Background Digital mental health is a promising paradigm for individualized, patient-driven health care. For example, cognitive bias modification programs that target interpretation biases (cognitive bias modification for interpretation [CBM-I]) can provide practice thinking about ambiguous situations in less threatening ways on the web without requiring a therapist. However, digital mental health interventions, including CBM-I, are often plagued with lack of sustained engagement and high attrition rates. New attrition detection and mitigation strategies are needed to improve these interventions. Objective This paper aims to identify participants at a high risk of dropout during the early stages of 3 web-based trials of multisession CBM-I and to investigate which self-reported and passively detected feature sets computed from the participants interacting with the intervention and assessments were most informative in making this prediction. Methods The participants analyzed in this paper were community adults with traits such as anxiety or negative thinking about the future (Study 1: n=252, Study 2: n=326, Study 3: n=699) who had been assigned to CBM-I conditions in 3 efficacy-effectiveness trials on our team’s public research website. To identify participants at a high risk of dropout, we created 4 unique feature sets: self-reported baseline user characteristics (eg, demographics), self-reported user context and reactions to the program (eg, state affect), self-reported user clinical functioning (eg, mental health symptoms), and passively detected user behavior on the website (eg, time spent on a web page of CBM-I training exercises, time of day during which the exercises were completed, latency of completing the assessments, and type of device used). Then, we investigated the feature sets as potential predictors of which participants were at high risk of not starting the second training session of a given program using well-known machine learning algorithms. Results The extreme gradient boosting algorithm performed the best and identified participants at high risk with macro–F1-scores of .832 (Study 1 with 146 features), .770 (Study 2 with 87 features), and .917 (Study 3 with 127 features). Features involving passive detection of user behavior contributed the most to the prediction relative to other features. The mean Gini importance scores for the passive features were as follows: .033 (95% CI .019-.047) in Study 1; .029 (95% CI .023-.035) in Study 2; and .045 (95% CI .039-.051) in Study 3. However, using all features extracted from a given study led to the best predictive performance. Conclusions These results suggest that using passive indicators of user behavior, alongside self-reported measures, can improve the accuracy of prediction of participants at a high risk of dropout early during multisession CBM-I programs. Furthermore, our analyses highlight the challenge of generalizability in digital health intervention studies and the need for more personalized attrition prevention strategies.

Objective: Web-based cognitive bias modification for interpretation (CBM-I) can improve interpretation biases and anxiety symptoms but faces high rates of dropout. This study tested the effectiveness of web-based CBM-I relative to an active psychoeducation condition and the addition of low-intensity telecoaching for a subset of CBM-I participants. Method: 1,234 anxious community adults (M_age = 35.09 years, 81.2% female, 72.1% white, 82.6% not Hispanic) were randomly assigned at Stage 1 of a sequential, multiple-assignment randomized trial to complete five weekly sessions of CBM-I or psychoeducation on our team’s public research website. After the first session, for Stage 2, an algorithm attempted to classify CBM-I participants as higher (vs. lower) risk for dropping out; those classified as higher risk were then randomly assigned to complete four brief weekly telecoaching check-ins (vs. no coaching). Results: As hypothesized (https://doi.org/j2xr; Daniel, Eberle, & Teachman, 2020), CBM-I significantly outperformed psychoeducation at improving positive and negative interpretation biases (Recognition Ratings, Brief Body Sensations Interpretation Questionnaire) and anxiety symptoms (Overall Anxiety Severity and Impairment Scale, Anxiety Scale from Depression Anxiety Stress Scales–Short Form), with smaller treatment gains remaining significant at 2-month follow-up. Unexpectedly, CBM-I had significantly worse treatment dropout outcomes than psychoeducation, and adding coaching (vs. no coaching) did not significantly improve efficacy or dropout outcomes (notably, many participants chose not to interact with their coach). Conclusions: Web-based CBM-I appears effective, but supplemental coaching may not mitigate the challenge of dropout.

