Psychological tools used for monitoring training responses of athletes

Professional soccer practitioners' perceptions of using performance analysis technology to monitor technical and tactical player characteristics within an academy environment: A category 1 club case study

This study aimed to identify professional soccer practitioners' perceptions of the application of performance analysis technology within a single academy club. Secondary aims were to understand the importance that practitioners place on monitoring technical and tactical player characteristics, current practices, and barriers to implementing wearable technology. Utilising a mixed method design, forty-four professional soccer academy practitioners (Age = 32 ± 5.8; Years of experience = 8.5 ± 6.2) completed an online survey intended to examine present trends, professional practices, and perceptions regarding the monitoring of technical and tactical metrics. Frequency and percentages of responses for individual items were calculated. Subsequently, eleven participants who were directly involved with the monitoring of players were recruited to participate in a semi-structured interview. Interview data was transcribed and analysed using a combination of deductive and inductive approaches to identify key themes. The main findings across both phases of the study were that (1) technical and tactical metrics are monitored more frequently in matches (Technical: 89%; tactical: 91%) than training (Technical: 80%; Tactical 64%), predominantly due to time constraints and staffing numbers. Accordingly, practitioners believe that it would be beneficial to have an automated way of tracking technical (79%) and tactical (71%) metrics and would consider using a foot-mounted IMU to do so (technical (68%) and tactical (57%)). (2) Monitoring technical and tactical metrics is beneficial to assist with player development and to enrich feedback provision (3) Key stake holders, coaches and players should be informed of the relevance and rationale for monitoring. (4) For successful implementation and continued uptake, the information delivered needs to be both meaningful and easy to understand. Findings suggest that although participants appreciate the need to collect technical and tactical metrics, they are keen to ensure that wearable-derived data does not replace experiential and contextual knowledge. Accordingly, practitioners need to work closely with coaches to determine the contexts in which metrics may or may not prove useful. However, as the sample comprised of participants from a single academy, further studies including more practitioners are warranted. Likewise, future research could also extend to include academy soccer players perceptions too.

The Athletic Intelligence Quotient and performance in the National Basketball Association

Prior to selecting an NBA player, teams consider multiple factors, including game film and tests of agility, strength, speed, anthropometry, and personality. In recent years, as the other major professional sports have begun to place greater emphasis on the measurement of cognitive abilities, so too have representatives in the NBA. In this study, the predictive validity of an empirically-supported measure of cognitive ability (AIQ) was examined vis-à-vis performance outcomes in the NBA. Specifically, AIQ scores were obtained from 356 NBA prospects prior to their draft between 2014 and 2019. The players' professional status and subsequent performance were assessed through composite and isolated NBA statistics. ANOVAs demonstrated that there were significant differences between NBA and non-NBA players, and subsequent independent samples t-tests revealed that NBA players had significantly higher AIQ scores than non-NBA players for 3 out of 4 factors and the Full Scale AIQ Score. Additionally, using hierarchical multiple regression analyses, it was demonstrated that the AIQ predicted some modest statistically significant relationships with multiple NBA stats (e.g., Player Efficiency Rating, Effective Field Goal Percentage), after controlling for the impact of draft placement. While the effect sizes for these differences and relationships were somewhat small, such findings are consistent with sport analytics and the restricted range when evaluating professional athletes. Given the expanding role of analytics and cognitive assessment in the NBA, the potential importance of the AIQ is considered in the draft process.

Assessment of Fatigue and Recovery in Sport: Narrative Review

Fatigue is a phenomenon associated with decreases in both physical and cognitive performances and increases in injury occurrence. Competitive athletes are required to complete demanding training programs with high workloads to elicit the physiological and musculoskeletal adaptations plus skill acquisition necessary for performance. High workloads, especially sudden rapid increases in training loads, are associated with the occurrence of fatigue. At present, there is limited evidence elucidating the underlying mechanisms associating the fatigue generated by higher workloads and with an increase in injury risk. The multidimensional nature and manifestation of fatigue have led to differing definitions and dichotomies of the term. Consequently, a plethora of physiological, biochemical, psychological and performance markers have been proposed to measure fatigue and recovery. Those include self-reported scales, countermovement jump performance, heart rate variability, and saliva and serum biomarker analyses. The purpose of this review is to provide an overview of fatigue and recovery plus methods of assessments.

