Nutritional Mobile Applications for CKD Patients: Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Mobile health applications offer the potential to help people living with chronic kidney disease (CKD) manage diet-related challenges. This systematic review examined CKD dietary mobile app interventions; specifically, app characteristics, feasibility, and effectiveness in changing user behavior, as well as user satisfaction. METHODS: This review was reported in accordance with PRISMA guidelines. We searched scholarly databases, as well as the gray literature, for all randomized controlled trials, observational studies, needs assessments, and pilot testing/studies/trials focused on the development or evaluation of CKD dietary mobile app interventions. The characteristics, user satisfaction with, usability/feasibility, and effectiveness in changing dietary behavior of the mobile application were summarized using descriptive statistics and in a narrative manner. RESULTS: Thirteen full-text studies were included, of which 11 were single center, with a mean sample size of 23. Of the 7 studies that measured usability/feasibility, all found at least some aspects of the application feasible/useful. Of the 5 studies that reported an evaluation of changes in behavior/diet related to self-management, all reported some positive change. CONCLUSION: According to current studies, nutritional apps show promise in CKD self-management.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".