Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments.

Background: Longitudinal tracking of multiple sclerosis (MS) symptoms in an individual's own environment may improve self-monitoring and clinical management for people with MS (pwMS).

Objective: We present a machine learning approach that enables longitudinal monitoring of clinically relevant patient-reported symptoms for pwMS by harnessing passively collected data from sensors in smartphones and fitness trackers.

Methods: We divide the collected data into discrete periods for each patient.

Bivalent boosters and risk of post-acute sequelae following vaccine-breakthrough SARS-CoV-2 Omicron infection: a cohort study.

Background: Vaccination has been shown to attenuate the risk of post-acute sequelae following SARS-CoV-2 infection. However, no prior population-based studies have evaluated if updated bivalent boosters reduce risk of post-acute sequelae following Omicron-variant infection, versus ancestral vaccines.

Methods: National databases were utilised to construct a population-based cohort of adult individuals infected during Omicron-predominant transmission.

Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping.

Background: The COVID-19 pandemic has broad negative impact on the physical and mental health of people with chronic neurological disorders such as multiple sclerosis (MS).

Objective: We presented a machine learning approach leveraging passive sensor data from smartphones and fitness trackers of people with MS to predict their health outcomes in a natural experiment during a state-mandated stay-at-home period due to a global pandemic.

Methods: First, we extracted features that capture behavior changes due to the stay-at-home order.

Objective Measurement of Hyperactivity Using Mobile Sensing and Machine Learning: Pilot Study.

Background: Although hyperactivity is a core symptom of attention-deficit/hyperactivity disorder (ADHD), there are no objective measures that are widely used in clinical settings.

Objective: We describe the development of a smartwatch app to measure hyperactivity in school-age children. The LemurDx prototype is a software system for smartwatches that uses wearable sensor technology and machine learning to measure hyperactivity.

EarBit: Using Wearable Sensors to Detect Eating Episodes in Unconstrained Environments.

Chronic and widespread diseases such as obesity, diabetes, and hypercholesterolemia require patients to monitor their food intake, and food journaling is currently the most common method for doing so. However, food journaling is subject to self-bias and recall errors, and is poorly adhered to by patients. In this paper, we propose an alternative by introducing EarBit, a wearable system that detects eating moments.

Use of a Smartphone App to Assess Neonatal Jaundice.

Background: The assessment of jaundice in outpatient neonates is problematic. Visual assessment is inaccurate, and more exact methodologies are cumbersome and/or expensive. Our goal in this study was to assess the accuracy of a technology based on the analysis of digital images of newborns obtained using a smartphone application called BiliCam.

Intravenous diazepam, midazolam and lorazepam in acute seizure control.

Objective: To evaluate the safety and efficacy of three benzodiazepine drugs: Lorazepam, Midazolam and Diazepam, when given parenterally in the control of acute seizure.

Methods: One hundred and twenty children of either sex in the age group 6 month to 14 years brought convulsing to the pediatric emergency services, were enrolled in the study. These were randomised to three equal groups of 40 patients each; Group A-received diazepam, Group B-received midazolam, Group C-received lorazepam.