(searched for: doi:10.1007/s40279-021-01551-5)
Published: 24 January 2023
Journal: Strength and Conditioning Journal
Strength and Conditioning Journal; https://doi.org/10.1519/ssc.0000000000000765
Quantifying training load is important to ensure that athletes correctly respond to training prescription and reduce injury risk. Training load can be divided into internal training load, the response of an individual to the training demand, and external training load, the physical “work” of the players. We aimed to analyze training load during a training week (microcycle) in soccer players. Systematic searches of 3 electronic databases (PubMed, SPORTDiscus, and Web of Science) were conducted, and Preferred Reporting Items for Systematic Review and Meta-analysis guidelines were followed. From 1,718 studies initially found, 16 were selected after screening. Descriptive and Z-score analyses were performed for each variable (acceleration and deceleration [DEC], average speed, high-speed running, sprint, total distance, player load, percentage of maximal heart rate, and rating of perceived exertion [RPE]). A sample of this review was 317 male soccer players aged 16.4–27.6 years, competing in elite, professional, and youth levels. Three days prior to match day was the most demanding session of the week, except for DEC, average speed, and player load demands. The day prior to match day was the least demanding session, except for sprint and RPE. In conclusion, midweek sessions can be chosen to apply higher training loads, whereas training sessions immediately before and after the match can be used to taper or recover.
Published: 1 January 2023
International Journal of Sports Physiology and Performance, Volume -1, pp 1-7; https://doi.org/10.1123/ijspp.2021-0500
Purpose: To investigate the internal training loads of a professional Spanish female futsal team throughout 26 weeks of training including preseason and in-season weeks and verify the impact of training period and/or training load magnitudes on heart-rate variability responses. Furthermore, we aimed to assess, intraindividually, the relationship between training load and the coefficient of variation (CV) of weekly natural log of the root mean square difference of successive normal interbeat (RR) intervals (lnRMSSDCV), obtained from ∼5 measures per week, and recorded in the seated position. Methods: A within-subject design involved 12 high-level outfield female futsal players (mean [SD] age: 23.9 [3.4] y). Results: lnRMSSD was significantly lower and lnRMSSDCV was significantly higher during the preseason (weeks 1–6) compared to in-season (weeks 7–26) (P < .001). Individually, players presented moderate to large negative correlations between lnRMSSDCV and lnRMSSD during the 26 weeks of observation. Correlations ranged between rplayer4 = −.41 (95% CI, −.69 to −.02) and rplayer12 = −.55 (−.78 to −.18). Players also presented moderate to verylarge positive correlations between lnRMSSDCV and session rating of perceived exertion. Correlations ranged between rplayer7 = .41 (.04 to .71) and rplayer1 = .71 (.45 to .86). Conclusion: Professional female futsal players in this study presented increased lnRMSSD and reduced lnRMSSDCV during 20 weeks into the competitive season compared to 6 weeks of preseason. Furthermore, lnRMSSDCV was negatively associated with lnRMSSD on an intraindividual basis. Finally, higher internal training loads were positively correlated with lnRMSSDCV, indicating that heart-rate variability is responsive to weekly training loads.
Published: 1 January 2023
International Journal of Sports Physiology and Performance, Volume 18, pp 1-2; https://doi.org/10.1123/ijspp.2022-0417
"Study Designs to Reduce the Gap Between Science and Practice in Sport" published on 07 Dec 2022 by Human Kinetics.
