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COVID-19 is exacerbating isolation issues faced by older adults, which may lead to increased risk for depression and other mental health issues. Social robots are being explored for their potential to alleviate these challenges through conversational therapy, companionship, and connectedness regardless of where older adults chose to age in place—from home to long-term care facilities. This is a discussion piece with the objective of raising awareness to the topic of social isolation within older adults and current limitations in the field of social robotics. We discuss the state of the art in social robotics for aging in place and bring attention to remaining challenges for addressing isolation and mental health especially during and after COVID-19. This paper provides a discussion on critical differences between environments where older individuals age, and how designs should account for these variations. Lastly, this paper highlights the physical and mental health issues of caregivers and provides a discussion of challenges that remain toward using social robotics to assist those who take care of the aging population.
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Evidence and theory for lower rates of depression in larger US urban areas
It is commonly assumed that cities are detrimental to mental health. However, the evidence remains inconsistent and at most, makes the case for differences between rural and urban environments as a whole. Here, we propose a model of depression driven by an individual’s accumulated experience mediated by social networks. The connection between observed systematic variations in socioeconomic networks and built environments with city size provides a link between urbanization and mental health. Surprisingly, this model predicts lower depression rates in larger cities. We confirm this prediction for US cities using four independent datasets. These results are consistent with other behaviors associated with denser socioeconomic networks and suggest that larger cities provide a buffer against depression. This approach introduces a systematic framework for conceptualizing and modeling mental health in complex physical and social networks, producing testable predictions for environmental and social determinants of mental health also applicable to other psychopathologies.
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- Award ID(s):
- 1952050
- PAR ID:
- 10297038
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 118
- Issue:
- 31
- ISSN:
- 0027-8424
- Page Range / eLocation ID:
- e2022472118
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1073/pnas.2022472118
