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SCIENCE DISCUSSION(NO PLAGIARISM, A++ WORK, QUALITY, ON TIME)

Open Posted By: highheaven1 Date: 04/03/2021 High School Report Writing

Science Discussion

Category: Mathematics & Physics Subjects: Algebra Deadline: 12 Hours Budget: $150 - $300 Pages: 3-6 Pages (Medium Assignment)

Attachment 1

nutrients

Article

Pre-Sleep Consumption of Casein and Whey Protein: Effects on Morning Metabolism and Resistance Exercise Performance in Active Women

Takudzwa A. Madzima * , Jared T. Melanson, Jonas R. Black and Svetlana Nepocatych

Department of Exercise Science, Energy Metabolism and Body Composition Laboratory, Elon University, 100 Campus Drive, Elon, NC 27244, USA; [email protected] (J.T.M.); [email protected] (J.R.B.); [email protected] (S.N.) * Correspondence: [email protected]; Tel.: +1-336-278-6791

Received: 1 August 2018; Accepted: 6 September 2018; Published: 10 September 2018 ���������� �������

Abstract: Consuming milk proteins (casein (CP) and whey (WP)) at night before sleep has been shown to positively influence next morning resting metabolic rate (RMR). No data exist regarding the effect of pre-sleep consumption of CP and WP on the ability to perform resistance exercise (RE) the following morning. The present study compared the effects of low (24 g) and high (48 g) doses of CP and WP and a non-energetic placebo (PLA) consumed 30 min before sleep on morning RMR, and RE performance. Nine active women participated in this randomized, double-blind, crossover study. Next morning RMR was measured via indirect calorimetry. RE was performed on six machines for 2 sets of 10 repetitions, and a 3rd set to failure at 60% of one-repetition maximum to calculate RE volume (weight lifted × sets × repetitions). Magnitude based inferences were used. Compared to the PLA, 48 g CP had a likely increase in RMR (4.0 ± 4.8%) and possibly trivial (1.1 ± 7.0%) effect on RE volume. There were no clear effects of 24 g CP, 24 g and 48 g of WP on RMR and RE volume. In conclusion, 48 g CP elicited favorable changes in morning RMR, with only trivial changes in RE performance.

Keywords: pre-sleep feeding; whey protein; casein protein; metabolism; resistance exercise

1. Introduction

Pre-sleep protein feeding within 30 min of sleep has been posited as a new window of opportunity in nutrient timing research [1], conferring benefits including increased next morning resting metabolic rate (RMR) [2], and overnight muscle protein synthesis (MPS) and recovery [3,4]. Previous concerns about pre-sleep feeding have been related to the belief that eating late at night leads to weight gain [5]. While the concern of weight gain is understandable as RMR is lower overnight [6], recent pre-sleep feeding studies have shown that next morning RMR was increased [2,7] or unhindered [8,9] after consumption of low energy (~600 kJ; 150 kcals), protein dense foods prior to sleep. Improvements in morning RMR may have an impact on total daily energy expenditure, which may help individuals seeking to maintain or improve body composition.

Commonly consumed pre-sleep snacks include the milk proteins casein (CP), and whey (WP), with CP often recommended as the pre-sleep protein type for active individuals [3]. The acidic environment of the stomach causes CP to clot, thereby delaying the gastric emptying into the small intestine resulting in a moderate, sustained increase in plasma amino acid concentrations [10]. On the other hand, WP is acid-soluble and empties into the small intestine more rapidly, resulting in a more pronounced but temporal rise in plasma amino acid levels [10,11]. Thus, CP may be a more ideal protein type to consume prior to sleep, which will prolong overnight hyperaminoacidemia and provide

Nutrients 2018, 10, 1273; doi:10.3390/nu10091273 www.mdpi.com/journal/nutrients

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the precursors for overnight protein metabolism, thereby increasing RMR. However, it is inconclusive whether CP is superior to WP, as daytime studies observed no differences in postprandial RMR between isoenergetic (1465 kJ; 350 kcals) mixed breakfast meals, each containing either 34 g CP or 36 g WP [12]. In contrast, a mixed meal containing WP increased RMR more than isoenergetic (1921 kJ; 459 kcals) CP in lean men and women [13].

When consumed in close proximity to sleep, a recent acute study by Madzima et al. [2] found similar increases in next morning RMR after consuming 30 g of both CP and WP prior to sleep in active men, when compared to a non-energetic placebo (PLA). Interestingly, next morning fat oxidation was greater after the PLA compared to WP, but not CP. In addition, Kinsey et al. [8,9] did not observe an advantage of consuming 30 g of CP over 30 g of WP in obese women, nor 30 g of CP compared to a PLA in obese men. Following the reported lack of differences between CP and WP, a more recent acute pre-sleep feeding study sought to investigate the impact of 10 g and 30 g protein in the form of milk, which constitutes 80% CP and 20% WP, on next morning RMR in moderately overweight men, and did not find any changes in RMR when compared to a non-caloric placebo [14]. Therefore, the composition and dose of an optimal pre-sleep meal to alter morning RMR is yet to be elucidated. Further, current evidence suggests that the only increases in next morning RMR after pre-sleep consumption of single macronutrients or low energy snacks (e.g., milk), have been observed in physically active men [2] and women [7]. Thus, a reasonable follow-up question is to determine whether the increases in RMR and protein type consumed prior to sleep have an impact on performance during a morning exercise bout in active individuals.

Although CP has been the most extensively studied pre-sleep protein [4,8,15], it is yet to be determined whether CP is superior. WP not only contains a greater essential amino acid content than CP, but also has a higher proportion of leucine, the branched chain amino acid important for stimulating MPS [16,17]. In addition, over a 6 h postprandial period during daytime protein feeding studies, WP has been reported to stimulate MPS more than CP [18], but it is yet to be established whether this remains true during an overnight period, which can last approximately 8–10 h. It is plausible that the timeline for benefits conferred from protein consumption during waking hours, may be extended during the overnight postprandial period when individuals are asleep. Previously, MPS has not been augmented during the overnight period when 20–25 g CP was consumed during an evening exercise bout [19]; however, 40 g of CP administered immediately prior to sleep was properly digested and absorbed and increased overnight MPS and recovery in active individuals, compared to a placebo [4]. Further, pre-sleep protein augmented overnight MPS when a prior evening session of resistance exercise was performed [15]. Thus, pre-sleep protein may be a strategy to improve overnight recovery from an evening session of exercise and potentially improve performance if exercise was performed early the following morning. It should be noted that overnight MPS in the study by Trommelen et al. [15], was also stimulated after pre-sleep protein consumption alone. Therefore, it is possible that even in the absence of an evening exercise session, consumption of a bolus of protein prior to sleep will acutely stimulate overnight MPS, although not to the extent as if an evening exercise session had been performed prior.

It is not uncommon for active individuals to perform morning exercise in a fasted state for several reasons, including seeking to avoid gastrointestinal distress, availability of time, or intending to increase fat oxidation [20–22]. The period between dinner and breakfast is the longest post-absorptive period during the day, which could result in >8 h without provision of nutrients. Although this length of time without nutrition may enhance next morning fat oxidation [20,23,24], it may ultimately hinder performance in individuals seeking to exercise in the early morning [25]. Consumption of a pre-sleep protein supplement may be a feeding opportunity to provide nutrients to improve overnight recovery from activities of daily living in active individuals, compared to not consuming any energy after their evening meal.

