Digital Phenotypes of Mobile Keyboard Backspace Rates and Their Associations With Symptoms of Mood Disorder: Algorithm Development and Validation.

Background: Passive sensing through smartphone keyboard data can be used to identify and monitor symptoms of mood disorders with low participant burden. Behavioral phenotyping based on mobile keystroke data can aid in clinical decision-making and provide insights into the individual symptoms of mood disorders.

Objective: This study aims to derive digital phenotypes based on smartphone keyboard backspace use among 128 community adults across 2948 observations using a Bayesian mixture model.

Using a Novel Digital Go/No-Go to Dissociate Intra-subject Temporal Fluctuations in Reaction Time and Accuracy.

Impulsivity can be a risk factor for serious complications for those with mood disorders. To understand intra-individual impulsivity variability, we analyzed longitudinal data of a novel gamified digital Go/No-Go (GNG) task in a clinical sample (n=43 mood disorder participants, n=17 healthy controls) and an open-science sample (n=121, self-reported diagnoses). With repeated measurements within-subject, we disentangled two aspects of GNG: reaction time and accuracy in response inhibition (i.

Trends in telemedicine visits among pediatric asthma patients during COVID-19.

Background: Environmental and social factors, including lack of access to asthma care, contribute to persistent inequities in asthma outcomes among children from historically marginalized ethnoracial groups. Telemedicine, which expanded rapidly during the coronavirus disease 2019 (COVID-19) pandemic, may be an approach to augment access to pediatric asthma care.

Objectives: We sought to describe characteristics of pediatric (0-17 years) telemedicine users with asthma and characterize use trends throughout the COVID-19 pandemic.

Smartphone-derived Virtual Keyboard Dynamics Coupled with Accelerometer Data as a Window into Understanding Brain Health: Smartphone Keyboard and Accelerometer as Window into Brain Health.

We examine the feasibility of using accelerometer data exclusively collected during typing on a custom smartphone keyboard to study whether typing dynamics are associated with daily variations in mood and cognition. As part of an ongoing digital mental health study involving mood disorders, we collected data from a well-characterized clinical sample (N = 85) and classified accelerometer data per typing session into orientation (upright vs. not) and motion (active vs.

Physicians differ in their perceptions of sensitive medical records: Survey and interview study.

Physician categorizations of electronic health record (EHR) data (e.g., depression) into sensitive data categories (e.

Evaluation of behavioral economic strategies to raise influenza vaccination rates across a health system: Results from a randomized clinical trial.

Influenza vaccination rates are low. Working with a large US health system, we evaluated three health system-wide interventions using the electronic health record's patient portal to improve influenza vaccination rates. We performed a two-arm RCT with a nested factorial design within the treatment arm, randomizing patients to usual-care control (no portal interventions) or to one or more portal interventions.

A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity.

The treatment of mood disorders, which can become a lifelong process, varies widely in efficacy between individuals. Most options to monitor mood rely on subjective self-reports and clinical visits, which can be burdensome and may not portray an accurate representation of what the individual is experiencing. A passive method to monitor mood could be a useful tool for those with these disorders.

Real-time associations among MS symptoms and cognitive dysfunction using ecological momentary assessment.

Introduction: Multiple sclerosis (MS) is characterized by a wide range of disabling symptoms, including cognitive dysfunction, fatigue, depression, anxiety, pain, and sleep difficulties. The current study aimed to examine real-time associations between non-cognitive and cognitive symptoms (latter measured both objectively and subjectively in real-time) using smartphone-administered ecological momentary assessment (EMA).

Methods: Forty-five persons with MS completed EMA four times per day for 3 weeks.

Smartphone accelerometer data as a proxy for clinical data in modeling of bipolar disorder symptom trajectory.

Being able to track and predict fluctuations in symptoms of mental health disorders such as bipolar disorder outside the clinic walls is critical for expanding access to care for the global population. To that end, we analyze a dataset of 291 individuals from a smartphone app targeted at bipolar disorder, which contains rich details about their smartphone interactions (including typing dynamics and accelerometer motion) collected everyday over several months, along with more traditional clinical features. The aim is to evaluate whether smartphone accelerometer data could serve as a proxy for traditional clinical data, either by itself or in combination with typing dynamics.

