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Using technology to improve longitudinal studies: self-reporting with ChronoRecord in bipolar disorder
Michael Bauer1, Paul Grof, Laszlo Gyulai
1Department of Psychiatry and Psychotherapy, Charité-University Medicine Berlin, Berlin, Germany. michael.bauer@charite.de
Bipolar Disorders
|March 6, 2004
Summary
This study validates ChronoRecord, a computer system for bipolar disorder patients to self-report mood daily. The system showed high patient acceptance and reduced missing data, correlating well with clinician ratings.
Area of Science:
- Psychiatry
- Digital Health
- Clinical Research
Background:
- Longitudinal studies are crucial for understanding bipolar disorder but face challenges with cost and data completeness.
- Patient self-reporting offers a valuable data source, but its accuracy and acceptance in technology-based systems require validation.
Purpose of the Study:
- To validate patient self-reported mood ratings from the ChronoRecord system against clinician assessments (HAMD, YMRS).
- To assess patient acceptance of the ChronoRecord computer-based system for daily data collection in bipolar disorder.
- To evaluate the impact of automated data collection on data completeness and accuracy.
Main Methods:
- Outpatients with bipolar disorder used ChronoRecord software for 3 months to log daily mood, medications, sleep, and life events.
- Data collected via ChronoRecord was compared with clinician ratings using the Hamilton Depression Rating Scale (HAMD) and Young Mania Rating Scale (YMRS).
Main Results:
- 83% of patients returned substantial data (mean 114.7 days), with only 6.1% missing mood data.
- Patient self-reported mood ratings demonstrated strong concurrent validity with clinician HAMD ratings (r=-0.683, p<0.001).
Conclusions:
- The ChronoRecord system is a valid tool for collecting self-reported mood data in bipolar disorder patients.
- Patients exhibited high acceptance of the technology, suggesting its feasibility for long-term studies.
- Automated data collection via ChronoRecord can significantly reduce missing data and entry errors, facilitating ongoing patient and researcher feedback.