Published: 1 January 2023
International Journal of Sports Physiology and Performance, Volume 18, pp 18-26; https://doi.org/10.1123/ijspp.2022-0179
Purpose: This study aimed to examine the responsiveness of commonly used measurement instruments to a short training camp by examining the time course of psychophysiological responses in high-level youth soccer players. Methods: Monitoring was carried out in 14 U15 male soccer players of 1 professional youth academy. Players provided data 3 days prior to (D − 3), during (D2−D4), and 1 (D + 1) and 4 days (D + 4) after the camp: 4 items for the Short Recovery and Stress Scale (SRSS), a countermovement jump (CMJ), and a submaximal run to assess exercise heart rate and heart-rate recovery. Training load during the camp followed an alternating low–high pattern, with lower training loads on D1 and D3 and higher training loads on D2 and D4. Results: Changes in SRSS physical performance capability, emotional balance, overall recovery, muscular stress, and overall stress were small to moderate on D3 and moderate to large on D + 1, while changes were trivial on D + 4. Some CMJ parameters related to the eccentric phase were slightly improved on D3, and these parameters were slightly impaired on D4. Changes in CMJ parameters were trivial on D + 1 and D + 4. After a moderate decrease in exercise heart rate on D3, there was a small decrease on D + 4 and a moderate increase in heart-rate recovery. Conclusion: Measurement instruments such as the SRSS and submaximal runs can be used to monitor acute psychophysiological responses to load, while the CMJ may provide little insight during periods of intensified training load.
Frontiers in Sports and Active Living, Volume 4; https://doi.org/10.3389/fspor.2022.970152
Injury reduction remains a hot topic in professional football due to the economic and competitive implications of time lost (1, 2). Current strategies to reduce injury burden involve either reducing primary injuries through prevention-based strategies or lowering the risk of secondary injuries when they occur. It appears that primary injury reduction strategies are largely effective (3, 4), and might have supported reduced incidence across the past two decades (5, 6). Strategies concerning re-injury risk, however, are less than optimal, particularly when concerning recurrent and/or high-grade muscle and ligament injuries (1, 5). Whilst return to play (RTP) rates for such injuries are high in elite football, players often return with heightened risk of re-injury and may experience lower performance levels, especially after severe injuries such as anterior cruciate ligament (ACL) ruptures (7–14). Injuries are thought to occur due to a complex web of determinants (15), with previous injury remaining one of the most reported risk factors (16). Re-injuries (i.e., to the same location) or subsequent injuries (i.e., in a different location) typically occur early in the RTP process, suggesting players might be returned too quickly for sufficient tissue healing, or they are inadequately prepared for RTP demands (6, 16–18). The role of previous injury as a risk factor for future injury can be mitigated through effective rehabilitation (19). As such, improving RTP practice and processes appears warranted to improve outcomes after certain injuries (e.g., high-grade muscle/severe ligament injuries). There is a lack of consensus on effective rehabilitation for such injuries, with current evidence suggesting that players should embark on a criterion-based process through a series of stages (20). These typically include early-, mid- and late-stage rehabilitation, followed by a RTP continuum, involving on-field rehabilitation (OFR), return to team training, return to competitive match-play and finally a return to performance (Figure 1) (21–26). Recently, there has been an increase in translational research published to support football medicine departments with their late-stage rehabilitation processes, specifically that of OFR (21, 22, 26, 27). OFR as a service is not new with numerous practitioners establishing unpublished frameworks before evidence-based practice and load monitoring technologies existed. Scientific developments however have facilitated two separate published frameworks for OFR, which use competency-based continua to provide evidential structures to support long-established practices (21, 22, 26). Despite improving clarity, such research is currently restricted to expert opinion and/or case studies. Although this is a complex topic with numerous inherent challenges, future research should attempt validation of such frameworks. Figure 1. A return to sport process involving a gradual transition from rehabilitation to performance training along a continuum of OFR, RTT, RTC, and RTPer. ORF, on-field rehabilitation; RTT, return to training; RTC, return to competition; RTPer, return to performance. Modified and re-printed with permission from Buckthorpe et al. (21). The purpose of this article is to (i) review injury incidence literature to assess the prevalence of re-injuries and postulate OFR as a potential tool to mitigate future risk, (ii) consider injury aetiology and the