Examining the benefits and harms of Alzheimer's disease screening for family members of older adults: study protocol for a randomized controlled trial

Relationship between health-related quality of life, depression, and anxiety in older primary care patients and their family members

OBJECTIVES

Patient-family member dyads experience transitions through illness as an interdependent team. This study measures the association of depression, anxiety, and health-related quality of life (HRQOL) of older adult primary care patient-family member dyads.

METHODS

Baseline data from 1,808 patient-family member dyads enrolled in a trial testing early detection of Alzheimer's disease and related dementias in primary care. Actor-Partner Independence Model was used to analyze dyadic relationships between patients' and family members' depression (PHQ-9), anxiety (GAD-7), and HRQOL (SF-36 Physical Component Summary score and Mental Component Summary score).

RESULTS

Family member mean (SD) age is 64.2 (13) years; 32.2% male; 84.6% White; and 64.8% being the patient's spouse/partner. Patient mean (SD) age is 73.7 (5.7) years; 47% male; and 85.1% White. For HRQOL, there were significant actor effects for patient and family member depression alone and depression and anxiety together on their own HRQOL (p < 0.001). There were significant partner effects where family member depression combined with anxiety was associated with the patient's physical component summary score of the SF-36 (p = 0.010), and where the family member's anxiety alone was associated with the patient's mental component summary score of the SF-36 (p = 0.031).

CONCLUSION

Results from this study reveal that many dyads experience covarying health status (e.g. depression, anxiety) even prior to entering a caregiving situation.

The methodology of the Agile Nudge University

Introduction

The Agile Nudge University is a National Institute on Aging-funded initiative to engineer a diverse, interdisciplinary network of scientists trained in Agile processes.

Methods

Members of the network are trained and mentored in rapid, iterative, and adaptive problem-solving techniques to develop, implement, and disseminate evidence-based nudges capable of addressing health disparities and improving the care of people living with Alzheimer's disease and other related dementias (ADRD).

Results

Each Agile Nudge University cohort completes a year-long online program, biweekly coaching and mentoring sessions, monthly group-based problem-solving sessions, and receives access to a five-day Bootcamp and the Agile Nudge Resource Library.

Discussion

The Agile Nudge University is evaluated through participant feedback, competency surveys, and tracking of the funding, research awards, and promotions of participating scholars. The Agile Nudge University is compounding national innovation efforts in overcoming the gaps in the ADRD discovery-to-delivery translational cycle.

The Impact of the COVID-19 Pandemic on the Mental Health of Older Primary Care Patients and Their Family Members

The COVID-19 pandemic introduced mandatory stay-at-home orders and concerns about contracting a virus that impacted the physical and mental health of much of the world's population. This study compared the rates of depression and anxiety in a sample of older primary care patients (aged ≥65 years old) and their family members recruited for a clinical trial before and during the COVID-19 pandemic. Participants were dyads enrolled in the Caregiver Outcomes of Alzheimer's Disease Screening (COADS) trial, which included 1,809 dyads of older primary care patients and one of their family members. Mean scores on the Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder Scale-7 (GAD-7) were measured and compared before and during the pandemic. We found no difference in depression and anxiety among dyads of older primary care patients and their family members recruited before and during COVID-19. Additionally, we found that older primary care patients and family members who reported their income as comfortable had significantly lower depression and anxiety compared to those who reported having not enough to make ends meet. Along with this, older primary care patients with a high school education or less were more likely to have anxiety compared to those with a postgraduate degree. Moreover, our findings support the notion that certain demographics of older primary care patients and family members are at a higher risk for depression and anxiety, indicating who should be targeted for psychological health interventions that can be adapted during COVID-19. Future research should continue monitoring older primary care patients and their family members through the remainder of the COVID-19 pandemic.

Automated Cognitive Health Assessment Using Partially Complete Time Series Sensor Data

BACKGROUND

Behavior and health are inextricably linked. As a result, continuous wearable sensor data offer the potential to predict clinical measures. However, interruptions in the data collection occur, which create a need for strategic data imputation.

OBJECTIVE

The objective of this work is to adapt a data generation algorithm to impute multivariate time series data. This will allow us to create digital behavior markers that can predict clinical health measures.

METHODS

We created a bidirectional time series generative adversarial network to impute missing sensor readings. Values are imputed based on relationships between multiple fields and multiple points in time, for single time points or larger time gaps. From the complete data, digital behavior markers are extracted and are mapped to predicted clinical measures.

RESULTS

We validate our approach using continuous smartwatch data for n = 14 participants. When reconstructing omitted data, we observe an average normalized mean absolute error of 0.0197. We then create machine learning models to predict clinical measures from the reconstructed, complete data with correlations ranging from r = 0.1230 to r = 0.7623. This work indicates that wearable sensor data collected in the wild can be used to offer insights on a person's health in natural settings.

