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
Bibliographic record
Abstract
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.
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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 | 0.054 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".