Assessing the Quality of Mobile Phone Apps for Weight Management: User-Centered Study With Employees From a Lebanese University
Bibliographic record
Abstract
BACKGROUND: Evaluating the quality of mobile health apps for weight loss and weight management is important to understand whether these can be used for obesity prevention and treatment. Recent reviews call for more research on multidimensional aspects of app quality, especially involving end users, as there are already many expert reviews on this domain. However, no quantitative study has investigated how laypersons see popular apps for weight management and perceive different dimensions of app quality. OBJECTIVE: This study aimed to explore how laypersons evaluate the quality of 6 free weight management apps (My Diet Coach, SparkPeople, Lark, MyFitnessPal, MyPlate, and My Diet Diary), which achieved the highest quality ratings in a related and recent expert review. METHODS: A user-centered study was conducted with 36 employees of a Lebanese university. Participants enrolled in the study on a rolling basis between October 2016 and March 2017. Participants were randomly assigned an app to use for 2 weeks. App quality was evaluated at the end of the trial period using the Mobile App Rating Scale user version (uMARS). uMARS assesses the dimensions of engagement, functionality, aesthetics, information, and subjective quality on 5-point scales. Internal consistency and interrater agreement were examined. The associations between uMARS scores and users' demographic characteristics were also explored using nonparametric tests. Analyses were completed in November 2017. RESULTS: Overall, the 6 apps were of moderately good quality (median uMARS score 3.6, interquartile range [IQR] 0.3). The highest total uMARS scores were achieved by Lark (mean 4.0 [SD 0.5]) and MyPlate (mean 3.8 [SD 0.4]), which also achieved the highest subjective quality scores (Lark: mean 3.3 [SD 1.4]; MyPlate: mean 3.3 [SD 0.8]). Functionality was the domain with the highest rating (median 3.9, IQR 0.3), followed by aesthetics (median 3.7, IQR 0.5), information (median 3.7, IQR 0.1), and engagement (median 3.3, IQR 0.2). Subjective quality was judged low (median 2.5, IQR 0.9). Overall, subjective quality was strongly and positively related (P<.001) with total uMARS score (ρ=.75), engagement (ρ=.68), information, and aesthetics (ρ=.60) but not functionality (ρ=.40; P=.02). Higher engagement scores were reported among healthy (P=.003) and obese individuals (P=.03), who also showed higher total uMARS (P=.04) and subjective quality (P=.05) scores. CONCLUSIONS: Although the apps were considered highly functional, they were relatively weak in engagement and subjective quality scores, indicating a low propensity of using the apps in the future. As engagement was the subdomain most strongly associated with subjective quality, app developers and researchers should focus on creating engaging apps, holding constant the functionality, aesthetics, and information quality. The tested apps (in particular Lark and MyPlate) were perceived as more engaging and of higher quality among healthy, obese individuals, making them a promising mode of delivery for self-directed interventions promoting weight control among the sampled population or in similar and comparable settings.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".