An Investigation of the Feasibility and Acceptability of Using a Commercial DASH (Dietary Approaches to Stop Hypertension) App in People With High Blood Pressure: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: The use of smartphone apps for dietary self-management among patients with high blood pressure is becoming increasingly common. Few commercially available DASH (Dietary Approaches to Stop Hypertension) diet apps have the potential to be effective, and only a few of these have adequate security and privacy measures. In previous studies, we identified 2 high-quality apps that are likely effective and safe. One of these, the Noom app, was selected as the most suitable app for use in the Saudi Arabian context based on health care professionals' and patients' preferences. OBJECTIVE: This study aims to determine the feasibility and acceptability of using the Noom app to support DASH diet self-management among people with high blood pressure in Saudi Arabia. METHODS: This mixed methods study evaluated the feasibility and acceptability of using the Noom app among people with high blood pressure in Riyadh, Saudi Arabia. Fourteen participants with high blood pressure were recruited and asked to use the app for 8 weeks. The quantitative outcome measures were DASH diet adherence and self-efficacy. Feasibility and acceptability were assessed during and after the intervention via the Noom diet-tracking engagement questionnaire, the System Usability Scale, and semistructured interviews. RESULTS: Most participants (8/13, 62%) logged their meals for 3 to 5 days a week; the frequency of logging increased over time. Snacks were the foods they most often forgot to log. The interviews revealed four main themes: (1) acceptance, (2) app usability, (3) technical issues, and (4) suggestions for improvement. Most participants found the Noom app acceptable, and most had no difficulties integrating it into their daily routines. The results of this feasibility study provided insights into the app's educational content, some of which was deemed unsuitable for Saudi Arabian users. App usability was identified as a critical theme: the app and its database were easy to use, convenient, and valuable to most of the participants. Despite this, some of the participants reported difficulties in identifying some foods because of a lack of local options on the app. Technical issues included the app freezing or responding slowly. Most participants also suggested developing an Arabic version of the app and simplifying the method of food logging. The participants showed some improvement in self-efficacy and adherence to the DASH diet, although these improvements were not statistically significant. The mean self-efficacy score increased from 18 (SD 4.7) to 20 (SD 6.3), and the mean DASH diet score increased from 3.4 (SD 1.4) to 4.3 (SD 1.1). CONCLUSIONS: The app was feasible and acceptable among the participants who completed the study. Further studies are needed to examine the potential of smartphone apps in promoting adherence to the DASH diet and their impact on blood pressure among individuals with hypertension in Saudi Arabia.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".