Time Series Analysis for Forecasting Cognitive Deterioration After The Covid-19 Pandemic in Healthy Japanese Elderly: A Pilot Study
Bibliographic record
Abstract
In Kyoto, Japan, government agencies implemented stay-at-home and social distancing measures from April 2020 to September 2022 to curb the spread of COVID-19. Mild cognitive impairment (MCI) is a precursor to dementia. Given the rapid progression from MCI to dementia, there is a pressing need to identify robust predictors of MCI.The study sample was 19 healthy elderly people (aged ≥ 65 years) enrolled in the Open University Program. This study employed a two-phase dataset: a pre-lockdown phase (October 2018, October 2019, and March 2020) and a post-lockdown phase (September 2022 onwards). The Japanese version of the Montreal Cognitive Assessment (MoCA-J) was used as a cognitive function test and was administered by skilled occupational therapists. We evaluated the applicability of exponential smoothing models to forecast MoCA-J scores in the 5 years following the COVID-19 pandemic. Data were analyzed using SPSS Statistics version 29.0 for Windows (IBM Japan).The COVID-19 pandemic significantly decreased the MoCA-J scores of 11 older adults who had an initial MoCA-J score of 26 or higher (p<0.05) and 5 individuals were diagnosed with MCI. The only item that showed a decrease in all participants was delayed recall, with an average score of 2.6. Exponential smoothing models were used to analyze time-series data from 5 individuals who developed MCI after the COVID-19 pandemic. The models predicted a decrease of 4 to 6 points in the target by 2027 after the COVID-19 pandemic.The findings of this study suggest that individuals who received stay-at-home orders during the COVID-19 pandemic experienced significant reductions in their MoCA-J scores. The time series analysis revealed a rapid decline in cognitive function among those who developed MCI. These findings highlight the urgent need for timely interventions tailored to individual needs to prevent progression to dementia.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".