Developing the Workforce of the Digital Future: mHealth Competency and Fidelity Measurement in Community-Based Care.

Integrating mobile health (mHealth) interventions into settings that serve diverse patient populations requires that prerequisite professional competencies are delineated and that standards for clinical quality assurance can be pragmatically assessed. Heretofore, proposed mHealth competencies have been broad and have lacked a framework to support specific applications. We outline the meta-competencies identified in the literature relevant to mHealth interventions and demonstrate how these meta-competencies can be integrated with population- and intervention-related competencies to help guide a pragmatic approach to competency assessment. We present a use case based on FOCUS-an evidence-based mHealth intervention designed for individuals with serious mental illness and currently being implemented in geographically and demographically diverse community behavioral health settings. Subsequent to identifying the cross-cutting competencies relevant to the target population (outpatients experiencing psychotic symptoms), substratal intervention (Cognitive Behavioral Therapy for psychosis), and treatment modality (mHealth), we detail the development process of an mHealth fidelity monitoring system (mHealth-FMS). We adhered to a published sequential 5-step process to design a fidelity monitoring system that aligns with our integrated mHealth competency framework and that was guided by best practices prescribed by the Treatment Fidelity Workgroup of the National Institutes of Health Behavior Change Consortium. The mHealth-FMS is intended to enhance both clinical and implementation outcomes by grounding the mHealth interventionist and the system of care in which they operate in the core functions, tasks, knowledge, and competencies associated with system-integrated mHealth delivery. Future research will explore acceptability and feasibility of the mHealth-FMS.

Identifying mechanisms of persecutory ideation maintenance with smartphone technology: Examining threat importance, certainty, rumination, and behavior change.

Previous cross-sectional and laboratory research has identified risk factors for persecutory ideation including rumination, negative affect, and safety-seeking behaviors. Questions remain about what in-the-moment factors link general negative affect to PI as well as which maintain PI over time. In the present study, N = 219 individuals completed momentary assessments of PI as well as four factors (attributing threats as certain and important, ruminating, and changing one's behavior in response) proposed to maintain PI over time. Linear mixed effects models were used to analyze multiple time-varying relationships, including these factors predicting negative affect and vice versa, as well as factors predicting maintenance of PI over time. Linear mixed effects models were used to analyze multiple time-varying relationships, examining each PI-related factor predicting negative affect, negative affect predicting each PI-related factor, as well as each factor predicting maintenance of PI over time. All four factors were associated with increases in subsequent day self-reported severity of PI, suggesting all four increased the likelihood of maintaining or worsening next-day PI. Results of this study confirm that the proposed factors are key in maintaining a cycle by which PI and negative affect are maintained over time. These factors may represent targets for momentary interventions.

Cost of Implementing mHealth in Community Mental Health Settings: External Versus Internal Facilitation.

OBJECTIVE: This study aimed to compare the costs of two implementation models for the mobile health (mHealth) intervention FOCUS in community mental health settings. The external facilitation (EF) approach uses a hub-and-spoke model, in which a central specialist provides support to clinicians and clients at multiple agencies. With the internal facilitation (IF) approach, frontline clinical staff at each center are trained to serve as their organization's local specialists. METHODS: Financial and economic cost data were collected in the context of a hybrid type 3 effectiveness-implementation trial by using a mixed-methods, top-down expenditure analysis with microcosting approaches. The analysis compared the incremental costs of both models and the costs of successfully engaging clients (N=210) at 20 centers. Costs were characterized as start-up or recurrent (personnel, supplies, contracted services, and indirect costs). RESULTS: The average annual financial cost per site was $23,517 for EF and $19,118 for IF. EF yielded more FOCUS users at each center, such that the average monthly financial costs were lower for EF ($167 per client [N=129]) than for IF ($177 per client [N=81]). When using a real-world scenario based on economic costs and a lower organizational indirect rate, the average monthly cost per client was $73 for EF and $59 for IF. Both models reflected substantial cost reductions (about 50%) relative to a previous deployment of FOCUS in a clinical trial. CONCLUSIONS: Compared with IF, EF yielded more clients who received mHealth at community mental health centers and had comparable or lower costs.

