The impact of mental health outcomes on cognition during the COVID‐19 pandemic in older adults with remitted depression, mild cognitive impairment, or normal cognition
Bibliographic record
Abstract
Abstract Background Older adults may be vulnerable to worsening cognition during pandemics because of public health restrictions that increase social isolation and negatively impact mental health. In the current study, we assessed mental health outcomes during the COVID‐19 pandemic and their relationship with cognitive status at 6 months in older adults with remitted major depressive disorder (rMDD), mild cognitive impairment (MCI), or normal cognition (NC). Method The sample consisted of 108 older adults enrolled from ongoing studies [37M, mean age = 70.6 (SD = ±5.75)], including 71 NC, 21 rMDD, and 16 MCI]. Participants completed self‐report measures including the Patient Health Questionnaire‐9 to assess depression, Patient Reported Outcomes Measurement Information System to assess anxiety, Perceived Stress Scale to assess general stress, Impact of Events Scale‐Revised to assess post‐traumatic stress, and the Clinical Dementia Rating Scale (CDR) at baseline, 3 and 6 months. Repeated measures ANCOVA of mood and cognitive measures with time as a within‐subject factor and covariates of parent study and mood/memory diagnosis assessed change over time in mental health and cognition. Regression modelling of CDR Sum of Boxes (CDR‐SB) score at 6 months with baseline mood and anxiety scores as predictors and age, gender, parent study, and mood/memory diagnosis as covariates were used to assess the association between mental health and future cognition. Result Mood and cognition did not significantly vary over the three time‐points. Depression and general stress at baseline were predictive of CDR‐SB at 6‐month follow‐up, accounting for 14% of its variance (F(6,55) = 2.69, p = .023, B = .28, p = .038; F(6,72) = 3.16, p = .008, B = .21, p = .088, respectively). Neither anxiety nor post‐traumatic stress symptom severity predicted CDR‐SB. Conclusion These findings are consistent with epidemiological studies supporting associations between depression or stress and risk of dementia (Byers & Yaffe, 2011; Stuart & Padgett, 2020). Limitations of the study include non‐random sampling and small rMDD and MCI samples. Nevertheless, these preliminary findings suggest the need to address adverse mental health outcomes in seniors during the COVID‐19 pandemic to potentially reduce risk of future cognitive decline.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".