complexity of OFR, (iii) describe existing OFR frameworks, and (iv) offer future directions related to the development of OFR in professional football. Understanding injury occurrence, healing timeframes and RTP rates are vital when designing, implementing, and evaluating OFR frameworks. When injuries occur, they are often categorised based on their severity, or the potential for time loss. Most injuries are mild (≤7 days), and overall RTP rates from all injuries are high, however those returning from severe injuries (>28 days) such as ACL ruptures often face long absence, elevated re-injury risk and reduced performance levels (1, 9). Overall, injuries have reduced by ~3% per year over the past 18 years, with muscle injury rates remaining unchanged (5). Although this should be considered in the context of greater frequencies and intensities of matches nowadays, muscle injuries remain a concern given their susceptibility to re-injury (17, 28). Indeed, injuries involving musculature of the lower limbs remain notable (~15%) (1). Ekstrand et al. (1) reported ACL re-injury rates at 6.6%, which is in-keeping with others (29), but less than the 18% reported by Della Villa et al. (9). However, it is perhaps severity and not incidence which is of concern for ACL injuries, with a mean absence of 205 days (1). Although, re-injury rates were low in the study of Waldén et al. (29), five out of the nine re-ruptures occurred during the final phase of rehabilitation or before the first match, and all others were within the first 3 months after the first match. The timing of these re-injuries suggests an increased risk during on-field activities and reinforces the importance of effective OFR frameworks. All injuries are related to an overload of some type, whether they involve trauma (i.e., contact), mechanical failure (i.e., non-contact) or a combination of both (i.e., indirect contact) (30, 31). They occur when the stress and/or strain on the body tissue exceeds the maximal strength or failure strain of that tissue (32). Injury prevention models have traditionally been based on a reductionist view (15, 33) that simplifies multifaceted components into units, attempting to identify relationships and sequence events (e.g., isolating the mechanism, site, type, and treatment of injury) (34, 35). In reality, injury involves complex interactions between numerous factors, and so seemingly comparable situations may yield different outcomes (15). Contributing factors might include any combination of neural...
Frontiers in Psychology, Volume 13; https://doi.org/10.3389/fpsyg.2022.1101052
Editorial on the Research TopicOptimizing player health, recovery, and performance in basketball Basketball is one of the most popular team sports globally, with participation rates ranging from 2 to 5% among adults (aged >18 years), 7–14% among adolescents (aged 13–17 years), and 5–25% among children (aged 5–12 years) in African, American, and Western Pacific regions (Hulteen et al., 2017). This participation rate has grown recently in many countries—for example, 27.1 million people from the United States over 6 years of age participated in basketball in 2021 compared to 22.3 million in 2016 (Statista, 2022). Furthermore, basketball is played across many competitive levels ranging from recreational settings to international tournaments such as the Olympics. In line with this broad appeal and increased participation, the number of journal publications focused on basketball has grown in the past 20 years (Figure 1), placing it second in publication outputs among Olympic team sports (Millet et al., 2021). Consequently, this Research Topic, Optimizing player health, recovery, and performance in basketball, was conceptualized as an outlet for this increased scientific interest to further strengthen the available evidence base for basketball end-users. Figure 1. Growth in the number of basketball Scopus-indexed journal publications between 2002 and 2021. Search conducted using Scopus on 27 October 2022 for “basketball” within “Article title, Abstract, Keywords” field, with “Journal” selected as source type and “Article in Press” excluded. The development of relevant research questions that meet the needs of end-users and provide real-world impact is essential to evidence-based practice (Fullagar et al., 2019a). In this way, the different focal areas of this Research Topic (i.e., player health, recovery, and performance) align with preferences for research evidence among end-users working in competitive sport (Fullagar et al., 2019b; Schwarz et al., 2021). For instance, most surveyed practitioners employed within a sports organization (at the collegiate, professional, or Olympic level) in the United States (n = 67, with 16% working in basketball) indicated they used research evidence for health-related functions [injury prevention (91%), nutrition (85%), and rehabilitation (81%)], recovery (94%), and performance-related functions [fitness (79%) and load monitoring (73%)], with research contributing most to developing individualized preparation/recovery strategies and optimizing individual performance (Fullagar et al., 2019a). Furthermore, most of the basketball