Assessing momentary stress responses to dynamic real-time greenspace exposure: Unveiling algorithmic uncertainty and...

Mental stress issues are emerging among residents of modern cities. Among environmental factors associated with stress mitigation, greenspace has consistently been shown to have significant stress-reducing properties. However, the temporality of greenspace exposure, particularly the cumulative threshold effect in urban environments, has been largely neglected in past studies. In addition, different algorithms and their related measurements of greenspace have led to inconsistent mental health outcomes. To address both gaps, we evaluated the dynamic greenspace exposure of 221 Hong Kong residents by integrating three distinct green space measurements: the Normalized Difference Vegetation Index (NDVI), the Green Space Area Ratio (GSAR), and the Eye-level Green View Index (GVI) based on individual real-time GPS data. We subsequently gauged individual momentary stress levels via Ecological Momentary Assessment (EMA) and modeled its association with dynamic green space exposure using mixed ordinal logistic regression across diverse cumulative time frames. The results reveal great disparities in greenspace-stress association between different greenspace measurements and different cumulative time frames: (1) Among the three measurements, GVI is the most robust and effective measurement in assessing the stress-reducing effect in urban environments. (2) Within specific time frames, cumulative exposure has a more pronounced stress-reducing influence than momentary exposure. (3) The stress-reducing effects of cumulative eye-level greenspace exposure exhibit two temporal phases: A continuous exposure spanning 12-36 min leads to a progressive enhancement in the stress-mitigating effect of eye-level greenspace, peaking initially and then diminishing after 36 min. Upon extended exposure reaching 2.3 h, the stress-alleviating impact of eye-level green space peaks once again before gradually waning. Our research underscores the need for multiple measurements of environmental exposure to address the algorithmic uncertainty in environmental health research and deeper insights into the temporality of the greenspace-mental relationship.