arXiv subjects

Collin Sakal

Publications and source records attributed to Collin Sakal.

4 recordsLinked to original sources

arXiv2023

Associations Between Sleep Efficiency Variability and Cognition Among Older Adults: Cross-Sectional Accelerometer Study

Objective: We aimed to determine the relationship between day-to-day sleep efficiency variability and cognitive function among older adults using accelerometer data and three cognitive tests. Methods: Older adults aged 65+ with 5 days of accelerometer data from the National Health and Nutrition Examination Survey (NHANES) who completed the Digit Symbol Substitution Test (DSST), the Consortium to Establish a Registry for Alzheimers Disease Word-Learning subtest (CERAD WL), and Animal Fluency Test (AFT) were included in this study. Associations between sleep efficiency variability and each cognitive test were examined adjusted for age, sex, education, household income, marital status, depressive symptoms, diabetes, smoking habits, alcohol consumption, arthritis, heart disease, prior heart attack, prior stroke, activities of daily living, and instrumental activities of daily living. Results: A total of 1074 older adults were included in this study. Greater sleep efficiency variability was univariably associated with worse cognitive function based on the DSST (per 10% increase, Beta -3.34, 95% CI -5.33 to -1.34), CERAD-WL (per 10% increase, Beta -1.00, 95% CI -1.79 to -0.21), and AFT (per 10% increase, Beta -1.02, 95% CI -1.68 to -0.36). In adjusted models, greater sleep efficiency variability remained associated with lower DSST (per 10% increase, Beta -2.01, 95% CI -3.62 to -0.40) and AFT (per 10% increase, Beta -0.84, 95% CI -1.47 to -0.21) scores but not CERAD WL scores. Conclusions: Targeting consistency regarding sleep quality may be useful for interventions seeking to preserve cognitive function among older adults.

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arXiv2023

Assessing cognitive function among older adults using machine learning and wearable device data: a feasibility study

Timely implementation of interventions to slow cognitive decline among older adults requires accurate monitoring to detect changes in cognitive function. Data gathered using wearable devices that can continuously monitor factors known to be associated with cognition could be used to train machine learning models and develop wearable-based cognitive monitoring systems. Using data from over 2,400 older adults in the National Health and Nutrition Examination Survey (NHANES) we developed prediction models to differentiate older adults with normal cognition from those with poor cognition based on outcomes from three cognitive tests measuring different domains of cognitive function. During repeated cross-validation, CatBoost, XGBoost, and Random Forest models performed best when predicting cognition based on processing speed, working memory, and attention (median AUCs >0.82) compared to immediate and delayed recall (median AUCs >0.72) and categorical verbal fluency (median AUC >0.68). Activity and sleep parameters were also more strongly associated with processing speed, working memory, and attention compared to other cognitive subdomains. Our work provides proof of concept that wearable-based cognitive monitoring systems may be a viable alternative to traditional methods for monitoring processing speeds, working memory, and attention. We further identified novel metrics that could be targets in future causal studies seeking to better understand how sleep and activity parameters influence cognitive function among older adults.

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arXiv2023

Identifying the most predictive risk factors for future cognitive impairment among elderly Chinese

Introduction. The societal burden of cognitive impairments in China has prompted researchers to develop clinical prediction models aimed at making risk assessments that enable preventative interventions. However, it is unclear which risk factors best predict future cognitive impairment and if predictive ability is consistent across different socioeconomic groups. Methods. We quantified the ability of demographics, instrumental activities of daily living, activities of daily living, cognitive tests, social factors, psychological factors, diet, exercise and sleep, chronic diseases, and three recently published prediction models predict future cognitive impairments in the general Chinese population and among male, female, rural, urban, educated, and uneducated elderly. Data were taken from the 2011 and 2014 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Results. The risk factor groups with the most predictive ability in the general population were demographics (AUC, 0.78, 95% CI, 0.77-0.78), cognitive tests (AUC, 0.72, 95% CI, 0.72-0.73), and instrumental activities of daily living (AUC, 0.71, 95% CI, 0.70-0.71). Demographics, cognitive tests, instrumental activities of daily living, and all three re-created prediction models had significantly higher AUCs when making predictions among women compared to men and among the uneducated compared to the educated. Discussion. This study suggests that demographics, cognitive tests, and instrumental activities of daily living are the most useful risk factors for predicting future cognitive impairment among elderly Chinese. However, the most useful risk factors and existing models have lower predictive power among male, urban, and educated elderly. More efforts are needed to ensure that equally accurate risk assessments can be conducted across different socioeconomic groups in China.

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arXiv2022

TreatmentEstimatoR: a Dashboard for Estimating Treatment Effects from Observational Health Data

Observational health data can be leveraged to measure the real-world use and potential benefits or risks of existing medical interventions. However, lack of programming proficiency and advanced knowledge of causal inference methods excludes some clinicians and non-computational researchers from performing such analyses. Code-free dashboard tools provide accessible means to estimate and visualize treatment effects from observational health data. We present TreatmentEstimatoR, an R Shiny dashboard that facilitates the estimation of treatment effects from observational data without any programming knowledge required. The dashboard provides effect estimates from multiple algorithms simultaneously and accommodates binary, continuous, and time-to-event outcomes. TreatmentEstimatoR allows for flexible covariate selection for treatment and outcome models, comprehensive model performance metrics, and an exploratory data analysis tool. TreatmentEstimatoR is available at https://github.com/CollinSakal/TreatmentEstimatoR. We provide full installation instructions and detailed vignettes for how to best use the dashboard.

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