Training Monitoring in Sports: It Is Time to Embrace Cognitive Demand

Appropriate training burden monitoring is still a challenge for the support staff, athletes, and coaches. Extensive research has been done in recent years that proposes several external and internal indicators. Among all measurements, the importance of cognitive factors has been indicated but has never been really considered in the training monitoring process. While there is strong evidence supporting the use of cognitive demand indicators in cognitive neuroscience, their importance in training monitoring for multiple sports settings must be better emphasized. The aims of this scoping review are to (1) provide an overview of the cognitive demand concept beside the physical demand in training; (2) highlight the current methods for assessing cognitive demand in an applied setting to sports in part through a neuroergonomics approach; (3) show how cognitive demand metrics can be exploited and applied to our better understanding of fatigue, sport injury, overtraining and individual performance capabilities. This review highlights also the potential new ways of brain imaging approaches for monitoring in situ. While assessment of cognitive demand is still in its infancy in sport, it may represent a very fruitful approach if applied with rigorous protocols and deep knowledge of both the neurobehavioral and cognitive aspects. It is time now to consider the cognitive demand to avoid underestimating the total training burden and its management.

"The Competitive Season and Off-Season": Preliminary Research concerning the Sport-Specific Performance, Stress, and Sleep in Elite Male Adolescent Basketball Athletes

BACKGROUND

Through scholastic sports programs, adolescent athletes compete to represent their communities. However, few studies investigate the changes in physiological and mental profiles during varied sport periodization among this population. Therefore, the purpose of this study was to compare the changes in sports performance and stress-related biomarkers between the competitive season (CS) and off-season (OS) in elite adolescent basketball players.

METHOD

Nine elite Division I male basketball players (age: 15-18 years. old) participated in this study. Basketball-specific performance, salivary dehydroepiandrosterone sulfate (DHEA-S)/cortisol levels, mood state, and sleep quality were all accessed during the CS and OS periods.

RESULTS

The training load during OS was 26.0% lower than CS (p = 0.001). Muscle mass, aerobic capacity, 10 m sprint, and Abalakov jump (AJ) power during OS were greater than that during CS (+2.2-9.8%, p < 0.05), but planned agility was greater during CS (p = 0.003). The salivary DHEA-S/cortisol was greater during CS than during OS (p = 0.039). The overall mood state and sleep quality did not differ between periods, but the POMS-tension was higher during CS (p = 0.005).

CONCLUSION

The present study demonstrates that muscle mass, aerobic capacity, peak AJ power, and 10 m sprint performance, but not planned agility, were greater during OS compared to CS among elite adolescent basketball players. Furthermore, the stress-related responses reflected by the D/C ratio and mood tension were relatively lower during the OS in these athletes. Thus, this study suggests that coaches and sport science professionals should closely monitor athletes' training states across varied training/competition periods to better react to modifying training or recovery plans.

Development and Initial Validation of an Acute Readiness Monitoring Scale in Military Personnel

Personnel in many professions must remain “ready” to perform diverse activities. Managing individual and collective capability is a common concern for leadership and decision makers. Typical existing approaches for monitoring readiness involve keeping detailed records of training, health and equipment maintenance, or – less commonly – data from wearable devices that can be difficult to interpret as well as raising privacy concerns. A widely applicable, simple psychometric measure of perceived readiness would be invaluable in generating rapid evaluations of current capability directly from personnel. To develop this measure, we conducted exploratory factor analysis and confirmatory factor analysis with a sample of 770 Australian military personnel. The 32-item Acute Readiness Monitoring Scale (ARMS) demonstrated good model fit, and comprised nine factors: overall readiness; physical readiness; physical fatigue; cognitive readiness; cognitive fatigue; threat-challenge (i.e., emotional/coping) readiness; skills-and-training readiness; group-team readiness, and equipment readiness. Readiness factors were negatively correlated with recent stress, current negative affect and distress, and positively correlated with resilience, wellbeing, current positive affect and a supervisor’s rating of solider readiness. The development of the ARMS facilitates a range of new research opportunities: enabling quick, simple and easily interpreted assessment of individual and group readiness.

Troubleshooting a Nonresponder: Guidance for the Strength and Conditioning Coach

Ideally an athlete would continue to improve performance indefinitely over time, however improvement slows as the athlete approaches their genetic limits. Measuring performance is complex—performance may be temporarily depressed following aggressive training for multiple reasons, physiological and psychosocial. This reality may be vexing to the strength and conditioning coach, who, as a service provider, must answer to sport coaches about an athlete’s progress. Recently an evaluation mechanism for strength and conditioning coaches was proposed, in part to help coaches establish their effectiveness within the organization. Without formal guidance and realistic expectations, if an athlete is not bigger, leaner, stronger, etc. as a result of training within a specified timeframe, blame is often placed upon the strength and conditioning coach. The purpose of this article is to explore possible causes of what may be perceived as athlete non-responses to training and to provide guidance for the coach on how to handle those issues within their domain. A process of investigation is recommended, along with resources to assist coaches as they consider a broad range of issues, including enhancing existing testing methods, improving athlete behaviors, and adjusting processes designed to bring about performance improvement.

