Biochemical and Perceptual Markers of Physiological Stress During Acute Exercise Overload in U20 Elite Basketball Players

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Authors

Generally, I think the authors conducted a satisfactory study, which they clearly outlined in the methodology and results. However, I believe the introduction and discussion sections require further analysis. The study also has some limitations that could be addressed or should at least be acknowledged in the discussion.

In the study's methodology, it is stated that the athletes are in the U20 category, whereas the title refers to high-level athletes. I believe the age group should be included in the study's title.

Introduction

Although the part of the study concerning internal load is thoroughly analysed, the same does not apply to external load. It is recommended to enrich the section on the external load of U20 basketball players with a paragraph and highlight that there are no studies examining the condensed game schedule in this age category.

Additionally, in the introduction, the paragraph on CK, urea, etc., can be combined and converted into indices that can be used to calculate the allostatic index in basketball.

 

 

Methodology

The methodology is presented in a way that enables the study to be replicated. However, this chapter also highlights most of the limitations. The study does not include the measurement of external load during the games. Data such as distances, speeds, accelerations, decelerations, and aggregated indices like Player Load could offer additional insights to the reader. Nevertheless, it is understandable that the researchers may not have had access to such equipment. The authors could have also mentioned the participation times per game and per athlete, which represents the minimum data necessary for estimating external load.

According to the CMJ evaluation, VALD force platform was used. I suggest to the authors to examine another marker such as Reactive Strength Index or Time to Take Off. In literature, there is information indicating that RSI and Time to Take Off are more sensitive markers than jump height.

 

Results

The results are clearly presented and are arguably the study's main strength, which also underpins its innovation.

 

Discussion

One of the study's limitations is that it lacks sufficient details about the athletes' external load. Furthermore,

 

Finally, I would like to inform you that I propose three bibliographic references that I believe will provide useful information. These references examine the impact of one or three basketball games on performance and biochemical indices and the evaluation of external load parameters in U20 athletes. I believe the analysis of these studies can improve the study's introduction and discussion. Of course, the authors are not obliged to use them.

https://pubmed.ncbi.nlm.nih.gov/24479464/

https://pubmed.ncbi.nlm.nih.gov/17138630/

https://pubmed.ncbi.nlm.nih.gov/39796561/

 

Author Response

Reviewer 1

Generally, I think the authors conducted a satisfactory study, which they clearly outlined in the methodology and results.

We appreciate the reviewer’s comments. Several amendments have been performed to improve the manuscript. Point-by-point answers for each reviewer’s comment have been created. We have also included the requested modifications in the revised manuscript. All changes have been clearly highlighted in red (tracking changes) so that they can be easily visible to the editor and reviewers.

However, I believe the introduction and discussion sections require further analysis. The study also has some limitations that could be addressed or should at least be acknowledged in the discussion.

Response: Thank you for your comments. The introduction and discussion sections have been revised in response to reviewers’ feedback. Additionally, subsection 4.1 outlines the study's limitations, and we have included a few more for transparency.

In the study's methodology, it is stated that the athletes are in the U20 category, whereas the title refers to high-level athletes. I believe the age group should be included in the study's title.

Response: Thank you for your suggestions. Indeed, they were elite athletes representing their national teams at the elite level. However, we agree with your point and have added "U20" to the title.

Introduction

Although the part of the study concerning internal load is thoroughly analysed, the same does not apply to external load. It is recommended to enrich the section on the external load of U20 basketball players with a paragraph and highlight that there are no studies examining the condensed game schedule in this age category.

Response: Thank you for your comment. You’ve raised an important point. As suggested, we have expanded our discussion to explicitly address the scarcity of research on external load in U20 basketball players, particularly during condensed game schedules.

Additionally, in the introduction, the paragraph on CK, urea, etc., can be combined and converted into indices that can be used to calculate the allostatic index in basketball.

Response: Thank you for your comment. This has been addressed in the introduction section as requested. We added the following statement as intro for the later description of the ALindex:

“It is worth noting that combining several validated biomarkers increases sensitivity, enabling earlier detection of exercise-induced physiological stress when monitoring performance, recovery, and health in athletes.”

Reviewer 2 Report

Comments and Suggestions for Authors

This study focuses on the construction of a PCA-based physiological stress index (PSI) based on serum creatine kinase (CK) and subjective training load (Session-RPE), which can be used to differentiate the physiological load status of basketball players under low-load and high-load training microcycles. The study has a good practical orientation and tries to integrate multimodal indexes for assessing allostatic load (ALindex), which is innovative to a certain extent. However, the manuscript still has substantial deficiencies in sample design, statistical processing, model construction logic, variable definition and generalizability, which make it difficult to support its current scientific validity and reproducibility. It is recommended that the manuscript be evaluated after sufficiently reconstructing the research hypotheses, expanding the database base and improving the transparency of the analysis.

