Visualization Analysis of International Research of Physical Activity Promoted built environment
Bibliographic record
Abstract
Zhenduo Liu1, Liping Jiang2, Yuning Jia1, Penghui Xie1, Yanan Wang1, Joonyoung Lee3, & Brooke Doherty3 East China Normal University, Shanghai, China1; Tongji University, Shanghai, China2; University of North Texas, Denton, TX3 Email: [email protected] BACKGROUND: Recently, studies regarding the relationship between the building environment, physical activity, and health have flourished in the areas of public health promotion among urbanized countries. (Chen, 2014). The researchers found that land use structure, residential density, and street connectivity were positively correlated with the amount of daily moderate physical activity (Frank, 2005). Although the importance of building environment to promote physical activity has been emphasized, very little is known about the development trend and “hotspots” in this field. PURPOSE: Through sorting out the process of studies focused on international physical activity promoting-type built environment, this paper aimed to reveal the basic characteristics and research “hotspots” in this field through software analysis and to provide suggestions for future research. METHODS: Based on the literature about international physical activity promoting-type building environment from the Web of Science, The researchers searched 3,678 research papers and references in the field of health promotion during 2004—2018 and used Citespace Version 5.2 (Chen, 2018) for bibliometric analysis and visualized analysis. RESULTS: The results revealed that: (1) current studies mainly come from western countries (i.e., primarily the United States, Canada, and Australia); (2) the research “hotspots” focus on different forms of physical activity, obesity, and body mass index control in built environment. CONCLUSION: Transportation planning and management, urban planning, and behavioral science have focused on building environments that can promote physical activity. Majority of the research has mainly emphasized the relationships between health and built environment and physical activity assessments. While facing the serious problem of childhood obesity, it is important to consider building environment construction as one of the main solutions.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.064 | 0.085 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".