Technology-Assisted Assessment of Post-Hospital Adherence in Schizophrenia Chronic mental illnesses, including schizophrenia and schizoaffective disorder, are significant public health concerns because they often are severe, debilitating, and put patients at risk for negative outcomes such as suicide. Psychotic disorders, in particular, are associated with disproportionately high societal costs related to treatment, disability, and readmission, which are often the result of treatment no adherence. Medications and illness self-management interventions have been shown to be effective in the treatment of psychotic disorders, but patients'adherence to medications frequently is poor and patients are at high risk for premature termination from behavioral treatment programs, putting them at risk for relapse and hospitalization. The month after inpatient hospitalization discharge is a particularly critical period in which patients are at highest risk for no adherence to discharge plans and subsequent poor outcomes. The goal of this R21 Exploratory/Developmental Research application is to establish a feasible and acceptable mobile device-based research protocol to identify ecologically-valid, contextual predictors of treatment adherence that will be potentially modifiable in a future technology-assisted self- management intervention. To address the current gap in knowledge, we will use Ecological Momentary Assessment (EMA), which is a dynamic assessment procedure that has shown promise in preliminary research for understanding health behaviors and illness management in patients with chronic illnesses. EMA assesses variables in natural settings in real-time through the use of brief measures conducted via mobile electronic devices (e.g., smartphones), making data less subject to retrospective biases (e.g., memory bias) and more sensitive to fluctuating environmental factors (e.g., reactivity to cues). EMA will be used to examine adherence barriers in 60 hospitalized patients with schizophrenia or schizoaffective disorder beginning at discharge and continuing 4-weeks post-discharge to identify predictors of medication and behavioral no adherence to post-discharge plans (e.g., appointment attendance). We will assess the feasibility and acceptability of the EMA procedures in this population. In addition, the four-week post- discharge EMA phase will assess the association of the predictor variables with future medication and behavioral adherence and be compared to behavioral and retrospective data collected over follow-up (one, three, and six months) to determine incremental validity. If successful, we believe that the current study will lead to applications for improving self-management in psychosis and other chronic illnesses as well.

Public Health Relevance

Chronic mental disorders, such as schizophrenia, are significant public health concerns because they are severe, debilitating conditions that place patients at risk for a host of negative outcomes often due to treatment no adherence following hospital discharge. The current project is a necessary first step to identify ecologically- valid ad potentially modifiable contextual variables that predict treatment adherence in this clinical population following hospital discharge using innovative new technologies. We envision that the adherence barriers identified will be used to inform future technology-assisted self-management programs that can be implemented during the critical transitional period from inpatient to outpatient treatment to decrease negative outcomes (e.g., hospitalization, relapse) in patients with schizophrenia, as well as other chronic medical illnesses.

National Institute of Health (NIH)
Exploratory/Developmental Grants (R21)
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Social Psychology, Personality and Interpersonal Processes Study Section (SPIP)
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Azrin, Susan
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Butler Hospital (Providence, RI)
United States
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