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.

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.

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.

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.

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.

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.

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.

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.

Multiscale modeling intervention development and perspectives from early-stage breast cancer survivors on technology to improve long-term adherence to endocrine therapy

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Background: Despite the important role of endocrine therapy (ET) in preventing cancer recurrence, rates of long-term adherence are poor among certain breast cancer survivors (BCS). Traditional medication adherence (MA) interventions that have primarily incorporated medication-taking reminders mainly focused on a “one size fits all” approach, which may explain why many interventions have proven unsuccessful. However, when combined with other context, sensors (i.e., wearable sensors, smartphone sensors) can facilitate a better understanding of medication-taking behaviors leading to individualized interventions that are time and context appropriate. The project team is developing a multiscale modeling and intervention (MMI) system designed to improve adherence to ET among BCS. This study describes MMI development. Methods: MMI development has included: 1) usability testing; 2) review of research literature regarding factors associated with ET MA; and 3) a neural network analysis of previously collected ET MA data. In usability testing, 20 BCS were recruited via social media posts to participate in semi-structured usability interviews. Interviews were conducted via videoconferencing and assessed perceptions of and willingness to use an ecological momentary assessment (EMA) smartphone app, smartwatch, smart pill bottle, and smart pill box. The literature review examined multiple systematic reviews to identify constructs associated with ET MA. Randomized neural network analysis with 32 early stage BCS taking ET was used to determine important features of ET MA 4 weeks following completion of a 346-item survey. Four-week medication adherence was measured daily with a medication event monitoring system (MEMS). Results: Usability testing participants were accepting of each technology and willing to use each technology at various frequencies. Forty-two surveys were reviewed as predictors of MA in systematic reviews. Randomized neural network analysis found 104 survey items had absolute weights at the 70th percentile, indicating a strong influence on week 4 ET MA, and 11 surveys were determined theoretically relevant. Conclusions: BCS are willing to use 4 components of the MMI system. The MMI system will soon be deployed for 6 months of data collection. If shown to be effective, the MMI framework can be used by oncologists and researchers to develop personalized interventions focused on understanding and increasing ET MA.

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.

When and Where Do People Regulate Their Emotions? Patterns of Emotion Regulation in Unselected and Socially Anxious Young Adults

Introduction: The current studies examined how smartphone-assessed contextual features (i.e., location, time-of-day, social situation, and affect) contribute to the relative likelihood of emotion regulation strategy endorsement in daily life. Methods: Emotion regulation strategy endorsement and concurrent contextual features were assessed either passively (e.g., via GPS coordinates) or via self-report among unselected (Study 1: N = 112; duration = 2 weeks) and socially anxious (Study 2: N = 106; duration = 5 weeks) young adults. Results: An analysis of 2,891 (Study 1) and 12,289 (Study 2) mobile phone survey responses indicated small differences in rates of emotion regulation strategy endorsement across location (e.g., home vs. work/education settings), time-of-day (e.g., afternoon vs. evening), time-of-week (i.e., weekdays vs. weekends) and social context (e.g., with others vs. alone). However, emotion regulation patterns differed markedly depending on the set of emotion regulation strategies examined, which likely partly explains some inconsistent results across the studies. Also, many observed effects were no longer significant after accounting for state affect in the models. Discussion: Results demonstrate how contextual information collected with relatively low (or no) participant burden can add to our understanding of emotion regulation in daily life, yet it is important to consider state affect alongside other contextual features when drawing conclusions about how people regulate their emotions.

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.

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. Efforts to disseminate cancer-related information may focus on identifying key breast cancer bloggers or linking key bloggers of various cancers to create a more interconnected network and expand its reach within this online community.

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.

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.

Can Text Messages Identify Suicide Risk in Real Time? A Within-Subjects Pilot Examination of Temporally Sensitive Markers of Suicide Risk

Objective tools to assess suicide risk are needed to determine when someone is at imminent risk. This pilot laboratory investigation utilized a within-subjects design to identify patterns in text messaging (SMS) unique to high-risk periods preceding suicide attempts. Individuals reporting a history of suicide attempt (N=33) retrospectively identified past attempts and periods of lower risk (e.g., suicide ideation). Language analysis software scored 189,478 text messages to capture three psychological constructs: self-focus, sentiment, and social engagement. Mixed-effects models tested whether these constructs differed in general (means) and over time (slopes) two weeks before a suicide attempt, relative to lower-risk periods. Regarding mean differences, no language features uniquely differentiated suicide attempts from other episodes. However, when examining patterns over time, anger increased and positive emotion decreased to a greater extent as one approached a suicide attempt. Results suggest private electronic communication has the potential to provide real-time digital markers of suicide risk.

