Nutrition Knowledge Is Associated with Energy Availability and Carbohydrate Intake in Young Female Cross-Country Skiers

f this study was to provide information on energy availability (EA), macronu- trient intake, nutritional periodization practices, and nutrition knowledge in young female cross- country skiers. A total of 19 skiers lled in weighted food and training logs before and during a training camp. Nutrition knowledge was assessed via a validated questionnaire. EA was opti- mal in 11% of athletes at home (mean 33.7 9.6 kcal kgFFM 1 d 1 ) and in 42% at camp (mean 40.3 17.3 kcal kgFFM 1 d 1 ). Most athletes (74%) failed to meet recommendations for carbo- hydrate intake at home (mean 5.0 1.2 g kg 1 d 1 ) and 63% failed to do so at camp (mean 7.1 1.6 g kg 1 d 1 ). The lower threshold of the pre-exercise carbohydrate recommendations was met by 58% and 89% of athletes while percentages were 26% and 89% within 1 h after exercise, at home and at camp, respectively. None of the athletes met the

1 d 1 ) and 63% failed to do so at camp (mean 7.1 1.6 g kg 1 d 1 ). The lower threshold of the pre-exercise carbohydrate recommendations was met by 58% and 89% of athletes while percentages were 26% and 89% within 1 h after exercise, at home and at camp, respectively. None of the athletes met the recommendations within 4 h after exercise. Nutrition knowledge was associated with EA at home (r = 0.52,p= 0.023), and with daily carbohydrate intake at home (r = 0.62,p= 0.005) and at camp (r = 0.52,p= 0.023). Carbohydrate intake within 1 and 4 h post-exercise at home was associated with better nutrition knowledge (r = 0.65, p= 0.003; r = 0.53,p= 0.019, respectively). In conclusion, young female cross-county skiers had dif culties meeting recommendations for optimal EA and carbohydrate intake. Better nutrition knowledge may help young athletes to meet these recommendations. Keywords: endurance athlete; macronutrient; periodized nutrition; protein; sports nutrition; win- ter sport 1. Introduction Cross-country (XC) skiing is a demanding sport, where success requires high aerobic and anaerobic capacities, as well as the ability to produce high power and speed [1,2]. To reach these requirements XC skiers periodize high amounts of training by varying exercise type, volume, intensity, and frequency between single workouts, days, weeks, and months [3,4]. The aim of the periodized training is to rst overload the trainee's physiological systems and then allow adequate recovery to develop training adaptations [5]. However, exercise-induced adaptations may be promoted or impaired by nutrition [6]. In fact, nutrition should be periodized to support the different goals of the training and to optimize competition performance [7,8]. Furthermore, XC ski training consumes high amounts of energy, which needs to be compensated for with high energy intake (EI) to maintain adequate energy availability (EA; the amount of dietary energy remaining after exercise for all other metabolic processes) and to avoid negative performance and health outcomes such as poor training response, hormonal dysfunction, impairment of bone Nutrients2021,13, 1769.

energy intake (EI) to maintain adequate energy availability (EA; the amount of dietary energy remaining after exercise for all other metabolic processes) and to avoid negative performance and health outcomes such as poor training response, hormonal dysfunction, impairment of bone Nutrients2021,13, 1769.

