A systematic review of measurement tools and senior engagement in urban nature: Health benefits and behavioral patterns analysis
Bibliographic record
Abstract
The engagement of senior citizens with urban nature has been shown to provide multiple health benefits and mitigate health issues associated with demographic aging. This review utilized the PRISMA methodology to systematically analyze the relationship between monitoring tools, seniors' behaviors in urban nature, and influencing factors. The main findings are as follows: (1) 4 main types, including self-reports, on-site observations, sensors, and third-party data, and 24 sub-types of measurement tools: ranging from questionnaires to crowdsourced imagery services. Self-reports capture participants' awareness of behaviors, on-site observations record various types of behaviors, sensors collect indicators to detect the body's direct responses, and third-party data provide representative behavior data from large samples. (2) 4 categories and 45 types of behaviors: physical and sports behaviors, leisure and recreational behaviors, relaxation, and passive behaviors, social and care behaviors, based on their characteristics and purposes. Physical and sports behaviors are the most common types for the elderly in urban nature, with walking being the most frequently measured behavior. (3) 36 influencing factors: ranging from diabetes risk to balanced meal habits, classified into 4 categories from physical and vitality health to social and lifestyle health. Physical and vitality health are the most affected category, receiving more academic attention. Gardening is identified as having the most health benefits. This review provides a classification of tools and behaviors, and a detailed discussion of future trends in the field. It provides actionable insights for researchers, urban designers, city managers, and policymakers to select the appropriate measurement tool from 24 sub-tools to better understand behaviors of elderly people in urban nature. It can also help them select the right type of behavior from 45 sub-behaviors to investigate in line with their research goals to improve seniors' health and well-being.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".