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. Increasing adoption of mobile and wearable sensing systems presents new opportunities for understanding how medication-taking behaviors unfold in natural settings, especially in populations who have difficulty adhering to medications. When combined with survey and ecological momentary assessment data, these mobile and wearable sensing systems can directly capture the context of medication adherence in situ, including personal, behavioral, and environmental factors. The purpose of this paper is to present a new transdisciplinary research framework in medication adherence, highlight critical advances in this rapidly-evolving research field, and outline potential future directions for both research and clinical applications.

Leveraging Mobile Sensing and Machine Learning for Personalized Mental Health Care

Mental illness is widespread in our society, yet remains difficult to treat due to challenges such as stigma and overburdened health care systems. New paradigms are needed for treating mental illness outside the practitioner’s office. We propose a framework to guide the design of mobile sensing systems for personalized mental health interventions. This framework guides researchers in constructing interventions from the ground up through four phases: sensor data collection, digital biomarker extraction, health state detection, and intervention deployment. We highlight how this framework advances research in personalized mHealth and address remaining challenges, such as ground truth fidelity and missing data.

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. This phenomenon is typically analyzed as the spectral intensity increasing from the summation of these neurons firing. Non-linear analysis methods (e.g., entropy) have been explored to characterize neuronal firings, but only analyze temporal information and not the frequency spectrum. By examining temporal and spectral entropic relationships simultaneously, we can better characterize how neurons are isolated, (the signal’s inability to propagate to adjacent neurons), an indicator of impairment. A novel time-frequency entropic analysis method, referred to as Activation Complexity (AC), was designed to quantify these dynamics from key EEG frequency bands. The data was collected during a cognitive impairment study at NASA Langley Research Center, involving hypoxia induction in 49 human test subjects. AC demonstrated significant changes in EEG firing patterns characterize within explanatory (p < 0.05) and predictive models (10% increase in accuracy). The proposed work sets the methodological foundation for quantifying neuronal isolation and introduces new potential technique to understand human cognitive impairment for a range of neurological diseases and insults.

Do I really feel better? Effectiveness of emotion regulation strategies depends on the measure and social anxiety

BACKGROUND

Effective emotion regulation (ER) is important to long-term healthy functioning, but little is known about what constitutes effective ER in the moment or how social anxiety symptoms and different strategies influence short-term effectiveness outcomes.

METHODS

Intensive ecological momentary data from N = 124 college students illustrate how different ways of operationalizing ER effectiveness leads to different conclusions about the short-term effectiveness of different strategies in daily life.

RESULTS

When effectiveness is operationalized as the degree to which participants judged that their ER attempts made them feel better, social anxiety severity was negatively associated with effectiveness, and avoidance-oriented strategies were judged to be less effective than engagement-oriented strategies. In contrast, when effectiveness is operationalized as the degree of change in self-reported affect following ER attempts, social anxiety severity was not related to effectiveness, and avoidance-oriented strategies were more effective than engagement-oriented strategies. Social anxiety and ER strategy type did not interact in either model, regardless of how effectiveness was measured.

CONCLUSIONS

The study highlights discrepancies when examining two common but distinct ways of measuring the same overarching effectiveness construct, and raises intriguing questions about how forms of psychopathology that are intimately tied to emotion dysregulation, like social anxiety, moderate different ways of measuring the effectiveness of ER attempts.

Relationships between trait emotion dysregulation and emotional experiences in daily life: an experience sampling study

Few studies have examined how trait emotion dysregulation relates to momentary affective experiences and the emotion regulation (ER) strategies people use in daily life. In the current study, 112 college students completed a trait measure of emotion dysregulation and completed experience sampling and end-of-day surveys over a two- to three-week period, asking about their emotional experiences and ER strategy use. Participants completed a total of 3798 experience sampling (in-the-moment) and 995 nightly diary surveys. We examined the top 40% of each participant's reported instances of negative affect (to capture times when emotions more likely need regulation). Results indicated that a higher level of trait emotion dysregulation was associated with the following in-the-moment responses: (a) higher level of negative affect; (b) greater desire to change emotions; (c) more attempts to regulate emotion; (d) higher relative endorsements of avoidant (e.g. thought suppression) versus engagement (e.g. acceptance) ER strategy use; and (e) lower perceived effectiveness of ER. Further, individuals with a higher (vs. lower) level of trait emotion dysregulation were less able to identify emotions over the course of the day. Findings demonstrate how trait emotion dysregulation may predict emotional experiences and ER in daily life.

