Back

Remote Assessment of Mental Health and Physical Activity in Older Black Americans during the COVID-19 Pandemic

Huang, C.-H. S.; Shutes-David, A.; Payne, S.; Wilson, K.; Brown, K.; Vintch, K.; Seto, E.; Tsuang, D. W.

2025-05-25 health informatics
10.1101/2025.05.23.25328188 medRxiv
Show abstract

Older Black Americans (BAs) face disparities in diagnosing and treating cognitive and mental health conditions. The shift to remote care during the COVID-19 pandemic may have exacerbated these inequities through digital barriers. This pilot study evaluated the feasibility of remote mental-health assessments and accelerometry among older BAs (n=6, age 65+, with subjective memory complaints but no dementia diagnosis). Participants completed three 2-week assessment modalities: pen and paper, telephone, and online/videoconferencing, and wore wrist accelerometers to measure physical activity. All modalities were well-tolerated, with participants expressing the strongest preference for the personal contact of telephone and online/videoconferencing and enthusiasm for accelerometry. The UCLA-Loneliness score demonstrated significant positive correlations with GAD-7 and significant negative correlations with LSNS-R and SF-20 mental-health domain. Although significant correlations between physical activity levels and mental-health assessment scores were not observed, trends in correlation coefficients suggests that mean daily, daytime, and nighttime hourly activity counts were negatively correlated with GAD-7 and UCLA-L and positively with LSNS-R scores. These findings suggest older BAs are amenable to accelerometry and remote assessments involving personal contact with providers. Observed trends suggest physical activity may be associated with reduced anxiety, loneliness, and social isolation. Larger studies are necessary to confirm these potential findings.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.