Authors: Sam T. Lawson, Julia C. Gardner, Mary Jo Carnot, Samuel S. Lackey, Nanette V. Lopez, and Jay T. Sutliffe

Corresponding Author:
Jay Sutliffe, PD, RD
Flagstaff AZ, 86011
Jay.sutliffe@nau.edu
928-523-7596

Sam T. Lawson is an undergraduate research assistant and student at Northern Arizona University.

Julia C. Gardner is a research coordinator with the PRANDIAL Lab at Northern Arizona University. Mary Jo Carnot is professor of Counseling, Psychological Sciences, and Social Work at Chadron State College in Chadron, NE.

Samuel S. Lackey is the Head Strength and Conditioning Coach at Northern Arizona University.

Nanette V. Lopez is Assistant Professor in Health Sciences at Northern Arizona University.

Jay T. Sutliffe is Professor of Nutrition and Foods and the Director of the PRANDIAL Lab at Northern Arizona University in Flagstaff, AZ.

Assessing the Outcomes of a Brief Nutrition Education Intervention Among Division 1 Football Student-Athletes at Moderate Altitude

Abbreviations
HEI: healthy eating index
g: grams
mg: milligrams
oz: ounces
kcal: kilocalories
std.: standard
DGA: Dietary Guidelines for Americans
USDA: United States Department of Agriculture
RDA: recommended dietary allowance
RM: repetition maximum

ABSTRACT

College students are notorious for having poor quality diets and student-athletes are no exception. Collegiate football student-athletes often fail to meet overall energy requirements necessary to meet activity demands (65). The research herein assessed diet quality, body composition and physical performance of selected student athletes following completion of a brief, 8-week nutrition education intervention. The participants consisted of 55 Division I collegiate football players, aged 18-24 years (mean age 19.8±1.2yrs). Results indicated that group education sessions on nutrition had minimal impact on outcomes, perhaps due to the voluntary nature of the training. However, independent of the intervention, there were significant changes across time for the total scores on the Healthy Eating Index-2015 (HEI-2015), strength performance measures, and total body water. Participants with higher HEI-2015 scores versus lower scores did not differ on strength performance or body composition outcomes. Specific nutrients, including sodium, protein, and solid fats negatively impacted strength performance, especially for the bench press measures. At moderate altitudes, athletes may struggle to maintain sufficient hydration (41). In this study, athletes with higher hydration levels (based on total body water and extracellular water) improved performance from pre to post assessments of strength performance in bench press, back squat, and power clean. The results highlight the importance of nutrition on athletic performance, especially the negative impact of unhealthy choices. Educational sessions on nutrition designed to improve eating habits may need to consider social influences, including everyday eating situations, via a combination of group and individualized approaches.

Keywords: micronutrients, nutrition intervention, athlete, body-composition, moderate altitude

INTRODUCTION

College students tend to have poor dietary habits that include low micronutrient intake and high amounts of processed foods (36). Studies indicate that college students report low fruit and vegetable intake, with an average consumption of two servings of combined fruits and vegetables daily which fails to meet dietary guidelines (18, 21, 22). Although college students often adopt new dietary habits that are frequently maintained throughout life, their eating behaviors are typically unhealthy and include excessive consumption of processed foods, skipping meals, and/or eating at irregular times (62). Specifically, students who report following a “Western” diet consume the highest quantities of refined and energy-dense foods labeled high in fat and sugar, resulting in an increased disease risk (5). In this period of nutrition transition, college-aged individuals are consuming diets high in animal-source foods and eating more highly processed grains and carbohydrate rich meals resulting in lower fiber intake (53).

College student-athletes have higher energy demands due to exercise, training, and competition, but often consume nutrient intakes similar to or below recommended dietary allowances (RDA) (29), with many failing to meet energy requirements for their training style and intensity (46,60). Among those student-athletes who fail to meet their minimum energy requirements, football student-athletes have been identified in at least one study as having the greatest energy deficit (65). Research has noted that optimal nutrient intake along with supplementation, if needed, improves athletic performance and ultimately aids in recovery (11,30,63). Research has also noted that student-athletes who work with a sports dietitian have better dietary habits than those who seek nutrition knowledge from strength and conditioning coaches or athletic trainers (26).  Among these positive dietary behaviors are consuming less fast food, not skipping meals, and eating a greater amount of whole foods (26).