Uncertainty in heart rate complexity metrics caused by R-peak perturbations

Heart rate complexity (HRC) is a proven metric for gaining insight into human stress and physiological deterioration. To calculate HRC, the detection of the exact instance of when the heart beats, the R-peak, is necessary. Electrocardiogram (ECG) signals can often be corrupted by environmental noise (e.g., from electromagnetic interference, movement artifacts), which can potentially alter the HRC measurement, producing erroneous inputs which feed into decision support models. Current literature has only investigated how HRC is affected by noise when R-peak detection errors occur (false positives and false negatives). However, the numerical methods used to calculate HRC are also sensitive to the specific location of the fiducial point of the R-peak. This raises many questions regarding how this fiducial point is altered by noise, the resulting impact on the measured HRC, and how we can account for noisy HRC measures as inputs into our decision models. This work uses Monte Carlo simulations to systematically add white and pink noise at different permutations of signal-to-noise ratios (SNRs), time segments, sampling rates, and HRC measurements to characterize the influence of noise on the HRC measure by altering the fiducial point of the R-peak. Using the generated information from these simulations provides improved decision processes for system design which address key concerns such as permutation entropy being a more precise, reliable, less biased, and more sensitive measurement for HRC than sample and approximate entropy.

Predicting Social Anxiety From Global Positioning System Traces of College Students: Feasibility Study

BACKGROUND

Social anxiety is highly prevalent among college students. Current methodologies for detecting symptoms are based on client self-report in traditional clinical settings. Self-report is subject to recall bias, while visiting a clinic requires a high level of motivation. Assessment methods that use passively collected data hold promise for detecting social anxiety symptoms and supplementing self-report measures. Continuously collected location data may provide a fine-grained and ecologically valid way to assess social anxiety in situ.

OBJECTIVE

The objective of our study was to examine the feasibility of leveraging noninvasive mobile sensing technology to passively assess college students' social anxiety levels. Specifically, we explored the different relationships between mobility and social anxiety to build a predictive model that assessed social anxiety from passively generated Global Positioning System (GPS) data.

METHODS

We recruited 228 undergraduate participants from a Southeast American university. Social anxiety symptoms were assessed using self-report instruments at a baseline laboratory session. An app installed on participants' personal mobile phones passively sensed data from the GPS sensor for 2 weeks. The proposed framework supports longitudinal, dynamic tracking of college students to evaluate the relationship between their social anxiety and movement patterns in the college campus environment. We first extracted the following mobility features: (1) cumulative staying time at each different location, (2) the distribution of visits over time, (3) the entropy of locations, and (4) the frequency of transitions between locations. Next, we studied the correlation between these features and participants' social anxiety scores to enhance the understanding of how students' social anxiety levels are associated with their mobility. Finally, we used a neural network-based prediction method to predict social anxiety symptoms from the extracted daily mobility features.

RESULTS

Several mobility features correlated with social anxiety levels. Location entropy was negatively associated with social anxiety (during weekdays, r=-0.67; and during weekends, r=-0.51). More (vs less) socially anxious students were found to avoid public areas and engage in less leisure activities during evenings and weekends, choosing instead to spend more time at home after school (4 pm-12 am). Our prediction method based on extracted mobility features from GPS trajectories successfully classified participants as high or low socially anxious with an accuracy of 85% and predicted their social anxiety score (on a scale of 0-80) with a root-mean-square error of 7.06.

CONCLUSIONS

Results indicate that extracting and analyzing mobility features may help to reveal how social anxiety symptoms manifest in the daily lives of college students. Given the ubiquity of mobile phones in our society, understanding how to leverage passively sensed data has strong potential to address the growing needs for mental health monitoring and treatment.

"Is this a STD? Please help!": Online Information Seeking for Sexually Transmitted Diseases on Reddit

Increasing incidence of sexually transmitted diseases (STDs) has prompted the public health and technology communities to innovate new measures to understand how individuals use Internet resources to attain relevant information, particularly for sensitive or stigmatized conditions. The purpose of this study is to examine recent health information seeking and needs of the r/STD community, a subreddit focused exclusively on STDs. We found that the majority of posts crowd-source information about intermediate, non-reportable STDs such as human papillomavirus (HPV). Crowdsourced information in this community focused on symptoms, treatment, as well as the social and emotional aspects of sexual health such as fear of misdiagnosis. From our analysis, it is clear that online communities focused on discussion of health symptoms have the ripe potential to influence information-seeking behavior and consumer action.

Design of a Culturally-Informed Virtual Human for Educating Hispanic Women about Cervical Cancer

Significant health disparities exist between Hispanics and the general US population, complicated in part by communication, literacy, and linguistic factors. There are few available Spanish-language interactive, technology-driven health education programs that engage patients who have a range of health literacy levels. We describe the development of an interactive virtual patient educator for educating and counseling Hispanic women about cervical cancer and human papillomavirus. Specifically, we describe the iterative design methodology and rationale, usability evaluation, and pilot testing of the system with Hispanic women in a rural community in Florida. The pilot study findings provide preliminary evidence of the feasibility of the proposed patient education approach. The proposed application and the lessons learned will prove beneficial for future work targeted towards different cultural populations.