Using Smartphones to Identify Momentary Characteristics of Persecutory Ideation Associated With Functional Disability.

Objectives: Though often a feature of schizophrenia-spectrum disorders, persecutory ideation (PI) is also common in other psychiatric disorders as well as among individuals who are otherwise healthy. Emerging technologies allow for a more thorough understanding of the momentary phenomenological characteristics that determine whether PI leads to significant distress and dysfunction. This study aims to identify the momentary phenomenological features of PI associated with distress, dysfunction, and need for clinical care. Methods: A total of 231 individuals with at least moderate PI from 43 US states participated in a study involving 30 days of data collection using a smartphone data collection system combining ecological momentary assessment and passive sensors, wherein they reported on occurrence of PI as well as related appraisals, responses, and cooccurring states. Most (N = 120, 51.9%) participants reported never having received treatment for their PI, while 50 participants had received inpatient treatment (21.6%), and 60 (26.4%) had received outpatient care only. Results: Individuals with greater functional disability did not differ in PI frequency but were more likely at the moment to describe threats as important to them, to ruminate about those threats, to experience distress related to them, and to change their behavior in response. Groups based on treatment-seeking patterns largely did not differ in baseline measures or momentary phenomenology of PI as assessed by self-report or passive sensors. Conclusions: Smartphone data collection allows for granular assessment of PI-related phenomena. Functional disability is associated with differences in appraisals of and responses to PI at the moment.

A preliminary study examining terminology used by individuals with lived experience describing beliefs about being targeted or harmed by others.

While recent studies have prompted re-evaluation of the term "schizophrenia," few have examined the use of terms to describe persecutory ideation (PI) or paranoia. This study examines the preferences and terms used by a cross-diagnostic population of individuals (N = 184) with lived experience using an online survey. Participants most commonly described their PI in terms of the perceived source of threat, followed by clinical language, most commonly variants of "paranoia" and "anxiety." Of five selected terms assessed quantitatively - "anxiety," "paranoia," "persecutory thoughts," "suspiciousness," and "threat thoughts" - participants were more likely to report that "anxiety" aligned with their experience of PI, followed by "suspiciousness." Endorsement of terms more specific to PI was associated with self-report PI severity, while a preference for "anxiety" over other terms was both associated with less severe PI and lower scores on a measure of stigma. These results suggest that the heterogeneity of terms used by individuals with lived experience support a person-centered approach to language describing such experiences.

Determinants of preference for telehealth versus in-person treatment for substance use disorders: A discrete choice experiment.

INTRODUCTION: Access to substance use disorder (SUD) treatment remains a significant issue in the United States. Telehealth has potential for increasing access to services; however, it is underutilized in SUD treatment compared to mental health treatment. This study uses a discrete choice experiment (DCE) to examine stated preferences for telehealth (videoconferencing, text-based + video, text only) versus in-person SUD treatment (community-based, in-home) and the attributes (location, cost, therapist choice, wait time, evidence-based practices) most important when choosing between modalities. Subgroup analyses are reported about preference differences based on type of substance and substance use severity. METHODS: Four hundred participants completed a survey containing a DCE with eighteen choice sets, the alcohol use disorders inventory test, drug abuse screening test, and a brief demographic questionnaire. The study collected data between April 15, 2020, and April 22, 2020. Conditional logit regression provided a measure of strength for participant preferences for technology-assisted treatment compared to in-person care. The study provides willingness to pay estimates as a real-world measure for the importance of each attribute in participants' decision-making. RESULTS: Telehealth options that include a video conference option were equally preferrable to in-person care modalities. Text-only treatment was significantly less preferable to all other modalities of care. The ability to choose one's own therapist was a significant driver of treatment preference beyond modality, while wait time did not appear significant in making decisions. Participants with the most severe substance use differed in that they were open to text-based care without video conferencing, did not express a preference for evidence-based care, and valued therapist choice significantly more than those with only moderate substance use. CONCLUSIONS: Telehealth for SUD treatment is equally preferable to in-person care offered in the community or at home, signifying preference is not a barrier for utilization. Text-only modalities may be enhanced by offering videoconference options for most individuals. Individuals with the most severe substance use issues may be willing to engage in text-based support without synchronous meetings with a provider. This approach may offer a less intensive method to engage individuals in treatment who may not otherwise access services.

