Objective Engaging users during physical exercise is crucial for fostering long-term commitment, however, sustaining that engagement remains a significant challenge. This study explores the design of a voice-enabled exoskeleton-based virtual exercise coach (VEC) that provides real-time verbal feedback to enhance user engagement. The objectives of this study are twofold: (i) to compare user engagement with real-time verbal feedback from both VEC and human exercise coach (HEC) during physical exercise, and (ii) to understand users’ perceptions and gather their recommendations for improving future VEC technologies. Methods We developed an exoskeleton-based VEC that delivers real-time verbal feedback on users’ exercise performance. To evaluate its impact on user engagement, we conducted a lab-based mixed-methods study ( N = 32 ) over a period of 6 weeks comparing users’ engagement with the VEC and HEC using User Engagement Scale (UES) questionnaire and conducted semi-structured interviews to understand users’ perceptions of the VEC. Results Participants in this study found the VEC more engaging than the HEC, in terms of focused attention ( Z = 156.5 , p < .001 ) and perceived usability ( Z = 32 , p < .001 ). Post-interaction interviews revealed that (i) users found the VEC to be engaging, intuitive, easy to use, and convenient; (ii) users perceived the VEC as a valuable training companion that could help reduce the emotional insecurities often associated with going to the gym; and (iii) users expressed a desire for the VEC to have a personality and embodiment that motivates and supports personalized interactions. Conclusion Based on our results, we discuss the challenges and implications for designing future voice-enabled VECs that support engaging physical exercises.

Objective Post-traumatic stress disorder (PTSD) is a pervasive health concern affecting millions of individuals. However, there remain significant barriers to providing resources and addressing the needs of individuals living with PTSD. To address this treatment gap, we have collaborated with clinical experts to develop PTSDialogue—a conversational agent (CA) that aims to support effective self-management of PTSD. In this work, we have focused on assessing the feasibility and acceptance of PTSDialogue for individuals living with PTSD. Methods We conducted semi-structured interviews with individuals living with PTSD ( N = 12 ). Participants were asked about their experiences with the PTSDialogue and their perceptions of its usefulness in managing PTSD. We then used bottom-up thematic analysis with a qualitative interpretivist approach to analyze the interview data. Results All participants expressed that PTSDialogue could be beneficial for supporting PTSD treatment. We also uncovered key opportunities and challenges in using CAs to complement existing clinical practices and support longitudinal self-management of PTSD. We highlight important design features of CAs to provide effective support for this population, including the need for personalization, education, and privacy-sensitive interactions. Conclusion We demonstrate the acceptability of CAs to support longitudinal self-management of PTSD. Based on these findings, we have outlined design recommendations for technologies aiming to reduce treatment and support gaps for individuals living with serious mental illnesses.

Objective:Chronic pain is a critical public health issue affecting approximately 20% of the adult population in the United States. Given the opioid crisis, there has been an urgent focus on non-addictive pain management methods including mindfulness-based stress reduction (MBSR). Prior work has successfully used MBSR for pain management. However, ensuring longitudinal engagement in MBSR practices remains a serious challenge. In this work, we explore the utility of a voice interface to support MBSR home practice.Methods:We interviewed 10 mindfulness program facilitators to understand how such a technology might fit in the context of the MBSR class and identify potential usability issues with our prototype. We then used directed content analysis to identify key themes and sub-themes within the interview data.Results:Our findings show that facilitators supported the use of the voice interface for MBSR, particularly for individuals with limited motor function. Facilitators also highlighted the unique affordances of voice interfaces, including perceived social presence, to support sustained engagement.Conclusion:We demonstrate the acceptability of a voice interface to support home practice for MBSR participants among trained mindfulness facilitators. Based on our findings, we outline design recommendations for technologies aiming to provide longitudinal support for mindfulness-based interventions. Future work should further these efforts toward making non-addictive pain management interventions accessible and efficacious for a wide audience of users.