Identification of Imminent Suicide Risk Among Young Adults using Text Messages

Suicide is the second leading cause of death among young adults but the challenges of preventing suicide are significant because the signs often seem invisible. Research has shown that clinicians are not able to reliably predict when someone is at greatest risk. In this paper, we describe the design, collection, and analysis of text messages from individuals with a history of suicidal thoughts and behaviors to build a model to identify periods of suicidality (i.e., suicidal ideation and non-fatal suicide attempts). By reconstructing the timeline of recent suicidal behaviors through a retrospective clinical interview, this study utilizes a prospective research design to understand if text communications can predict periods of suicidality versus depression. Identifying subtle clues in communication indicating when someone is at heightened risk of a suicide attempt may allow for more effective prevention of suicide.

Environmental Reservoirs of Nosocomial Infection: Imputation Methods for Linking Clinical and Environmental Microbiological Data to Understand Infection Transmission

The transmission of hospital-acquired Carbapenem-resistant Enterobacteriaceae (CRE) is a serious and growing concern in hospitals worldwide. Previous research of CRE found that traditional patient-to-patient transmission of the bacteria does not fully account for all cases of transmission. Recent efforts to further understand modes of transmission found identical genomes of CRE in patient sinks as was found in cultures collected from patients, indicating that environmental reservoirs could be playing a larger role in transmission than was first realized. This study evaluated imputation methods for linking multiscale clinical and environmental microbiological data. We then utilized the imputed data set to model the risk of CRE presence in sinks between culture dates. We demonstrated that imputation based on expert knowledge of the unique factors of the physical hospital layout and patterns of occurrence throughout hospital sinks provided the best representation of sink positivity and also identified several significant risk factors for explaining environmental contamination. This work helps to more clearly define the mechanism and risk of transmission from a wastewater source to hospitalized patients in a world with increasingly antibiotic-resistant bacteria which can thrive in wastewater environments and cause infections in vulnerable patients.

A Social Cognitive Theory-based Framework for Monitoring Medication Adherence Applied to Endocrine Therapy in Breast Cancer Survivors

Poor adherence to long-term therapies for chronic diseases, such as cancer, compromises effectiveness of treatment and increases the likelihood of disease progression, making medication adherence a critical issue in population health. While the field has documented many eers to adherence to medication, it has also come up with few efficacious solutions to medication adherence, indicating that new and innovative approaches are needed. In this paper, we evaluate medication-taking behaviors based on social cognitive theory (SCT), presenting patterns of adherence stratified across SCT constructs in 33 breast cancer survivors over an 8-month period. Findings indicate that medication adherence is a very personal experience influenced by many simultaneously interacting factors, and a deeper contextual understanding is needed to understand and develop interventions targeting non-adherence.

Utilization of primary care among college students with mental health disorders

OBJECTIVE

Our goal was to assess whether integration of behavioral and medical services in college settings is associated with decreased use of primary care by patients with mental health diagnoses (MHDs).

METHOD

The cohort consisted of 80,219 patients with at least 1 MHD in 21 universities. Two levels of service integration were defined: "standard"-universities with separate electronic health records (EHR) systems and minimal clinical collaboration between services, and "enhanced"-universities with shared EHR and collaborative patient care. Frequency, the total number of primary care visits, and complexity, the total visit length for primary care per unit time, were compared by using multilevel models.

RESULTS

Nine schools met the criteria of enhanced clinical integration; a tenth school shifted to enhanced service during the study period. Student and patient demographics and clinical diagnoses were similar between the 2 categories. When controlling for variance in age, sex, and total time in school, patients with MHDs in standard systems had 15.72% (95% confidence interval [CI]: 10.77%-20.44%) more primary care visits and 22.88% (95% CI: 21.42%-24.38%) more time than patients in enhanced systems.

CONCLUSIONS

Students with MHDs have significantly lower utilization of primary care services in integrated health care systems, but only a minority of institutions nationally have adopted this model of care. Although further research is needed to specifically assess differences in health outcomes and perceived suffering, it is possible that reduced primary care visits in enhanced integrative service settings with robust mental health support indicates overall reduction in perceived suffering for patients/clients. (PsycINFO Database Record

Derivation and validation of a universal vital assessment (UVA) score: a tool for predicting mortality in adult hospitalised patients in sub-Saharan Africa

Background

Critical illness is a leading cause of morbidity and mortality in sub-Saharan Africa (SSA). Identifying patients with the highest risk of death could help with resource allocation and clinical decision making. Accordingly, we derived and validated a universal vital assessment (UVA) score for use in SSA.