To help student-athletes improve the quality of their diet, the Healthy Eating Index (HEI)-2015 assessment which generates a diet quality score based upon nutrient intakes, is a useful tool (68). Developed with key recommendations from the Dietary Guidelines for Americans (DGA), the HEI-2015 can be used to assess health risks among specific populations (58). For example, populations with adherence to a high HEI-2015 dietary pattern have a reduced risk of cardiovascular disease and certain cancers (47).

Diet quality plays an essential role in desired weight gain as many micronutrients have synergistic qualities allowing for better nutrient absorption from a wide variety of foods (32). This factor can impact football athletes because of documented evidence that a proportion of coaches falsely believe that certain positions require maintaining a higher weight or specific physique (9,10). Deliberate weight gain by football athletes through consumption of unhealthy foods can lead to metabolic syndrome and an increased risk of cardiovascular disease (6,15). Intentional dietary programming should be considered for football athletes, specifically offensive and defensive linemen due to their elevated risk of cardiovascular disease (69). However, athletes should be warned against the sudden or excessive gain in body fat as that may increase their risk for metabolic syndrome (67).

Student-athletes who receive nutrition counseling could have improved physical performance while increasing their lean body mass and maintaining a minimum threshold of energy (1). Many student-athletes receive nutrition information from athletic trainers and strength and conditioning coaches. Unfortunately, these individuals often lack nutrition knowledge, certifications, and/or adequate time to properly counsel student-athletes on dietary information. Therefore, consultations with a trained dietetics professional may benefit student-athletes (31). According to Hull et al. (27), student-athletes with access to a sports dietician reported improved dietary patterns such as eating before exercise, healthy post-exercise meals, and more nutrient dense meals while traveling; all of these dietary improvements may lead to improved performance and recovery.

The primary aims of this study were to improve diet quality hydration, body composition, and performance outcomes among football student-athletes. Exploratory aims included examining intake of specific nutrients and their impact on performance. Specifically, this study was designed to address the following hypotheses:

  1. Student-athletes who attended between one and three nutrition education sessions will show greater improvements in diet quality, as measured by the HEI-2015 score.
  2. Improved diet quality will positively affect (a) body composition, as measured by fat free mass, fat mass, phase angle, and body mass index (BMI) and (b) strength performance outcomes, measured by 1 repetition maximum (RM) in power clean, squat, and bench press.

Materials and Methods

Participants
Participants were recruited from the men’s football team at a National Collegiate Athletic Association (NCAA) Division I program. These student-athletes primarily train, practice, and compete at 7,000 feet above sea level, which is considered to be at moderate altitude (14). A total of 65 participants were enrolled, with 10 lost to follow-up procedures, resulting in 55 participants who completed measures at both the initial stage and 8-weeks post-baseline period. Participants were 18-24 years of age (mean age 19.8 ± 1.2yrs) and ethnically diverse, self-identifying as African-American (41.8%), Caucasian (49.1%), Southeast Asian (1.8%), and ‘other’ (7.3%). No exclusionary health criteria were developed for this study, as all NCAA student-athletes are required to complete yearly athletic physicals to screen for possible health risks. Participants were informed of the minimal risks of the study and provided written informed consent. The experimental research procedures were approved by the Institutional Review Board #982568.

Study Design
By design, this was a non-randomized pilot study where each subject served as his own control for measurements completed at baseline and 8-weeks post-baseline. The purpose of this pilot study was to examine the feasibility and effectiveness of implementation for consideration of future studies with collegiate sports teams. Following the baseline body composition testing, all participants received a five-minute individualized nutrition education with specific dietary recommendations to help improve their body composition parameters. After completion of baseline measures, participants were encouraged to attend three optional nutrition education sessions. To minimize scheduling conflicts, nutrition education sessions were offered every other week, during the middle of the week and on weekends. Sessions occurring in the same week covered identical material allowing all participants to receive the information uniformly. Each 45-minute face-to-face session included a lecture-style presentation that began with a food demonstration, followed by a lecture with a slideshow presentation, and time for open discussion. Sessions started with 10 minutes of the food demonstrations, followed by 20-minutes of nutrition education and 15 minutes of open discussion which typically centered on personal questions about the participants’ diet. The food demonstration included a discussion about why a particular meal would be considered effective fuel for the athletes. The participants were made aware of the nutrition education sessions through a group messaging app utilized by the athletic department which contained a link for an online sign-up sheet for testing and nutrition education sessions. Reminders were sent to participants via text messaging and email.