"To Tech or Not to Tech?" A Critical Decision-Making Framework for Implementing Technology in Sport

The current technological age has created exponential growth in the availability of technology and data in every industry, including sport. It is tempting to get caught up in the excitement of purchasing and implementing technology, but technology has a potential dark side that warrants consideration. Before investing in technology, it is imperative to consider the potential roadblocks, including its limitations and the contextual challenges that compromise implementation in a specific environment. A thoughtful approach is therefore necessary when deciding whether to implement any given technology into practice. In this article, we review the vision and pitfalls behind technology's potential in sport science and medicine applications and then present a critical decision-making framework of 4 simple questions to help practitioners decide whether to purchase and implement a given technology.

Competitive Recovery-Stress and Mood States in Mexican Youth Athletes

Background

Monitoring recovery–stress balance in sport is becoming more relevant to prevent training maladaptation and reach the optimal performance for each athlete. The use of questionnaires that identify the athlete’s recovery–stress state have much acceptance in sports due to reliability and useful, furthermore for its low cost. Identifying possible differences between sport modalities and sex is important to determine specific needs and possible intervention ways to keep a recovery–stress balance. The aim was to analyze the differences in the recovery–stress state and mood states by sex and sport type during the competitive phase in young Mexican athletes. As a secondary objective, the psychometric properties of the Mexican version of the Recovery–Stress Questionnaire for Athletes (RESTQ-Sport) were analyzed.

Methods

A cross-sectional study was carried on with 461 athletes (61% women and 39% men), 17.95 (±1.2) years old, from six sports disciplines. The RESTQ-Sport and Profile of Mood States (POMS) were applied in a single moment. Differences by sex and sports modality were analyzed. RESTQ-Sport’s confirmatory factor analysis was performed after the stress and recovery theoretical structure of two stress (general and sport) and two recovery (general and sport) dimensions, and last, the concurrent validation with the POMS was carried on.

Results

Significant differences by sex were found in the General Recovery and Sport Stress dimensions of the RESTQ-Sport as well as Vigor factor of the POMS, being higher for men; furthermore, both the Sport Recovery dimension of RESTQ-Sport and Cholera and the Fatigue and Depression factors from POMS also had differences by sport type, showing a less recovery and high stress for individual sport athletes. Goodness-of-fit indexes of the model for the RESTQ-Sport were acceptable. Pearson’s correlation between questionnaires was moderate (p < 0.05).

Conclusion

The recovery–stress state shows differences in the function of sex and sport modality. More special attention is suggested for women and individual sport athletes. The higher punctuation for men compared with women in sport stress dimension did not negatively affect the recovery–stress balance for male athletes. Finally, the Mexican context adaptation of the RESTQ-Sport provides a psychometric instrument suitable to assess the recovery–stress balance in Mexican athletes.

Analysing the predictive capacity and dose-response of wellness in load monitoring

This study aimed to identify the predictive capacity of wellness questionnaires on measures of training load using machine learning methods. The distributions of, and dose-response between, wellness and other load measures were also examined, offering insights into response patterns. Data (n= 14,109) were collated from an athlete management systems platform (Catapult Sports, Melbourne, Australia) and were split across three sports (cricket, rugby league and football) with data analysis conducted in R (Version 3.4.3). Wellness (sleep quality, readiness to train, general muscular soreness, fatigue, stress, mood, recovery rating and motivation) as the dependent variable, and sRPE, sRPE-TL and markers of external load (total distance and m.min^-1) as independent variables were included for analysis. Classification and regression tree models showed high cross-validated error rates across all sports (i.e., > 0.89) and low model accuracy (i.e., < 5% of variance explained by each model) with similar results demonstrated using random forest models. These results suggest wellness items have limited predictive capacity in relation to internal and external load measures. This result was consistent despite varying statistical approaches (regression, classification and random forest models) and transformation of wellness scores. These findings indicate practitioners should exercise caution when interpreting and applying wellness responses.