Nutrients2021,13, 1769 2 of 11 health, and increased injury risk [9,10]. Notably, teenage female athletes may be at higher risk for stress fractures caused by low EA [11], and therefore adequate dietary intake is especially important among young female athletes. XC skiers compete and perform most of their key training sessions at intensities that are highly dependent on carbohydrate (CHO) based fuels for muscle metabolism [2,3,12]. Thus, pre-exercise meals should ensure adequate CHO availability in key training sessions, while this is less important before easy sessions [6,8,13]. As restoration of muscle CHO stores may take up to 24 h, the recovery process should be started as soon as possible after the high intensity training session in situations when recovery time between key training sessions is limited [14]. Meanwhile, performing part of the easy sessions with low CHO availability may increase the muscle adaptations to endurance training and therefore it might be bene cial to limit CHO intake before the training session where training intensity and quality are less important [6,8]. While CHO is an important macronutrient as a fuel, protein is needed for tissue repair and adaptation as it acts both as a trigger and substrate in anabolic processes [13,15]. Recovery and post-exercise muscle protein synthesis are optimized by ingesting adequate amounts of protein within two hours after the training session [13]. In addition to CHO and proteins, dietary fats are a vital component of the diet of XC skier as they are an important fuel during low intensity exercise and may help maintain adequate EA during hard training periods due to their high energy density [16]. Furthermore, dietary fats promote immune function and participate in the metabolism of fat-soluble vitamins [13]. Although timing macronutrient intake to support training goals is one of the key components of athletes' nutrition, to our knowledge only one study has explored the topic among XC skiers, which demonstrated that even elite skiers had dif culties meeting adequate CHO intake around training and competition [17]. Optimizing nutrition is dif cult without adequate knowledge. This may especially be the case among high school athletes, many of

is one of the key components of athletes' nutrition, to our knowledge only one study has explored the topic among XC skiers, which demonstrated that even elite skiers had dif culties meeting adequate CHO intake around training and competition [17]. Optimizing nutrition is dif cult without adequate knowledge. This may especially be the case among high school athletes, many of whom have moved away from their parental home and started to take more responsibility over purchasing and preparing food. Indeed, some [18,19], but not all [20], studies have shown that older athletes have better nutrition knowledge compared to younger ones. Although there is mild evidence to suggest a positive association between nutrition knowledge and dietary intake, a very limited number of studies have used valid tools to assess both nutrition knowledge and dietary intake [21]. Furthermore, to the best of our knowledge there are no studies reporting how nutrition knowledge is associated with timing and periodization of macronutrient intake in athletes. Therefore, the aim of the present study was to provide novel information on how CHO and protein intake were periodized around different training sessions, and then to clarify how nutrition knowledge affects dietary intake and periodization practices in young female XC skiers presenting at the top national level. In addition, we investigated how well recommendations for EA and macronutrient intake were met during different training situations. 2. Materials and Methods 2.1. Participants A total of 31 female XC skiers from the Finnish Ski Association's under 18-year- old national team were invited to join the study. Of those, a total of 19 athletes (age 16.7 0.7 years ) participated in the study. The participants provided written informed consent prior to their involvement in the study and were allowed to drop out of the study at any time. The ethical board of the University of Jyväskylä approved the study and included procedures, and the study was conducted in accordance with the Declaration of Helsinki. 2.2. Experimental Overview The study was carried out before and during a 5-day training camp in a speci c prepa- ration season in October. Participants lled in

out of the study at any time. The ethical board of the University of Jyväskylä approved the study and included procedures, and the study was conducted in accordance with the Declaration of Helsinki. 2.2. Experimental Overview The study was carried out before and during a 5-day training camp in a speci c prepa- ration season in October. Participants lled in 48-hour food and training logs before and

Nutrients2021,13, 1769 3 of 11 during the training camp. Nutrition knowledge questionnaires [22] and anthropometric measurements were completed at the beginning of the training camp. 2.3. Anthropometric Measurements Anthropometric measurements were carried out in fasted state on the rst morning of the training camp. The height of the participants was measured with a stadiometer. Body mass and body composition were measured following an overnight fast using a bioimpedance (Inbody 720, Biospace Co., Seoul, Korea) measurement. 2.4. Food and Training Logs 48-hour food and training logs were lled in twice during the study. The rst logs were lled in between 12 and 2 days before the training camp (HOME) on self-selected days and the second logs were lled in during the second and third day of the 5-day training camp (CAMP). At CAMP, participants had three prescheduled meals in the restaurant of the local sport institute. Participants selected the contents of their meals from a buffet, which included salad and bread tables, several main course options, and dessert. Most ingredients were produced in Finland, and sport institute food is not highly processed. Typical drinks included juice from fruits, milk, and water. The energy and macronutrient contents of the dishes were obtained from the chef. Participants were allowed to have their own snacks between meals. Participants recorded the type, amount, and timing of foods and uid consumed, measuring intake using kitchen scales. Written and verbal instructions were given for accurate record keeping. The food logs were analyzed using Aivodiet-software (version 2.0.2.3, Mashie, Malmö, Sweden), which employs the national food composition database Fineli Release 16 (2013). Energy and macronutrient intake were recorded as daily intake, as well as at pre- and post-exercise time points, and expressed in relation to body weight. Training logs were analyzed for exercise energy expenditure (EEE) using equations by Charlot et al. [23]. Participants' average heart rate (HR), the duration of each training session, the subject's body mass, self-reported maximal oxygen uptake, resting HR, and maximum HR were used for calculations. Resting energy expenditure was estimated using the Cunningham equation [24]. Resting energy expenditure that would have occurred during

