Change in Nutrient and Dietary Intake in European Children with Cystic Fibrosis after a 6-Month Intervention with a Self-Management mHealth Tool
Bibliographic record
Abstract
Cystic Fibrosis (CF) is a life-long genetic disease, causing increased energy needs and a healthy diet with a specific nutrient distribution. Nutritional status is an indicator of disease prognosis and survival. This study aimed at assessing the effectiveness of a self-management mobile app in supporting patients with CF to achieve the dietary goals set by the CF nutrition guidelines. A clinical trial was conducted in pancreatic insufficient children with CF, followed in six European CF centres, where the self-management app developed within the MyCyFAPP project was used for six months. To assess secondary outcomes, three-day food records were compiled in the app at baseline and after 3 and 6 months of use. Eighty-four subjects (mean 7.8 years old) were enrolled. Compared to baseline, macronutrient distribution better approximated the guidelines, with protein and lipid increasing by 1.0 and 2.1% of the total energy intake, respectively, by the end of the study. Consequently, carbohydrate intake of the total energy intake decreased significantly (-2.9%), along with simple carbohydrate intake (-2.4%). Regarding food groups, a decrease in ultra-processed foods was documented, with a concomitant increase in meat and dairy. The use of a self-management mobile app to self-monitor dietary intake could become a useful tool to achieve adherence to guideline recommendations, if validated during a longer period of time or against a control group.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 source (direct Gemma or distilled Codex), 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".