The Impact of Nutrition Education Intervention with and Without a Mobile Phone Application on Nutrition Knowledge Among Young Endurance Athletes

thletes often have signi cant gaps in their nutrition knowledge. Thus, the aim of this study was to investigate whether young Finnish endurance athletes' nutrition knowledge and dietary intake can be improved through an education intervention with or without a mobile food application. Seventy-nine endurance athletes, 18.0 years (SD: 1.4), participated in this randomized, controlled intervention. We compared the e ects of participatory nutrition education sessions alone (group EDU) to those including the use of a mobile food application (group EDU+APP) for four days after each session. Both groups attended three 90-min education sessions fortnightly. The participants completed a validated nutrition knowledge questionnaire in Weeks 0, 5, and 17, and a three-day food diary in Weeks 0 and 17. The education plan was based on the Self-Determination Theory and the concept of meaningful learning process. The EDU group's nutrition knowledge scores were: 78 (week 0), 85 (week 5), and 84 (week 17) and the EDU+APP group's 78, 86, and 85, respectively. Nutrition knowledge increased signi cantly (main e ect of time (p<0.001)), but we observed no signi cant group time interaction (p=0.309). The changes in dietary intakes were minor (p>0.05). The amount of carbohydrates was below endurance athletes' recommendations throughout the intervention. The reported energy intakes were also below the estimated energy expenditures. In conclusion, nutrition knowledge improved signi cantly after only three education sessions and food diary feedback, but the mobile app did not improve learning further. However, the nutrition education intervention alone was not

intakes were minor (p>0.05). The amount of carbohydrates was below endurance athletes' recommendations throughout the intervention. The reported energy intakes were also below the estimated energy expenditures. In conclusion, nutrition knowledge improved signi cantly after only three education sessions and food diary feedback, but the mobile app did not improve learning further. However, the nutrition education intervention alone was not enough to change dietary intake. Keywords: sports nutrition; nutrition knowledge; dietary habits; intervention; adolescents; endurance sports; energy intake; carbohydrates 1. Introduction Training, rest and proper nutrition are the keys for optimal athletic performance. For young athletes, the role of proper nutrition is particularly important because of its role in growth and development [1]. Adequate nutrition knowledge is needed to understand the importance of daily food choices for performance, health and recovery [2,3]. Unfortunately, athletes and coaches often have limited nutrition knowledge [2,4]. Information regarding this knowledge can be used for planning targeted and e ective education interventions [5]. By identifying knowledge gaps, an intervention can focus on groups needing more nutrition education, or on speci c dietary challenges, such as inadequate carbohydrate consumption [4,6]. To achieve lasting dietary changes, traditional education Nutrients2019,11, 2249; doi:10.3390 /nu11092249 /journal/nutrients

Nutrients2019,11, 2249 2 of 12 should be combined with behavioural change strategies and the practical skills needed for following an appropriate diet [7–9]. The bene ts of prudent dietary changes should be made attractive [7]. Earlier interventions aiming to improve athletes' nutrition knowledge have varied greatly in their duration and content [9–16]. The education has been provided in either group sessions, mainly lectures [10,15,16] or individual face-to-face sessions [9,11,12]. Athletes' nutrition knowledge has increased in many of these studies [9–12,14–16]. Other positive outcomes have also been reported, such as increases in self-e cacy, overall number of positive dietary changes and body composition [9,10,16]. While education interventions may require a great deal of time and resources [15], mobile applications (apps) may be helpful in replacing or complementing the use of traditional methods, thus making the interventions more convenient and time-e cient, especially for younger participants preferring new technology [17,18]. In addition to serving as tools for assessing dietary intake in real time, apps may be useful for increasing nutrition knowledge [19]. However, mobile apps have been used for any kind of nutrition promotion, such as improving nutrition knowledge, only for some years and can be regarded as still being in their infancy [19]. Therefore, the best practices for such actions might not be known yet. The primary aim of this study was to create a new scalable, exible education programme to improve nutritional knowledge among young Finnish endurance athletes. This target group was chosen because majority of the nutrition knowledge studies have been conducted among team sport athletes far from the Nordic Countries. The randomized, controlled intervention compared the e ects of participatory nutrition education sessions alone to those enhanced by a mobile app. We hypothesized that nutrition knowledge would improve in both groups but more among the athletes using the mobile application. In addition to nutrition knowledge, we wanted to study whether any intervention-induced changes occurred in dietary intake. 2. Materials and Methods 2.1. Participants We asked endurance sport coaches in two Finnish sports academies, the Finnish Military Sport Federation, and two sports clubs to invite their national- and international-level

