Predicting Cognitive Function from Clinical Measures of Physical Function and Health Status in Older Adults

Abstract

IntroductionCurrent research suggests that the neuropathology of dementia—including brain changes leading to memory impairment and cognitive decline—is evident years before the onset of this disease. Older adults with cognitive decline have reduced functional independence and quality of life, and are at greater risk for developing dementia. Therefore, identifying biomarkers that can be easily assessed within the clinical setting and predict cognitive decline is important. Early recognition of cognitive decline could promote timely implementation of preventive strategies.MethodsWe included 89 community-dwelling adults aged 70 years and older in our study, and collected 32 measures of physical function, health status and cognitive function at baseline. We utilized an L1–L2 regularized regression model (elastic net) to identify which of the 32 baseline measures were strongly predictive of cognitive function after one year. We built three linear regression models: 1) based on baseline cognitive function, 2) based on variables consistently selected in every cross-validation loop, and 3) a full model based on all the 32 variables. Each of these models was carefully tested with nested cross-validation.ResultsOur model with the six variables consistently selected in every cross-validation loop had a mean squared prediction error of 7.47. This number was smaller than that of the full model (115.33) and the model with baseline cognitive function (7.98). Our model explained 47% of the variance in cognitive function after one year.DiscussionWe built a parsimonious model based on a selected set of six physical function and health status measures strongly predictive of cognitive function after one year. In addition to reducing the complexity of the model without changing the model significantly, our model with the top variables improved the mean prediction error and R-squared. These six physical function and health status measures can be easily implemented in a clinical setting.

Afternoon Napping and Cognition in Chinese Older Adults: Findings from the China Health and Retirement Longitudinal Study Baseline Assessment

“…C ognitive impairment significantly increases the risk of functional dependence and poor quality of life in older adults. 1,2 Current research has focused on the prevention or delay of cognitive decline through risk factor identification and modification, with growing interest in the role of sleep behaviors, such as the optimal duration of sleep and napping. [3][4][5] The prevalence of habitual daytime napping ranges from 22% to 69% in older adults around the world, which is significantly higher than in younger individuals.…”

Collection of human reaction times and supporting health related data for analysis of cognitive and physical performance

“…[4], [5], [6]) but to the authors best knowledge, there are no datasets publicly available that contain reaction time data together with other supportive health related data and metadata.The resulting data collection allows other researchers to perform further analysis, e.g. to detect early symptoms of starting chronic diseases [7], [8].…”

“…[4], [5], [6]) but to the authors best knowledge, there are no datasets publicly available that contain reaction time data together with other supportive health related data and metadata.…”

Association between rural-to-urban migration and the cognitive aging trajectories of older Chinese adults: results from a prospective cohort analysis

“…Estimates indicate that more than 10.4 million individuals with cognitive impairment resided in China in 2016 [1]. Cognitive impairment is associated with a poor quality of life among older adults and imposes heavy burdens on families and societies [2]. In 2018, the number of Chinese adults aged ≥60 years was 249 million, and this group accounted for 17.9% of the total population [3].…”