Optimizing the frequency of ecological momentary assessments using signal processing.

ABSTRACT:

Background

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 be queried to 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.

Methods

We applied the Nyquist-Shannon theorem to analyze two EMA datasets on depressive symptoms, encompassing a combined total of 35,452 data points collected over periods ranging from 30 to 90 days per individual.

Result

SUBMITTER: Jamalabadi H 

PROVIDER: S-EPMC12671912 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature