3 Exploring the Relationship Between Cognition, Adherence, and Engagement in Compensatory Strategy Training in Mild Cognitive Impairment
Notice bibliographique
Résumé
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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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 | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».