To date, only one study has investigated the effect of pre-sleep feeding on next morning exercise performance in competitive female runners [7]. Although, Ormsbee et al. [7] reported that a pre-sleep,

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low-calorie (~753 kJ; 180 kcals), protein-rich source in the form of chocolate milk had a likely small increase in next morning RMR, there was no improvement in aerobic performance compared to a non-caloric placebo. Therefore, it is unclear whether pre-sleep feeding influences next morning exercise performance. Furthermore, determining the optimal dose of pre-sleep protein consumption that will increase next morning RMR, fat oxidation, and exercise performance may be beneficial for active individuals seeking to exercise early in the morning. Our previous pre-sleep feeding study [2] has shown increased next morning RMR, following a 30 g dose of pre-sleep protein. However, to the best of our knowledge, no acute pre-sleep feeding studies have investigated whether a single dose of CP or WP less than or more than the typical 30 g dose, elicits similar increases in RMR and possibly fat oxidation. Further, no acute studies have investigated the effect of pre-sleep protein on next morning resistance exercise (RE) performance. Therefore, the primary purpose of the present study was to investigate whether pre-sleep consumption of a low dose (24 g) and a high dose (48 g) of CP and WP increases next morning RMR, fat oxidation, and RE performance, when compared to a non-energetic placebo (PLA). We hypothesize that pre-sleep consumption of protein (CP or WP) would be superior in increasing next morning RMR, fat oxidation, and total volume performed during an RE bout, when compared to a PLA (which will simulate not eating anything before bed the night before). The rationale for our hypothesized increase in morning total RE volume performed, after pre-sleep protein feeding, is based on the improvement in overnight recovery from pre-sleep feeding reported in the aforementioned studies. A secondary aim of the present study was to determine any differences between CP and WP, and whether there is a dose response of CP and WP on the aforementioned variables. Owing to moderate, sustained rise in plasma amino acid concentrations after CP consumption [10], we hypothesize that pre-sleep CP will result in a greater increase in next morning RMR, fat oxidation, and volume of RE performed, compared to WP. We also hypothesize that pre-sleep consumption of the high dose of CP and WP, would be superior to the low doses of each respective protein.

2. Materials and Methods

2.1. Participants

Nine physically active young women, (age: 25 ± 6.4 years; body fat: 22.0 ± 6.2%; BMI 22.6 ± 2.4 kg/m2) participated. To be eligible, participants had to have regularly resistance trained ≥2 days per week for 12 months. Participants were excluded if they had uncontrolled hypertension (blood pressure (BP) > 160/100 mmHg), taking BP or cholesterol medications, had been diagnosed with cardiovascular disease, stroke, diabetes, thyroid, kidney dysfunction, or had dairy allergies. In addition, participants were excluded if they were currently a smoker. If they were consuming any nutritional supplements (except for a multivitamin), they were asked to refrain from taking the supplements two weeks before their first visit and during the entire study period. Participants were asked to maintain their normal exercise regimens for the duration of the study. The present study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human participants were approved by the University Institutional Review Board. Written informed consent was obtained before participation in the study.

2.2. Study Design

The present study was a randomized double-blinded crossover trial. This study included one familiarization visit and 5 experimental trials, separated by 48–72 h (Figure 1). Participants were randomly assigned using a computer generated randomization program to consume one of five supplements the night prior to each experimental trial: (1) 24 g WP (31 g, 502 kJ (120 kcals), 24 g protein, 4 g carbohydrate, 1 g fat); (2) 48 g WP (62 g, 1004 kJ (240 kcals), 48 g protein, 8 g carbohydrate, 2 g fat; (3) 24 g CP: 33 g, 502 kJ (120 kcals), 24 g protein, 4 g carbohydrate, 1 g fat; (4) 48 g CP: 66 g, 1004 kJ (240 kcals), 48 g protein, 8 g carbohydrate, 2 g fat; (5) PLA: Propel Zero™ (The Gatorade

Nutrients 2018, 10, 1273 4 of 11

Company, Chicago, IL, USA), 2.9 g, 0 kJ (0 kcals) (placebo). The rationale for the use of the 24 g dosage for the present study, was to match typical serving sizes of most commercially available protein supplements (24–25 g per serving). Further, we sought to investigate the effect of a double serving of protein on the dependent variables. Each protein powder (Optimum Nutrition®, Aurora, IL, USA) was identically flavored (vanilla) and had similar texture, however the PLA that was also in powdered form had a different texture and flavor. Thus, only the PLA was single-blinded to the participants. Participants were provided each supplement in a Ziplock® plastic bag (S. C. Johnson & Son, Racine, WI, USA) and instructed to consume each supplement with 12 oz of water at least two hours after dinner, and within 30 min before sleep. To confirm compliance with time of supplement consumption, participants were asked to complete a log documenting the time they completed the supplement and the time they lay down to sleep. Prior to each experimental trial, participants were asked to refrain from alcohol, caffeine, and exercise for 24 h. In addition, each participant was asked to record their dietary intake for the 24 h period prior to each experimental trial using the nutrient tracking smartphone application, MyFitnessPal® (Under Armour Inc., Baltimore, MD, USA). Participants reported to the laboratory for each experimental trial the morning after consuming their assigned pre-sleep supplements, upon waking, and in a fasted state between 0600 and 0900 h.

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in powdered form had a different texture and flavor. Thus, only the PLA was single-blinded to the participants. Participants were provided each supplement in a Ziplock® plastic bag (S. C. Johnson & Son, Racine, WI, USA) and instructed to consume each supplement with 12 oz of water at least two hours after dinner, and within 30 min before sleep. To confirm compliance with time of supplement consumption, participants were asked to complete a log documenting the time they completed the supplement and the time they lay down to sleep. Prior to each experimental trial, participants were asked to refrain from alcohol, caffeine, and exercise for 24 h. In addition, each participant was asked to record their dietary intake for the 24 h period prior to each experimental trial using the nutrient tracking smartphone application, MyFitnessPal® (Under Armour Inc., Baltimore, MD, USA). Participants reported to the laboratory for each experimental trial the morning after consuming their assigned pre-sleep supplements, upon waking, and in a fasted state between 0600 and 0900 h.

Figure 1. Study Timeline. 1-RM, one-repetition strength testing; RMR, resting metabolic rate measurements; the morning after pre-sleep consumption of a single serving of 24 or 48 g whey protein (WP), 24 or 28 g casein protein (CP), and a non-energetic placebo (PLA).

2.3. Laboratory Visit 1: Baseline Testing and Familiarization

Height and weight was assessed using a stadiometer and standardized scale (Detecto®, Webb City, MO, USA), respectively. Body composition was assessed via bioelectrical impedance analysis (BIA 450, Biodynamics Corp, Shoreline, WA). Upon completion of body composition assessments, one-repetition maximum (1-RM) tests were performed on the following exercise machines (Precor®, Woodinville, WA, USA): Chest press, leg press, lat pull-down, shoulder press, leg extension, and leg curl. After a warm-up, participants progressed towards the maximum weight that they could lift one time through a full range of motion. All measurements were recorded, with the goal of achieving a 1-RM within 3 to 5 attempts. Their achieved 1-RM was used to calculate 60% of their 1-RM, which was the load used for testing days. Participants were then familiarized with the ventilated hood (ParvoMedics, Sandy, UT, USA) to be used for metabolic testing by laying under the hood for several minutes until they felt accustomed to it. Upon completion of familiarization, participants were provided with their first supplement and instructed to take it the night before their first testing day (at least 48 h) later.

2.4. Experimental Trials (Visits 2, 3, 4 and 5)

Metabolic Testing

Upon arrival to the laboratory, participants lay supine in a quiet, dark, and climate-controlled room (20–23 °C), whilst gas exchange was measured. Gas exchange was measured continuously for 30 min to assess resting oxygen consumption (VO2; mL/kg per min), RMR (kJ/day), and respiratory exchange ratio (RER) via indirect calorimetry using the ventilated hood connected to a metabolic cart (ParvoMedics, TrueOne 2400, Sandy, UT, USA). Metabolic data were averaged every 30 s, and the last 20 min were used for analysis. The RER measurements were used to determine substrate

Figure 1. Study Timeline. 1-RM, one-repetition strength testing; RMR, resting metabolic rate measurements; the morning after pre-sleep consumption of a single serving of 24 or 48 g whey protein (WP), 24 or 28 g casein protein (CP), and a non-energetic placebo (PLA).

