59 Effects of Cognitive Impairment, Geriatric Depression, and Anxiety on the Texas Functional Living Scale (TFLS) in a Memory Disorder Clinic
Notice bibliographique
Résumé
Objective: The Texas Functional Living Scale (TFLS) is a measure of adaptive functioning commonly utilized across the geriatric population. Current research suggests that those with Alzheimer’s disease and other dementias perform poorly on the TFLS, compared to those with mild cognitive impairment (MCI) and normal cognition (Cullum et al., 2001). Additional research is needed to examine the influence anxiety and depressive symptoms have on activities of daily living (ADLS) in individuals being evaluated for memory disorders. This study will examine the effects of anxiety and depression on adaptive functioning across all patients, and within samples of those with dementia and MCI. It is hypothesized that higher reported anxiety and depressive symptoms will predict lower scores of ADLS. Participants and Methods: Patients at a memory disorder clinic (N = 756; 58.2% female) were screened for cognitive impairment using the Montreal Cognitive Assessment (MoCA). A brief neuropsychological evaluation (BNE) was then conducted in which the TFLS, Geriatric Depression Scale (GDS), and Geriatric Anxiety Inventory (GAI) were administered, among other measures. Results: A stepwise hierarchical regression was conducted on the entire sample to examine the effects of anxiety and depressive symptoms on TFLS performance, controlling for cognitive impairment using the MoCA. Lower MoCA scores explained a significant amount of variance in TFLS performance (R2 = 0.456, F(1, 754) = 632.78, p < .001). MoCA scores (b = 1.27, p < .001), the GAI (b = 0.14, p = .019), and the GDS (b = 0.10, p = 0.039) were significant predictors of poor TFLS performance across the entire sample. Although the MoCA, GDS, and GAI were each significant predictors of the TFLS, the increased variance explained by the GDS and GAI individually was incremental (AR2 = 0.003, F(1, 752) = 3.90, p = .049). Stepwise hierarchical regressions were also conducted on subsamples diagnosed with MCI (N = 171) and dementia (N = 394). For those with MCI, MoCA scores explained a significant amount of variance in TFLS performance (R2 = 0.044, F(1, 169) = 7.80, p = .006). Neither the GAI nor GDS explained significant additional variance. Only MoCA scores (b = .30, p =.006) predicted TFLS performance. For those with dementia, MoCA scores explained significant variance in TFLS scores (R2 = 0.338, F(1, 392) = 200.47, p < .001). The GAI explained additional significant variance when added (AR2 = 0.009, F(1, 391) = 5.26, p = .022). The GDS did not explain any additional variance. Both the MoCA (b = 1.29, p < .001) and the GAI (b = -0.15, p = .002) significantly predicted TFLS performance. Conclusions: While results suggest that anxiety and depressive symptoms alone do not explain a significant degree of variance within scores of adaptive functioning across the entire sample, elevated ratings of anxiety and depressive symptoms were significant predictors of lower scores of ADLS, suggesting some support for our hypothesis. Additionally, anxiety symptoms significantly explained increased variance in TFLS scores for those diagnosed with dementia, suggesting a potential relationship between anxiety levels and poor adaptive functioning for dementia patients.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».