Development of an ultrasound-based metric of muscle functional capacity for use in patients with neuromuscular disease.

Introduction/aims: Spinal muscular atrophy (SMA) and Duchenne muscular dystrophy (DMD) are progressive neuromuscular disorders characterized by severe muscle weakness and functional decline (Pillen et al., Muscle Nerve 2008; 37(6):679-693). With new therapeutics, objective methods with increased sensitivity are needed to assess muscle function.

Web-based interpretation bias training to reduce anxiety: A sequential, multiple-assignment randomized trial.

Objective: Web-based cognitive bias modification for interpretation (CBM-I) can improve interpretation biases and anxiety symptoms but faces high rates of dropout. This study tested the effectiveness of web-based CBM-I relative to an active psychoeducation condition and the addition of low-intensity telecoaching for a subset of CBM-I participants.

Method: 1,234 anxious community adults (Mage = 35.

Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious Individuals.

Mobile sensing is a ubiquitous and useful tool to make inferences about individuals' mental health based on physiology and behavior patterns. Along with sensing features directly associated with mental health, it can be valuable to detect different features of social contexts to learn about social interaction patterns over time and across different environments. This can provide insight into diverse communities' academic, work and social lives, and their social networks.

Online cognitive bias modification for interpretation to reduce anxious thinking during the COVID-19 pandemic.

Anxiety disorders are highly prevalent, and rates increased during the COVID-19 pandemic. However, most individuals with elevated anxiety do not access treatment due to barriers such as stigma, cost, and availability. Digital mental health programs, such as cognitive bias modification for interpretation (CBM-I), hold promise in increasing access to care.

Personalized State Anxiety Detection: An Empirical Study with Linguistic Biomarkers and A Machine Learning Pipeline.

Individuals high in social anxiety symptoms often exhibit elevated state anxiety in social situations. Research has shown it is possible to detect state anxiety by leveraging digital biomarkers and machine learning techniques. However, most existing work trains models on an entire group of participants, failing to capture individual differences in their psychological and behavioral responses to social contexts.

Understanding Privacy Risks versus Predictive Benefits in Wearable Sensor-Based Digital Phenotyping: A Quantitative Cost-Benefit Analysis.

Wearable devices with embedded sensors can provide personalized healthcare and wellness benefits in digital phenotyping and adaptive interventions. However, the collection, storage, and transmission of biometric data (including processed features rather than raw signals) from these devices pose significant privacy concerns. This quantitative, data-driven study examines the privacy risks associated with wearable-based digital phenotyping practices, with a focus on user , which is the process of identifying participants' IDs from deidentified digital phenotyping datasets.

Shifting Episodic Prediction With Online Cognitive Bias Modification: A Randomized Controlled Trial.

Negative future thinking pervades emotional disorders. This hybrid efficacy-effectiveness trial tested a four-session, scalable online cognitive bias modification program for training more positive episodic prediction. 958 adults (73.

Feasibility and acceptability testing of CommSense: A novel communication technology to enhance health equity in clinician-patient interactions.

Background: Quality patient-clinician communication is paramount to achieving safe and compassionate healthcare, but evaluating communication performance during real clinical encounters is challenging. Technology offers novel opportunities to provide clinicians with actionable feedback to enhance their communication skills.

Methods: This pilot study evaluated the acceptability and feasibility of CommSense, a novel natural language processing (NLP) application designed to record and extract key metrics of communication performance and provide real-time feedback to clinicians.

Assessing Emotion Polyregulation in Daily Life: Who Uses It, When Is It Used, and How Effective Is It?

Unlabelled: Most research on emotion regulation has focused on understanding individual emotion regulation strategies. Preliminary research, however, suggests that people often use several strategies to regulate their emotions in a given emotional scenario (polyregulation). The present research examined who uses polyregulation, when polyregulation is used, and how effective polyregulation is when it is used.

Mobile Sensing in the COVID-19 Era: A Review.

Background: During the COVID-19 pandemic, mobile sensing and data analytics techniques have demonstrated their capabilities in monitoring the trajectories of the pandemic, by collecting behavioral, physiological, and mobility data on individual, neighborhood, city, and national scales. Notably, mobile sensing has become a promising way to detect individuals' infectious status, track the change in long-term health, trace the epidemics in communities, and monitor the evolution of viruses and subspecies.

Methods: We followed the PRISMA practice and reviewed 60 eligible papers on mobile sensing for monitoring COVID-19.

Scene-dependent, feedforward eye gaze metrics can differentiate technical skill levels of trainees in laparoscopic surgery.

Introduction: In laparoscopic surgery, looking in the target areas is an indicator of proficiency. However, gaze behaviors revealing feedforward control (i.e.

