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Abstract Background Physical activity can improve function and decrease healthcare spending among overweight and obese older adults. Although unstructured physical activity has been related to cardiometabolic improvements, the relationship between unstructured activity and movement quality is unclear. Aims This study aimed to evaluate the association of amount of unstructured free-living moderate-vigorous physical activity (MVPA) with measures of movement quality in overweight and obese older adults. Methods The association of MVPA with movement quality was assessed in 165 overweight and obese older adults (Age: 77.0(8.0) years; Body mass index (BMI): 29.2(5.3) kg/m 2 ). Participants performed overground walking, the Figure of 8 Walk test, and the Five-Times Sit to Stand. Weekly physical activity was measured using a waist-worn Actigraph activity monitor. Results Movement quality during straight path (gait speed (ρ = 0.30, p  < 0.01), stride length (ρ = 0.33, p  < 0.01), double-limb support time (ρ=-0.26, p  < 0.01), and gait symmetry (ρ = 0.17, p  = 0.02)) and curved path (F8W time (ρ=-0.22, p  < 0.01) and steps (ρ=-0.22, p  < 0.01)) walking were associated with weekly minutes of MVPA after controlling for age. Five-Times Sit to Stand performance was not significantly associated with weekly minutes of MVPA (ρ=-0.10, p  = 0.13). Conclusions Older adults with high BMIs who are less active also demonstrate poorer movement quality which should be targeted in interventions to promote healthy aging, decrease falls, and delay disability development. Future work should explore if these associations are observed in middle-aged adults so targeted interventions can be implemented even earlier in the disability development continuum.

Purpose: The development and evaluation of machine learning models that automatically identify the body part(s) imaged, axis of imaging, and the presence of intravenous contrast material of a CT series of images. Methods: This retrospective study included 6955 series from 1198 studies (501 female, 697 males, mean age 56.5 years) obtained between January 2010 and September 2021. Each series was annotated by a trained board-certified radiologist with labels consisting of 16 body parts, 3 imaging axes, and whether an intravenous contrast agent was used. The studies were randomly assigned to the training, validation and testing sets with a proportion of 70%, 20% and 10%, respectively, to develop a 3D deep neural network for each classification task. External validation was conducted with a total of 35,272 series from 7 publicly available datasets. The classification accuracy for each series was independently assessed for each task to evaluate model performance. Results: The accuracies for identifying the body parts, imaging axes, and the presence of intravenous contrast were 96.0% (95% CI: 94.6%, 97.2%), 99.2% (95% CI: 98.5%, 99.7%), and 97.5% (95% CI: 96.4%, 98.5%) respectively. The generalizability of the models was demonstrated through external validation with accuracies of 89.7 - 97.8%, 98.6 - 100%, and 87.8 - 98.6% for the same tasks. Conclusions: The developed models demonstrated high performance on both internal and external testing in identifying key aspects of a CT series. Graphical Abstract

Objective: Walking is a key component of daily-life mobility. We examined associations between laboratory-measured gait quality and daily-life mobility through Actigraphy and Global Positioning System (GPS). We also assessed the relationship between two modalities of daily-life mobility i.e., Actigraphy and GPS. Methods: In community-dwelling older adults (N = 121, age = 77±5 years, 70% female, 90% white), we obtained gait quality from a 4-m instrumented walkway (gait speed, walk-ratio, variability) and accelerometry during 6-Minute Walk (adaptability, similarity, smoothness, power, and regularity). Physical activity measures of step-count and intensity were captured from an Actigraph. Time out-of-home, vehicular time, activity-space, and circularity were quantified using GPS. Partial Spearman correlations between laboratory gait quality and daily-life mobility were calculated. Linear regression was used to model step-count as a function of gait quality. ANCOVA and Tukey analysis compared GPS measures across activity groups [high, medium, low] based on step-count. Age, BMI, and sex were used as covariates. Results: Greater gait speed, adaptability, smoothness, power, and lower regularity were associated with higher step-counts (0.20<|ρp| < 0.26, p < .05). Age(β = −0.37), BMI(β = −0.30), speed(β = 0.14), adaptability(β = 0.20), and power(β = 0.18), explained 41.2% variance in step-count. Gait characteristics were not related to GPS measures. Participants with high (>4800 steps) compared to low activity (steps<3100) spent more time out-of-home (23 vs 15%), more vehicular travel (66 vs 38 minutes), and larger activity-space (5.18 vs 1.88 km2), all p < .05. Conclusions: Gait quality beyond speed contributes to physical activity. Physical activity and GPS-derived measures capture distinct aspects of daily-life mobility. Wearable-derived measures should be considered in gait and mobility-related interventions.

