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BackgroundWe examined whether digital phenotyping of spontaneous speech, such as the use of specific word categories during speech samples, was associated with depressive symptoms in youth who were at familial and clinical risk for mood disorders.MethodsParticipants (ages 13-19) had active mood symptoms, mood instability, and at least one parent with bipolar or major depressive disorder. During a randomized trial of family-focused therapy, participants were instructed to make weekly calls to a central voice server and leave speech samples in response to automated prompts. We coded youths' speech samples with the Linguistic Inquiry and Word Count system and used machine learning to identify the combination of speech features that were most closely associated with the course of depressive symptoms over 18 weeks.ResultsA total of 253 speech samples were collected from 44 adolescents (mean age = 15.8 years; SD = 1.6) over 18 weeks. Speech containing affective processes, social processes, drives toward risk or reward, nonfluencies, and time orientation words were correlated with depressive symptoms at concurrent time periods (ps

BackgroundOpen design formats for mobile apps help clinicians and stakeholders bring their needs to direct, co-creative solutions. Palliative care for patients with advanced cancers requires intensive monitoring and support and remains an area in high need for innovation.ObjectiveThis study aims to use community-partnered participatory research to co-design and pretest a mobile app that focuses on palliative care priorities of clinicians and patients with advanced cancer.MethodsIn-person and teleconference workshops were held with patient and family stakeholders, researchers, and clinicians in palliative care and oncology. Question prompts, written feedback, semistructured interviews, and facilitated group discussions identified the core palliative care needs. Using Chorus, a no-code app-building platform, a mobile app was co-designed with the stakeholders. A pretest with 11 patients was conducted, with semistructured interviews of clinician and patient users for feedback.ResultsKey themes identified from the focus groups included needs for patient advocacy and encouragement, access to vetted information, patient-clinician communication support, and symptom management. The initial prototype, My Wellness App, contained a weekly wellness journal to track patient-reported symptoms, goals, and medication use; information on self-management of symptoms; community resources; and patient and caregiver testimonial videos. Initial pretesting identified value in app-based communication for clinicians, patients, and caregivers, with suggestions for improving user interface, feedback and presentation of symptom reports, and gamification and staff coordinators to support patient app engagement.ConclusionsThe development of a mobile app using community-partnered participatory research is a low-technology and feasible intervention for palliative care. Iterative redesign and user interface expertise may improve implementation.

Expressed emotion (EE), a measure of attitudes among caregivers towards a patient with a psychiatric disorder, is a robust predictor of relapse across mood and psychotic disorders. Because the measurement of EE is time-intensive and costly, its use in clinical settings has been limited. In an effort to automate EE classification, we evaluated whether machine learning (ML) applied to lexical features of speech samples can accurately categorize parents as high or low in EE and in its subtypes (criticism, overinvolvement, and warmth). The sample was 123 parents of youth who had active mood symptoms and a family history of bipolar disorder. Using ML algorithms, we achieved 75.2-81.8% accuracy (sensitivities of ~0.7 and specificities of ~0.8) in classifying parents as high or low in EE and EE subtypes. Additionally, machine-derived EE classifications and observer-rated EE classifications had simiar relationships with youth mood symptoms, parental distress, and family conflict. Of note, criticism related to greater manic severity, parental distress, and family conflict. Study findings indicate that EE classification can be automated through lexical analysis and suggest potential for facilitating larger-scale applications in clinical settings. The results also provide initial indications of the digital phenotypes that underlie EE and its subtypes.

Individuals with serious mental illness experience changes in their clinical states over time that are difficult to assess and that result in increased disease burden and care utilization. It is not known if features derived from speech can serve as a transdiagnostic marker of these clinical states. This study evaluates the feasibility of collecting speech samples from people with serious mental illness and explores the potential utility for tracking changes in clinical state over time. Patients (n = 47) were recruited from a community-based mental health clinic with diagnoses of bipolar disorder, major depressive disorder, schizophrenia or schizoaffective disorder. Patients used an interactive voice response system for at least 4 months to provide speech samples. Clinic providers (n = 13) reviewed responses and provided global assessment ratings. We computed features of speech and used machine learning to create models of outcome measures trained using either population data or an individual's own data over time. The system was feasible to use, recording 1101 phone calls and 117 hours of speech. Most (92%) of the patients agreed that it was easy to use. The individually-trained models demonstrated the highest correlation with provider ratings (rho = 0.78, p

