Older adults bear a high risk of multiple chronic diseases and face high healthcare cost expenditures. Physical activity such as walking is considered the most effective preventive strategy to reduce health risks and burdens. Strong evidence shows that built environmental factors such as walkability are strongly linked with older adults' walking and overall physical activity, with frail older adults being more susceptible to environmental barriers/challenges. However, traditional urban planning/design practices that target the 'average person' have failed to meet the special needs of older adults experiencing age-related physiological and psychosocial conditions. As a result, neighborhood environments in most communities can be the source of significant physical and emotional distress to older adults, thereby inhibiting their mobility and outdoor physical activity. This project thus aims to (1) create a digital twin city (DTC) model that reveals older adults' collective distress and associated environmental conditions, and (2) leverage the DTC model to develop and implement technological and environmental interventions that alleviate such distress and promote older adults' independent mobility and physical activity. The DTC model will be constructed by matching or twinning crowdsourced biosignals (i.e., physiological sensing data from unobtrusive wearable devices worn by older adults) with street-level visual data from participatory sensing and Google Street View, enabling the establishment of a city's affective map. Thus, this project will leverage the DTC model to design, implement, and/or evaluate stress-responsive interventions, in collaboration with local stakeholders and older adults in an underserved neighborhood in Houston, TX. The empirical outcomes of our work will quantify the experience of community members on a daily basis, which will promote productive conversations and targeted interventions at both the individual and the community levels. While this research leverages Houston, TX as a testbed, research findings are expected to generalize to other communities in the U.S. and artifacts from this work (e.g., DTC model) will be made widely available to the general public.

This project will make methodological advancements in (1) constructing group-specific saliency models from reliable physiological measures, (2) developing personalized distress prediction using multimodal data, and (3) formulating distress-responsive routing algorithms. The research team with community partners will incorporate these outcomes into designing, implementing, and evaluation three technology/environmental stress-responsive interventions: (1) providing community affective maps to enhance older adults' awareness of environmental stressors in their communities; (2) offering stress-responsive routing plans that optimize older adults' route plans responding to individuals' health and mobility conditions; and (3) implementing environmental interventions (i.e., promoting age-friendly Privately Owned Public Space (POPS) redesigning/retrofitting neighborhood infrastructure/facilities) to alleviate location-based distress. Additionally, the community engagement activities contained within this project will critically enhance older adults' understanding of safety risks and of mobility challenges in the community environment and will contribute to the community's capacity building in promoting health and reducing distress.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.