Abstract
Background
Measuring pre-service dieticians’ sport nutrition knowledge levels seems important in order to determine their real educational needs, monitor the improvements and make required adjustments to the educational process. However, there is no valid and reliable sport nutrition knowledge test for pre-service dieticians. This study aimed to develop a valid and reliable sport nutrition knowledge test (SNK) for Turkish pre-service dieticians.
Methods
Participants were 201 pre-service dieticians who had successfully completed sport nutrition course at their departments previously. Rasch modeling was utilized to evaluate the validity and reliability of the sport nutrition knowledge test. Indices of item fit, person fit, person item/wright maps, separation index and separation-reliability index were checked to confirm test’ validity and reliability.
Results
Results showed that item difficulty and item discrimination of the SNK test met the required standards for educational assessment. According to fit statistics, all items were within acceptable MNSQ (0.5 to 1.5) and ZSTD (+ 2.0 to -2.0) ranges, except four items (items 1, 13, 16 and 26). Moreover, high item and person reliability indices and excellent level of item and person separation indices were determined.
Conclusions
SNK test for pre-service dieticians has been developed and validated using multiple relevant methods. The test can be used to assess the SNK levels of pre-service dieticians and advocate for the importance of adding and/or increasing a specific sport nutrition education for whom working with athletic population.
Supplementary Information
The online version contains supplementary material available at 10.1186/s13102-026-01596-x.
Keywords: Dietician, Department of dietetics, Rasch modelling, Pre-service education
Introduction
Nutrition knowledge (NK) has been suggested as an important phenomenon which play an essential role to follow appropriate dietary behaviors and practices [1, 2]. Yet, there have still been rooms for improvements considering the inadequate NK and misunderstanding of general and sport-specific NK in different populations (e.g., adults, athletes) [3–5]. NK refers to the knowledge of nutrients and nutrition [6], which may affect health status of individuals considering the potential impact of NK on eating habits and practices. Within this conceptualization, general nutrition knowledge (GNK) and sports nutrition knowledge (SNK) are not identical to each other with an obvious interaction though. GNK refers to knowledge on healthy eating and nutrition-related disease prevention, while SNK refers to knowledge on dietary concepts with an emphasis on sport performance and recovery [7]. From a psychometric standpoint, SNK is defined as a multidimensional latent construct encompassing knowledge related to sport-specific nutrients, hydration strategies, supplementation, nutrition timing, and dietary practices [8, 9]. This construct has been operationalized in prior research through rigorously developed and validated measurement instruments, demonstrating acceptable reliability and construct validity and enabling objective quantification of SNK [5, 8–10].
In SNK, there appears an obvious stress on supporting individuals to meet nutritional requirements for sport, exercise and physical activity, optimizing dietary intake before, during and following activities and improving athletic performance [11]. Considering these concepts and needs, sport participants and athletes are stated as better to have adequate SNK and easily accessible resources for guidance [12].
In sport settings coaches, strength and conditioning specialists and registered dieticians (RDs) are the first group of staff to come to mind as sources of such guidance and SNK [13–15]. Yet, expertise and specializations of them should be considered comprehensively while aiming to reach the most appropriate guidance. Coaches and strength and conditioning specialists may guide sport participants and athletes in settings with no easy access to RD and/or nutritionist. Studies indicated that coaches did not have sufficient SNK level [15–17]. In addition, athletes’ SNK has been determined inadequate in the relevant literature too [15, 17–19]. Nevertheless, RDs are the primary resources for sport nutrition guidance for sport participants. Due to the lack of adequate SNK of athletes and sport participants and their dietary behaviors and practices, RDs may need to be questioned whether they meet the requirements of professing as an RD in the field.
In the department of nutrition and dietetics at higher education institutions, teaching sports nutrition to pre-service dietitians is crucial, as it equips them with the knowledge and skills necessary to provide evidence-based dietary guidance to physically active populations [5]. Pre-service dietitians are increasingly expected to work in diverse settings such as sports clubs, fitness centers, and federations, where clients often seek advice on performance optimization, recovery, and safe supplementation [20]. Research shows that many athletes and coaches lack adequate nutrition knowledge and frequently rely on registered dietitians for reliable, evidence-based guidance [3, 5]. In this context, a strong foundation in sports nutrition is essential to prevent the dissemination of misinformation and to promote scientifically sound dietary practices [20]. Measuring pre-service dietitians’ sports nutrition knowledge not only helps identify gaps in understanding but also provides a basis for tailoring educational interventions, monitoring improvements, and ensuring readiness for professional practice [21]. In Turkey, sports nutrition is offered only as an elective course in dietetics programs, and there are currently no mandatory prerequisites or competency criteria for graduates entering sports-related roles. Given the prevalence of myths and misconceptions in the field, assessing and strengthening sports nutrition knowledge in pre-service dietitians is therefore vital to ensure competent, ethical, and evidence-based practice [22]. To assess SNK of those who practice or disseminate such knowledge accurately, several tools have been developed and validated in the relevant literature for different groups such as athletes [9, 23], coaches [12] and adolescents [9]. Yet, to the best of our knowledge, no valid test has been developed to assess SNK of pre-service dieticians and nutritionists.
