A Content Analysis of Free, Popular Low FODMAPs Diet mHealth Apps
Bibliographic record
Abstract
Canada has one of the highest Irritable Bowel Syndrome rates (IBS) globally (18% vs. 11%). There is growing interest in the low FODMAPs (Fermentable Oligo-, Di-, Mono-saccharides, And Polyols) diet as nutrition therapy for IBS. However, recognizing IBS symptoms and identifying low FODMAPs foods has been challenging for users. Mobile technology may be a convenient and cost-effective tool in helping users adhere to the low FODMAPs diet. The objective was to assess free, popular mobile Health (mHealth) Apps supporting a low FODMAPs diet. Apple iTunes and GooglePlay App stores were searched on December 22, 2020, using pre-selected search terms. The top 100 returns were screened for eligibility. Free and popular (≥3 out of 5 ratings; ≥100 total reviews) mHealth Apps available in English and primarily marketed to help follow FODMAPs diets were included. Apps were downloaded and assessed by 3 nutrition professionals using the Mobile App Rating Scale (MARS) and the App Quality Evaluation (AQEL) tool. Of the 1304 screened Apps, n = 9 (mean ratings ± SEM = 4.4 ± 0.1) met the eligibility criteria for assessment, comprising 5 FODMAPs identifiers and 4 tracking/monitoring symptoms Apps. Apps targeted the general population, and 70% focused on nutrition education and food shopping. The Apps used education (9), skills training (5), and/or goal setting (5) as a behavior change tool. 3 Apps provided a cognitive behavioral therapy component. The MARS (rated out of 5) revealed a high overall App quality score (3.6 ± 0.2), credibility (3.1 ± 0.5) and subjective quality scores (2.9 ± 0.3). The AQEL (rated out of 10) revealed high scores in App function (6.2 ± 0.7) and purpose (7.4 ± 0.8), but low scores in behavioral change potential (5.2 ± 0.7), knowledge acquisition (5.4 ± 0.7) and skills development (2.4 ± 1.2). A small number of free FODMAPs diet Apps are available, with limited information on the nutritional adequacy and users’ compliance in following a low FODMAPs diet. Considering the need for careful assessment and management of IBS symptoms with the highly restrictive low FODMAPs diet, Apps should be evidence-based and designed with Behavioral Change Techniques and easy access to healthcare professionals that support Canadians in acquiring knowledge about the diet and in self-monitoring IBS. None.
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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 | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".