The 100 most cited articles on wearable technology in the area of Medical Informatics: A bibliometric analysis using Web of Science
Bibliographic record
Abstract
INTRODUCTION: Wearable technology has revolutionized healthcare in recent years thanks to its ability to collect accurate data on the health status of patients. Wearable devices, such as smartwatches, wristbands, and fitness trackers, are designed to be worn on the body and can measure various body parameters, including heart rate, blood pressure, physical activity, and sleep quality. OBJECTIVES: To analyze the 100 most cited articles on wearable technology in the area of Medical Informatics. METHODS: The Web of Science database carried out a bibliometric analysis of the 100 most cited articles on wearable technology in the area of Medical Informatics. The objective is to identify the main trends and themes in this area of research. RESULTS: There is an increasing trend in the number of papers published and citations received in recent years, with some years with low publications but high citations and others with high publications but low citations. A positive and statistically significant correlation (r = 0.66; P<0.001) was found between the number of documents published by the authors and the number of citations they received. The analysis of publications by country, reveals that the United States is the most productive country, with 49 documents, followed by the United Kingdom, China, and Italy. However, when considering the impact of the research, other countries such as Canada, Germany, China, and South Korea have significantly high average citations per paper and leadership. CONCLUSION: The results of this study have several important implications for the research and development of wearable technology in the area of Medical Informatics. The increase in the number of papers published and citations received in recent years suggests a growing interest and advances in research. This indicates an increasing need to develop innovative real-time solutions for measuring and monitoring physical activity and health.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.199 | 0.195 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".