in both groups but more among the athletes using the mobile application. In addition to nutrition knowledge, we wanted to study whether any intervention-induced changes occurred in dietary intake. 2. Materials and Methods 2.1. Participants We asked endurance sport coaches in two Finnish sports academies, the Finnish Military Sport Federation, and two sports clubs to invite their national- and international-level endurance athletes aged 16–20 to participate in our study. The participants completed an informed consent form before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the study protocol and questionnaires used were reviewed and approved by the University of Helsinki Ethical Review Board (statement 45/2017). Groups: The participants were randomized into two groups using random permuted blocks, strati ed by sex. The EDU group refers to participatory nutrition education sessions. The EDU+ APP group refers to participatory nutrition education sessions and the use of the mobile app on a smartphone. The power calculations were based on the results from a prior study on nutrition knowledge [4]. The mean di erence in knowledge between athletes and coaches in that study was 8 points (SD: 9), measured using a 79-item questionnaire. Thus, we estimated that the change in the total knowledge score that was possible to achieve and which would bene t the participants, would be approximately eight points. Based on this, we calculated that each group should have 29 athletes ( =0.01, desired power=0.80). Due to an estimated drop-out of 25%, 40 athletes were recruited to both groups. 2.2. Instruments Nutrition knowledge questionnaire: We investigated nutrition knowledge using a validated questionnaire for young endurance athletes and coaches [20], which consists of demographic questions and 78 statements (after the deletion of one item that was inappropriate for the target group from the original 79-item questionnaire) in true/false format in sections: (1) nutrition recommendations for endurance athletes, (2) dietary supplements, (3) uid balance and hydration, (4) energy intake

that was inappropriate for the target group from the original 79-item questionnaire) in true/false format in sections: (1) nutrition recommendations for endurance athletes, (2) dietary supplements, (3) uid balance and hydration, (4) energy intake

Nutrients2019,11, 2249 3 of 12 and recovery, and (5) the association between food choices and body image. The content, face, and construct validities both as the test-retest and internal consistency reliabilities were veri ed during the questionnaire's development and they were within appropriate limits [20]. Food diary: The athletes recorded everything they ate and drank in a food diary for three successive days (of which one was a weekend day), at baseline and three months after the last education session, to indicate possible long-term changes in the food intakes. The amounts of food were estimated using The Children's Food Picture Book (2015) developed by the University of Helsinki, Seinäjoki University of Applied Sciences and Folkhälsan Research Center. In this book, three to four di erent portion sizes of foods and ingredients typical to the Finnish diet were presented. Food diary data were entered and nutritional composition of diet was calculated using AivoDiet dietary software (version 2.2.0.1, Mashie, Malmo, Sweden) which employs the national food composition database Fineli Release 16 (2013). 2.3. Procedures Pilot study: The questionnaire, app, and the structure of the education sessions were tested in a pilot study with eleven 16–22-year old athletes from the Helsinki metropolitan area. They lled in the questionnaire before and after the sessions, which were held on three consecutive days. Minor changes were made after the pilot to keep the schedule of the sessions. After the sessions, we asked the athletes to use the mobile app for a week. Due to their feedback, the food logging period for the actual intervention was shortened to four days. Intervention: Figure completed before (week 0), after the education intervention (week 5) and after follow-up (week 17). The food diary was completed at baseline and after follow-up. We held three education sessions fortnightly, which were all attended by both groups. Figure 1.Setting and schedule of the intervention. 2.4. Education Sessions The aim of the education sessions was to increase the nutrition knowledge among the athletes. The education sessions were planned according to the idea of meaningful learning process (Figure), explaining the conversion of real-life challenges (e.g., inadequate

We held three education sessions fortnightly, which were all attended by both groups. Figure 1.Setting and schedule of the intervention. 2.4. Education Sessions The aim of the education sessions was to increase the nutrition knowledge among the athletes. The education sessions were planned according to the idea of meaningful learning process (Figure), explaining the conversion of real-life challenges (e.g., inadequate energy intake) into interesting and motivating educational challenges [21–23]. Motivation was the key element of the education plan and student's learning. Increase in motivation was based on Self-Determination Theory, which is used to explain the role of humans' inner resources for personality development and behavioural changes [24]. The education sessions aimed to expand the athletes' feelings of autonomy, competence and relatedness by allowing them to participate in the sessions in the form of discussions, tasks and goal setting, to achieve intrinsic motivation [25]. However, it is impossible to divide the education sessions into parts that addressed only relatedness, only competence or only autonomy. The main idea behind the education sessions was to strengthen all of these feelings.

