Optimizing the Frequency of Ecological Momentary Assessments Using Signal Processing

Ecological Momentary Assessment (EMA) is increasingly recognized as a vital tool for tracking the fluctuating nature of mental states and symptoms in psychiatric research. However, determining the optimal sampling rate — that is, deciding how often participants should report their symptoms — remains a significant challenge. To address this issue, our study utilizes the Nyquist-Shannon Theorem from signal processing, which establishes that any sampling rate more than twice the highest frequency component of a signal is adequate. In our study, we applied this insight to analyze two EMA datasets on depressive symptoms with a combined 35,452 data points: dataset 1 from the WARN-D study features daily 7-point Likert measurements of the two core depression symptoms from 368 participants over 85 days, dataset 2 includes four daily 0-100 visual analogue scale measurements of the same core depression symptoms from 39 participants over a minimum of 30 days. Our analysis of both datasets suggests that the most effective sampling strategy involves measurements at least every other week. We find that measurements at higher frequencies provide valuable and consistent information across both datasets, with significant peaks at weekly and daily intervals. Furthermore, a robustness analysis using a broader set of symptoms aligning with the DSM-5 criteria for depression in dataset 2 leads to similar results. This suggests that the ideal frequency for measurements remains largely consistent, regardless of the specific symptoms used to estimate depression severity. We discuss our results in the context of aligning the ideal frequency for EMA measurements with specific study goals and needs. For conditions where abrupt or transient symptom dynamics are expected, such as during treatment, more frequent data collection is recommended. However, for regular monitoring, weekly assessments of depressive symptoms may be sufficient. We discuss the implications of our findings for EMA study optimization, address our study's limitations, and outline directions for future research.