A digital therapeutic (CT-152) as adjunct to antidepressant medication: A phase 3 randomized controlled trial (the Mirai study).

Objective: Digital therapeutics (DTx) are a new treatment class for major depressive disorder (MDD). This study evaluated the effectiveness and safety of a novel DTx, CT-152 (Rejoyn™), for MDD adjunctive to antidepressant medication monotherapy.

Methods: Adults aged 22-64 years with MDD having an inadequate response to current antidepressant medication monotherapy were enrolled in a phase 3 multicenter, randomized, blinded, sham-controlled, remote trial with a 6-week intervention and 4-week extension.

The evolution of psychotherapy: from Freud to prescription digital therapeutics.

The evolution of psychotherapeutic treatments from Freud to digitally administered evidence-based treatments reflects a history of progressive advance. This history is characterized by identification of problems with the current state of the art, followed by solutions inspired and supported by advances in basic science and technology leading to subsequent recognition of other limitations revealed by the new advance. The common thread running through this process is (a) increasing specificity of the psychotherapeutic interventions, (b) increasing evidence of efficacy and safety, (c) increasing integrity and reliability in the delivery of the intervention, (d) increased equality of access, and (e) recognition of the need for regulation to provide protection for the public from unsafe or ineffective products.

Designing mHealth Apps to Incorporate Evidence-Based Techniques for Prolonging User Engagement.

Maintaining user engagement with mobile health (mHealth) apps can be a challenge. Previously, we developed a conceptual model to optimize patient engagement in mHealth apps by incorporating multiple evidence-based methods, including increasing health literacy, enhancing technical competence, and improving feelings about participation in clinical trials. This viewpoint aims to report on a series of exploratory mini-experiments demonstrating the feasibility of testing our previously published engagement conceptual model.

A New Approach to Enhancing Engagement in eHealth Apps.

This viewpoint presents a 3-phase conceptual model of the process of user engagement with eHealth apps. We also describe how knowledge gleaned from psychosocial, behavioral, and cognitive science can be incorporated into this model to enhance user engagement with an eHealth app in each phase of the engagement process.

Use of technology for care coordination initiatives for patients with mental health issues: a systematic literature review.

This systematic literature review investigates the use of technology for the coordination and management of mental health care with an emphasis on outcomes. Searches of MEDLINE/PubMed, Scopus, and EMBASE were conducted between January 1, 2003, and January 4, 2018, to identify articles that assessed patient outcomes associated with care coordination, evaluated technology to improve care, or discussed management of mental health care using technology. A total of 21 articles were included in a qualitative review based on the recommendations set forth by the PRISMA statement.

Expert Consensus Survey on Digital Health Tools for Patients With Serious Mental Illness: Optimizing for User Characteristics and User Support.

Background: Digital technology is increasingly being used to enhance health care in various areas of medicine. In the area of serious mental illness, it is important to understand the special characteristics of target users that may influence motivation and competence to use digital health tools, as well as the resources and training necessary for these patients to facilitate the use of this technology.

Objective: The aim of this study was to conduct a quantitative expert consensus survey to identify key characteristics of target users (patients and health care professionals), barriers and facilitators for appropriate use, and resources needed to optimize the use of digital health tools in patients with serious mental illness.

Predicting Future High-Cost Schizophrenia Patients Using High-Dimensional Administrative Data.

Background: The burden of serious and persistent mental illness such as schizophrenia is substantial and requires health-care organizations to have adequate risk adjustment models to effectively allocate their resources to managing patients who are at the greatest risk. Currently available models underestimate health-care costs for those with mental or behavioral health conditions.

Objectives: The study aimed to develop and evaluate predictive models for identification of future high-cost schizophrenia patients using advanced supervised machine learning methods.

Why do psychiatric patients stop antipsychotic medication? A systematic review of reasons for nonadherence to medication in patients with serious mental illness.

Background: Antipsychotic medication reduces the severity of serious mental illness (SMI) and improves patient outcomes only when medicines were taken as prescribed. Nonadherence to the treatment of SMI increases the risk of relapse and hospitalization and reduces the quality of life. It is necessary to understand the factors influencing nonadherence to medication in order to identify appropriate interventions.

Integrated multisystem analysis in a mental health and criminal justice ecosystem.

Background: Patients with a serious mental illness often receive care that is fragmented due to reduced availability of or access to resources, and inadequate, discontinuous, and uncoordinated care across health, social services, and criminal justice organizations. This article describes the creation of a multisystem analysis that derives insights from an integrated dataset including patient access to case management services, medical services, and interactions with the criminal justice system.

Methods: Data were combined from electronic systems within a US mental health ecosystem that included mental health and substance abuse services, as well as data from the criminal justice system.

Usability of a novel digital medicine system in adults with schizophrenia treated with sensor-embedded tablets of aripiprazole.

Objective: Digital medicine system (DMS) is a novel drug-device combination that objectively measures and reports medication ingestion. The DMS consists of medication embedded with an ingestible sensor (digital medicine), a wearable sensor, and software applications. This study evaluated usability of the DMS in adults with schizophrenia rated by both patients and their health care providers (HCPs) during 8-week treatment with prescribed doses of digital aripiprazole.

