EMindLog: Self-measurement of anxiety and depression using mobile technology

Abstract

Background: Quantifying anxiety and depressive experiences permits individuals to calibrate where they are and monitor intervention-associated changes. eMindLog is a novel self-report measure for anxiety and depression that is grounded in psychology with an organizing structure based on neuroscience. Objective: Our aim was to explore the psychometric properties of eMindLog in a nonclinical sample of subjects. Methods: In a cross-sectional study of eMindLog, a convenience sample of 198 adults provided informed consent and completed eMindLog and the Hospital Anxiety and Depression Scale (HADS) as a reference. Brain systems (eg, negative and positive valence systems, cognitive systems) and their functional states that drive behavior are measured daily as emotions, thoughts, and behaviors. Associated symptoms, quality of life, and functioning are assessed weekly. eMindLog offers ease of use and expediency, using mobile technology across multiple platforms, with dashboard reporting of scores. It enhances precision by providing distinct, nonoverlapping description of terms, and accuracy through guidance for scoring severity. Results: eMindLog daily total score had a Cronbach alpha of .94. Pearson correlation coefficient for eMindLog indexes for anxiety and sadness/anhedonia were r=.66 (P