The Twazon Arabic Weight Loss App: App-Based Intervention for Saudi Women With Obesity
Bibliographic record
Abstract
BACKGROUND: By 2022, it is estimated that the rate of female obesity (78%) in Saudi Arabia will almost double that of males (41%). Despite being mainly attributed to poor diet, sedentary lifestyle, and a lack of health awareness, behavioral modification interventions are relatively new to the population; bariatric surgery continues to be the treatment of choice for comorbidities. However, neither pre nor postoperative diet and exercise are promoted. Evidence-informed mobile health (mHealth) weight loss apps and interventions may be an effective tool for delivering a culturally relevant intervention. OBJECTIVE: This study aimed to determine the feasibility of a weight loss intervention that tests the effectiveness of Twazon, an originally designed Arabic weight-loss app that promotes lifestyle modification specific to Arab populations. METHODS: A pre-post single-arm pilot study was carried out among a sample of 240 overweight volunteer Saudi women residing in Riyadh, Saudi Arabia who used the Twazon app over a 4-month period. Anthropometric, diet, and physical activity measures were assessed 3 times: baseline, 2-months and 4-months; frequency of app use and system usability were evaluated during the 2 latter data collection periods. Repeated measures analysis of variance was used to identify changes over time. RESULTS: A total of 40 participants completed the 4-month intervention with an attrition rate of 83%. An evaluation of the frequency of app use fostered 2 groups: engaged users (65%) and unengaged users (35%). At 4 months, the engaged users experienced more successful outcomes; body weight was lowered on average by 1.3 (SD 0.6) kg (P=.18), waist circumference (WC) was reduced by 4.9 (SD 1.1) cm (P<.001), and daily energy consumption was decreased by >600 calories (P=.002). Unengaged users experienced minor changes in body weight, WC, and reduced energy intake. CONCLUSIONS: The findings have demonstrated that engagement with the Twazon app renders positive changes in body weight, WC, and energy intake. mHealth weight loss apps and interventions have the potential to be effective in promoting weight loss and healthy lifestyle modification in Saudi Arabia and similar populations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".