In recent years, new treatments have become available which have improved survival rates in lung cancer patients. One promising treatment option is the rapidly growing field of oral targeted therapies, which employs drugs that interfere with specific molecules involved in the growth, progression, and spread of cancer. However, these therapies can cause a variety of symptoms and adverse events that can impair quality of life. mHealth technologies may help individuals with lung cancer better track their side effects and manage medications on a day-to-day basis. However, understanding patients’ attitudes toward smart devices such as smartphones, smartwatches, and smart pill bottles, as well as their specific needs when using these devices, is critical before design and deployment studies of medication adherence can be carried out. In this study, we conducted interviews with 9 individuals with stage III-IV lung cancer at a National Cancer Institute-designated comprehensive cancer center in the Mid-Atlantic region of the United States to assess the feasibility of using such devices for managing medication and medication related side-effects. We evaluated patients’ attitudes towards the design and function of smart devices and how these devices fit into their daily life. Our results may help clinicians and researchers to co-develop effective mHealth system deployments for side effect and medication management in oncology populations.

This study evaluated the effectiveness of different recruitment messages for encouraging enrollment in a digital mental health intervention (DMHI) for anxiety among 1,600 anxious patients in a large healthcare system. Patients were randomly assigned to receive a standard message, or one of five messages designed to encourage enrollment: Three messages offered varying financial incentives, one message offered coaching, and one message provided consumer testimonials. Patients could then click a link in the message to visit the DMHI website, enroll, and start the first session. We examined the effects of message features and message length (short vs. long) on rates of site clicks, enrollment, and starting the first session. We also tested whether demographic and clinical factors derived from patients' electronic health records were associated with rates of enrollment and starting the first session to understand the characteristics of patients most likely to use DMHIs in this setting. Across messages, 19.4% of patients clicked a link to visit the DMHI website, but none of the messages were significantly associated with rates of site clicks, enrollment, or starting the first session. Females (vs. males) had a greater probability of enrollment. No other demographic or clinical variables were significantly associated with enrollment or starting the first session. Findings provide guidance for resource allocation decisions in larger scale DMHI implementations in healthcare settings.

The use of integrative medicine (IM) practices during pregnancy are on the rise in the United States and a growing number of individuals seek pregnancy support outside of the conventional medical system every year. There is a variety of integrative medicine providers (IMPs) who work outside the clinical system to fill the gaps in pregnancy support by providing a set of services such as midwifery, doula care, childbirth education, and lactation support. Evidence suggests integrative medicine providers positively impact pregnancy experiences, yet IMPs are often marginalized and excluded from the conventional pregnancy healthcare system. We present results from an ongoing investigation that seeks to understand the roles IMPs play in pregnancy healthcare journeys, and the pregnancy ecology. We interviewed 12 integrative medicine providers to understand their needs and challenges concerning technology, business models, and communication practices. Our findings reflect on a broad range of topics including the intersection of personal and professional journeys, legitimacy within the healthcare system, and the provision of personalized care. We discuss the potential for technology to support IMPs and present implications for the design of future pregnancy support technologies. Improving collaborative care and communication technologies for the IM community has the potential to improve professional experiences and the quality of care provided by IMPs. WARNING: This paper includes detailed personal narratives of individuals' pregnancy healthcare journey including information about, pregnancy, labor, delivery, birth stories, and pregnancy loss.