1. The sample size is seriously insufficient. Although it is a high-level athlete team, the analysis involves complex statistical processing such as PCA, Bayesian modeling, linear regression, etc. It is recommended to supplement the sample size efficacy analysis or reconstruct the research design.
2. PCA has low explanatory power and questionable structural stability: the two principal components explain only 67.5% of the variance, and the KMO=0.565 suggests that the correlation between the variables is not strong, which may affect the construct validity of the Physiological Stress Index. For the application basis and methodological detailing of PCA, the authors may consider referring to the following updated relevant studies: A new method proposed for realizing human gait pattern recognition: Inspirations for the application of sports and clinical gait analysis (https://doi.org/10.1016/j.gaitpost.2023.10.019).
3. The title and objective of the article point to a “composite index of physiological load”, but the inclusion of only CK and EWMA-RPE in the results does not reflect the proper characterization of “multi-system integration”.
4. Indicator selection lacks theoretical support: why CMJ, RPE and CK were chosen to construct PCA is not adequately explained, and the rationality of the options should be demonstrated in the context of existing physiological models.
5. Lack of reliability/validity statements for subjective questionnaires such as fatigue and recovery. It is recommended that the source of the questionnaire, scale reliability, and applicability to young elite athletes be clarified.
6. The u-rea did not show a significant difference, yet it was still included in the center of the discussion with an inadequate explanation.
7. Statistical methods were used in a mixed manner, and some processing details were missing, such as the a priori setting of the Bayesian model, normalization of PCA variables, and other treatments were not fully accounted for.
8. There is a risk of excessive extrapolation in the interpretation of the results. The conclusion of “generalizing the ALindex construct” is not appropriate based on data from 12 individuals and a short period and should be expressed with caution.
9. The chart structure is repetitive and the information is redundant, such as Fig. 3-5 all show PCA results, which should be consolidated into one multi-panel chart to enhance the information density.
10. There was no control group or randomized comparison design, making it difficult to isolate the interaction between “changes in training content” and “individual differences”. The presentation of variables such as sleep and mood is inconsistent with the main findings. A distinction should be made between “main effects” and “trend” variables to avoid confusing the focus. Some of the language expressions are complicated, with too much terminology. It is recommended that native speakers touch up the language to improve the clarity of logic and structural coherence.

Author Response

Reviewer 2

This study focuses on the construction of a PCA-based physiological stress index (PSI) based on serum creatine kinase (CK) and subjective training load (Session-RPE), which can be used to differentiate the physiological load status of basketball players under low-load and high-load training microcycles. The study has a good practical orientation and tries to integrate multimodal indexes for assessing allostatic load (ALindex), which is innovative to a certain extent. However, the manuscript still has substantial deficiencies in sample design, statistical processing, model construction logic, variable definition and generalizability, which make it difficult to support its current scientific validity and reproducibility. It is recommended that the manuscript be evaluated after sufficiently reconstructing the research hypotheses, expanding the database base and improving the transparency of the analysis.

We appreciate the reviewer’s comments. Several amendments have been performed to improve the manuscript. Point-by-point answers for each reviewer’s comment have been created. We have also included the requested modifications in the revised manuscript. All changes have been clearly highlighted in red (tracking changes) so that they can be easily visible to the editor and reviewers. We sincerely appreciate your valuable feedback, which has significantly contributed to improving the quality of this work.

1. The sample size is seriously insufficient. Although it is a high-level athlete team, the analysis involves complex statistical processing such as PCA, Bayesian modeling, linear regression, etc. It is recommended to supplement the sample size efficacy analysis or reconstruct the research design.