Identifying individual social risk factors using unstructured data in electronic health records and their relationship with adverse clinical outcomes.

Objective: To determine the prevalence of individual-level social risk factors documented in unstructured data from electronic health records (EHRs) and the relationship between social risk factors and adverse clinical outcomes.

Study Setting: Inpatient encounters for adults (≥18 years) at the University of Virginia Medical Center during a 12-month study period between July 2018 and June 2019. Inpatient encounters for labor and delivery patients were excluded, as well as encounters where the patient was discharged to hospice, left against medical advice, or expired in the hospital.

Exploring the Use of Wearable Sensors and Natural Language Processing Technology to Improve Patient-Clinician Communication: Protocol for a Feasibility Study.

Background: Effective communication is the bedrock of quality health care, but it continues to be a major problem for patients, family caregivers, health care providers, and organizations. Although progress related to communication skills training for health care providers has been made, clinical practice and research gaps persist, particularly regarding how to best monitor, measure, and evaluate the implementation of communication skills in the actual clinical setting and provide timely feedback about communication effectiveness and quality.

Objective: Our interdisciplinary team of investigators aims to develop, and pilot test, a novel sensing system and associated natural language processing algorithms (CommSense) that can (1) be used on mobile devices, such as smartwatches; (2) reliably capture patient-clinician interactions in a clinical setting; and (3) process these communications to extract key markers of communication effectiveness and quality.

Theory-Guided Randomized Neural Networks for Decoding Medication-Taking Behavior.

Long-term endocrine therapy (e.g. Tamoxifen, aromatase inhibitors) is crucial to prevent breast cancer recurrence, yet rates of adherence to these medications are low.

Cognitive bias modification for threat interpretations: using passive Mobile Sensing to detect intervention effects in daily life.

Background: Social anxiety disorder is associated with distinct mobility patterns (e.g., increased time spent at home compared to non-anxious individuals), but we know little about if these patterns change following interventions.

Emotion Malleability Beliefs and Emotion Experience and Regulation in the Daily Lives of People with High Trait Social Anxiety.

Background: The extent to which a person believes they can change or control their own emotions is associated with trait-level symptoms of mood and anxiety-related psychopathology. Method: The present study examined how this belief relates to momentary and daily self-reports of affect, emotion regulation tendencies, and perceived effectiveness of emotion regulation attempts throughout a five-week experience sampling study conducted in = 113 high socially anxious people (https://osf.io/eprwt/).

Building an emotion regulation recommender algorithm for socially anxious individuals using contextual bandits.

Objectives: Poor emotion regulation (ER) has been implicated in many mental illnesses, including social anxiety disorder. To work towards a scalable, low-cost intervention for improving ER, we developed a novel contextual recommender algorithm for ER strategies.

Design: N = 114 socially anxious participants were prompted via a mobile app up to six times daily for five weeks to report their emotional state, use of 19 different ER strategies (or no strategy), physical location, and social context.

Leveraging Mobile Sensing to Understand and Develop Intervention Strategies to Improve Medication Adherence.

Interventions to improve medication adherence have had limited success and can require significant human resources to implement. Research focused on improving medication adherence has undergone a paradigm shift, of late, with a shift towards developing personalized, theory-driven interventions. The current research integrates foundational and translational science to implement a mechanisms-focused, context-aware approach.

Predictive Models of Fever, ICU Transfer, and Mortality in Hospitalized Patients With Neutropenia.

Unlabelled: Neutropenia is a common side effect of myelosuppressive chemotherapy and is associated with adverse outcomes. Early Warning Scores are used to identify at-risk patients and facilitate rapid clinical interventions. Since few Early Warning Scores have been validated in patients with neutropenia, we aimed to create predictive models and nomograms of fever, ICU transfer, and mortality in hospitalized neutropenic patients.

Activation Complexity: A Cognitive Impairment Tool for Characterizing Neuro-isolation.

Electroencephalography (EEG) is a method for recording electrical activity, indicative of cortical brain activity from the scalp. EEG has been used to diagnose neurological diseases and to characterize impaired cognitive states. When the electrical activity of neurons are temporally synchronized, the likelihood to reach their threshold potential for the signal to propagate to the next neuron, increases.

Mapping the online social network of cancer bloggers.

While social media has the ability to quickly disseminate information and reach large audiences, cancer blogs as a communication platform have not yet been well studied. A social network analysis was conducted on 89 active cancer blogs. Results demonstrated the overall cancer-blog-network was widely distributed and decentralized, with blogs clustered by cancer type, and that breast cancer blogs were the most significant and highly linked blogs.