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. Before widely disseminating CBM-I, we must rigorously test its effectiveness and determine whom it is best positioned to benefit. The present study (which is a substudy of a parent trial) compared CBM-I against psychoeducation offered through the public website MindTrails, and also tested whether baseline anxiety tied to COVID-19 influenced the rate of change in anxiety and interpretation bias during and after each intervention. Adults with moderate-to-severe anxiety symptoms were randomly assigned to complete five sessions of either CBM-I or psychoeducation as part of a larger trial, and 608 enrolled in this substudy after Session 1. As predicted (https://osf.io/2dyzr), CBM-I was superior to psychoeducation at reducing anxiety symptoms (on the OASIS but not the DASS-21-AS: d = -0.31), reducing negative interpretation bias (d range = -0.34 to -0.43), and increasing positive interpretation bias (d = 0.79) by the end of treatment. Results also indicated that individuals higher (vs. lower) in baseline COVID-19 anxiety had stronger decreases in anxiety symptoms while receiving CBM-I but weaker decreases in anxiety symptoms (on the DASS-21-AS) while receiving psychoeducation. These findings suggest that CBM-I may be a useful anxiety-reduction tool for individuals experiencing higher anxiety tied to uncertain events such as the COVID-19 pandemic.

Physician perceptions of barriers to infection prevention and control in labor and delivery.

OBJECTIVE: To learn about the perceptions of healthcare personnel (HCP) on the barriers they encounter when performing infection prevention and control (IPC) practices in labor and delivery to help inform future IPC resources tailored to this setting. DESIGN: Qualitative focus groups. SETTING: Labor and delivery units in acute-care settings. PARTICIPANTS: A convenience sample of labor and delivery HCP attending the Infectious Diseases Society for Obstetrics and Gynecology 2022 Annual Meeting. METHODS: Two focus groups, each lasting 45 minutes, were conducted by a team from the Centers for Disease Control and Prevention. A standardized script facilitated discussion around performing IPC practices during labor and delivery. Coding was performed by 3 reviewers using an immersion-crystallization technique. RESULTS: In total, 18 conference attendees participated in the focus groups: 67% obstetrician-gynecologists, 17% infectious disease physicians, 11% medical students, and 6% an obstetric anesthesiologist. Participants described the difficulty of consistently performing IPC practices in this setting because they often respond to emergencies, are an entry point to the hospital, and frequently encounter bodily fluids. They also described that IPC training and education is not specific to labor and delivery, and personal protective equipment is difficult to locate when needed. Participants observed a lack of standardization of IPC protocols in their setting and felt that healthcare for women and pregnant people is not prioritized on a larger scale and within their hospitals. CONCLUSIONS: This study identified barriers to consistently implementing IPC practices in the labor and delivery setting. These barriers should be addressed through targeted interventions and the development of obstetric-specific IPC resources.

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. To address this concern, in Study 1, we collected linguistic data from N=35 high socially anxious participants in a variety of social contexts, finding that digital linguistic biomarkers significantly differ between evaluative vs. non-evaluative social contexts and between individuals having different trait psychological symptoms, suggesting the likely importance of personalized approaches to detect state anxiety. In Study 2, we used the same data and results from Study 1 to model a multilayer personalized machine learning pipeline to detect state anxiety that considers contextual and individual differences. This personalized model outperformed the baseline's F1-score by 28.0%. Results suggest that state anxiety can be more accurately detected with personalized machine learning approaches, and that linguistic biomarkers hold promise for identifying periods of state anxiety in an unobtrusive way.

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 (73.3% female, 86.5% White, 83.4% from United States) were randomized to positive conditions with ambiguous future scenarios that ended positively, 50/50 conditions that ended positively or negatively, or a control condition with neutral scenarios. As hypothesized (preregistration: https://osf.io/jrst6), positive training participants improved more than control participants in negative expectancy bias (d = -0.58), positive expectancy bias (d = 0.80), and self-efficacy (d = 0.29). Positive training was also superior to 50/50 training for expectancy bias and optimism (d = 0.31). Training gains attenuated yet remained by 1-month follow-up. Unexpectedly, participants across conditions improved comparably in anxiety and depression symptoms and growth mindset. Targeting a transdiagnostic process with a scalable program may improve bias and outlook; however, further validation of outcome measures is required.

