Mobile Fitness and Weight Management Apps: Protocol for a Quality Evaluation
Bibliographic record
Abstract
BACKGROUND: Obesity is a contributing factor for many noncommunicable diseases and a growing problem worldwide. Many mobile apps have been developed to help users improve their fitness and weight management behaviors. However, the speed at which apps are created and updated means that it is important to periodically assess their quality. OBJECTIVE: The purpose of this study is to evaluate the quality of fitness and weight management mobile health apps using the Mobile Application Rating Scale (MARS). It will also describe the features of the included apps and compare the results to a previous evaluation conducted in 2015. METHODS: Searches for "fitness," "weight," "exercise," "physical activity," "diet," "eat*," and "food" will be conducted in the Apple App Store and Google Play. Apps that have been updated over the past 5 years will be included. Two reviewers will rate the apps' quality using the MARS objective and subjective quality subscales. Interrater reliability will also be assessed. Features included in high-quality apps will be assessed, and changes in quality, features, and behavior change techniques made during the past 5 years will be described. RESULTS: The results will be included in the evaluation paper, which we aim to publish in 2020. CONCLUSIONS: This evaluation will assess the quality of currently available fitness and weight management apps. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/17685.
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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 | 0.148 | 0.227 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.103 | 0.031 |
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".