3 Exploring the Relationship Between Cognition, Adherence, and Engagement in Compensatory Strategy Training in Mild Cognitive Impairment
Bibliographic record
Abstract
Objective: Compensatory strategy training has been identified as a useful mechanism to improve everyday cognitive function among older adults with Mild Cognitive Impairment (MCI). Despite this, few studies have looked at cognitive factors that support adherence and engagement in these programs, which are key to maximizing benefit. The present study aimed to evaluate the relationship between cognition, adherence, and engagement during a group-based compensatory strategy training for people with MCI. We hypothesized individuals with better memory and executive function performance would show better adherence and higher engagement scores in cognitive training classes. Participants and Methods: Twenty-five participants enrolled in Emory University's Charles and Harriet Schaffer Cognitive Empowerment Program (CEP) completed an 11-week compensatory strategy training group (CEP-CT). CEP-CT is adapted from Ecologically Oriented Neurorehabilitation to be suitable for people with MCI. Participants enrolled were on average 74.3 years old (SD= 5.4), 52% Male, primarily Caucasian (80%; 16% African American), and college educated (M= 16.5 years; SD= 2.7). All participants received clinical diagnoses of MCI prior to enrollment in the program. Participants completed multiple cognitive measures, including Montreal Cognitive Assessment (MoCA), Hopkins Verbal Learning Test (HVLT), Trail Making Test A & B (TMT), Number Span Forward (NSF) and verbal fluency (S-words and Animals). For all group sessions, class attendance (present vs. not present) was recorded for each participant and their care partner, and engagement ratings for participants were recorded by the facilitator on a 1 to 5 scale (higher scores indicate better engagement). Outcomes include adherence to cognitive training (percentage of sessions attended; M= 82% class attendance, SD= 18%) as well as the average engagement ratings across 11 weeks (M= 3.25, SD= .40). Results: Bivariate Pearson correlations revealed that individuals who attended more classes also demonstrated better engagement in class, r= .44, p= .03. Class attendance was significantly related to performance on measures of memory and executive function (HVLT: r= -.42, p= .04; TMT-B: r= .69, p= .04), such that participants who performed worse on these measures attended more CEP-CT classes. Average engagement ratings were unrelated to cognitive performance. Conclusions: Results did not support initial hypotheses, and instead indicate individuals with poorer performance on measures of memory and executive function had better adherence to CEP-CT classes, as measured by attendance. These results may indicate individuals experiencing cognitive difficulties are more likely to attend cognitive training classes. Subjective engagement ratings were unrelated to cognition; however, individuals who attended more sessions were more engaged in cognitive training classes. Future areas of research include objective measurement of class engagement as well as the incorporation of nuanced adherence metrics to further elucidate the relationship between these factors and cognition in MCI.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".