BACKGROUND:Posttraumatic stress disorder (PTSD) is a serious public health concern. However, individuals with PTSD often do not have access to adequate treatment. A conversational agent (CA) can help to bridge the treatment gap by providing interactive and timely interventions at scale. Toward this goal, we have developed PTSDialogue-a CA to support the self-management of individuals living with PTSD. PTSDialogue is designed to be highly interactive (eg, brief questions, ability to specify preferences, and quick turn-taking) and supports social presence to promote user engagement and sustain adherence. It includes a range of support features, including psychoeducation, assessment tools, and several symptom management tools.OBJECTIVE:This paper focuses on the preliminary evaluation of PTSDialogue from clinical experts. Given that PTSDialogue focuses on a vulnerable population, it is critical to establish its usability and acceptance with clinical experts before deployment. Expert feedback is also important to ensure user safety and effective risk management in CAs aiming to support individuals living with PTSD.METHODS:We conducted remote, one-on-one, semistructured interviews with clinical experts (N=10) to gather insight into the use of CAs. All participants have completed their doctoral degrees and have prior experience in PTSD care. The web-based PTSDialogue prototype was then shared with the participant so that they could interact with different functionalities and features. We encouraged them to "think aloud" as they interacted with the prototype. Participants also shared their screens throughout the interaction session. A semistructured interview script was also used to gather insights and feedback from the participants. The sample size is consistent with that of prior works. We analyzed interview data using a qualitative interpretivist approach resulting in a bottom-up thematic analysis.RESULTS:Our data establish the feasibility and acceptance of PTSDialogue, a supportive tool for individuals with PTSD. Most participants agreed that PTSDialogue could be useful for supporting self-management of individuals with PTSD. We have also assessed how features, functionalities, and interactions in PTSDialogue can support different self-management needs and strategies for this population. These data were then used to identify design requirements and guidelines for a CA aiming to support individuals with PTSD. Experts specifically noted the importance of empathetic and tailored CA interactions for effective PTSD self-management. They also suggested steps to ensure safe and engaging interactions with PTSDialogue.CONCLUSIONS:Based on interviews with experts, we have provided design recommendations for future CAs aiming to support vulnerable populations. The study suggests that well-designed CAs have the potential to reshape effective intervention delivery and help address the treatment gap in mental health.

Despite the increasing sophistication of voice assistant (VA) technology, most major VAs subscribe to a onevoice-fits-all model of interaction. This study examines if offering users a VA similar to them, or letting users customize the VA's voice personality, would affect their perceptions and experience. We test this in a unique scenario where a VA delivers misinformation about COVID-19. Data from a pre-registered experiment (N = 401) suggest that extroverted users appreciate being matched with an extroverted VA, whereas introverted users do not have a specific preference. In addition, perceived homophily in voice increases user attraction toward the VA, and enhances credibility perceptions for those who customize their VA. Those not given the option to customize prefer VAs with an extroverted voice. Data also suggest that automated similarity matching of VA personality can evoke user resistance toward the persuasive information provided-in our case, changing as many as 38% of unvaccinated individuals' mind toward vaccination after exposure to misinformation.

Evidence shows green space exposure has beneficial impacts on psychological and physiological wellbeing. However, aesthetic differences in color use in cultivated garden landscapes on wellbeing remains unexplored. This study investigates how warm and cool colored garden landscapes affect psychological and physiological wellbeing and how responses differ geographically.Our between subjects design used USA and UK participants exposed to videos of static garden landscapes consisting of (a) warm colors, (b) cool colors and (c) control images. Measures of subjective psychological wellbeing (UWIST Mood Adjective Checklist (MACL)) and biometrics of stress using the Empatica E4 watch (Heart rate; Heart Rate Variability (HRV); Skin Temperature; Galvanic Skin Response (GSR) and Photoplethysmography) were obtained to ascertain if warm and cool colored cultivated garden landscapes affected psychological and physiological responses.Results showed statistical differences between locations in psychological and physiological wellbeing. USA participants experienced increases in hedonic tone and decreases in perceived stress after viewing warm and cool colored garden landscapes, a result not found in UK participants. Physiological indicators show geographical differences with beneficial effects of warm colors in the USA, shown in HRV and GSR measures relative to control. The UK sample presented mixed evidence regarding positive effects of warm and cool colored garden landscapes on physiological measures.These findings show stronger psychological and physiological responses to color in the US sample compared to a UK sample, suggesting geographic disparities in these responses to plant color. This should be further explored to understand color choice for landscape design to optimize outdoor settings that maximize wellbeing.