were analyzed for exercise energy expenditure (EEE) using equations by Charlot et al. [23]. Participants' average heart rate (HR), the duration of each training session, the subject's body mass, self-reported maximal oxygen uptake, resting HR, and maximum HR were used for calculations. Resting energy expenditure was estimated using the Cunningham equation [24]. Resting energy expenditure that would have occurred during exercise regardless of exercise was subtracted from EEE so that only the additional energy cost of the exercise is included in the EEE. EA was estimated as EI minus EEE and expressed in kcal kg fat-free mass (FFM) 1 day (d) 1 and compared with cut-off values for low (<30 kcal kgFFM 1 d 1 ) and optimal EA (>45 kcal kgFFM 1 d 1 ), as speci ed by Loucks et al. [10]. Sessions that involved over 30 min duration were divided into KEY or EASY sessions, as described in detail by Heikura et al. [25]. Brie y, KEY sessions were de ned as speed or strength training, high intensity work (above aerobic threshold [26]) or duration of exercise over 150 min, and all other trainings were categorized as EASY sessions. The amount of ingested CHO was recorded in the 4-hour pre-exercise period as well as in 1- and 4-hour post-exercise periods, while protein intake was recorded in the 2-hour post- exercise period to compare intake with current consensus statement recommendations [13]. Mean CHO and protein intake around KEY and EASY exercises were calculated for each athlete separately at HOME and at CAMP. Thus, every athlete had single values describing her intake around different training situations (KEY at HOME; EASY at HOME; KEY at CAMP; EASY at CAMP). 2.5. Nutrition Knowledge A validated nutrition knowledge questionnaire for young endurance athletes [22] was completed on paper at the beginning of the training camp. The questionnaire included 79 statements in true/false format, divided into ve sections: (1) nutrition recommen- dations for endurance athletes, (2) dietary supplements, (3) uid balance and hydration, (4) energy intake and recovery, and (5) the association between food choices and body im-

athletes [22] was completed on paper at the beginning of the training camp. The questionnaire included 79 statements in true/false format, divided into ve sections: (1) nutrition recommen- dations for endurance athletes, (2) dietary supplements, (3) uid balance and hydration, (4) energy intake and recovery, and (5) the association between food choices and body im-

