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Evaluation of a Theory-Based Intervention Aimed at Improving Coaches’ Recommendations on Sports Nutrition to Their Athletes
RESEARCH
Original Research: Brief
Evaluation of a Theory-Based Intervention Aimed at Improving Coaches’ Recommendations on Sports Nutrition to Their Athletes Raphaëlle Jacob, MSc, RD*; Benoît Lamarche, PhD; Véronique Provencher, PhD, RD*; Catherine Laramée, MSc, RD*; Pierre Valois, PhD; Claude Goulet, PhD; Vicky Drapeau, PhD, RD* ARTICLE INFORMATION Article history: Submitted 25 September 2015 Accepted 4 April 2016 Available online 24 May 2016
Keywords: Sports nutrition Coaches Theory-based intervention Knowledge Health promotion 2212-2672/Copyright ª 2016 by the Academy of Nutrition and Dietetics. http://dx.doi.org/10.1016/j.jand.2016.04.005 *
Certified in Canada.
ABSTRACT Background Coaches are a major source of nutrition information and influence for young athletes. Yet, most coaches do not have training in nutrition to properly guide their athletes. Objective The aim of this study was to evaluate the effectiveness of an intervention aimed at improving the accuracy of coaches’ recommendations on sports nutrition. Design This was a quasi-experimental study with a comparison group and an intervention group. Measurements were made at baseline, post-intervention, and after a 2-month follow-up period. Coaches’ recommendations on sports nutrition during the follow-up period were recorded in a diary. Participants/setting High school coaches from various sports (n¼41) were randomly assigned to a comparison group or an intervention group. Intervention Both groups attended two 90-minute sessions of a theory-based intervention targeting determinants of coaches’ intention to provide recommendations on sports nutrition. The intervention group further received an algorithm that summarizes sports nutrition guidelines to help promote decision making on sports nutrition recommendations. Main outcome measures Nutrition knowledge and accuracy of coaches’ recommendations on sports nutrition. Statistical analysis performed c2 analyses and t-tests were used to compare baseline characteristics; mixed and general linear model analyses were used to assess the change in response to the intervention and differences in behaviors, respectively. Results Coaches in the intervention vs comparison group provided more nutrition recommendations during the 2-month post-intervention period (mean number of recommendations per coach 25.722.0 vs 9.46.5, respectively; P¼0.004) and recommendations had a greater accuracy (mean number of accurate recommendations per coach 22.419.9 [87.1%] vs 4.33.2 [46.1%], respectively; P<0.001). Knowledge was significantly increased post-intervention in both groups, but was maintained only in the intervention group during the 2-month follow-up (Pgroup*time¼0.04). Conclusions A theory-based intervention combined with a decision-making algorithm maintained coaches’ sports nutrition knowledge level over time and helped them to provide more accurate recommendations on sports nutrition. J Acad Nutr Diet. 2016;116:1308-1315.
S
PORTS NUTRITION PLAYS AN IMPORTANT ROLE IN athletic performance.1 Adolescent athletes have particular dietary needs to meet requirements of daily training, but also to ensure optimal growth and health.2 A significant proportion of adolescent athletes have a relatively low energy intake, inadequate intakes of certain nutrients, or adopt dietary practices that are not always in accordance with guidelines.3-6 It has been reported that although young athletes generally have adequate protein intakes, they do not consume enough carbohydrates daily4-9 and have suboptimal hydration status both before and during training.10-12 1308
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Coaches are recognized as a major source of information and influence for young athletes regarding food and supplement choices, as well as nutritional behaviors.13-17 Yet most coaches do not have specific or formal training in nutrition, and studies have characterized their knowledge in sports nutrition as being insufficient to properly guide their athletes on this topic.13,17-19 It has been reported that 46% of high school coaches intended to recommend consumption of foods rich in carbohydrates to their athletes, while proportions were 45% for foods rich in proteins and 92% for hydration (R. Jacob, MSc, RD; B. Lamarche, PhD; V. Provencher, PhD, RD; É. Morissette, MSc, RD; S. Couture, MSc, RD; ª 2016 by the Academy of Nutrition and Dietetics.
RESEARCH P. Valois, PhD; C. Goulet, PhD; V. Drapeau, PhD, RD: unpublished data, May 2011). Based on the theory of planned behavior (TPB), subjective norm (ie, the perceived social pressure to engage or not to engage in a behavior) was also identified as the main significant determinant of coaches’ intention to recommend carbohydrates and proteins (R. Jacob, MSc, RD; B. Lamarche, PhD; V. Provencher, PhD, RD; É. Morissette, MSc, RD; S. Couture, MSc, RD; P. Valois, PhD; C. Goulet, PhD; V. Drapeau, PhD, RD: unpublished data, May 2011). In order to develop a successful intervention, it is appropriate to rely on theoretical frameworks (such as the TPB), because these models have been identified as a factor of the success of an intervention.20 To the best of our knowledge, no study has yet investigated the effectiveness of a theory-based intervention designed to specifically improve the accuracy of coaches’ recommendations on sports nutrition to their athletes and to enhance their nutrition knowledge. Based on our previous work, the main purpose of this study was to evaluate the effectiveness of a TPB-based intervention combined with a decision-making algorithm, aimed at improving coaches’ recommendations on sports nutrition to their athletes compared with a TPB-based intervention only. The intervention was also intended to increase sports nutrition knowledge among coaches. It was hypothesized that adding the use of a decisionmaking algorithm that informs on proper sports nutrition recommendations improves nutrition knowledge retention as well as the number and accuracy of recommendations to a greater extent than a theory-based intervention only.
METHODS Participants Coaches of athletes aged 12 to 17 years were recruited through e-mail and phone contact within local competitive sport communities in 2013. To take part in this study, coaches had to work with adolescent athletes during the entire length of the study. Participants were met by the study coordinator to explain the study purpose and procedures. The Research Ethics Committee of Laval University approved study procedures and written informed consent was obtained from all participants before the study. All participants received a gift certificate at a local sports store at the end of the study. Participants were randomly assigned to the comparison group (ie, an intervention based on the TPB) or the intervention group (ie, the same intervention with the addition of a decision-making algorithm regarding recommendations on sports nutrition). Randomization was stratified by types of sports (aesthetic, eg: gymnastic, cheerleading, figure skating; endurance, eg: cycling, cross-country skiing, triathlon; or power/team, eg: swimming, basketball, tennis) to ensure similar numbers in each group because dietary needs might differ more between the types of sports than within sports of the same type. Coaches from the same team or club were randomized as a “group” into either condition to reduce contamination bias. Coaches were not aware of the two conditions.