2.3. Laboratory Visit 1: Baseline Testing and Familiarization

Height and weight was assessed using a stadiometer and standardized scale (Detecto®, Webb City, MO, USA), respectively. Body composition was assessed via bioelectrical impedance analysis (BIA 450, Biodynamics Corp, Shoreline, WA). Upon completion of body composition assessments, one-repetition maximum (1-RM) tests were performed on the following exercise machines (Precor®, Woodinville, WA, USA): Chest press, leg press, lat pull-down, shoulder press, leg extension, and leg curl. After a warm-up, participants progressed towards the maximum weight that they could lift one time through a full range of motion. All measurements were recorded, with the goal of achieving a 1-RM within 3 to 5 attempts. Their achieved 1-RM was used to calculate 60% of their 1-RM, which was the load used for testing days. Participants were then familiarized with the ventilated hood (ParvoMedics, Sandy, UT, USA) to be used for metabolic testing by laying under the hood for several minutes until they felt accustomed to it. Upon completion of familiarization, participants were provided with their first supplement and instructed to take it the night before their first testing day (at least 48 h) later.

2.4. Experimental Trials (Visits 2, 3, 4 and 5)

Metabolic Testing

Upon arrival to the laboratory, participants lay supine in a quiet, dark, and climate-controlled room (20–23 ◦C), whilst gas exchange was measured. Gas exchange was measured continuously for 30 min to assess resting oxygen consumption (VO2; mL/kg per min), RMR (kJ/day), and respiratory

Nutrients 2018, 10, 1273 5 of 11

exchange ratio (RER) via indirect calorimetry using the ventilated hood connected to a metabolic cart (ParvoMedics, TrueOne 2400, Sandy, UT, USA). Metabolic data were averaged every 30 s, and the last 20 min were used for analysis. The RER measurements were used to determine substrate utilization of fat (RER = 0.7; 100% fat) and carbohydrate (RER = 1.0; 100% carbohydrate) as a fuel source the morning, after pre-sleep protein or PLA consumption.

2.5. Resistance Exercise Performance Testing

Following metabolic testing, participants were asked to perform RE at 60% of their 1-RM, for two sets of 10 repetitions, and a third set to muscular failure on the following exercise machines: Chest press, leg press, lat pull-down, shoulder press, leg extension, and leg curl. To standardize time under tension and minimize the variation in repetition speed between experimental trials, repetitions were performed at a metronome cadence of 30 beats per minute, which was equated to a 2-s concentric and 2-s eccentric phase. Total RE volume performed was calculated by multiplying the weight lifted by 3 sets and by the number of repetitions performed.

2.6. Statistical Analyses

Probabilistic magnitude-based inferences and 90% confidence intervals (CI) were used to assess the effect of pre-sleep WP and CP on the metabolic variables and RE volume, when compared to the PLA using the methods detailed by Batterham and Hopkins [26]. Several performance and sports nutrition studies [7,27,28] have used this approach as an alternative to traditional null hypothesis testing. A published spreadsheet was used to assess the likelihood of a true treatment effect, based on the smallest meaningful threshold [29]. The smallest meaningful treatment effect thresholds were determined by multiplying 0.2 and 0.3, by the back-transformed SD as a percent of the mean of the PLA (control condition) for metabolic and performance variables, respectively [30]. Inferences were based on the spread of the 90% CI, in relation to the threshold values. An effect was classified as unclear if the 90% confidence interval overlapped both the positive and negative thresholds. The clinical version of magnitude-based inferences was used to determine the clear effects of the treatment as having a >25% chance of benefit and <0.5% chance of harm. Qualitative inferences were based on the chance of benefit (increase) and harm (decrease) of each supplement on the outcome variables, compared to the PLA as follows: <1%, almost certainly none; 1–5%, very unlikely; 5–25% unlikely; 25–75%, possible; 75–95%, likely; 95–99%, very likely; and >99%, almost certainly. Effect sizes (ES) were calculated by standardizing the differences of all treatments to the SD of the PLA; and to control for small sample bias, the SD of the PLA was divided by 1–3(4v-1), where v is equal to the degrees of freedom [30]. The ES magnitudes for metabolic variables were qualified as follows: trivial, 0.0–0.2; small, 0.2–0.6; moderate, 0.6–1.2; large, 1.2–2.0; very large, 2.0–4.0; and extremely large, >4.0. For RE performance, ES was qualified as follows: trivial, 0.0–0.3 (4%); small, 0.3–0.9 (18%); moderate, 0.9–1.6 (32%); large, 1.6–2.5 (50%); very large, 2.5–4.0 (80%); and extremely large, >4.0 [30]. Data were log-transformed to account for heteroscedastic error [31]. In addition, within-subjects repeated measures ANOVA was conducted to measure differences for metabolic variables and RE volume for each trial. When appropriate, a Tukey’s post hoc analysis was used to examine differences. Statistical analyses were performed using SPSS software (Ver. 24; IBM-SPSS Inc., Armonk, NY, USA). Significance was set at p < 0.05. Data presented were back-transformed means ± SD and/or mean difference (%) ± CI. Qualitative inferences were presented along associated p-values.

3. Results

3.1. Dietary Intake

Analysis of pre-experimental trial 24 h dietary logs showed dietary intake was similar between trials, with a mean intake of 5933 ± 866 kJ/day (1418 ± 207 kcals/day), with 48.0 ± 3.4% energy from carbohydrates, 17.9 ± 3.1% energy from protein, and 34.1 ± 5.7% energy from fat.

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3.2. Metabolism

Mean VO2 (ml/kg per min), RMR (kJ/day), and RER, the morning after pre-sleep CP and WP, as well as the mean effect differences when compared to PLA, are presented in Table 1. With 48 g CP, there was a possibly and likely (70%, 84% likelihood, respectively) increase in VO2 (ES = 0.31; p = 0.375) and RMR; (ES = 0.43; p = 0.119), respectively, when compared to PLA, with no clear effect of 24 g of CP. There were no clear effects of 48 g CP on RER; however, 24 g CP elicited a possibly (70% likelihood) lower fat oxidation than PLA (ES = 0.44; p = 0.397). There were no clear effects of 24 g and 48 g of WP on VO2 and RMR. However, RER measures revealed a likely (76% likelihood) lower fat oxidation (increase in carbohydrate utilization), following 48 g WP, compared to PLA (ES = −0.53; p = 0.327).

Comparison between protein type and dose are displayed in Table 2. When compared to 48 g WP, pre-sleep consumption of 48 g CP was likely beneficial (91%, 93%, 81% likelihood, respectively) to increasing VO2 (ES = 0.72; p = 0.128), RMR (ES = 0.61; p = 0.066) and fat oxidation (ES = 0.64; p = 0.241). Whereas, differences in VO2 and RMR were unclear between 24 g CP and 24 g WP, with 24 g CP having a likely (95% likelihood) lower fat oxidation, compared to 24 g WP (ES = −0.65; p = 0.040). Comparison of protein dose within protein type revealed that 48 g CP had a likely (85%, 86% likelihood, respectively) increase in VO2 (ES = 0.67; p = 0.231) and RMR (ES = 0.65; p = 0.163), whilst RER remained unclear. Mean differences between 48 g WP and 24 g WP were unclear for VO2, whilst 48 g WP possibly (56% likelihood) increased RMR (ES = 0.25; p = 0.651) and very likely (96% likelihood) decreased fat oxidation (ES = −0.74; p = 0.033).