Nutrients2021,13, 1769 4 of 11 age. Each correct answer yielded one point and each wrong answer zero points. Nutrition knowledge score refers to the proportion (percentage) of correct answers. 2.6. Statistical Analysis Statistical analyses were conducted using SPSS Statistics 26 (IBM, Armonk, NY, USA). Results are reported as means SD. Normality was assessed via Shapiro–Wilk, and nonparametric tests were used with non-normally distributed data. Changes between HOME and CAMP as well as between different training situations were assessed using a repeated-measures analysis of variance, followed by a Student´s pairedttest as a post hoc test to determine thepvalues in the pairwise comparisons. Wilcoxon signed rank test was used in the case of non-normally distributed variables. Effect sizes were calculated as Cohen'sdwith threshold values of <0.2 (trivial), 0.2–0.5 (small), 0.5–0.8 (moderate), and >0.8 (large) [27]. Pearson's (normally distributed data) or Spearman's (non-normally distributed data) correlation coef cient was used to analyze correlations between nutrition knowledge score and other variables. To test knowledge about nutrition recommendations more speci cally, part one (44 questions) of the 79 question nutrition knowledge ques- tionnaire [22] was used in the correlation analysis. Statistical signi cance was de ned as p< 0.05. 3. Results 3.1. Dietary Intake Table Daily intake of energy (p< 0.001, d = 1.69), protein (p= 0.002, d = 0.84), and CHO (p< 0.001, d = 2.36), as well as EEE (p< 0.001, d = 2.21) were lower at HOME compared to CAMP, while an increased trend from HOME to CAMP was observed in EA(p= 0.065,d = 0.47). A high percentage, 89% and 58% of athletes, had suboptimal EA at HOME and at CAMP, respectively. Furthermore, ve (26%) athletes had low EA at HOME and seven (37%) athletes at CAMP. Table 1. Mean ( SD) daily training volume, energy and macronutrient intake, exercise energy expenditure, and energy availability in young female cross-country skiers (n = 19). HOME % CAMP % Recommendation [10,13] Training (min d 1 ) 120 26 NA 214 20 *** NA NA EI (kcal kg 1 d 1 ) 43.1 9.1 NA 60.4 13.1 *** NA NA EEE (kcal kg 1 d

daily training volume, energy and macronutrient intake, exercise energy expenditure, and energy availability in young female cross-country skiers (n = 19). HOME % CAMP % Recommendation [10,13] Training (min d 1 ) 120 26 NA 214 20 *** NA NA EI (kcal kg 1 d 1 ) 43.1 9.1 NA 60.4 13.1 *** NA NA EEE (kcal kg 1 d 1 ) 14.9 4.5 NA 26.8 4.3 *** NA NA EA (kcal kg 1 d 1 ) 33.7 9.6 11 40.3 17.3 42 45 Protein (g kg 1 d 1 ) 2.1 0.3 100 2.5 0.5 ** 100 1.2–2.0 CHO (g kg 1 d 1 ) 5.0 1.2 26 7.1 1.6 *** 37 6–10/8–12 Fat (% of EI) 30 6 95 32 7 100 20–35 EI = energy intake; EEE = exercise energy expenditure; EA = energy availability; CHO = carbohydrate; HOME = during home training; CAMP = during training camp; % = the percentage of athletes who met the lower threshold for recommended intake for optimal performance and recovery [10,13]; NA = Not Applicable. ** p< 0.01; ***p< 0.001 signi cant difference between HOME and CAMP. EEE and macronutrient intake around KEY and EASY sessions are presented inTable. EEE was signi cantly higher in KEY sessions compared to EASY sessions at HOME and at CAMP (p= 0.007, d = 0.72;p< 0.001, d = 1.64, respectively). Furthermore, EEE in KEY sessions was lower at HOME than at CAMP (p< 0.001; d = 1.14). CHO intake during the 4-hour period before KEY sessions was lower at HOME than at CAMP (p= 0.002,d = 0.74), while CHO intake before EASY sessions remained similar (p= 0.37, d = 0.28). CHO intake both in 1 and 4 hours after exercise period was signi cantly lower at HOME compared to CAMP after KEY (p< 0.001, d = 1.17;p< 0.001; d = 1.40, respectively) and EASY(p= 0.002, d = 1.04;p< 0.001; d = 1.09, respectively) sessions. Protein intake was higher after KEY sessions at CAMP compared to EASY sessions at CAMP (p= 0.033, d = 0.41) and KEY sessions at HOME (p= 0.018, d = 0.59).

lower at HOME compared to CAMP after KEY (p< 0.001, d = 1.17;p< 0.001; d = 1.40, respectively) and EASY(p= 0.002, d = 1.04;p< 0.001; d = 1.09, respectively) sessions. Protein intake was higher after KEY sessions at CAMP compared to EASY sessions at CAMP (p= 0.033, d = 0.41) and KEY sessions at HOME (p= 0.018, d = 0.59). Many athletes failed to meet the lower end of the

This study examines the relationship between nutrition knowledge and dietary practices in young female cross-country skiers.