Popular Mobile Phone Apps for Diet and Weight Loss: A Content Analysis
Bibliographic record
Abstract
BACKGROUND: A review of the literature has revealed that the rates of overweight and obesity have been increasing in Australia over the last two decades and that wellness mobile phone apps play a significant role in monitoring and managing individuals' weight. Although mobile phone app markets (iTunes and Google Play) list thousands of mobile phone health apps, it is not always clear whether those apps are supported by credible sources. Likewise, despite the prevailing use of mobile phone apps to aid with weight management, the usability features of these apps are not well characterized. OBJECTIVE: The research explored how usability taxonomy could inform the popularity of downloaded, socially focused wellness mobile phone apps, in particular weight loss and diet apps. The aim of the study was to investigate the Australian mobile phone app stores (iTunes and Google Play) in order to examine the usability features of the most popular (ie, most downloaded) wellness apps. METHODS: The design of this study comprises 3 main stages: stage 1, identifying apps; stage 2, development of weight loss and diet evaluation framework; and stage 3, application of the evaluation framework. Each stage includes specific data collection, analysis tools, and techniques. RESULTS: The study has resulted in the development of a justified evaluation framework for weight loss and diet mobile phone apps. Applying the evaluation framework to the identified apps has shown that the most downloaded iTunes and Google Play apps are not necessarily the most usable or effective. In addition, the research found that search algorithms for iTunes and Google Play are biased toward apps' titles and keywords that do not accurately define the real functionality of the app. Moreover, the study has also analyzed the apps' user reviews, which served as justification for the developed evaluation framework. CONCLUSIONS: The analysis has shown that ease of use, reminder, bar code scanning, motivation, usable for all, and synchronization are significant attributes that should be included in weight loss and diet mobile phone apps and ultimately in potential weight loss and diet evaluation frameworks.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".