OBJECTIVES Devices such as mobile phones and smart speakers could be useful to remotely identify voice alterations associated with alcohol intoxication, which could be used to deliver just-in-time interventions, but data to support such approaches for the English language are lacking. In this controlled lab study, we compare how well English spectrographic voice features identify alcohol intoxication. METHODS 18 participants (72% male, aged 21-62 y) read a different randomly-assigned tongue twister prior to drinking and each hour for up to 7 hours after drinking a weight-based dose of alcohol. Vocal segments were cleaned and split into 1 second windows. We built support vector machine models for detecting alcohol intoxication, defined as breath alcohol concentration [BrAC] >0.08%, comparing the baseline voice spectrographic signature to each subsequent timepoint and present ensemble examine accuracy with 95% confidence intervals (CIs). RESULTS Alcohol intoxication was predicted with an accuracy of 98% (95% CI 97.1 to 98.6); mean sensitivity = .98; specificity = .97; positive predictive value = .97; and negative predictive value = .98. CONCLUSIONS In this small controlled lab study, voice spectrographic signatures collected from brief recorded English segments were useful in identifying alcohol intoxication. Larger studies using varied voice samples are needed to validate and expand models.

Objective: Dysphagia management relies on the evaluation of the temporospatial kinematic events of swallowing performed in videofluoroscopy (VF) by trained clinicians. The upper esophageal sphincter (UES) opening distension represents one of the important kinematic events that contribute to healthy swallowing. Insufficient distension of UES opening can lead to an accumulation of pharyngeal residue and subsequent aspiration which in turn can lead to adverse outcomes such as pneumonia. VF is usually used for the temporal and spatial evaluation of the UES opening; however, VF is not available in all clinical settings and may be inappropriate or undesirable for some patients. High resolution cervical auscultation (HRCA) is a noninvasive technology that uses neck-attached sensors and machine learning to characterize swallowing physiology by analyzing the swallow-induced vibrations/sounds in the anterior neck region. We investigated the ability of HRCA to noninvasively estimate the maximal distension of anterior-posterior (A-P) UES opening as accurately as the measurements performed by human judges from VF images. Methods and procedures: Trained judges performed the kinematic measurement of UES opening duration and A-P UES opening maximal distension on 434 swallows collected from 133 patients. We used a hybrid convolutional recurrent neural network supported by attention mechanisms which takes HRCA raw signals as input and estimates the value of the A-P UES opening maximal distension as output. Results: The proposed network estimated the A-P UES opening maximal distension with an absolute percentage error of 30% or less for more than 64.14% of the swallows in the dataset. Conclusion: This study provides substantial evidence for the feasibility of using HRCA to estimate one of the key spatial kinematic measurements used for dysphagia characterization and management. Clinical and Translational Impact Statement: The findings in this study have a direct impact on dysphagia diagnosis and management through providing a non-invasive and cheap way to estimate one of the most important swallowing kinematics, the UES opening distension, that contributes to safe swallowing. This study, along with other studies that utilize HRCA for swallowing kinematic analysis, paves the way for developing a widely available and easy-to-use tool for dysphagia diagnosis and management.

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting ECG are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but we currently have no accurate tools to identify them during initial triage. Herein, we report the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, significantly boosting both precision and sensitivity. Our derived OMI risk score provided superior rule-in and rule-out accuracy compared to routine care, and when combined with the clinical judgment of trained emergency personnel, this score helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.