Nutrients2019,11, 2249 4 of 12 Figure 2. The meaningful learning process. The gure is inspired by Autio [21] and Engeström [22] and is based on Davydov's concept of developmental teaching [23]. In this intervention, challenges and problems refers to: how to improve the nutrition knowledge among the athletes, learning content to: nutrition knowledge, and learner to: the endurance athletes. The education sessions with lectures, discussions, exercises, and individual and group work lasted 90 min each. They were held by a nutritionist (M.H.) and their themes were as follows, based on the ndings of the nutrition knowledge study [4] and literature [26]. 1. 2. Carbohydrates, fat, and protein (sources, quality, timing, trends) from the viewpoint of endurance athletes. 3. Certain minerals and vitamins (iron, calcium, magnesium, vitamin D), supplements and challenges (eating on competition days, eating on the road, disordered eating, and weight control). Feedback on food diaries: The participants received written feedback on their food diaries at baseline and after follow-up from a nutritionist (M.H.). Feedback was an integral part of the education. Energy intake, and macro- and micronutrient intakes from foods and drinks excluding dietary supplements, were shown and compared to recommendations (6–10 g kg 1 day 1 or 8–12 g kg 1 day 1 carbohydrates for moderate to high intensity endurance training (1–3 h day 1 or>4–5 h day 1 ), 1.5–2 g kg 1 day 1 proteins for high volume of intense training and ~1–2 g kg 1 day 1 fat) [27,28]. They also received written feedback highlighting the main strengths and targets for development in their diet, both as a rough estimation of daily total energy expenditure (TEE) and general advice. TEE was calculated using the Harris-Benedict equation for resting energy expenditure (REE) and multiplied by estimated average physical activity level (PAL), 2.1 [29]. Due to the similar training background and after consultation of coaches about athletes' typical training amounts, the same PAL was used for all athletes in the study. 2.5. Mobile App Intervention The athletes in the EDU+APP group used the photo food journal and nutritional network application MealLogger ® with their smartphones for

estimated average physical activity level (PAL), 2.1 [29]. Due to the similar training background and after consultation of coaches about athletes' typical training amounts, the same PAL was used for all athletes in the study. 2.5. Mobile App Intervention The athletes in the EDU+APP group used the photo food journal and nutritional network application MealLogger ® with their smartphones for four days after each session. The study used two functions of the app: First, during the four day photographing periods, the athletes were asked to take photos of everything they ate or drank. They were given speci c tasks (presented below) to concentrate on when taking photos and/or in additional written descriptions to reinforce their feelings of autonomy and competence. Second, they received written feedback from the nutritionist (M.H.) via the app to support their learning and feelings of relatedness.

Nutrients2019,11, 2249 5 of 12 Week 1, eating rhythm and uids: The athletes were asked to concentrate on the number and timing of meals, and the amount of uids in their diet. They were given feedback twice a week on these points. Week 2, healthy eating: Feedback was given nearly real-time on the quality of breakfast, lunch and dinner. Before receiving the feedback, the athletes were asked for a self-evaluation of their meals in terms of sources of carbohydrate, bre, protein, unsaturated fat, and something colourful (vegetables, fruits, berries). Week 3, variety of food+vitamin D: The athletes were asked to concentrate on the sources of vitamin D in their diet and to log alongside their photographs the food that contained vitamin D. They were encouraged to try new foods and to eat a wide variety of foods. They received feedback on these points twice a week. 2.6. Data Analysis The categorical data are presented as number and percentages. We identi ed the normality of the variable distributions using Kolmogorov-Smirnov test. For knowledge score or dietary intake variable comparisons between groups and changes in time, we used repeated measures ANOVA (SPSS version 24.0) with age, sex and eld of sports as covariates. For macronutrient intakes, we used direct data derived from dietary software instead of Willett's energy-adjusted intakes [30], as the di erences in the values were only minor. We did not exclude potential under-reporters (week 0,n=3; week 17, n=1), because the di erences in the analyses were minor. In this study, under-reporting was de ned as getting energy from diet less than the estimated REE. All the statistical analyses were conducted using IBM SPSS Statistics for Windows (version 24.0; IBM Corp., Armonk, NY, USA).p-values below 0.05 were considered statistically signi cant. In the knowledge questionnaire, each wrong answer yielded zero and each correct answer one point. In this paper, “nutrition knowledge score” always refers to the proportion (percentage) of correct answers. The maximum points were 78, in which case the score was 100. 3. Results Of the 79 participants, 62 completed all the nutrition knowledge questionnaires and were thus included

