A Model for Advancing Scale-Up of Complex Interventions for Vulnerable Populations: the ALACRITY Center for Health and Longevity in Mental Illness

McGinty, Emma E.; Murphy, Karly A.; Dalcin, Arlene; Stuart, Elizabeth A.; Wang, Nae Yuh; Dickerson, Faith; Gudzune, Kim; Jerome, Gerald J.; Thompson, David A.; Cullen, Bernadette; Gennusa, Joseph V.; Kilbourne, Amy M.; Daumit, Gail L.

doi:10.1007/s11606-020-06137-x

Cited by 7 publications

(4 citation statements)

References 57 publications

(50 reference statements)

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“…In the second example, we illustrate how a conceptual agent-based approach can be used to provide guidance for an implementation intervention that includes training and organizational strategies. This example is based in part on an ongoing pilot project focused on scaling up evidence-based tobacco smoking cessation practices in community mental health clinics in Maryland (McGinty et al, 2021).…”

Section: Resultsmentioning

confidence: 99%

Agent-based modeling for implementation research: An application to tobacco smoking cessation for persons with serious mental illness

Huang

Chang

Stuart

et al. 2021

Implementation Research and Practice

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Background: Implementation researchers have sought ways to use simulations to support the core components of implementation, which typically include assessing the need for change, designing implementation strategies, executing the strategies, and evaluating outcomes. The goal of this article is to explain how agent-based modeling could fulfill this role. Methods: We describe agent-based modeling with respect to other simulation methods that have been used in implementation science, using non-technical language that is broadly accessible. We then provide a stepwise procedure for developing agent-based models of implementation processes. We use, as a case study to illustrate the procedure, the implementation of evidence-based smoking cessation practices for persons with serious mental illness (SMI) in community mental health clinics. Results: For our case study, we present descriptions of the motivating research questions, specific models used to answer these questions, and a summary of the insights that can be obtained from the models. In the first example, we use a simple form of agent-based modeling to simulate the observed smoking behaviors of persons with SMI in a recently completed trial (IDEAL, Comprehensive Cardiovascular Risk Reduction Trial in Persons with SMI). In the second example, we illustrate how a more complex agent-based approach that includes interactions between patients, providers, and site administrators can be used to provide guidance for an implementation intervention that includes training and organizational strategies. This example is based in part on an ongoing project focused on scaling up evidence-based tobacco smoking cessation practices in community mental health clinics in Maryland. Conclusion: In this article, we explain how agent-based models can be used to address implementation science research questions and provide a procedure for setting up simulation models. Through our examples, we show how what-if scenarios can be examined in the implementation process, which are particularly useful in implementation frameworks with adaptive components. Plain Language Summary: The goal of this paper is to explain how agent-based modeling could be used as a supplementary tool to support the components of complex implementation processes. Such models have not yet been widely used in implementation science, partly because they are not straightforward to develop. To promote the use of agent-based modeling we provide a stepwise procedure using non-technical language and emphasizing the relationships between the model and implementation processes. We used two detailed examples to demonstrate our proposed approach. In the first example, we simulate the observed smoking behaviors of persons with serious mental illness in a recently completed trial (IDEAL, Comprehensive Cardiovascular Risk Reduction Trial in Persons with Serious Mental Illness). In the second example, we illustrate how agent-based models that include interactions between patients, providers and site administrators can be used to provide guidance for an implementation intervention that includes training and organizational strategies. This example is based in part on an ongoing project focused on scaling up evidence-based tobacco smoking cessation practices in community mental health clinics in Maryland. For this example, we show how the visual user interface of an agent-based model can be in the form of a dashboard with levers for simulating what-if scenarios that can be used to guide implementation decisions. In summary, this paper shows how agent-based models can provide insights into the processes in complex interventions, and guide implementation decisions for improving delivery of evidence-based practices in community mental health clinics.

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Section: Resultsmentioning

confidence: 99%

Agent-based modeling for implementation research: An application to tobacco smoking cessation for persons with serious mental illness

Huang

Chang

Stuart

et al. 2021

Implementation Research and Practice

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“…The Johns Hopkins ALACRITY Center for Health and Longevity in Mental Illness is a research-to-practice translation center funded by the National Institute of Mental Health that aims to reduce premature mortality from CVD in people with SMI [ 33 , 34 ]. There is an untapped opportunity to develop scalable and generalizable solutions that integrate physical health care into mental health service sectors, which have not historically provided such services.…”

Section: Methodsmentioning

confidence: 99%

Designing Practical Motivational Interviewing Training for Mental Health Practitioners Implementing Behavioral Lifestyle Interventions: Protocol for 3 Pilot Intervention Studies