Theory-Based Intervention Common to Both Groups Participants from both groups attended two 90-minute meetings delivered during a 2-week period, during which specific determinants of coaches’ intention to recommend August 2016 Volume 116 Number 8
different sports nutrition recommendations were targeted. Participants received nutrition information about current recommendations for healthy eating and sports nutrition in adolescent athletes with focus on macro- and micronutrients and hydration to achieve and maintain good health; carbohydrates, proteins and lipids, and their food sources, with particular focus on recommendations related to consumption of carbohydrates to achieve training requirements and sport performance; optimal hydration practices to achieve training requirements and sport performance; dietary and hydration strategies before, during, and after training and competitions; optimal food choices while eating out; and relevance of optimizing dietary strategies vs supplement use in improving performance in young athletes. The intervention was developed and implemented by a registered dietitian (RD). More specifically, the intervention was a lecture-based format with interactions between participants and the RD. Two specific behavior change strategies were used in both groups to improve coaches’ recommendations on sports nutrition to their athletes. In order to induce more rational decision making in coaches, strategies aimed at ensuring that subjective norm had less influence on the intention to pursue this behavior were used. First, persuasive communication, which involved use of arguments and repeated exposure to the message, was used to establish a positive attitude (ie, subjective analysis of advantages and disadvantages related to a given behavior) toward the impact of appropriate dietary and hydration practices on athletic performance, with focus on carbohydrate consumption as a major source of energy.20 Resistance to social pressure, which represents another specific behavior change strategy, was also used to enable coaches to convince parents and athletes of the importance of sound dietary strategies to improve sport performance.20
Intervention Group In the intervention group, all coaches also received, at the end of the second meeting, an algorithm aimed at facilitating decision making regarding sports nutrition recommendations in order to help coaches increase their perceived behavioral control (ie, perceived level of ease or difficulty regarding the adoption of a given behavior) over their nutrition recommendations. A decision-making algorithm is defined as a type of advance organizer,21 which is a schematic representation of a content, that is used to increase knowledge.20 Knowledge can positively influence perceived behavioral control by reducing barriers related to the lack of information or abilities and increasing the perceived ease to perform a behavior.22 More specifically, this decision-making algorithm illustrated evidence-based sports nutrition information in specific contexts (eg, before, during, and after training and competition). It also presented an example of an optimal plate for an athlete and provided examples of food sources of carbohydrates, proteins, best sources of lipids, and suggestions for recovery snacks, what to eat during effort/competition, and what to eat in particular situations (eg, not hungry before a competition). A different decision-making algorithm was made for each type of sports and coaches were provided with one algorithm. Understanding of the decision-making algorithm by coaches was reinforced through case studies.20 JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS
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RESEARCH Measures Coaches completed web-based questionnaires assessing sociodemographic characteristics, nutrition knowledge, and their intention to provide different dietary recommendations to their athletes. Questionnaires were completed at baseline, 1 week post-intervention, and after a 2-month follow-up period. Baseline questionnaires were completed in the presence of the research coordinator at the research unit or at the coaches’ workplace. Post-intervention and follow-up questionnaires were completed at home. Coaches were instructed to complete the questionnaires individually and were asked not to use any outside source of nutrition information, such as the Internet. Coaches also completed a sports nutrition diary in which they were asked to write down the dietary recommendations they provide to their athletes during the 2-month follow-up period. The intervention, questionnaires, and diary were first tested in a group of five coaches, providing an opportunity for feedback by coaches and investigators.
hydration. A mean number of recommendations per coach were calculated (ie, total number of recommendations on sports nutrition provided in each group divided by the number of coaches in each group). Accuracy of recommendations on sports nutrition was assessed on a 4-point score (score 0 to 4). More specifically, 2 points were given if the recommendation was in line with current sports nutrition guidelines1 and 2 points were given if the recommendation consisted of an example of a food (1 point) and a quantity of that food (1 point). An accurate recommendation corresponded to an accuracy score of 3. The number and accuracy of recommendations were evaluated independently by two RDs from the research team, and they discussed to achieve a consensus when necessary. When this was not achieved, the opinion of a third RD from the research team was used (ie, for 2.2% for the classification of recommendations [mean number per coach] and 3.4% for the accuracy of recommendations).
Data Analysis Nutrition Knowledge Evaluation The general and sports nutrition knowledge questionnaire has been developed for the purpose of our prior study19 and was based on literature23-25 as well as the investigators’ experiences in sports nutrition. For the present study, 10 questions mainly related to carbohydrates in sports were added to the questionnaire, bringing the number of questions to 69 (Cronbach’s a¼.78). Questions consisted of true or false and multiple-choices questions and were divided into six main subcategories: carbohydrates, proteins, lipids, supplement use, timing and hydration, and others. All questions included a “don’t know” option to minimize guessing.25 Correct answers were scored as 1, and incorrect and “don’t know” answers and missing values were scored as 0. Nutrition knowledge score was calculated as percent of correct answers provided.
Intention Measurement A web-based questionnaire developed according to the TBP guidelines22,26 was used to assess coaches’ intentions to recommend the three following dietary practices to their athletes during the next 3 months: increase consumption of foods rich in carbohydrates to improve sport performance, increase consumption of foods rich in proteins to promote muscle gain, and increase hydration to improve sport performance. These behaviors were based on sports nutrition guidelines and on professional experiences of the study investigators. Intention to recommend these dietary practices was assessed on a 6-point Likert scale (strongly disagree [3] to strongly agree [3]).
Statistical analyses were performed using SAS software.27 c2 analyses and t-test and were used to compare baseline characteristics between the two groups. Mixed models with Tukey’s post hoc test were conducted to assess the changes in nutrition knowledge and intention to recommend the three nutritional practices between groups and over time, using time, groups, and their interaction as fixed effects and subject as random effect. Mixed models included type of sports as a covariate when it showed a significant association with the outcome in the model. General linear model analysis was used to examine differences between groups in the number and accuracy of recommendations provided to athletes during a 2-month post-intervention period. Data are reported as meanstandard deviation, frequency, or as meanstandard error of the mean for knowledge scores. Differences were considered significant at P<0.05. A total of 41 coaches were recruited. Twenty and 21 coaches were randomly assigned to the intervention and comparison groups, respectively. One coach in the comparison group did not participate in the study due to a lack of interest. Another coach in the intervention group quit the study after having attended the two educational sessions because of a time constraint. These two individuals were not included in the analyses. One coach in the comparison group did not complete the diary during the 2-month follow-up due to maternity leave, but attended both meetings and completed all of the questionnaires and was thus included in the analyses. Participation rates at the 2-month follow-up were 95% in both groups.
Behavior Measurement
RESULTS Participant Characteristics
Dietary recommendations made by coaches during the 2-month follow-up period were recorded in a diary developed by the research team. For each recommendation, information regarding type, time, and context was reported by coaches. They were first classified as general nutrition or sports nutrition recommendations. The latter was then further classified into four subcategories related to carbohydrates, proteins, carbohydrates and proteins combined when the recommendation concerned both nutrients, and
Coaches randomized to the intervention group were involved in cycling (n¼2), mountain biking (n¼3), gymnastics (n¼6), swimming (n¼5), triathlon (n¼1), canoe-kayak (n¼1), basketball (n¼1), or ice hockey athletic trainer (n¼1), whereas coaches in the comparison group were involved in cross-country skiing (n¼7), gymnastics (n¼3), cheerleading (n¼4), figure skating (n¼1), tennis (n¼5), or judo (n¼1). Among coaches in the intervention group, 31.6%, 26.3%, and 42.1% were involved in sports considered as aesthetic,
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RESEARCH endurance, and power/team, respectively. A similar distribution was seen among coaches assigned to the comparison group (40.0%, 35.0%, and 25.0% for aesthetic, endurance, and power/team sports, respectively). There was no significant difference in baseline sociodemographic characteristics between groups (Table). Coaches in the comparison group appear to be more involved with athletes of national and international levels compared with coaches in the intervention group, but this difference did not achieve significance (c2[3]¼5.51; P¼0.14).
