycles provided higher load for non-starters. This information can alert coaches that non-starter players are likely to try too hard in training to demonstrate their abilities, leading to non-functional overreaching, overtraining syndrome, and then poor performance.

Competing Interests: Competing interests: None declared.

Background: The relation between training load, especially internal load, and the recovery-stress state is of central importance for avoiding negative adaptations in high-performance sports like swimming. The aim of this study was to analyze the individual time-delayed linear effect relationship between training load and recovery-stress state with single case time series methods and to monitor the acute recovery-stress state of high-performance swimmers in an economical and multidimensional manner over a macro cycle. The Acute Recovery and Stress Scale (ARSS) was used for daily monitoring of the recovery-stress state. The methods session-RPE (sRPE) and acute:chronic workload-ratio (ACWR) were used to compare different methods for quantifying the internal training load with regard to their interrelationship with the recovery-stress state.Methods: Internal load and recovery-stress state of five highly trained female swimmers [with a training frequency of 13.6 ± 0.8 sessions per week and specializing in sprint (50 and 100 m), middle-distance (200 and 400 m), or long distance (800 and 1,500 m) events] were daily documented over 17 weeks. Two different types of sRPE were applied: RPE∗duration (sRPEh) and RPE∗volume (sRPEkm). Subsequently, we calculated the ratios ACWRh and ACWRkm (sRPE last week: 4-week exponentially weighted moving average). The recovery-stress state was measured by using the ARSS, consisting of eight scales, four of which are related to recovery [Physical Performance Capability (PPC), Mental Performance Capability (MPC), Emotional Balance (EB), Overall Recovery (OR)], and four to stress [Muscular Stress (MS), Lack of Activation (LA), Negative Emotional State (NES), Overall Stress (OS)]. To examine the relation between training load and recovery-stress state a cross correlation (CCC) was conducted with sRPEh, sRPEkm, ACWRh, and ACWRkm as lead and the eight ARSS-scales as lag variables.Results: A large variation of training load can be observed in the individual week-to-week fluctuations whereby the single fluctuations can significantly differ from the overall mean of the group. The range also shows that the CCC individually reaches values above 0.3, especially with sRPEkm as lead variable. Overall, there is a large range with significant differences between the recovery and stress dimensions of the ARSS and between the training load methods, with sRPEkm having the largest span (Range = 1.16). High inter-individual differences between the athletes lie in strength and direction of the correlation | 0.66|≤ CCC ≥|-0.50|. The time delayed effects (lags 0–7) are highly individual, however, clear patterns can be observed.Conclusion: The ARSS, especially the physical and overall-related scales (PPC, OR, MS, OS), is a suitable tool for monitoring the acute recovery-stress state in swimmers. MPC, EB, LA, and NES are less affected by training induced changes. Comparably high CCC and Ranges result from the four internal load methods, whereby sRPE, especially sRPEkm, shows a stronger relation to recovery-stress state than ACWR. Based on these results and the individual differences in terms of time delay in training response, we recommend for swimming to use sRPE to monitor the internal training load and to use the ARSS, with a focus at the physical and overall-scales, to monitor the recovery-stress state.

The objective of this study was to quantify the training load involved in tennis training using relatively simple tools to provide practical information to coaches on the calculation and optimization of the training load, paying particular attention to the session-RPE method, the calculation of the monotony index and the acute to chronic workload ratio calculation. This article presents a case study of a 16-year-old player combining his education with his training in a tennis academy as well as with competition. The daily and weekly training load as well as the other indicators were calculated over 31 weeks and then explained, interpreted and discussed in this article. [ABSTRACT FROM AUTHOR]