Diet Quality and Assessment
Undergraduate and graduate students, in conjunction with faculty, were trained to collect Automated Self-Administered 24-Hour (ASA24) diet recall, blood pressure, and body composition from each participant. Nasco food models/replicas, depicting serving sizes of commonly eaten foods, were used to confirm the serving sizes of food and beverages consumed during the 24-hour diet recalls. The ASA24 is a web-based tool developed by the National Cancer Institute to accurately collect 24-hour diet recalls, commonly known as food diaries (“ASA24,” 2019). Although the ASA24 is a self-administered program, to ensure completion and accuracy, the 24-hour diet recalls were performed by trained study personnel.

Dietary measures of kilocalories, sugar, fiber, cholesterol, total vegetable, total fruit, total grain, total protein foods, total dairy, vitamin D, calcium, potassium, sodium, and solid fat were collected via ASA24. The HEI-2015 was generated to provide an overall diet quality score from the data collected from ASA24. The HEI-2015 diet scores range from 0 -100, with 0 being the lowest diet score and 100 being the highest. An HEI-2015 score of 50 was chosen to represent a cutoff score since scores below 50 have been classified to represent a “poor” diet (23).

Anthropometric and Body-Composition Measurements 
Evaluation of body composition was conducted using tetrapolar bioelectrical impedance analysis (BIA) via the Seca® mBCA 515 (8). BIA is an efficient and non-invasive technique that enables the determination of body composition based on the measurement of electrical characteristics of the human body over five body regions, including left and right arms and legs, and the torso. The data can be used to assess metabolic activity, energy consumption, energy reserves, fluid status, and abdominal fat. Phase angle (phA) in BIA is a validated measurement that correlates with the percentage of body fat (%BF), body mass index (BMI), fat mass (FM), and total body water (TBW) (37). A low phA is associated with increased morbidity and nutritional risk (39,51). Because phA is affected by body geometry, anthropometric measurements also need to be considered. Individuals with hydration outliers (i.e., unstable extracellular and intracellular water ratios) can obtain a phA measurement when using bioelectrical impedance vector analysis, which uses the plot resistance and reactance normalized per height (35,64).

Nutrition Intervention 
Participants were offered the opportunity to attend up to three optional, in-person sport-specific nutrition education sessions. The sessions were conducted over 8-weeks with those who participated typically averaging one session, every other week. The first session focused on the sport-specific nutrition topics related to macronutrients, micronutrients, and timed-eating. Macronutrient content focused primarily on the importance of proper carbohydrate and protein intake while information on micronutrients stressed the necessary diet for a body under physical stress due to training. Participants were encouraged to achieve adequate macronutrient and micronutrient intake through the consumption of whole foods, due to their greater nutrient density compared to processed foods and supplements. The second session focused on supplementation for an anaerobic training style with topics ranging from dietary supplements (e.g., protein powder and fish oil) to performance-based supplements (e.g., creatine and caffeine). The last session addressed the relationship between hydration and performance, including awareness of dietary, physical, and environmental factors that may promote dehydration. Participants were also provided information on how to calculate sweat rate in order to help them stay adequately hydrated through practices and training sessions. As previously mentioned, each session included a short food demonstration for preparing meals containing micronutrient dense-foods that met the minimum number of calories recommended per portion for football athletes.

Strength Performance
Assessment also included strength testing for participants in the study.  The primary goal of winter off-season training for football players is to increase their absolute strength and muscular hypertrophy, or more commonly known as increasing muscle size. The testing included a micro-cycle started by using a 1RM test on the power clean, squat, and bench. At the end of the training cycle, the 1RM was repeated to measure strength gains in each lift. The tests were conducted on three separate days to allow time for full recovery between testing days. Power cleans were tested first, followed by back squat and bench press.  The athletes were familiar with all testing protocols provided by the Head Strength Coach and the assistant strength coaches. 