Development of Written Materials for Participants in an Alzheimer's Disease and Related Dementias Screening Trial

Given that participants' experiences in clinical trials include a variety of communication touchpoints with clinical trial staff, these communications should be designed in a way that enhances the participant experience by paying attention to the self-determination theoretical concepts of competence, autonomy, and relatedness. In this feature, we argue that clinical trial teams need to consider the importance of how they design their written participant communication materials, and we explain in detail the process our multidisciplinary team took to design written materials for the patient and family caregiver participants in our Alzheimer's disease and related dementias (ADRD) screening trial. This article concludes with suggested guidance and steps for other clinical trial teams.

Connecting Cohorts to Diminish Alzheimer's Disease (CONCORD-AD): A Report of an International Research Collaboration Network

Longitudinal observational cohort studies are being conducted worldwide to understand cognition, biomarkers, and the health of the aging population better. Cross-cohort comparisons and networks of registries in Alzheimer's disease (AD) foster scientific exchange, generate insights, and contribute to the evolving clinical science in AD. A scientific working group was convened with invited investigators from established cohort studies in AD, in order to form a research collaboration network as a resource to address important research questions. The Connecting Cohorts to Diminish Alzheimer's Disease (CONCORD-AD) collaboration network was created to bring together global resources and expertise, to generate insights and improve understanding of the natural history of AD, to inform design of clinical trials in all disease stages, and to plan for optimal patient access to disease-modifying therapies once they become available. The network brings together expertise and data insights from 7 cohorts across Australia, Europe, and North America. Notably, the network includes populations recruited through memory clinics as well as population-based cohorts, representing observations from individuals across the AD spectrum. This report aims to introduce the CONCORD-AD network, providing an overview of the cohorts involved, reporting the common assessments used, and describing the key characteristics of the cohort populations. Cohort study designs and baseline population characteristics are compared, and available cognitive, functional, and neuropsychiatric symptom data, as well as the frequency of biomarker assessments, are summarized. Finally, the challenges and opportunities of cross-cohort studies in AD are discussed.

Analyzing Sensor-Based Individual and Population Behavior Patterns via Inverse Reinforcement Learning

Digital markers of behavior can be continuously created, in everyday settings, using time series data collected by ambient sensors. The goal of this work was to perform individual- and population-level behavior analysis from such time series sensor data. In this paper, we introduce a novel algorithm—Resident Relative Entropy-Inverse Reinforcement Learning (RRE-IRL)—to perform an analysis of a single smart home resident or a group of residents, using inverse reinforcement learning. By employing this method, we learnt an individual’s behavioral routine preferences. We then analyzed daily routines for an individual and for eight smart home residents grouped by health diagnoses. We observed that the behavioral routine preferences changed over time. Specifically, the probability that the observed behavior was the same at the beginning of data collection as it was at the end (months later) was lower for residents experiencing cognitive decline than for cognitively healthy residents. When comparing aggregated behavior between groups of residents from the two diagnosis groups, the behavioral difference was even greater. Furthermore, the behavior preferences were used by a random forest classifier to predict a resident’s cognitive health diagnosis, with an accuracy of 0.84.

Risks and Benefits of Screening for Dementia in Primary Care: The Indiana University Cognitive Health Outcomes Investigation of the Comparative Effectiveness of Dementia Screening (IU CHOICE)Trial

BACKGROUND/OBJECTIVE

The benefits and harms of screening of Alzheimer disease and related dementias (ADRDs) are unknown. This study addressed the question of whether the benefits outweigh the harms of screening for ADRDs among older adults in primary care.

DESIGN, SETTING, AND PARTICIPANTS

Single-blinded, two-arm, randomized controlled trial (October 2012-September 2016) in urban, suburban, and rural primary care settings in Indiana. A total of 4005 primary care patients (aged ≥65 years) were randomized to ADRD screening (n = 2008) or control (n = 1997).

INTERVENTION

Patients were screened using the Memory Impairment Screen or the Mini-Cog and referred for a voluntary follow-up diagnostic assessment if they screened positive on either or both screening tests.

MEASUREMENTS

Primary measures were health-related quality of life (HRQOL; Health Utilities Index) at 12 months, depressive symptoms (Patient Health Questionnaire-9), and anxiety symptoms (Generalized Anxiety Disorder seven-item scale) at 1 month.

RESULTS

The mean age was 74.2 years (SD = 6.9 years); 2257 (66%) were female and 2301 (67%) were white. At 12 months, we were unable to detect differences in HRQOL between the groups (effect size = 0.009 [95% confidence interval {CI} = -0.063 to 0.080]; P = .81). At 1 month, differences in mean depressive symptoms (mean difference = -0.23 [90% CI = -0.42 to -0.039]) and anxiety symptoms (mean difference = -0.087 [90% CI = -0.246 to 0.072]) were within prespecified equivalency range. Scores for depressive and anxiety symptoms were similar between the groups at all time points. No differences in healthcare utilization, advance care planning, and ADRD recognition by physicians were detected at 12 months.

CONCLUSION

We were unable to detect a difference in HRQOL for screening for ADRD among older adults. We found no harm from screening measured by symptoms of depression or anxiety. Missing data, low rates of dementia detection, and high rate of refusal for follow-up diagnostic assessments after a positive screen may explain these findings. J Am Geriatr Soc 68:535-543, 2020.