Dalcin¹,

Yuan²,

Jerome³

et al. 2023

JMIR Res Protoc

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Background Motivational interviewing (MI) is an evidence-based, patient-centered communication method shown to be effective in helping persons with serious mental illness (SMI) to improve health behaviors. In clinical trials where study staff conducted lifestyle interventions incorporating an MI approach, cardiovascular disease (CVD) risk profiles of participants with SMI showed improvement. Given the disproportionate burden of CVD in this population, practitioners who provide somatic and mental health care to persons with SMI are ideally positioned to deliver patient-centered CVD risk reduction interventions. However, the time for MI training (traditionally 16-24 hours), follow-up feedback, and the coaching required to develop and maintain patient-centered skills are significant barriers to incorporating MI when scaling up these evidence-based practices. Objective We describe the design and development of the following 2 scalable MI training approaches for community mental health practitioners: real-time brief workshops and follow-up asynchronous avatar training. These approaches are being used in 3 different pilot implementation research projects that address weight loss, smoking cessation, and CVD risk reduction in people with SMI who are a part of ALACRITY Center, a research-to-practice translation center funded by the National Institute of Mental Health. Methods Clinicians and staff in community mental health clinics across Maryland were trained to deliver 3 distinct evidence-based physical health lifestyle interventions using an MI approach to persons with SMI. The real-time brief MI workshop training for ACHIEVE-D weight loss coaches was 4 hours; IMPACT smoking cessation counselors received 2-hour workshops and prescribers received 1-hour workshops; and RHYTHM CVD risk reduction program staff received 4 hours of MI. All workshop trainings occurred over videoconference. The asynchronous avatar training includes 1 common didactic instructional module for the 3 projects and 1 conversation simulation unique to each study’s target behavior. Avatar training is accessible on a commercial website. We plan to assess practitioners’ attitudes and beliefs about MI and evaluate the impact of the 2 MI training approaches on their MI skills 3, 6, and 12 months after training using the MI Treatment Integrity 4.2.1 coding tool and the data generated by the avatar-automated scoring system. Results The ALACRITY Center was funded in August 2018. We have implemented the MI training for 126 practitioners who are currently delivering the 3 implementation projects. We expect the studies to be complete in May 2023. Conclusions This study will contribute to knowledge about the effect of brief real-time training augmented with avatar skills practice on clinician MI skills. If MI Treatment Integrity scoring shows it to be effective, brief videoconference trainings supplemented with avatar skills practice could be used to train busy community mental health practitioners to use an MI approach when implementing physical health interventions. International Registered Report Identifier (IRRID) DERR1-10.2196/44830

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“…Some programs also report a lack of capacity for population health management, a staple nowadays in chronic disease care and monitoring [20,71]. Greater investment in information technology, workforce development, and financing is required [72].…”

Section: Bring Physical Health Services To Mental Health Programsmentioning

confidence: 99%

Establishing a Care Continuum for Cardiometabolic Conditions for Patients with Serious Mental Illness

Murphy

Daumit²

2023

Curr Cardiol Rep

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Purpose of Review Addressing cardiometabolic risk factors in persons with serious mental illness requires early screening and proactive medical management in both medical and mental health settings. Recent Findings Cardiovascular disease remains the leading cause of death for persons with serious mental illness (SMI), such as schizophrenia or bipolar disorder, much of which is driven by a high prevalence of metabolic syndrome, diabetes, and tobacco use. We summarize barriers and recent approaches to screening and treatment for metabolic cardiovascular risk factors within physical health and specialty mental health settings. Summary Incorporating system-based and provider-level support within physical health and psychiatric clinical settings should contribute to improvement for screening, diagnosis, and treatment for cardiometabolic conditions for patients with SMI. Targeted education for clinicians and leveraging multi-disciplinary teams are important first steps to recognize and treat populations with SMI at risk of CVD.

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A Model for Advancing Scale-Up of Complex Interventions for Vulnerable Populations: the ALACRITY Center for Health and Longevity in Mental Illness

Cited by 7 publications

References 57 publications

Agent-based modeling for implementation research: An application to tobacco smoking cessation for persons with serious mental illness

Agent-based modeling for implementation research: An application to tobacco smoking cessation for persons with serious mental illness

Designing Practical Motivational Interviewing Training for Mental Health Practitioners Implementing Behavioral Lifestyle Interventions: Protocol for 3 Pilot Intervention Studies

Establishing a Care Continuum for Cardiometabolic Conditions for Patients with Serious Mental Illness

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