Considering the importance of establishing SNK, monitor the improvements and make the required adjustments for the education programs, the need for developing a valid and reliable test for pre-service dieticians has become vital. Thus, the present study aims to develop a valid and reliable SNK test for Turkish pre-service dieticians.
Methods
Ethical considerations
The ethics statement was provided from the institutional review board of Karamanoğlu Mehmetbey University (Decision no 06-2022/126). Informed consent was obtained from all participants with online form which explained aim of the study. Participants were informed that the study had no risk and they could leave the study at any time. The study was conducted in adherence to the Declaration of Helsinki guidelines. Participants were also informed that their names would not be requested in the study and that the data obtained would be used solely by the principal author for purely scientific purposes. Participants did not receive any compensation or incentives.
Setting
In Türkiye, in order to be a licensed dietician, completing a 4-years bachelor’s degree at the Department of Nutrition and Dietetics of the Faculty or School of Health Sciences at higher education institutions is a prerequisite [24].
A four-years bachelor’s degree is sufficient for individuals to work professionally after graduation and to receive titles such as clinical dietician, cardiology dietician, administrative dietician and sport dietician, and no additional certificate program is required [25, 26]. During the undergraduate education period, the core curriculum of Nutrition and Dietetics program consists of five main areas; (I) basic knowledge and skills, (II) nutritional sciences, (III) dietetics, (IV) community nutrition and (V) collective nutrition systems [27]. Courses related to exercise and nutrition are provided within the nutritional sciences area, and they are in the group of elective courses. In particular, “exercise and nutrition” and “sports nutrition” are given as two different courses in some universities, while in others only “sports nutrition” course is provided.
In order to graduate, the student who receives nutrition and dietetics education must complete his/her internship in the places that meet the criteria in the core curriculum at the end of the third and the fourth grade. Internships are compulsory and there are five different internship programs as clinical nutrition adult internship, clinical nutrition child internship, collective nutrition systems internship, community health nutrition internship and external institution internship [28]. Students are required to complete their internships separately in each of these programs. In particular, if a student is interested in sports nutrition, s/he could prefer to do his/her internship in a sport club or sport center as an external institution internship under the condition that the center has been accepted and approved by the university.
Study design
Convenience sampling method was utilized for this study. Convenience sampling is used as a data collection strategy in which participants are drawn from a research population that is readily accessible to the researcher. This approach exemplifies non-probability sampling and is conceptually distinct from probability-based sampling methods [29]. This method was utilized because pre-service dietitians are a specific group and it is difficult to collect data from pre-service dietitians who completed sport nutrition course.
Participants
A total of 201 pre-service dieticians studying at Department of Nutrition and Dietetics at 29 universities in Türkiye participated in the study, voluntarily. These 29 Department of Nutrition and Dietetics accept their undergraduate students with similar ways. Their age ranged from 19 to 29 (Mage= 24.53 SD = 4.49) years. Of participants, 178 were females, 22 were males, and one participant did not want to state his/her gender. Sample size for Rasch modelling is expected to be more than 100 participants [30]. Our sample size is adequate for this Rasch modelling analysis.
Participants were invited to study with departmental mailing lists. Inclusion criteria were (a) Successfully completing Sport Nutrition course, (b) Being volunteer to participate. Exclusion criteria were (a) Not completing Sport Nutrition course or being unsuccessful, (b) Being not volunteer to participate. All mailed participants met required inclusion criteria and no participants were excluded from the study.
Test development procedure
During the literature review, some validated SNK tests [9, 23, 31] have been recognized; however, to the best of our knowledge, no valid and reliable SNK test for measuring pre-service dieticians is available. This lack and the need for such a test to better manage the teaching-learning process led to the test development process. Four steps were followed to develop the test. First of all, a panel including four professors lecturing SNK course (three in Türkiye and one in UK who is also International Olympic Committee certified and registered sports nutritionist), a professor having expertise on curriculum development and a Turkish language expert was consisted. They met together and discussed on construct definition, and possible outcomes of sport nutrition course. This group determined that SNK test for pre-service dieticians should include eight main topics as follows; general sport nutrition principles, carbohydrates, proteins, fats, vitamins-minerals, fluid-water, nutritional ergogenic aids, nutrition pre-, during and post-competition or/and training. Each group member prepared a least five items for each main topics of SNK course. Then they met together again and compared their items. After this discussion, they concluded 34 items, including six questions for general sport nutrition principles and four questions for other each main topics. In the second step, the group checked all questions according to the expected outcomes. Draft test was given to two professors having expertise on sport nutrition. They checked the questions and decided that six questions should be removed because they are too easy (n = 2), too difficult (n = 1) or inappropriate (n = 3) for the pre-service dieticians. The third step was possible participant check. The last version of the test was given to another 20 pre-service dieticians who had already completed elective sport nutrition course. This group read the test in terms of its readability and comprehensibility. They stated that all questions were straightforward and understandable for pre-service dieticians. At the fourth and last step, final version of the test was determined as 28 questions (six questions for general sport nutrition principles and three questions for other each main topics) consisting of multiple-choice with four alternative options, yet there is only one correct answer. Examples of questions are presented below in Table 1. In addition, data were collected online.