Response: Thank you for your comments. Increasing sample size or reconstruct the research design is unsuitable at this point. Importantly, there is a key consideration in sports research: the fundamental purpose shifts from seeking broad generalizability to providing actionable insights for athletic performance. This perspective is particularly relevant when studying elite athletes, where practical application takes precedence over traditional research paradigms. When working with elite athletes, small sample sizes are an inherent challenge; however, robust analyses (Bayesian and multivariate statistics, as employed in our study) can effectively address this limitation. In agreement with this, we share thoughts from other colleagues on this concern: 

“In this case what is called for is not the evidence the current data set can provide but a summary of the currently available evidence specific for the framework conditions in question. Therefore, formally including previous knowledge (“Bayesian updating”) can offer a legitimate head start to a small data set. Previous trials on a lower performance level or practitioner’s experiential knowledge are potential sources for valuable “prior” knowledge. Importantly, when combined with single-subject designs, valid individualized estimates (with appropriate precision) can be provided to the participating athletes.” --> Coping With the “Small Sample–Small Relevant Effects” Dilemma in Elite Sport Research https://doi.org/10.1123/ijspp.2021-0467

“What do elite athletes offer the investigator?-Elite athletes tend to exist in coherent intact groups for many years, are exposed to considerable stress, possess high skill and fitness, and can usually be ranked through competition and performance scores that arise naturally in the athletic environment. Given their involvement in high-level training with concomitant physical, emotional, and mental stressors, elite athletes are particularly attractive for studies of various forms of stress. However, they also bring a number of challenges to the researcher in search of ideal models for stress-related investigations. These challenges include rarity, inability to randomize, limited control of confounding variables, and sometimes limited generalizability of results.” --> Plaudits and Pitfalls in Studying Elite Athletes http://doi.org/10.2466/pms.100.1.22-24  

“While the shift towards a magnitude-based inference model may act as a better fit for inference in sport science, a commitment towards a fully Bayesian model may act as a better solution for small effects and small sample sizes [38]. Criticism towards the magnitude inference model claim that a fully Bayesian approach may be a better solution [38,39]. In a study performed by Mengersen et al. [38], a fully Bayesian approach was shown to provide more direct probabilistic comparisons of treatments and able to identify small effects of interest, even with small sample sizes. Conclusions based off the Bayesian model were consistent with a magnitude-based inference approach and was determined to be a simple and effective way of analysing small effects while providing a rich set of results that are straightforward to interpret in terms of probabilistic statements [38].” --> Current Research and Statistical Practices in Sport Science and a Need for Change https://doi.org/10.3390/sports5040087 

 

2. PCA has low explanatory power and questionable structural stability: the two principal components explain only 67.5% of the variance, and the KMO=0.565 suggests that the correlation between the variables is not strong, which may affect the construct validity of the Physiological Stress Index. For the application basis and methodological detailing of PCA, the authors may consider referring to the following updated relevant studies: A new method proposed for realizing human gait pattern recognition: Inspirations for the application of sports and clinical gait analysis (https://doi.org/10.1016/j.gaitpost.2023.10.019).

Response: Thanks for your comments and suggestions. After standardization as z-scores, we conducted the PCA on 15 variables of the study: sleep, stress, fatigue, DOMS, mood, cumulative wellness, RPE, time, session RPE, EW-MA session RPE, urea, CK, CMJ post-blood sample, CMJ pre-workout, and CMJ post-workout. This information was added to the manuscript in the statistical methods section. We are aware that the greater the sample, the better the performance, but we analyzed in average N = 42 [95% CI: 38.72, 45.44] (as athlete per time points for each variable) for 15 variables which results in a ratio greater than 1. Previous findings have shown that:

“Evidently, PCAs based on N = 40 and N = 50 returned stable and consistent eigenvectors (Figs 6 and 7) compared to those of sample sizes N = 20 and N = 30.” à IMPACT OF SAMPLE SIZE ON PRINCIPAL COMPONENT ANALYSIS ORDINATION OF AN ENVIRONMENTAL DATA SET: EFFECTS ON EIGENSTRUCTURE file:///C:/Users/dabio/Downloads/Impact-of-sample-size-on-principal-component-analysis-ordination-of-an-environmental-data-set-effects-on-eigenstructure.pdf

 “PCA showed smaller MSEs of loading estimates than did MLFA when the ratio of sample size to the number of observed variables was greater than 1, whereas MLFA yielded smaller MSEs than did PCA when the regularized covariance matrix was factor-analyzed.” https://doi.org/10.3758/s13428-011-0077-9

To our knowledge, in PCA, the first two components together typically explain between 50% and 80% of the total variance. If the data is highly correlated, the first few components may explain a very high proportion (e.g., >80% combined). In this case, rules like Kaiser’s criterion or Scree plot may fail if variance is overly concentrated in PC1. To support decisions and the use of the PCA, here we share additional statistics information supporting our analysis:

Component	Sum of Squared Loadings	% of Variance	Cumulative %
1	1.72	34.3%	34.3%
2	1.66	33.1%	67.5%

Bartlett’s Test of Sphericity (no issues with correlation)

χ²	Degrees of Freedom (df)	p-value
112	10	< .001

Even though some metrics require further study and refinement with a higher sample (e.g., KMO), we decided to retain the variables as they are theoretically critical and have practical utility (e.g., for coaches or clinicians) which justify the inclusion. This is clearly highlighted in the discussion and limitations sections. We also added this statement based on the reviewer’s comments: Future studies should explore alternative measures for this construct.