The Factorial Validity of the Norwegian Version of the Multicomponent Training Distress Scale (MTDS-N)

BACKGROUND

Athlete self-report measures (ASRM) are methods of athlete monitoring, which have gained considerable popularity in recent years. The Multicomponent Training Distress Scale (MTDS), consisting of 22 items, is a promising self-report measure to assess training distress among athletes. The present study aimed to investigate the factorial validity of the Norwegian version of MTDS (MTDS-N) among student-athletes (n = 632) attending the optional program subject "Top-Level Sports" in upper secondary schools in Norway.

METHODS

A confirmatory factor analysis (CFA) was conducted to assess the six-factor model proposed by Main and Grove (2009). McDonald's omega (ω) along with confidence intervals (CIs) were used to estimate scale reliability. After examining the fit of the CFA model in the total sample, covariates were included to investigate group differences in latent variables of MTDS-N, resulting in the multiple indicators multiple causes (MIMIC) model. Further, direct paths between the covariates and the factor indicators were included in an extended MIMIC model to investigate whether responses to items differed between groups, resulting in differential item functioning (DIF).

RESULTS

When modification indices (MIs) were taken into consideration, the alternative CFA model revealed that MTDS-N is an acceptable psychometric tool with a good fit index. The factors in MTDS-N all constituted high scale reliability with McDonald's ω ranging from 0.725-0.862. The results indicated statistically significant group differences in factor scores for gender, type of sport, hours of training per week, school program, and school level. Further, results showed that DIF occurred in 13 of the MTDS-N items. The student-athletes' reports of training distress were moderate.

CONCLUSION

The MTDS-N may be suitable for use in a Norwegian population to assess student-athletes' training distress in a reliable manner. The indications of group effects suggest that caution should be used if one is interested in making group comparisons when the MTDS-N is used among student-athletes in Norway until further research is conducted.

Stress in Academic and Athletic Performance in Collegiate Athletes: A Narrative Review of Sources and Monitoring Strategies

College students are required to manage a variety of stressors related to academic, social, and financial commitments. In addition to the burdens facing most college students, collegiate athletes must devote a substantial amount of time to improving their sporting abilities. The strength and conditioning professional sees the athlete on nearly a daily basis and is able to recognize the changes in performance and behavior an athlete may exhibit as a result of these stressors. As such, the strength and conditioning professional may serve an integral role in the monitoring of these stressors and may be able to alter training programs to improve both performance and wellness. The purpose of this paper is to discuss stressors experienced by collegiate athletes, developing an early detection system through monitoring techniques that identify the detrimental effects of stress, and discuss appropriate stress management strategies for this population.

Does exercise intensity affect wellness scores in a dose-like fashion?

Wellness questionnaires are common in monitoring systems, yet the sensitivity to variations in acute training intensity is unclear. This study examined the controlled dosage effects of differing exercise intensities on wellness variables and subsequent associations with neuromuscular performance. Participants (n = 10) completed low-, moderate- and high-intensity conditions of a 90 min simulated football match shuttle running protocol scaled relative to beep test scores. The protocols were completed in a randomised and counterbalanced fashion matched for time of day. Wellness (sleep quality, readiness to train, soreness, fatigue, stress, mood, motivation) and neuromuscular performance (maximal voluntary contraction, countermovement jump, 6 s cycle-ergometer sprint) were assessed pre-, post- and 24 h post-exercise. Heart rate (HR) and rating of perceived exertion (RPE) were recorded during, and session RPE (sRPE) after exercise. Generalised linear mixed models demonstrated main effects between conditions with increased HR, RPE and sRPE (P < 0.03; d > 0.8) responses from the low-high condition. Total and z-score wellness showed no significant differences between trials at any time-point (P > 0.05; d = 0.03-0.91). Fatigue was lower 24 h post-exercise for the low, compared to moderate and high conditions (P = 0.006-0.047; d = 1.20-1.77). Ratings of fatigue and soreness increased from pre- to 24 h post-trial (P < 0.003; d = 0.96-2.48), while total wellness and readiness to train decreased over time (P < 0.04; d = 0.91-1.86). Wellness showed limited capacity to differentiate training intensities. Practitioners should be aware while wellness may be highly practical, it may be limited to solely determine athlete accommodation of load considering the strength of association observed with the applied load.