Burnout and staff turnover among certified nursing assistants working in acute care hospitals during the COVID-19 pandemic.

INTRODUCTION: Healthcare worker burnout is a growing problem in the United States which affects healthcare workers themselves, as well as the healthcare system as a whole. The goal of this qualitative assessment was to understand factors that may lead to healthcare worker burnout and turnover through focus groups with Certified Nursing Assistants who worked in acute care hospitals during the COVID-19 pandemic. METHODS: Eight focus group discussions lasting approximately 30 minutes each were held remotely from October 2022-January 2023 with current and former Certified Nursing Assistants who worked during the COVID-19 pandemic in acute care hospitals. Participants were recruited through various sources such as social media and outreach through professional organizations. The focus groups utilized open-ended prompts including topics such as challenges experienced during the pandemic, what could have improved their experiences working during the pandemic, and motivations for continuing or leaving their career in healthcare. The focus groups were coded using an immersion-crystallization technique and summarized using NVivo and Microsoft Excel. Participant demographic information was summarized overall and by current work status. RESULTS: The focus groups included 58 Certified Nursing Assistants; 33 (57%) were current Certified Nursing Assistants and 25 (43%) were Certified Nursing Assistants who no longer work in healthcare. Throughout the focus groups, five convergent themes emerged, including staffing challenges, respect and recognition for Certified Nursing Assistants, the physical and mental toll of the job, facility leadership support, and pay and incentives. CONCLUSIONS: Focus group discussions with Certified Nursing Assistants identified factors at individual and organizational levels that might contribute to burnout and staff turnover in healthcare settings. Suggestions from participants on improving their experiences included ensuring staff know they are valued, being included in conversations with leadership, and improving access to mental health resources.

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. Metrics of communication performance were established from a review of the literature and technical feasibility verified. CommSense was deployed on a wearable (smartwatch), and participants were recruited from an academic medical center to test the technology. Participants completed a survey about their experience; results were exported to SPSS (v.28.0) for descriptive analysis. Results: Forty (n = 40) healthcare participants (nursing students, medical students, nurses, and physicians) pilot tested CommSense. Over 90% of participants "strongly agreed" or "agreed" that CommSense could improve compassionate communication (n = 38, 95%) and help healthcare organizations deliver high-quality care (n = 39, 97.5%). Most participants (n = 37, 92.5%) "strongly agreed" or "agreed" they would be willing to use CommSense in the future; 100% (n = 40) "strongly agreed" or "agreed" they were interested in seeing information analyzed by CommSense about their communication performance. Metrics of most interest were medical jargon, interruptions, and speech dominance. Conclusion: Participants perceived significant benefits of CommSense to track and improve communication skills. Future work will deploy CommSense in the clinical setting with a more diverse group of participants, validate data fidelity, and explore optimal ways to share data analyzed by CommSense with end-users.

NAPS Fusion: A framework to overcome experimental data limitations to predict human performance and cognitive task outcomes.

In the area of human performance and cognitive research, machine learning (ML) problems become increasingly complex due to limitations in the experimental design, resulting in the development of poor predictive models. More specifically, experimental study designs produce very few data instances, have large class imbalances and conflicting ground truth labels, and generate wide data sets due to the diverse amount of sensors. From an ML perspective these problems are further exacerbated in anomaly detection cases where class imbalances occur and there are almost always more features than samples. Typically, dimensionality reduction methods (e.g., PCA, autoencoders) are utilized to handle these issues from wide data sets. However, these dimensionality reduction methods do not always map to a lower dimensional space appropriately, and they capture noise or irrelevant information. In addition, when new sensor modalities are incorporated, the entire ML paradigm has to be remodeled because of new dependencies introduced by the new information. Remodeling these ML paradigms is time-consuming and costly due to lack of modularity in the paradigm design, which is not ideal. Furthermore, human performance research experiments, at times, creates ambiguous class labels because the ground truth data cannot be agreed upon by subject-matter experts annotations, making ML paradigm nearly impossible to model. This work pulls insights from Dempster-Shafer theory (DST), stacking of ML models, and bagging to address uncertainty and ignorance for multi-classification ML problems caused by ambiguous ground truth, low samples, subject-to-subject variability, class imbalances, and wide data sets. Based on these insights, we propose a probabilistic model fusion approach, Naive Adaptive Probabilistic Sensor (NAPS), which combines ML paradigms built around bagging algorithms to overcome these experimental data concerns while maintaining a modular design for future sensor (new feature integration) and conflicting ground truth data. We demonstrate significant overall performance improvements using NAPS (an accuracy of 95.29%) in detecting human task errors (a four class problem) caused by impaired cognitive states and a negligible drop in performance with the case of ambiguous ground truth labels (an accuracy of 93.93%), when compared to other methodologies (an accuracy of 64.91%). This work potentially sets the foundation for other human-centric modeling systems that rely on human state prediction modeling.