literature has been identified to focus on topics related to physiology (Millet et al., 2021), injury (Scanlan and Dalbo, 2019; Millet et al., 2021), testing/assessment (Millet et al., 2021), load monitoring (Scanlan and Dalbo, 2019), and game statistics (Scanlan and Dalbo, 2019), which likely encompasses various health-, recovery-, and performance-related research questions. Consequently, the studies published in this Research Topic provide novel evidence in areas that are relevant to basketball end-users by extending upon popularized areas and expanding areas in need of further attention such as technical/tactical components and skill acquisition (Fullagar et al., 2019a). Three studies published in this Research Topic focused on external load monitoring among basketball players. Player monitoring is commonly employed by basketball practitioners (Fox et al., 2020), with external load data indicating what players do and being an integral part of the physical training process to impact health, recovery, and performance outcomes among players (Jeffries et al., 2022). Firstly, Russell et al. provide the most comprehensive analysis of external loads imposed upon a National Basketball Association (NBA) team to date, reporting demands during different tasks according to player role, experience, and position across a season. Secondly, Stone et al. provide insight into the utility of different external load variables measured using microsensors according to position among male, National Collegiate Athletic Association Division I players. Thirdly, Pernigoni et al. used video-based time-motion and microsensor technologies to quantify the demands experienced during jumps, sprints, and high-intensity specific movements, as well as with and without ball possession according to position among semi-professional, male basketball players. Four further descriptive studies focused on quantifying anthropometric, fitness, behavioral, or technical/tactical attributes among basketball players, generating evidence that may inform practical strategies in health- and performance-related areas including player assessment, selection, and nutrition. Firstly, Sato et al. described the associations between facial width-to-height ratio measurements and performance during games (i.e., efficiency rating) among professional, male basketball players. Secondly, Popowczak et al. highlighted the importance of anthropometrical attributes when elucidating associations between physical and cognitive variables during reactive agility and change-of-direction speed tests in professional, female basketball players. Thirdly, Rösch et al. concluded that the Basketball Learning and Performance Assessment Instrument possessed adequate reliability in assessing various performance and technical variables but lacked diagnostic validity in identifying selected (vs. non-selected) youth (under-15 years), male players within a national program. Fourthly, Sánchez-Díaz et al. demonstrated male players had superior physical fitness and led more active lifestyles than female players, with all players possessing inadequate nutritional habits and knowledge among youth (under-14 years) players from a national program. An additional three studies...
Published: 1 December 2022
BMC Sports Science, Medicine and Rehabilitation, Volume 14, pp 1-13; https://doi.org/10.1186/s13102-022-00596-x
Background: The non-linear index alpha 1 of Detrended Fluctuation Analysis (DFA a1) of heart rate variability, has been shown to be a marker of fatigue during endurance exercise. This report aims to explore its ability to assess the physiological status as a surrogate metric for “readiness to train” while performing simulated warm-up sessions the day after two different exercise sessions. Methods: 11 triathletes were recruited to determine the first ventilatory threshold (VT1) during a baseline assessment and to perform 10-min of cycling at 90% of VT1 (simulating a warm-up bout) before (PRE) and within 36 h after (POST) light and heavy running exercise. RR intervals were recorded for DFA a1 analysis along with neuromuscular testing to verify the effects of the performed exercise sessions. In addition to common statistical methods, magnitude-based inferences (MBI) were applied to assess the changes in true score and thus also the practical relevance of the magnitude. Results: Rating of perceived exertion for the heavy exercise session showed a significant higher rating as opposed to the light exercise session (p < 0.001, d = 0.89). In regard of MBIs, PRE versus POST comparisons revealed a significant reduced DFA a1 with large effect size after the heavy exercise session (p = 0.001, d = − 1.44) and a 99% chance that this negative change was clinically relevant. Conclusions: Despite inter-individual differences, DFA a1 offers potential to assess physiological status and guide athletes in their training as an easy-to-apply monitoring procedure during a standardized warm-up. A regular assessment including individual data history and statistical references for identification of response is recommended. Further data are necessary to confirm the results in a larger and more homogeneous population.
