Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis.

Early recognition and treatment of sepsis are linked to improved patient outcomes. Machine learning-based early warning systems may reduce the time to recognition, but few systems have undergone clinical evaluation. In this prospective, multi-site cohort study, we examined the association between patient outcomes and provider interaction with a deployed sepsis alert system called the Targeted Real-time Early Warning System (TREWS).

Smartphone motor testing to distinguish idiopathic REM sleep behavior disorder, controls, and PD.

Objective: We sought to identify motor features that would allow the delineation of individuals with sleep study-confirmed idiopathic REM sleep behavior disorder (iRBD) from controls and Parkinson disease (PD) using a customized smartphone application.

Methods: A total of 334 PD, 104 iRBD, and 84 control participants performed 7 tasks to evaluate voice, balance, gait, finger tapping, reaction time, rest tremor, and postural tremor. Smartphone recordings were collected both in clinic and at home under noncontrolled conditions over several days.

Automated Quality Control for Sensor Based Symptom Measurement Performed Outside the Lab.

The use of wearable sensing technology for objective, non-invasive and remote clinimetric testing of symptoms has considerable potential. However, the accuracy achievable with such technology is highly reliant on separating the useful from irrelevant sensor data. Monitoring patient symptoms using digital sensors outside of controlled, clinical lab settings creates a variety of practical challenges, such as recording unexpected user behaviors.

Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity: The Mobile Parkinson Disease Score.

Importance: Current Parkinson disease (PD) measures are subjective, rater-dependent, and assessed in clinic. Smartphones can measure PD features, yet no smartphone-derived rating score exists to assess motor symptom severity in real-world settings.

Objectives: To develop an objective measure of PD severity and test construct validity by evaluating the ability of the measure to capture intraday symptom fluctuations, correlate with current standard PD outcome measures, and respond to dopaminergic therapy.

Measuring Patient Mobility in the ICU Using a Novel Noninvasive Sensor.

Objectives: To develop and validate a noninvasive mobility sensor to automatically and continuously detect and measure patient mobility in the ICU.

Design: Prospective, observational study.

Setting: Surgical ICU at an academic hospital.

Development of an Exergame for Urban-dwelling Older Adults With Functional Limitations: Results and Lessons Learned.

Background: Falls at home are common and potentially fatal for disabled older adults. To address this problem, we created an academic-community partnership involving disabled, urban-dwelling older adults and their families, the housing authority, a Tai Chi master, and a university.

Objectives: We conducted a pilot to assess safety, acceptability, and feasibility of a Wii-based exergame designed to increase disabled older adults' strength and balance.