We also thank the reviewer for sharing the article 'A New Method Proposed for Realizing Human Gait Pattern Recognition: Inspirations for the Application of Sports and Clinical Gait Analysis.' However, we note that this study included a significantly larger participant sample (n=12) compared to our work. So it will be considered for future studies.

“This study collected gait data from 80 healthy runners during the running stance phase, including 40 higher-mileage runners and 40 lower-mileage runners.” – “The current 476 research results only rely on 800 sets of gait sample data…” https://myresearchspace.uws.ac.uk/ws/files/45103681/2023_10_24_Xu_et_al_Gait_accepted.pdf

 

3. The title and objective of the article point to a “composite index of physiological load”, but the inclusion of only CK and EWMA-RPE in the results does not reflect the proper characterization of “multi-system integration”.

Response: Thank you for your comments. However, we disagree, as the title does not refer to what the reviewer highlights and is actually: “Biochemical and Perceptual Markers of Physiological Stress During Acute Exercise Overload in U20 Elite Basketball Players.”

In the objective, we included a rationale for assessing multiple components related to multi-system integration, such as physiological, biochemical, and performance metrics (15 variables in total). The goal of deriving a streamlined set of biomarkers (with fewer elements while maintaining sensitivity) was to reduce impracticality and complexity compared to proposals requiring 60+ biomarkers. We acknowledge the limitations of this research (clearly stated in the corresponding section), as a single study cannot fully establish or validate an entire framework. However, we demonstrated that a select few key biomarkers can be used to construct parsimonious AL_indexes, aiding coaches, nutritionists, and physicians in monitoring and preventing allostatic overload in athletes.

 

4. Indicator selection lacks theoretical support: why CMJ, RPE and CK were chosen to construct PCA is not adequately explained, and the rationality of the options should be demonstrated in the context of existing physiological models.

Response: Thanks for your comments. It should be noted that the PCA was conducted with varimax rotation on all 15 perceptual, physiological, and performance variables (sleep, stress, fatigue, DOMS, mood, cumulative wellness, RPE, time, session RPE, EWMA session RPE, urea, CK, CMJ post-blood sample, CMJ pre-workout, and CMJ post-workout). The analysis was validated through Bartlett's test of sphericity (con-firming sufficient intercorrelations) and the Kaiser-Meyer-Olkin (KMO) measure for sampling adequacy (both overall and for individual variables). This information was added to the statistical methods section.

In addition, the relevance of CMJ, RPE and CK as biomarkers in exercise and sports is very clear in the field. The following meta-analytic evidence supports the use of each one in sports practice - we have introduced these references supporting the biomarkers and the reason for prioritization:

CMJ:

• Claudino, J. G., Cronin, J., Mezêncio, B., McMaster, D. T., McGuigan, M., Tricoli, V., Amadio, A. C., & Serrão, J. C. (2017). The countermovement jump to monitor neuromuscular status: A meta-analysis. Journal of science and medicine in sport, 20(4), 397–402. https://doi.org/10.1016/j.jsams.2016.08.011
• Edwards, T., Spiteri, T., Piggott, B., Bonhotal, J., Haff, G. G., & Joyce, C. (2018). Monitoring and Managing Fatigue in Basketball. Sports (Basel, Switzerland), 6(1), 19. https://doi.org/10.3390/sports6010019
• Alba-Jiménez, C., Moreno-Doutres, D., & Peña, J. (2022). Trends Assessing Neuromuscular Fatigue in Team Sports: A Narrative Review. Sports (Basel, Switzerland), 10(3), 33. https://doi.org/10.3390/sports10030033

RPE:

• Inoue, A., Dos Santos Bunn, P., do Carmo, E. C., Lattari, E., & da Silva, E. B. (2022). Internal Training Load Perceived by Athletes and Planned by Coaches: A Systematic Review and Meta-Analysis. Sports medicine - open, 8(1), 35. https://doi.org/10.1186/s40798-022-00420-3
• Pillitteri, G., Petrigna, L., Ficarra, S., Giustino, V., Thomas, E., Rossi, A., Clemente, F. M., Paoli, A., Petrucci, M., Bellafiore, M., Palma, A., & Battaglia, G. (2024). Relationship between external and internal load indicators and injury using machine learning in professional soccer: a systematic review and meta-analysis. Research in sports medicine (Print), 32(6), 902–938. https://doi.org/10.1080/15438627.2023.2297190

