Feasibility of Measuring Smartphone Accelerometry Data During a Weekly Instrumented Timed Up-and-Go Test After Emergency Department Discharge: Prospective Observational Cohort Study.

Background: Older adults discharged from the emergency department (ED) face elevated risk of falls and functional decline. Smartphones might enable remote monitoring of mobility after ED discharge, yet their application in this context remains underexplored. Objective: This study aimed to assess the feasibility of having older adults provide weekly accelerometer data from an instrumented Timed Up-and-Go (TUG) test over an 11-week period after ED discharge. Methods: This single-center, prospective, observational, cohort study recruited patients aged 60 years and older from an academic ED. Participants downloaded the GaitMate app to their iPhones that recorded accelerometer data during 11 weekly at-home TUG tests. We measured adherence to TUG test completion, quality of transmitted accelerometer data, and participants' perceptions of the app's usability and safety. Results: Of the 617 approached patients, 149 (24.1%) consented to participate, and of these 149 participants, 9 (6%) dropped out. Overall, participants completed 55.6% (912/1639) of TUG tests. Data quality was optimal in 31.1% (508/1639) of TUG tests. At 3-month follow-up, 83.2% (99/119) of respondents found the app easy to use, and 95% (114/120) felt safe performing the tasks at home. Barriers to adherence included the need for assistance, technical issues with the app, and forgetfulness. Conclusions: The study demonstrates moderate adherence yet high usability and safety for the use of smartphone TUG tests to monitor mobility among older adults after ED discharge. Incomplete TUG test data were common, reflecting challenges in the collection of high-quality longitudinal mobility data in older adults. Identified barriers highlight the need for improvements in user engagement and technology design.

Detection of Alcohol Intoxication Using Voice Features: A Controlled Laboratory Study.

OBJECTIVE: Devices such as mobile phones and smart speakers could be useful to remotely identify voice alterations associated with alcohol intoxication that could be used to deliver just-in-time interventions, but data to support such approaches for the English language are lacking. In this controlled laboratory study, we compare how well English spectrographic voice features identify alcohol intoxication. METHOD: A total of 18 participants (72% male, ages 21-62 years) read a randomly assigned tongue twister before 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 > .08%, comparing the baseline voice spectrographic signature to each subsequent timepoint and examined accuracy with 95% confidence intervals (CIs). RESULTS: Alcohol intoxication was predicted with an accuracy of 98% (95% CI [97.1, 98.6]); mean sensitivity = .98; specificity = .97; positive predictive value = .97; and negative predictive value = .98. CONCLUSIONS: In this small, controlled laboratory 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.

Minimizing cotton retention in neurosurgical procedures: which imaging modality can help?

Cotton balls are used in neurosurgical procedures to assist with hemostasis and improve vision within the operative field. Although the surgeon can reshape pieces of cotton for multiple intraoperative uses, this customizability and scale also places them at perpetual risk of being lost, as blood-soaked cotton balls are visually similar to raw brain tissue. Retained surgical cotton can induce potentially life-threatening immunologic responses, impair postoperative imaging, lead to a textiloma or misdiagnosis, and/or require reoperation. This study investigated three imaging modalities (optical, acoustic, and radiographic) to find the most effective method of identifying foreign bodies during neurosurgery. First, we examined the use of dyes to increase contrast between cotton and surrounding parenchyma (optical approach). Second, we explored the ability to distinguish surgical cotton on or below the tissue surface from brain parenchyma using ultrasound imaging (acoustic approach). Lastly, we analyzed the ability of radiography to differentiate between brain parenchyma and cotton. Our preliminary testing demonstrated that dark-colored cotton is significantly more identifiable than white cotton on the surface level. Additional testing revealed that cotton has noticeable different acoustic characteristics (eg, speed of sound, absorption) from neural tissue, allowing for enhanced contrast in applied ultrasound imaging. Radiography, however, did not present sufficient contrast, demanding further examination. These solutions have the potential to significantly reduce the possibility of intraoperative cotton retention both on and below the surface of the brain, while still providing surgeons with traditional cotton material properties without affecting the surgical workflow.