Convenient, automated, objective measure of depression

DESCRIPTION (provided by applicant): This research is aimed at demonstrating the feasibility of obtaining measures of depression severity using interactive voice response (IVR) technology that are equivalent or superior to clinician-administered Hamilton Depression Rating Scale (HAMD) interviews. Physicians will refer forty patients beginning treatment for a new episode of depression to the naturalistic, open-label study. Clinician HAMDs will be obtained at baseline and Weeks 2, 4 and 6. Beginning at baseline, subjects will call an IVR system daily to provide severity ratings of eight symptoms frequently associated with depression and a rating of clinical change since their last call. Beginning at baseline, and weekly thereafter, subjects will complete a validated IVR version of the HAMD, an IVR implementation of the Quick Inventory of Depressive Symptomatology (QIDS), provide a rating of clinical change since baseline enhanced by personalized recording of their experiences at baseline, and will provide speech samples elicited by a standardized protocol for subsequent acoustical analysis by Dr. Peter Snyder. Principal axis factoring will be used to define a statistically constrained, theoretically interpretable multivariate factor of depressive severity. Derived factor scores will be analyzed for between- and within-subjects variance related to clinician HAMD assessments and compared to depression metrics derived from the Daily Questions on Depression (DQD), the Memory Enhanced Retrospective Evaluation of Treatment (MERET), the IVR HAMD and QIDS, and speech characteristics extracted from the speech samples collected by IVR and analyzed in Dr. Snyder's laboratory. Improving the quality of assessment instruments used in depression treatment research might reverse the currently increasing rates of placebo response in randomized clinical trials, reduce the number of failed trials, provide more accurate measurement of therapeutic onset, and provide a more level playing field for comparing efficacy between compounds. Ultimately such efforts may decrease the drug development cycle at lower developmental costs.