Published: 21 November 2022
Journal: Strength and Conditioning Journal
Strength and Conditioning Journal; https://doi.org/10.1519/ssc.0000000000000750
Because of fundamental mechanical misconceptions, workload is a contested and nonsensical term that has been erroneously used in sports science literature. When the term workload is used, readers may interpret the term to mean: (a) load, referring to the weight force of an object, or an external or internal force, applied in a specified direction and, when using the International System of Units (SI), the outcome measure must be reported in newtons, or (b) the amount of work performed, which should be reported in joules. Solutions consistent with the SI and using proper scientific terminology are simple and would improve the advancement and use of knowledge in sports science. During an endurance training program, exercise duration, relative or absolute mean velocity, distance traveled, and power output are manipulated. Within strength and power training programs, variables to be considered are repetitions and sets, rest period durations, and the load lifted. In team sports, performance quantification includes displacement, distance traveled, velocity, and acceleration. These physical quantities should replace the vague and inaccurate term workload. The quantification of physical performance should be accomplished using the SI for clarity of communication and seamless use across all subdisciplines of sports science.
Sports Medicine pp 1-18; https://doi.org/10.1007/s40279-022-01791-z
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Current Reviews in Musculoskeletal Medicine, Volume 15, pp 561-569; https://doi.org/10.1007/s12178-022-09799-8
The publisher has not yet granted permission to display this abstract.
Frontiers in Sports and Active Living, Volume 4; https://doi.org/10.3389/fspor.2022.919228
This study investigated how two slightly different athlete groups would differ in acute neuromuscular and endocrine responses to specific resistance exercise loadings and recovery compared to untrained participants. Power athletes (PA, n = 8), strength athletes (SA, n = 8) and non-athletes (NA, n = 7) performed power (PL, 7 × 6 × 50% of 1RM), maximal strength (MSL, 7 × 3 × 3RM), and hypertrophic (HL, 5 × 10 × 10RM) loadings in Smith-machine back-squat. Neuromuscular performance, serum testosterone, growth hormone, and cortisol concentrations, and blood lactate (BL) were measured before (Pre), at Mid and after (Post) loading, and after recovery for 24 and 48 h. All loadings led to acute decreases in neuromuscular performance and elevations in hormone concentrations and BL. During PL, a significant group × time interactions occurred in maximal isometric force [F(4, 40) = 4.189, p = 0.006, = 0.295] indicating a greater decrease in PA compared to SA from Pre to Mid (p < 0.05), and in countermovement jump height [F(4, 40) = 2.895, p = 0.034, = 0.224] indicating a greater decrease in NA compared to SA from Pre to Mid (p < 0.05). During HL, growth hormone was higher in Mid and Post in SA compared to NA (p < 0.05). No significant interactions were found during recovery. The differences during PL and HL suggest that the training background may enhance acute responses during the present loadings, whereas it seemed to have a limited effect on the recovery.
Sports Medicine, Volume 52, pp 2605-2626; https://doi.org/10.1007/s40279-022-01712-0
Team-sports staff often administer non-exhaustive exercise assessments with a view to evaluating physiological state, to inform decision making on athlete management (e.g., future training or recovery). Submaximal fitness tests have become prominent in team-sports settings for observing responses to a standardized physical stimulus, likely because of their time-efficient nature, relative ease of administration, and physiological rationale. It is evident, however, that many variations of submaximal fitness test characteristics, response measures, and monitoring purposes exist. The aim of this scoping review is to provide a theoretical framework of submaximal fitness tests and a detailed summary of their use as proxy indicators of training effects in team sports. Using a review of the literature stemming from a systematic search strategy, we identified five distinct submaximal fitness test protocols characterized in their combinations of exercise regimen (continuous or intermittent) and the progression of exercise intensity (fixed, incremental, or variable). Heart rate-derived indices were the most studied outcome measures in submaximal fitness tests and included exercise (exercise heart rate) and recovery (heart rate recovery and vagal-related heart rate variability) responses. Despite the disparity between studies, these measures appear more relevant to detect positive chronic endurance-oriented training effects, whereas their role in detecting negative transient effects associated with variations in autonomic nervous system function is not yet clear. Subjective outcome measures such as ratings of perceived exertion were less common in team sports, but their potential utility when collected alongside objective measures (e.g., exercise heart rate) has been advocated. Mechanical outcome measures either included global positioning system-derived locomotor outputs such as distance covered, primarily during standardized training drills (e.g., small-sided games) to monitor exercise performance, or responses derived from inertial measurement units to make inferences about lower limb neuromuscular function. Whilst there is an emerging interest regarding the utility of these mechanical measures, their measurement properties and underpinning mechanisms are yet to be fully established. Here, we provide a deeper synthesis of the available literature, culminating with evidence-based practical recommendations and directions for future research.
