COVID-19 Impact on Older African Americans in the Minority Aging Research Study: Survey Engagement, Self-Reported Health, and Actigraphy Data (Preprint)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».