Post-traumatic stress disorder (PTSD) is a serious public health issue. Approximately 8 million adults in the United States suffer from PTSD in any given year, and 7–8% of the U.S. population will have PTSD at some point in their lives. Recent studies have explored eHealth technologies to support persons living with PTSD. However, current approaches are often unable to sustain adherence, leading to sub-optimal clinical outcomes. Conversational agents (CAs) can help to improve longitudinal adherence by interactively engaging users and maintaining social presence. In this work, we present prototypes of PTSDialogue — a finite-state CA to deliver evidence-based strategies and support self-management for individuals living with PTSD. We also discuss the design requirements and process of adapting existing eHealth content to interactive dialogues. Furthermore, we detail design decisions to address safety and ethical concerns to develop a CA for a vulnerable population.

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.

Exploiting the capabilities of smartphones for monitoring social anxiety shows promise for advancing our ability to both identify indicators of and treat social anxiety in natural settings. Smart devices allow researchers to collect passive data unobtrusively through built-in sensors and active data using subjective, self-report measures with Ecological Momentary Assessment (EMA) studies. Prior work has established the potential to predict subjective measures from passive data. However, the majority of the past work on social anxiety has focused on a limited subset of self-reported measures. Furthermore, the data collected in real-world studies often results in numerous missing values in one or more data streams, which ultimately reduces the usable data for analysis and limits the potential of machine learning algorithms. We explore several approaches for addressing these problems in a smartphone based monitoring and intervention study of eighty socially anxious participants over a five week period. Our work complements and extends prior work in two directions: (i) we show the predictability of seven different self-reported dimensions of social anxiety, and (ii) we explore four imputation methods to handle missing data and evaluate their effectiveness in the prediction of subjective measures from the passive data. Our evaluation shows imputation of missing data reduces prediction error by as much as 22%. We discuss the implications of these results for future research.

Over 35% of the world's population uses social media. Platforms like Facebook, Twitter, and Instagram have radically influenced the way individuals interact and communicate. These platforms facilitate both public and private communication with strangers and friends alike, providing rich insight into an individual's personality, health, and wellbeing. To date, many researchers have employed a variety of methods for extracting mental health-centric features from digital text communication (DTC) data, including natural language processing, social network analysis, and extraction of temporal discourse patterns. However, none have explored a hierarchical framework for extracting features from private messages with the goal of unifying approaches across methodological domains. Furthermore, while analyses of large, public corpora abound in existing literature, limited work has been done to explore the relationship between of private textual communications, personality traits, and symptoms of mental illness. We present a framework for constructing rich feature spaces from digital text communications. We then demonstrate the efficacy of our framework by applying it to a dataset of private Facebook messages in a college student population (N=103). Our results reveal key individual differences in temporal and relational behaviors, as well as language usage in relation to validated measures of trait-level anxiety, loneliness, and personality. This work represents a critical step forward in linking features of private social media messages to validated measures of mental health, wellbeing, and personality.

In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in real-world problems are discussed.

As social media platforms have grown to form the foundation of modern digital communication, digital text message datasets that document interpersonal exchanges on these platforms have proliferated. These exchanges comprise a rich corpus of social context data, which can provide insight into how mental health challenges manifest in social contexts. To date, researchers have employed a variety of methods for extracting mental health-centric features from digital text communication data, including natural language processing, social network analysis, sentiment analysis, time series analysis, and discourse analysis. However, there is a marked divide in current literature between qualitative and quantitative feature extraction methods. To effectively identify and analyze key underlying social contexts and related mental health factors from digital text communication data, researchers must extract a comprehensive corpus of features from raw textual data streams. In this paper, we present a generalized framework for extracting features from digital text communication datasets that leverages methodological approaches from diverse fields. This framework will serve to bridge the gap between quantitative and qualitative research approaches to analyzing digital text communications with respect to mental health.

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.

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.

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 complex decision 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 and HRC measurements to characteristize the influence of noise on the HRC measure by altering the fiducial point of the Rpeak. 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.