Nutrition Knowledge Mean nutrition knowledge score at baseline was 72.3% in the intervention group vs 70.0% in the comparison group (t[74]¼0.79; P¼0.97) (Figure 1). Participants in both groups showed a significant increase in nutrition knowledge postintervention compared with baseline values, with scores of 81.7% in the intervention group and 82.7% in the comparison group, indicating no difference between groups 1-week postintervention (t[74]¼9.47; P<0.0001 for within-group differences; t[74]¼0.51; P¼1.0 for between-group differences). However, this increase in nutrition knowledge was maintained during the 2-month follow-up period in the intervention group only, with scores of 81.5% in the intervention group and 78.0% in the comparison group (F[2, 74]¼3.36; Pgroup*time¼0.04).
Table. Baseline characteristics of coaches in the intervention (n¼19) and comparison (n¼20) groups of a theory-based intervention aimed at improving coaches’ recomendations on sports nutritiona Characteristics
Intervention Comparison P value meanstandard deviation!
Age, y
26.15.3
28.611.4
0.39
Years in sport
8.65.6
10.09.6
0.61
n (%)! Sex of coaches Male
47.4
45.0
Female
52.6
55.0
High school
10.5
15.8
College
31.6
47.4
University
57.9
36.8
None
15.8
10.0
Levels 1 to 2
42.1
45.0
Coaches’ intention to recommend the consumption of foods rich in carbohydrates to their athletes increased over time in both groups compared to baseline (F[2, 73]¼6.12; Ptime¼0.004; F[2, 73]¼0.02; Pgroup*time¼0.98), but this increase was not maintained during the 2-month postintervention period. Coaches’ intention to recommend the consumption of foods rich in proteins tended to decrease in both groups after the intervention, but this did not achieve significance (F[2, 70]¼2.85; Ptime¼0.06; F[2, 70]¼0.06; Pgroup*time¼0.94). Coaches’ intention to recommend hydration did not change over time in both groups (F[2, 69]¼1.30; Ptime¼0.28; F[2, 69]¼0.60; Pgroup*time¼0.55) (data not shown).
Levels 3 to 4
42.1
45.0
Aesthetic
31.6
40.0
Endurance
26.3
35.0
Power/team sport
42.1
25.0
Behavior
Competitive level
Coaches in the intervention group provided more recommendations on sports nutrition than those in the comparison group (mean number of recommendations per coach¼25.722.0 [range¼8 to 103] vs 9.46.5 [range¼2 to 26], respectively; F(1, 36)¼9.67; P¼0.004) during the 2-month post-intervention period (Figure 2). Recommendations related to consumption of carbohydrates and hydration were provided more frequently among coaches in the intervention group than those in the comparison group (6.65.2 [range¼1 to 21] vs 3.33.0 [range¼0 to 12], respectively for carbohydrates, F[1, 36]¼5.83; P¼0.02 and 13.812.9 [range¼2 to 56] vs 3.32.7 [range¼1 to 11], respectively for hydration, F[1, 36]¼12.14; P¼0.001). The accuracy of recommendations on sports nutrition was also greater in the intervention group than in the comparison group (mean number of accurate recommendations per coach 22.419.9 [range¼7 to 93] vs 4.33.2 [range¼0 to 13], respectively; F[1, 36]¼15.39; P<0.001). Indeed, 87.1% of recommendations on sports nutrition made by coaches in the intervention
Local
0.0
0.0
Regional
5.3
10.0
Provincial
57.9
25.0
National
31.6
40.0
5.3
25.0
Intention
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0.88
Coaches’ education level 0.43
NCCPb level 0.86
Sports groups 0.53
Sex of athletes trained Male
5.3
0.0
Female
26.3
10.0
Male and female
68.4
90.0
International
0.22
0.14
Competitive network Civilc
73.7
70.0
Schoold and civil
21.1
30.0
5.3
0.3
School
0.50
a
Intervention and comparison group received the same two 90-min sessions of a theory-based intervention on sports nutrition and a decision-making algorithm on sports nutrition was provided only for the intervention group. b NCCP¼National Coaching Certification Program. c Competitions between sports clubs from a same city or from different cities, regions, provinces or countries. d Competition between schools.
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RESEARCH 85
a
Pgroup*time=0.04
% nutrition knowledge score
83 a
81
a
79 ab
77
comparison intervention
75 73 71 69 67 65
Baseline
Post-intervention
Follow-up
Figure 1. Sports nutrition knowledge score (meanstandard error of the mean) at baseline, post-intervention, and after the 2month follow-up period in the intervention (n¼19) and comparison (n¼20) groups of high school coaches. Intervention and comparison group received the same two 90-minute sessions of a theory-based intervention on sports nutrition and a decisionmaking algorithm on sports nutrition was provided only for the intervention group. aSignificant difference between post-intervention and baseline. bSignificant difference between follow-up and post-intervention. group were evaluated as being accurate compared to 46.1% in the comparison group. Specifically, the mean number (range and percent) of accurate recommendations per coach was higher in the intervention group compared to the comparison
group for the recommendations related to carbohydrate consumption (5.24.7 [range¼0 to 19; 77.7%] vs 1.31.8 [range¼0 to 6; 38.1%], respectively; F[1, 36]¼11.18; P¼0.002), recommendations on both carbohydrate and protein
30 P=0.004 25
Intervention
Mean number of recommendations
Comparison 20
P=0.001
15
10 P=0.02 P=0.07 5 P=0.2 0 Sports nutrition Carbohydrates
Proteins
Carbohydrates and proteins
Hydration
Figure 2. Mean number of recommendations on sports nutrition made by coaches during the 2-month follow-up period in the intervention (n¼19) and comparison (n¼19) groups. Intervention and comparison group received the same two 90-minute sessions of a theory-based intervention on sports nutrition and a decision-making algorithm on sports nutrition was provided only for the intervention group. 1312
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RESEARCH consumption (3.94.6 [range¼0 to 18; 77.1%] vs 1.11.6 [range¼0 to 6; 51.2%], respectively; F[1, 36]¼6.19; P¼0.02) and recommendations on hydration (13.312.9 [range¼2 to 56; 96.2%] vs 1.91.4 [range¼0 to 4; 58.7%], F[1, 36]¼14.63; P<0.001), respectively.