cant. In the knowledge questionnaire, each wrong answer yielded zero and each correct answer one point. In this paper, “nutrition knowledge score” always refers to the proportion (percentage) of correct answers. The maximum points were 78, in which case the score was 100. 3. Results Of the 79 participants, 62 completed all the nutrition knowledge questionnaires and were thus included in the nutrition knowledge analyses. Sixty-seven completed both food diaries and were thus included in the food intake analyses. Table Athletes' mean age was 18.0 years (SD: 1.4). Of the athletes, 56% were male and 44% female. Their main sports were cross-country skiing (n=33) and endurance running/race-walking (n=18). Apart from one athlete, all the participants attended the rst lecture. All the athletes completed the rst questionnaire, 66 the second, and 67 the third. At baseline, 73 athletes completed the food diary and at the end 67. Of the 42 athletes in the EDU+APP group, 34 used the app (81%). Athletes' voluntary written (given by 13 athletes) or verbal (given by multiple athletes either individually or in the classrooms during the sessions) feedback on the di erent parts of the intervention was highly positive, in general. The mean weight of the athletes in the EDU group was 64.2 kg (SD: 8.8) at baseline and 64.9 kg (SD 8.6) after follow-up. In the EDU+APP group these were 66.4 kg (SD: 8.6) and 67.8 kg (SD: 8.6), respectively. There was a signi cant increase in weight during the study (main e ect of time:p=0.001). Table 1.Background information on athletes presented as numbers and percentages of participants in group. Group EDU (n=37) Group EDU +APP (n=42) Sex female 18 (49%) 17 (40%) male 19 (51%) 25 (60%) Main sport cross-country skiing 15 (41%) 18 (43%)

Nutrients2019,11, 2249 6 of 12 Table 1.Cont. Group EDU (n=37) Group EDU +APP (n=42) biathlon 5 (14%) 8 (19%) orienteering 8 (22%) 5 (12%) endurance running and race-walking 9 (24%) 9 (21%) triathlon 0 (0%) 2 (5%) Attendance of lectures Lecture 1 (Week 0) 36 (97%) 42 (100%) Lecture 2 (Week 2) 31 (84%) 39 (93%) Lecture 3 (Week 4) 30 (81%) 29 (69%) Completion of questionnaire 1st questionnaire (Week 0) 37 (100%) 42 (100%) 2nd questionnaire (Week 5) 31 (84%) 35 (83%) 3rd questionnaire (Week 17) 29 (78%) 38 (90%) Completion of food diary 1st food diary (Week 0) 33 (89%) 40 (95%) 2nd food diary (Week 17) 30 (81%) 37 (88%) 3.1. Nutrition Knowledge Figure were 77.7 (SD: 7.6) at their weakest and 87.4 (SD: 7.6) at their best. There was a signi cant increase in the mean scores during the study (p<0.001). The knowledge scores between the groups did not di er (p=0.309). A comparison of the scores at the end of three months follow-up (week 17) to those at baseline showed that the mean change in knowledge was 6.1 3.7 points in the EDU group and 7.3 6.0 in the EDU+APP group. Immediately after the nutrition education, the mean change in knowledge among the athletes was 6.9 6.2 points in EDU group and 9.3 6.7 in EDU+APP group. Figure 3. Mean nutrition knowledge scores with SD at baseline, after nutrition education and after follow-up. Black line with squares refers to the EDU+APP group (education sessions+the use of mobile food app) and dotted line with circles to the EDU group (education sessions only).p=0.309 for group time interaction,p<0.001 for main e ect of time, andp=0.309 for main e ect of group. We found a similar tendency in all sections of the questionnaire (Table). That is, the athletes in the EDU+APP group scored slightly higher than those in the EDU group, but the di erences were not

This study evaluates the effectiveness of nutrition education interventions on knowledge and dietary intake in young athletes.