The coronavirus pandemic of 2019 (COVID-19) has created unprecedented changes to everyday life for millions of Americans due to job loss, school closures, stay-at-home orders and health and mortality consequences. In turn, physicians, academics, and policymakers have turned their attention to the public mental health toll of COVID-19. This commentary reporting from the field integrates perceptions of academic, community, health system, and policy leaders from state, county, and local levels in commenting on community mental health needs in the COVID-19 pandemic. Stakeholders noted the broad public health scope of mental health challenges while expressing concern about exacerbation of existing disparities in access and adverse social determinants, including for communities with high COVID-19 infection rates, such as African Americans and Latinos. They noted rapid changes toward telehealth and remote care, and the importance of understanding impacts of changes, including who may benefit or have limited access, with implications for future services delivery. Needs for expanded workforce and training in mental health were noted, as well as potential public health value of expanding digital resources tailored to local populations for enhancing resilience to stressors. The COVID-19 pandemic has led to changes in delivery of health care services across populations and systems. Concerns over the mental health impact of COVID-19 has enhanced interest in remote mental care delivery and preventive services, while being mindful of potential for enhanced disparities and needs to address social determinants of health. Ongoing quality improvement across systems can integrate lessons learned to enhance a public mental well-being.

This manuscript presents the protocol and participatory planning process for implementing the Community Resilience Learning Collaborative and Research Network (C-LEARN) study. C-LEARN is designed to determine how to build a service program and individual client capacity to improve mental health-related quality of life among individuals at risk for depression, with exposure to social risk factors or concerns about environmental hazards in areas of Southern Louisiana at risk for events such as hurricanes and storms. The study uses a Community Partnered Participatory Research (CPPR) framework to incorporate community priorities into study design and implementation. The first phase of C-LEARN is assessment of community priorities, assets, and opportunities for building resilience through key informant interviews and community agency outreach. Findings from this phase will inform the implementation of a two-level (program-level and individual client level) randomized study in up to four South Louisiana communities. Within communities, health and social-community service programs will be randomized to Community Engagement and Planning (CEP) for multi-sector coalition support or Technical Assistance (TA) for individual program support to implement evidence-based and community-prioritized intervention toolkits, including an expanded version of depression collaborative care and resources (referrals, manuals) to address social risk factors such as financial or housing instability and for a community resilience approach to disaster preparedness and response. Within each arm, the study will randomize individual adult clients to one of two mobile applications that provide informational resources on services for depression, social risk factors, and disaster response or also provide psychoeducation on Cognitive Behavioral Therapy to enhance coping with stress and mood. Planned data collection includes baseline, six-month and brief monthly surveys for clients, and baseline and 12-month surveys for administrators and staff.

BackgroundFloatation-REST (Reduced Environmental Stimulation Therapy), an intervention that attenuates exteroceptive sensory input to the nervous system, has recently been found to reduce state anxiety across a diverse clinical sample with high levels of anxiety sensitivity (AS). To further examine this anxiolytic effect, the present study investigated the affective and physiological changes induced by Floatation-REST and assessed whether individuals with high AS experienced any alterations in their awareness for interoceptive sensation while immersed in an environment lacking exteroceptive sensation.MethodsUsing a within-subject crossover design, 31 participants with high AS were randomly assigned to undergo a 90-minute session of Floatation-REST or an exteroceptive comparison condition. Measures of self-reported affect and interoceptive awareness were collected before and after each session, and blood pressure was measured during each session.ResultsRelative to the comparison condition, Floatation-REST generated a significant anxiolytic effect characterized by reductions in state anxiety and muscle tension and increases in feelings of relaxation and serenity (p < .001 for all variables). Significant blood pressure reductions were evident throughout the float session and reached the lowest point during the diastole phase (average reduction >12 mm Hg). The float environment also significantly enhanced awareness and attention for cardiorespiratory sensations.ConclusionsFloatation-REST induced a state of relaxation and heightened interoceptive awareness in a clinical sample with high AS. The paradoxical nature of the anxiolytic effect in this sample is discussed in relation to Wolpe's theory of reciprocal inhibition and the regulation of distress via sustained attention to present moment visceral sensations such as the breath.

Predictive analytics in health is a complex, transdisciplinary field requiring collaboration across diverse scientific and stakeholder groups. Pilot implementation of participatory research to foster team science in predictive analytics through a partnered-symposium and funding competition. In total, 85 stakeholders were engaged across diverse translational domains, with a significant increase in perceived importance of early inclusion of patients and communities in research. Participatory research approaches may be an effective model for engaging broad stakeholders in predictive analytics.

ObjectiveWe describe the rationale, development, and progress on the Community and Patient Partnered Research Network (CPPRN). The CPPRN builds on more than a decade of partnered work and is designed to promote health equity by developing partnered research on behavioral health and social risk factors in Los Angeles and New Orleans.SettingA community-academic partnership across Los Angeles County and New Orleans.MethodsReview of rationale, history, structure, activities and progress in applying community partnered participatory research (CPPR) to CPPRN.FindingsPatient and community stakeholders participated in all phases of development, including local and national activities. Key developments include partnered planning efforts, progress on aggregating a large, de-identified dataset across county agencies, and development of an information technology-supported screening approach for behavioral and social determinants in health care, social, and community-based settings.ConclusionThe CPPRN represents a promising approach for research data networks, balancing the potential benefit of information technology and data analytic approaches while addressing potential risks and priorities of relevant stakeholders.