Optimization of a Digital Medicine System in Psychiatry.

Background: Nonadherence to medication compromises the effectiveness of psychiatric treatments in patients with serious mental illness (SMI). A newly developed digital medicine system (DMS) offers an opportunity to objectively assess and report patient medication adherence. DMS includes a wearable sensor that receives a data signal from a medication tablet with an embedded ingestible sensor after ingestion of the medication and transmits that data to the patient's mobile device to display health care information for the patient and treatment team.

Developing a Digital Medicine System in Psychiatry: Ingestion Detection Rate and Latency Period.

Background: A digital medicine system (DMS) has been developed to measure and report adherence to an atypical antipsychotic, aripiprazole, in psychiatric patients. The DMS consists of 3 components: ingestible sensor embedded in a medication tablet, wearable sensor, and secure mobile and cloud-based applications. An umbrella study protocol was designed to rapidly assess the technical performance and safety of the DMS in multiple substudies to guide the technology development.

Multi-Agent-Based Simulation of a Complex Ecosystem of Mental Health Care.

This paper discusses the creation of an Agent-Based Simulation that modeled the introduction of care coordination capabilities into a complex system of care for patients with Serious and Persistent Mental Illness. The model describes the engagement between patients and the medical, social and criminal justice services they interact with in a complex ecosystem of care. We outline the challenges involved in developing the model, including process mapping and the collection and synthesis of data to support parametric estimates, and describe the controls built into the model to support analysis of potential changes to the system.

Racial differences in antipsychotic use: Claims database analysis of Medicaid-insured patients with schizophrenia.

Background: Database analyses have indicated that medical treatment for schizophrenia varies among racial groups. This study assessed antipsychotic use and healthcare utilization across races in Medicaid-insured patients with schizophrenia.

Methods: A Medicaid database of inpatient/outpatient medical claims and outpatient prescription claims for more than 28 million enrollees in 11 geographically diverse states was analyzed.

Reduction in inpatient resource utilization and costs associated with long-acting injectable antipsychotics across different age groups of Medicaid-insured schizophrenia patients.

Objective: Evaluate utilization of inpatient healthcare resources and associated costs after 12 months of treatment using long-acting injectable (LAI) antipsychotic medications among a large sample of Medicaid-insured patients categorized by different age groups.

Method: Adult patients with schizophrenia were identified from the Thomson Reuters MarketScan Research database (1/1/2006-12/31/2010) before initiation of treatment using LAI antipsychotic agents. Utilization of inpatient healthcare resources and associated direct medical costs were compared for 12-month baseline and 12-month follow-up periods.

Integrated multisystem analysis in a mental health and criminal justice ecosystem.

Patients with a serious mental illness often receive care that is fragmented due to reduced availability of or access to resources, and inadequate, discontinuous, and uncoordinated care across health, social services, and criminal justice organizations. These gaps in care may lead to increased mental health disease burden and relapse, as well as repeated incarcerations. Further, the complex health, social service, and criminal justice ecosystem within which the patient may be embedded makes it difficult to examine the role of modifiable risk factors and delivered services on patient outcomes, particularly given that agencies often maintain isolated sets of relevant data.

Mental health needs of returning veterans.

As more service members return from war zones around the world, clinicians must be prepared to treat them. Veterans with PTSD and other mental disorders must overcome the stigma associated with seeking treatment, and clinicians must equip these patients and their family members to deal with challenging symptoms. Clinicians should learn about military culture and jargon to better understand these patients and should become familiar with veteran resources to direct veterans and their families to the appropriate services.

The impact of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) on prescribing practices: an analysis of data from a large midwestern state.

Background: The Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) was a series of effectiveness trials. The results of these trials began publication in September 2005. Among other findings, these studies were interpreted to suggest that (1) second-generation antipsychotics might have fewer advantages over first-generation antipsychotics than had been generally thought; (2) among the agents assessed, olanzapine had the best efficacy outcome; and (3) after treatment failure with a second-generation antipsychotic, the most efficacious second-line medication is clozapine.

Strategies for addressing adherence problems in patients with serious and persistent mental illness: recommendations from the expert consensus guidelines.

Poor adherence to medication can have devastating consequences for patients with serious mental illness. The literature review and recommendations in this article are reprinted from The Expert Consensus Guideline Series: Adherence Problems in Patients with Serious and Persistent Mental Illness, published in 2009. The expert consensus survey (39 questions, 521 options) on adherence problems in schizophrenia and bipolar disorder was completed by 41 experts in 2008.

A hypothesized role for dendritic remodeling in the etiology of mood and anxiety disorders.

An elegant theory that links hippocampal neurogenesis to mood and anxiety disorders and to the mechanism of action of antidepressant drugs has gained widespread attention. However, depression and anxiety disorders involve multiple areas of the brain, such as the amygdala and prefrontal cortex, where neurogenesis does not appear to occur in the adult mammalian brain. A complementary theory is proposed here in which neurogenesis is seen as an epiphenomenon of a more widespread alteration in dendritic length and spine number.