DISCUSSION The main purpose of this study was to evaluate the effectiveness of a TPB-based intervention, combined with a decision-making algorithm, aimed at supporting coaches in providing adequate general sports nutrition recommendations to their adolescent athletes. A secondary objective was to increase coaches’ knowledge in sports nutrition. The results suggest that complementing a theory-based intervention with a decision-making algorithm that targets perceived behavioral control improves the number and accuracy of recommendations on sports nutrition that coaches make to their athletes compared with the same intervention without a decision-making algorithm. Baseline sports nutrition knowledge of high school coaches was similar to data from our previous study (68%)19 and other studies (70% and 60%).17,28 Baseline knowledge was also lower than the threshold of 75% used by one study to suggest an adequate level of knowledge.29 Thus, the relatively low nutrition knowledge score among coaches emphasizes the need for more adequate education in this area. The intervention was effective in both groups in improving knowledge, and data showed that adding the use of a decision-making algorithm on sports nutrition recommendations as part of the theory-based intervention contributed to better knowledge retention during a 2-month postintervention period. This suggests that coaches could have relied on this tool to remember the sports nutrition guidelines after the intervention. The most important added effect of the use of a decisionmaking algorithm vs no algorithm was the higher number and accuracy of coaches’ recommendations on sports nutrition, and recommendations specifically related to carbohydrates, provided during the 2-month post-intervention period. The results also showed an increase in coaches’ intention to recommend the consumption of foods rich in carbohydrate after the intervention in both groups. Two hypotheses can be proposed to explain these effects. First, the decision-making algorithm may have helped coaches to implement their intention to provide dietary recommendations to their athletes. Fishbein and Ajzen22 reported that failure to act on an existing intention is often due to a low actual or perceived control over the behavior. Thus, the decision-making algorithm could have helped coaches to act directly on their intention by increasing their skills and abilities to provide sports nutrition recommendations. Increasing skills and abilities could also have an indirect effect by increasing the accuracy of judgment of control (actual control) and, thus, could strengthen the perceived behavioral controlbehavior relation.30 The second hypothesis is that the decision-making algorithm may have attenuated the impact of perceived barriers, such as a lack of knowledge, on the behavior. Indeed, as discussed previously, knowledge can positively influence perceived behavioral control by reducing barriers related to the lack of information and increasing the perceived ease and self-efficacy to perform a behavior.22,30 A August 2016 Volume 116 Number 8
higher self-efficacy could then have a direct influence on the behavior.30 These hypotheses need to be validated in future studies. Both conditions—that is, adding the use of the decisionmaking algorithm or not—had the same effects on the intention toward the recommendations of proteins and hydration. The apparent, but not significant, decrease in coaches’ intention to recommend the protein consumption may be explained by the emphasis given during the intervention on the fact that athletes already reach their daily protein needs only from food.31 The low number of protein recommendations provided during the 2-month follow-up period is consistent with this result. The absence of the intervention effect on coaches’ intention to recommend hydration can be explained by the fact that this intention was already high at baseline in both groups. However, in contrast to the comparison group, a higher frequency and accuracy of hydration recommendations provided by coaches was observed in the intervention group during the 2-month follow-up period. Thus, the decision-making algorithm seems to have helped coaches to implement their favorable intention and accurately recommend hydration to their athletes by reducing the barriers related to the lack of knowledge or skills.
Strengths and Limitations This study has several strengths and limitations. To our knowledge, this is the first study to assess the effect of an intervention designed to promote coaches’ accurate recommendations on sports nutrition, based on the determinants influencing coaches’ recommendations on sports nutrition measured in a previous study (R. Jacob, MSc, RD; B. Lamarche, PhD; V. Provencher, PhD, RD; É. Morissette, MSc, RD; S. Couture, MSc, RD; P. Valois, PhD; C. Goulet, PhD; V. Drapeau, PhD, RD: unpublished data, May 2011). Several studies have evaluated the determinants of intention to adopt a behavior in various domains, but very few studies developed subsequent interventions to encourage the desired behaviors.32 The 2-month follow-up evaluation of the actual behavior was also a strength because most studies investigating the impact of an intervention on behaviors assessed intentions rather than the actual behaviors. The methods used to modify behaviors, intentions, and their determinants and beliefs were based on available literature. On the other hand, the small number of subjects limited some of the analyses. Specifically, data do not inform on how the intervention modified the behavior (ie, coaches’ recommendations on sports nutrition). Because no written document on sports nutrition was provided in the comparison group after the intervention, it is not possible to conclude that the positive results observed in the intervention group are due to the decision-making algorithm “format” or the “sports nutrition information.” The absence of a pre-intervention measure of the behavior or a control group (ie, without intervention) can also represent a limitation of this study. However, the results regarding the intention to recommend carbohydrates (which was not different between groups and low at baseline) suggest that behaviors were similar between groups at baseline and that there was room for improvement. The nutrition knowledge questionnaire involving “true/false/I don’t know” question format could have incorporated an element of JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS
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RESEARCH chance in the answers.25 In addition, coaches in both groups were asked not to seek nutrition information from outside sources during the project, although this does not guarantee that other sources were not consulted. However, the possibility that coaches have learned from outside sources should be the same in both groups.
CONCLUSIONS Coaches have significant influences on their athletes, even in the area of sports nutrition, thereby providing great opportunities for nutrition education. Results suggest that using a decision-making algorithm as part of a theory-based intervention appears to be effective for maintaining coaches’ knowledge in sports nutrition over time and to help them to provide better recommendations on sports nutrition. This study shows that simple tools can be effective at facilitating evidence-based nutrition practices through coaches in a sports environment involving young athletes. Therefore, the development and availability of effective and practical sports nutrition tools for coaches, athletes, and parents are encouraged to promote not only athletic performance, but also healthy dietary habits in the sports environment.
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Cockburn E, Fortune A, Briggs M, Rumbold P. Nutritional knowledge of UK coaches. Nutrients. 2014;6(4):1442-1453.
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Torres-McGehee TM, Pritchett KL, Zippel D, Minton DM, Cellamare A, Sibilia M. Sports nutrition knowledge among collegiate athletes, coaches, athletic trainers, and strength and conditioning specialists. J Athl Train. 2012;47(2):205-211.
30.
Sheeran P, Trafimow D, Armitage CJ. Predicting behaviour from perceived behavioural control: Tests of the accuracy assumption of the theory of planned behaviour. Br J Soc Psychol. 2003;42(Pt 3): 393-410.
31.
Lun V, Erdman KA, Reimer RA. Evaluation of nutritional intake in Canadian high-performance athletes. Clin J Sport Med. 2009;19(5): 405-411.
32.
Hardeman W, Johnston M, Johnston D, Bonetti D, Wareham N, Kinmonth AL. Application of the theory of planned behaviour in behaviour change interventions: A systematic review. Psychol Health. 2002;17:123-158.
7.
8.
Hassapidou MN, Valasiadou V, Tzioumakis L, Vrantza P. Nutrient intake and anthropometric characteristics of adolescent Greek swimmers. Nutr Diet. 2002;59(1):38-42.
9.
Nikic M, Pedisic Z, Satalic Z, Jakovljevic S, Venus D. Adequacy of nutrient intakes in elite junior basketball players. Int J Sport Nutr Exerc Metab. 2014;24(5):516-523.
10.
Gibson JC, Stuart-Hill LA, Pethick W, Gaul CA. Hydration status and fluid and sodium balance in elite Canadian junior women’s soccer players in a cool environment. Appl Physiol Nutr Metab. 2012;37(5): 931-937.
11.
Rivera-Brown AM, De Felix-Davila RA. Hydration status in adolescent judo athletes before and after training in the heat. Int J Sports Physiol Perform. 2012;7(1):39-46.
