In multiple facets of subjective memory decline sex moderates memory predictions

Abstract

Introduction Two established subjective memory decline facets (SMD; complaints, concerns) are early indicators of memory decline and Alzheimer's disease. We report (1) a four‐facet SMD inventory (memory complaints, concerns, compensation, self‐efficacy) and (2) prediction of memory change and moderation by sex. Methods The longitudinal design featured 40 years (53 to 97) of non‐demented aging ( n = 580) from the Victoria Longitudinal Study. Statistical analyses included confirmatory factor analyses and conditional latent growth modeling. Results The four‐facet SMD Inventory was psychometrically confirmed. Longitudinal analyses revealed significant variability in level and change for SMD and memory. Prediction analyses showed complaints and concerns predicted lower level and steeper memory decline; however, follow‐up moderation analyses revealed selective predictions for females. Memory compensation predicted decline overall. Lower memory self‐efficacy predicted steeper decline selectively for males. Discussion Although traditional and novel SMD facets predicted memory decline, differential sex moderation was observed. SMD research benefits from conceptual complementarity and precision prediction.

Subjective memory concerns, poor vascular health, and male sex predict exacerbated memory decline trajectories: An integrative data-driven class and prediction analysis.

“…As described in the previous study (Drouin et al, 2020), a set of exclusionary criteria were applied to a source sample of participants with data collected since 2002 (N = 652): (a) a diagnosis or indication of AD or any other dementia (n = 4), (b) a Mini-Mental State Examination (MMSE) score of less than 24 (n = 1), (c) a self-report of "severe" for potential comorbid conditions (e.g., epilepsy, head injury, depression, alcohol dependence; n = 60), (d) use of antipsychotic medication (n = 2), and (e) a self-report of "severe" or "moderate" for potential comorbid diseases such as neurological conditions (e.g., stroke, Parkinson's disease; n = 5). After exclusions, the final sample for this study consisted of 580 nondemented older adults (M age = 70.2; range = 53-95 at baseline; % female = 65).…”

“…Beliefs about changes in memory per se (i.e., subjective memory decline [SMD]) have been reported to be predictive of future objective memory decline (Drouin et al, 2020; Glodzik-Sobanska et al, 2007; Koppara et al, 2015; Snitz et al, 2015) and even increased risk of AD (Buckley et al, 2016; Mitchell et al, 2014; Slot et al, 2019; Wolfsgruber et al, 2016). Typically, SMD is represented by two facets: memory complaints and memory concerns.…”

“…Typically, SMD is represented by two facets: memory complaints and memory concerns. Memory complaints are often captured by a single item (e.g., “Do you feel like your memory is becoming worse?”) reflecting the endorsement of recent memory change (Drouin et al, 2020; Jessen, Amariglio, et al, 2014; Jessen, Wolfsgruber, et al, 2014). For memory concerns, a follow-up item is used to assess whether an individual is additionally worried or concerned about their perceived memory decline.…”

“…We defined the two additional facets as follows. Memory compensation referred to one’s awareness of possible memory decline and subsequent actions or compensatory strategies deployed to mitigate or address this decline (de Frias & Dixon, 2005; Dixon & de Frias, 2007; Drouin et al, 2020). Memory self-efficacy referred to one’s own beliefs about their ability to manage their memory ability/decline as well as the extent to which they believe they can control memory aging changes (Drouin et al, 2020; Hertzog et al, 1990; Valentijn et al, 2006).…”

Data-driven approaches to executive function performance and structure in aging: Integrating person-centered analyses and machine learning risk prediction.

“…RFA is a machine learning analytic technique specifically applicable to biomarker prediction analyses, especially when multiple predictors are examined in a quantitatively competitive context. We have deployed this technology in several recent studies on brain aging and dementia (e.g., Caballero et al, 2021; Drouin et al, 2020; McFall et al, 2019; Sapkota et al, 2018). Specifically, RFA is an ensemble of decision trees constructed from a training data set that is internally validated to yield a prediction of an outcome (such as differential clinical status) and indicate the relative importance of tested predictors (Boulesteix et al, 2012).…”