Table 1.
Example questions from each category of the SNK test. Correct answers are underlined
| General sport nutrition principles |
| Which of the following is recommended for an athlete who wants to lose weight while preserving muscle mass? |
| a. Losing an average of 0.5–1 kg per week during the off-season |
| b. Losing an average of 1–1.5 kg per week during the season |
| c. Losing an average of 1.5–2 kg per week during the off-season |
| d. Losing an average of 2–2.5 kg per week during the season |
| Carbohydrates |
| Where are carbohydrates stored most abundantly in the human body? |
| a. As glucose in the blood |
| b. As glycogen in the liver |
| c. As glycogen in the skeletal muscles |
| d. As glycogen in the blood |
| Fats |
| Which of the following is the nutrient used predominantly to obtain energy during long-term and low-intensity exercise? |
| a. Carbohydrate |
| b. Protein |
| c. Vitamin-mineral |
| d. Fat |
| Proteins |
| Which protein is recommended before sleep due to its slow digestion and effectiveness in increasing muscle protein synthesis overnight? |
| a. Gluten |
| b. Whey |
| c. Casein |
| d. Albumin |
| Vitamins & Minerals |
| Which group of minerals, when deficient, may cause muscle cramps? |
| a. Iron-Calcium-Zinc-Sodium |
| b. Calcium-Magnesium-Sodium-Potassium |
| c. Iron-Zinc-Potassium-Phosphorus |
| d. Calcium-Sodium-Copper-Zinc |
| Fluid& Water |
| Which of the following statements is definitely false? |
| a. Magnesium is the electrolyte lost the highest quantities through sweat |
| b. The athlete must consume 1–1.5 L of fluid for each kilogram of body weight lost after the exercise or/and event. |
| c. The feeling of thirst begins after dehydration |
| d. Dehydration can be a cause of muscle cramps |
| Nutritional ergogenic aids |
| Which of the following is not a scientifically proven nutritional ergogenic aid that enhances athletic performance? |
| a. Creatine |
| b. Caffeine |
| c. Carnitine |
| d. Sports gels |
| Pre- during post- competition or training |
| Which method should be followed after a competition to promote recovery, replenish glycogen stores, and stimulate muscle protein synthesis? |
| a. High protein and high fat foods should be consumed together. |
| b. High carbohydrate and protein foods should be consumed together. |
| c. Low protein and low carbohydrate foods should be consumed together |
| d. Low-carbohydrate and high-protein foods should be consumed together. |
Data were collected from participants and transferred from excel spreadsheet to Winsteps Software Version 3.72.4 which is used for Rasch measurement [32]. Rasch modelling measures the model in terms of a participant’s probability of answering a question depends on the difficulty of the item and the participant’s ability [33]. It is different from other traditional measurement models because it checks the data as ‘fit the model’, not the data as “fit the data” which traditional methods use [33]. In this study, we used dichotomous Rasch modelling because items in this test have only two response options [34].
The model includes five basic levels of analysis;
-
Item fit: Item fit statistic was determined by infit and outfit statistics [35]. Infit statistics scores are delicate to expectations regarding participants’ potential responses. Outfit statistics are sensitive to participants’ unexpected answers. In an example, if a participant had high knowledge in sport nutrition and answered correctly most of the questions, data showed that model was a good fit. On the other hand, if a participant had no sufficient knowledge about sport nutrition and answered correctly even very difficult questions, the model indicates poor fit [33].
To check whether the model has a good fit or not, the mean square residual (MNSQ) and the standardized mean square residual (ZSTD) are used. MNSQ which concentrates on difference between estimated and monitored responses on the pattern is squared residual. ZSTD is the gauge that utilizes t-score of the residual. In rasch modelling, both MNSQ and ZSTD variables hinge on what is predicted and what is monitored [36].
Person fit: Person-fit statistics which is utilized to determine appropriateness of item-score pattern in the model [33, 35]. Person-fit indices check the relevance of participants’ response scores. MNSQ values, which are expected to be scores between 0.5 and 1.5, are utilized to detect person-fit measurement. If a respondent has lower than 0.5 (e.g. little variation in responses) or higher than 1.5 (e.g. large variation in responses) in mean square statistic of infit and outfit, there is a poor person-fit in model [33].