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RESEARCH AUTHOR INFORMATION R. Jacob is a doctoral candidate, Institute of Nutrition and Functional Foods, School of Nutrition, Laval University, Québec City, Québec, Canada; at the time of the study, she was a master’s of science student, Institute of Nutrition and Functional Foods, School of Nutrition, Laval University, Québec City, Québec, Canada. B. Lamarche is a professor and V. Provencher is an associate professor, both at the Institute of Nutrition and Functional Foods, School of Nutrition, Laval University, Québec City, Québec, Canada. C. Laramée is a research assistant, Institute of Nutrition and Functional Foods, School of Nutrition, Laval University, Québec City, Québec, Canada; at the time of the study, she was a master’s of science student, Institute of Nutrition and Functional Foods, School of Nutrition, Laval University, Québec City, Québec, Canada. P. Valois is a professor, Department of Educational Fundamentals and Practices, and C. Goulet is a professor and V. Drapeau is an associate professor, Physical Education Department, PEPS, all at Laval University, Québec City, Québec, Canada. Address correspondence to: Vicky Drapeau, PhD, RD, Physical Education Department, PEPS, Laval University (bur. 2214), 2300, rue de la Terrasse, Quebec City, Québec, Canada G1V 0A6. E-mail: [email protected]
STATEMENT OF POTENTIAL CONFLICT OF INTEREST No potential conflict of interest was reported by the authors.
FUNDING/SUPPORT This study was funded by the Danone Institute of Canada.
ACKNOWLEDGEMENTS The authors would like to personally thank all the coaches for their participation and Maya Purcell, MA, for her contribution to the evaluation of the advice provided by coaches.
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Original Research: Brief
Evaluation of a Theory-Based Intervention Aimed at Improving Coaches’ Recommendations on Sports Nutrition to Their Athletes Raphaëlle Jacob, MSc, RD*; Benoît Lamarche, PhD; Véronique Provencher, PhD, RD*; Catherine Laramée, MSc, RD*; Pierre Valois, PhD; Claude Goulet, PhD; Vicky Drapeau, PhD, RD* ARTICLE INFORMATION Article history: Submitted 25 September 2015 Accepted 4 April 2016 Available online 24 May 2016
Keywords: Sports nutrition Coaches Theory-based intervention Knowledge Health promotion 2212-2672/Copyright ª 2016 by the Academy of Nutrition and Dietetics. http://dx.doi.org/10.1016/j.jand.2016.04.005 *
Certified in Canada.
ABSTRACT Background Coaches are a major source of nutrition information and influence for young athletes. Yet, most coaches do not have training in nutrition to properly guide their athletes. Objective The aim of this study was to evaluate the effectiveness of an intervention aimed at improving the accuracy of coaches’ recommendations on sports nutrition. Design This was a quasi-experimental study with a comparison group and an intervention group. Measurements were made at baseline, post-intervention, and after a 2-month follow-up period. Coaches’ recommendations on sports nutrition during the follow-up period were recorded in a diary. Participants/setting High school coaches from various sports (n¼41) were randomly assigned to a comparison group or an intervention group. Intervention Both groups attended two 90-minute sessions of a theory-based intervention targeting determinants of coaches’ intention to provide recommendations on sports nutrition. The intervention group further received an algorithm that summarizes sports nutrition guidelines to help promote decision making on sports nutrition recommendations. Main outcome measures Nutrition knowledge and accuracy of coaches’ recommendations on sports nutrition. Statistical analysis performed c2 analyses and t-tests were used to compare baseline characteristics; mixed and general linear model analyses were used to assess the change in response to the intervention and differences in behaviors, respectively. Results Coaches in the intervention vs comparison group provided more nutrition recommendations during the 2-month post-intervention period (mean number of recommendations per coach 25.722.0 vs 9.46.5, respectively; P¼0.004) and recommendations had a greater accuracy (mean number of accurate recommendations per coach 22.419.9 [87.1%] vs 4.33.2 [46.1%], respectively; P<0.001). Knowledge was significantly increased post-intervention in both groups, but was maintained only in the intervention group during the 2-month follow-up (Pgroup*time¼0.04). Conclusions A theory-based intervention combined with a decision-making algorithm maintained coaches’ sports nutrition knowledge level over time and helped them to provide more accurate recommendations on sports nutrition. J Acad Nutr Diet. 2016;116:1308-1315.
S
PORTS NUTRITION PLAYS AN IMPORTANT ROLE IN athletic performance.1 Adolescent athletes have particular dietary needs to meet requirements of daily training, but also to ensure optimal growth and health.2 A significant proportion of adolescent athletes have a relatively low energy intake, inadequate intakes of certain nutrients, or adopt dietary practices that are not always in accordance with guidelines.3-6 It has been reported that although young athletes generally have adequate protein intakes, they do not consume enough carbohydrates daily4-9 and have suboptimal hydration status both before and during training.10-12 1308
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Coaches are recognized as a major source of information and influence for young athletes regarding food and supplement choices, as well as nutritional behaviors.13-17 Yet most coaches do not have specific or formal training in nutrition, and studies have characterized their knowledge in sports nutrition as being insufficient to properly guide their athletes on this topic.13,17-19 It has been reported that 46% of high school coaches intended to recommend consumption of foods rich in carbohydrates to their athletes, while proportions were 45% for foods rich in proteins and 92% for hydration (R. Jacob, MSc, RD; B. Lamarche, PhD; V. Provencher, PhD, RD; É. Morissette, MSc, RD; S. Couture, MSc, RD; ª 2016 by the Academy of Nutrition and Dietetics.
RESEARCH P. Valois, PhD; C. Goulet, PhD; V. Drapeau, PhD, RD: unpublished data, May 2011). Based on the theory of planned behavior (TPB), subjective norm (ie, the perceived social pressure to engage or not to engage in a behavior) was also identified as the main significant determinant of coaches’ intention to recommend carbohydrates and proteins (R. Jacob, MSc, RD; B. Lamarche, PhD; V. Provencher, PhD, RD; É. Morissette, MSc, RD; S. Couture, MSc, RD; P. Valois, PhD; C. Goulet, PhD; V. Drapeau, PhD, RD: unpublished data, May 2011). In order to develop a successful intervention, it is appropriate to rely on theoretical frameworks (such as the TPB), because these models have been identified as a factor of the success of an intervention.20 To the best of our knowledge, no study has yet investigated the effectiveness of a theory-based intervention designed to specifically improve the accuracy of coaches’ recommendations on sports nutrition to their athletes and to enhance their nutrition knowledge. Based on our previous work, the main purpose of this study was to evaluate the effectiveness of a TPB-based intervention combined with a decision-making algorithm, aimed at improving coaches’ recommendations on sports nutrition to their athletes compared with a TPB-based intervention only. The intervention was also intended to increase sports nutrition knowledge among coaches. It was hypothesized that adding the use of a decisionmaking algorithm that informs on proper sports nutrition recommendations improves nutrition knowledge retention as well as the number and accuracy of recommendations to a greater extent than a theory-based intervention only.
METHODS Participants Coaches of athletes aged 12 to 17 years were recruited through e-mail and phone contact within local competitive sport communities in 2013. To take part in this study, coaches had to work with adolescent athletes during the entire length of the study. Participants were met by the study coordinator to explain the study purpose and procedures. The Research Ethics Committee of Laval University approved study procedures and written informed consent was obtained from all participants before the study. All participants received a gift certificate at a local sports store at the end of the study. Participants were randomly assigned to the comparison group (ie, an intervention based on the TPB) or the intervention group (ie, the same intervention with the addition of a decision-making algorithm regarding recommendations on sports nutrition). Randomization was stratified by types of sports (aesthetic, eg: gymnastic, cheerleading, figure skating; endurance, eg: cycling, cross-country skiing, triathlon; or power/team, eg: swimming, basketball, tennis) to ensure similar numbers in each group because dietary needs might differ more between the types of sports than within sports of the same type. Coaches from the same team or club were randomized as a “group” into either condition to reduce contamination bias. Coaches were not aware of the two conditions.
