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Impact of Cognitive Measures and Sleep on Acute Squat Strength Performance and Perceptual Responses Among Well-Trained Men and Women. | LitMetric

AI Article Synopsis

  • The study examined how cognitive factors like sleep, anxiety, and stress affect back squat strength performance in trained men and women.
  • Fifty-three men and fifteen women participated, using various questionnaires to measure sleep quality, anxiety levels, and perceived exertion before performing a 1-repetition maximum (1RM) squat test.
  • Results indicated that sleep hours inversely correlated with average concentric velocity and positively correlated with perceived exertion, while certain anxiety measures negatively impacted actual performance outcomes.

Article Abstract

Haischer, MH, Cooke, DM, Carzoli, JP, Johnson, TK, Shipherd, AM, Zoeller, RF, Whitehurst, M, and Zourdos, MC. Impact of cognitive measures and sleep on acute squat strength performance and perceptual responses among well-trained men and women. J Strength Cond Res 35(2S): S16-S22, 2021-This study assessed the efficacy of currently used assessments for sleep, anxiety, and stress in predicting 1-repetition maximum (1RM) back squat performance. Fifty-three men (age, 23 ± 3 years; body mass, 86.67 ± 13.93 kg; training age, 6.0 ± 2.5 years; 1RM = 163.5 ± 39.5 kg) and 15 women (age, 21 ± 1.5 years; body mass, 63.34 ± 9.6 kg; training age, 4 ± 1.5 years; 1RM = 81.5 ± 12.5 kg) participated. Subjects completed the Daily Analysis of Life Demands for Athletes (DALDA), the revised Competitive State Anxiety Inventory-2 (CSAI-2R), and Oviedo Sleep Questionnaire (OSQ) to evaluate stress, anxiety, and sleep, respectively. Subjects then completed the perceived self-efficacy (PSE) scale, to predict what loads they were 100, 75, and 50% confident that they could lift for a 1RM; then completed 1RM testing with rating of perceived exertion (RPE) and average concentric velocity (ACV) obtained on each attempt. The performance-dependent variable was calculated by subtracting the PSE responses from the actual 1RM (1RM-PSE difference). Bootstrapping with 1,000 replicate samples was used with linear regression to increased robustness of the statistical analyses, and 95% confidence intervals (CIs) were calculated. Hours of sleep was an inverse predictor of ACV (p = 0.014; 95% CI = 0.046 to-0.011) and a positive predictor of RPE (p = 0.005; 95% CI = 0.068-0.342). Furthermore, the hypersomnia subscale of the OSQ was a negative predictor of 1RM-PSE difference at 50% confidence (p = 0.028; 95% CI = -3.507 to -0.528), and CSAI-2R total score was a negative predictor of RPE at 1RM (p = 0.043; 95% CI = -0.041 to -0.003); however, the DALDA did not exhibit any significant relationships. These data highlight the importance of monitoring anxiety and sleep when assessing readiness for maximal strength performance.

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Source
http://dx.doi.org/10.1519/JSC.0000000000003202DOI Listing

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