Digital Health in Saudi Arabia: A Descriptive Study of User Perspectives, Adoption Rates, Benefits, and Challenges of Digital Health Applications
Bibliographic record
Abstract
Background: Digital health applications have emerged as transformative tools within healthcare systems globally. This study investigates the perceptions, adoption, perceived benefits and challenges, and factors involved in the selection of digital health apps among the residents of Riyadh city, Saudi Arabia. Methodology: A cross-sectional study was conducted using a validated, self-administered online questionnaire targeting residents of Riyadh aged 18 years and older. A total of 444 participants were recruited through convenience sampling via social media platforms. Results: Among the respondents, 90% reported using digital health applications, with Sehhaty being the most commonly used. Over 75% rated these applications as effective in improving quality of life (QoL). Key benefits included improved healthcare access, appointment booking, health awareness, and time efficiency. Fitness tracking was the most used app category (57.2%). Despite positive perceptions, 22% reported challenges, including technical difficulties and limited app compatibility. Ease of use was the most important factor (92.1%) when choosing a health app. Conclusion: Digital health applications have been widely adopted in Riyadh and are perceived to enhance QoL through improved access, convenience, and personal health management. The findings highlight strong user satisfaction and a growing interest in preventive care. Expanding digital health features, enhancing awareness, and integrating AI-based tools are recommended to further support health outcomes and national digital health goals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".