Methods

We pooled data from hospital-based cohort studies conducted in six countries in SSA spanning the years 2009–2015. We derived and internally validated a UVA score using decision trees and linear regression and compared its performance with the modified early warning score (MEWS) and the quick sepsis-related organ failure assessment (qSOFA) score.

Results

Of 5573 patients included in the analysis, 2829 (50.8%) were female, the median (IQR) age was 36 (27–49) years, 2122 (38.1%) were HIV-infected and 996 (17.3%) died in-hospital. The UVA score included points for temperature, heart and respiratory rates, systolic blood pressure, oxygen saturation, Glasgow Coma Scale score and HIV serostatus, and had an area under the receiver operating characteristic curve (AUC) of 0.77 (95% CI 0.75 to 0.79), which outperformed MEWS (AUC 0.70 (95% CI 0.67 to 0.71)) and qSOFA (AUC 0.69 (95% CI 0.67 to 0.72)).

Conclusion

We identified predictors of in-hospital mortality irrespective of the underlying condition(s) in a large population of hospitalised patients in SSA and derived and internally validated a UVA score to assist clinicians in risk-stratifying patients for in-hospital mortality. The UVA score could help improve patient triage in resource-limited environments and serve as a standard for mortality risk in future studies.

Daehr

Electronic health records (EHR) provide a rich source of temporal data that present a unique opportunity to characterize disease patterns and risk of imminent disease. While many data-mining tools have been adopted for EHR-based disease early detection, linear discriminant analysis (LDA) is one of the most commonly used statistical methods. However, it is difficult to train an accurate LDA model for early disease diagnosis when too few patients are known to have the target disease. Furthermore, EHR data are heterogeneous with significant noise. In such cases, the covariance matrices used in LDA are usually singular and estimated with a large variance.

This article presents Daehr , an extension of the LDA framework using electronic health record data to address these issues. Beyond existing LDA analyzers, we propose Daehr to (1) eliminate the data noise caused by the manual encoding of EHR data and (2) lower the variance of parameter (covariance matrices) estimation for LDA models when only a few patients’ EHR are available for training. To achieve these two goals, we designed an iterative algorithm to improve the covariance matrix estimation with embedded data-noise/parameter-variance reduction for LDA. We evaluated Daehr extensively using the College Health Surveillance Network, a large, real-world EHR dataset. Specifically, our experiments compared the performance of LDA to three baselines (i.e., LDA and its derivatives) in identifying college students at high risk for mental health disorders from 23 U.S. universities. Experimental results demonstrate Daehr significantly outperforms the three baselines by achieving 1.4%--19.4% higher accuracy and a 7.5%--43.5% higher F1-score.

Using Mobile Sensing to Test Clinical Models of Depression, Social Anxiety, State Affect, and Social Isolation Among College Students

Background

Research in psychology demonstrates a strong link between state affect (moment-to-moment experiences of positive or negative emotionality) and trait affect (eg, relatively enduring depression and social anxiety symptoms), and a tendency to withdraw (eg, spending time at home). However, existing work is based almost exclusively on static, self-reported descriptions of emotions and behavior that limit generalizability. Despite adoption of increasingly sophisticated research designs and technology (eg, mobile sensing using a global positioning system [GPS]), little research has integrated these seemingly disparate forms of data to improve understanding of how emotional experiences in everyday life are associated with time spent at home, and whether this is influenced by depression or social anxiety symptoms.

Objective

We hypothesized that more time spent at home would be associated with more negative and less positive affect.

Methods

We recruited 72 undergraduate participants from a southeast university in the United States. We assessed depression and social anxiety symptoms using self-report instruments at baseline. An app (Sensus) installed on participants’ personal mobile phones repeatedly collected in situ self-reported state affect and GPS location data for up to 2 weeks. Time spent at home was a proxy for social isolation.

Results

We tested separate models examining the relations between state affect and time spent at home, with levels of depression and social anxiety as moderators. Models differed only in the temporal links examined. One model focused on associations between changes in affect and time spent at home within short, 4-hour time windows. The other 3 models focused on associations between mean-level affect within a day and time spent at home (1) the same day, (2) the following day, and (3) the previous day. Overall, we obtained many of the expected main effects (although there were some null effects), in which higher social anxiety was associated with more time or greater likelihood of spending time at home, and more negative or less positive affect was linked to longer homestay. Interactions indicated that, among individuals higher in social anxiety, higher negative affect and lower positive affect within a day was associated with greater likelihood of spending time at home the following day.

