Dataset Information

The nutrition for sport knowledge questionnaire (NSKQ): development and validation using classical test theory and Rasch analysis.

ABSTRACT:

Background

Appropriate dietary intake can have a significant influence on athletic performance. There is a growing consensus on sports nutrition and professionals working with athletes often provide dietary education. However, due to the limitations of existing sports nutrition knowledge questionnaires, previous reports of athletes' nutrition knowledge may be inaccurate.

Methods

An updated questionnaire has been developed based on a recent review of sports nutrition guidelines. The tool has been validated using a robust methodology that incorporates relevant techniques from classical test theory (CTT) and Item response theory (IRT), namely, Rasch analysis.

Results

The final questionnaire has 89 questions and six sub-sections (weight management, macronutrients, micronutrients, sports nutrition, supplements, and alcohol). The content and face validity of the tool have been confirmed based on feedback from expert sports dietitians and university sports students, respectively. The internal reliability of the questionnaire as a whole is high (KR = 0.88), and most sub-sections achieved an acceptable internal reliability. Construct validity has been confirmed, with an independent T-test revealing a significant (p < 0.001) difference in knowledge scores of nutrition (64 ± 16%) and non-nutrition students (51 ± 19%). Test-retest reliability has been assured, with a strong correlation (r = 0.92, p < 0.001) between individuals' scores on two attempts of the test, 10 days to 2 weeks apart. Three of the sub-sections fit the Rasch Unidimensional Model.

Conclusions

The final version of the questionnaire represents a significant improvement over previous tools. Each nutrition sub-section is unidimensional, and therefore researchers and practitioners can use these individually, as required. Use of the questionnaire will allow researchers to draw conclusions about the effectiveness of nutrition education programs, and differences in knowledge across athletes of varying ages, genders, and athletic calibres.

SUBMITTER: Trakman GL

PROVIDER: S-EPMC5543556 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

The nutrition for sport knowledge questionnaire (NSKQ): development and validation using classical test theory and Rasch analysis.

Trakman Gina Louise GL Forsyth Adrienne A Hoye Russell R Belski Regina R

Journal of the International Society of Sports Nutrition 20170803

<h4>Background</h4>Appropriate dietary intake can have a significant influence on athletic performance. There is a growing consensus on sports nutrition and professionals working with athletes often provide dietary education. However, due to the limitations of existing sports nutrition knowledge questionnaires, previous reports of athletes' nutrition knowledge may be inaccurate.<h4>Methods</h4>An updated questionnaire has been developed based on a recent review of sports nutrition guidelines. Th ...[more]

PMID: 28785177

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Project description:BackgroundThe Nutrition for Sport Knowledge Questionnaire (NSKQ) is an 89-item, valid and reliable measure of sports nutrition knowledge (SNK). It takes 25 min to complete and has been subject to low completion and response rates. The aim of this study was to develop an abridged version of the NSKQ (A-NSKQ) and compare response rates, completion rates and NK scores of the NSKQ and A-NSKQ.MethodsRasch analysis was used for the questionnaire validation. The sample (n?=?181) was the same sample that was used in the validation of the full-length NSKQ. Construct validity was assessed using the known-group comparisons method. Temporal stability was assessed using the test-retest reliability method. NK assessment was cross-sectional; responses were collected electronically from members of one non-elite Australian football (AF) and netball club, using Qualtrics Software (Qualtrics, Provo, UT).ResultsValidation - The A-NSKQ has 37 items that assess general (n?=?17) and sports (n?=?20) nutrition knowledge (NK). Both sections are unidimensional (Perc5%?=?2.84% [general] and 3.41% [sport]). Both sections fit the Rasch Model (overall-interaction statistic mean (SD)?=?-?0.15?±?0.96 [general] and 0.22?±?1.11 [sport]; overall-person interaction statistic mean (SD)?=?-?0.11?±?0.61 [general] and 0.08?±?0.73 [sport]; Chi-Square probability?=?0.308 [general] and 0.283 [sport]). Test-retest reliability was confirmed (r?=?0.8, P?<?0.001 [general] and r?=?0.7, P?<?0.001 [sport]). Construct validity was demonstrated (nutrition students?=?77% versus non-nutrition students?=?60%, P?<?0.001 [general] and nutrition students?=?60% versus non-nutrition students?=?40%, P?<?0.001 [sport]. Assessment of NK - 177 usable survey responses from were returned. Response rates were low (7%) but completion rates were high (85%). NK scores on the A-NSKQ (46%) are comparable to results obtained in similar cohorts on the NSKQ (49%). The A-NSKQ took on average 12 min to complete, which is around half the time taken to complete the NSKQ (25 min).ConclusionsThe A-NSKQ is a valid and reliable, brief questionnaire designed to assess general NK (GNK) and SNK.