Statistical Analysis
To address the hypothesis regarding the impact of educational sessions on macro- and micronutrient consumption, supplementation, and sport hydration, participants were grouped based on whether they attended any of the three optional educational sessions. Initial grouping was based on comparing those who attended any educational sessions (experimental) with those who did not (control). Strength training outcomes, diet quality, and body composition variables were measured twice, at baseline and at 8-weeks post-baseline. Multiple 2×2 ANOVAs with time as a within-subjects variable and education as a between-subject variable were analyzed. Because attendance at educational sessions did not result in significant effects, groups were collapsed to consider change across time, with the initial consultation with individual athletes considered an educational session. Paired sample t-tests were used to compare selected variables across the two time periods.

Additional analyses were performed on specific dietary, body composition, and performance variables measured at 8-weeks post-baseline. Independent samples t-tests used median split comparisons for sodium, protein, and dietary solid fat to compare high and low groups on fat-free mass and performance measures. The HEI-2015 total score of 50 (USDA, 2019) was similarly used to separate participants into two groups, who were then compared using independent samples t-tests for BMI, weight change, fat-free mass, absolute fat mass and phase angle. Median splits were also examined based on extracellular water and total body water to determine impact on performance measures. All analyses were conducted using IBM SPSS Statistics version 26 software (28).

RESULTS

Analyses from the 2×2 ANOVAs using educational session attendance and time as independent variables indicated few differences between experimental and control groups.  This unanticipated pattern of results suggested that there might be preexisting differences in our groups, such as ethnicity differences. Participation in the educational sessions was not well attended. Out of the initial group of 65 participants, 60% did not attend any educational sessions. Twenty percent attended one educational session, 12.3% attended two sessions, and 7.7% attended all three. When groups were collapsed to compare measures at baseline and 8-weeks post-baseline using paired sample t-tests, significant changes were seen in phase angle (t(53) = -2.301, p=.025) HEI-2015 total score (t(54)  = -2.046, p = .046), total body water (t(53)  = -2.501, p = .015),  bench press  (t(54)  = -6.420, p < .001), power clean  (t(54)  = -3.494, p = .001) and squat  (t(54)  = -6.006 , p < .001). Marginal changes (p < .10) occurred for calcium and energy deficit measures (Table 1).

Table 1: Outcome Measures Collapsed Across Educational Session Attendance 

Measure N Baseline Week 8
    Mean SD Mean SD
Calories, Kcal 55 4008.2 1550.4 4080.9 1529.5
Sugar, g 55 133.5 80.4 143.3 76.5
Fiber, g 55 29.9 16.2 31.2 14.5
Cholesterol, mg 55 882.8 541.0 781.5 564.9
Total Vegetable, cups 55 2.5 2.4 2.3 1.6
Total Fruit, cups 55 0.9 0.9 1.1 1.4
Total Grains, oz 55 13.8 7.6 13.9 6.9
Total Protein Foods, oz 55 15.2 8.5 15.4 9.0
Total Dairy, cups 55 3.3 2.3 3.8 3.1
Vitamin D, mcg 55 8.8 6.5 10.1 6.6
Calcium, mg 55 1834.0 826.2 2105.0 1075.2
Potassium, mg 55 4494.6 2080.0 4721.0 1781.3
Sodium, mg 55 7758.0 3141.3 7530.0 3276.0
Solid Fat, g 55 78.3 47.5 73.2 41.4
Phase Angle, degrees* 54 6.8 0.5 6.9 0.5
BMI, kg/m2 54 29.0 4.3 29.2 4.2
Energy Deficit, kcal 55 646.0 1573.7 220.8 1761.6
HEI-2015 Score* 55 47.7 11.9 51.3 12.3
Total Body Water, %* 54 55.6 5.9 56.2 5.3
Extracellular by Total Body Water, % 54 38.9 1.1 38.8 1.0
Extracellular Water, % 54 21.7 2.6 21.8 2.4
Power Clean, kg* 55 112.0 12.4 116.2 13.3
Squat, kg** 55 167.1 60.2 177.7 64.0
Bench Press, kg** 55 122.4 41.5 127.3 43.8