Perceived Burdensomeness and Thwarted Belongingness in Text Messages of Suicide Attempt Survivors.

OBJECTIVE: Perceived burdensomeness and thwarted belongingness are considered interpersonal risk factors for suicide. Examining these themes in personal text messages may help identify proximal suicide risk. METHOD: Twenty-six suicide attempt survivors provided personal text messages and reported dates for past periods characterized by positive mood, depressed mood, suicidal ideation (with no attempt), or the two-week period leading up to suicide attempt(s). Texts were then classified into the applicable period based on matching dates. Texts (N = 194,083; including n = 86,705 outgoing texts) were coded for perceived burdensomeness and thwarted belongingness by masked trained raters. Multilevel models were fit to examine whether the target themes (combined into one overall interpersonal risk variable due to low base rate) were more prevalent in texts sent during higher risk episodes (e.g., suicide attempt vs. depressed mood episodes). RESULTS: 0.57% of outgoing texts contained either target theme. As hypothesized, logistic models showed participants were more likely to send texts containing the target themes during suicide attempt episodes relative to suicidal ideation (with no attempt) episodes, depressed mood episodes, and positive mood episodes, and during suicidal ideation (with no attempt) episodes relative to positive mood episodes. All contrasts were robust to post-hoc correction except for suicide attempt episodes vs. ideation (with no attempt) episodes. No other significant pairwise differences for episode type emerged. CONCLUSIONS: Despite the small sample size and low base rate of target themes in the texts, perceived burdensomeness and thwarted belongingness were associated with intra-individual suicide risk severity in personal text messages.

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

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. College students (N = 128; 65.6% female; 54.7% White) completed an in-person lab visit followed by a 2-week ecological momentary assessment protocol with six randomly timed survey prompts per day for up 2 weeks. At baseline, participants completed measures assessing past-week depression symptoms, social anxiety-related traits, and trait emotion dysregulation. During each randomly timed prompt, participants reported up to eight strategies used to change their thoughts or feelings, negative and positive affect, motivation to change emotions, their social context, and how well they felt they were managing their emotions. In pre-registered analyses examining the 1,423 survey responses collected, polyregulation was more likely when participants were feeling more intensely negative and when their motivation to change their emotions was stronger. Neither sex, psychopathology-related symptoms and traits, social context, nor subjective effectiveness was associated with polyregulation, and state affect did not moderate these associations. This study helps address a key gap in the literature by assessing emotion polyregulation in daily life. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-022-00166-x.

Inferring sleep disturbance from text messages of suicide attempt survivors: A pilot study.

OBJECTIVE: Identifying digital markers of sleep disturbance-a known suicide risk factor-may aid in the detection of imminent suicide risk. This study examined sleep-related communication and texting patterns in personal text messages (N = 86,705) of suicide attempt survivors. METHOD: Twenty-six participants provided dates of past suicide attempts and 2-week periods of positive mood, depressed mood, or suicidal ideation. Linguistic Inquiry Word Count was used to identify sleep-related texts via a custom dictionary. Mixed effect models were fitted to test the association between suicide/mood episode type (e.g., attempt versus ideation) and three outcomes: likelihood of a text including sleep-related content, nightly count of texts sent from midnight to 5:00 AM, and sum of unique hour bins from midnight to 5:00 AM with outgoing texts. RESULTS: Analyses with a sleep dictionary that was manually revised to be more accurate (but not the original unedited dictionary) showed sleep-related communication was more likely during depressed mood episodes than positive mood episodes. Otherwise, there were no significant differences in sleep-related communication or objective texting patterns across episode type. CONCLUSIONS: Although we did not detect differences in sleep-related communication tied to suicidal thoughts or behaviors, sleep-related communication may differ as a function of within-person mood level.

