Optimizing the frequency of ecological momentary assessments using signal processing.
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...
| Publicado en: | Psychological Medicine Vol. 55; pp. 1 - 10 |
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| Formato: | research tables/charts Journal Article |
| Publicado: |
Cambridge University Press
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191245401&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191245401 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00332917 6Q3 jtl: Psychological Medicine issn: 00332917 maglogo: N pubinfo: dt: 2025 vid: 55 pid: 15979 pub: Cambridge University Press artinfo: ui: 191245401 191245401 191245401 10.1017/S003329172510264X 191245401 ppf: 1 ppct: 9 formats: tig: atl: Optimizing the frequency of ecological momentary assessments using signal processing. aug: sug: subj: Depression Diagnosis Psychological Tests Signal Processing, Computer Assisted Methods Human Male Female Adult Prospective Studies Monitoring, Physiologic Descriptive Statistics Comparative Studies Summated Rating Scaling Questionnaires Anxiety Depression Post Hoc Analysis Psychotherapy Methods Retrospective Design Record Review Visual Analog Scaling DSM Power Analysis Adult: 19-44 years Male Female ab: 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. Results 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. Conclusions Ideal frequency for measurements remains largely consistent, regardless of the specific symptoms used to estimate depression severity. For conditions in which 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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