A Content Analysis of Free, Popular Plant-Based Mobile Health Apps
Bibliographic record
Abstract
There has been an increased emphasis on plant-based foods and diets in numerous dietary guidelines worldwide. Although mobile technology has the potential to be a convenient and cost-effective tool to aid adherence to dietary guidelines, little is known about the content and the quality of available mobile Apps on plant-based diets. Therefore, the objective of the study was to assess free, popular mobile health (mHealth) Apps supporting plant-based diets for Canadians. Using pre-defined search terms, Apple iTunes and GooglePlay App stores were searched on December 22, 2020; the top 100 returns for each search term were screened for eligibility. Free and popular (≥3 out of 5 ratings; ≥100 total reviews) mHealth Apps available in English, primarily marketed to help users follow plant-based diets were included. Included Apps were downloaded and assessed for quality by three research assistants/dietitians using the Mobile App Rating Scale (MARS) and the App Quality Evaluation (AQEL) tool. Of the 998 Apps screened, 16 Apps (mean ratings ± SEM = 4.5 ± 0.08) met the eligibility criteria for assessment, comprising 10 recipe manager and meal planners, 2 food scanners, 2 vegan community builders, 1 restaurant identifier, and 1 sustainability-focused App. All included Apps targeted the general population and focused on changing behaviours using education (14 Apps), skills training (14 Apps), and/or goal setting (5 Apps). Vegan, vegetarian or other plant-based (e.g., pescatarian, flexitarian) settings were available in 13 Apps, while 3 Apps offered no plant-based diet settings. The MARS (rated out of 5) revealed a high overall App quality score (3.84 ± 0.66) and subjective quality score (3.63 ± 0.62), but low credibility score (2.09 ± 0.36). The AQEL (rated out of 10) revealed high scores in App function (8.29 ± 0.47), purpose (8.11 ± 0.39), and behavioral change potential (8.35 ± 0.45), but a low score in support of knowledge acquisition (4.82 ± 0.43). Although a variety of free plant-based Apps with different focuses and behavioural change techniques are available to help Canadians follow plant-based diets, our findings suggest a need for credible Apps and other resources to complement the low support of knowledge acquisition in plant-based Apps. Banting & Best Diabetes Centre.
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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 | 0.011 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".