Person item/Wright maps: Wright maps (also called person-item maps) are the scale measurement method which shows distributions of item difficulties and responses of data collected sample. In viewing of the wright map, the left side of it shows responses of the participants. Participants’ highest scores take on the top of the side while lowest scores take the bottom part of the left side. On the right side, the rank of the item difficulty was demonstrated. Uppermost of the side indicates the most difficult items and lowest section of the side takes the easiest items.
Separation index and separation-reliability index: Item separation index, which provides construct validity of the model, is utilized to confirm hierarchy of low to high difficulty ranking. Person separation index is used to discriminate respondents who have high level knowledge and those haven’t. A person or item separation index scores are presented as, (a) 3.00 or over is excellent level, (b) 2.00 represents good level, and (c) 1.50 is acceptable level [35]. The separation-reliability index is a reliability indicator that is utilized to report the probability of replicating an item’s or person’s placements [35]. Either item or person take score between 0 and 1.00. The closer to 1.00 indicates high degree of confidence [35].
Unidimensionality: The Rasch model assumes unidimensionality, meaning that test items measure a single underlying construct rather than multiple dimensions [33]. In this study, all items targeted to measure pre-service dieticians’ SNK levels. Unidimensionality was assessed through standardized residual analysis, focusing on the eigenvalue of the first contrast with a criterion of 2.0. When this value exceeded the criterion, the standardized residual contrast plot was examined to determine whether items represented distinct dimensions. Residual values between − 0.4 and 0.4 supported the unidimensionality of the test [33].
Some studies in literature proposes one more analysis which is Differential item functioning (DIF) for checking Rasch modelling findings [37]. DIF analysis was not performed in this study because DIF is not a core assumption of the Rasch model. The sample was largely homogeneous across relevant demographic and experiential variables. Furthermore, group comparisons were not a primary objective of the study; therefore, the omission of DIF analysis was not expected to influence interpretation of the Rasch results [33, 35].
Results
The dimensionality of the SNK test was examined through analysis of the unexplained variance in the first residual contrast. The eigenvalue of this contrast was 1.76 which is within expected score of 2.0. Moreover, further evaluation of the standardized residual contrast one plot revealed that item residuals were predominantly distributed within the − 0.4 to 0.4 range. Collectively, these findings provide empirical support for the unidimensionality of the instrument and indicate that it functions as a valid measure of SNK test [33].
Results of infit and outfit values are presented in Table 2. Infit results of MNSQ values were ranged from 1.32 to 0.83 which was within the acceptable values [32]. ZSTD values of infit statistics were acceptable level of 2.0 and − 2.0, except item 1 and item 16 which are more than 2.0 (3.8 and 3.6, respectively). In outfit statistics, MNSQ values of all items were within acceptable range, except item 1 (1.62). Results of ZSTD values showed that four items (items 1, 13, 16 and 26) were out of the acceptable range [28]. Item difficulty of the model was presented in Table 2. Range of item difficulty was from 1.54 to 1.90 (M = 1.66, SD=0.14). This shows that the difficulty of items was in a narrow range of values. Wright maps are represented in Fig. 1. Wright maps clearly indicated that there was a cumulative norm on distribution of items on left side and person on right side. It also shows a good distribution for both person and item separations.
Table 2.
