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We used ecological momentary assessment (EMA) to track symptoms during a clinical trial. Thirty-six participants with major depressive disorder (MDD) and MADRS scores ≥20 were enrolled in a nonrandomized 6-week open-label trial of commercially available antidepressants. Twice daily, a mobile device prompted participants to self-report the 6 items of the HamD6 sub-scale derived from the Hamilton rating scale for depression (HamD17). Morning EMA reports asked “how do you feel now” whereas evening reports gathered a full-day impression. Clinicians who were blinded to the EMA data rated the MADRS, HamD17 and HamD6 at screen, baseline and weeks 2,4, and 6. Hierarchical linear modeling (HLM) examined the course of the EMA assessments and convergence between EMA scores and clinician ratings. HLM analyses revealed strong correlations between AM and PM EMA derived HamD6 scores and revealed significant improvements over time. EMA improvements were significantly correlated with the clinician rated HamD6 scores at endpoint and predicted clinician rated HamD6 score changes from baseline to endpoint (p < .001). There was a large correlation between EMA and clinician derived HamD6 scores at each in-person assessment after baseline. Treatment response defined by EMA matched the clinician rated HamD6 treatment responses in 33 of 36 cases (91.7%). EMA derived symptom scores appear to be efficient and valid measures to track daily symptomatic change in clinical trials and may provide more accurate measures of symptom severity than the episodic “snapshots” that are currently used as clinical outcomes. These findings support further investigation of EMA for assessment in clinical trials.
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