CK:

• Simmons, R., Doma, K., Sinclair, W., Connor, J., & Leicht, A. (2021). Acute Effects of Training Loads on Muscle Damage Markers and Performance in Semi-elite and Elite Athletes: A Systematic Review and Meta-analysis. Sports medicine (Auckland, N.Z.), 51(10), 2181–2207. https://doi.org/10.1007/s40279-021-01486-x
• Greenham, G., Buckley, J. D., Garrett, J., Eston, R., & Norton, K. (2018). Biomarkers of Physiological Responses to Periods of Intensified, Non-Resistance-Based Exercise Training in Well-Trained Male Athletes: A Systematic Review and Meta-Analysis. Sports medicine (Auckland, N.Z.), 48(11), 2517–2548. https://doi.org/10.1007/s40279-018-0969-2

 

5. Lack of reliability/validity statements for subjective questionnaires such as fatigue and recovery. It is recommended that the source of the questionnaire, scale reliability, and applicability to young elite athletes be clarified.

Response: Thank you for your comments. The Hooper index is a well-established, valid, and reliable instrument in exercise and sports sciences. As we initially stated in the manuscript:

Self-reported measures assessing perceptual or psychological states were collected post-exercise [18,34]. Each player accessed Google Forms on their mobile devices and completed the required questionnaires when requested by the researchers. These forms included the RPE scale (from 1 to 10 – not measured on the final study day) and a cu-mulative wellness score based on the Hooper Index [35], which assessed subjective perceptions of sleep quality, stress levels, fatigue levels, muscle soreness, and mood (on a 1 to 7 Likert scale from “very very low-or-good” [point 1] to “very very high-or-bad” [point 7]) [13].

However, to further substantiate the instrument's reliability and validity, we have incorporated additional references pertaining to the subjective wellness/well-being measure (commonly known as the Hooper Index) and its application in athletic populations, particularly team sport athletes:

• Hooper, S. L., & Mackinnon, L. T. (1995). Monitoring overtraining in athletes. Recommendations. Sports medicine (Auckland, N.Z.), 20(5), 321–327. https://doi.org/10.2165/00007256-199520050-00003
• Jeffries, A. C., Wallace, L., Coutts, A. J., McLaren, S. J., McCall, A., & Impellizzeri, F. M. (2020). Athlete-Reported Outcome Measures for Monitoring Training Responses: A Systematic Review of Risk of Bias and Measurement Property Quality According to the COSMIN Guidelines. International journal of sports physiology and performance, 15(9), 1203–1215. https://doi.org/10.1123/ijspp.2020-0386
• Fitzpatrick, J. F., Hicks, K. M., Russell, M., & Hayes, P. R. (2021). The Reliability of Potential Fatigue-Monitoring Measures in Elite Youth Soccer Players. Journal of strength and conditioning research, 35(12), 3448–3452. https://doi.org/10.1519/JSC.0000000000003317
• Andersen, T. R., Kästner, B., Arvig, M., Larsen, C. H., & Madsen, E. E. (2023). Monitoring load, wellness, and psychological variables in female and male youth national team football players during international and domestic playing periods. Frontiers in sports and active living, 5, 1197766. https://doi.org/10.3389/fspor.2023.1197766
• Wellm, D., Willberg, C., & Zentgraf, K. (2023). Differences In Player Load Of Professional Basketball Players As A Function Of Distance To The Game Day During A Competitive Season. International Journal of Strength and Conditioning, 3(1). https://doi.org/10.47206/ijsc.v3i1.219
• Sansone, P., Rago, V., Kellmann, M., & Alcaraz, P. E. (2023). Relationship Between Athlete-Reported Outcome Measures and Subsequent Match Performance in Team Sports: A Systematic Review. Journal of strength and conditioning research, 37(11), 2302–2313. https://doi.org/10.1519/JSC.0000000000004605
• Burger, J., Henze, A. S., Voit, T., Latzel, R., & Moser, O. (2024). Athlete Monitoring Systems in Elite Men's Basketball: Challenges, Recommendations, and Future Perspectives. Translational sports medicine, 2024, 6326566. https://doi.org/10.1155/2024/6326566

 

6. The urea did not show a significant difference, yet it was still included in the center of the discussion with an inadequate explanation.

