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. Then, we adapted and applied an existing algorithm to these behavior-change features to predict the presence of depression, high global MS symptom burden, severe fatigue, and poor sleep quality during the stay-at-home period. RESULTS: Using data collected between November 2019 and May 2020, the algorithm detected depression with an accuracy of 82.5% (65% improvement over baseline; F1-score: 0.84), high global MS symptom burden with an accuracy of 90% (39% improvement over baseline; F1-score: 0.93), severe fatigue with an accuracy of 75.5% (22% improvement over baseline; F1-score: 0.80), and poor sleep quality with an accuracy of 84% (28% improvement over baseline; F1-score: 0.84). CONCLUSIONS: Our approach could help clinicians better triage patients with MS and potentially other chronic neurological disorders for interventions and aid patient self-monitoring in their own environment, particularly during extraordinarily stressful circumstances such as pandemics, which would cause drastic behavior changes.

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. The goal is to differentiate children with ADHD combined presentation (a combination of inattentive and hyperactive/impulsive presentations) or predominantly hyperactive/impulsive presentation from children with typical levels of activity. METHODS: In this pilot study, we recruited 30 children, aged 6 to 11 years, to wear a smartwatch with the LemurDx app for 2 days. Parents also provided activity labels for 30-minute intervals to help train the algorithm. Half of the participants had ADHD combined presentation or predominantly hyperactive/impulsive presentation (n=15), and half were in the healthy control group (n=15). RESULTS: The results indicated high usability scores and an overall diagnostic accuracy of 0.89 (sensitivity=0.93; specificity=0.86) when the motion sensor output was paired with the activity labels. CONCLUSIONS: State-of-the-art sensors and machine learning may provide a promising avenue for the objective measurement of hyperactivity.

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. METHODS: Paired BiliCam images and total serum bilirubin (TSB) levels were obtained in a diverse sample of newborns (<7 days old) at 7 sites across the United States. By using specialized software, data on color values in the images ("features") were extracted. Machine learning and regression analysis techniques were used to identify features for inclusion in models to predict an estimated bilirubin level for each newborn. The correlation between estimated bilirubin levels and TSB levels was calculated. In addition, the sensitivity and specificity of the estimated bilirubin levels in identifying newborns with high TSB levels were calculated by using 2 recommended decision rules for jaundice screening. RESULTS: Estimated bilirubin levels were calculated and compared with TSB levels in a diverse sample of 530 newborns (20.8% African American, 26.3% Hispanic, and 21.2% Asian American). The overall correlation was 0.91, and correlations among white, African American, Hispanic, and Asian American newborns were 0.92, 0.90, 0.91, and 0.88, respectively. The sensitivities of BiliCam in identifying newborns with high TSB levels were 84.6% and 100%, respectively, by using 2 decision rules; specificities were 75.1% and 76.4%, respectively. CONCLUSIONS: BiliCam provided accurate estimates of TSB values, demonstrating that an inexpensive technology that uses commodity smartphones could be used to effectively screen newborns for jaundice.

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. We evaluate the performance of inertial, optical, and acoustic sensing modalities and focus on inertial sensing, by virtue of its recognition and usability performance. Using data collected in a simulated home setting with minimum restrictions on participants' behavior, we build our models and evaluate them with an unconstrained outside-the-lab study. For both studies, we obtained video footage as ground truth for participants activities. Using leave-one-user-out validation, EarBit recognized all the eating episodes in the semi-controlled lab study, and achieved an accuracy of 90.1% and an F1-score of 90.9% in detecting chewing instances. In the unconstrained, outside-the-lab evaluation, EarBit obtained an accuracy of 93% and an F1-score of 80.1% in detecting chewing instances. It also accurately recognized all but one recorded eating episodes. These episodes ranged from a 2 minute snack to a 30 minute meal.

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. End of seizure episode (clinically) was defined as cessation of visible epileptic phenomenon or return of purposeful response to external stimuli within 15 min of drug administration. A stopwatch was used to measure various time intervals accurately. The patient's vitals were monitored and recorded in a predesigned performa. The primary outcome was the time to seizure cessation and secondary outcome was the side effects of the drugs. Data obtained was analysed statistically using student's t-test and chi-square test. RESULTS: Mean duration to clinical seizure cessation was comparable among the three groups. For diazepam group it was 84.94 ± 38.56 s, for midazolam group it was 92.69 ± 25.97 s, for lorazepam group it was 91.12 ± 23.58 s. Number of patients with any abnormality in seizure cessation were significantly higher in diazepam group [11/40 (27.5%)] when compared to the midazolam [4/40 (10%)] and lorazepam group [2/40 (5%)]. Number of patients requiring 2nd dose to control seizures was significantly higher [4/40 (10%)] in diazepam group when compared to lorazepam group [0/40 (0%)] but diazepam and midazolam and midazolam and lorazepam were comparable in this aspect.All the three drugs were comparable in terms of side effects except excessive somnolence which was significantly higher in diazepam group. CONCLUSIONS: All the three groups were comparable in terms of time to clinical seizure cessation, seizure recurrence and uncontrolled seizures after drug administration. However, number of patients requiring second dose to control seizures were significantly higher in diazepam group when compared to lorazepam group. Excessive somnolence and sedation occurred more frequently with diazepam.