Association Of Sleep Quantity And Mood State With Countermovement Jump Performance In Male Football Athletes
Bibliographic record
Abstract
PURPOSE: To discern the association of self-reported sleep quantity and mood state with countermovement jump (CMJ) performance in male football athletes, and to determine whether differences existed depending on starter status. METHODS: Seventy-seven male football athletes (age: 20 (2) years; height: 185 (7) cm; mass: 98 (19) kg; training experience at the collegiate level: 1.5 (1.0) years; starters: n = 21; dressers: n = 18; non-dressers: n = 38) from McMaster University participated in a 5-week longitudinal study. Participants self-reported their sleep quantity (amended from the Pittsburgh Sleep Quality Index) and mood state (i.e., 0-10 Likert scale) weekly. Additionally, three maximal CMJ attempts were completed twice per week after a standardized 5-minute dynamic warm-up with a minimum of 60 seconds rest between successive attempts on two portable force plates. The variables obtained from CMJ testing included: jump height, countermovement depth, peak relative propulsive power, the modified reactive strength index, and time to takeoff, all of which were computed as weekly average values. Pearson’s correlation coefficients (α = 0.05, and β = 0.20) were used to determine the association between average weekly sleep quantity and mood state, and CMJ metrics. This was computed across all athletes, as well as for each individual starter category. RESULTS: Across all athletes, sleep quantity was associated with CMJ metrics, but associations vary for starter status (Table 1). CONCLUSION: Sleep quantity was related to jump performance, but the mood state of the athlete was not. These findings support previous literature showing a relationship between sleep and jump performance, while highlighting a potential lack of sensitivity in assessments of mental health. These discrepancies may be attributed to the stigmatization surrounding mental health in athletic populations. Table 1. Association between Psychological State and Countermovement Jump Biomechanics. - All Athletes Psychological State JH CMD mRSI PrPP TT Mood State 0.13 (-0.06, 0.32) 0.01 (-0.18, 0.20) 0.17 (-0.02, 0.36) 0.11 (-0.09, 0.29) -0.16 (-0.34, 0.03) Sleep Quantity 0.36 (0.18, 0.52) *** -0.21 (-0.39, -0.02) * 0.30 (0.11, 0.47) ** 0.28 (0.09, 0.45) ** -0.17 (-0.35, 0.02) Starters Psychological State JH CMD mRSI PrPP TT Mood State 0.16 (-0.19, 0.47) -0.23 (-0.53, 0.12) 0.09 (-0.25, 0.42) 0.07 (-0.27, 0.40) 0.03 (-0.31, 0.37) Sleep Quantity 0.42 (0.09, 0.66) * -0.01 (-0.35, 0.33) 0.40 (0.07, 0.65) * 0.36 (0.02, 0.62) * -0.52 (-0.73, -0.21) ** Dressers Psychological State JH CMD mRSI PrPP TT Mood State 0.03 (-0.29, 0.35) 0.20 (-0.13, 0.49) 0.12 (-0.21, 0.43) 0.05 (-0.28, 0.37) -0.26 (-0.54, 0.07) Sleep Quantity 0.43 (0.13, 0.66) ** -0.23 (-0.52, 0.10) 0.42 (0.11, 0.66) ** 0.31 (-0.02, 0.58) -0.24 (-0.53, 0.09) Non-Dressers Psychological State JH CMD mRSI PrPP TT Mood State 0.28 (-0.07, 0.57) 0.32 (-0.02, 0.60) 0.44 (0.11, 0.68) * 0.35 (0.003, 0.62) * -0.56 (-0.76, -0.27) *** Sleep Quantity -0.05 (-0.39, 0.30) 0.04 (-0.31, 0.38) -0.06 (-0.39, 0.29) -0.06 (-0.40, 0.29) 0.10 (-0.25, 0.43) *All values represented as r (95% confidence intervals). JH = Jump height; CMD = countermovement depth; mRSI = the modified reactive strength index; PrPP = peak relative propulsive power; TT = time to takeoff. * = p < 0.05; ** = p < 0.01; *** = p < 0.001.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".