Item difficulty, standard error, fit and point-measure correlation
| Entry Number | Item Difficulty | Model Standard Error | Infit | Outfit | PT- measure | ||
|---|---|---|---|---|---|---|---|
| MNSQ | ZSTD | MNSQ | ZSTD | ||||
| 1 | 1.67 | 0.23 | 1.32 | 3.8 | 1.62 | 3.8 | 0.05 |
| 13 | 1.66 | 0.22 | 1.15 | 1.9 | 1.4 | 3.1 | 0.24 |
| 26 | 1.64 | 0.21 | 1.09 | 1.2 | 1.37 | 2.9 | 0.29 |
| 16 | 1.54 | 0.21 | 1.2 | 3.6 | 1.31 | 3.4 | 0.22 |
| 27 | 1.54 | 0.2 | 1.08 | 1.5 | 1.17 | 1.9 | 0.32 |
| 5 | 1.54 | 0.2 | 1.06 | 1.1 | 1.16 | 1.9 | 0.34 |
| 6 | 1.55 | 0.19 | 1.06 | 1.1 | 1.15 | 1.6 | 0.33 |
| 3 | 1.64 | 0.19 | 0.98 | − 0.2 | 1.09 | 0.8 | 0.4 |
| 11 | 1.58 | 0.18 | 1.02 | 0.3 | 1.05 | 0.5 | 0.39 |
| 17 | 1.65 | 0.18 | 1.04 | 0.6 | 0.99 | 0 | 0.38 |
| 21 | 1.56 | 0.18 | 1.04 | 0.7 | 0.99 | 0 | 0.37 |
| 9 | 1.62 | 0.17 | 1.02 | 0.3 | 1 | 0 | 0.36 |
| 2 | 1.97 | 0.17 | 1 | 0.1 | 0.92 | − 0.3 | 0.31 |
| 15 | 1.6 | 0.16 | 0.96 | − 0.5 | 0.99 | − 0.1 | 0.43 |
| 20 | 1.54 | 0.17 | 0.99 | − 0.2 | 0.96 | − 0.4 | 0.41 |
| 23 | 1.63 | 0.16 | 0.91 | -1.3 | 0.99 | − 0.1 | 0.43 |
| 8 | 1.54 | 0.15 | 0.97 | − 0.5 | 0.94 | − 0.7 | 0.43 |
| 24 | 1.63 | 0.15 | 0.95 | − 0.9 | 0.91 | -1 | 0.45 |
| 19 | 1.75 | 0.15 | 0.92 | -1 | 0.94 | − 0.3 | 0.41 |
| 10 | 1.63 | 0.14 | 0.94 | − 0.8 | 0.9 | − 0.8 | 0.42 |
| 12 | 1.54 | 0.13 | 0.93 | -1.4 | 0.88 | -1.5 | 0.47 |
| 18 | 1.65 | 0.12 | 0.91 | − 0.7 | 0.72 | -1.2 | 0.39 |
| 22 | 1.69 | 0.12 | 0.9 | -1.3 | 0.8 | -1.5 | 0.45 |
| 4 | 1.9 | 0.13 | 0.9 | − 0.9 | 0.84 | − 0.7 | 0.4 |
| 14 | 1.54 | 0.13 | 0.87 | -2 | 0.83 | -2.1 | 0.52 |
| 25 | 1.68 | 0.14 | 0.87 | -1.8 | 0.79 | -1.6 | 0.48 |
| 7 | 1.8 | 0.15 | 0.85 | -1.7 | 0.73 | -1.7 | 0.47 |
| 28 | 1.9 | 0.15 | 0.83 | -1.6 | 0.71 | -1.5 | 0.46 |
| Mean | 1.66 | 0.16 | 0.99 | 0 | 1.01 | 0.2 | |
| SD | 0.14 | 0.03 | 0.11 | 1.5 | 0.21 | 1.6 | |
MNSQ (mean square residual)
ZSTD (standardised mean square residual)
SD (standard deviation)
Fig. 1.
Person- MAP- Item
Item and person separation indexes are presented in Tables 3 and 4, respectively. Real estimate values were checked instead of model estimate because it is more reliable and conservative [38]. Item separation index was determined as 5.21 which shows excellent level of separation. Reliability level of item separation was 0.96. This shows a high level of reliability [38]. Person separation index score was determined as 1.85. This is a good separation index. Separation score of person reliability was 0.81 which shows acceptable level of reliability.
Table 3.
Summary of 203 assessed persons
| Total Score | Model | Infit | Outfit | ||||
|---|---|---|---|---|---|---|---|
| Count | SE | MNSQ | ZSTD | MNSQ | ZSTD | ||
| MEAN | 16.1 | 28.0 | 2.91 | 1.00 | 0.0 | 1.01 | 0.0 |
| SD | 5.0 | 0.0 | 0.89 | 0.17 | 0.9 | 0.29 | 1.0 |
| MAX. | 27.0 | 28.0 | 10.31 | 1.52 | 2.4 | 2.12 | 2.4 |
| MIN. | 3.0 | 28.0 | 3.09 | 0.62 | -2.6 | 0.39 | -2.5 |
| Real RMSE = 4.77 | True SD= 8.84 | Separation = 1.87 | Person reliability= 0.81 | ||||
| Model RMSE= 4.69 | True SD= 8.89 | Separation = 1.93 | Person reliability= 0.80 | ||||
| SE of item mean= 0.73 | |||||||
MNSQ (mean square residual)
ZSTD (standardised mean square residual)
SE (standard error)
SD (standard deviation)
RMSE (root mean square error)
Table 4.
