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BackgroundAccounting for how end users engage with technologies is imperative for designing an efficacious mobile behavioral intervention. ObjectiveThis mixed methods analysis examined the translational potential of user-centered design and basic behavioral science to inform the design of a new mobile intervention for obesity and binge eating. MethodsA total of 22 adults (7/22, 32% non-Hispanic White; 8/22, 36% male) with self-reported obesity and recurrent binge eating (≥12 episodes in 3 months) who were interested in losing weight and reducing binge eating completed a prototyping design activity over 1 week. Leveraging evidence from behavioral economics on choice architecture, participants chose treatment strategies from 20 options (aligned with treatment targets composing a theoretical model of the relation between binge eating and weight) to demonstrate which strategies and treatment targets are relevant to end users. The process by which participants selected and implemented strategies and their change in outcomes were analyzed. ResultsAlthough prompted to select one strategy, participants selected between 1 and 3 strategies, citing perceived achievability, helpfulness, or relevance as selection reasons. Over the week, all practiced a strategy at least once; 82% (18/22) struggled with implementation, and 23% (5/22) added a new strategy. Several themes emerged on successes and challenges with implementation, yielding design implications for supporting users in behavior change. In postexperiment reflections, 82% (18/22) indicated the strategy was helpful, and 86% (19/22) planned to continue use. One-week average within-subject changes in weight (–2.2 [SD –5.0] pounds) and binge eating (–1.6 [SD –1.8] episodes) indicated small clinical improvement. ConclusionsApplying user-centered design and basic behavioral science yielded design insights to incorporate personalization through user choice with guidance, which may enhance engagement with and potential efficacy of digital health interventions.

BackgroundUser engagement is key to the effectiveness of digital mental health interventions. Considerable research has examined the clinical outcomes of overall engagement with mental health apps (eg, frequency and duration of app use). However, few studies have examined how specific app use behaviors can drive change in outcomes. Understanding the clinical outcomes of more nuanced app use could inform the design of mental health apps that are more clinically effective to users. ObjectiveThis study aimed to classify user behaviors in a suite of mental health apps and examine how different types of app use are related to depression and anxiety outcomes. We also compare the clinical outcomes of specific types of app use with those of generic app use (ie, intensity and duration of app use) to understand what aspects of app use may drive symptom improvement. MethodsWe conducted a secondary analysis of system use data from an 8-week randomized trial of a suite of 13 mental health apps. We categorized app use behaviors through a mixed methods analysis combining qualitative content analysis and principal component analysis. Regression analyses were used to assess the association between app use and levels of depression and anxiety at the end of treatment. ResultsA total of 3 distinct clusters of app use behaviors were identified: learning, goal setting, and self-tracking. Each specific behavior had varied effects on outcomes. Participants who engaged in self-tracking experienced reduced depression symptoms, and those who engaged with learning and goal setting at a moderate level (ie, not too much or not too little) also had an improvement in depression. Notably, the combination of these 3 types of behaviors, what we termed “clinically meaningful use,” accounted for roughly the same amount of variance as explained by the overall intensity of app use (ie, total number of app use sessions). This suggests that our categorization of app use behaviors succeeded in capturing app use associated with better outcomes. However, anxiety outcomes were neither associated with specific behaviors nor generic app use. ConclusionsThis study presents the first granular examination of user interactions with mental health apps and their effects on mental health outcomes. It has important implications for the design of mobile health interventions that aim to achieve greater user engagement and improved clinical efficacy.

As digital mental health interventions (DMHIs) proliferate, there is a growing need to understand the complexities of moving these tools from concept and design to service-ready products. We highlight five case studies from a center that specializes in the design and evaluation of digital mental health interventions to illustrate pragmatic approaches to the development of digital mental health interventions, and to make transparent some of the key decision points researchers encounter along the design-to-product pipeline. Case studies cover different key points in the design process and focus on partnership building, understanding the problem or opportunity, prototyping the product or service, and testing the product or service. We illustrate lessons learned and offer a series of questions researchers can use to navigate key decision points in the digital mental health intervention (DMHI) development process.