Theory-Based Intervention Common to Both Groups Participants from both groups attended two 90-minute meetings delivered during a 2-week period, during which specific determinants of coaches’ intention to recommend August 2016 Volume 116 Number 8
different sports nutrition recommendations were targeted. Participants received nutrition information about current recommendations for healthy eating and sports nutrition in adolescent athletes with focus on macro- and micronutrients and hydration to achieve and maintain good health; carbohydrates, proteins and lipids, and their food sources, with particular focus on recommendations related to consumption of carbohydrates to achieve training requirements and sport performance; optimal hydration practices to achieve training requirements and sport performance; dietary and hydration strategies before, during, and after training and competitions; optimal food choices while eating out; and relevance of optimizing dietary strategies vs supplement use in improving performance in young athletes. The intervention was developed and implemented by a registered dietitian (RD). More specifically, the intervention was a lecture-based format with interactions between participants and the RD. Two specific behavior change strategies were used in both groups to improve coaches’ recommendations on sports nutrition to their athletes. In order to induce more rational decision making in coaches, strategies aimed at ensuring that subjective norm had less influence on the intention to pursue this behavior were used. First, persuasive communication, which involved use of arguments and repeated exposure to the message, was used to establish a positive attitude (ie, subjective analysis of advantages and disadvantages related to a given behavior) toward the impact of appropriate dietary and hydration practices on athletic performance, with focus on carbohydrate consumption as a major source of energy.20 Resistance to social pressure, which represents another specific behavior change strategy, was also used to enable coaches to convince parents and athletes of the importance of sound dietary strategies to improve sport performance.20
Intervention Group In the intervention group, all coaches also received, at the end of the second meeting, an algorithm aimed at facilitating decision making regarding sports nutrition recommendations in order to help coaches increase their perceived behavioral control (ie, perceived level of ease or difficulty regarding the adoption of a given behavior) over their nutrition recommendations. A decision-making algorithm is defined as a type of advance organizer,21 which is a schematic representation of a content, that is used to increase knowledge.20 Knowledge can positively influence perceived behavioral control by reducing barriers related to the lack of information or abilities and increasing the perceived ease to perform a behavior.22 More specifically, this decision-making algorithm illustrated evidence-based sports nutrition information in specific contexts (eg, before, during, and after training and competition). It also presented an example of an optimal plate for an athlete and provided examples of food sources of carbohydrates, proteins, best sources of lipids, and suggestions for recovery snacks, what to eat during effort/competition, and what to eat in particular situations (eg, not hungry before a competition). A different decision-making algorithm was made for each type of sports and coaches were provided with one algorithm. Understanding of the decision-making algorithm by coaches was reinforced through case studies.20 JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS
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RESEARCH Measures Coaches completed web-based questionnaires assessing sociodemographic characteristics, nutrition knowledge, and their intention to provide different dietary recommendations to their athletes. Questionnaires were completed at baseline, 1 week post-intervention, and after a 2-month follow-up period. Baseline questionnaires were completed in the presence of the research coordinator at the research unit or at the coaches’ workplace. Post-intervention and follow-up questionnaires were completed at home. Coaches were instructed to complete the questionnaires individually and were asked not to use any outside source of nutrition information, such as the Internet. Coaches also completed a sports nutrition diary in which they were asked to write down the dietary recommendations they provide to their athletes during the 2-month follow-up period. The intervention, questionnaires, and diary were first tested in a group of five coaches, providing an opportunity for feedback by coaches and investigators.
hydration. A mean number of recommendations per coach were calculated (ie, total number of recommendations on sports nutrition provided in each group divided by the number of coaches in each group). Accuracy of recommendations on sports nutrition was assessed on a 4-point score (score 0 to 4). More specifically, 2 points were given if the recommendation was in line with current sports nutrition guidelines1 and 2 points were given if the recommendation consisted of an example of a food (1 point) and a quantity of that food (1 point). An accurate recommendation corresponded to an accuracy score of 3. The number and accuracy of recommendations were evaluated independently by two RDs from the research team, and they discussed to achieve a consensus when necessary. When this was not achieved, the opinion of a third RD from the research team was used (ie, for 2.2% for the classification of recommendations [mean number per coach] and 3.4% for the accuracy of recommendations).
Data Analysis Nutrition Knowledge Evaluation The general and sports nutrition knowledge questionnaire has been developed for the purpose of our prior study19 and was based on literature23-25 as well as the investigators’ experiences in sports nutrition. For the present study, 10 questions mainly related to carbohydrates in sports were added to the questionnaire, bringing the number of questions to 69 (Cronbach’s a¼.78). Questions consisted of true or false and multiple-choices questions and were divided into six main subcategories: carbohydrates, proteins, lipids, supplement use, timing and hydration, and others. All questions included a “don’t know” option to minimize guessing.25 Correct answers were scored as 1, and incorrect and “don’t know” answers and missing values were scored as 0. Nutrition knowledge score was calculated as percent of correct answers provided.
Intention Measurement A web-based questionnaire developed according to the TBP guidelines22,26 was used to assess coaches’ intentions to recommend the three following dietary practices to their athletes during the next 3 months: increase consumption of foods rich in carbohydrates to improve sport performance, increase consumption of foods rich in proteins to promote muscle gain, and increase hydration to improve sport performance. These behaviors were based on sports nutrition guidelines and on professional experiences of the study investigators. Intention to recommend these dietary practices was assessed on a 6-point Likert scale (strongly disagree [3] to strongly agree [3]).
Statistical analyses were performed using SAS software.27 c2 analyses and t-test and were used to compare baseline characteristics between the two groups. Mixed models with Tukey’s post hoc test were conducted to assess the changes in nutrition knowledge and intention to recommend the three nutritional practices between groups and over time, using time, groups, and their interaction as fixed effects and subject as random effect. Mixed models included type of sports as a covariate when it showed a significant association with the outcome in the model. General linear model analysis was used to examine differences between groups in the number and accuracy of recommendations provided to athletes during a 2-month post-intervention period. Data are reported as meanstandard deviation, frequency, or as meanstandard error of the mean for knowledge scores. Differences were considered significant at P<0.05. A total of 41 coaches were recruited. Twenty and 21 coaches were randomly assigned to the intervention and comparison groups, respectively. One coach in the comparison group did not participate in the study due to a lack of interest. Another coach in the intervention group quit the study after having attended the two educational sessions because of a time constraint. These two individuals were not included in the analyses. One coach in the comparison group did not complete the diary during the 2-month follow-up due to maternity leave, but attended both meetings and completed all of the questionnaires and was thus included in the analyses. Participation rates at the 2-month follow-up were 95% in both groups.
