Feature Availability Comparison in Free and Paid Versions of Popular Smartphone Weight Management Applications
Bibliographic record
Abstract
Objective Characterize capabilities of nutrition applications (apps) for weight management and associations between features, ratings, and app installations. Design Calorie tracking apps with weight management as a primary outcome were selected from the Apple App Store and Google Play Store using keywords "diet" and "weight loss." Methods Reviewers assessed free and upgraded versions of nutrition apps (n = 15) for features within 4 categories: (1) dietary intake, (2) anthropometrics, (3) physical activity, and (4) behavior change strategies. Outcome Measures Presence of specific app features, app ratings, and app installations. Analysis Descriptive statistics of free and paid app versions. Spearman rank-order correlations were used to determine associations between feature inclusion, app ratings, and installations. Results The apps had the greatest number of features in the dietary intake category. Additional dietary intake features were those most likely obtained through a subscription purchase. Behavior change content was absent from most apps. The macronutrient adjustment feature was strongly associated with average app ratings ( r s = 0.74; P < 0.002) and with subscription costs ( r s = 0.60; P < 0.019). Conclusions and Implications This study found most nutrition apps possess an abundance of features dedicated to dietary intake, anthropometric, and physical activity tracking while also being notably devoid of behavior change content features.
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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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".