Conclusions

Results demonstrate the feasibility and utility of modeling the relationship between affect and homestay using fine-grained GPS data. Although these findings must be replicated in a larger study and with clinical samples, they suggest that integrating repeated state affect assessments in situ with continuous GPS data can increase understanding of how actual homestay is related to affect in everyday life and to symptoms of anxiety and depression.

Acceptability of a Virtual Patient Educator for Hispanic Women

There are few Spanish language interactive, technology-driven health education programs. Objectives of this feasibility study were to (a) learn more about computer and technology usage among Hispanic women living in a rural community and (b) evaluate acceptability of the concept of using an embodied conversational agent (ECA) computer application among this population. A survey about computer usage history and interest in computers was administered to a convenience sample of 26 women. A sample video prototype of a hospital discharge ECA was administered followed by questions to gauge opinion about the ECA. Data indicate women exhibited both a high level of computer experience and enthusiasm for the ECA. Feedback from community is essential to ensure equity in state of the art dissemination of health information.

Spanish-Language Consumer Health Information Technology Interventions: A Systematic Review

Background

As consumer health information technology (IT) becomes more thoroughly integrated into patient care, it is critical that these tools are appropriate for the diverse patient populations whom they are intended to serve. Cultural differences associated with ethnicity are one aspect of diversity that may play a role in user-technology interactions.

Objective

Our aim was to evaluate the current scope of consumer health IT interventions targeted to the US Spanish-speaking Latino population and to characterize these interventions in terms of technological attributes, health domains, cultural tailoring, and evaluation metrics.

Methods

A narrative synthesis was conducted of existing Spanish-language consumer health IT interventions indexed within health and computer science databases. Database searches were limited to English-language articles published between January 1990 and September 2015. Studies were included if they detailed an assessment of a patient-centered electronic technology intervention targeting health within the US Spanish-speaking Latino population. Included studies were required to have a majority Latino population sample. The following were extracted from articles: first author’s last name, publication year, population characteristics, journal domain, health domain, technology platform and functionality, available languages of intervention, US region, cultural tailoring, intervention delivery location, study design, and evaluation metrics.

Results

We included 42 studies in the review. Most of the studies were published between 2009 and 2015 and had a majority percentage of female study participants. The mean age of participants ranged from 15 to 68. Interventions most commonly focused on urban population centers and within the western region of the United States. Of articles specifying a technology domain, computer was found to be most common; however, a fairly even distribution across all technologies was noted. Cancer, diabetes, and child, infant, or maternal health were the most common health domains targeted by consumer health IT interventions. More than half of the interventions were culturally tailored. The most frequently used evaluation metric was behavior/attitude change, followed by usability and knowledge retention.

Conclusions

This study characterizes the existing body of research exploring consumer health IT interventions for the US Spanish-speaking Latino population. In doing so, it reveals three primary needs within the field. First, while the increase in studies targeting the Latino population in the last decade is a promising advancement, future research is needed that focuses on Latino subpopulations previously overlooked. Second, preliminary steps have been taken to culturally tailor consumer health IT interventions for the US Spanish-speaking Latino population; however, focus must expand beyond intervention content. Finally, the field should work to promote long-term evaluation of technology efficacy, moving beyond intermediary measures toward measures of health outcomes.

Trauma Survival Margin Analysis: A Dissection of Trauma Center Performance through Initial Lactate

Measurement of trauma center performance presently relies on W-score calculation and comparison to national data sets. A limitation to this practice is a skewing of the W score, as it determines overall performance of a trauma population that is often heavily weighted by patients of low acuity. The University of Virginia relative mortality metric (RMM) was formulated to provide higher resolution in identifying areas of performance improvement within subpopulations of a trauma center using traditional Trauma Injury Severity Score methodology. Lactic acidosis has been established as a risk factor for mortality in the setting of trauma. This study aims to compare survival margin, defined as the area between actual and predicted mortality curves, in patients with either normal or elevated initial lactate. W score and RMM were calculated and compared in these cohorts. Whereas the W score suggested increased survival within the high initial lactate group, the RMM demonstrated the expected finding of increased survival margin in the normal lactate cohort. The RMM is a potentially valuable tool for trauma centers to monitor and improve performance. In addition, these findings validate the use of lactate as a triage and risk adjustment tool in the trauma setting.

