Article Title



E. C. Fonken, C. M. Tarvin, J. M. Loo, R. E. Rumann, and W. M. Silvers

Whitworth University, Spokane, WA.

Sleep is an integral part of proper physiological functioning and can affect physical performance as well as perceived exercise intensity. PURPOSE: To determine if there was a difference between the effects of full rest and acute sleep deprivation on anaerobic exercise performance. METHODS: Seventeen recreationally active male (n = 12) and female (n = 5) participants ages 20-23 yrs. old performed two separate 30 sec Wingate tests (WT): one in a sleep deprived state (less than 4 hrs. sleep) and one in well rested state (two consecutive nights of 7-9 hrs. sleep). The order of the sleep conditions was randomly assigned and at least 48 hrs. separated each condition. During each WT, peak power (PP), mean power (MP), fatigue index (FI), and rating of perceived exertion (RPE) were measured. RPE was measured using the 10 pt. Borg scale and recorded immediately after each WT. All WTs were performed with a workload equivalent to 7.5% of participants’ body weight in kg. Dependent group’s t-tests were used to determine differences between conditions for each dependent variable. Alpha was set at p ≤ 0.05. RESULTS: No significant differences were found (p = 0.11-0.45) between the well-rested and sleep deprived states for PP (10.48 ± 1.41W vs. 9.92 ± 1.62W), MP (7.73 ± 0.82W vs. 7.49 ± 0.89W) and FI (48.81 ± 6.38% vs. 45.90 ± 13.48%). However, RPE was significantly lower (p = 0.02; 7.65 ± 1.00 vs. 8.24 ± 1.20) for the well-rested state. CONCLUSIONS: The descriptive data indicated trends for higher PP and MP for the well-rested state. Statistical power was very low, due to high β values, which suggests the lack of significant differences for PP and MP may have been due to Type II error. The difference in RPE was attributed to the cognitive effects of sleep loss, which have been observed in previous research and are also known to negatively affect exercise fatigue. Future research should utilize a much larger participant population to increase statistical power, monitor sleep and diet more accurately, focus on specific populations, and control for time of day.

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