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Optimizing the frequency of ecological momentary assessments using signal processing.

Hamidreza Jamalabadi1,2,3, Tahmineh A Koosha1, Elina Stocker1

  • 1Department of Psychiatry and Psychotherapy, https://ror.org/01rdrb571Marburg University, Marburg, Germany.

Psychological Medicine
|November 25, 2025
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Summary

Ecological momentary assessment (EMA) requires optimal sampling rates. Weekly or bi-weekly symptom measurements are sufficient for regular monitoring, informed by the Nyquist-Shannon theorem.

Keywords:
ecological momentary assessment (EMA)major depression disorder (MDD)sampling ratesignal processing

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Area of Science:

  • Psychiatric research
  • Signal processing
  • Mental health monitoring

Background:

  • Ecological momentary assessment (EMA) is crucial for tracking fluctuating mental states in psychiatric research.
  • Determining optimal EMA sampling rates is a significant challenge.
  • The Nyquist-Shannon theorem provides a framework for determining adequate sampling frequencies.

Purpose of the Study:

  • To apply the Nyquist-Shannon theorem to determine optimal sampling rates for EMA in psychiatric research.
  • To analyze EMA datasets for depressive symptoms to identify effective measurement frequencies.
  • To provide evidence-based recommendations for EMA study design.

Main Methods:

  • Analysis of two EMA datasets on depressive symptoms (35,452 data points).
  • Application of the Nyquist-Shannon theorem to assess signal frequency components.
  • Evaluation of data consistency and information value at different sampling frequencies.

Main Results:

  • The most effective sampling strategy involves measurements at least every other week.
  • Higher frequency measurements (weekly, daily) provide valuable and consistent information.
  • Optimal sampling frequency is largely consistent across different depression severity indicators.

Conclusions:

  • Weekly assessments may be sufficient for regular monitoring of depressive symptoms.
  • More frequent data collection is recommended for conditions with transient symptom dynamics.
  • Findings inform EMA study optimization and future research directions.