Table 1. Comparison of Proteins versus Non-Energetic Placebo on Metabolic Variables.

Mean ± SD and Mean Effect Difference (%) ± 90% Confidence Interval Treatment VO2 (mL/kg per min) RMR kJ/day (kcal/day) RER

24 g CP Mean 3.62 ± 0.57 6268 ± 753 (1498 ± 180) 0.73 ± 0.04

Mean effect −3.0; ±7.6 −1.9; ±8.0 2.8; ±5.8 Interpretation Unclear Unclear Possibly increase

48 g CP Mean 3.84 ± 0.42 6653 ± 628 (1590 ± 150) 0.70 ± 0.03

Mean effect 2.7; ±5.8 4.0; ±4.8 −0.7; ±2.7 Interpretation Possibly increase Likely increase Unclear

Mean 3.59 ± 0.39 6180 ± 678 (1477 ± 162) 0.70 ± 0.02 24 g WP Mean effect −3.8; ±3.2 −3.3; ±3.3 −1.3; ±3.6

Interpretation Unclear Unclear Unclear Mean 3.61 ± 0.32 6292 ± 444 (1504 ± 106) 0.73 ± 0.05

48 g WP Mean effect −3.4; ±3.2 −1.6; ±3.5 3.4; ±3.2 Inference Unclear Unclear Likely increase

Mean ± SD, mean effect difference, and inferences on variables the morning after pre-sleep consumption of a single serving of 24 or 48 g whey protein (WP), 24 or 28 g casein protein (CP), compared to a non-energetic placebo (PLA).

Table 2. Comparison of Metabolic Variables between Protein Type and Dose.

Mean Difference (%); ± 90% Confidence Interval Comparison VO2 RMR RER

24 g CP vs. 24 g WP Mean effect 0.9; ±8.6 1.4; ±8.7 4.2; ±3.2

Interpretation Unclear Unclear Likely increase

48 g CP vs. 48 g WP Mean effect 6.3; ±7.0 5.7; ±5.2 −4.2; ±6.3

Interpretation Likely increase Likely increase Likely decrease

48 g CP vs. 24 g CP Mean effect 5.9; ±8.4 6.1; ±7.8 −3.5; ±5.5

Interpretation Likely increase Likely increase Unclear

48 g WP vs. 24 g WP Mean effect 0.5; ±5.8 1.8; ±5.9 4.8; ±3.4

Interpretation Unclear Possibly increase Very likely increase

Mean effect comparisons and inferences on variables the morning after pre-sleep consumption of a single serving of 24 or 48 g whey protein (WP), 24 or 28 g casein protein (CP).

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3.3. Resistance Exercise Performance

Total RE volume performed the morning after pre-sleep CP and WP, when compared to PLA are presented in Figure 2A. There were no clear effects of 24 g WP, 48 g WP, and 24 g CP. Only 48 g CP elicited a possibly trivial response in next morning RE training volume (+1.1 ± 7.0% mean effect, ES = 0.1; p = 0.779). Figure 2B displays the comparison between protein type and dose for total RE volume performed. When compared to 48 g WP, pre-sleep consumption of 48 g CP possibly (38% likelihood) increased RE volume (+3.0 ± 6.0% mean effect, ES = 0.13; p = 0.351), whilst the difference between 24 g CP and 24 g WP was unclear. Total RE volume performed was possibly (61% likelihood) greater after 48 g CP, compared to 24 g CP (+5.7 ± 10.8% mean effect, ES = 0.24; p = 0.440), but the comparison between 48 g WP and 24 g WP was likely trivial (+0.1 ± 3.5% mean effect, ES = 0.00; p = 0.899).

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3.3. Resistance Exercise Performance

Total RE volume performed the morning after pre-sleep CP and WP, when compared to PLA are presented in Figure 2A. There were no clear effects of 24 g WP, 48 g WP, and 24 g CP. Only 48 g CP elicited a possibly trivial response in next morning RE training volume (+1.1 ± 7.0% mean effect, ES = 0.1; p = 0.779). Figure 2B displays the comparison between protein type and dose for total RE volume performed. When compared to 48 g WP, pre-sleep consumption of 48 g CP possibly (38% likelihood) increased RE volume (+3.0 ± 6.0% mean effect, ES = 0.13; p = 0.351), whilst the difference between 24 g CP and 24 g WP was unclear. Total RE volume performed was possibly (61% likelihood) greater after …

Attachment 2

Measuring Drug and Alcohol Use Among College Student-Athletes∗

James N. Druckman, Northwestern University

Mauro Gilli, Northwestern University

Samara Klar, University of Arizona

Joshua Robison, Aahus University

Objective. Few issues in athletics today receive more attention than drug and alcohol usage, especially when it comes to college athletics. We seek to address self-report biases related to drug usage and heavy drinking. Methods. We employ an experimental measurement technique. Results. Our results suggest that a greater percentage of student-athletes from a major conference knowingly engage in these two behaviors than self-reports indicate. Specifically, we find 37 percent of respondents seem to have taken banned performance-enhancing drugs (compared to 4.9 percent who directly admit to doing so when asked), and 46 percent seem to have consumed more than five drinks in a week (compared to about 3 percent who openly admit to doing so). Conclusions. We provide evidence for the extent of self-underreporting when it comes to drug and alcohol usage among college athletes. That said, future work is needed to accurately pinpoint specific substances and the frequency with which they are taken; for example, it could be the percentage of individuals using banned substances stems from consuming significant concentrations of caffeine (e.g., multiple cups of coffee).

Drug and alcohol use by college students is a frequently debated and often controversial topic. This subject has received particular attention when it comes to student-athletes. Evidence of the importance of assessing drug and alcohol usage among student-athletes is exemplified by a 2012 National Collegiate Athletic Association (NCAA) report whose “primary objective [was] to update NCAA policy makers with both current and historical information concerning levels of drug and alcohol use by student-athletes within college athletics” (2012: 4). In this article, we employ an experimental technique that allows us to offer a more accurate assessment of usage than extant studies provide. We begin in the next section with a literature review that leads us to an explication of our approach. We then present results from our survey. Our evidence demonstrates that the commonly used self-report method for estimating drug and alcohol use found in existing studies, including in the aforementioned NCAA report, seem to understate usage.

The Challenge of Measuring Drug and Alcohol Usage

To our knowledge, there is surprisingly little written on drug use among college student- athletes and, when it comes to student-athletes’ own input on this controversial issue,

∗Direct correspondence to James N. Druckman, Department of Political Science, Northwestern University, Scott Hall, 601 University Place, Evanston, IL 60208 〈[email protected]〉. All data and coding for replication purposes are available at James N. Druckman’s professional webpage 〈http://faculty.wcas.northwestern.edu/�jnd260/publications.html〉. The authors thank the many students at Northwestern University who assisted with data collection.

