Bipolar disorder (BP) is commonly researched in digital settings. As a result, standardized digital tools are needed to measure mood. We sought to validate a new survey that is brief, validated in digital form, and able to separately measure manic and depressive severity.
We introduce a 6-item digital survey, called digiBP, for measuring mood in BP. It has 3 depressive items (depressed mood, fidgeting, fatigue), 2 manic items (increased energy, rapid speech), and 1 mixed item (irritability); and recovers two scores (m and d) to measure manic and depressive severity. In a secondary analysis of individuals with BP who monitored their symptoms over 6 weeks (n = 43), we perform a series of analyses to validate the digiBP survey internally, externally, and as a longitudinal measure.
We first verify a conceptual model for the survey in which items load onto two factors (“manic” and “depressive”). We then show weekly averages of m and d scores from digiBP can explain significant variation in weekly scores from the YMRS (R = 0.47) and SIGH-D (R = 0.58). Lastly, we examine the utility of the survey as a longitudinal measure by predicting an individual’s future m and d scores from their past m and d scores.
While further validation is warranted in larger, diverse populations, these validation analyses should encourage researchers to consider digiBP for their next digital study of BP.