Automated Detection of Cognitive Distortions in Text Exchanges Between Clinicians and People With Serious Mental Illness.

OBJECTIVE: The authors tested whether natural language processing (NLP) methods can detect and classify cognitive distortions in text messages between clinicians and people with serious mental illness as effectively as clinically trained human raters. METHODS: Text messages (N=7,354) were collected from 39 clients in a randomized controlled trial of a 12-week texting intervention. Clinical annotators labeled messages for common cognitive distortions: mental filtering, jumping to conclusions, catastrophizing, "should" statements, and overgeneralizing. Multiple NLP classification methods were applied to the same messages, and performance was compared. RESULTS: A tuned model that used bidirectional encoder representations from transformers (F1=0.62) achieved performance comparable to that of clinical raters in classifying texts with any distortion (F1=0.63) and superior to that of other models. CONCLUSIONS: NLP methods can be used to effectively detect and classify cognitive distortions in text exchanges, and they have the potential to inform scalable automated tools for clinical support during message-based care for people with serious mental illness.

Developing the Workforce of the Digital Future: Leveraging Technology to Train Community-Based Mobile Mental Health Specialists.

Challenges in training, dissemination, and implementation have impeded the ability of providers to integrate promising digital health tools in real-world services. There is a need for generalizable strategies to rapidly train real-world providers at scale to support the adoption of digital health. This study describes the development of principles guiding rapid training of community-based clinicians in the support of digital health. This training approach was developed in the context of an ongoing trial examining implementation strategies for FOCUS, a mobile mental health intervention designed for people with serious mental illness. The SAIL (Simple, Accessible, Inverted, Live) model introduces how digital tools can be leveraged to facilitate rapid training of community agency-based personnel to serve as digital mental health champions, promoters, and providers. This model emphasizes simple and flexible principles of intervention delivery, accessible materials in a virtual learning environment, inverted or "flipped" live training structure, and live consultation calls for ongoing support. These initial insights lay the groundwork for future work to test and replicate generalizable training strategies focused on real-world delivery of digital mental health services. These strategies have the potential to remove key obstacles to the implementation and dissemination of digital health interventions for mental health.

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<.001), GAD-7 (F1,313=5.87; P=.01), RAS (F1,313=23.42; P<.001), RSES (F1,313=19.28; P<.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. TRIAL REGISTRATION: ClinicalTrials.gov NCT04068467; https://clinicaltrials.gov/ct2/show/NCT04068467.

mHealth for Young Adults with Early Psychosis: User Preferences and Their Relationship to Attitudes About Treatment-Seeking.

A long duration of untreated psychosis reduces benefits of early intervention for early psychosis. Digital technologies have potential to encourage help-seeking and reduce barriers to care. Because of high rates of smartphone ownership, mobile health (mHealth) interventions may be particularly well-suited to increase access. There is a lack of available information on the specific features that may be most appealing to young adults with early psychosis. The present study remotely recruited 77 young adults with psychosis and surveyed their interest in mHealth features, delivery modalities, and attitudes toward treatment. Overall, respondents reported high utilization of digital health and high interest in psychosis-specific mHealth. They expressed the highest interest (ordered by mean score by item) in information about medications and side effects (n = 69, 89.6% reporting being "interested" or "very interested"), managing stress and improving mood (n = 67, 89.3%) and symptoms of psychosis (n = 66, 88%), as well as in tracking changes in symptoms (n = 70, 90.9%), and goals (n = 66, 86.9%). They also reported high interest in content being delivered as text (n = 69, 89.6%) and also in communicating directly with providers. Respondents were less interested in social features, and those with most negative attitudes toward help-seeking had particularly low interest in features related to disclosing symptoms to others. These results suggest mHealth may have potential to engage individuals with early psychosis, and that the most effective strategies may be those that are most straightforward, including direct psychoeducational information.

