Research Combining Physical Activity and Sleep: A Bibliometric Analysis
Bibliographic record
Abstract
This study used a bibliometric analysis through the Scopus database to examine papers that combined physical activity and sleep, published between 1979 and 2018. Bibliometric indicators of productivity included publication volume and citation distribution, top 10 authors, average authors per paper, single- and multicountry collaboration, collaborative index, top 10 countries, leading journals, highly cited papers and network visualization for coauthorship, international collaboration, and co-occurrence of author keywords. The initial search identified 1,509 papers, of which 607 passed through comprehensive screening and were included in the final analysis. Most of the papers were research articles (90.8%) and published in English (90.8%). Most papers (81.4%) were published within the past decade, 2009–2018. The mean number of papers published per year was 15.2, the mean number of citations per paper was 257.3, and the mean number of authors per paper was 5.5. International collaboration was evident for 21.6% of the papers, and 95.6% of papers were multiauthored. The most prolific publishing institutions and authors were from the United States, Canada, Australia, Switzerland, and Brazil. Keyword analysis suggested that almost all age groups and study designs were covered, but most papers focus on noncommunicable diseases. Although there has been a rise in scientific production on combined physical activity and sleep research in recent years, future work in this area should include researchers from developing countries.
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 | 0.022 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.269 | 0.307 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".