Background: The relation between training load, especially internal load, and the recovery-stress state is of central importance for avoiding negative adaptations in high-performance sports like swimming. The aim of this study was to analyze the individual time-delayed linear effect relationship between training load and recovery-stress state with single case time series methods and to monitor the acute recovery-stress state of high-performance swimmers in an economical and multidimensional manner over a macro cycle. The Acute Recovery and Stress Scale (ARSS) was used for daily monitoring of the recovery-stress state. The methods session-RPE (sRPE) and acute:chronic workload-ratio (ACWR) were used to compare different methods for quantifying the internal training load with regard to their interrelationship with the recovery-stress state. Methods: Internal load and recovery-stress state of five highly trained female swimmers [with a training frequency of 13.6 ± 0.8 sessions per week and specializing in sprint (50 and 100 m), middle-distance (200 and 400 m), or long distance (800 and 1,500 m) events] were daily documented over 17 weeks. Two different types of sRPE were applied: RPE∗duration (sRPEh) and RPE∗volume (sRPEkm). Subsequently, we calculated the ratios ACWRh and ACWRkm (sRPE last week: 4-week exponentially weighted moving average). The recovery-stress state was measured by using the ARSS, consisting of eight scales, four of which are related to recovery [Physical Performance Capability (PPC), Mental Performance Capability (MPC), Emotional Balance (EB), Overall Recovery (OR)], and four to stress [Muscular Stress (MS), Lack of Activation (LA), Negative Emotional State (NES), Overall Stress (OS)]. To examine the relation between training load and recovery-stress state a cross correlation (CCC) was conducted with sRPEh, sRPEkm, ACWRh, and ACWRkm as lead and the eight ARSS-scales as lag variables. Results: A large variation of training load can be observed in the individual week-to-week fluctuations whereby the single fluctuations can significantly differ from the overall mean of the group. The range also shows that the CCC individually reaches values above 0.3, especially with sRPEkm as lead variable. Overall, there is a large range with significant differences between the recovery and stress dimensions of the ARSS and between the training load methods, with sRPEkm having the largest span (Range = 1.16). High inter-individual differences between the athletes lie in strength and direction of the correlation | 0.66|≤ CCC ≥|-0.50|. The time delayed effects (lags 0–7) are highly individual, however, clear patterns can be observed. Conclusion: The ARSS, especially the physical and overall-related scales (PPC, OR, MS, OS), is a suitable tool for monitoring the acute recovery-stress state in swimmers. MPC, EB, LA, and NES are less affected by training induced changes. Comparably high CCC and Ranges result from the four internal load methods, whereby sRPE, especially sRPEkm, shows a stronger relation to recovery-stress state than ACWR. Based on these results and the individual differences in terms of time delay in training response, we recommend for swimming to use sRPE to monitor the internal training load and to use the ARSS, with a focus at the physical and overall-scales, to monitor the recovery-stress state. [ABSTRACT FROM AUTHOR]

Background: The main purpose of the current study was to compare the within-season variations of workload, training duration, acute/chronic workload ratio (ACWR), training monotony ™, and training strain (TS) through session rating perceived exertion (s-RPE) between starters and non-starters. Methods: Seventeen under-17 European male soccer players (age, 16.2 ± 0.3 y, height, 1.8 ± 0.1 m; body mass, 66.5 ± 4.0 kg) divided in two groups: nine starters and eight non-starters, were evaluated over 50 weeks throughout the season. Results: In general, there were load variations for all players during the full-season. RPE tended to decrease during in-season and RPE, training duration and s-RPE did not present significant differences between starters and non-starters. TM and TS presented lower values for starters in mesocycle (M) 4 and M11 compared to non-starters. TS presented lower values for starters in M4 and M11 compared to non-starters, while in M10 a higher value was found for starters when compared to non-starters. ACWR showed differences between starters and non-starters in two of the mesocycles. Conclusions: This study showed that some mesocycles provided higher load for non-starters. This information can alert coaches that non-starter players are likely to try too hard in training to demonstrate their abilities, leading to non-functional overreaching, overtraining syndrome, and then poor performance. [ABSTRACT FROM AUTHOR]