What do clinicians want? Understanding frontline addiction treatment clinicians' preferences and priorities to improve the design of measurement-based care technology.

BACKGROUND: Measurement-based care (MBC) is the practice of routinely administering standardized measures to support clinical decision-making and monitor treatment progress. Despite evidence of its effectiveness, MBC is rarely adopted in routine substance use disorder (SUD) treatment settings and little is known about the factors that may improve its adoptability in these settings. The current study gathered qualitative data from SUD treatment clinicians about their perceptions of MBC, the clinical outcomes they would most like to monitor in MBC, and suggestions for the design and implementation of MBC systems in their settings. METHODS: Fifteen clinicians from one publicly-funded and two privately-funded outpatient SUD treatment clinics participated in one-on-one research interviews. Interviews focused on clinicians' perceived benefits, drawbacks, and ideas related to implementing MBC technology into their clinical workflows. Interviews were audio recorded, transcribed, and coded to allow for thematic analysis using a mixed deductive and inductive approach. Clinicians also completed a card sorting task to rate the perceived helpfulness of routinely measuring and monitoring different treatment outcomes. RESULTS: Clinicians reported several potential benefits of MBC, including improved patient-provider communication, client empowerment, and improved communication between clinicians. Clinicians also expressed potential drawbacks, including concerns about subjectivity in patient self-reports, limits to personalization, increased time burdens, and needing to learn to use new technologies. Clinicians generated several ideas and preferences aimed at minimizing burden of MBC, illustrating clinical changes over time, improving ease of use, and improving personalization. Numerous patient outcomes were identified as "very helpful" to track, including coping skills, social support, and motivation for change. CONCLUSIONS: MBC may be a beneficial tool for improving clinical care in SUD treatment settings. MBC tools may be particularly adoptable if they are compatible with existing workflows, help illustrate gradual and nonlinear progress in SUD treatment, measure outcomes perceived as clinically useful, accommodate multiple use cases and stakeholder groups, and are framed as an additional source of information meant to augment, rather than replace, existing practices and information sources.

Using a Clinical Workflow Analysis to Enhance eHealth Implementation Planning: Tutorial and Case Study.

eHealth apps often fail to improve clinical outcomes due to poor integration with clinical workflow-the sequence and personnel needed to undertake a series of tasks for clinical care. Our central thesis is that eHealth interventions will be more effective if the clinical workflow is studied and taken into consideration for intervention implementation. This paper aims to provide an introductory tutorial on when and how to use a clinical workflow analysis to guide the implementation of eHealth interventions. The tutorial includes a step-by-step guide to conducting a clinical workflow analysis in planning for eHealth implementation. We began with a description of why a clinical workflow analysis is best completed before the implementation of eHealth interventions. Next, we described 4 steps needed to perform the clinical workflow analysis: the identification of discrete workflow components, workflow assessment, triangulation, and the stakeholder proposal of intervention implementation. Finally, we presented a case study of a clinical workflow analysis, which was conducted during patient visits of patients aged 11 or 12 years from 4 diverse pediatric or family medicine clinics to plan the implementation of a tablet-based app for adolescent vaccination. Investigators planning the implementation of new eHealth interventions in health care settings can use the presented steps to assess clinical workflow, thereby maximizing the match of their intervention with the clinical workflow. Conducting a prospective workflow study allows for evidence-based planning, identifying potential pitfalls, and increasing stakeholder buy-in and engagement. This tutorial should aid investigators in increasing the successful implementation of eHealth interventions.

Distrust in health care and cultural factors are associated with uptake of colorectal cancer screening in Hispanic and Asian Americans.

