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21 Neurocognitive Differences Between Lifestyle Profiles of Women Across the Menopausal Transition
- Hannah Hagy, Amy Bohnert
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 334-335
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Objective:
Women are at greater risk of developing Alzheimer’s disease (AD) than men. The menopausal transition, which involves a neuroendocrine shift, is a potential contributor to this sex difference. Multiple estrogen-regulated systems (i.e., circadian rhythms) are disrupted during this transition which affects cognitive functioning (Barha & Liu-Ambrose, 2020), most notably verbal learning and memory. Little is known about how lifestyle factors (i.e., sleep, physical activity (PA), stress) may promote neurocognitive functioning across this transition (Maki & Weber, 2021). Utilizing data from the Human Connectome Aging project (HCP-A), the current study will examine whether distinct lifestyle profiles including sleep, PA, and stress relate to multiple domains of cognitive performance among a sample of perimenopausal/menopausal women.
Participants and Methods:Perimenopausal/menopausal women (ages 45 to 65) from the HCP-A were included (n =150, M age = 54.6, SD = 5.5). Demographic information, menopausal status, sleep problems (Pittsburgh Sleep Quality Index), PA (International Physical Activity Questionnaire), stress (Distress subscale of the Perceived Stress Scale) were assessed with surveys, and participants completed several lab-based tasks including: dimensional change card sort (DCCS), flanker, pattern recognition processing speed (PS), working memory (WM), picture sequencing, oral reading, Trails Making Test A and B (TMT), and Rey Auditory Verbal Learning (RAVLT) tasks. Using latent profile analysis (LPA), lifestyle profiles were identified via sleep problems, PA, and stress levels. A MANOVA compared cognitive performance between these lifestyle profiles, above and beyond age and education status.
Results:Fit indices indicated that a three-class solution fit the sample best: high PA, low stress and sleep problems (Class 1, n=38), high PA, stress, and sleep problems (Class 2, n= 17), and low PA, high stress and sleep problems (Class 3, n= 95) which were not significantly different based on age or menopausal status (p>0.05). A significant multivariate effect of age and education on cognitive performance (p<.001) emerged. There was a significant multivariate effect of lifestyle profile on cognitive performance, F (18, 260) = 1.73, p=.034, eta squared = .11, after controlling for age and education. Univariate analyses determined that certain lifestyle profiles were associated with better performance on all cognitive tasks except verbal memory. Contrary to expectation, Class 3 performed better on TMT A & B, DCCS, flanker, WM, and PS tasks as compared to Class 1. Class 3 performed better on reading and picture sequencing tasks than Class 2. There was no difference in performance between Class 1 and 2.
Conclusions:Results suggest three distinct lifestyle profiles exist in this analytic sample. After controlling for age and education, cognitive performance on all tasks except for verbal memory significantly differed between lifestyle profiles. The profile characterized by low PA and high stress and sleep problems demonstrated superior performance as compared to other classes. These findings provide preliminary evidence that women who have high levels of stress and sleep problems with low PA are performing better on cognitive tasks, but replication of these findings utilizing longitudinal designs are needed.
73 Changes in Sleep Negatively Impacts Next Day Inhibition Performance Among College Females
- Laura M Nicholson, Diana Ohanian, Amy Egbert, Becky Silton, Amy Bohnert
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 582-583
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Objective:
Sleep is a restorative function that supports various aspects of well-being, including cognitive function. College students, especially females, report getting less sleep than recommended and report more irregular sleep patterns than their male counterparts. Inadequate and irregular sleep are associated with neuropsychological deficits including more impulsive responding in lab-based tasks. Although many lab-based experiments ask participants to report their sleep patterns, few studies have analyzed how potential changes in sleep affect their findings. Utilizing data from a previously collected study, this study aims to investigate relations between sleep (i.e., sleep duration and changes in sleep duration) and performance-based measures of inhibition among female college students.
Participants and Methods:Participants (n = 39) were majority first-year students (Mage =19.27) and Caucasian (51%). Participants were recruited to participate in a larger study exploring how food commercials affect inhibitory control. Participants were randomized to each study condition (watching a food or non-food commercial) over two visits to the lab (T1 and T2). During both visits, they completed questionnaires asking about their 1) sleep duration the night before and 2) their “typical” sleep duration to capture changes in sleep duration. They also completed a computer-based stop signal task (SST) which required them to correctly identify healthy food images (stop signal accuracy [SSA] healthy) and unhealthy food images (SSA unhealthy) while inhibiting their response during a stop signal delay (SSD) which became increasingly more difficult (or delayed) as they successfully progressed. Since the main aim of the study was to explore the impact of sleep, analyses controlled for study condition. Analyses involving changes in sleep also accounted for sleep duration the night before the study visit.
Results:On average, students reported being under slept the night before the lab visit, reporting that they got 38 minutes less sleep than their “typical” sleep (7 hrs 3 min). Hierarchical regression analyses demonstrated that sleep duration the night before the lab visit was not associated with inhibition (i.e., SSA unhealthy, SSA healthy, SSD). In contrast, a greater change in sleep, or getting less sleep than “typical,” was associated with worsened inhibition across inhibition variables (SSA healthy, SSA unhealthy, SSD) above and beyond sleep duration at T1. At T2, only one analysis remained significant, such that getting less sleep than “typical” was associated with lower accuracy of appropriately identifying unhealthy images (SSA unhealthy) whereas other analyses only approached statistical significance.