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., looking ahead) and their importance have been under-investigated in surgery. This study aims to establish the sensitivity and relative importance of different scene-dependent gaze and motion metrics for estimating trainee proficiency levels in surgical skills. METHODS: Medical students performed the Fundamentals of Laparoscopic Surgery peg transfer task while recording their gaze on the monitor and tool activities inside the trainer box. Using computer vision and fixation algorithms, five scene-dependent gaze metrics and one tool speed metric were computed for 499 practice trials. Cluster analysis on the six metrics was used to group the trials into different clusters/proficiency levels, and ANOVAs were conducted to test differences between proficiency levels. A Random Forest model was trained to study metric importance at predicting proficiency levels. RESULTS: Three clusters were identified, corresponding to three proficiency levels. The correspondence between the clusters and proficiency levels was confirmed by differences between completion times (F2,488 = 38.94, p < .001). Further, ANOVAs revealed significant differences between the three levels for all six metrics. The Random Forest model predicted proficiency level with 99% out-of-bag accuracy and revealed that scene-dependent gaze metrics reflecting feedforward behaviors were more important for prediction than the ones reflecting feedback behaviors. CONCLUSION: Scene-dependent gaze metrics revealed skill levels of trainees more precisely than between experts and novices as suggested in the literature. Further, feedforward gaze metrics appeared to be more important than feedback ones at predicting proficiency.

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. We posit that passively detecting social contexts can be particularly useful for social anxiety research, as it may ultimately help identify changes in social anxiety status and patterns of social avoidance and withdrawal. To this end, we recruited a sample of highly socially anxious undergraduate students (N=46) to examine whether we could detect the presence of experimentally manipulated virtual social contexts via wristband sensors. Using a multitask machine learning pipeline, we leveraged passively sensed biobehavioral streams to detect contexts relevant to social anxiety, including (1) whether people were in a social situation, (2) size of the social group, (3) degree of social evaluation, and (4) phase of social situation (anticipating, actively experiencing, or had just participated in an experience). Results demonstrated the feasibility of detecting most virtual social contexts, with stronger predictive accuracy when detecting whether individuals were in a social situation or not and the phase of the situation, and weaker predictive accuracy when detecting the level of social evaluation. They also indicated that sensing streams are differentially important to prediction based on the context being predicted. Our findings also provide useful information regarding design elements relevant to passive context detection, including optimal sensing duration, the utility of different sensing modalities, and the need for personalization. We discuss implications of these findings for future work on context detection (e.g., just-in-time adaptive intervention development).

Making a C-DIFFerence: Implementation of a prevention collaborative to reduce hospital-onset Clostridioides difficile infection rates.

Objective: To assist hospitals in reducing Clostridioides difficile infections (CDI), the Centers for Disease Control and Prevention (CDC) implemented a collaborative using the CDC CDI prevention strategies and the Targeted Assessment for Prevention (TAP) Strategy as foundational frameworks. Setting: Acute-care hospitals. Methods: We invited 400 hospitals with the highest cumulative attributable differences (CADs) to the 12-month collaborative, with monthly webinars, coaching calls, and deployment of the CDC CDI TAP facility assessments. Infection prevention barriers, gaps identified, and interventions implemented were qualitatively coded by categorizing them to respective CDI prevention strategies. Standardized infection ratios (SIRs) were reviewed to measure outcomes. Results: Overall, 76 hospitals participated, most often reporting CDI testing as their greatest barrier to achieving reduction (61%). In total, 5,673 TAP assessments were collected across 46 (61%) hospitals. Most hospitals (98%) identified at least 1 gap related to testing and at least 1 gap related to infrastructure to support prevention. Among 14 follow-up hospitals, 64% implemented interventions related to infrastructure to support prevention (eg, establishing champions, reviewing individual CDIs) and 86% implemented testing interventions (eg, 2-step testing, testing algorithms). The SIR decrease between the pre-collaborative and post-collaborative periods was significant among participants (16.7%; P < .001) but less than that among nonparticipants (25.1%; P < .001). Conclusions: This article describes gaps identified and interventions implemented during a comprehensive CDI prevention collaborative in targeted hospitals, highlighting potential future areas of focus for CDI prevention efforts as well as reported challenges and barriers to prevention of one of the most common healthcare-associated infections affecting hospitals and patients nationwide.