BACKGROUND: There are racial/ethnic disparities in colorectal cancer (CRC) screening, including lower uptake rates among Hispanic Americans (HAs) and Asian Americans (AAs) relative to non-Hispanic white Americans. The objective of this study was to explore pathways associated with the use of health services and to characterize multifaceted associations with the uptake of CRC screening among HAs and AAs. METHODS: Data were obtained from the Medical Expenditure Panel Survey (2012-2013). Participants included HA (n = 3731) and AA (n = 1345) respondents ages 50 to 75 years who met CRC screening recommendations. A modified Andersen behavioral model was used to examine pathways that lead to CRC screening uptake, including predisposing characteristics (education, economic, and cultural factors), health insurance, health needs (perceived health status and several comorbidities), and health provider contextual factors (access to care, perceived quality of health services, and distrust in health care). Structural equation modeling was used to examine the models for HAs and AAs. RESULTS: In the HA model, cultural factors (standardized regression coefficient [ß] = -0.04; P = .013) and distrust in health care (ß = -0.05; P = .007) directly and negatively affected CRC screening. Similarly, cultural factors (ß = -0.11; P = .002) negatively affected CRC screening in the AA model, but distrust in health care was not significant (P = .103). In both models, perceived quality of health services was positively associated with CRC screening uptake and mediated the negative association between cultural factors and CRC screening. Access to care was not associated with CRC screening. CONCLUSIONS: Correlations between CRC screening and associated factors differ among HAs and AAs, suggesting a need for multilevel interventions tailored to race/ethnicity. The current findings suggest that facilitating access to care without improving perceived quality of health services may be ineffective for increasing the uptake of CRC screening among HAs and AAs. Cancer 2018;124:335-45. © 2017 American Cancer Society.

Developing the WorkingWell mobile app to promote job tenure for individuals with serious mental illnesses.

OBJECTIVE: Individuals living with serious mental illnesses are key stakeholders in user experience design and the development of the WorkingWell mobile app to enhance on-the-job follow-along support. In this study, Individual Placement and Support (IPS) consumers identify challenges in sustaining employment, provide data regarding their use of technology, and suggest technology-based solutions for coping on the job to inform app development. METHOD: Focus groups were conducted in 3 agencies providing IPS services to examine consumers' perspectives on supported employment, work, and their preferences for technology-based supports. Qualitative data were coded thematically in a multistep, collaborate approach to ensure trustworthiness. Survey data were collected to describe participants and their current technology use; these data were analyzed descriptively. RESULTS: A total of 25 IPS consumers reported work challenges related to interpersonal relationships and social situations; job characteristics, tasks, and expectations; illness- and treatment-related issues; lifestyle/wellness and conditions apart from work; and motivation. The majority owned mobile phones, felt comfortable using technology, and could see how technology-based tools could help sustain employment. Participants highlighted the potential benefits of technology-based supports for work challenges, and underscored the potential for independence and empowerment as a consequence. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Study findings suggest the value of a WorkingWell mobile app that is innovative, easy to access, self-directed, and individually tailored to enhance IPS follow-along support. The WorkingWell app, if proven effective, will provide an empowering set of tools designed with input from individuals with serious mental illnesses, and integrated into a single, accessible interface. (PsycINFO Database Record

The potential of technology for enhancing individual placement and support supported employment.

TOPIC: The potential of technology to enhance delivery and outcomes of Individual Placement and Support (IPS) supported employment. PURPOSE: IPS supported employment has demonstrated robust success for improving rates of competitive employment among individuals with psychiatric disabilities. Still, a majority of those with serious mental illnesses are not employed (Bond, Drake, & Becker, 2012). The need to promote awareness of IPS and expand services is urgent. In this study, we describe ways that technologies may enhance delivery of IPS supported employment across the care continuum and stakeholder groups. Directions for research are highlighted. SOURCES USED: published literature, clinical observations, IPS learning collaborative. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Technology has the potential to enhance direct service as well as workflow in the IPS supported employment process, which may lead to improved fidelity and client outcomes. Mobile and cloud technologies open opportunities for collaboration, self-directed care, and ongoing support to help clients obtain and maintain meaningful employment. Research is needed to evaluate efficacy of technology-based approaches for promoting client employment outcomes, to identify provider and organization barriers to using technology for IPS delivery, and to determine effective strategies for implementing technology with IPS in different settings and with diverse client audiences.