SOCIAL SCIENCE QUARTERLY, Volume 96, Number 2, June 2015 C© 2014 by the Southwestern Social Science Association DOI: 10.1111/ssqu.12135

370 Social Science Quarterly

the literature is scarce. We have identified those few instances in which student-athletes’ attitudes are measured.1 While existing studies on this subject are illustrative of college athletes in many ways, the nature of the samples used and the method for measuring usage limit what can be said about the extent of drug and alcohol use. For example, Buckman et al. (2008) find that among male student-athletes, 9.7 percent say they use “banned perfor- mance enhancers” and 55.8 percent say they used “performance-enhancing drugs” (which might include legal nutritional supplements). Among female student-athletes, no one said they use “banned performance enhancers” and 29.8 percent said they used “performance- enhancing drugs.” While these are intriguing and important findings, the sample is of limited generalizability since it comes only from those who took part in a mandatory alcohol education program. Green et al. (2001) survey student-athletes in Divisions 1, 2, and 3 and find 80.5 percent use alcohol, but the specifics of the survey are unclear and the survey also was part of a NCAA-sponsored project, for which research teams conducted the survey at each participating school. While this result is clearly important evidence, the way the data were collected creates the possibility that demand effects influenced the validity of usage estimates. For instance, the presence of NCAA authorities during the administration of the survey may have had a substantial influence over respondents’ candor, especially given usage was measured via self-reports (also see Wechsler et al., 1997 who similarly rely on self-reports in a study of alcohol use).2

Perhaps the most impressive and exhaustive survey of athlete drug use was done by the NCAA (2012) itself in 2009. It drew a stratified sample of institutions from all 1,076 active member institutions of the NCAA and surveyed three prespecified teams per school with an ultimate sample of 20,474 respondents. The survey took several steps to ensure anonymity, such as providing a preaddressed and stamped envelope for re- turn to a third-party vendor, and it did not ask for identifying information from the respondent. The survey asked about a host of drug and alcohol behaviors, finding—for example—that only 0.4 percent of respondents report using anabolic steroids within the last 12 months and over 50 percent of respondents indicate using alcohol in the past year. The NCAA survey provides vital information. However, like the other studies described above, the NCAA’s survey relied on self-reports of behavior that may lead to underreports even with the survey’s efforts to ensure anonymity. Indeed, the report acknowledged that “Even with these measures to ensure anonymity, self-report data of this kind can be prob- lematic due to the sensitive nature of the issues. Therefore, absolute levels of use might be underestimated in a study such as this” (2012:5).

In sum, while research to date provides valuable information, it is plagued by the nontrivial threat of arriving at substantial understatements of usage. Reliance on self- reports leads to underreporting due to social desirability and threat of disclosure influences (Tourangeau and Smith, 1996; Tourangeau et al., 2000). The former refers to respondents’ hesitation to provide an answer that may be deemed as socially unacceptable (e.g., that violates expectations or norms). The latter, meanwhile, occurs when there are “concerns about the possible consequences of giving a truthful answer should the information become known to a third party . . . [Such a] question . . . raises fears about the likelihood or consequences of disclosure of the answers to agencies or individuals not directly involved in the survey. For example, a question about use of marijuana is sensitive to teenagers when

1Some of this work looks at other drugs that we do not study, such as chewing tobacco and marijuana, so we do not discuss those studies here.

2Other studies, such as those by Lisha and Sussman (2010), Terry and Overman (1991), Ford (2007), and Tricker and Connolly (1997), explore drug and/or alcohol use among athletes (and some nonathletes), but they do not look directly at distinct types of behaviors as we do below.

Drug and Alcohol Use Among College Student-Athletes 371

their parents might overhear their answers” (Tourangeau and Yan, 2007:860). Questions about drug or alcohol usage in general have long been noted as carrying with them social desirability and threat of disclosure problems. For example, Tourangeau and Yan state: “To cite just one line of research . . . studies that compared self-reports about illicit drug use with results from urinalyses . . . found that some 30%–70% of those who test positive for cocaine or opiates deny having used drugs recently. The urinalyses have very low false positive rates . . . so those deniers who test positive are virtually all misreporting” (2007:859).

When it comes to student-athletes and drug/alcohol usage, there is undoubtedly a threat of disclosure issue such that if these student-athletes were discovered to be using banned substances or drinking heavily, they could be prevented from participating in their sport according to NCAA rules. Specifically, the NCAA bans a number of substances, including anabolic agents, stimulants, street drugs, and even large amounts of caffeine; individuals identified as using such substances are banned from participation.3 While the NCAA only has a limited ban on alcohol usage, it explicitly warns against overusage in stating the following: “The following is a list of substances that are commonly abused, and how they can impact a student-athlete’s performance and eligibility. Alcohol: Alcohol is a nervous system depressant. At high dosages, effects include mood swings, im- paired judgment and inability to control motor functions. Alcohol can impair an athlete’s performance through dehydration, depleting vital nutrients and interfering with restful sleep and recovery.”4 This statement makes reporting use socially undesirable (e.g., it would be violating a possible norm of avoiding any product that may harm perfor- mance). Moreover, it may be potentially threatening for athletes to overdrink since their individual school or conference may enforce distinct policies that could put caps on alcohol usage. It is for these reasons that the literature on underreporting often accentuates biases in self-reported drug and alcohol usage, as the aforementioned NCAA report explicitly recognizes (Tourangeau and Yan, 2007:860). Our goal is to remedy this underreporting problem and identify more accurate rates of usage by employing a procedure that has been shown to overcome underreporting challenges.5

There are various ways to elicit more accurate responses (e.g., minimize underreporting), including the previously discussed anonymity approach employed by the NCAA (for a fuller discussion, see Traugott and Lavrakas, 2008). However, perhaps the most powerful approach, and the one we pursue, is called the list experiment or item count technique. This approach has been employed to gauge racial prejudice, homophobia, and substance abuse in other populations than our focus, where it has not been used (e.g., Kuklinski et al., 1997; Druckman and Lupia, 2012; Coffman et al., 2013). The technique provides a solid estimate of aggregate responses, although it does not allow for individual-level analyses (and again we are unaware of it being employed as we do below when it comes to college athletics).

In this approach, the researcher randomly divides respondents into two groups: one treat- ment and one control. The respondents in the treatment count the number of arguments with which they agree (or disagree/cause them to be upset) among the (for example) four arguments listed in the questionnaire. Of those four arguments provided, one addresses

3See 〈http://www.ncaa.org/wps/wcm/connect/public/NCAA/Health+and+Safety/Drug+Testing/ Resources/〉.

4See 〈http://www.ncaa.org/wps/wcm/connect/public/NCAA/Health+and+Safety/Drug+Testing/ Resources/Commonly+Abused+Substances〉.

5The problem of underreporting also occurs in the related areas of mental illness (e.g., Calhoun et al., 2000) and morbidity and healthcare (e.g., Ansah and Powell-Jackson, 2013; Khantzian, 1997; Mountjoy et al., 2010).

372 Social Science Quarterly

an item of social undesirability (e.g., racism or, in our case, drug usage). By contrast, respondents in the control group are provided with the same question, except that their argument pool is only composed of, for example, three arguments (e.g., all but the socially undesirable item). Random assignment to the control and treatment groups means that the two groups should be equivalent, on average, in how they answer the items presented on both forms. In turn, this allows for an unbiased estimate for the proportion of respondents who have the socially undesirable trait by subtracting the average number of agreement in the control group from the treatment group.

One notable application is Kuklinski et al. (1997), who employ a list experiment to elicit the extent to which citizens are willing to admit racial anxiety or animus. In the experiment, subjects are presented with a list of items and are asked: “How many of them upset you?” Some subjects randomly were assigned to assess a total of three items (e.g., increased gasoline tax, athletes receiving millions of dollars, corporations polluting the environment). Others receive a four-item list where the added item is “a black family moving in next door.” Kuklinski et al. report that, among white survey respondents in the American South, the four-item group reported that an average 2.37 items made them upset, compared to 1.95 items in the three-item group. Since the groups are otherwise identical, the implication is that 42 percent of these respondents (i.e., (2.37 – 1.95) × 100) are upset by the thought of a black neighbor. By contrast, when subjects were asked this question directly, only 19 percent of respondents claimed to be upset. More recently, the National Bureau of Economic Research released a list experiment regarding sexual orientation among Americans (Coffman et al., 2013). It reports that the use of a list experiment indicates “substantial underestimation” of nonheterosexuality in conventional surveys. Survey experiments such as these can help us observe opinions that citizens do not readily express due to social desirability and/or threat of disclosure problems. Also note that the experimental (random assignment) nature of this measure means that multivariate analyses are not needed as the groups are, on average, equivalent, and thus the focus is on average percentage differences.