Response: Thank you for your comments. While the following paragraph may not have been as clear as intended, we sought to emphasize that although urea has been proposed as a potential biomarker, our findings do not support its use in this context (we have revised the text for greater conciseness):

While urea concentration has been proposed previously as a marker of increased nucleotide cycle turnover and protein breakdown (potentially indicating muscle damage following competition or high-load microcycles [47,48]), our study found no significant differences between deload and overload microcycles. Furthermore, its limited explanatory power at the microcycle level and low variance in the PCA suggest urea may not be suitable for training load monitoring in U20 elite basketball players.

 

7. Statistical methods were used in a mixed manner, and some processing details were missing, such as the a priori setting of the Bayesian model, normalization of PCA variables, and other treatments were not fully accounted for.

Response: Thank you for your valuable comments. You have raised an important point. In line with current statistical recommendations for sports science research, we employed robust estimation methods and Bayesian statistics (Bernards et al. 2017, PMID: 29910447). Furthermore, in response to your suggestion, we have incorporated the following statement to enhance methodological transparency and reproducibility:

We standardized all continuous predictor variables using z-score normalization prior to analysis. To assess neuromuscular fatigue and muscle damage, we also performed Bayesian linear regression analyses. The coefficients used a Jeffreys-Zellner-Siow prior with a scale of 0.354, as a robust approach for small effect sizes in physiological studies. Model selection employed a uniform prior to avoid comparison bias, which might be appropriate for exploratory research. We reported BF₁₀ to quantify evidence strength for the alternative hypothesis (H₁: effect exists) versus the null (H₀: no effect), along with highest density intervals (HDI). Finally, we conducted a principal component analysis (PCA) with varimax rotation on all 15 perceptual, physiological, and performance variables (sleep, stress, fatigue, DOMS, mood, cumulative wellness, RPE, time, session RPE, EWMA session RPE, urea, CK, CMJ post-blood sample, CMJ pre-workout, and CMJ post-workout). The analysis was validated through Bartlett's test of sphericity (confirming sufficient intercorrelations) and the Kaiser-Meyer-Olkin (KMO) measure for sampling adequacy (both overall and for individual variables).

 

8. There is a risk of excessive extrapolation in the interpretation of the results. The conclusion of “generalizing the ALindex construct” is not appropriate based on data from 12 individuals and a short period and should be expressed with caution.

Response: Thanks for your comments. We partially agree with your comment as we believe the revisions have improved the manuscript's clarity. The limitations section has been revised to provide greater precision and appropriate caution in our interpretation:

Although in a pioneering attempt to assess and integrate molecular, neuromuscu-lar, and psychometric biomarkers for an ALindex in elite basketball players during de-load and overload microcycles, we are aware that this study is not except for limita-tions. Firstly, the small sample size of elite athletes; however, they represented the complete U20 Antioquia team with extensive competitive experience (+5 years), and a robust Bayesian statistical approach was implemented. It should be noted that sub-analyses by player position were not performed due to sample size limitations. Secondly, only CK and urea concentrations were biochemically assessed after blood sampling. Thirdly, the inclusion of perception-related variables in the ALindex requires further validation in other athletic populations, though our results demonstrate their potential utility, at least in U20 elite basketball players. In this study, we employed a modified version of the previously validated Hooper index [34,74], adding mood to the classical components of sleep quality, stress levels, fatigue levels, and muscle soreness. Other perceptual questionnaires, such as the profile of mood states (POMS), the recov-ery-stress questionnaire for athletes (RESTQ-Sport), and the multicomponent training distress scale (MTDS), could further elucidate the relationship with physiological var-iables [25,50]. These tools may help unravel the complexity underlying human health and performance through diverse statistical approaches.

Finally, we developed a parsimonious yet practical index combining CK and ses-sion-RPE that demonstrated exceptional discriminative power between deload and overload phases, providing coaches with a potential monitoring tool for assessing readiness in competitively scheduled basketball players. Notwithstanding, we techni-cally did not validate the ALindex since this implies measuring several biomarkers of cardiovascular, neuroendocrine, metabolic, and inflammatory domains. In fact, the modest KMO of our PCA-based index indicates limited sampling adequacy for the current variable set. Future studies should explore alternative measures for this con-struct.

.

9. The chart structure is repetitive and the information is redundant, such as Fig. 3-5 all show PCA results, which should be consolidated into one multi-panel chart to enhance the information density.

Response: Thanks for your suggestions. We have combined information presented in figures 3-5 to avoid redundancy as requested.