Note. One participant was unable to complete the BIA measures.  *p<.05, ** p<.001 Abbreviations: mcg, micrograms; mg, milligram, g, gram; kg, kilogram; %, percent; kcal, kilocalorie; oz, ounces; sd, standard deviation; BMI, Body Mass Index, HEI-2015, Healthy Eating Index- 2015

Education (see Table 2) indicated a participant attended at least one of the three optional intervention sessions. For energy deficit, there were marginal but nonsignificant changes over time (p < .10) (Table 2). Number of education sessions attended had no significant effect on HEI-2015 total score (p > .05) (Table 2).

Table 2: Energy Deficit and Total HEI Score Differences Based Upon Nutrition Education Session Attendance

Measure   N Baseline Week 8
      Mean SD Mean SD
Energy Deficit, kcal* No Ed 30 658.6 1136.3 303.2 1591.9
  Ed 25 630.8 2002.9 121.9 1975.2
HEI Total Score** No Ed 30 46.6 11.1 53.2 12.8
  Ed 25 49.1 12.9 49.1 11.6

*p<.05, ** p<.001
Abbreviations: Ed education; HEI, healthy eating index; kcal, kilocalories; SD, standard deviation

Following the initial group comparisons, an analysis was conducted at week 8.  The examination was intended to assess whether making healthier diet choices impacted performance measures. HEI-2015 total scores were examined, as well as specific nutrients (i.e., sodium, protein, and solid fats) using data from the ASA24.

A HEI-2015 total score of 50, data taken at week 8, was used to separate participants into two groups to compare 8-week body composition outcomes of weight change and performance outcome measures including, bench, power clean, and squat. Table 3 evaluated the relationship between the two groups differentiated by HEI-2015 total score and body composition parameters. There were no significant differences in outcomes between the two groups (Table 3).  Additionally, the two HEI-2015 groups were compared on 8-week outcomes including BMI, fat free mass, absolute fat mass and phase angle (Table 3). There were no significant differences between HEI-2015 groups on any of these outcome measures.

Table 3: Diet Quality and Body Composition Assessment at 8-weeks

  HEI-2015 Total Score N Mean Std. Deviation Std. Error Mean
BMI, kg/m2 ≥ 50.0 28 28.6 3.4 0.6
< 50.0 26 29.7 4.8 0.9
Weight Change, kg ≥ 50.0 27 0.4 4.7 0.9
< 50.0 26 .8 5.8 1.1
Fat Free Mass, kg ≥ 50.0 28 75.7 14.6 2.8
< 50.0 26 76.3 16.6 3.3
Fat Mass, kg ≥ 50.0 28 22.5 21.3 4.0
< 50.0 26 23.7 25.9 5.1
Phase Angle, degrees ° ≥ 50.0 28 6.9 0.6 0.1
< 50.0 26 7.0 0.5 0.1

Abbreviations: HEI, healthy eating index; BMI, body mass index; kg, kilogram; std., standard

Median splits of sodium, protein, and solid fats were used to divide participants into two groups and compared on the outcome measures of power clean, squat, bench press, and weight change at week 8. Participants who consumed lower levels of sodium (< 7427.5 g daily) performed better on squat (t(49) = -2.147, p = .036) and bench press (t(49) = -2.390, p = .021) measures, and tended to perform better on power clean, although this difference was not significant (t(48) = -1.685, p= .098) (Table 4). Participants who consumed higher levels of protein (>186.9 g) were not significantly different in power clean (t(48) = -.835, p = .408), squat (t(49) = -1.539, p = .130) or bench (t(49) = -1.807, p = .077), although bench press measures had a non-significant tendency to be higher for those in the lower protein group (Table 4). Participants who consumed fewer solid fats (< 66.0 g) were not significantly different in power clean (t(48) = -1.453, p = .153) or squat measures (t(49) = -1.825, p = .111), but performed better on bench press measures (t(49) = -2.50, p = .014)  (Table 4). Due to the moderate altitude location of the research, median splits on extracellular water and total body water were examined in respect to the effects on performance outcome measures. All differences in performance were significant indicating better performance outcomes for student athletes with higher extracellular water and total body water. Specifically, those with higher levels of extracellular water (≥21.7 %) had a better performance for the bench press (t(49) = 4.216, p < .001) , power clean (t(47) = 2.819, p = .007) and squat  (t(49) = 3.420, p = .001). (Table 4). Additionally, those with higher level of total body water (≥ 56.4 %) had a better performance for the bench press (t(49) = 4.482, p < .001) , power clean (t(47) = 2.819, p < .001) and squat (t(49) = 3.419, p = .001) (Table 4).