Considerable research shows that list experiments reveal a clear causal dynamic of un- derreporting. Indeed, differences between the groups have been found to not stem from measurement error. This argument is supported by three types of evidence. First, studies that have available validation data show that reliance on self-reports, even when coupled with assurances of anonymity as found in the NCAA report cited earlier, generate substantial underreporting of behaviors in comparison to estimates generated by list experiments; this difference is substantial and is on the order of 40 percent (see Tourangeau et al., 2000). Second, this argument is consistent with Tourangeau and Yan’s (2007:872) finding that “the use of item count techniques [i.e., list experiments] generally elicits more reports of socially undesirable behaviors than direct questions” (also see Blair and Imai, 2012:47–48, for a long list of examples that employ this approach in other domains). Finally, Kiewiet de Jonge and Nickerson (2014) directly investigate the possibility that the added item found in the treatment version of the list experiment by itself leads to a higher number of responses. Their results “imply that the ICT [item count technique] does not overestimate socially undesirable attitudes and behaviors and may even provide conservative estimates” (2014: 661). In short, they find that there is no evidence that the differing lengths of the lists generate any measurement bias, and instead, differences come only from the ex- perimental treatment of the added “undesirable” item (also see Himmelfarb and Lickteig, 1982; Tourangeau et al., 2000:278; Lensvelt-Mulders et al., 2005; Tourangeau and Yan 2007:872 for more confirmatory evidence along these lines). Finally, we will later provide direct evidence that measurement error is unlikely since the two groups responded to direct

Drug and Alcohol Use Among College Student-Athletes 373

self-report questions in proportions that do not significantly differ and thus the treatment group was not per se more likely to count the extra item.

Our causal claim, which is supported by a wealth of prior work as just discussed, is that social desirability and disclosure issues cause underreporting in direct self-reports relative to a list experiment. Again, this is so because the experimental (random assignment) nature of the approach means the groups are on average equivalent, so any difference in responses is due to distinctions in treatment (see Druckman et al., 2011 for details on the experimental approach and the need for only proportion or mean comparisons between groups and not multivariate analyses). In short, differences reveal a causal source of underreporting.

Data and Methodology

Our survey focuses on the NCAA Big Ten conference, which is located primarily in the Midwest, with Nebraska as the western-most point and Penn State to the east (circa 2013, which is relevant since the conference is expanding in 2014). Despite its name, the Big Ten included, at the time of our survey, 12 major universities, all of whom compete in Division I NCAA Athletics. While we recognize the limitations of restricting our sample to only one conference, the Big Ten conference is a strong starting point as it includes a large amount of variance among universities and includes schools that recruit nationally (for another fruitful study of a single conference, see Fountain and Finley, 2009).

In the spring of 2012, we accessed the athletic websites of all 12 Big Ten schools and obtained the full rosters for all sports at every school. We then accessed each school’s website to locate and record the e-mail address of every student-athlete listed on those rosters. This information was publicly available at all schools except for the University of Nebraska. We contacted officials at the University of Nebraska to obtain directory information for their student-athletes but were declined and thus they are excluded from our sample.

Overall, we located 6,375 names on rosters. We found no e-mails for 479 student-athletes and subsequently we sent out 5,896 e-mails. Of them, 1,803 bounced back as no longer in service (which could be due to the students no longer being enrolled, database errors, website errors, or some other reason). Thus, we successfully sent a total of 4,093 e-mails that, to our knowledge, reached their intended targets. We also sent out one reminder to all respondents. Sample size varied across schools, in part due to variations in the number of sports each school sponsors (e.g., Ohio State fields 37 total teams, Michigan has 27 teams, while Northwestern has just 19 teams). We received 1,303 responses, leading to response rate of 1303/4093 = 31.8 percent. This rate exceeds the typical response rate in e-mail surveys of this length, especially those that do not employ incentives (see Couper, 2008:310; Shih and Fan, 2008; Sue and Ritter, 2007:36 for discussion of typical response rates on similar surveys).6

6While we found notable variance in the success of our e-mails reaching their targets (i.e., not bouncing back), of the 4,093 e-mails that were ostensibly received, we found near-identical response rates across universities with a minimum response rate of 29.59 percent at Michigan State, a maximum rate of 35.57 percent at Wisconsin, and an average response rate of 31.79 percent (SD = 0.018). In terms of the sample makeup, the following lists the number of respondents and the percentage of the sample that came from each university: Indiana (128; 9.82 percent), Ohio State (122; 9.36 percent), Illinois (104; 7.98 percent), Minnesota (120; 9.21 percent), Michigan State (100; 7.67 percent), Purdue (100; 7.67 percent), Iowa (110; 8.44 percent), Wisconsin (154; 11.82 percent), Northwestern (122; 9.36 percent), Pennsylvania State (116; 8.90 percent), and Michigan (127; 9.75 percent). Our responses came from over 24 different sports; the specific breakdown is available from the authors. The highest responding sport was track and field, which made up 15.12 percent of the respondents, followed by swimming with 12.97 percent of the respondents. All other sports consisted of less than 10 percent of the respondents.

374 Social Science Quarterly

While our sample may not be perfectly representative of Big Ten student-athletes, it provides a telling view of drug and alcohol use among student-athletes given the diversity of the schools sampled and given that we have no reason to suspect they differ in terms of reporting relative to other conferences/sports.7 Additionally, the experimental nature of our key measurement approach means that obtaining a perfectly representative sample is of much less importance than is the random assignment of our experimental treatment between groups (for an extended discussion why this is the case, see Druckman and Kam (2011), who show that given sufficient variance, which we have, experimental findings are robust sampling considerations aside). In short, our sample permits us, as far as we know, to carry out the first study of its kind.

Results

Before turning to our list experiments, we first compare some of our own self-report measures with those from the annual College Senior Survey (sponsored by the UCLA Higher Education Research Institute; see Franke et al., 2010); the Senior Survey provides an important baseline of comparison between athletes (our survey) and a sample of largely nonathletes.8 Indeed, the vast majority of the UCLA respondents are, based on probability, nonvarsity athletes, given that the sample includes 24,457 individuals from 111 colleges and universities. For a baseline, we used identical questions to those employed by the UCLA survey.

In Table 1 (where we use the label “Ath” for our student-athlete survey and “Gen” for the general survey), the first column lists the question in both surveys. The other columns list the results from the general survey (Gen) and our survey (Ath), with N/A indicating that there were no other response categories on that question.9 What we clearly find is that, relative to the general student survey, student-athletes are substantially less likely—in self-reports—to drink beer, liquor (in general or over the two weeks preceding the survey), and to frequently “party,” which can be defined as a social gathering that “typically involves eating, drinking, and entertainment” (Oxford Dictionaries). Nearly 75 percent of the general student population say they “frequently” or “occasionally” drink beer, whereas only 46 percent of our student-athlete sample say the same (z = 20.55; p < 0.01). Similarly, 84 percent of the general population report drinking wine or liquor “frequently” or “occasionally” over the previous year, but only about 36 percent of student-athletes do so (z = 39.58; p < 0.01). In the two weeks prior to the survey, about 53 percent of the general sample state they partied for three or more hours, whereas only 38 percent of student-athletes do (z = 9.05; p < 0.01).10

7The demographics of our sample are as follows: 60.94 percent female; 6.29 African American; 78.00 percent are underclassmen and 22.00 are seniors; 4 percent having family incomes below $30,000, 16 percent between $30,000 and 69,999, 26 percent between $70,000 and $99,999, 35.5 percent between $100,000 and $100,999, and 18.5 percent $200,000 or over; and 51 percent on athletic scholarship.

