The Landscape of Mobile Apps for Healthy Eating: Case Study for a Systematic Review and Quality Assessment
Bibliographic record
Abstract
BACKGROUND: Mobile apps are being increasingly used to foster healthy lifestyles. There is a growing need for clear, standardized guidelines to help users select safe and effective health apps. OBJECTIVE: Our study aimed to highlight the importance of establishing a structured framework for quality evaluation in mobile health (mHealth) through a case study of mobile apps promoting healthy eating. METHODS: We conducted a systematic review of apps promoting healthy eating that had already been evaluated by one or more of 28 recognized health app certification bodies. Three rounds of app evaluations were conducted by experts in nutrition and behavior change. The first two rounds focused on the quality of the content of the recommendations and were performed pairwise using the Quality Evaluation Scoring Tool (QUEST), which has not been previously used by the certification bodies. In addition, in the second and third rounds, each reviewer answered the question "How probable is it that you would recommend this app?" using a subjective scale score from 0 to 10. In the third round, this score was weighed by usability (30%), content quality (40%), and promotion of behavior change (30%). Discussions were held to resolve scoring discrepancies and to identify the top-quality apps. We also assessed correlations among QUEST, Google Play Store, and certification body scores. RESULTS: Of the 41 apps identified by five certification bodies, 19 (46.3%) met the inclusion criteria and were examined. Only 16 (84.2%) of these remained accessible for the second round. Eight of these surpassed 20 points (out of a maximum of 28) on the QUEST scale and were evaluated by all six experts in the third round, and the top 5 (62.5%) apps were selected. No correlations were found among QUEST, Google Play Store, and certification body scores. CONCLUSIONS: Despite numerous evaluations by various certification bodies, only 5 (12.2%) of the 41 apps met the quality standards set by our experts. Our results mark the importance of rigorous, transparent, and standardized app evaluation processes to guide users toward making informed decisions about health apps. Guidelines for developers for the design of evidence-based, unbiased, high-quality apps, as well as technological solutions for real-time monitoring of the health apps, would address these challenges and improve reliability.
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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 | 0.150 | 0.275 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".