ArticleEnvironmental health insights2024
Small Urban Green Spaces: Insights into Perception, Preference, and Psychological Well-being in a Densely Populated Areas of Tehran, Iran.
Article in Environmental health insights, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Pathways of urban green space in shaping population aging across China via spatial spillover and non-linear threshold effects.BMC public health · 2026Article
- Spatial spillovers and nonlinear effects of urban green space on population aging in China.Frontiers in public health · 2026Article
- How Green Space Justice in urban built-up areas affects public mental health: a moderated chain mediation model.Frontiers in public health · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In metropolitan areas worldwide, abandoned properties are prevalent, prompting a need for small urban green spaces (SUGS) to meet the growing demand. Understanding residents' preferences and perceptions of transformed spaces is vital for effective urban design. This study delves into residents' preferences and perceptions regarding the transformation of such spaces into SUGS and their impact on psychological well-being. By examining how these preferences and perceived health benefits shape the value of transformed spaces, the research aims to inform effective urban design strategies. The participants underwent visual stimulation, with psychological reactions recorded through Electroencephalogram (EEG) readings and assessed via Questionnaire. Machine learning techniques analyzed EEG sub-band data, achieving an average accuracy of 92.8% when comparing leftover and designed spaces. Results revealed that different types of transformed spaces provoke distinct physiological and preference responses. Specifically, viewing SUGS was associated with significant changes in gamma wave power, suggesting a correlation between enhanced gamma activity and increased feelings of empathy. Moreover, participants also reported enhanced comfort, relaxation, and overall mood, and a strong preference for SUGS over untransformed spaces, emphasizing the value placed on these areas for their health benefits. This research highlights the positive impact of even SUGS on mental health, using EEG data to assess emotional states triggered by urban spaces. The study concludes with a call for further research to investigate the long-term benefits of SUGS on well-being, alongside an exploration of the gamma band as a neural marker for emotional restoration in urban green spaces. This research highlights the crucial role of urban design in fostering psychological well-being through the strategic development of green spaces, suggesting a paradigm shift toward more inclusive, health-promoting urban environments.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.