Optimizing the frequency of ecological momentary assessments using signal processing
Bibliographic record
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. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".