A remote digital memory composite to detect cognitive impairment in memory clinic samples in unsupervised settings using mobile devices

Remote monitoring of cognition holds the promise to facilitate case-finding in clinical care and the individual detection of cognitive impairment in clinical and research settings. In the context of Alzheimer's disease, this is particularly relevant for patients who seek medical advice due to memory problems. Here, we develop a remote digital memory composite (RDMC) score from an unsupervised remote cognitive assessment battery focused on episodic memory and long-term recall and assess its construct validity, retest reliability, and diagnostic accuracy when predicting MCI-grade impairment in a memory clinic sample and healthy controls. A total of 199 participants were recruited from three cohorts and included as healthy controls (n = 97), individuals with subjective cognitive decline (n = 59), or patients with mild cognitive impairment (n = 43). Participants performed cognitive assessments in a fully remote and unsupervised setting via a smartphone app. The derived RDMC score is significantly correlated with the PACC5 score across participants and demonstrates good retest reliability. Diagnostic accuracy for discriminating memory impairment from no impairment is high (cross-validated AUC = 0.83, 95% CI [0.66, 0.99]) with a sensitivity of 0.82 and a specificity of 0.72. Thus, unsupervised remote cognitive assessments implemented in the neotiv digital platform show good discrimination between cognitively impaired and unimpaired individuals, further demonstrating that it is feasible to complement the neuropsychological assessment of episodic memory with unsupervised and remote assessments on mobile devices. This contributes to recent efforts to implement remote assessment of episodic memory for case-finding and monitoring in large research studies and clinical care.

The importance of the dyad: Participant perspectives on sharing biomarker results in Alzheimer's disease research

BACKGROUND

In the asymptomatic "preclinical" phase of Alzheimer's disease (AD), abnormal biomarkers indicate risk for developing cognitive impairment. Biomarker information is increasingly being disclosed to participants in research settings, and biomarker testing and results disclosure will be implemented in clinical settings in the future. Biomarker disclosure has potential psychosocial benefits and harms, impacting affected individuals and their support person(s). Limited data are available about with whom research participants share their results, information that will be necessary to develop disclosure protocols and post-disclosure resources. Additionally, existing research has been conducted in largely White cohorts, limiting applicability to future clinical populations.

METHODS

We enrolled a diverse cohort of 329 adults (184 non-Hispanic White and 145 Black/African American individuals) who previously participated in AD research. After reviewing a vignette describing a hypothetical biomarker research study, participants indicated their anticipated willingness to share biomarker results with loved ones, and what reactions they anticipated from others. Using mixed-methods analysis, we identified responses related to willingness to share results.

RESULTS

A majority (78.7%) were willing to share their results with support persons. Many (59.6%) felt it would not be difficult to share, and most (90.6%) believed their loved ones would be supportive. The most common reasons for sharing were to prepare for possible future AD (41.0% of respondents), while the most common reason for not sharing was to avoid worrying loved ones (4.8% of respondents). A total of 7.3% of respondents related reasons regarding being unsure about sharing.

DISCUSSION

Participants' interest in sharing results supports integrating support persons into AD biomarker research, and may help maximize potential benefits for participants. Communicating with this "dyad" of research participant and support person(s) may improve involvement in research, and help prepare for implementation of clinical biomarker testing by clarifying communication preferences and the influence of support persons on psychosocial outcomes.

Anticipated reactions to learning Alzheimer's disease biomarker results

INTRODUCTION

We developed the Alzheimer's Biomarker Survey to assess willingness to enroll in biomarker studies that disclose results and anticipated reactions to an elevated biomarker result.

METHODS

Participants included cognitively unimpaired adults enrolled in longitudinal AD studies (n = 334, mean age = 64.8 ± 7.7, 44% non-Hispanic Black or African American). Exploratory and confirmatory factor analyses determined the latent structure comprising anticipated reactions to learning AD biomarker results. Measurement invariance was tested across racial groups.

RESULTS

Two models comprising behavior change and psychological impact fit well for the total sample and the two racial groups. The 2-factor behavior change model assessed constructs of planning and dementia risk-reduction. The 3-factor psychological impact model assessed constructs of distress, cognitive symptoms, and stigma. Both models exhibited measurement invariance across racial groups.

DISCUSSION

The 28-item Anticipated Reactions to AD Biomarker Disclosure scale is a reliable and valid measure of anticipated reactions when communicating AD biomarker results to research participants.

Spatio-Semantic Graphs From Picture Description: Applications to Detection of Cognitive Impairment

Clinical assessments often use complex picture description tasks to elicit natural speech patterns and magnify changes occurring in brain regions implicated in Alzheimer's disease and dementia. As The Cookie Theft picture description task is used in the largest Alzheimer's disease and dementia cohort studies available, we aimed to create algorithms that could characterize the visual narrative path a participant takes in describing what is happening in this image. We proposed spatio-semantic graphs, models based on graph theory that transform the participants' narratives into graphs that retain semantic order and encode the visuospatial information between content units in the image. The resulting graphs differ between Cognitively Impaired and Unimpaired participants in several important ways. Cognitively Impaired participants consistently scored higher on features that are heavily associated with symptoms of cognitive decline, including repetition, evidence of short-term memory lapses, and generally disorganized narrative descriptions, while Cognitively Unimpaired participants produced more efficient narrative paths. These results provide evidence that spatio-semantic graph analysis of these tasks can generate important insights into a participant's cognitive performance that cannot be generated from semantic analysis alone.