 

10. There was no control group or randomized comparison design, making it difficult to isolate the interaction between “changes in training content” and “individual differences”. The presentation of variables such as sleep and mood is inconsistent with the main findings. A distinction should be made between “main effects” and “trend” variables to avoid confusing the focus. Some of the language expressions are complicated, with too much terminology. It is recommended that native speakers touch up the language to improve the clarity of logic and structural coherence.

Response: Thank you for your comments, but we do not fully understand the point of the reviewer. This was a prospective cohort study on U20 elite basketball athletes that looked at the internal load changes (15 variables) during deload and overload microcycles - we did not perform a randomized clinical trial or evaluate a given intervention. Thus, this design does not allow causal inference as the objective was to explore associations (not isolate effects).

Due to the nature of the athletes, the sample was limited and no "individual" or position-based differences were suitable for comparison. While sleep quality correlated weakly with CK, it did not load significantly on the PCA components or Bayesian model. Thus, we prioritized CK and EWMA of session-RPE for the parsimonious index due to their stronger discriminative validity between microcycles. As mentioned in other comments before, this aligns with current clinical practice, as both biomarkers (CK and session-RPE) have demonstrated utility in team sports, supported by meta-analytic evidence (PMID 35244801; 38146925; 34097298; 30141022).

In addition, we think that the suggestion "A distinction should be made between 'main effects' and 'trend' variables to avoid confusing the focus" does not correspond to our work since we consistently report associations rather than causal effects throughout the manuscript. We have revised the text to enhance grammatical precision and overall readability. 

Reviewer 3 Report

Comments and Suggestions for Authors

General comments

Author aimed to examine relationships between molecular, physical, and psychometric biomarkers across deload and overload microcycles. Given the versatility of the allostatic load index (ALindex) in component selection, they expect to identify key elements suitable for inclusion in this measure.

The design of the study is appropriate and the manuscript is in general well written, and manuscript deals with very interesting and scientifically popular area. Further, the author state that creatine kinase (CK) and session rating of perceived exertion (s-RPE) may serve as sensitive biomarkers for inclusion in the ALindex for professional team sport athletes. Main strength of the research is myriad of objective and subjective variables for examining mentioned associations with elite level athletes. Also, it must be noted that stability of this relationships was examined in two workload conditions independent factors. On the other hand, this research has a lot of statistics procedures, and doesn’t follow the methodological principles: 1) less is more, 2) simple is better. Saying in other words, authors could display their “scientific message” in much simpler manner.

Specific comments:

Comment 1: Abstract aim doesn’t include mention of deload and overload microcycles, which is very important to mention, as authors goal was to show stability of analyzed relationships through this two microcycles.

Comment 2: Can authors explain why is nonparametric statistics used (i.e., spearman correlation), as RPE measures in the most researches are analyzed through parametric statistic procedures.  

Comment 3: Discussion section at the end should contain pointing out of main strengths of the research.

Comment 4: What is the application of the study in real-world settings?

 

Author Response

Reviewer 3

General comments

Author aimed to examine relationships between molecular, physical, and psychometric biomarkers across deload and overload microcycles. Given the versatility of the allostatic load index (ALindex) in component selection, they expect to identify key elements suitable for inclusion in this measure.

The design of the study is appropriate and the manuscript is in general well written, and manuscript deals with very interesting and scientifically popular area. Further, the author state that creatine kinase (CK) and session rating of perceived exertion (s-RPE) may serve as sensitive biomarkers for inclusion in the ALindex for professional team sport athletes. Main strength of the research is myriad of objective and subjective variables for examining mentioned associations with elite level athletes. Also, it must be noted that stability of this relationships was examined in two workload conditions independent factors.

We appreciate the reviewer’s comments. Several amendments have been performed to improve the manuscript. Point-by-point answers for each reviewer’s comment have been created. We have also included the requested modifications in the revised manuscript. All changes have been clearly highlighted in red (tracking changes) so that they can be easily visible to the editor and reviewers.

On the other hand, this research has a lot of statistics procedures, and doesn’t follow the methodological principles: 1) less is more, 2) simple is better. Saying in other words, authors could display their “scientific message” in much simpler manner.

Response: Thank you for your suggestions. We acknowledge the nonconformity from conventional statistical analysis; however, based on current recommendations, the statistical approach in applied science should prioritize robust statistics and Bayesian methods. Additionally, contrary to your comments, another reviewer requested supplementary calculations/specifications that, while enriching the analysis, demand greater complexity. Given the divergence between reviewers’ perspectives (one favoring simplicity and the other advocating for complexity), we opted to strike a balance between both suggestions. With this in mind, we have revised specific discussion and conclusion statements to present the scientific message more simply, as required.