Table 4: Strength Assessment and HEI Scores, Sodium, Protein, Solid Fat, Extracellular Water, and Total Body Water at 8-weeks

  HEI-2015 Total Score N Mean Std. Deviation Std. Error Mean
Power Clean, kg ≥ 50.0 25 117.4 9.6 1.9
< 50.0 25 117.9 16.0 3.2
Squat, kg ≥ 50.0 26 180.8 61.6 12.1
< 50.0 25 176.8 63.6 12.7
Bench Press, kg ≥ 50.0 27 129.6 45.4 8.7
< 50.0 24 130.0 43.1 8.8
Weight Change, kg  ≥ 50.0 28 98.2 32.4 6.1
< 50.0 26 100.1 40.2 7.9
  Sodium, mgª N Mean Std. Deviation Std. Error Mean
Power Clean, kg ≥ 7427.5 24 114.5 12.3 2.5
< 7427.5 26 120.6 13.2 2.6
Squat, kg ≥ 7427.5 24 170.1 62.0 12.7
< 7427.5 27 186.6 58.2 11.2
Bench Press, kg ≥ 7427.5 26 123.5 39.5 7.7
< 7427.5 25 136.3 44.4 8.9
Weight Change, kg ≥ 7427.5 27 96.8 30.7 5.9
< 7427.5 27 101.4 40.7 7.8
  Protein, gª N Mean Std. Deviation Std. Error Mean
Power Clean, kg ≥ 186.9 23 116.0 12.0 2.5
< 186.9 27 119.1 13.9 2.7
Squat, kg ≥ 186.9 24 172.5 61.8 12.6
< 186.9 27 184.5 60.9 11.7
Bench Press, kg ≥ 186.9 25 124.7 41.0 8.2
< 186.9 26 134.6 44.7 8.8
Weight Change, kg  ≥ 186.9 26 98.8 30.3 5.9
< 186.9 28 99.4 41.3 7.8
  Solid Fat, gª N Mean Std. Deviation Std. Error Mean
Power Clean, kg ≥ 66.0 24 114.9 12.6 2.6
< 66.0 26 120.2 13.1 2.6
Squat, kg ≥ 66.0 26 172.6 58.2 11.4
< 66.0 25 185.2 64.1 12.8
Bench Press, kg ≥ 66.0 26 123.2 46.1 9.1
< 66.0 25 136.6 36.5 7.3
Weight Change, kg ≥ 66.0 27 96.9 34.3 6.6
< 66.0 27 101.3 37.8 7.3
  Extracellular Water, %ª N Mean Std. Deviation Std. Error Mean
Power Clean, kg ≥ 21.7 24 121.9 11.9 2.4
< 21.7 26 113.4 12.9 2.6
Squat, kg ≥ 21.7 26 191.2 59.9 11.5
< 21.7 25 166.5 52.9 10.8
Bench Press, kg ≥ 21.7 26 139.3 36.3 7.0
< 21.7 25 119.0 39.8 8.1
  Total Body Water, %ª N Mean Std. Deviation Std. Error Mean
Power Clean, kg ≥ 56.4 25 121.9 12.6 2.6
< 56.4 25 113.4 13.1 2.6
Squat, kg ≥ 56.4 26 191.6 58.2 11.4
< 56.4 25 167.0 64.1 12.8
Bench Press, kg ≥ 56.4 26 140.3 46.1 9.1
< 56.4 25 118.8 36.5 7.3

Note. a, median splits based upon participant results at week 8
Abbreviations: HEI, healthy eating index; mg, milligrams; g, grams; kg, kilogram; std., standard

DISCUSSION

The purpose of this study was to determine the effects of sport-specific nutrition education on diet quality, body composition, and strength training performance. The results indicated (i) improvements in diet quality (ii) body composition parameters remained constant (iii) dietary intake of sodium, excessive protein, and solid fat negatively impacted strength performance, and (iv) increased hydration status have proven statistically significant and can positively impact strength performance.

