Individual Differences and Features of Self-reported Memory Lapses as Risk Factors for Alzheimer Disease Among Adults Aged 50 Years and Older: Protocol for a Coordinated Analysis Across Two Longitudinal Data Sets
Notice bibliographique
Résumé
BACKGROUND: Increasing evidence has promoted the clinical utility of self-reported memory problems for detecting early impairment associated with Alzheimer disease (AD). However, previous studies investigating memory problems often conflated the types of problems (ie, retrospective and prospective) with their features (ie, frequency and consequences). This bias limits the specificity of traditional measures of memory problems and minimizes their ability to detect differential trajectories associated with cognitive decline. In this study, we use a novel measure of self-reported memory problems that uses daily reports of memory lapses to disentangle types from features for analyzing the impact of each dimension in two longitudinal data sets. Furthermore, this study explores the individual difference factors of age and gender as potential moderators of the relationships between self-reported memory lapses and objective cognitive decline. OBJECTIVE: The aim of this study is to describe the protocol for a secondary data analysis project that explores the relationship between experiences of daily memory lapses and their associations with cognitive decline in middle-aged and older adults. METHODS: This study uses multilevel, coordinated analyses across two measurement burst data sets to examine the links between features and consequences of memory lapses (retrospective and prospective) and their association with objective cognitive decline. This study's sample (N=392; aged 50-85 years; n=254, 64.8% women) is drawn from two ongoing, nationally funded research studies: The Effects of Stress on Cognitive Aging, Physiology, and Emotion study and the Einstein Aging Study. Both studies assess the daily experience of memory lapses, including the type as well as the emotional and functional outcomes, and objective measures of cognition, such as processing speed and episodic memory. We will use multilevel modeling to test our conceptual model demonstrating that differences in frequency and types of memory lapses show differential trends in their relationships with cognitive decline and that these relationships vary by the age and gender of participants. RESULTS: This project was funded in August 2019. The approval for secondary data analysis was given by the institutional review board in February 2020. Data analysis for this project has not yet started. CONCLUSIONS: The early and accurate identification of individuals most at risk for cognitive decline is of paramount importance. Previous research exploring self-reported memory problems and AD is promising; however, limitations in measurement may explain previous reports of inconsistences. This study addresses these concerns by examining daily reports of memory lapses, how these vary by age and gender, and their relationship with objective cognitive performance. Overall, this study aims to identify the key features of daily memory lapses and the differential trajectories that best predict cognitive decline to help inform future AD risk screening tools. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/25233.
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 | 0,054 | 0,066 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,005 | 0,005 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,004 |
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 ».