Long-term effects of cognitive training in Parkinson’s disease: A randomized, controlled trial
Bibliographic record
Abstract
Computerized cognitive training may be promising to improve cognitive impairment in Parkinson’s disease and has even been suggested to delay cognitive decline. However, evidence to date is limited. The aim of this study was to assess the durability of eight-week cognitive training effects at up to two years follow-up. One hundred and thirty-six (1 3 6) individuals with Parkinson’s disease, subjective cognitive complaints but without severe cognitive impairment (Montreal Cognitive Assessment ≥ 22) participated in this double-blind RCT. Participants underwent an eight-week home-based intervention of either adaptive, computerized cognitive training with BrainGymmer (n = 68) or an active control (n = 68). They underwent extensive neuropsychological assessment, psychiatric questionnaires and motor symptom assessment at baseline and one and two years after the intervention. We used mixed-model analyses to assess changes in cognitive function at follow-up and performed Fisher’s exact tests to assess conversion of cognitive status. There were no group differences on any neuropsychological assessment outcome at one- and two-year follow-up. Groups were equally likely to show conversion of cognitive status at follow-up. A considerable amount of assessments was missed (1y: n = 27; 2y: n = 33), most notably due to COVID-19 regulations. Eight-week cognitive training did not affect long-term cognitive function in Parkinson’s disease. Future studies may focus on one cognitive subgroup to enhance reliability of study results. Intervention improvements are needed to work towards effective, lasting treatment options.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".