Sugar, fiber, cholesterol, total vegetable, total fruit, total grains, total dairy, vitamin D, calcium, and potassium outcomes did not result in any significant improvement over time, and were not associated with strength performance. The nutrition education intervention did not significantly improve HEI-2015 total scores, but diet quality improved over time. Although prior research indicated dietary compliance and nutritional knowledge improved following an 8-week nutrition education intervention among adolescent swimmers (50), the majority of participants in the present study did not complete the optional sessions. In the current study, the nutrition education intervention did substantially decrease energy deficit. Prior research demonstrated that energy deficit among athletes was reduced following attendance at four nutrition educational sessions (55). It is possible that participants in the study herein may have been seeking nutrition information from different sources such as the Internet, coaches, family, and friends (13), resulting in increases in calorie consumption. In the current study, the nutrition education sessions intervention yielded mixed results on HEI-2015 total scores and energy deficit. However, all participants received a brief individualized dietary consultation following baseline measures. Therefore, brief individualized recommendations may be an effective intervention strategy to make improvements in diet quality and reduce energy deficit.

Dietary quality was not a predictor of body composition in this study. Participants with HEI-2015 total scores of 50 and above were comparable to those with scores below 50 on BMI, fat-free mass, weight maintenance, and phase angle. Results from a previous study indicate that a higher diet quality score was associated with positive body composition parameters such as, lower body fat in adult men (16), and weight maintenance among university students (38). Additional research indicates that diet quality was negatively associated with snacking processed foods, but positively associated with body fat (4). The negative impact of poor snack choices may explain why our participants who scored lower on the HEI-2015 had greater, although statistically insignificant, fat mass. However, in contrast to the results presented by a different study (71), phase angle was not a useful assessment for measuring nutritional status because participants with lower diet quality scores had higher phase angle scores. 

Dietary intake of sodium was a negative predictor of strength performance as measured by power clean, squat, and bench press. Participants reported consumption of foods with excessive amounts of sodium which is common among college students who frequently consume processed foods in campus cafeterias or fast food restaurants (3,49). Previous research suggests that slightly elevated sodium intake above the suggested daily amount (i.e. 2,000 mg) may help improve athletic performance (34,43). However, sodium consumption is typically timed in accordance to exercise (12). In the current study, not only was excessive sodium consumption detrimental to physical performance, but consuming higher than recommended amounts of sodium (2,300mg/day) was identified as resulting in negative implications for future health, including increased risk for hypertension and subsequent cardiovascular disease (CVD), stroke (45), and death (42).

There is a common assumption that protein supplementation is associated with greater gains in muscle mass and strength. This study found a trend toward greater strength gains when protein was not consumed in excess (＞1.8g/kg). In fact, protein supplementation has been shown to have little to no effect on trained individuals when dietary protein needs are met (48,54), including attenuating exercise-induced muscle damage (17). Protein supplements are processed food products and lack many essential nutrients necessary to sustain a healthy lifestyle (56). Because of the nutrient deficiency of protein supplements, it is recommended that collegiate football student-athletes avoid intake if they are already meeting their needs through a healthy diet (54).

Dietary intake of solid fat was negatively associated with physical performance; athletes who consumed less solid fat had greater improvement in strength performance. Non-athlete, college students have also reported a high intake of dietary fat consumption (70). High intake of dietary solid fats, which are common in processed food and fast food, can hinder physical performance (7,2). Elite athletes showed the greatest increase in sport performance when their diet consisted of a high consumption of protein and carbohydrates, but limited consumption of dietary fat (2). Although not measured in the study, frequent consumption of fast food (e.g., French fries and pizza) among college students could explain the high intake of solid fats reported by participants in the current study (20,52). The fact that university students tend to rate the most important factors for food selection to be taste, value, convenience, and cost may explain the prevalence of consuming high-fat processed, fast food (66).

