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The use of Autocorrelation Analysis in the Longitudinal Study of Mood Patterns in Depressed Patients

Published online by Cambridge University Press:  29 January 2018

G. J. Huba ,
W. G. Lawlor ,
F. Stallone  and
R. R. Fieve
G. J. Huba
Affiliation:
New York State Psychiatric Institute. Psychology Department, Tale University, 2 Hillhouse Avenue, New Haven, Conn. 06520, U.S.A.
W. G. Lawlor
Affiliation:
Fordham University. Psychology Department, Fordham University, Bronx, N.T. 10458, U.S.A.
F. Stallone
Affiliation:
New York State Psychiatric Institute. Psychiatric Institute, 722 West 168 Street, New York, N.Y. 10032, U.S.A.
R. R. Fieve
Affiliation:
Dept. of Internal Medicine, New York State Psychiatric Institute, 722 West 168 Street, New York, N.Y. 10032, U.S.A.
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Summary

The statistical method of autocorrelation, commonly used in econometrics and engineering, was applied to the daily mood scores of ten depressive hospital in-patients. The analyses made possible the quantification of two aspects of the longitudinal course of individual patients' psychopathology, the degree of day-to-day stability and the degree of periodicity in mood. Quantification of the degree of day-to-day mood stability yielded wide variations between patients and suggested that patients might be usefully categorized in terms of this characteristic. Mood stability during periods of severe depression was found to be less pronounced than during periods of relatively moderate depression. Furthermore, the existence of ‘mini-cycles', cyclical fluctuations in mood of one to two weeks' duration occurring during the course of depressive episodes, was demonstrated in three cases.

Type
Research Article
Information
The British Journal of Psychiatry , Volume 128 , Issue 2 , February 1976 , pp. 146 - 155
Copyright
Copyright © Royal College of Psychiatrists, 1976 

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