Conclusions:These findings suggest that changes in sleep, or getting less sleep than typical, may impact inhibition performance measured in a lab, even when accounting for how much sleep they got the night before. Specifically, getting less sleep than typical was associated with reduced accuracy in selecting unhealthy images, a finding that was consistent across two visits to the lab. These preliminary findings offer opportunities for lab-based experiments to investigate the role of sleep when measuring inhibition performance. Further, clinicians conducting neuropsychological assessments in clinical settings may benefit from assessing sleep the night before the appointment and determine if this represents a change from their typical sleep pattern.
29120 Classification of Individuals Across the Spectrum of Problematic Opioid Use: Clinical Correlates and Longitudinal Associations with Mortality
- Victoria Powell, Colin MacLeod, Lewei A. Lin, Amy S.B. Bohnert, Pooja Lagisetty
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- Journal:
- Journal of Clinical and Translational Science / Volume 5 / Issue s1 / March 2021
- Published online by Cambridge University Press:
- 30 March 2021, p. 138
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ABSTRACT IMPACT: A better understanding of the spectrum of problematic opioid use will lead to more targeted treatments. OBJECTIVES/GOALS: It is unclear how to approach treatment of individuals with problematic opioid use who do not clearly meet criteria for opioid use disorder (OUD). We aim to characterize clinical, demographic, and medication use at time of identification of problematic opioid use across the spectrum as well as identify predictors of poor outcomes. METHODS/STUDY POPULATION: A national sample of Veterans coded as having opioid abuse or dependence were previously categorized as (1) high likelihood of OUD, (2) limited aberrant opioid use, and (3) prescribed opioid use with no evidence of aberrant use based on chart review. We will describe how individuals in these three categories differ demographically and clinically. We will then use a trained binary logistic regression model to predict whether individuals with limited aberrant opioid use more closely resemble category (1) or (3). Cox proportional hazards models will be used to predict all-cause mortality, suicide-related mortality, opioid-overdose related mortality, and hospitalization over a three-year period using the three categories as predictors and adjusting for relevant covariates. RESULTS/ANTICIPATED RESULTS: We anticipate that Veterans with a high likelihood of OUD will be more likely to experience homelessness and have more psychiatric comorbidities (especially PTSD). We hypothesize that Veterans with prescribed opioid use and no evidence of misuse will be significantly older, more likely to have disability, medical comorbidities (ie., chronic pain, cancer), more prescriptions for non-opioid analgesics, and be prescribed higher doses of opioids. Using a trained binary logistic regression model, we predict that Veterans with limited aberrant opioid use will more closely resemble Veterans with a high likelihood of OUD. We expect that all categories of problematic opioid use will have a high risk of mortality, with a high likelihood of OUD associated with the greatest risk of premature death. DISCUSSION/SIGNIFICANCE OF FINDINGS: Identifying and better characterizing individuals with limited aberrant opioid use may be an important opportunity to intervene prior to development of severe OUD. Future research will focus on targeting interventions to this population, which may have specific needs that are separate from classic OUD or simple pain-related opioid dependence.
3142 U.S. Counties with High Opioid-Overdose Mortality and Low Capacity to Deliver Medications for Opioid Use Disorder: an Observational Study
- Rebecca Haffajee, Lewei Allison Lin, Amy S.B. Bohnert, Jason E. Goldstick
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- Journal:
- Journal of Clinical and Translational Science / Volume 3 / Issue s1 / March 2019
- Published online by Cambridge University Press:
- 26 March 2019, p. 129
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OBJECTIVES/SPECIFIC AIMS: To identify characteristics of counties with persistently high opioid-overdose rates and low capacity to deliver medications for OUD (MOUD). METHODS/STUDY POPULATION: Setting: County-level opioid-overdose death data, 2013-2016, and 2017 publicly-available treatment provider data for MOUD: buprenorphine-waivered providers, opioid treatment programs (OTPs), and extended-release naltrexone providers. Participants: Populations in 3,142 U.S. counties. 24,851 buprenorphine-waivered providers; 1,517 OTPs; and 5,222 extended-release naltrexone providers. Measurements: The outcome variable, “opioid high-risk county”, was a binary indicator of high (above average) opioid-overdose rates with low (below median) MOUD availability rates. We used spatial logistic regression models to determine correlates of being a high-risk county. RESULTS/ANTICIPATED RESULTS: 46.4% of all counties, and 71.2% of rural counties, lacked a publicly-available MOUD provider in 2017. In adjusted models, rural counties had 53% greater odds of being high-risk than urban counties. Counties in the East South Central, West South Central, and South Atlantic divisions had over twice the odds of being high-risk than counties in the West North Central division. Primary care provider density, greater traversability, and a higher proportion of the population under age 25 were all protective against a county being opioid high-risk. DISCUSSION/SIGNIFICANCE OF IMPACT: Counties with both low MOUD provider availability and high opioid-overdose death rates are significantly more likely to be rural, have less primary care providers per capita, and in the southern regions. Strategies to increase MOUD must account for these factors.