ObjectiveTo assess the feasibility of a novel, partnered technology development process to co-create mobile health applications (apps) addressing community health priorities, using psychoeducation of cognitive behavioral therapy (CBT) principles for enhancing resilience as an example.DesignStakeholder engagement, workgroups, pilot feasibility study using mixed methods during October 2013 through January 2016 over three phases: 1) defining the vision of the project and increasing technical capacity, 2) co-development and pilot testing of the app, and 3) planning for sustainability.SettingAn academic-community partnership in South Los Angeles, California.ParticipantsEight stakeholders; 30 pilot participants from the community.Main outcome measuresQualitative analysis of audio-recordings of the app development process and stakeholder interviews, surveys of stakeholders' perception of the development process, app use data, and feedback from pilot participants.ResultsThe participatory technology development process resulted in creation and pilot-testing of a resiliency-focused text messaging app. Of the 1,107 messages sent, 23 out of 30 (77%) app users responded to explore interactive content. Stakeholders reported increased perceived competency in creating mobile apps and that the process fostered a culture of co-leadership. There was also sustained engagement in mobile app development by stakeholders beyond the initial project period.ConclusionsThis is the first study, to our knowledge, to demonstrate the feasibility of participatory technology development, an approach involving direct participation in the development, tailoring and maintenance of a mobile app by a broad set of stakeholders with high representation from racial/ethnic minorities from an under-resourced community. Participatory technology development is a promising approach for creating sustainable, relevant and engaging health technologies across different technological, clinical and community settings.

Individuals with anorexia nervosa (AN) typically display anxious traits prior to the onset of food avoidance and weight loss that characterize the disorder. Meal associated anxiety is an especially common clinical feature in these patients, and heightened sensitivity to sympathetically mediated interoceptive sensations has also been observed. However, it remains unclear how heightened interoceptive sensitivity relates to experiences of anxiety before and after meals. To investigate this relationship, we experimentally induced anxiety and panic symptoms with isoproterenol, a peripheral sympathetic agonist similar to adrenaline, across several different conditions: during panic provocation, during anticipation of a 1,000 Calorie meal, and after meal consumption. Fifteen AN and 15 age- and sex-matched healthy comparisons received bolus infusions of isoproterenol and saline in a double-blinded, randomized design. Participants rated anxiety symptoms after each infusion, completed panic rating scales, and traced the location of perceived palpitations on a manikin to index interoceptive "body map" representation. The AN group reported significantly elevated anxiety relative to healthy comparisons during infusions before and after the meal, but surprisingly, not during panic provocation. These symptoms were accompanied by geographical differences in patterns of perceived heartbeat sensations across each condition. In particular, the AN group localized heartbeat sensations disproportionately to the chest during meal related saline infusions, when no cardiorespiratory modulation actually occurred. The AN group also showed a trend toward higher panic attack rates during the meal anticipation period. Correcting for anxiety levels reported during saline infusions abolished group differences in anxiety change across all conditions, suggesting a significant contribution of anxious traits in AN. The observation of meal related "visceral illusions" provides further evidence that AN is associated with abnormal interoceptive representation of the heartbeat and suggests that meal consumption, particularly when anticipated, preferentially alters the processing of interoception related signals in AN.

Prominent theories emphasize key roles for the insular cortex in the central representation of interoceptive sensations, but how this brain region responds dynamically to changes in interoceptive state remains incompletely understood. Here, we systematically modulated cardiorespiratory sensations in humans using bolus infusions of isoproterenol, a rapidly acting peripheral beta-adrenergic agonist similar to adrenaline. To identify central neural processes underlying these parametrically modulated interoceptive states, we used pharmacological functional magnetic resonance imaging (phMRI) to simultaneously measure blood-oxygenation-level dependent (BOLD) and arterial spin labelling (ASL) signals in healthy participants. Isoproterenol infusions induced dose-dependent increases in heart rate and cardiorespiratory interoception, with all participants endorsing increased sensations at the highest dose. These reports were accompanied by increased BOLD and ASL activation of the right insular cortex at the highest dose. Different responses across insula subregions were also observed. During anticipation, insula activation increased in more anterior regions. During stimulation, activation increased in the mid-dorsal and posterior insula on the right, but decreased in the same regions on the left. This study demonstrates the feasibility of phMRI for assessing brain activation during adrenergic interoceptive stimulation, and provides further evidence supporting a dynamic role for the insula in representing changes in cardiorespiratory states.This article is part of the themed issue 'Interoception beyond homeostasis: affect, cognition and mental health'.