Specific comments:

Comment 1: Abstract aim doesn’t include mention of deload and overload microcycles, which is very important to mention, as authors goal was to show stability of analyzed relationships through this two microcycles.

Response: Thank you for your comment. We have revised the abstract to emphasize the microcycles in the study's aim.

Comment 2: Can authors explain why is nonparametric statistics used (i.e., spearman correlation), as RPE measures in the most researches are analyzed through parametric statistic procedures.  

Response: Thanks for your comments. Beyond the central limit theorem and data transformations, normality was not assessed given that the Gaussian distributions (“normal”) are less common than thought. This is particularly important in small samples of elite athletes, such as in our study. In a Gaussian distribution, the curve is entirely symmetrical around the mean, such that x = μ. This symmetrical distribution shows that with the mean, the median and mode must also coincide (DOI 10.4135/9781506326139.n476). This almost never happens in ‘real-world’ situations, so we deal with non-Gaussian distributions contrary to common beliefs (DOI: 10.2134/appliedstatistics.2015.0081.c16). Considering all this, we selected non-parametric tests to compare our data. This is supported by the fact that distributions were not specified a priori but were instead determined from data. In addition, we should highlight the following advantages:

• More statistical power/accuracy.
• It can be used for all data types, including ordinal, nominal and interval.
• Useful when group sample sizes are unequal.
• Importantly: The term non-parametric is not meant to imply that such models completely lack parameters but that the number and nature of the parameters are flexible and not fixed in advance.

 

In addition, in agreement with Bernards et al. (2017) in their article “Current Research and Statistical Practices in Sport Science and a Need for Change”:

“The inclusion of confidence intervals, effect statistics, and other descriptive metrics to accompany the p-value under the NHST model is an easy and effective first step an investigator can make to produce more transparent research that is also more informative. However, a shift towards a magnitude-based inference model, and eventually a fully Bayesian approach, may be a better fit from a statistical standpoint, a reproducibility standpoint, and may be an improved way to address biases within the literature. All while being a superior model to deal with smaller samples sizes and small effects, two fundamental struggles in the field.”

Bernards, J. R., Sato, K., Haff, G. G., & Bazyler, C. D. (2017). Current Research and Statistical Practices in Sport Science and a Need for Change. Sports (Basel, Switzerland), 5(4), 87. https://doi.org/10.3390/sports5040087 

 

Comment 3: Discussion section at the end should contain pointing out of main strengths of the research.

Response: Thanks for your suggestion. We have added a couple of  statements.

Comment 4: What is the application of the study in real-world settings?

Response: Thank you for your question. We have revised sevearl manuscript sections to emphasize practical applications, including sharing the 4Rs app (https://dbss.shinyapps.io/4RsApp/) developed in our previous work of the theoretical and practical framework of the AL_index for athletes, along with additions to the discussion's concluding section:

“Finally, we developed a parsimonious yet practical index combining CK and session-RPE that demonstrated exceptional discriminative validity between deload and overload phases, providing coaches with a potential monitoring tool for assessing readiness in competitively scheduled basketball players.”

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors made a significant effort to answer my comments. 

The manuscript could be accepted in the present form. 

After the acceptance, I would like to send you some references in which I am the first or

corresponding author, who is related to your work. Of course, I do not ask to use it. 

https://pubmed.ncbi.nlm.nih.gov/24479464/

https://pubmed.ncbi.nlm.nih.gov/39796561/

 

Author Response

Dear Reviewer,

Thank you for your comments.

We have added the reference PMID: 24479464, as we found it relevant to the topic of our study. Thanks!

Sincerely,
The Authors

Reviewer 2 Report

Comments and Suggestions for Authors

Thank the authors for their efforts in improving the quality of their papers. The quality of the article has already improved a bit with the revisions. It is recommended to improve the clarity of the graph and the font of the axis scale so that the results can be presented more clearly. In addition, for the application and details of the PCA method, if appropriate, it is recommended to refer to the relevant literature mentioned earlier to provide a more detailed description: A new method proposed for realizing human gait pattern recognition: Inspirations for the application of sports and clinical gait analysis.

Author Response

Dear Reviewer,

Thank you for your comments.

We have added the reference (PMID: 37926657) as requested. We have included this reference in the "Future Directions" section as an example for further studies in this field.

Sincerely,
The Authors