Nutrition-Related Mobile Apps in the Spanish App Stores: Quality and Content Analysis
Bibliographic record
Abstract
Background: Mobile apps represent accessible and cost-effective tools to improve nutrition and prevent chronic diseases. However, most of these apps have been characterized as having limited functionality, raising concerns about their effectiveness, acceptability, and efficacy. Objective: The aims of the study were to assess the quality of popular nutrition-related app platforms in Spain and to describe their characteristics and functionalities. Methods: We screened apps providing information on dietary advice, food advice, and nutritional content in the Apple App Store and Google Play Store in Spain from March 2 to March 16, 2024. Apps with a star rating of ≥4 (of 5 stars), those available in Spanish, those that were free of charge, those last updated after January 2022, those with >500 reviews, and those with >500,000 downloads were included. The quality of apps was assessed using the user version of the Mobile App Rating Scale (uMARS). General characteristics and nutritional, health, and market-related functionalities of the nutrition-related apps were described. Correlations among total and uMARS sections, star ratings, and number of reviews and downloads were evaluated. Results: Among the 1460 apps identified in the search, 42 apps met the criteria. The majority of these (n=20, 48%) aimed at recording and analyzing food intake, followed by those providing nutritional plans or diets (n=9, 21%), advising on healthy habits (n=7, 17%), and offering recipes (n=6, 14%). The most prevalent nutritional functionalities offered were recording and monitoring body measurements (n=30, 71%), food tracking (n=26, 62%), and dietary analysis (n=25, 60%), whereas nutrition education was less common (n=16, 38%). Among market-related functionalities, advertisements were the most common among the study apps (n=30, 71%), followed by the option of sharing on social media (n=29, 69%) and customizable reminders (n=26, 62%). Sharing the recorded information in the app with health professionals was infrequent (n=1, 2%). The mean (SD) total uMARS score (maximum 5 points) was 3.78 (0.35), while the mean (SD) uMARS scores for functionality, aesthetics, engagement, and information were 4.21 (0.38), 3.94 (0.54), 3.51 (0.46), and 3.48 (0.44), respectively. Lower mean scores were observed for the subjective quality (mean 2.65, SD 0.56) and perceived impact (mean 3.06, SD 0.67). Moderate to strong positive significant correlations were mostly observed between total uMARS and section-specific uMARS scores, while the correlations between the uMARS section scores were mostly moderate positive. Total uMARS scores were very weakly correlated with user rating, number of reviews, and number of downloads. Conclusions: The quality of popular nutrition-related app platforms in Spain was acceptable, with observed remarkable differences between sections. The majority of the apps were appealing due to their user-friendly interfaces. Only a few apps, however, provided dietary structure analysis or nutritional education. Further research is needed to assess the long-term impact of these apps on users.
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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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".