BackgroundMobile health (mHealth) apps for weight loss (weight loss apps) can be useful diet and exercise tools for individuals in need of losing weight. Most studies view weight loss app users as these types of individuals, but not all users have the same needs. In fact, users with disordered eating behaviors who desire to be underweight are also utilizing weight loss apps; however, few studies give a sense of the prevalence of these users in weight loss app communities and their perceptions of weight loss apps in relation to disordered eating behaviors. ObjectiveThe aim of this study was to provide an analysis of users’ body mass indices (BMIs) in a weight loss app community and examples of how users with underweight BMI goals perceive the impact of the app on disordered eating behaviors. MethodsWe focused on two aspects of a weight loss app (DropPounds): profile data and forum posts, and we moved from a broader picture of the community to a narrower focus on users’ perceptions. We analyzed profile data to better understand the goal BMIs of all users, highlighting the prevalence of users with underweight BMI goals. Then we explored how users with a desire to be underweight discussed the weight loss app’s impact on disordered eating behaviors. ResultsWe found three main results: (1) no user (regardless of start BMI) starts with a weight gain goal, and most users want to lose weight; (2) 6.78% (1261/18,601) of the community want to be underweight, and most identify as female; (3) users with underweight BMI goals tend to view the app as positive, especially for reducing bingeing; however, some acknowledge its role in exacerbating disordered eating behaviors. ConclusionsThese findings are important for our understanding of the different types of users who utilize weight loss apps, the perceptions of weight loss apps related to disordered eating, and how weight loss apps may impact users with a desire to be underweight. Whereas these users had underweight goals, they often view the app as helpful in reducing disordered eating behaviors, which led to additional questions. Therefore, future research is needed.

Human computer interaction (HCI) and implementation science (IS) each have been applied to improve the adoption and delivery of innovative health interventions, and the two fields have complementary goals, foci, and methods. While the IS community increasingly draws on methods from HCI, there are many unrealized opportunities for HCI to draw from IS and to catalyze bidirectional collaborations. This workshop will explore similarities and differences between fields, with a goal of articulating a research agenda at their intersection.

BackgroundThe prenatal care visit structure has changed little over the past century despite the rapid evolution of technology including Internet and mobile phones. Little is known about how pregnant women engage with technologies and the interface between these tools and medical care, especially for women of lower socioeconomic status. ObjectiveWe sought to understand how women use technology during pregnancy through a qualitative study with women enrolled in the Women, Infants, and Children (WIC) program. MethodsWe recruited pregnant women ages 18 and older who owned a smartphone, at a WIC clinic in central Pennsylvania. The focus group guide included questions about women’s current pregnancy, their sources of information, and whether they used technology for pregnancy-related information. Sessions were audiotaped and transcribed. Three members of the research team independently analyzed each transcript, using a thematic analysis approach. Themes related to the topics discussed were identified, for which there was full agreement. ResultsFour focus groups were conducted with a total of 17 women. Three major themes emerged as follows. First, the prenatal visit structure is not patient-centered, with the first visit perceived as occurring too late and with too few visits early in pregnancy when women have the most questions for their prenatal care providers. Unfortunately, the educational materials women received during prenatal care were viewed as unhelpful. Second, women turn to technology (eg, Google, smartphone applications) to fill their knowledge gaps. Turning to technology was viewed to be a generational approach. Finally, women reported that technology, although frequently used, has limitations. ConclusionsThe results of this qualitative research suggest that the current prenatal care visit structure is not patient-centered in that it does not allow women to seek advice when they want it most. A generational shift seems to have occurred, resulting in pregnant women in our study turning to the Internet and smartphones to fill this gap, which requires significant skills to navigate for useful information. Future steps may include developing interventions to help health care providers assist patients early in pregnancy to seek the information they want and to become better consumers of Internet-based pregnancy resources.

Current technologies designed for mental health support often have low adoption rates and may not fit people's routines. However, recent literature demonstrates that individuals managing mental illness often incorporate a variety of technologies into their self-management routines, including texting, music, wearables, online communities, games, and social media. Therefore, adopting a perspective focused on understanding the technology ecosystem of mental health management may be a better approach than focusing on singular platforms and applications. In this CHI workshop, we will examine a constellation of digital and non-digital mental health support resources. We will discuss the needs and practices of individuals managing mental illness, how these needs relate to future ecosystems of technologies and services, and the potentials and repercussions of current technologies. By convening a group of interdisciplinary researchers and practitioners, we will collectively address these topics to create resources attentive to individuals' lived experiences with mental illness, goals with respect to management and recovery, and interactions with existing forms of resources, care, and technology.

While the HCI field increasingly examines how digital tools can support individuals in managing mental health conditions, it remains unclear how these tools can accommodate these conditions' temporal aspects. Based on weekly interviews with five individuals with depression, conducted over six weeks, this study identifies design opportunities and challenges related to extending technology-based support across fluctuating symptoms. Our findings suggest that participants perceive events and contexts in daily life to have marked impact on their symptoms. Results also illustrate that ebbs and flows in symptoms profoundly affect how individuals practice depression self-management. While digital tools often aim to reach individuals while they feel depressed, we suggest they should also engage individuals when they are less symptomatic, leveraging their energy and motivation to build habits, establish plans and goals, and generate and organize content to prepare for symptom onset.