Background: Health interventions delivered via smart devices are increasingly being used to address mental health challenges associated with cancer treatment. Engagement with mobile interventions has been associated with treatment success; however, the relationship between mood and engagement among patients with cancer remains poorly understood. A reason for this is the lack of a data-driven process for analyzing mood and app engagement data for patients with cancer. Objective: This study aimed to provide a step-by-step process for using app engagement metrics to predict continuously assessed mood outcomes in patients with breast cancer. Methods: We described the steps involved in data preprocessing, feature extraction, and data modeling and prediction. We applied this process as a case study to data collected from patients with breast cancer who engaged with a mobile mental health app intervention (IntelliCare) over 7 weeks. We compared engagement patterns over time (eg, frequency and days of use) between participants with high and low anxiety and between participants with high and low depression. We then used a linear mixed model to identify significant effects and evaluate the performance of the random forest and XGBoost classifiers in predicting weekly mood from baseline affect and engagement features. Results: We observed differences in engagement patterns between the participants with high and low levels of anxiety and depression. The linear mixed model results varied by the feature set; these results revealed weak effects for several features of engagement, including duration-based metrics and frequency. The accuracy of predicting depressed mood varied according to the feature set and classifier. The feature set containing survey features and overall app engagement features achieved the best performance (accuracy: 84.6%; precision: 82.5%; recall: 64.4%; F1 score: 67.8%) when used with a random forest classifier. Conclusions: The results from the case study support the feasibility and potential of our analytic process for understanding the relationship between app engagement and mood outcomes in patients with breast cancer. The ability to leverage both self-report and engagement features to analyze and predict mood during an intervention could be used to enhance decision-making for researchers and clinicians and assist in developing more personalized interventions for patients with breast cancer.

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.

Music therapists provide critical, evidence-based care to a diverse range of clients. However, despite their active role in empowering individuals affected by disability, stigma, grief, and trauma, music therapists remain understudied by the HCI community. We present the results of a mixed methods study of 10 interviewees and 20 survey respondents in the U.S., all of whom are practicing music therapists. Our results show that music therapists engage in technology-aided practices such as making personalized connections with clients, assisting in identity formation, encouraging musicking (music-making), and preserving legacies. Results also show that music therapists face key challenges such as environmental, societal, and financial constraints, including high workload, lack of awareness of the value of music therapy among the general community, and limited access to secure technologies for remote client care. In light of these challenges, we present a set of design implications for creating future technologies for music therapists. This work diverges from previous studies on music therapy technologies, which focus largely on interventions with music therapy clients, by highlighting the often-neglected perspectives from music therapists.

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.

Tyrosine kinase inhibitors (TKIs) are widely used in the treatment of metastatic non-small cell lung cancer (NSCLC) with a driver mutation. These oral therapies offer increased efficacy and convenience for patients when compared to intravenous chemotherapy. However, reduced direct provider supervision with oral therapy makes the detection of treatment-related adverse events (trAEs) challenging. Though clinical trials investigating TKI use in NSCLC document highest-grade trAE frequency, data regarding the frequency and duration of these trAEs in the real-world setting is lacking.

Approximately one in five adults in the United States have been diagnosed with some form of mental illness, but less than half received treatment in this past year [1]. An interdisciplinary team at the University of Virginia aims to reduce this gap in mental health coverage through its freely accessible online research platform, the MindTrails Project. The MindTrails Calm Thinking study evaluates cognitive bias modification for interpretation (CBM-I), an intervention that aims to reframe the thinking patterns of highly anxious individuals when they respond to ambiguous situations that they might interpret as stressful. MindTrails is experiencing a high attrition (dropout) rate, which is common to eHealth interventions. In response to this, our project utilized two novel approaches to online anxiety interventions to improve engagement and retention: (1) personalization of training content and (2) implementation intentions and goal setting. We designed a prototype for a new mobile interface that engages users with a journal to record implementation intentions and goals. Users also have the ability to choose the domain of anxiety (e.g., relationships, health) that they would like to work on. To further incorporate these psychological principles into the MindTrails program, suggestions for future work are also discussed. We hypothesize that, with its new user-centered mobile interface, the Calm Thinking mobile application will further connect users with an evidence-based mental health intervention and increase the efficacy of the program.

People living with HIV experience a high level of stigma in our society. Public HIV-related stigma often leads to anxiety and depression and hinders access to social support and proper medical care. Technologies for HIV, however, have been mainly designed for treatment management and medication adherence rather than for helping people cope with public HIV-related stigma specifically. Drawing on empirical data obtained from semi-structured interviews and design activities with eight social workers and 29 people living with HIV, we unpack the ways in which needs for privacy and trust, intimacy, and social support create tensions around key coping strategies. Reflecting on these tensions, we present design implications and opportunities to empower people living with HIV to cope with public HIV-related stigma at the individual level.

