Development of ‘Twazon’: An Arabic App for Weight Loss
Bibliographic record
Abstract
BACKGROUND: Weight gain and its related illnesses have become a major public health issue across the world, with Saudi Arabia and other Gulf countries seeing dramatic increases in obesity and overweight, and yet there is very little information on how to intervene with this demographic due to cultural and linguistic barriers. As the use of smartphones and apps has also increased in the region, information communication technologies could be a cost-effective means of facilitating the delivery of behavior-modification interventions directly to the target population. Although there are existing apps that offer lifestyle-modification tools, they do not give consideration to the evidence-based practices for weight management. This offers an opportunity to create an Arabic language weight loss app that offers localized content and adheres to evidence-informed practices that are needed for effective weight loss. OBJECTIVE: This paper describes the process of developing an Arabic weight loss app designed to facilitate the modification of key nutritional and physical activity behaviors among Saudi adults, while taking into consideration cultural norms. METHODS: The development of the Twazon app involved: (1) reviewing all available Arabic weight loss apps and compared with evidence-based practices for weight loss, (2) conducting a qualitative study with overweight and obese Saudi women to ascertain their preferences, (3) selecting which behavioral change strategies and guidelines to be used in the app, (4) creating the Saudi Food Database, (5) deciding on graphic design for both iPhone operating system and Android platforms, including user interface, relational database, and programming code, and (6) testing the beta version of the app with health professionals and potential users. RESULTS: The Twazon app took 23 months to develop and included the compilation of an original Saudi Food database. Eight subjects gave feedback regarding the content validity and usability of the app and its features during a pilot study. The predominant issue among the group was the lack of information explaining how to use the app. This has since been resolved through the implementation of a tutorial. No other changes were required to be made. CONCLUSIONS: Information communication technologies, such as smartphone apps, may be an effective tool for facilitating the modification of unhealthy lifestyle habits in Saudi; however, consideration must be given to the target population, cultural norms, and changing trends in the global market. The effectiveness of the app will be better determined during a 6-month intervention with 200 overweight and obese Saudi women.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".