“My attitude on telehealth has completely changed.”: Facilitators and Barriers to Implementing Technology for Care Delivery in Community Mental Health Centers

The purpose of this study was to explore facilitators and barriers aiding community mental health centers in implementing technology-assisted care during the COVID-19 pandemic. Six key informants were interviewed and 28 clinicians were surveyed from three community mental health centers. Interviews focused on technology-assisted care implementation efforts and factors that facilitated adoption. Surveys focused on clinician beliefs and experience with technology-assisted care in addition to training needs. Barriers to technology-assisted care implementation included beliefs about the quality of virtual services and a lack of technology access. An increase in service utilization was reported. Technology-assisted care facilitators included reimbursement policy changes and clinic-based factors such as clinician training and supervision efforts. Clinicians reported having the skills necessary to implement technology-assisted care however endorsed a need for training. Implementation of technology-assisted care in community mental health centers was largely successful however support is needed to help clinicians adapt services to client needs

Routine Clustering of Mobile Sensor Data Facilitates Psychotic Relapse Prediction in Schizophrenia Patients

We aim to develop clustering models to obtain behavioral representations from continuous multimodal mobile sensing data towards relapse prediction tasks. The identified clusters could represent different routine behavioral trends related to daily living of patients as well as atypical behavioral trends associated with impending relapse. We used the mobile sensing data obtained in the CrossCheck project for our analysis. Continuous data from six different mobile sensing-based modalities (e.g. ambient light, sound/conversation, acceleration etc.) obtained from a total of 63 schizophrenia patients, each monitored for up to a year, were used for the clustering models and relapse prediction evaluation. Two clustering models, Gaussian Mixture Model (GMM) and Partition Around Medoids (PAM), were used to obtain behavioral representations from the mobile sensing data. The features obtained from the clustering models were used to train and evaluate a personalized relapse prediction model using Balanced Random Forest. The personalization was done by identifying optimal features for a given patient based on a personalization subset consisting of other patients who are of similar age. The clusters identified using the GMM and PAM models were found to represent different behavioral patterns (such as clusters representing sedentary days, active but with low communications days, etc.). Significant changes near the relapse periods were seen in the obtained behavioral representation features from the clustering models. The clustering model based features, together with other features characterizing the mobile sensing data, resulted in an F2 score of 0.24 for the relapse prediction task in a leave-one-patient-out evaluation setting. This obtained F2 score is significantly higher than a random classification baseline with an average F2 score of 0.042

A smartphone intervention for people with serious mental illness: Fully remote randomized controlled trial of CORE

Background: People with serious mental illness (SMI) have significant unmet mental health needs. Development and testing of digital interventions that can alleviate the suffering of people with SMI is a public health priority. Objective: The aim of this study is to conduct a fully remote randomized waitlist-controlled trial of CORE, a smartphone intervention that comprises daily exercises designed to promote reassessment of dysfunctional beliefs in multiple domains. Methods: Individuals were recruited via the web using Google and Facebook advertisements. Enrolled participants were randomized into either active intervention or waitlist control groups. Participants completed the Beck Depression Inventory-Second Edition (BDI-II), Generalized Anxiety Disorder-7 (GAD-7), Hamilton Program for Schizophrenia Voices, Green Paranoid Thought Scale, Recovery Assessment Scale (RAS), Rosenberg Self-Esteem Scale (RSES), Friendship Scale, and Sheehan Disability Scale (SDS) at baseline (T1), 30-day (T2), and 60-day (T3) assessment points. Participants in the active group used CORE from T1 to T2, and participants in the waitlist group used CORE from T2 to T3. Both groups completed usability and accessibility measures after they concluded their intervention periods. Results: Overall, 315 individuals from 45 states participated in this study. The sample comprised individuals with self-reported bipolar disorder (111/315, 35.2%), major depressive disorder (136/315, 43.2%), and schizophrenia or schizoaffective disorder (68/315, 21.6%) who displayed moderate to severe symptoms and disability levels at baseline. Participants rated CORE as highly usable and acceptable. Intent-to-treat analyses showed significant treatment×time interactions for the BDI-II (F1,313=13.38; P\u3c.001), GAD-7 (F1,313=5.87; P=.01), RAS (F1,313=23.42; P\u3c.001), RSES (F1,313=19.28; P\u3c.001), and SDS (F1,313=10.73; P=.001). Large effects were observed for the BDI-II (d=0.58), RAS (d=0.61), and RSES (d=0.64); a moderate effect size was observed for the SDS (d=0.44), and a small effect size was observed for the GAD-7 (d=0.20). Similar changes in outcome measures were later observed in the waitlist control group participants following crossover after they received CORE (T2 to T3). Approximately 41.5% (64/154) of participants in the active group and 60.2% (97/161) of participants in the waitlist group were retained at T2, and 33.1% (51/154) of participants in the active group and 40.3% (65/161) of participants in the waitlist group were retained at T3. Conclusions: We successfully recruited, screened, randomized, treated, and assessed a geographically dispersed sample of participants with SMI entirely via the web, demonstrating that fully remote clinical trials are feasible in this population; however, study retention remains challenging. CORE showed promise as a usable, acceptable, and effective tool for reducing the severity of psychiatric symptoms and disability while improving recovery and self-esteem. Rapid adoption and real-world dissemination of evidence-based mobile health interventions such as CORE are needed if we are to shorten the science-to-service gap and address the significant unmet mental health needs of people with SMI during the COVID-19 pandemic and beyond

CrossCheck:toward passive sensing and detection of mental health changes in people with schizophrenia

