Use of Weight-Management Mobile Phone Apps in Saudi Arabia: A Web-Based Survey
Bibliographic record
Abstract
Background: In recent years, the use of mobile phone weight-management apps has increased significantly. Weight-management apps have been found effective in promoting health and managing weight. However, data on user perception and on barriers to app usage are scarce. Objective: This study aimed to investigate the use of weight-management apps and barriers to use as well as reasons for discontinuing use in a sample of mobile phone users in Saudi Arabia. Methods: Mobile phone users aged 18 years and above from the general public in Saudi Arabia completed a Web-based survey. The survey included questions on weight-management app usage patterns, user perceptions concerning weight management, efficacy of weight-management apps, and reasons for discontinuing use. Participants were classified into normal weight (body mass index [BMI]: 18.5 to 24.9 kg/m2) and overweight or obese (BMI: ≥25.0 kg/m2). Results: The survey included 1191 participants; 513 of them used weight-management apps. More overweight or obese respondents used these apps compared with normal weight respondents (319/513, 62.2% vs 194/513, 37.8%, respectively). App features that overweight or obese users were most interested in were mainly the possibility to be monitored by a specialist and barcode identification of calorie content, whereas normal weight users mostly preferred availability of nutrition information of food items. Reasons for discontinuing use among overweight or obese respondents were mainly that monitoring by a specialist was not offered (80/236, 33.9%) and the app was not in the local language (48/236, 20.3%). Among normal weight users, the main reason for noncontinuance was the app language (45/144, 31.3%) and difficulty of use (30/144, 20.8%). Conclusions: To better address the needs of both normal weight and overweight or obese adults, improved app designs that offer monitoring by a specialist are needed. Developers may consider ways of overcoming barriers to use, such as language, by developing local language apps, which can improve the efficacy of such apps and help spread their use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".