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Neighborhood walkability has a significant influence on older adults’ physical and mental health. These effects are amplified in underserved communities (e.g., low-income groups, ethnic minorities) which are often associated with worsening pedestrian infrastructure and safety concerns. This paper investigates environmental stressors linked with decreased walkability of older adults from a low-income Latino community, and how these are associated with physiological, physical, environmental, and sociological variables. 68 older adults were recruited from a primarily Hispanic neighborhood, and each collected two-weeks of multimodal data using wearable and smartphone devices. The data included location, acceleration, and physiological data, such as heart rate and electrodermal activity, from participants’ outdoor walking trips. Environmental stressors participants encountered during each walking trip were self-reported through a mobile application. The first part of this paper discusses unique challenges faced when working with this under-studied population and strategies used to address these challenges while maintaining scientific rigor. The second part of the paper describes results from the preliminary analysis employing linear mixed models (LMM) and machine learning classifiers to examine potential associations between self-reported and objectively-measured stress levels among participants, as well as the effect of environmental, sociological, and individual variables on physiological stress responses while walking. Findings from this study support new avenues for engaging with and gaining deeper insights into a unique and often overlooked population while laying the groundwork for developing new computational models for quantifying environmental stress using wearable and smartphone devices.
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Stored only when both papers are in the corpus — 3 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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