The Place of Mobile Health in the Health Sector, Barriers and Opportunities, Integrated Technologies and Usage Areas Affecting the Development of Mobile Health: A Review of the Literature in All Aspects
Bibliographic record
Abstract
Mobile health (m-Health) is a crucial component of electronic health, and eHealth involves utilizing the possibilities provided by information and communication technologies to enhance the diagnosis, treatment, and accessibility of healthcare services, aiming to deliver high-quality, efficient, and effective healthcare to all stakeholders in the healthcare sector. Mobile health specifically refers to the provision of healthcare services using mobile technologies and communication tools such as mobile phones, patient monitoring devices, and personal digital assistants. With the increasing adoption of mobile technologies, mobile health is gaining greater importance within healthcare systems. Mobile applications are utilized in various domains such as disease prevention, reduction of risk factors, promotion of physical activity and quality of life, as well as diagnosis, treatment, feedback, and monitoring. In our research, we accessed over 600 documents via Google Scholar and 916 documents via Web of Science using the keywords "Health Sector Mobile Technology." We evaluated and synthesized the findings within the framework of topic headings identified by researchers, encompassing research and review articles included in Web of Science. Our study primarily involves document analysis, focusing on the potential transformation of healthcare service delivery globally through the use of mobile and wireless technologies to achieve health goals worldwide. The literature review identified 15 distinct areas where the healthcare sector, health research, and mobile technology can be grouped under the umbrella of mobile health research. Each topic was evaluated with regard to its scope and application areas in the literature.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".