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Using technology to improve longitudinal studies: self‐reporting with ChronoRecord in bipolar disorder
Author(s) -
Bauer Michael,
Grof Paul,
Gyulai Laszlo,
Rasgon Natalie,
Glenn Tasha,
Whybrow Peter C
Publication year - 2004
Publication title -
bipolar disorders
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.285
H-Index - 129
eISSN - 1399-5618
pISSN - 1398-5647
DOI - 10.1046/j.1399-5618.2003.00085.x
Subject(s) - mood , young mania rating scale , bipolar disorder , mania , psychology , clinical psychology , rating scale , psychiatry , missing data , depression (economics) , hamd , developmental psychology , computer science , machine learning , economics , macroeconomics
Objectives: Longitudinal studies are an optimal approach to investigating the highly variable course and outcome associated with bipolar disorder, but are expensive and often have missing data. This study validates patient self‐reported mood ratings using a home computer‐based system (ChronoRecord) with clinician mood ratings on the Hamilton Depression Rating scale (HAMD) and Young Mania Rating scale (YMRS), and investigates the patient acceptance of the technology. Methods: After brief training, outpatients with bipolar disorder were given the software version of an established paper based self‐reporting form (ChronoSheet) to install on a home computer. Every day for 3 months, patients entered mood, medications, sleep, life events, and menstrual data. Weight was entered weekly. Results: Eighty of 96 (83%) patients returned 8662 days of data. The mean days of data returned was 114.7 ± 32.3 SD The mean percentage of days missing for mood data was 6.1% ± 9.3 SD, equivalent to missing 7.3 day of the 114.7 days. Self‐reported ratings were strongly correlated with clinician HAMD ratings (−0.683, p < 0.001). Conclusions: This study demonstrates concurrent validity between ChronoRecord and HAMD. Patients with bipolar disorder showed high acceptance of a computer‐based system for self‐reporting of daily data. Automation of data collection can reduce missing data and eliminate errors associated with data entry. This technology also enables on‐going feedback for both patient and researcher during a long‐term study.