The Hamilton rating scale for depression (HRSD) is considered the gold standard for the assessment of major depressive disorder. Nevertheless, it has drawbacks such as reliance on retrospective reports and a relatively long administration time. Using a combination of an experience sampling method with mobile health technology, the present study aimed at developing and conducting initial validation of HRSD-D, the first digital image-based assessment of the HRSD. Fifty-three well-trained HRSD interviewers selected the most representative image for each item from an initial sample of images. Based on their responses, we developed the prototype of HRSD-D in two versions: trait-like (HRSD-DT) and state-like (HRSD-DS). HRSD-DT collects one-time reports on general tendencies to experience depressive symptoms; HRSD-DS collects daily reports on the experience of symptoms. Using a total of 1933 responses collected in a preclinical sample (N = 86), we evaluated the validity and feasibility of HRSD-D, based on participant reports of HRSD-DT at baseline, and 28 consecutive daily reports of HRSD-DS, using smartphone devices. HRSD-D showed good convergent validity with respect to the original HRSD, as evident in high correlations between HRSD-DS and HRSD (up to Bstd = 0.80). Our combined qualitative and quantitative analyses indicate that HRSD-D captured both dynamic and stable features of symptomatology, in a user-friendly monitoring process. HRSD-D is a promising tool for the assessment of trait and state depression and contributes to the use of mobile technologies in mental health research and practice.
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