ClinicalTrials.Veeva

Menu

Augmenting Hospitalization for Serious Mental Illness: Cognitive Bias Modification

Mass General Brigham logo

Mass General Brigham

Status

Completed

Conditions

Psychiatric Disorder
Bipolar Disorder
Depression
Anxiety Disorders

Treatments

Behavioral: I-Change
Behavioral: Symptom Tracking

Study type

Interventional

Funder types

Other
NIH

Identifiers

NCT03509181
R34MH113600 (U.S. NIH Grant/Contract)
MH113600

Details and patient eligibility

About

Approximately 4.1% of the adult US population meets the criteria for SMI, a mental disorder associated with significant functional impairment. Even when effective, pharmacologic and psychological treatments often leave individuals with SMI with residual symptoms, impairment, and at risk for re-hospitalization and suicide. The month following hospitalization is a particularly risky time; thus, augmentation treatments that can speed up improvement during brief hospital stays, as well as provide a bridge to outpatient care are urgently needed. Thus, the investigators propose to develop an augmentation to psychiatric hospital care (called "I-Change") that can be continued at home following discharge. I-Change targets interpretation bias, the tendency to resolve ambiguous situations negatively. Interpretation bias is a well-established cognitive vulnerability for psychopathology and is associated with poor emotion regulation, rumination, symptom severity, and suicidal ideation. For example, in a psychiatric hospital sample, interpretation bias upon admission accounted for 28% of the variance in treatment response, and predicted suicidal ideation at discharge, controlling for ideation at admission. Although some existing treatments target this mechanism, most notably Cognitive Behavioral Therapy (CBT), they require individuals to be able to recognize their automatic interpretations and use complex techniques to reappraise them. Individuals with SMI who are experiencing symptoms acute enough to require hospitalization are often treatment refractory and may experience particular difficulty applying these techniques. It is therefore critical to more efficiently and effectively engage this target. Over the past 14 years, the Principal Investigator has developed and validated a training task that utilizes repetition and feedback to reinforce a healthier interpretive style. The computer-delivered version of the task was acceptable to an SMI population and led to better treatment response than a placebo task in patients who exhibited interpretation bias at baseline. The investigators seek to develop this task into a personalized smart-phone delivered intervention. The investigators will harness smart-phone technology to enhance skill acquisition and generalization by improving user engagement and prompting participants to complete a session at set times to ensure adequate dosage and spacing of sessions. The investigators will conduct an open trial (n = 16) and a randomized controlled trial (n = 64) to confirm target engagement (improvement in interpretation bias), evaluate the feasibility and acceptability of delivering I-Change during and following discharge from a partial hospital, and examine clinical outcomes (global improvement, functioning) related to changes in interpretation. I-Change is expected to shift interpretation bias, be acceptable to patients with SMI, and lead to greater global improvement compared to a Symptom Tracking control. Results will support a fully-powered effectiveness trial.

Full description

Treatment in acute psychiatric hospital settings is brief, and many individuals continue to experience residual symptoms and impairment upon discharge. The months following discharge from hospitalization are particularly risky, as individuals transition from a highly structured and supportive environment to home, acute stressors, and uncertain aftercare. Currently, there are few interventions available to accelerate improvement during brief hospital stays, or to provide a bridge to outpatient care. Thus, there is an urgent need to develop augmentations to hospital care that both more efficiently reduce symptoms during the acute hospital stay and provide continuation of care during the transition to home. Such new interventions are critical to reduce the risk of relapse, re-hospitalization, and suicide in individuals with Serious Mental Illness (SMI).

The long-term goal of this study is to develop effective and scalable interventions that target key mechanisms in psychopathology and are easily implemented in real world settings. The overall objective of this study is to develop a low-intensity augmentation to psychiatric partial hospital care that can be continued during the transition to home. "I-Change", a personalized, smart-phone delivered cognitive bias modification (CBM) treatment, is expected to hasten improvement in pathological cognitive processes and clinical outcomes during hospitalization and following discharge compared to a control. This hypothesis is based on the principal investigator's (PI) 14 years of research developing and testing CBM treatments, including a pilot study of 65 patients attending a partial hospital program that showed excellent feasibility, acceptability, large effects on cognitive bias, and moderate effects on clinical outcomes compared to a placebo control.