Trauma Survival Margin Analysis: A Dissection of Trauma Center Performance through Initial Lactate

Measurement of trauma center performance presently relies on W-score calculation and comparison to national data sets. A limitation to this practice is a skewing of the W score, as it determines overall performance of a trauma population that is often heavily weighted by patients of low acuity. The University of Virginia relative mortality metric (RMM) was formulated to provide higher resolution in identifying areas of performance improvement within subpopulations of a trauma center using traditional Trauma Injury Severity Score methodology. Lactic acidosis has been established as a risk factor for mortality in the setting of trauma. This study aims to compare survival margin, defined as the area between actual and predicted mortality curves, in patients with either normal or elevated initial lactate. W score and RMM were calculated and compared in these cohorts. Whereas the W score suggested increased survival within the high initial lactate group, the RMM demonstrated the expected finding of increased survival margin in the normal lactate cohort. The RMM is a potentially valuable tool for trauma centers to monitor and improve performance. In addition, these findings validate the use of lactate as a triage and risk adjustment tool in the trauma setting.

Effects of Stereoscopic Depth on Vigilance Performance and Cerebral Hemodynamics

OBJECTIVE

We tested the possibility that monitoring a display wherein critical signals for detection were defined by a stereoscopic three-dimensional (3-D) image might be more resistant to the vigilance decrement, and to temporal declines in cerebral blood flow velocity (CBFV), than monitoring a display featuring a customary two-dimensional (2-D) image.

BACKGROUND

Hancock has asserted that vigilance studies typically employ stimuli for detection that do not exemplify those that occur in the natural world. As a result, human performance is suboptimal. From this perspective, tasks that better approximate perception in natural environments should enhance performance efficiency. To test that possibility, we made use of stereopsis, an important means by which observers interact with their everyday surroundings.

METHOD

Observers monitored a circular display in which a vertical line was embedded. Critical signals for detection in a 2-D condition were instances in which the line was rotated clockwise from vertical. In a 3-D condition, critical signals were cases in which the line appeared to move outward toward the observer.

RESULTS

The overall level of signal detection and the stability of detection over time were greater when observers monitored for 3-D changes in target depth compared to 2-D changes in target orientation. However, the 3-D display did not retard the temporal decline in CBFV.

CONCLUSION

These results provide the initial demonstration that 3-D displays can enhance performance in vigilance tasks.

APPLICATION

The use of 3-D displays may be productive in augmenting system reliability when operator vigilance is vital.

Design of Interactive Cancer Education Technology for Latina Farmworkers

Latinas in the United States experience higher levels of cervical cancer (CC) incidence and mortality rates than the general population, and many lack access to healthcare or face communication, literacy, and knowledge barriers preventing proper CC screening. Interactive technological interventions, like embodied conversational agents (ECA)/virtual agents, are currently used in other populations, settings, and for other health topics, however, no known initiative has used culturally- and literacy-appropriate technological interventions to deliver Spanish-language CC education. This study aims to create a culturally tailored Spanish-language Virtual Patient Educator (VPE) application to augment a patient navigator (PN) intervention for increasing CC screening rates among Hispanic women in a rural agricultural community. The VPE is a computer character that can simulate face-to-face conversation with an actual person and will embody the characteristics of a PN. Through iterative interviews with the target population, key cultural design factors were identified to inform the design and implementation of a prototype VPE. This paper discusses design and usability issues associated with development of the VPE for low-literacy users in addition to a framework methodology for development of similar tools and a cultural matrix of design factors. A VPE might help close the knowledge gap between Hispanic women and the general population regarding cervical cancer screening. Incorporation of culturally tailored features in technology aids in increasing overall understanding and trust of health information presented. An iterative approach that engages the patient population in design of technology is important to identify population-specific patient preferences.

Towards a classification model to identify hospice candidates in terminally ill patients

This paper presents a Rough Set Theory (RST) based classification model to identify hospice candidates within a group of terminally ill patients. Hospice care considerations are particularly valuable for terminally ill patients since they enable patients and their families to initiate end-of-life discussions and choose the most desired management strategy for the remainder of their lives. Unlike traditional data mining methodologies, our approach seeks to identify subgroups of patients possessing common characteristics that distinguish them from other subgroups in the dataset. Thus, heterogeneity in the data set is captured before the classification model is built. Object related reducts are used to obtain the minimum set of attributes that describe each subgroup existing in the dataset. As a result, a collection of decision rules is derived for classifying new patients based on the subgroup to which they belong. Results show improvements in the classification accuracy compared to a traditional RST methodology, in which patient diversity is not considered. We envision our work as a part of a comprehensive decision support system designed to facilitate end-of-life care decisions. Retrospective data from 9105 patients is used to demonstrate the design and implementation details of the classification model.

Rough Set Theory based prognostication of life expectancy for terminally ill patients

We present a novel knowledge discovery methodology that relies on Rough Set Theory to predict the life expectancy of terminally ill patients in an effort to improve the hospice referral process. Life expectancy prognostication is particularly valuable for terminally ill patients since it enables them and their families to initiate end-of-life discussions and choose the most desired management strategy for the remainder of their lives. We utilize retrospective data from 9105 patients to demonstrate the design and implementation details of a series of classifiers developed to identify potential hospice candidates. Preliminary results confirm the efficacy of the proposed methodology. We envision our work as a part of a comprehensive decision support system designed to assist terminally ill patients in making end-of-life care decisions.