The increasing ubiquity of health sensing technology holds promise to enable patients and health care providers to make more informed decisions based on continuously-captured data. The use of sensor-captured patient-generated data (sPGD) has been gaining greater prominence in the assessment of physical health, but we have little understanding of the role that sPGD can play in mental health. To better understand the use of sPGD in mental health, we interviewed care providers in an intensive treatment program (ITP) for veterans with post-traumatic stress disorder. In this program, patients were given Fitbits for their own voluntary use. Providers identified a number of potential benefits from patients' Fitbit use, such as patient empowerment and opportunities to reinforce therapeutic progress through collaborative data review and interpretation. However, despite the promise of sensor data as offering an "objective" view into patients' health behavior and symptoms, the relationships between sPGD and therapeutic progress are often ambiguous. Given substantial subjectivity involved in interpreting data from commercial wearables in the context of mental health treatment, providers emphasized potential risks to their patients and were uncertain how to adjust their practice to effectively guide collaborative use of the FitBit and its sPGD. We discuss the implications of these findings for designing systems to leverage sPGD in mental health care.

ObjectivesThis study sought to characterize ventricular tachycardia (VT) ablation outcomes across nonischemic cardiomyopathy (NICM) etiologies and adjust these outcomes by patient-related comorbidities that could explain differences in arrhythmia recurrence rates.BackgroundOutcomes of catheter ablation of VT in patients with NICM could be related to etiology of NICM.MethodsData from 2,075 patients with structural heart disease referred for catheter ablation of VT from 12 international centers was retrospectively analyzed. Patient characteristics and outcomes were noted for the 6 most common NICM etiologies. Multivariable Cox proportional hazards modeling was used to adjust for potential confounders.ResultsOf 780 NICM patients (57 ± 14 years of age, 18% women, left ventricular ejection fraction 37 ± 13%), underlying prevalence was 66% for dilated idiopathic cardiomyopathy (DICM), 13% for arrhythmogenic right ventricular cardiomyopathy (ARVC), 6% for valvular cardiomyopathy, 6% for myocarditis, 4% for hypertrophic cardiomyopathy, and 3% for sarcoidosis. One-year freedom from VT was 69%, and freedom from VT, heart transplantation, and death was 62%. On unadjusted competing risk analysis, VT ablation in ARVC demonstrated superior VT-free survival (82%) versus DICM (p ≤ 0.01). Valvular cardiomyopathy had the poorest unadjusted VT-free survival, at 47% (p < 0.01). After adjusting for comorbidities, including age, heart failure severity, ejection fraction, prior ablation, and antiarrhythmic medication use, myocarditis, ARVC, and DICM demonstrated similar outcomes, whereas hypertrophic cardiomyopathy, valvular cardiomyopathy, and sarcoidosis had the highest risk of VT recurrence.ConclusionsCatheter ablation of VT in NICM is effective. Etiology of NICM is a significant predictor of outcomes, with ARVC, myocarditis, and DICM having similar but superior outcomes to hypertrophic cardiomyopathy, valvular cardiomyopathy, and sarcoidosis, after adjusting for potential covariates.

BACKGROUND:The increase in availability of patient data through consumer health wearable devices and mobile phone sensors provides opportunities for mental health treatment beyond traditional self-report measurements. Previous studies have suggested that wearables can be effectively used to benefit the physical health of people with mental health issues, but little research has explored the integration of wearable devices into mental health care. As such, early research is still necessary to address factors that might impact integration including patients' motivations to use wearables and their subsequent data. OBJECTIVE:The aim of this study was to gain an understanding of patients' motivations to use or not to use wearables devices during an intensive treatment program for post-traumatic stress disorder (PTSD). During this treatment, they received a complementary Fitbit. We investigated the following research questions: How did the veterans in the intensive treatment program use their Fitbit? What are contributing motivators for the use and nonuse of the Fitbit? METHODS:We conducted semistructured interviews with 13 veterans who completed an intensive treatment program for PTSD. We transcribed and analyzed interviews using thematic analysis. RESULTS:We identified three major motivations for veterans to use the Fitbit during their time in the program: increase self-awareness, support social interactions, and give back to other veterans. We also identified three major reasons certain features of the Fitbit were not used: lack of clarity around the purpose of the Fitbit, lack of meaning in the Fitbit data, and challenges in the veteran-provider relationship. CONCLUSIONS:To integrate wearable data into mental health treatment programs, it is important to understand the patient's perspectives and motivations in using wearables. We also discuss how the military culture and PTSD may have contributed to our participants' behaviors and attitudes toward Fitbit usage. We conclude with possible approaches for integrating patient-generated data into mental health treatment settings that may address the challenges we identified.