I-Change will target the maladaptive interpretative style that maintains emotional disorders. The way in which individuals automatically resolve the countless ambiguous situations encountered each day has a large impact on their affect and behavior. Interpretation bias, the tendency to resolve such ambiguity negatively, is a crucial therapeutic target because it is associated with poor emotion regulation, rumination, symptom severity, suicidal ideation, and treatment response. Although existing treatments target interpretation bias, most notably Cognitive Behavioral Therapy (CBT), they require individuals to recognize their automatic interpretations and use complex techniques to reappraise them. Individuals experiencing symptoms sufficiently acute to require hospital care often experience difficulty applying these techniques. In contrast, the PI validated a computerized training task that utilizes quick, repeated practice and feedback to more efficiently reinforce a healthier interpretive style. Ten studies demonstrate that the task engages interpretation bias and leads to improved clinical outcomes in individuals with mood and anxiety disorders, including a psychiatric hospital sample. The CBM task is highly acceptable and uniquely suited to acute psychiatric settings due to its low complexity and engaging qualities.

Specific Aim 1: Develop a smart-phone delivered intervention to augment hospital care.

This study will harness smart-phone technology to enhance the acquisition of a healthier interpretive style by personalizing the situations presented, prompting participants to complete sessions to ensure adequate dosage, and incorporating features to enhance adherence. Delivery via smart phone increases accessibility of the intervention by overcoming barriers (e.g., transportation, computer access) within the hospital and at home and allows better assessment of outcomes in "real time" via ecological momentary assessment. An Advisory Board of patients, hospital providers, experts in CBM and mobile health technology, and other stakeholders (i.e., directors of acute psychiatric clinics) will inform the development of I-Change.

Specific Aim 2: Obtain pilot data to support a fully-powered randomized controlled trial (RCT), including measures of (a) target engagement (improvement in interpretation bias), (b) feasibility and acceptability of I-Change and procedures for hospital and home delivery, and (c) global improvement and functioning Participants will complete I-Change daily while admitted to the partial hospital and three times per week at home during the 1-month following discharge. Consistent with a precision medicine and RDoC approach, participants will be selected based on baseline level of interpretation bias (not diagnosis). The investigators will first conduct an open trial of I-Change (n = 16) to inform refinements. The investigators will conduct a pilot RCT (n = 64) to obtain data to inform the design of a future trial. Participants will be randomly assigned to I-Change or a Symptom Tracking control and assessed at admission, discharge, 1-month and 3-months following discharge. Obtained data will be compared to a priori benchmarks of feasibility, acceptability, target engagement, and clinical improvement.

The final products of this study will be the I-Change app, RCT protocol, and pilot data to support a future confirmatory effectiveness trial. Achievement of these aims will result in a simple, scalable augmentation to psychiatric partial hospital care that can improve outcomes following hospital care.

Enrollment

68 patients

Sex

All

Ages

18+ years old

Volunteers

No Healthy Volunteers

Inclusion criteria

  • currently receiving partial hospital care at the study site
  • age ≥18
  • at least moderate symptom severity (PHQ-9 or GAD-7 score > 10)
  • signing a release of information for treatment providers
  • a minimal level of interpretation bias (<80% accuracy on the Word Sentence Association Paradigm)

Exclusion criteria

* current psychiatric symptoms that would prevent informed consent or understanding of research procedures (e.g., active symptoms of psychosis, mania)

Trial design

Primary purpose

Treatment

Allocation

Randomized

Interventional model

Parallel Assignment

Masking

Single Blind

68 participants in 2 patient groups

CBM
Experimental group
Description:
Cognitive Bias Modification for Interpretation delivered via smartphone
Treatment:
Behavioral: Symptom Tracking
Behavioral: I-Change
Symptom Tracking
Active Comparator group
Description:
Weekly symptom monitoring smartphone app with anxiety and depression symptom scores
Treatment:
Behavioral: Symptom Tracking

Trial documents
3

Trial contacts and locations

1

Loading...

Data sourced from clinicaltrials.gov

Clinical trials

Find clinical trialsTrials by location
© Copyright 2024 Veeva Systems