Associations between smartphone keystroke dynamics and cognition in MS.

Objective: Examine the associations between smartphone keystroke dynamics and cognitive functioning among persons with multiple sclerosis (MS).

Methods: Sixteen persons with MS with no self-reported upper extremity or typing difficulties and 10 healthy controls (HCs) completed six weeks of remote monitoring of their keystroke dynamics (i.e.

Barriers and facilitators of electronic patient portal uptake for asthma management.

Objective: An active patient-practitioner partnership is a key aspect of asthma management and patient-reported data helps with shared decision making. Technological advances such as the electronic patient portal can facilitate partnership, with the goal of improved asthma outcomes. However, uptake of portals by end-users for asthma management has been low.

Daily Associations of Air Pollution and Pediatric Asthma Risk Using the Biomedical REAI-Time Health Evaluation (BREATHE) Kit.

Background: Exposure to air pollution is associated with acute pediatric asthma exacerbations, including reduced lung function, rescue medication usage, and increased symptoms; however, most studies are limited in investigating longitudinal changes in these acute effects. This study aims to investigate the effects of daily air pollution exposure on acute pediatric asthma exacerbation risk using a repeated-measures design. Methods: We conducted a panel study of 40 children aged 8−16 years with moderate-to-severe asthma.

Partnered decision support: Parental perspectives of completing a pre-visit pediatric asthma questionnaire via the patient portal.

Background: Collection of patient-reported data has been demonstrated to improve asthma outcomes. One method to collect information is through the electronic patient portal. In practice, patient portal use in pediatrics and, specifically for asthma management, has had low uptake.

Naturalistic smartphone keyboard typing reflects processing speed and executive function.

Objective: The increase in smartphone usage has enabled the possibility of more accessible ways to conduct neuropsychological evaluations. The objective of this study was to determine the feasibility of using smartphone typing dynamics with mood scores to supplement cognitive assessment through trail making tests.

Methods: Using a custom-built keyboard, naturalistic keypress dynamics were unobtrusively recorded in individuals with bipolar disorder (n = 11) and nonbipolar controls (n = 8) on an Android smartphone.

Interstitial lung disease in children with Rubinstein-Taybi syndrome.

Introduction: Rubinstein-Taybi syndrome (RSTS) is a rare genetic syndrome caused primarily by a mutation in the CREBBP gene found on chromosome 16. Patients with RSTS are at greater risk for a variety of medical problems, including upper airway obstruction and aspiration. Childhood interstitial lung disease (ILD) thus far has not been definitively linked to RSTS.

Accuracy of Asthma Computable Phenotypes to Identify Pediatric Asthma at an Academic Institution.

Objectives: Asthma is a heterogenous condition with significant diagnostic complexity, including variations in symptoms and temporal criteria. The disease can be difficult for clinicians to diagnose accurately. Properly identifying asthma patients from the electronic health record is consequently challenging as current algorithms (computable phenotypes) rely on diagnostic codes (e.

Adverse Childhood Experiences and Association With Pediatric Asthma Severity in the 2016-2017 National Survey of Children's Health.

Objectives: Prior studies have found that adverse childhood experiences (ACEs) are associated with asthma prevalence and onset, presumably related to stress and increased inflammation. We hypothesized that ACEs may be associated with asthma severity as well. We studied the 2016-2017 US National Survey of Children's Health dataset to explore the relationship between ACEs and pediatric asthma severity.

Asthma clustering methods: a literature-informed application to the children's health study data.

Objective: The heterogeneity of asthma has inspired widespread application of statistical clustering algorithms to a variety of datasets for identification of potentially clinically meaningful phenotypes. There has not been a standardized data analysis approach for asthma clustering, which can affect reproducibility and clinical translation of results. Our objective was to identify common and effective data analysis practices in the asthma clustering literature and apply them to data from a Southern California population-based cohort of schoolchildren with asthma.