Modification and Applicability of Questionnaires to Assess the Recovery-Stress State Among Adolescent and Child Athletes

Despite the general consensus regarding the implementation of self-report measures in the training monitoring, there is a lack of research about their applicability and comprehensibility among developing athletes. However, this target group needs special considerations to manage the increasing training demands while maintaining health and performance. This study deals with challenges of applying recovery-stress questionnaires which were validated with adult populations among developing athletes and presents a possible approach to enhance their applicability. In two phases, the Acute Recovery and Stress Scale (ARSS), a 32-adjective list covering eight scales, and the 8-item derived version, the Short Recovery and Stress Scale (SRSS) were answered by 1052 athletes between 10 and 16 years. Phase 1 included 302 14- to 16-year-old athletes who used the original questionnaires with the additional option to mark "I don't understand," while modified versions with additional explanations (phase 2) were applied to 438 adolescents (14.7 ± 0.6 years) and 312 child athletes (11.8 ± 1.1 years). Data of the original validation sample (n = 442) were reanalyzed to examine measurement invariance between adults and adolescents. The results showed comparable psychometric properties to the validation sample (e.g., r it > 0.30) and acceptable fit indices via confirmatory factor analyses (CFA), although more difficulties and limitations were present within the younger groups (e.g., Cronbach's α between 0.50 and 0.87), especially among 10- and 11-year-olds. The original as well as the modified SRSS, on the other hand, indicated good applicability (Cronbach's α between 0.72 and 0.80). Multigroup CFA revealed measurement invariance of the original ARSS among adults and adolescents and of the modified ARSS among adolescents and children. Overall, the present study confirmed the assumption that questionnaires designed by and for adults cannot be directly transferred to younger athletes. The peculiarities and differences in the cognitive and affective development of each age group need to be considered. Future research needs to identify a cut-off age to start the proper use of psychometric tools, especially for state-oriented assessments for routine application in training monitoring. Further modifications and long-term investigations are necessary to implement psychometric monitoring in high-performance environments within youth sport.

Validation of the Acute Recovery and Stress Scale (ARSS) and the Short Recovery and Stress Scale (SRSS) in three English-speaking regions

The Acute Recovery and Stress Scale (ARSS) and the Short Recovery and Stress Scale (SRSS) are well-established monitoring tools in German-speaking countries. This study aimed at validating them for English-speaking populations. Overall, 996 athletes (536 males, 24.9 ± 9.1 years) of Australia/New Zealand (n = 380), the United Kingdom (n = 316), and North America (n = 300) participated. The 32-item ARSS consists of eight scales. These scales constitute the eight items of the SRSS with the corresponding ARSS items as descriptors. Confirmatory factor analysis (CFA), internal consistency (α), and discriminatory power of the items (r_it) were calculated for the total and subsamples separately. Satisfactory discriminatory power (r_it > .30) for all ARSS and SRSS items and acceptable internal consistency (α > .70) was achieved. CFA indicated good fit indices for the total sample and subsamples, and strong measurement invariance was found across subsamples and gender. Correlations between corresponding scales and items (r_s = .68 - .78) support theoretical congruency as well as independent usage of both questionnaires. Construct validity of both tools is shown through hypothesis-conforming correlations with the Recovery-Stress Questionnaire for Athletes. Future studies may apply the ARSS and SRSS as monitoring tools in English-speaking regions worldwide.

Multidimensional Monitoring of Recovery Status and Implications for Performance

Monitoring recovery in the context of athletic performance has gained significant importance during recent years. As a systematic process of data collection and evaluation, the monitoring of recovery can be implemented for various purposes. It may help prevent negative outcomes of training or competition, such as underrecovery, overtraining, or injuries. Furthermore, it aims to establish routines and strategies necessary to guarantee athletes' readiness for performance by restoring their depleted resources. Comprehensive monitoring of recovery ideally encompasses a multidimensional approach, thereby considering biological, psychological, and social monitoring methods. From a biological perspective, physiological (eg, cardiac parameters), biochemical (eg, creatine kinase), hormonal (eg, salivary cortisol), and immunological (eg, immunoglobulin A) markers can be taken into account to operationalize training loads and recovery needs. Psychological approaches suggest the application of validated and reliable psychometric questionnaires (eg, Recovery-Stress Questionnaire for Athletes) to measure a subjective perception of recovery, as well as the subjective degree of training- or competition-induced fatigue. Social aspects also play a role in performance monitoring and may hence provide essential performance-related information. The implementation of a monitoring routine in athletic environments represents a continuous process that functions as an effective addition to training and depends on a range of conditions (eg, organizational regulations, commitment of athletes). Current research in the field of monitoring aims to establish individualized monitoring regimens that refer to intraindividual reference values with the help of innovative technological devices.