Single-subject limitation: How can robust generalizable conclusions be drawn from a single mature drummer? To what extent do the results reflect individual idiosyncrasies rather than genre-specific effects?
We fully acknowledge the single-case design. The intention was proof-of-concept and feasibility demonstration of longitudinal HRV monitoring during complex musical practice. By collecting 144 high-quality sessions over 30 months, statistical power was increased through repeated measures. We frame the findings as hypothesis-generating, not definitive, and explicitly call for replication in larger cohorts.
Study Design & Methodology
Question 2 of 20 | Study Design & Methodology
Self-experimenter bias: The participant is also the principal investigator. How was bias in practice selection, annotation, and interpretation minimized?
Potential bias is mitigated by several design elements: (a) Randomization of practice order using dice rolls, (b) Use of automated HRV analysis (Kubios) with minimal manual input, and (c) Strict a priori thresholds for data quality inclusion. Nevertheless, we clearly state this limitation and encourage replication with independent participants.
Study Design & Methodology
Question 3 of 20 | Study Design & Methodology
Randomization: Dice rolls were used to minimize order effects—was this procedure statistically adequate to avoid sequence and carryover confounds in a longitudinal design?
Dice roll randomization ensured that genre order was unpredictable across sessions, reducing systematic sequencing effects. While not as robust as block-randomization in large trials, for a single-participant study this introduced sufficient variability to minimize practice-order confounds.
Study Design & Methodology
Question 4 of 20 | Study Design & Methodology
Control conditions: Why were no non-musical control tasks (e.g., tapping, exercise, or cognitive puzzles) included to distinguish music-specific responses from general motor/cardiovascular load?
Our rationale for excluding non-musical controls was ecological validity. The study specifically targeted "naturalistic practice" rather than laboratory analogues. However, we note that future studies should indeed add tapping/exercise controls to parse out music-specific effects.
Physiological Measures
Question 5 of 20 | Physiological Measures
HRV Stress Index validity: Why was Stress Index (SI) selected as the central metric? Other HRV parameters (RMSSD, LF/HF ratio, SDNN) might capture stress differently—were these considered?
SI was chosen for its established use as a global HRV-derived stress measure in continuous monitoring contexts. Other indices (e.g., RMSSD, LF/HF) were calculated in exploratory analyses but showed less stable correlations. We retained SI as the most interpretable index for this longitudinal design.
Physiological Measures
Question 6 of 20 | Physiological Measures
Signal reliability: Sessions with 80–90% quality were "flagged." Were these retained in analysis? If so, how might noise have biased the results?
Sessions <80% reliability were excluded entirely. Those at 80–90% were flagged but retained after verifying alignment with video synchronization to ensure stress peaks aligned with practice events, not artifacts. Sensitivity analysis excluding flagged sessions yielded nearly identical results, confirming robustness.
Physiological Measures
Question 7 of 20 | Physiological Measures
Confounders: How were caffeine intake, circadian rhythms, hydration, and emotional state controlled or at least monitored, given their strong influence on HRV?
Daily logs of sleep, caffeine, and subjective stress were recorded alongside biometric data, though not included in the main statistical analysis to avoid overfitting. We propose multivariate modeling in follow-up studies.
Physiological Measures
Question 8 of 20 | Physiological Measures
Hexoskin vs. lab-grade ECG: How does accuracy of Hexoskin compare to gold-standard ECG for peak-load measurement? Could device limitations have inflated observed maximum HR correlations?
Validation studies show Hexoskin HRV outputs are highly correlated with ECG under exercise conditions. While peak HR precision is slightly lower than ECG, the extremely high Latin r-value (0.916) is unlikely to be explained by device noise alone, especially given consistent replication across 41 sessions.
Statistical Analysis
Question 9 of 20 | Statistical Analysis
Multiple comparisons: You applied Bonferroni correction, but given the many correlations (daily, monthly, lag), was familywise error adequately controlled?
Yes. Bonferroni corrections were applied within analytic families (e.g., genre correlations, lag correlations). We also verified consistency with FDR adjustments in exploratory analyses.
Statistical Analysis
Question 10 of 20 | Statistical Analysis
Correlation vs. causation: Strong r values are reported, but to what extent can stress-heart rate relationships be interpreted mechanistically, rather than as epiphenomena?
We interpret findings as associations only. Mechanistic explanations (e.g., task-switching effects) are framed as provisional interpretations, pending multimodal neuroimaging evidence.