Behavior Measurement
RESULTS Participant Characteristics
Dietary recommendations made by coaches during the 2-month follow-up period were recorded in a diary developed by the research team. For each recommendation, information regarding type, time, and context was reported by coaches. They were first classified as general nutrition or sports nutrition recommendations. The latter was then further classified into four subcategories related to carbohydrates, proteins, carbohydrates and proteins combined when the recommendation concerned both nutrients, and
Coaches randomized to the intervention group were involved in cycling (n¼2), mountain biking (n¼3), gymnastics (n¼6), swimming (n¼5), triathlon (n¼1), canoe-kayak (n¼1), basketball (n¼1), or ice hockey athletic trainer (n¼1), whereas coaches in the comparison group were involved in cross-country skiing (n¼7), gymnastics (n¼3), cheerleading (n¼4), figure skating (n¼1), tennis (n¼5), or judo (n¼1). Among coaches in the intervention group, 31.6%, 26.3%, and 42.1% were involved in sports considered as aesthetic,
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RESEARCH endurance, and power/team, respectively. A similar distribution was seen among coaches assigned to the comparison group (40.0%, 35.0%, and 25.0% for aesthetic, endurance, and power/team sports, respectively). There was no significant difference in baseline sociodemographic characteristics between groups (Table). Coaches in the comparison group appear to be more involved with athletes of national and international levels compared with coaches in the intervention group, but this difference did not achieve significance (c2[3]¼5.51; P¼0.14).
Nutrition Knowledge Mean nutrition knowledge score at baseline was 72.3% in the intervention group vs 70.0% in the comparison group (t[74]¼0.79; P¼0.97) (Figure 1). Participants in both groups showed a significant increase in nutrition knowledge postintervention compared with baseline values, with scores of 81.7% in the intervention group and 82.7% in the comparison group, indicating no difference between groups 1-week postintervention (t[74]¼9.47; P<0.0001 for within-group differences; t[74]¼0.51; P¼1.0 for between-group differences). However, this increase in nutrition knowledge was maintained during the 2-month follow-up period in the intervention group only, with scores of 81.5% in the intervention group and 78.0% in the comparison group (F[2, 74]¼3.36; Pgroup*time¼0.04).
Table. Baseline characteristics of coaches in the intervention (n¼19) and comparison (n¼20) groups of a theory-based intervention aimed at improving coaches’ recomendations on sports nutritiona Characteristics
Intervention Comparison P value meanstandard deviation!
Age, y
26.15.3
28.611.4
0.39
Years in sport
8.65.6
10.09.6
0.61
n (%)! Sex of coaches Male
47.4
45.0
Female
52.6
55.0
High school
10.5
15.8
College
31.6
47.4
University
57.9
36.8
None
15.8
10.0
Levels 1 to 2
42.1
45.0
Coaches’ intention to recommend the consumption of foods rich in carbohydrates to their athletes increased over time in both groups compared to baseline (F[2, 73]¼6.12; Ptime¼0.004; F[2, 73]¼0.02; Pgroup*time¼0.98), but this increase was not maintained during the 2-month postintervention period. Coaches’ intention to recommend the consumption of foods rich in proteins tended to decrease in both groups after the intervention, but this did not achieve significance (F[2, 70]¼2.85; Ptime¼0.06; F[2, 70]¼0.06; Pgroup*time¼0.94). Coaches’ intention to recommend hydration did not change over time in both groups (F[2, 69]¼1.30; Ptime¼0.28; F[2, 69]¼0.60; Pgroup*time¼0.55) (data not shown).
Levels 3 to 4
42.1
45.0
Aesthetic
31.6
40.0
Endurance
26.3
35.0
Power/team sport
42.1
25.0
Behavior
Competitive level
Coaches in the intervention group provided more recommendations on sports nutrition than those in the comparison group (mean number of recommendations per coach¼25.722.0 [range¼8 to 103] vs 9.46.5 [range¼2 to 26], respectively; F(1, 36)¼9.67; P¼0.004) during the 2-month post-intervention period (Figure 2). Recommendations related to consumption of carbohydrates and hydration were provided more frequently among coaches in the intervention group than those in the comparison group (6.65.2 [range¼1 to 21] vs 3.33.0 [range¼0 to 12], respectively for carbohydrates, F[1, 36]¼5.83; P¼0.02 and 13.812.9 [range¼2 to 56] vs 3.32.7 [range¼1 to 11], respectively for hydration, F[1, 36]¼12.14; P¼0.001). The accuracy of recommendations on sports nutrition was also greater in the intervention group than in the comparison group (mean number of accurate recommendations per coach 22.419.9 [range¼7 to 93] vs 4.33.2 [range¼0 to 13], respectively; F[1, 36]¼15.39; P<0.001). Indeed, 87.1% of recommendations on sports nutrition made by coaches in the intervention
Local
0.0
0.0
Regional
5.3
10.0
Provincial
57.9
25.0
National
31.6
40.0
5.3
25.0
Intention
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0.88
Coaches’ education level 0.43
NCCPb level 0.86
Sports groups 0.53
Sex of athletes trained Male
5.3
0.0
Female
26.3
10.0
Male and female
68.4
90.0
International
0.22
0.14
Competitive network Civilc
73.7
70.0
Schoold and civil
21.1
30.0
5.3
0.3
School
0.50
a
Intervention and comparison group received the same two 90-min sessions of a theory-based intervention on sports nutrition and a decision-making algorithm on sports nutrition was provided only for the intervention group. b NCCP¼National Coaching Certification Program. c Competitions between sports clubs from a same city or from different cities, regions, provinces or countries. d Competition between schools.
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RESEARCH 85
a
Pgroup*time=0.04
% nutrition knowledge score
83 a
81
a
79 ab
77
comparison intervention
75 73 71 69 67 65
Baseline
Post-intervention
Follow-up
Figure 1. Sports nutrition knowledge score (meanstandard error of the mean) at baseline, post-intervention, and after the 2month follow-up period in the intervention (n¼19) and comparison (n¼20) groups of high school coaches. Intervention and comparison group received the same two 90-minute sessions of a theory-based intervention on sports nutrition and a decisionmaking algorithm on sports nutrition was provided only for the intervention group. aSignificant difference between post-intervention and baseline. bSignificant difference between follow-up and post-intervention. group were evaluated as being accurate compared to 46.1% in the comparison group. Specifically, the mean number (range and percent) of accurate recommendations per coach was higher in the intervention group compared to the comparison
group for the recommendations related to carbohydrate consumption (5.24.7 [range¼0 to 19; 77.7%] vs 1.31.8 [range¼0 to 6; 38.1%], respectively; F[1, 36]¼11.18; P¼0.002), recommendations on both carbohydrate and protein
30 P=0.004 25
Intervention
Mean number of recommendations
Comparison 20
P=0.001
15
10 P=0.02 P=0.07 5 P=0.2 0 Sports nutrition Carbohydrates
Proteins
Carbohydrates and proteins
Hydration
Figure 2. Mean number of recommendations on sports nutrition made by coaches during the 2-month follow-up period in the intervention (n¼19) and comparison (n¼19) groups. Intervention and comparison group received the same two 90-minute sessions of a theory-based intervention on sports nutrition and a decision-making algorithm on sports nutrition was provided only for the intervention group. 1312
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RESEARCH consumption (3.94.6 [range¼0 to 18; 77.1%] vs 1.11.6 [range¼0 to 6; 51.2%], respectively; F[1, 36]¼6.19; P¼0.02) and recommendations on hydration (13.312.9 [range¼2 to 56; 96.2%] vs 1.91.4 [range¼0 to 4; 58.7%], F[1, 36]¼14.63; P<0.001), respectively.