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. We proposed a taxonomy system to summarize literature by the time duration and population scale under mobile sensing studies. Results: We found that existing literature can be naturally grouped in four clusters, including remote detection, long-term tracking, contact tracing, and epidemiological study. We summarized each group and analyzed representative works with regard to the system design, health outcomes, and limitations on techniques and societal factors. We further discussed the implications and future directions of mobile sensing in communicable diseases from the perspectives of technology and applications. Conclusion: Mobile sensing techniques are effective, efficient, and flexible to surveil COVID-19 in scales of time and populations. In the post-COVID era, technical and societal issues in mobile sensing are expected to be addressed to improve healthcare and social outcomes.

Evaluation of a Virtual Training to Enhance Public Health Capacity for COVID-19 Infection Prevention and Control in Nursing Homes.

CONTEXT: Between April 2020 and May 2021, the Centers for Disease Control and Prevention (CDC) awarded more than $40 billion to health departments nationwide for COVID-19 prevention and response activities. One of the identified priorities for this investment was improving infection prevention and control (IPC) in nursing homes. PROGRAM: CDC developed a virtual course to train new and less experienced public health staff in core healthcare IPC principles and in the application of CDC COVID-19 healthcare IPC guidance for nursing homes. IMPLEMENTATION: From October 2020 to August 2021, the CDC led training sessions for 12 cohorts of public health staff using pretraining reading materials, case-based scenarios, didactic presentations, peer-learning opportunities, and subject matter expert-led discussions. Multiple electronic assessments were distributed to learners over time to measure changes in self-reported knowledge and confidence and to collect feedback on the course. Participating public health programs were also assessed to measure overall course impact. EVALUATION: Among 182 enrolled learners, 94% completed the training. Most learners were infection preventionists (42%) or epidemiologists (38%), had less than 1 year of experience in their health department role (75%), and had less than 1 year of subject matter experience (54%). After training, learners reported increased knowledge and confidence in applying the CDC COVID-19 healthcare IPC guidance for nursing homes (≥81%) with the greatest increase in performing COVID-19 IPC consultations and assessments (87%). The majority of participating programs agreed that the course provided an overall benefit (88%) and reduced training burden (72%). DISCUSSION: The CDC's virtual course was effective in increasing public health capacity for COVID-19 healthcare IPC in nursing homes and provides a possible model to increase IPC capacity for other infectious diseases and other healthcare settings. Future virtual healthcare IPC courses could be enhanced by tailoring materials to health department needs, reinforcing training through applied learning experiences, and supporting mechanisms to retain trained staff.

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. The study population included 21,402 inpatient admissions, representing 15,116 unique patients who had at least one inpatient admission during the study period. Study design: We identified measures related to individual social risk factors in EHRs through existing workflows, flowsheets, and clinical notes. Multivariate binomial logistic regression was performed to determine the association of individual social risk factors with unplanned inpatient readmissions, post-discharge emergency department (ED) visits, and extended length of stay (LOS). Other predictors included were age, sex, severity of illness, location of residence, and discharge destination. Results: Predictors of 30-day unplanned readmissions included severity of illness (OR = 3.96), location of residence (OR = 1.31), social and community context (OR = 1.26), and economic stability (OR = 1.37). For 30-day post-discharge ED visits, significant predictors included location of residence (OR = 2.56), age (OR = 0.60), economic stability (OR = 1.39), education (OR = 1.38), social and community context (OR = 1.39), and neighborhood and built environment (OR = 1.61). For extended LOS, significant predictors were age (OR = 0.51), sex (OR = 1.18), severity of illness (OR = 2.14), discharge destination (OR = 2.42), location of residence (OR = 0.82), economic stability (OR = 1.14), neighborhood and built environment (OR = 1.31), and education (OR = 0.79). Conclusions: Individual-level social risk factors are associated with increased risk for unplanned hospital readmissions, post-discharge ED visits, and extended LOS. While individual-level social risk factors are currently documented on an ad-hoc basis in EHRs, standardized SDoH screening tools using validated metrics could help eliminate bias in the collection of SDoH data and facilitate social risk screening.