Sports Medicine - Open, Volume 8, pp 1-11; https://doi.org/10.1186/s40798-022-00433-y
This article addresses several key issues that have been raised related to subjective training load (TL) monitoring. These key issues include how TL is calculated if subjective TL can be used to model sports performance and where subjective TL monitoring fits into an overall decision-making framework for practitioners. Regarding how TL is calculated, there is conjecture over the most appropriate (1) acute and chronic period lengths, (2) smoothing methods for TL data and (3) change in TL measures (e.g., training stress balance (TSB), differential load, acute-to-chronic workload ratio). Variable selection procedures with measures of model-fit, like the Akaike Information Criterion, are suggested as a potential answer to these calculation issues with examples provided using datasets from two different groups of elite athletes prior to and during competition at the 2016 Olympic Games. Regarding using subjective TL to model sports performance, further examples using linear mixed models and the previously mentioned datasets are provided to illustrate possible practical interpretations of model results for coaches (e.g., ensuring TSB increases during a taper for improved performance). An overall decision-making framework for determining training interventions is also provided with context given to where subjective TL measures may fit within this framework and the determination if subjective measures are needed with TL monitoring for different sporting situations. Lastly, relevant practical recommendations (e.g., using validated scales and training coaches and athletes in their use) are provided to ensure subjective TL monitoring is used as effectively as possible along with recommendations for future research.
Sports Medicine, Volume 52, pp 949-951; https://doi.org/10.1007/s40279-021-01629-0
Sports Medicine pp 1-1; https://doi.org/10.1007/s40279-021-01628-1
Published: 1 January 2022
International Journal of Sports Physiology and Performance, Volume -1, pp 1-3; https://doi.org/10.1123/ijspp.2022-0247
Published: 1 January 2022
International Journal of Sports Physiology and Performance, Volume -1, pp 1-2; https://doi.org/10.1123/ijspp.2022-0154
Published: 1 January 2022
International Journal of Sports Physiology and Performance, Volume -1, pp 1-1; https://doi.org/10.1123/ijspp.2022-0147
Frontiers in Physiology, Volume 12; https://doi.org/10.3389/fphys.2021.711634
Locomotor and collision actions that rugby players complete during match-play often lead to substantial fatigue, and in turn, delays in recovery. The methods used to quantify post-match fatigue and recovery can be categorised as subjective and objective, with match-related collision characteristics thought to have a primary role in modulating these recovery measures. The aim of this review was to (1) evaluate how post-match recovery has been quantified in the rugby football codes (i.e., rugby league, rugby union, and rugby sevens), (2) to explore the time-course of commonly used measures of fatigue post-match, and (3) to investigate the relationships between game-related collisions and fatigue metrics. The available evidence suggests that upper-, and lower-body neuromuscular performance are negatively affected, and biomarkers of muscular damage and inflammation increase in the hours and days following match-play, with the largest differences being at 12–36 h post-match. The magnitude of such responses varies within and between neuromuscular performance (Δ ≤ 36%, n = 13 studies) and tissue biomarker (Δ ≤ 585%, n = 18 studies) measures, but nevertheless appears strongly related to collision frequency and intensity. Likewise, the increase in perceived soreness in the hours and days post-match strongly correlate to collision characteristics across the rugby football codes. Within these findings, there are specific differences in positional groups and recovery trajectories between the codes which relate to athlete characteristics, and/or locomotor and collision characteristics. Finally, based on these findings, we offer a conceptual model of fatigue which details the multidimensional latent structure of the load to fatigue relationship contextualised to rugby. Research to date has been limited to univariate associations to explore relationships between collision characteristics and recovery, and multivariate methods are necessary and recommended to account for the latent structures of match-play external load and post-match fatigue constructs. Practitioners should be aware of the typical time windows of fatigue recovery and utilise both subjective and objective metrics to holistically quantify post-match recovery in rugby.