8We use results from 2009, as these were the most recent available data. We did not use the freshmen survey as it would provide an incorrect point of comparison since nearly 75 percent of our respondents were not freshmen.

9In our survey, respondents always had the choice to not respond to a particular question and thus the sample size varies across different questions for this reason.

10Our main concern is with measuring overall levels of alcohol and drug use, but we also found some intriguing differences between male and female respondents on these items. Specifically, we find that male respondents reported significantly higher rates of drinking and partying and were significantly more likely to admit taking banned drugs (results available from authors). This is in line with Wechsler et al. (1997) and Buckman et al. (2008). For additional analyses on gender, see the online appendix at 〈http://faculty.wcas.northwestern.edu/�jnd260/publications.html〉.

Drug and Alcohol Use Among College Student-Athletes 375

TA B

L E

1

S tu

d e

n t-

A th

le te

a n

d S

tu d

e n

t S

e lf-

R e

p o

rt e

d B

e h

a vi

o rs

Q u e st

io n

R e sp

o n se

C a te

g o ri e s

D u ri n g

th e

p a st

a c a d

e m

ic ye

a r,

h o w

o ft e n

h a ve

yo u

d ru

n k

b e e r?

(N fo

r a th

le te

s =

1 ,0

4 1 )

F re

q u e n tly

A th

.: 3 0 .5

% G

e n :

3 3 .4

%

O c

c a

si o

n a

lly A

th .:

1 5 .9

% G

e n :

4 1 .4

%

N o t

a t a

ll A

th .:

5 3 .7

% G

e n :

2 5 .1

% N

/A N

/A N

/A N

/A

D u ri n g

th e

p a st

a c a d

e m

ic ye

a r,

h o w

o ft e n

h a ve

yo u

d ru

n k

w in

e o r

liq u

o r?

(N =

1 ,0

3 5 )

F re

q u e n tly

A th

.: 2 0 .4

% G

e n :

3 1 .5

%

O c

c a

si o

n a

lly A

th .:

1 5 .5

6 %

G e n :

5 2 .5

%

N o t

a t a

ll A

th .:

6 4 .0

6 %

G e n :

1 5 .9

% N

/A N

/A N

/A N

/A

T h in

k b

a c k

o ve

r th

e p

a st

tw o

w e e ks

. H

o w

m a n y

tim e s

in th

e p

a st

tw o

w e e ks

, if

a n y,

h a ve

yo u

h a d

fiv e

o r

m o re

a lc

o h

o lic

d ri n

ks in

a ro

w ?a

(N =

1 ,0

4 2 )

N e ve

r A

th .:

5 3 %

G e n :

4 4 .7

%

O n c e

A th

.: 2 0 .6

% G

e n :

1 4 .7

%

Tw ic

e A

th .:

1 4 .2

% G

e n :

1 3 .6

%

3 –5

tim e

s A

th .:

9 .3

% G

e n :

1 6 .8

%

6 –9

tim e

s A

th .:

2 .1

1 %

G e n :

6 .8

%

1 0

o r

m o

re tim

e s

A th

.: 0 .7

% G

e n :

3 .4

% N

/A

D u ri n g

th e

p a st

a c a d

e m

ic ye

a r,

h o w

m u c h

tim e

d id

yo u

sp e n d

d u ri n g

a ty

p ic

a lw

e e k

p a rt

yi n g

? (N

= 1 ,0

4 2 )

N o n e

A th

.: 1 8 .9

% G

e n :

1 9 .7

%

L e ss

th a n

1 h o u r

A th

.: 2 2 %

G e n :

1 1 .3

%

1 –2

h o u rs

A th

.: 2 1 %

G e n :

1 6 .4

%

3 –5

h o u rs

A th

.: 2 5 .4

% G

e n :

2 4 .7

%

6 –1

0 h o u rs

A th

.: 1 0 .1

% G

e n :

1 7 .2

%

1 1 –1

5 h o u rs

A th

.: 1 .9

% G

e n :

6 .1

%

1 6 –2

0 h o u rs

A th

.: 0 .4

% G

e n :

2 .3

% O

ve r

2 0

h o u rs

A th

.: 0 .5

% G

e n :

2 .3

%

a T h e

q u e st

io n

c o n tin

u e d

w ith

th e

fo llo

w in

g :

“( A

d ri n k

c a n

b e

a 1 2 -o

u n c e

b e e r

o r

w in

e c o o le

r, a

4 o u n c e

g la

ss o f

w in

e ,

o r

a sh

o t

o f

liq u o r

e ith

e r

st ra

ig h t

o r

in a

m ix

e d

d ri n

k. )”

N /A

, n o t

a re

sp o n se

c a te

g o ry

.

376 Social Science Quarterly

We speculate that the vast differences in self-report stem from two factors. First, student- athletes may genuinely spend less time engaging in these activities, perhaps due to the fact that they spend much of their time either in classes, practicing their sport, or receiving tutoring. Second, student-athletes may be increasingly aware of the negative influence that alcoholic consumption has on their athletic performance, particularly since on-campus education programs highlight the impact of drinking and other related behaviors on performance. They may share concerns about drugs or perhaps worry about being caught for using drugs banned by their university sports department, their conference, or the NCAA (i.e., threat of disclosure).11 Of course, the lack of individual-level data from the UCLA survey prevents us from making causal claims about the difference between athletes and nonathletes. However, our evidence is suggestive.

Estimating Drug and Alcohol Use with List Experiments

We now turn to our list experiment to gauge the aggregate usage of drugs and alcohol; as explained, this approach is a fruitful one for addressing documented underreporting in these areas due to social desirability and threat of disclosure issues (e.g., Tourangeau and Smith, 1996). As far as we know, we are the first to apply this method to the domain of student-athlete drug use and drinking.

Drug Use12

We use the list experiment approach to estimate the percentage of student-athletes who knowingly take banned performance-enhancing drugs and consume over five alcoholic drinks per week—both behaviors to which student-athletes may be extremely hesitant to admit on a self-reported survey. We asked all respondents: “Below is a list of activities that you may have engaged in over your time in college. We are interested in HOW MANY of these activities you have engaged – NOT which ones. Thus, please simply choose the number at the end of the list.” All respondents were provided the list of the following four items: “Sustained an injury during a practice or game that prevented you from playing at least one other game,” “joined a social club whose majority of members did/does not include varsity athletes,” “skipped a class because you felt so tired from a practice or a game,” and “was unable to take a class that you hoped to take …

Attachment 3

SPECIAL ISSUE

Improving Optimal Performance—And Life—For Young Athletes

Troy Todd, PhD, BCN

United States Air Force Academy, Colorado Springs, CO

Keywords: optimal performance, collegiate athletes, neurofeedback, biofeedback, cognitive therapy

A composite case study illustrates how a multidisciplinary

approach can be used to improve athletic performance and

overall life functioning. The use of heart rate/respiration

biofeedback and QEEG-guided neurofeedback are built on

a foundation of cognitive therapy. The elements in the

article are taken from several successful cases over the

course of several years. The athletes represented in this

article range from 18 to 24 years old, are from a variety of

sports, and are from higher levels of achievement.

The phone rings in my office at the United States Air

Force Academy Peak Performance Center, the Academy’s

college counseling center. Coach Johnson, head coach of the

Track and Field team says, ‘‘Hey, Troy, I have this really

talented athlete, but her performance is just too unpredict-

able. If this keeps up, I’ll have to cut her from the team!

Can you help her?’’