Summary of 28 assessed items
| Total Score | Model | Infit | Outfit | ||||
|---|---|---|---|---|---|---|---|
| Count | SE | MNSQ | ZSTD | MNSQ | ZSTD | ||
| MEAN | 117.7 | 204.0 | 1.66 | 0.99 | 0.0 | 1.01 | 0.2 |
| SD | 34.6 | 0.0 | 0.14 | 0.11 | 1.5 | 0.21 | 1.6 |
| MAX. | 173.0 | 204.0 | 2.06 | 1.32 | 3.8 | 1.62 | 3.8 |
| MIN. | 63.0 | 204.0 | 1.54 | 0.83 | -2.5 | 0.71 | -2.1 |
| Real RMSE = 1.70 | True SD = 8.83 | Separation = 5.21 | Item reliability=0.96 | ||||
| Model RMSE = 1.67 | True SD = 8.84 | Separation = 5.31 | Item reliability= 0.97 | ||||
| SE of item mean = 1.73 | |||||||
MNSQ (mean square residual)
ZSTD (standardised mean square residual)
SE (standard error)
SD (standard deviation)
RMSE (root mean square error)
Discussion
The aim of this study was to develop and validate SNK test for Turkish pre-service dieticians. The findings of the present study showed that item difficulty estimates were clustered within a relatively narrow range. The clustering of item difficulty estimates suggested that the instrument was well targeted to the ability level of the sample [33, 35].
Item discrimination of the SNK test met the required standards for educational assessment [38]. According to fit statistics, all items were within acceptable MNSQ (0.5 to 1.5) and ZSTD (+ 2.0 to -2.0) ranges, except four items [35]. These items were from general sport nutrition principles (item 1), fats (item 13), proteins (item 16) and nutritional ergogenic aids (item 26). Moreover, high item and person reliability indices and excellent level of item and person separation indices were determined [38, 39]. Overall, the results of the present study indicate that the developed SNK test is a valid and reliable tool in order to assess SNK level of the pre-service dieticians.
Given that nutrition and dietetics students can work as sports dieticians after graduating, they are expected to have sufficient SNK. Otherwise, sport dieticians cannot provide appropriate sport nutrition education and program to athletes, which can possibly cause negative impacts on athletes’ performances [20]. In the relevant literature, various SNK tests have been validated previously for athletes [9, 23], coaches [12] and adolescents [9] for last two decades. Trakman et al. [23] developed SNK questionnaire including 89 items and six sub-categories for athletes. Authors collected data from expert sport dieticians and university sport students. Vázquez-Espino et al. [31]. similarly validated short version of SNK questionnaire for athletes. Authors collected data from athletes, nutrition students and non-athletes with no formal nutrition knowledge. Further, Zinn et al. [22] studied general SNK and validated the questionnaire related it. In that study, the questionnaire included 84 items and five sub-categories, and data were collected from university business staff, dieticians and nutrition students. Authors reported high test-retest reliability and content and construct validity. However, the present study, to the best of our knowledge, is the first study to develop a SNK test to assess SNK levels of pre-service dieticians who successfully completed sport nutrition course.
The present study contributes the literature in two ways. Firstly, the results of this study indicated that the test specifically assesses pre-service dieticians who completed the sport nutrition course. The developed test can be used for both pre-service and continuing professional development of sport dieticians in addition to allowing academics to assess whether pre-service dieticians have sufficient knowledge about sport nutrition. Secondly, we used Rasch modeling to validate the test, which is more reliable than those validated with traditional methods. It is ideal for validating instruments which focus on latent variables and measuring item and person distribution in a knowledge test [38].
There are some limitations in this study. At current SKN test has only been validated in pre-service dieticians. Future researchers could focus on validation to confirm reliability and validity in-service dieticians and nutritionists. The moderate sample size can be considered another limitation. While there is evidence to show that Rasch model with varying sample sizes (samples of around 100) can provide stable results [30], future studies are suggested to use larger sample sizes. Test-retest reliability and external validity were not checked in this study. Future studies are highly advised to check these two important points. Moreover, future studies are suggested to check different validity domains such as factor structure, known-group validity, predictive validity.
Conclusion
In the present study, a 24-questions SNK test shows preliminary evidence of validity. The test can be used to assess the SNK levels of pre-service dietitians and to highlight the importance of incorporating or enhancing sport nutrition education for those working with athletic populations. Additional evaluation may be required before using it in educational or professional settings.
Supplementary Information
Acknowledgements
We thank all pre-service dietitians participating in this study voluntarily.
Authors’ contributions
ÖÖ: Conceptualization, Methodology, Writing – review & editing; NT: Methodology, Writing – review & editing; MB: Writing - Original Draft, Writing – review & editing; AU: Investigation, Writing - Original Draft; ED: Conceptualization, Methodology, Formal Analysis, Writing – review & editing; OU: Investigation, Writing - Original Draft; EYT: Investigation, Writing - Original Draft; ÖE: Writing – review & editing.
Funding
The authors declare that no financial support was received for the research, authorship, and/or publication of this article.