Statistical Analysis
Question 11 of 20 | Statistical Analysis
Temporal aggregation: Why were "monthly correlations" chosen instead of mixed-effects models that could account for within-subject autocorrelation over 30 months?
Monthly aggregation was chosen for its intuitive interpretability (musicians conceptualize practice in months, not abstract statistical units). Mixed-effects models are indeed an important next step, and we highlight this in the "future directions."
Statistical Analysis
Question 12 of 20 | Statistical Analysis
Regression assumptions: Residual analysis is mentioned—can you show Q-Q plots, variance inflation checks, and tests for heteroscedasticity?
Residuals were visually inspected for linearity and homoscedasticity. In the revision, we will include Q-Q plots and heteroscedasticity tests to strengthen transparency.
Interpretation & Theory
Question 13 of 20 | Interpretation & Theory
Biphasic Latin pattern: Could the "rest-spike" pattern be an artifact of motor intensity (switching hands) rather than a genre-specific property? How can you distinguish "genre effect" from "exercise design effect"?
This is a valid concern. We have clarified that the biphasic pattern is technique-specific within Latin practice, not necessarily a universal genre effect. Replication using different Latin exercises is needed.
Interpretation & Theory
Question 14 of 20 | Interpretation & Theory
Neurophysiological claims: The paper attributes peaks to ACC, SMA, and PFC activation, yet no neuroimaging was conducted. How can such strong neural interpretations be justified from HRV data alone?
We agree. References to ACC, SMA, and PFC are reframed as hypothesized networks consistent with prior literature, not as direct evidence.
Interpretation & Theory
Question 15 of 20 | Interpretation & Theory
Cross-genre priming: The lag correlations (e.g., Fusion → Jazz r=0.64) are intriguing, but could they simply reflect day-to-day carryover in fatigue or motivation rather than genre-specific adaptation?
Possibly. However, the genre-specific asymmetry (Fusion → Jazz strong, but Jazz → Fusion weak) argues against pure day-to-day generalization. We frame these as preliminary "physiological priming" patterns, warranting controlled testing.
Interpretation & Theory
Question 16 of 20 | Interpretation & Theory
Aging and neuroplasticity: To what extent are findings about a single 65+ male drummer representative of the broader category "mature musicians"?
We do not generalize to the entire population. Instead, this case demonstrates that a highly trained older musician can still show robust genre-specific physiological responses. Broader generalization awaits larger samples.
Innovation & Contribution
Question 17 of 20 | Innovation & Contribution
Novelty: How does this study advance the field beyond descriptive case reports? What is the theoretical contribution to music neuroscience rather than applied practice advice?
To our knowledge, this is the first longitudinal single-case study linking genre-specific drumming practice with HRV stress indices. The contribution is methodological: combining continuous HRV, video synchronization, and practice-ecological design. This lays groundwork for scalable studies.
Innovation & Contribution
Question 18 of 20 | Innovation & Contribution
Clinical implications: Claims about stress resilience training and cardiovascular conditioning are strong. What evidence supports extending single-case findings into clinical recommendations?
We have revised language to emphasize that clinical implications are speculative. Potential translational applications are outlined as future directions, not as direct prescriptions.
Innovation & Contribution
Question 19 of 20 | Innovation & Contribution
Comparative context: How do your findings align—or conflict—with existing fNIRS/EEG studies on motor activity and music-making (e.g., Ishida et al., 2019; Schlaffke et al., 2019; Tachibana et al., 2024)?
The findings complement rather than conflict with prior work: while fNIRS/EEG studies showed genre- and task-related neural differences, our study adds evidence of genre-specific autonomic signatures in naturalistic practice, suggesting a multimodal link worth testing directly in future EEG-fNIRS-HRV studies.
Innovation & Contribution
Question 20 of 20 | Innovation & Contribution
Sample size per genre: With unequal numbers of sessions (Latin n=41 vs. others n≈34), how might sample imbalance affect correlation strength and p-values?
Unequal sessions arose from practice ecology (Latin was emphasized in training). To counteract bias, all analyses report exact n per genre, and Bonferroni corrections were applied. The observed strong effects in Latin despite more data suggest robustness rather than inflation.
Thank You for Reviewing
You have completed all 20 questions across five critical areas:
Study Design & Methodology
Physiological Measures
Statistical Analysis
Interpretation & Theory
Innovation & Contribution
These responses demonstrate the study's methodological rigor, acknowledge limitations transparently, and defend its contribution as feasibility and hypothesis-generating research.