Early detection of mental health changes in individuals with serious mental illness is critical for effective intervention. CrossCheck is the first step towards the passive monitoring of mental health indicators in patients with schizophrenia and paves the way towards relapse prediction and early intervention. In this paper, we present initial results from an ongoing randomized control trial, where passive smartphone sensor data is collected from 21 outpatients with schizophrenia recently discharged from hospital over a period ranging from 2-8.5 months. Our results indicate that there are statistically significant associations between automatically tracked behavioral features related to sleep, mobility, conversations, smartphone usage and self-reported indicators of mental health in schizophrenia. Using these features we build inference models capable of accurately predicting aggregated scores of mental health indicators in schizophrenia with a mean error of 7.6% of the score range. Finally, we discuss results on the level of personalization that is needed to account for the known variations within people. We show that by leveraging knowledge from a population with schizophrenia, it is possible to train accurate personalized models that require fewer individual-specific data to quickly adapt to new user

Perception and appropriation of a web-based recovery narratives intervention: qualitative interview study

Introduction: Mental health recovery narratives are widely available to the public, and can benefit people affected by mental health problems. The NEON Intervention is a novel web-based digital health intervention providing access to the NEON Collection of recovery narratives. The NEON Intervention was found to be effective and cost-effective in the NEON-O Trial for people with nonpsychosis mental health problems (ISRCTN63197153), and has also been evaluated in the NEON Trial for people with psychosis experience (ISRCTN11152837). We aimed to document NEON Intervention experiences, through an integrated process evaluation. Methods: Analysis of interviews with a purposive sample of intervention arm participants who had completed trial participation. Results: We interviewed 34 NEON Trial and 20 NEON-O Trial participants (mean age 40.4 years). Some users accessed narratives through the NEON Intervention almost daily, whilst others used it infrequently or not at all. Motivations for trial participation included: exploring the NEON Intervention as an alternative or addition to existing mental health provision; searching for answers about mental health experiences; developing their practice as a mental health professional (for a subset who were mental health professionals); claiming payment vouchers. High users (10 + narrative accesses) described three forms of appropriation: distracting from difficult mental health experiences; providing an emotional boost; sustaining a sense of having a social support network. Most participants valued the scale of the NEON Collection (n = 659 narratives), but some found it overwhelming. Many felt they could describe the characteristics of a desired narrative that would benefit their mental health. Finding a narrative meeting their desires enhanced engagement, but not finding one reduced engagement. Narratives in the NEON Collection were perceived as authentic if they acknowledged the difficult reality of mental health experiences, appeared to describe real world experiences, and described mental health experiences similar to those of the participant. Discussion: We present recommendations for digital health interventions incorporating collections of digital narratives: (1) make the scale and diversity of the collection visible; (2) provide delivery mechanisms that afford appropriation; (3) enable contributors to produce authentic narratives; (4) enable learning by healthcare professionals; (5) consider use to address loneliness

Perception and appropriation of a web-based recovery narratives intervention: qualitative interview study

IntroductionMental health recovery narratives are widely available to the public, and can benefit people affected by mental health problems. The NEON Intervention is a novel web-based digital health intervention providing access to the NEON Collection of recovery narratives. The NEON Intervention was found to be effective and cost-effective in the NEON-O Trial for people with nonpsychosis mental health problems (ISRCTN63197153), and has also been evaluated in the NEON Trial for people with psychosis experience (ISRCTN11152837). We aimed to document NEON Intervention experiences, through an integrated process evaluation.MethodsAnalysis of interviews with a purposive sample of intervention arm participants who had completed trial participation.ResultsWe interviewed 34 NEON Trial and 20 NEON-O Trial participants (mean age 40.4 years). Some users accessed narratives through the NEON Intervention almost daily, whilst others used it infrequently or not at all. Motivations for trial participation included: exploring the NEON Intervention as an alternative or addition to existing mental health provision; searching for answers about mental health experiences; developing their practice as a mental health professional (for a subset who were mental health professionals); claiming payment vouchers. High users (10 + narrative accesses) described three forms of appropriation: distracting from difficult mental health experiences; providing an emotional boost; sustaining a sense of having a social support network. Most participants valued the scale of the NEON Collection (n = 659 narratives), but some found it overwhelming. Many felt they could describe the characteristics of a desired narrative that would benefit their mental health. Finding a narrative meeting their desires enhanced engagement, but not finding one reduced engagement. Narratives in the NEON Collection were perceived as authentic if they acknowledged the difficult reality of mental health experiences, appeared to describe real world experiences, and described mental health experiences similar to those of the participant.DiscussionWe present recommendations for digital health interventions incorporating collections of digital narratives: (1) make the scale and diversity of the collection visible; (2) provide delivery mechanisms that afford appropriation; (3) enable contributors to produce authentic narratives; (4) enable learning by healthcare professionals; (5) consider use to address loneliness

Roadmap on label-free super-resolution imaging

Label-free super-resolution (LFSR) imaging relies on light-scattering processes in nanoscale objects without a need for fluorescent (FL) staining required in super-resolved FL microscopy. The objectives of this Roadmap are to present a comprehensive vision of the developments, the state-of-the-art in this field, and to discuss the resolution boundaries and hurdles that need to be overcome to break the classical diffraction limit of the label-free imaging. The scope of this Roadmap spans from the advanced interference detection techniques, where the diffraction-limited lateral resolution is combined with unsurpassed axial and temporal resolution, to techniques with true lateral super-resolution capability that are based on understanding resolution as an information science problem, on using novel structured illumination, near-field scanning, and nonlinear optics approaches, and on designing superlenses based on nanoplasmonics, metamaterials, transformation optics, and microsphere-assisted approaches. To this end, this Roadmap brings under the same umbrella researchers from the physics and biomedical optics communities in which such studies have often been developing separately. The ultimate intent of this paper is to create a vision for the current and future developments of LFSR imaging based on its physical mechanisms and to create a great opening for the series of articles in this field