Interventions to improve the 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 toward developing personalized, theory-driven interventions. The current research integrates foundational and translational science to implement a mechanism-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 article 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.

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.

Virtual coaching has rapidly evolved into a foundational component of modern clinical practice. At a time when healthcare professionals are in short supply and the demand for low-cost treatments is ever-increasing, virtual health coaches (VHCs) offer intervention-on-demand for those limited by finances or geographic access to care. More recently, AI-powered virtual coaches have become a viable complement to human coaches. However, the push for AI-powered coaching systems raises several important issues for researchers, designers, clinicians, and patients. In this paper, we present a novel framework to guide the design and development of virtual coaching systems. This framework augments a traditional data science pipeline with four key guiding goals: reliability, fairness, engagement, and ethics.

Approximately one in five people in the United States are affected by mental illness, with anxiety disorders being the most common. Barriers to treatment include limited access to trained professionals and high financial cost. eHealth applications are one alternative to treatment outside of a traditional clinical setting. Patients can readily access eHealth interventions on their own time via devices such as computers, tablets, and smartphones. Despite the scalability and accessibility of eHealth applications, their benefits are overshadowed by high attrition rates. MindTrails (MT), an existing eHealth platform, uses Cognitive Bias Modification (CBM) to treat anxiety through online interventions designed to change negative thinking patterns. The MindTrails program has the potential to treat a large population of anxious individuals. The objective of this work is to identify, analyze, and implement strategies to increase user engagement with MindTrails by exploring the integration of gamification/engagement strategies into the program. Our design for increasing engagement focuses on the Doherty Web Strategies, incorporating interactive, personal, supportive, and social elements. Using this new design, users will be able to set personalized goals that are clear, actionable, and reasonably challenging. To meet our objective, we developed high fidelity wireframes and prototypes, with the intent of utilizing user studies to evaluate the efficacy in MindTrails. Results of user tests are hypothesized to show the effectiveness of a personalized gamification feature in increasing user engagement while simultaneously reducing attrition. The improved design will be included in the next launch of the MindTrails program and demonstrates progress toward increasing the effectiveness of CBM treatment in eHealth applications.

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.

The experience of grief and death is an inevitable part of life. Grief, a natural response to death, can be a challenging and emotionally taxing journey. Bereaved individuals often feel lost in a fog, unaware of resources available to them and unsure of which resources could be useful for supporting their healing process. Complicated grief, a more intense form of grief that extends beyond six months following the death of a loved one, presents both a unique challenge and a design opportunity for the HCI community. In this work, we present the results of a survey and interview study on the technological practices of complicated grievers. Based on themes found in the data, we propose a new model for complicated grief in the digital age, consisting of the following phases: Fog, Isolation, Exploration, Immersion, and Stabilization. We then present a set of design considerations for designers seeking to create tools for complicated grievers navigating their unique grief journeys.

The experience of grief and death is an inevitable part of life. Grief, a natural response to death, can be a challenging and emotionally taxing journey. Bereaved individuals often feel lost in a fog of grief, unaware of the resources that are available to them or are unsure which resources could be the most useful. The intersection of grief and technology presents both an interesting challenge and opportunity for the HCI community to design technological tools to support the grieving process. In this paper, we present the results of our survey and interview study on the technological practices of the bereaved. We surveyed both online and local in-person support groups. We then used iterative, inductive analysis and open-coding to analyze our survey data. From this analysis, we identified four common themes: connection, research and reading, legacy, and finding a personal preference for support groups. Based on these themes, we prototyped a mobile application to support the bereaved and those close to them. This prototype creates a centralized space for resources for both the bereaved and those in their support network, and includes educational content on grief, links to local and online support groups, and a tool for sharing individual stories of grief and loss.