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. The long-term goal of this research is to use CommSense in a variety of health care contexts to provide real-time feedback to end users to improve communication and patient health outcomes. METHODS: This is a 1-year pilot study. During Phase I (Aim 1), we will identify feasible metrics of communication to extract from conversations using CommSense. To achieve this, clinical investigators will conduct a thorough review of the recent health care communication and palliative care literature to develop an evidence-based "ideal and optimal" list of communication metrics. This list will be discussed collaboratively within the study team and consensus will be reached regarding the included items. In Phase II (Aim 2), we will develop the CommSense software by sharing the "ideal and optimal" list of communication metrics with engineering investigators to gauge technical feasibility. CommSense will build upon prior work using an existing Android smartwatch platform (SWear) and will include sensing modules that can collect (1) physiological metrics via embedded sensors to measure markers of stress (eg, heart rate variability), (2) gesture data via embedded accelerometer and gyroscope sensors, and (3) voice and ultimately textual features via the embedded microphone. In Phase III (Aim 3), we will pilot test the ability of CommSense to accurately extract identified communication metrics using simulated clinical scenarios with nurse and physician participants. RESULTS: Development of the CommSense platform began in November 2021, with participant recruitment expected to begin in summer 2022. We anticipate that preliminary results will be available in fall 2022. CONCLUSIONS: CommSense is poised to make a valuable contribution to communication science, ubiquitous computing technologies, and natural language processing. We are particularly eager to explore the ability of CommSense to support effective virtual and remote health care interactions and reduce disparities related to patient-clinician communication in the context of serious illness. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/37975.

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. Information from passive sensors was also collected. METHODS: Given the large number of ER strategies, we used two different approaches for variable reduction: (1) grouping ER strategies into categories based on a prior meta-analysis, and (2) considering only the ten most frequently used strategies. For each approach, an algorithm that recommends strategies based on one's current context was compared with an algorithm that recommends ER strategies randomly, an algorithm that always recommends cognitive reappraisal, and the person's observed ER strategy use. Contextual bandits were used to predict the effectiveness of the strategies recommended by each policy. RESULTS: When strategies were grouped into categories, the contextual algorithm was not the best performing policy. However, when the top ten strategies were considered individually, the contextual algorithm outperformed all other policies. CONCLUSIONS: Grouping strategies into categories may obscure differences in their contextual effectiveness. Further, using strategies tailored to context is more effective than using cognitive reappraisal indiscriminately across all contexts. Future directions include deploying the contextual recommender algorithm as part of a just-in-time intervention to assess real-world efficacy. PRACTITIONER POINTS: Emotion regulation strategies vary in their effectiveness across different contexts. An algorithm that recommends emotion regulation strategies based on a person's current context may one day be used as an adjunct to treatment to help dysregulated individuals optimize their in-the-moment emotion regulation. Recommending flexible use of emotion regulation strategies across different contexts may be more effective than recommending cognitive reappraisal indiscriminately across all contexts.

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. The ubiquity of GPS-enabled smartphones offers new opportunities to assess the benefits of mental health interventions beyond self-reported data. OBJECTIVES: This pre-registered study (https://osf.io/em4vn/?view_only=b97da9ef22df41189f1302870fdc9dfe) assesses the impact of a brief, online cognitive training intervention for threat interpretations using passively-collected mobile sensing data. DESIGN: Ninety-eight participants scoring high on a measure of trait social anxiety completed five weeks of mobile phone monitoring, with 49 participants randomly assigned to receive the intervention halfway through the monitoring period. RESULTS: The brief intervention was not reliably associated with changes to participant mobility patterns. CONCLUSIONS: Despite the lack of significant findings, this paper offers a framework within which to test future intervention effects using GPS data. We present a template for combining clinical theory and empirical GPS findings to derive testable hypotheses, outline data processing steps, and provide human-readable data processing scripts to guide future research. This manuscript illustrates how data processing steps common in engineering can be harnessed to extend our understanding of the impact of mental health interventions in daily life.

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. To develop, evaluate, and sustain future interventions, individual-level modeling can be used to understand breast cancer survivors' behavioral mechanisms of medication-taking. This paper presents interdisciplinary research, wherein a model employing randomized neural networks was developed to predict breast cancer survivors' daily medication-taking behavior based on their survey data over three time periods (baseline, 4 months, 8 months). The neural network structure was guided by random utility theory developed in psychology and behavioral economics. Comparative analysis indicates that the proposed model outperforms existing computational models in terms of prediction accuracy under conditions of randomness.