Hydration is a crucial aspect in sport, especially when athletes are competing at elevation. Increased hydration status appears to positively impact strength performance (44). Extracellular water and total body water can be used as hydration status indicators; a deficit of total body water is predictive of dehydration (19, 24). In a study conducted among college age athletes, increases in intracellular water, which constitutes 65% of total body water, were predictive of improved performance level (61). Insensible evaporation of water is higher at altitude, increasing the likelihood of hypo-hydration (33). To allow for positive training adaptations at altitude, hydration status needs to be optimized (57).

CONCLUSIONS

The number of nutrition education sessions attended had no significance on improvements in HEI-2015 total score. However, there were significant increases in HEI-2015 total scores from baseline to week 8, indicating that the individualized nutrition intervention that every participant received may have been an effective intervention strategy. The HEI-2015 total score may indicate the impact of unhealthy diets as it is a combination of all aspects of one’s diet but the examination of specific nutrients may be a better indicator for how performance may be affected. These individual markers of performance could be hidden by a HEI-2015 total score as one part of a diet might be considered ‘good’ while another portion might be ‘poor’ resulting in what looks to be an average diet. The potential performance markers seen in this study were sodium, protein foods, and solid fats which, when eaten in greater amounts shown to have negative performance effects.

APPLICATIONS IN SPORT

High dietary intake of sodium, protein, and solid fat appeared to have a negative impact on strength performance. Although not measured in the current study, consumption of fast food and processed foods, which tend to be high in sodium and solid fats, should be limited in athletes due to their tendency to be detrimental to physical performance. A well-balanced diet should be encouraged as a variety in dietary intake improves performance and disease prevention (25,40,59). Participants with a HEI-2015 total score ≥ 50.0 had overall, though statistically insignificant, less fat mass, lower BMI, and better weight maintenance. Strength performance improved from baseline to week 8 in 1 RM power clean, squat, and bench press; athletes who consumed lower amounts of sodium, protein, and solid fat had greater physical performance than those who consumed higher amounts. Due to the lack of significant findings from the intervention, future research could consider using an equivalency trial to compare the effectiveness between an individualized nutrition intervention at baseline and a lecture/classroom style nutrition intervention conducted over time.

Strengths and Limitations

There were numerous strengths in this study, including expanding upon previously collected data from another research study (65). Participant follow-up was successful, despite the lack of incentives. Researchers assisting with data collection were blinded to nutrition education intervention status to avoid bias. Additionally, having the strength and conditioning staff perform data collection reduced potential bias from researchers. The established professional relationship with the strength and conditioning staff increased opportunities for nutrition-related research while assisting athletes improve their diet and performance.

However, this study was not without limitations. Dietary recalls were conducted over only one 24-hour period, which does not accurately represent a participant’s daily dietary intake. Additionally, reporting bias from participants may have resulted in lower reported amounts of less-nutrient dense foods, sweets, and alcohol. Limited variability in dietary intake reduced the likelihood of statistical significance. Lastly, nutrition educational sessions were optional, making it difficult to identify a clearly defined experimental group.

While scripted education at the time of testing body composition may impact athletes’ diet, there appears to be a disconnect from nutrition knowledge provided and what is actually implemented by athletes. Thus, application strategies for diet as opposed to knowledge enhancement may be more appropriate in determining the effect on performance. Individually reviewing the dietary analysis with each participant could improve understanding among the athletes regarding how their diet affects performance. Athletes who reside and train at altitude (e.g., ≥ 6,000 feet) are recommended to increase carbohydrate, hydration, and iron (on an individual basis) intake due to altered environmental conditions (41).

ACKNOWLEDGMENTS

Ethics Approval and Consent to Participate
This study was approved by the Institutional Review Board of Northern Arizona University.

Consent for Publication
Not applicable

Availability of Data and Materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing Interests
The authors declare that they have no competing interests.

Funding
This research was funded by the Eric M. Lehrman 2015 Trust.

Author’s Contributions
Jay Sutliffe: secured funding; developed study design; collected data; wrote and edited manuscript

Acknowledgements
The authors would like to acknowledge the support of the PRANDIAL Lab at Northern Arizona University as well as the individual athletes who participated in this research. Special mentions go to Jason Farrow, Heather Marquis, Chris Stanley, Steven Stanley, and Hannah Olsen for their help during data collection.

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