Actigraphic Predictors of Depressed Mood in a Cohort of Non-Psychiatric Adults

• Published in: Australian and New Zealand journal of psychiatry 1999-08, Vol.33 (4), p.553-558
• Main Authors: Mendlowicz, Mauro V., Jean-Louis, Girardin, von Gizycki, Hans, Zizi, Ferdinand, Nunes, João
• Format: Article
• Language: English
• Subjects: Activity level; Adolescent; Adult; Adult and adolescent clinical studies; Aged; Alzheimer Disease - diagnosis; Alzheimer Disease - psychology; Biological and medical sciences; Circadian Rhythm; Cross-Sectional Studies; Depressed; Depression; Depression - diagnosis; Depression - psychology; Depressive Disorder, Major - diagnosis; Depressive Disorder, Major - psychology; Female; Humans; Male; Medical sciences; Mental Status Schedule; Middle Aged; Monitoring, Physiologic - instrumentation; Mood disorders; Moods; Motor Activity; Polysomnography - instrumentation; Predictors; Psychiatric Status Rating Scales; Psychology. Psychoanalysis. Psychiatry; Psychopathology. Psychiatry; Regression Analysis; Signal Processing, Computer-Assisted - instrumentation; Sleep
• ISSN: 0004-8674; 1440-1614
• DOI: 10.1080/j.1440-1614.1999.00585.x

Objective: Depressed mood is one of the essential features for the diagnosis of major depression. Evidence from the three-site Epidemiologic Catchment Area study (EcA, Baltimore, Durham and Los Angeles) suggests a prevalence of 4.4% of depressive symptoms in the community. In this study, we examined whether depressed mood, as coded in the Alzheimer's Disease Assessment Scale, would be correlated with actigraphic-derived daytime activity and sleep/wake parameters in a non-psychiatric sample. Method: Consenting volunteers were monitored at home for 5 days with a wrist actigraph. On the last day of the recording, they were given a neuropsychological battery including the Alzheimer's Disease Assessment Scale. Results: Daytime activity level was the best predictor of depressed mood as indicated by a logistic regression analysis. The regression model further suggested that sleep onset latency, total time asleep, and time in bed were also significant predictors of depressed mood. Conclusion: This investigation demonstrates that daytime activity level could be used as an index of depressed mood even in a non-psychiatric sample. Further, the results support the notion that depression should be considered more as a continuum rather than as a set of rigid categories.