DISCUSSION The main purpose of this study was to evaluate the effectiveness of a TPB-based intervention, combined with a decision-making algorithm, aimed at supporting coaches in providing adequate general sports nutrition recommendations to their adolescent athletes. A secondary objective was to increase coaches’ knowledge in sports nutrition. The results suggest that complementing a theory-based intervention with a decision-making algorithm that targets perceived behavioral control improves the number and accuracy of recommendations on sports nutrition that coaches make to their athletes compared with the same intervention without a decision-making algorithm. Baseline sports nutrition knowledge of high school coaches was similar to data from our previous study (68%)19 and other studies (70% and 60%).17,28 Baseline knowledge was also lower than the threshold of 75% used by one study to suggest an adequate level of knowledge.29 Thus, the relatively low nutrition knowledge score among coaches emphasizes the need for more adequate education in this area. The intervention was effective in both groups in improving knowledge, and data showed that adding the use of a decision-making algorithm on sports nutrition recommendations as part of the theory-based intervention contributed to better knowledge retention during a 2-month postintervention period. This suggests that coaches could have relied on this tool to remember the sports nutrition guidelines after the intervention. The most important added effect of the use of a decisionmaking algorithm vs no algorithm was the higher number and accuracy of coaches’ recommendations on sports nutrition, and recommendations specifically related to carbohydrates, provided during the 2-month post-intervention period. The results also showed an increase in coaches’ intention to recommend the consumption of foods rich in carbohydrate after the intervention in both groups. Two hypotheses can be proposed to explain these effects. First, the decision-making algorithm may have helped coaches to implement their intention to provide dietary recommendations to their athletes. Fishbein and Ajzen22 reported that failure to act on an existing intention is often due to a low actual or perceived control over the behavior. Thus, the decision-making algorithm could have helped coaches to act directly on their intention by increasing their skills and abilities to provide sports nutrition recommendations. Increasing skills and abilities could also have an indirect effect by increasing the accuracy of judgment of control (actual control) and, thus, could strengthen the perceived behavioral controlbehavior relation.30 The second hypothesis is that the decision-making algorithm may have attenuated the impact of perceived barriers, such as a lack of knowledge, on the behavior. Indeed, as discussed previously, knowledge can positively influence perceived behavioral control by reducing barriers related to the lack of information and increasing the perceived ease and self-efficacy to perform a behavior.22,30 A August 2016 Volume 116 Number 8
higher self-efficacy could then have a direct influence on the behavior.30 These hypotheses need to be validated in future studies. Both conditions—that is, adding the use of the decisionmaking algorithm or not—had the same effects on the intention toward the recommendations of proteins and hydration. The apparent, but not significant, decrease in coaches’ intention to recommend the protein consumption may be explained by the emphasis given during the intervention on the fact that athletes already reach their daily protein needs only from food.31 The low number of protein recommendations provided during the 2-month follow-up period is consistent with this result. The absence of the intervention effect on coaches’ intention to recommend hydration can be explained by the fact that this intention was already high at baseline in both groups. However, in contrast to the comparison group, a higher frequency and accuracy of hydration recommendations provided by coaches was observed in the intervention group during the 2-month follow-up period. Thus, the decision-making algorithm seems to have helped coaches to implement their favorable intention and accurately recommend hydration to their athletes by reducing the barriers related to the lack of knowledge or skills.
Strengths and Limitations This study has several strengths and limitations. To our knowledge, this is the first study to assess the effect of an intervention designed to promote coaches’ accurate recommendations on sports nutrition, based on the determinants influencing coaches’ recommendations on sports nutrition measured in a previous study (R. Jacob, MSc, RD; B. Lamarche, PhD; V. Provencher, PhD, RD; É. Morissette, MSc, RD; S. Couture, MSc, RD; P. Valois, PhD; C. Goulet, PhD; V. Drapeau, PhD, RD: unpublished data, May 2011). Several studies have evaluated the determinants of intention to adopt a behavior in various domains, but very few studies developed subsequent interventions to encourage the desired behaviors.32 The 2-month follow-up evaluation of the actual behavior was also a strength because most studies investigating the impact of an intervention on behaviors assessed intentions rather than the actual behaviors. The methods used to modify behaviors, intentions, and their determinants and beliefs were based on available literature. On the other hand, the small number of subjects limited some of the analyses. Specifically, data do not inform on how the intervention modified the behavior (ie, coaches’ recommendations on sports nutrition). Because no written document on sports nutrition was provided in the comparison group after the intervention, it is not possible to conclude that the positive results observed in the intervention group are due to the decision-making algorithm “format” or the “sports nutrition information.” The absence of a pre-intervention measure of the behavior or a control group (ie, without intervention) can also represent a limitation of this study. However, the results regarding the intention to recommend carbohydrates (which was not different between groups and low at baseline) suggest that behaviors were similar between groups at baseline and that there was room for improvement. The nutrition knowledge questionnaire involving “true/false/I don’t know” question format could have incorporated an element of JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS
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RESEARCH chance in the answers.25 In addition, coaches in both groups were asked not to seek nutrition information from outside sources during the project, although this does not guarantee that other sources were not consulted. However, the possibility that coaches have learned from outside sources should be the same in both groups.
CONCLUSIONS Coaches have significant influences on their athletes, even in the area of sports nutrition, thereby providing great opportunities for nutrition education. Results suggest that using a decision-making algorithm as part of a theory-based intervention appears to be effective for maintaining coaches’ knowledge in sports nutrition over time and to help them to provide better recommendations on sports nutrition. This study shows that simple tools can be effective at facilitating evidence-based nutrition practices through coaches in a sports environment involving young athletes. Therefore, the development and availability of effective and practical sports nutrition tools for coaches, athletes, and parents are encouraged to promote not only athletic performance, but also healthy dietary habits in the sports environment.
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JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS
August 2016 Volume 116 Number 8
RESEARCH AUTHOR INFORMATION R. Jacob is a doctoral candidate, Institute of Nutrition and Functional Foods, School of Nutrition, Laval University, Québec City, Québec, Canada; at the time of the study, she was a master’s of science student, Institute of Nutrition and Functional Foods, School of Nutrition, Laval University, Québec City, Québec, Canada. B. Lamarche is a professor and V. Provencher is an associate professor, both at the Institute of Nutrition and Functional Foods, School of Nutrition, Laval University, Québec City, Québec, Canada. C. Laramée is a research assistant, Institute of Nutrition and Functional Foods, School of Nutrition, Laval University, Québec City, Québec, Canada; at the time of the study, she was a master’s of science student, Institute of Nutrition and Functional Foods, School of Nutrition, Laval University, Québec City, Québec, Canada. P. Valois is a professor, Department of Educational Fundamentals and Practices, and C. Goulet is a professor and V. Drapeau is an associate professor, Physical Education Department, PEPS, all at Laval University, Québec City, Québec, Canada. Address correspondence to: Vicky Drapeau, PhD, RD, Physical Education Department, PEPS, Laval University (bur. 2214), 2300, rue de la Terrasse, Quebec City, Québec, Canada G1V 0A6. E-mail: [email protected]
STATEMENT OF POTENTIAL CONFLICT OF INTEREST No potential conflict of interest was reported by the authors.
FUNDING/SUPPORT This study was funded by the Danone Institute of Canada.
ACKNOWLEDGEMENTS The authors would like to personally thank all the coaches for their participation and Maya Purcell, MA, for her contribution to the evaluation of the advice provided by coaches.
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