Data availability
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
Declarations
Ethics approval and consent to participate
Ethics approval for this study was granted by the Republic of Türkiye, Karamanoğlu Mehmetbey University Decisions of Scientific Research and Public Ethics Committee (Decision no 06-2022/126). Informed consent was obtained from all participants, and the study was conducted in adherence to the Declaration of Helsinki guidelines.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Alaunyte I, Perry JL, Aubrey T. Nutritional knowledge and eating habits of professional rugby league players: does knowledge translate into practice? J Int Soc Sports Nutr. 2015;12(1):18. 10.1186/s12970-015-0082-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Spronk I, Kullen C, Burdon C, O’Connor H. Relationship between nutrition knowledge and dietary intake. Br J Nutr. 2014;111(10):1713–26. 10.1017/S0007114514000087. [DOI] [PubMed] [Google Scholar]
- 3.Heaney S, O’Connor H, Michael S, Gifford J, Naughton G. Nutrition knowledge in athletes: a systematic review. Int J Sport Nutr Exerc Metab. 2011;21(3):248–61. 10.1123/ijsnem.21.3.248. [DOI] [PubMed] [Google Scholar]
- 4.Spendlove JK, Heaney SE, Gifford JA, Prvan T, Denyer GS, O’Connor HT. Evaluation of general nutrition knowledge in elite Australian athletes. Br J Nutr. 2012;107(12):1871–80. 10.1017/S0007114511005125. [DOI] [PubMed] [Google Scholar]
- 5.Trakman GL, Forsyth A, Devlin BL, Belski R. A systematic review of athletes’ and coaches’ nutrition knowledge and reflections on the quality of current nutrition knowledge measures. Nutrients. 2016;8(9):570. 10.3390/nu8090570. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Worsley A. Nutrition knowledge and food consumption: can nutrition knowledge change food behaviour? Asia Pac J Clin Nutr. 2002;11(3):579–85. 10.1046/j.1440-6047.11.supp3.7.x. [DOI] [PubMed] [Google Scholar]
- 7.Collette BD. Sport nutrition knowledge, dietary intake and quality of life status of curling athlete populations [Unpublished master’sthesis]. Winnipeg: University of Manitoba; 2020.
- 8.Jayawardena R, Weerasinghe K, Trakman G, Madhujith T, Hills AP, Kalupahana NS. Sports nutrition knowledge questionnaires developed for the athletic population: A systematic review. Curr Nutr Rep. 2023;12(4):767–77. [DOI] [PubMed] [Google Scholar]
- 9.Trakman GL, Forsyth A, Hoye R, Belski R. Development and validation of a brief general and sports nutrition knowledge questionnaire and assessment of athletes’ nutrition knowledge. J Int Soc Sports Nutr. 2018;15(1):17. 10.1186/s12970-018-0223-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Elsahoryi NA, Trakman G, Al Kilani A. General and sports nutrition knowledge among Jordanian adult coaches and athletes: A cross-sectional survey. PLoS ONE. 2021;16(11):e0258123. 10.1371/journal.pone.0258123. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Fink HH, Mikesky AE. Practical applications in sports nutrition. Burlington: Jones & Bartlett Learning; 2021.
- 12.Heikkilä M, Valve R, Lehtovirta M, Fogelholm M. Development of a nutrition knowledge questionnaire for young endurance athletes and their coaches. Scand J Med Sci Sports. 2018;28(3):873–80. 10.1111/sms.12987. [DOI] [PubMed] [Google Scholar]
- 13.Shifflett B, Timm C, Kahanov L. Understanding of athletes’ nutritional need Samong athletes, coaches, and athletic trainers. Res Q Exerc Sport. 2002;73(3):357–62. 10.1080/02701367.2002.10609032. [DOI] [PubMed] [Google Scholar]
- 14.Tawfik S, El Koofy N, Moawad EMI. Patterns of nutrition and dietary supplements use in young Egyptian athletes: a community-based cross-sectional survey. PLoS ONE. 2016;11(8):e0161252. 10.1371/journal.pone.0161252. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.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):205–11. 10.4085/1062-6050-47.2.205. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Botsis AE, Holden SL. Nutritional knowledge of college coaches. Sport Sci Rev. 2015;24(3–4):193–200. 10.1515/ssr-2015-0015. [Google Scholar]
- 17.Zinn C, Schofield G, Wall C. Evaluation of sports nutrition knowledge of new Zealand premier club rugby coaches. Int J Sport Nutr Exerc Metab. 2006;16(2):214–25. 10.1123/ijsnem.16.2.214. [DOI] [PubMed] [Google Scholar]
- 18.Jessri M, Jessri M, RashidKhani B, Zinn C. Evaluation of Iranian college athletes’ sport nutrition knowledge. Int J Sport Nutr Exerc Metab. 2010;20(3):257–63. 10.1123/ijsnem.20.3.257. [DOI] [PubMed] [Google Scholar]
