COVID-19 Impact on Older African Americans in the Minority Aging Research Study: Survey Engagement, Self-Reported Health, and Actigraphy Data (Preprint)
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic disrupted older adults' daily lives, particularly concerning social interaction, physical activity, and sleep quality. Older African Americans were disproportionately affected yet remain underrepresented in research documenting the impact of the COVID-19 pandemic. OBJECTIVE: This study investigated changes in self-reported health, survey engagement, physical activity, and sleep duration among older African American adults in the Minority Aging Research Study (MARS) before and after Illinois' March 21, 2020, COVID-19 stay-at-home order, using online surveys and actigraphy watch data. METHODS: MARS is a longitudinal observational cohort study of older African American adults who enroll initially without dementia. We examined a subset of MARS participants enrolled in the Collaborative Aging Research Using Technology initiative. Weekly online health survey responses to binary (yes/no) questions (eg, away from home overnight, overnight visitors, blue mood, loneliness, medication changes, falls, accidents, hospitalizations, health limitations, living space change, or assistance change) were analyzed for 32 weeks (November 30, 2019, to July 11, 2020) and actigraphy data for over 10 weeks (February 15, 2020, to April 15, 2020). Generalized linear mixed models with a logit link function for binary outcomes and linear mixed models for continuous outcomes, adjusted for age, sex, and education, were used to assess changes in self-reported experiences and actigraphy-derived daily steps and nightly sleep and reported as odds ratios (ORs) with 95% credible intervals (CrIs). RESULTS: Of 59 participants (mean age 76.6, SD 6.1 years; male: 11/59, 19%) included in the survey data analysis, 43 (73%) were classified as high-engagement (completed at least 50% of the weekly surveys) and 16 (27%) as low-engagement; these participants were more likely to have mild cognitive impairment (3/43, 7% vs 5/16, 31%; P=.03) and lower mean Mini-Mental State Examination scores (28.1, SD 1.4 vs 28.9, SD 1.0; P=.04). Generalized linear mixed models on the full analytic sample (N=59) showed significant reductions post-COVID-19 in being away from home overnight (OR 0.30, 95% CrI 0.20-0.46), having overnight visitors (OR 0.44, 95% CrI 0.31-0.64), a medication change (OR 0.60, 95% CrI 0.42-0.86), and a health limitation (OR 0.70, 95% CrI 0.52-0.95). COVID-19 and study-related technical disruptions limited actigraphy data availability. Among 15 participants with valid data, mean daily step count decreased significantly (20.1%; 1646, SD 1306 to 1315, SD 1149 steps; P<.001); nightly sleep duration decreased but not significantly (2.8%; 7.1, SD 2.3 hours to 6.9, SD 2.3 hours; P=.49). CONCLUSIONS: Despite widespread COVID-19 disruptions, older African American MARS participants maintained stable survey engagement. Participants with low survey engagement were more likely to have cognitive impairment, suggesting that mild cognitive challenges may hinder sustained participation with online responses. A subset with valid actigraphy data showed reduced physical activity. Although technical issues limited data availability, findings support the value of objective monitoring and highlight the challenges associated with public health disruptions to research infrastructure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".