Extensions to regret-based decision curve analysis: an application to hospice referral for terminal patients

Background

Despite the well documented advantages of hospice care, most terminally ill patients do not reap the maximum benefit from hospice services, with the majority of them receiving hospice care either prematurely or delayed. Decision systems to improve the hospice referral process are sorely needed.

Methods

We present a novel theoretical framework that is based on well-established methodologies of prognostication and decision analysis to assist with the hospice referral process for terminally ill patients. We linked the SUPPORT statistical model, widely regarded as one of the most accurate models for prognostication of terminally ill patients, with the recently developed regret based decision curve analysis (regret DCA). We extend the regret DCA methodology to consider harms associated with the prognostication test as well as harms and effects of the management strategies. In order to enable patients and physicians in making these complex decisions in real-time, we developed an easily accessible web-based decision support system available at the point of care.

Results

The web-based decision support system facilitates the hospice referral process in three steps. First, the patient or surrogate is interviewed to elicit his/her personal preferences regarding the continuation of life-sustaining treatment vs. palliative care. Then, regret DCA is employed to identify the best strategy for the particular patient in terms of threshold probability at which he/she is indifferent between continuation of treatment and of hospice referral. Finally, if necessary, the probabilities of survival and death for the particular patient are computed based on the SUPPORT prognostication model and contrasted with the patient's threshold probability. The web-based design of the CDSS enables patients, physicians, and family members to participate in the decision process from anywhere internet access is available.

Conclusions

We present a theoretical framework to facilitate the hospice referral process. Further rigorous clinical evaluation including testing in a prospective randomized controlled trial is required and planned.

Abstract A44: Feasibility study for technology-based cancer education for Latina women from an agricultural community

Introduction: Interactive, technology-driven health education programs improve on passive communication of health information by allowing users to interact with educational information in a highly visual manner. These interventions have not been fully explored or disseminated widely to vulnerable populations and few are available in Spanish. This study examines the feasibility of implementing a low-literacy, user friendly, interactive computer program for the delivery of cervical cancer education to primarily Spanish-speaking Latina women from an agricultural community in rural central Florida.

Methods: Study participants were recruited from the waiting room at Catholic Mobile Medical Services, a faith-based community primary care clinic in Dover, Florida. A bilingual research coordinator interviewed participants regarding their prior experience with computers, reasons for computer use, whether they have accessed health information on a computer, and their willingness to use a computer to get health information if they were taught how. Subsequently, they were shown a video depicting a computer program with health information delivered by an embodied conversational agent. Embodied conversational agents (ECAs) are computer characters that can simulate face-to-face conversation with an actual person and embody a person in appearance, behavior and dialect. The video was paused periodically for translation into Spanish. When the video presentation was completed, participants were asked a series of open-ended questions regarding their reactions to the ECA-delivered program. Data were recorded by the research coordinator using a standardized form and summarized descriptively.

Results: A total of 26 women participated in the study, all of whom spoke Spanish. One hundred percent of participants responded positively to the concept of the computer program, method of health information delivery, and stated they would trust information obtained through a similar educational program. Most women (69.2%) interviewed had a family member who used a computer in their home, half already had a computer in their home, and a minority (38.5%) had never used a computer before. Approximately one third (30.8%) of the women also reported utilizing a computer more than twice a week. Women who reported computer usage indicated that the most common reasons they used a computer were information seeking and checking e-mail. Half of the women interviewed said they had used a computer to obtain health information. Furthermore, half the women had used their cellular telephones to access the internet. Some of the descriptive terms the women used to describe the ECA-delivered program were: “Fabulous”, “Awesome”, “Useful”, “Practical”, “Fast” and “Helpful”. The only criticism of the computer program was that it seemed too robotic and was outdated in regards to the appearance and voices. Many women also agreed that such a program could assist women in preparing themselves and reducing anxiety before a Pap test or gynecological exam. Women also suggested including the following topics for future education: changes in menstrual cycle and menopause, sexually transmitted infections, breast and cervical cancer, exercise, nutrition, and disease prevention.

Conclusions: The results of this study suggest that technology-based cancer education for primarily Spanish-speaking Latina women from an agricultural community is feasible, and user acceptance is high. Women feel that computer programs could help them learn about their health. The data from this study will be used to inform the design and development of an interactive ECA program to deliver education about cervical cancer and the human papillomavirus.

Citation Information: Cancer Epidemiol Biomarkers Prev 2011;20(10 Suppl):A44.