• This study shows that the highest values of acute, chronic load, and training strain were verified in the mid-competitive season and the lowest values in the start-competitive season. • Wellness indicators could predict related acute load. • These results provide new insights for coaches and practitioners about perceived loads and well-being variations over a season in elite young soccer players. This study described the weekly variations of acute (wAL), chronic (wCL), acute:chronic workload ratio (wACWR), training monotony (wTM), and training strain (wTS) of perceived load, such as wellness indicators over a competitive season. In addition, we analyzed the associations between training load metrics and weekly reports. Twenty-nine under 16 years old elite players were daily monitored for 20 consecutive weeks during the season by individual observations. Training and match load were obtained using the session rating of perceived exertion. Well-being status relative to stress, fatigue, delayed onset muscle soreness (DOMS), and sleep quality/disorders were daily monitored using the Hooper index method. The results revealed that the highest values of wAL, wCL, and wTS were verified in the mid-season and the lowest values in the start-season. The highest values of accumulated weekly fatigue, stress, and DOMS were observed in the end-season, and the lowest values of sleep and stress in the start-season while the lowest values of fatigue and DOMS were observed in the mid-season. Regarding the load variability, the results showed the highest values between-week variations to wTS (15%; week-8 to 9) and the lowest reduction to wACWR (-19%; week-9 to 10). The highest within-week variations were verified to wACWR (coefficient of variation =19%; week-18) and the lowest to wCL (coefficient of variation =6%, week-19). Wellness indicators were moderate-large related to acute load, monotony and strain (r = 0.46−0.67). Overall Hooper index was the best predictor of the acute load (R2 = 0.45). These results provide new insights for coaches and practitioners about perceived loads and well-being variations over a season in elite youth level. [ABSTRACT FROM AUTHOR]

The aim of this study was twofold: (1) to describe the weekly acute workload (wAW), chronic workload (wCW), acute/chronic workload ratio (wACWR), training monotony (wTM), and strain (wTS) across the preparation season (PS), and (2) to analyze the variations of wAW, wCW, wACWR, wTM, and training strain (wTS) between periods of PS (early-, mid-, and end). Ten elite young wrestlers were monitored daily during the 32 weeks of the season. Internal loads were monitored using session rating of perceived exertion, and weekly workload measures of wACWR, wTM, and wTS were also calculated. Results revealed that the greatest differences were found between early- and mid-PS for wAW ( p = 0.004, g = 0.34), wCW ( p = 0.002, g = 0.90), wTM ( p = 0.005, g = 0.39), and wTS ( p = 0.009, g = -1.1), respectively. The wACWR showed significant differences between early- and end-PS ( p ≤ 0.001, g = -0.30). We concluded that wAW, wCW, and wTM are slightly lower during the first weeks of the PS. The wTM remained relatively high during the entire season, while wAW and wCW remained balanced throughout the PS. The greatest workload changes seem to happen from the early to mid-PS season.

Objective: To investigate whether the relationship between the acute-chronic workload ratio (ACWR) and health problems varies when different methodological approaches are used to quantify it., Design: Prospective cohort study., Methods: An online questionnaire was used to collect daily health and training information from 86 elite youth footballers for 105 days. The relationship between players' training load and health was analyzed using a range of different definitions of ACWR and health problems. We used 21-day and 28-day chronic periods, coupled and uncoupled calculations, and the exponentially weighted moving average and rolling average. Acute-chronic workload ratio data were categorized as low, medium, or high, using predefined categories and z scores. We compared medium to high, medium to low, and low to high categories. The outcome was defined in 3 ways: "all health problems," "all injuries," and "new noncontact injuries." We performed random-effects logistic regression analyses of all combinations, for a total of 108 analyses., Results: We recorded 6250 athlete-days and 196 health problems. Of the 108 analyses performed, 23 (21%) identified a statistically significant ( P <.05) association between the ACWR and health problems. A greater proportion of significant associations were identified when using an exponentially weighted moving average (44% of analyses), when comparing low to high categories (33%), and when using the "all health problems" definition (33%)., Conclusion: The relationship between the ACWR and health problems was dependent on methodological approach. J Orthop Sports Phys Ther 2021;51(4):162-173. Epub 20 Jan 2021. doi:10.2519/jospt.2021.9893 .