Project description:BackgroundSubjective Well-Being (SWB) is usually conceptualized in terms of an affective (i.e., judgements of biological emotional reactions and experiences) and a cognitive component (i.e., judgements of life satisfaction in relation to a psychological self-imposed ideal). Recently, researchers have suggested that judgements of harmony in life can replace or at least complement the cognitive component of SWB. Here, however, we go beyond that suggestion and propose that harmony in life should be seen as SWB's social component since it is the sense of balance between the individual and the world around her-a process that comprises acceptance, adaptation, and balance. By adding judgements of one's social interactions (i.e., harmony in life) to judgments of one's life satisfaction (psycho) and judgements of one's emotional reactions (bio), we propose a tentatively biopsychosocial model of SWB. As a first step, we used different factorial models in order to determine if both a general factor and specific sub-factors contribute to the biopsychosocial model of SWB.MethodA total of 527 participants responded to the Positive Affect Negative Affect Schedule (PANAS; 20 items), the Satisfaction with Life Scale (SWLS; five items), and the Harmony in life Scale (HILS; five items). We conducted exploratory and confirmatory factor analyses to validate the biopsychosocial model of subjective well-being and a general factor (SWBS).ResultsThe 20 PANAS items reflected a mixture of general latent structure saturation and specific latent structure saturation, but contributed to their respective specific latent factor (PA: 48%; NA: 49%) more than to the general latent SWBS factor (positive affect: 25%; negative affect: 32%). The five SWLS items contributed to a larger degree to the general SWBS factor (72%) than to life satisfaction itself (22%), while the five HILS items contributed to even a larger degree to the general SWBS factor (98%) than to harmony in life (0%). The bifactor model was the best model compared with all other models we tested (χ2 = 1,660.78, df = 375, p < 0.001); Satorra Bentler χ2 = 1,265.80, df = 375, p < 0.001; CFI = 0.92; Tucker-Lewis Index = 0.91; RMSEA = 0.067. This model of a general SWBS factor explained about 64% of the total variance in the model, while specific SWBS components together explained 15% of the total variance.ConclusionOur study suggests SWB as a general factor in a multidimensional biopsychosocial model. Indeed, as much as 64% of the variance of SWB was explained by this general factor. The SWB components, however, contributed to a different degree to each corresponding factor in the model. For instance, while the affective and cognitive components seem to be their own constructs and also part of the general SWB factor, the social component tested here contributed 0% to its own variance but 98% to the general factor.

Project description:BackgroundMobile health (mHealth) apps offer great opportunities to deliver large-scale, cost-efficient digital solutions for implementing lifestyle changes. Furthermore, many mHealth apps act as medical devices. Yet, there is little research on how to assess user satisfaction with an mHealth solution.ObjectiveThis study presents the development of the mHealth Satisfaction Questionnaire and evaluates its measurement properties.MethodsRespondents who took part in the Health Integrator Study and were randomized to use the Health Integrator smartphone app for lifestyle changes (n=112), with and without additional telephone coaching, rated their satisfaction with the app using the new 14-item mHealth Satisfaction Questionnaire. The ratings were given on a 5-point Likert scale and measurement properties were evaluated using Rasch measurement theory (RMT).ResultsOptimal scoring was reached when response options 2, 3, and 4 were collapsed, giving three response categories. After omitting two items that did not fit into the scale, fit residuals were within, or close to, the recommended range of ±2.5. There was no differential item functioning between intervention group, age group, or sex. The Person Separation Index was 0.79, indicating that the scale's ability to discriminate correctly between person leniency was acceptable for group comparisons but not for individual evaluations. The scale did not meet the criterion of unidimensionality; 16.1% (18/112) of the respondents were outside the desired range of -1.96 to 1.96. In addition, several items showed local dependency and three underlying dimensions emerged: negative experiences, positive experiences, and lifestyle consequences of using the mHealth solution.ConclusionsIn times where mHealth apps and digital solutions are given more attention, the mHealth Satisfaction Questionnaire provides a new possibility to measure user satisfaction to ensure usability and improve development of new apps. Our study is one of only a few cases where RMT has been used to evaluate the usability of such an instrument. There is, though, a need for further development of the mHealth Satisfaction Questionnaire, including the addition of more items and consideration of further response options. The mHealth Satisfaction Questionnaire should also be evaluated in a larger sample and with other mHealth apps and in other contexts.Trial registrationClinicalTrials.gov NCT03579342; http://clinicaltrials.gov/ct2/show/NCT03579342.

Dataset Information

The nutrition for sport knowledge questionnaire (NSKQ): development and validation using classical test theory and Rasch analysis.

Background

Methods

Results

Conclusions

Publications

The nutrition for sport knowledge questionnaire (NSKQ): development and validation using classical test theory and Rasch analysis.

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