Daily mobility, greenspace exposure and affective states: A systematic review of studies that use mobile methods
Bibliographic record
Abstract
Emotions, being individuals’ transient affective experiences, are shaped by relational dynamics and environmental interactions over time and cross places. Contact with greenspace as a vital health determinant and well-being resource, similarly, is situationally dependent and culturally influenced. Mobile methods, which involve collecting and analysing data from participants as they move through various settings, offer an innovative approach to studying the relationship between greenspace exposure and affective states during daily mobility. As these methods gain traction, it is essential to develop theoretical frameworks and methodological standards. This systematic review synthesises evidence from 33 studies that employ individual-level, high-resolution mobility data to examine the relationships among daily mobility, greenspace exposure, and affective states. While the overall quality of these studies was rated as ‘good’ with respect to bias risk, according to the Newcastle-Ottawa Scale, inconsistencies in the definitions of outcomes and exposures, as well as variations in measurement and analytical designs, pose significant challenges to forming a cohesive body of evidence. Our analysis focuses on five critical aspects of these studies: geographic scope, sampling strategies, methods for measuring affective states, approaches to assessing greenspace exposure, and statistical techniques. To overcome these challenges and build a more robust evidence base, we propose a unified and collaborative research framework. This framework aims to guide built environment research and inform urban planning practices, thereby enhancing our understanding of the connections between greenspace exposure and emotional well-being.
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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 | 0.014 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".