- 19.Janiczak A, Devlin BL, Forsyth A, Trakman GL. A systematic review update of athletes’ nutrition knowledge and association with dietary intake. Br J Nutr. 2022;128(6):1156–69. 10.1017/S0007114521004311. [DOI] [PubMed] [Google Scholar]
- 20.Thomas DT, Erdman KA, Burke LM. Position of the academy of nutrition and Dietetics, dietitians of Canada, and the American college of sports medicine: nutrition and athletic performance. J Acad Nutr Diet. 2016;116(3):501–28. 10.1016/j.jand.2015.12.006. [DOI] [PubMed] [Google Scholar]
- 21.Akman CT, Aydın CG, Ersoy G. The effect of nutrition education sessions on energy availability, body composition, eating attitude and sports nutrition knowledge in young female endurance athletes. Front Public Health. 2024;12:1289448. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Zinn C, Schofield G, Wall C. Development of a psychometrically valid and reliable sports nutrition knowledge question Naire. J Sci Med Sport. 2005;8(3):346–51. 10.1016/S1440-2440(05)80045-3. [DOI] [PubMed] [Google Scholar]
- 23.Trakman GL, Forsyth A, Hoye R, Belski R. The nutrition for sport knowledge question Naire (NSKQ): development and validation using classical test theory and Rasch analysis. J Int Soc Sports Nutr. 2017;14(1):26. 10.1186/s12970-017-0182-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Turkish Dietitians Association. About the Turkish Dietitians Association. [Online]. 2023. Official Website of the Turkish Dietitians Association. URL: http://www.tdd.org.tr/index.php/tdd-hakk-nda/tuezuek.
- 25.Sinan S. Determining the status of education, employment and job satisfaction of dietitians in Turkey. [Master Thesis]. İstanbul: Halic University; 2017.
- 26.Merdol TK. Past, present and future of nutrition and dietetics science. Izmir Katip Celebi Univ Fac Health Sci J. 2016;1(1):1–5. [Google Scholar]
- 27.Karabudak E, Turnagöl H. Farklı spor Dallarında Egzersiz ve Beslenme. Türkiye Diyetisyenler Derneği Yayını. 2018. p. 122.
- 28.The Council of Higher Education (YÖK). ÇEP (Çekirdek Eğitim Programı) - Nutrition and Dietetics. [Online]. URL: https://eski.yok.gov.tr/Documents/Kurumsal/egitim_ogretim_dairesi/Ulusal-cekirdek-egitimi-programlari/beslenme_ve_diyetetik.pdf. Accessed on 08 Oct 2025.
- 29.Rahi S Research design and methods: A systematic review of research paradigms, sampling issues and instruments development. Int J Econ Manage Sci. 2017;6(2):1–5. 10.4172/2162-6359.1000403. [Google Scholar]
- 30.Chen WH, Lenderking W, Jin Y, Wyrwich KW, Gelhorn H, Revicki DA. (2014). Is Rasch model analysis applicable in small sample size pilot studies for assessing item characteristics? An example using PROMIS pain behavior item bank data. Qual Life Res. 2014;23(2):485–493. 10.1007/s11136-013-04875. [DOI] [PubMed]
- 31.Vázquez-Espino K, Fernández-Tena C, Lizarraga-Dallo MA, Farran-Codina A. Development and validation of a short sport nutrition knowledge questionnaire for athletes. Nutrients. 2020;12(11):3561. 10.3390/nu12113561. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Rasch G. Probabilistic models for some intelligence and attainment tests. Chicago: University of Chicago Press; 1980. [Google Scholar]
- 33.Linacre JM. A User’s Guide to Winsteps Rasch-Model Computer Program (Version 3.65). 2008; Available at: https://www.winsteps.com. Accessed 10 May 2023.
- 34.Tennant A, Conaghan PG. The Rasch measurement model in rheumatology: what is it and why use it? When should it be applied, and what should one look for in a Rasch paper? Arthritis Care Res. 2007;57(8):1358–62. [DOI] [PubMed] [Google Scholar]
- 35.Bond TG, Fox MT. Applying the Rasch Model. Fundamental Measurement in the Human Sciences.2nd edition; Mahwah NJ: Erlbaum; 2007.
- 36.Liu X. Using and developing measurement instruments in science education: A Rasch modeling approach. Charlotte; NC: Information Age Publishing; 2010. [Google Scholar]
- 37.Krist AT, Wind S, Lakin JM. (2025). Rasch tree modeling for DIF analysis: how sample and item characteristics influence DIF detection. Applied Measurement in Education. 2025;38(2):149–164.
- 38.Boone WJ, Staver JR, Yale MS. Rasch analysis in the human sciences. Dordrecht: Springer; 2014. [Google Scholar]
- 39.Baghaei P, Amrahi N. Validation of a multiple choice english vocabulary test with the Rasch model. J Lang Teach Res. 2011;2(5):1052–60. 10.4304/jltr.2.5.1052-1060. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.