A New Paradigm for Illness Monitoring and Relapse Prevention in Schizophrenia
| Status: | Recruiting |
|---|---|
| Conditions: | Schizophrenia, Psychiatric |
| Therapuetic Areas: | Psychiatry / Psychology |
| Healthy: | No |
| Age Range: | 18 - Any |
| Updated: | 4/21/2016 |
| Start Date: | March 2015 |
| End Date: | July 2017 |
| Contact: | Rachel M Brian, MPH |
| Email: | rachel.m.brian@dartmouth.edu |
| Phone: | 603-448-0263 |
The study is a three year research project whose aims are to design and test a
state-of-the-science smartphone Ecological Momentary Assessment (EMA) and sensor
technologies to provide mobile monitoring of schizophrenia to detect early signs of relapse.
The proposed system will be used to continuously capture multidimensional behavior as it
occurs in real-time and in real-world environments, detect individual early warning signs,
and trigger targeted interventions that may mitigate the severity of relapses or prevent
their recurrence altogether. Mobile sensors include paralinguistic aspects of speech,
physical activity and location, and sleep. The first hypothesis is that mobile sensing and
EMA techniques will capture early behavioral warning signs. The second hypothesis is that
machine learning, coupled with user input, will enable development of individualized
predictive "risk signature" models. The third hypothesis is that automated system patient
and provider functions will help prevent psychotic relapses.
state-of-the-science smartphone Ecological Momentary Assessment (EMA) and sensor
technologies to provide mobile monitoring of schizophrenia to detect early signs of relapse.
The proposed system will be used to continuously capture multidimensional behavior as it
occurs in real-time and in real-world environments, detect individual early warning signs,
and trigger targeted interventions that may mitigate the severity of relapses or prevent
their recurrence altogether. Mobile sensors include paralinguistic aspects of speech,
physical activity and location, and sleep. The first hypothesis is that mobile sensing and
EMA techniques will capture early behavioral warning signs. The second hypothesis is that
machine learning, coupled with user input, will enable development of individualized
predictive "risk signature" models. The third hypothesis is that automated system patient
and provider functions will help prevent psychotic relapses.
Inclusion Criteria:
- DSM-IV criteria for schizophrenia or schizoaffective disorder based on the Structured
Clinical Interview for DSM-IV-TR Axis I Disorders (SCID)
- 18 years or older
- An inpatient psychiatric hospitalization, daytime psychiatric hospitalization,
outpatient crisis management, or short-term psychiatric hospital emergency room
within 12 months before study entry.
Exclusion Criteria:
- Hearing, vision, or motor impairment that make it impossible to operate a smartphone
(determined using a demonstration smartphone for screening)
- 6th grade reading level (determined by Wide Range Achievement Test- 4th Edition)
We found this trial at
1
site
Glen Oaks, New York 11004
Principal Investigator: Dror Ben-Zeev, PhD
Phone: 718-470-8141
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