BACKGROUND The increase in availability of patient data through consumer health wearable devices and mobile phone sensors provides opportunities for mental health treatment beyond traditional self-report measurements. Previous studies have suggested that wearables can be effectively used to benefit the physical health of people with mental health issues, but little research has explored the integration of wearable devices into mental health care. As such, early research is still necessary to address factors that might impact integration including patients' motivations to use wearables and their subsequent data. OBJECTIVE The aim of this study was to gain an understanding of patients’ motivations to use or not to use wearables devices during an intensive treatment program for post-traumatic stress disorder (PTSD). During this treatment, they received a complementary Fitbit. We investigated the following research questions: How did the veterans in the intensive treatment program use their Fitbit? What are contributing motivators for the use and nonuse of the Fitbit? METHODS We conducted semistructured interviews with 13 veterans who completed an intensive treatment program for PTSD. We transcribed and analyzed interviews using thematic analysis. RESULTS We identified three major motivations for veterans to use the Fitbit during their time in the program: increase self-awareness, support social interactions, and give back to other veterans. We also identified three major reasons certain features of the Fitbit were not used: lack of clarity around the purpose of the Fitbit, lack of meaning in the Fitbit data, and challenges in the veteran-provider relationship. CONCLUSIONS To integrate wearable data into mental health treatment programs, it is important to understand the patient’s perspectives and motivations in using wearables. We also discuss how the military culture and PTSD may have contributed to our participants' behaviors and attitudes toward Fitbit usage. We conclude with possible approaches for integrating patient-generated data into mental health treatment settings that may address the challenges we identified.

An increasingly large body of randomized controlled trials has demonstrated the efficacy of mental health technologies, such as Web-based and mobile interventions, to prevent and treat mental disorders and increase psychological well-being. However, there is little evidence that these tools can be successfully implemented in clinical settings. The authors highlight three widely held misconceptions that they believe are holding back the field, and they reconceptualize the issues to strengthen the path toward implementation and accelerate innovation.

The disruption of brain energy metabolism, leading to alterations in synaptic signaling, neural circuitry, and neuroplasticity, has been implicated in severe mental illnesses such as schizophrenia, bipolar disorder, and major depressive disorder. The therapeutic potential of ketogenic interventions in these disorders suggests a link between metabolic disturbances and disease pathology; however, the precise mechanisms underlying these metabolic disturbances, and the therapeutic effects of metabolic ketogenic therapy, remain poorly understood. In this study, we conducted an in silico analysis of transcriptomic data to investigate perturbations in metabolic pathways in the brain across severe mental illnesses via gene expression profiling. We also examined dysregulation of the same pathways in rodent or cell culture models of ketosis, comparing these expression profiles to those observed in the disease states. Our analysis revealed significant perturbations across all metabolic pathways, with the greatest perturbations in glycolysis, the tricarboxylic acid (TCA) cycle, and the electron transport chain (ETC) across all three disorders. Additionally, we observed some discordant gene expression patterns between disease states and ketogenic intervention studies, suggesting a potential role for ketone bodies in modulating pathogenic metabolic changes. Our findings highlight the importance of understanding metabolic dysregulation in severe mental illnesses and the potential therapeutic benefits of ketogenic interventions in restoring metabolic homeostasis. This study provides insights into the complex relationship between metabolism and neuropsychiatric disorders and lays the foundation for further experimental investigations aimed at appreciating the implications of the present transcriptomic findings as well as developing targeted therapeutic strategies. [ABSTRACT FROM AUTHOR]

BackgroundThe impact of catheter ablation of ventricular tachycardia (VT) on all-cause mortality remains unknown.ObjectiveThe purpose of this study was to examine the association between VT recurrence after ablation and survival in patients with scar-related VT.MethodsAnalysis of 2061 patients with structural heart disease referred for catheter ablation of scar-related VT from 12 international centers was performed. Data on clinical and procedural variables, VT recurrence, and mortality were analyzed. Kaplan-Meier analysis was used to estimate freedom from recurrent VT, transplant, and death. Cox proportional hazards frailty models were used to analyze the effect of risk factors on VT recurrence and mortality.ResultsOne-year freedom from VT recurrence was 70% (72% in ischemic and 68% in nonischemic cardiomyopathy). Fifty-seven patients (3%) underwent cardiac transplantation, and 216 (10%) died during follow-up. At 1 year, the estimated rate of transplant and/or mortality was 15% (same for ischemic and nonischemic cardiomyopathy). Transplant-free survival was significantly higher in patients without VT recurrence than in those with recurrence (90% vs 71%, P