Smartphone Diet Application Use & Dietary Assessment in Sports Dietetics
Bibliographic record
Abstract
Background: The availability of diet applications (diet apps) on smartphones is increasing, and this may help sports dietitians employ best practice. Sports dietetic best practices are informed by evidence-based guidelines, which include assessing and prescribing quantitative dietary intake. The use of smartphone apps and implementation of best practices in sports dietetics is currently unknown. \n \nObjectives: The aims of the study were to 1) assess the prevalence and perception of smartphone diet app use by sports dietitians; 2) to examine the methods and barriers of dietary assessment and nutrition intervention used by sports dietitians, with focus on best practice; and 3) to compare the prevalence of implementation of best practices by sports dietitians who use diet apps with those who do not use diet apps. \n \nMethods: A 27-item cross-sectional online survey was developed and sent to 549 sports dietitians to determine diet app use, as well as dietary assessment and nutrition interven- tion practices in sports dietetics. Respondents included sports dietitians from Australia, Canada, New Zealand, and the United Kingdom, who were surveyed between 22 June and 24 August 2012. \n \nResults: The questionnaire had a response rate of 25.1% (138/549). Of the 136 eligible respondents to the questionnaire, 25.0% (n=34) used diet apps with clients in sports dietetics. Diet app users had a positive perception of diet apps. Overall, sports dietitians primarily assessed dietary intake with a diet history (61/125, 48.8%), estimated energy (55/123, 44.7%) and/or macronutrient intakes (59/123, 48.0%), prescribed quantitative nutrient intakes (11/127, 8.7%), and most often referred to Clinical Sports Nutrition (L. M. Burke & Deakin, 2010) (97/118, 82.2%) for sports dietetic guidelines. Only 12.7% (16/126) of sports dietitians used the Nutrition Care Process. The majority of best practices in sports dietetics (5/7, 71.4%) were followed by less than half of the surveyed sports dietitians. More sports dietitians who used diet apps followed best practices compared with sports dietitians who did not use diet apps. \n \nConclusion: This study highlights the gap between best practice and actual practice in sports dietetics. More diet app users followed best practice, suggesting that diet apps can help close this gap. Smartphone diet apps may help implement best practices by enabling sports dietitians to quantitatively assess dietary intake and calculate nutrient intake, thus allowing nutrient goals to be set. As the adoption of smartphones increases and sophistication of the software improves, diet apps will grow in importance and become a valuable tool in the dietetic profession.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".