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Background: Current evaluation methods to assess physical and cognitive function are limited and often not feasible in emergency settings. The upper-extremity function (UEF) test to assess physical and cognitive performance using wearable sensors. The purpose of this study was to examine the (1) relationship between preoperative UEF scores with in-hospital outcomes; and (2) association between postoperative UEF scores with 30-d adverse outcomes among adults undergoing emergent abdominal surgery., Methods: We performed an observational, longitudinal study among adults older than 40 y who presented with intra-abdominal symptoms. The UEF tests included a 20-sec rapid repetitive elbow flexion (physical function), and a 60-sec repetitive elbow flexion at a self-selected pace while counting backwards by threes (cognitive function), administered within 24-h of admission and within 24-h prior to discharge. Multiple logistic regression models assessed the association between UEF and outcomes. Each model consisted of the in-hospital or 30-d post-discharge outcome as the dependent variable, preoperative UEF physical and cognitive scores as hypothesis covariates, and age and sex as adjuster covariates., Results: Using UEF physical and cognitive scores to predict in-hospital outcomes, an area under curve (AUC) of 0.76 was achieved, which was 17% more sensitive when compared to age independently. For 30-d outcomes, the AUC increased to 0.89 when UEF physical and cognitive scores were included in the model with age and sex., Discussion: Sensor-based measures of physical and cognitive function enhance outcome prediction providing an objective practicable tool for risk stratification in emergency surgery settings among aging adults presenting with intra-abdominal symptoms., (Copyright © 2021. Published by Elsevier Inc.)

Studying the dynamics of nonlinear systems can provide additional information about the variability structure of the system. Within the current study, we examined the application of regularity and local stability measures to capture motor function alterations due to dual-tasking using a previously validated upper-extremity function (UEF). We targeted young (ages 18 and 30 years) and older adults (65 years or older) with normal cognition based on clinical screening. UEF involved repetitive elbow flexion without counting (ST) and while counting backward by one (DT1) or three (DT3). We measured the regularity (measured by sample entropy (SE)), local stability (measured by the largest Lyapunov exponent (LyE)), as well as conventional peak-dependent variability measures (coefficient of variation of kinematics parameters) to capture motor dynamic alterations due to dual-tasking. Within both groups, only SE showed significant differences between all pairs of UEF condition comparisons, even ST vs DT1 (p = 0.007, effect size = 0.507), for which no peak-dependent parameter showed significant difference. Among all measures, the only parameter that showed a significant difference between young and older adults was LyE (p < 0.001, effect size = 0.453). Current findings highlight the potential of nonlinear analysis to detect aging-related alterations among cognitively healthy participants., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The purpose of the current study was to develop an objective tool based on dual-task performance for screening early-stage Alzheimer's disease (AD) and mild cognitive impairment (MCI of the Alzheimer's type). Dual-task involved a simultaneous execution of a sensor-based upper-extremity function (UEF) motor task (normal or rapid speed) and a cognitive task of counting numbers backward (by ones or threes). Motor function speed and variability were recorded and compared between cognitive groups using ANOVAs, adjusted for age, gender, and body mass index. Cognitive indexes were developed using multivariable ordinal logistic models to predict the cognitive status using UEF parameters. Ninety-one participants were recruited; 35 cognitive normal (CN, age = 83.8 ± 6.9), 34 MCI (age = 83.9 ± 6.6), and 22 AD (age = 84.1 ± 6.1). Flexion number and sensor-based motion variability parameters, within the normal pace elbow flexion, showed significant between-group differences (maximum effect size of 1.10 for CN versus MCI and 1.39 for CN versus AD, p < 0.0001). Using these parameters, the cognitive status (both MCI and AD) was predicted with a receiver operating characteristic area under curve of 0.83 (sensitivity = 0.82 and specificity = 0.72). Findings suggest that measures of motor function speed and accuracy within a more practical upper-extremity test (instead of walking) may provide enough complexity for cognitive impairment assessment.

Background: Dual-task actions challenge cognitive processing. The usefulness of objective methods based on dual-task actions to identify the cognitive status of older adults has been previously demonstrated. However, the properties of select motor and cognitive tasks are still debatable. We investigated the effect of cognitive task difficulty and motor task type (walking versus an upper-extremity function [UEF]) in identifying cognitive impairment in older adults., Methods: Older adults (≥65 years) were recruited, and cognitive ability was measured using the Montreal Cognitive Assessment (MoCA). Participants performed repetitive elbow flexion under three conditions: 1) at maximum pace alone (Single-task); and 2) while counting backward by ones (Dual-task 1); and 3) threes (Dual-task 2). Similar single- and dual-task gait were performed at normal speed. Three-dimensional kinematics were measured for both motor functions using wearable sensors., Results: One-hundred older adults participated in this study. Based on MoCA score <20, 21 (21%) of the participants were considered cognitively impaired (mean age =86±10 and 85±5 for cognitively impaired and intact participants, respectively). Within ANOVA models adjusted with demographic information, UEF dual-task parameters, including speed and range-of-motion variability were significantly higher by 52% on average, among cognitively impaired participant ( p <0.01). Logistic models with these UEF parameters plus age predicted cognitive status with sensitivity, specificity, and area under curve (AUC) of 71%, 81% and 0.77 for Dual-task 1. The corresponding values for UEF Dual-task 2 were 91%, 73% and 0.81, respectively. ANOVA results were non-significant for gait parameters within both dual-task conditions ( p >0.26)., Conclusion: This study demonstrated that counting backward by threes within a UEF dual-task experiment was a pertinent and challenging enough task to detect cognitive impairment in older adults. Additionally, UEF was superior to gait as the motor task component of the dual-task. The UEF dual-task could be applied as a quick memory screen in a clinical setting., Competing Interests: Disclosure The authors report no conflicts of interest in this work. Mrs Kathy O’Connor, Dr Edward Zamrini, Dr Martha Jane Mohler, and Dr Nima Toosizadeh report grants from National Institute of Aging, during the conduct of the study.