Here at the Academy, I devote a good portion of my

efforts offering optimal performance services to individual

athletes, teams, and coaches. Applying cognitive and

behavioral psychology, biofeedback, and neurofeedback to

high-achieving 18–24 year-old athletes from a variety of

athletic disciplines not only improves their athletic perfor-

mance, but also their quality of life. The names used in this

article are fictitious. The story I tell is a compilation of many

success stories and truthfully represents the progression and

improvements athletes realize when using these services. It

is based on 3 years of working with about 50 different

athletes on various athletic teams, all high achieving in their

sport and academics. About two-thirds of these are males,

the other third females. The rapidity of improvement

illustrated here is also accurate. The average number of

sessions required for goal completion for all these athletes

was five (a few very complex cases took as many as 25), with

an average self-report improvement of 27% (using a verbal

scale of 0–10). Many reported significantly reducing race

times or increasing scores (as appropriate to their sport),

remarkably increasing performance consistency, and/or

winning competitions they felt they would not have won

without optimal performance services. All of this was also

confirmed by coaches and recorded results.

Case Study: Cadet Amy Rollins At her first appointment, Cadet Amy Rollins told me of her

impressive high school track record: She consistently

achieved winning times and made it to state finals her

senior year, at which point she became recognized and was

highly recruited for college track. Since choosing to come

to the Academy, however, her performance had become

inconsistent. Her sport was a big part of her life, and when

she performed poorly, she struggled with confidence in

other areas of her life. She also felt bad that she was

unreliable for her coach and team. Nevertheless, despite

trying everything she and her coach could think of, she

could not figure out how to gain back the consistency she

enjoyed in high school. It was very frustrating to her not to

be improving on her high school achievements. ‘‘After all,’’

she concluded, ‘‘I’m competing at the collegiate level now!’’

I asked her what seems to affect her athletic performance.

‘‘I have no clue. I mean, even in high school I got a little

distracted by aches and pains or other competitors in the race.

I don’t eat as well here in the dorms, I stay up later studying,

and occasionally have arguments with my boyfriend, but

isn’t that normal college stuff?’’ ‘‘Well,’’ I responded, ‘‘if the

reasons for your performance change were obvious, you and

Coach Johnson would have figured it out long ago.’’

We explored many cognitive and emotional aspects of

life that affect optimal performance: social relationships,

attitudes about competing, the way she thinks about herself

as an athlete and person, her assessment of how others see

her, prerace rituals, team dynamics, and other aspects

unique to her. Most of these areas seemed to be in good

order. There was some negative self-talk and concern for

others’ assessment of her, but she reported that these were

not any worse than in high school. It was clear there was

something new affecting performance. Her family relations

were very strong and meaningful to her. As we explored

this further, we realized that she felt more distant from

Biofeedback Volume 39, Issue 3, pp. 109–111 DOI: 10.5298/1081-5937-39.3.08

EAssociation for Applied Psychophysiology & Biofeedback www.aapb.org

B io fe e d b a c k | F a ll 2 0 1 1

109

her family, and was not experiencing the same kind of

supportive interactions as she did living at home. ‘‘It is not

logical,’’ she said, ‘‘but it feels like, because I did well in

running and that helped me get to college, running takes

me away from my family.’’

It is funny how the mind works, inventing ridiculous

ways to correct bad feelings; unfortunately they work, but

usually at a great cost. We began to explore the full spectrum

of the feelings her mind was trying to correct. ‘‘How does

college actually bring you closer to your family?’’ I asked.

Amy discussed how her younger sister idolized her by

following her college running, how her parents supported

her by attending all her meets, that discussion of college life

filled many family interactions, and how they were able to

talk, text, and email regularly. She acknowledged that, even

though physically separated from her family, the college

experience had actually improved her emotional bond with

her family. ‘‘Yes,’’ we concluded, ‘‘Let’s remember that

while you practice and race. Although at first glance it may

feel like it takes you away from your family, winning races

actually brings you closer together.’’ This new concept

became Amy’s motivation to run her best.

In our next meeting Amy reported, ‘‘As long as I remind

myself that running actually brings my family and me

closer to one another, I don’t feel nervous before races, I

enjoy interacting with my friends more, and feel more like

myself around my boyfriend. I feel happier overall.’’ We

spent a few more meetings focusing on sleep habits, good

nutrition in the college environment, and better interac-

tions with her friends and boyfriend.

‘‘I never knew I could feel so relaxed,’’ Amy responded as

she mastered heart rate/respiration coherence biofeedback. In

just a few training sessions with biofeedback, she was able to

achieve noticeable benefits. She was able to get to sleep quicker,

her overall energy was increased, she was more relaxed during

school tests, and her end-of-race surge was more powerful.

‘‘I can’t believe it!’’ Coach Johnson said when I joined

him in his office. ‘‘Amy is like a new person: I can

completely count on her in events and she keeps cutting her

time. I can think of several other athletes that could benefit

from this, but I am not sure they will be as willing as

Amy,’’ I said. ‘‘I see. They aren’t comfortable with me and

don’t understand how I can help them. I have an interactive

presentation that outlines basic optimal performance

techniques. We could use this to introduce them to me

and my services.’’ We made plans for me to make this

presentation at the next team meeting. This allowed the

athletes to get basic techniques on board and seek more

customized services from me as they needed. As more

athletes took advantage of optimal performance training,

Coach Johnson was able to spend more time coaching,

while I improved the factors that affect performance besides

skill, talent, and physical conditioning.

‘‘If I keep this up,’’ Amy began at our next meeting, ‘‘I’ll

be able to qualify for the Olympic trials next year! I think

the only thing negatively affecting my races now is getting

distracted by the normal aches and pains and other

competitors, but I think most athletes struggle with these

things. Can you help with those?’’ Staying ‘‘in the zone’’

requires mental flexibility—considering a distraction, resolv-

ing it, and getting back to the zone quickly. Neurofeedback

improves mental flexibility by directly training the brain. It

is a great capstone for optimal performance services. ‘‘First,

we will do an assessment to determine what areas of your

brain are working too hard, or not enough, then we will

directly train those parts of your brain to work better for

you,’’ I explained. We began with a quantitative electroen-

cephalograph (QEEG), which maps brain activation levels at

19 sites and shows patterns of coherence and symmetry

among those sites. This showed that Amy’s brain was over-

activated in the parietal areas (specifically for her, increased

16–28 Hz across an area including P3, Pz, and P4, cortical

sites in the International 10–20 system), which may have

contributed to her essentially perseverative focus on and

worry about common race distractors.

The training protocol that appeared most parsimonious

was inhibiting 16–28 Hz at Pz while rewarding 8–10 Hz

(Alpha), and controlling for muscle artifact (30+ Hz). After about five sessions with this custom-designed protocol, she

noticed an improvement: ‘‘I can’t really explain it, but I just

don’t seem to get distracted by the other girls on the line. I

just think about my race. Sure, I notice the pain in my

body, but it is not a big deal: I just feel focused.’’ We

continued neurofeedback for several more sessions, re-

viewed breathing techniques and occasionally focused on a

solution for a problem with her friends or teammates. After

just a few months from our first meeting, Amy said,

‘‘Running is much more enjoyable now, my mind is clear,

my relationships are better, I am doing well in school, and I

am making the kind of times I know I am capable of.

Thanks for helping me get there.’’

Conclusion On my schedule the next week I notice a cadet from the

diving team. Our intake paperwork indicated that he was

interested in optimal performance services. When we sat

down in my office, I asked him how he heard about these

services. ‘‘I’m good friends with Amy; she said you helped

her a lot with her running. Does this stuff work for diving,

too?’’ I smiled and said, ‘‘It sure does.’’

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Troy Todd

Correspondence: Troy Todd, PhD, BCN, Personal Performance Solutions, 13550 Northgate Estates Dr. #110, Suite C, Colorado Springs, CO 80921-7653, email: [email protected]

Todd

B io fe e d b a c k | F a ll 2 0 1 1

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