Ecological Momentary Assessment in Eating Disorders Research: A Qualitative Examination of Participant Experience and Recommendations for Future Studies
Bibliographic record
Abstract
OBJECTIVE: Ecological momentary assessment (EMA) is a widely-used research method for investigating temporal relationships among eating disorder (ED) symptoms. Though EMA has many methodological advantages (e.g., reducing retrospective recall bias), little is known about the experience and effects of participating in this type of study from the perspective of individuals with EDs. The present study aimed to examine the experience of participants with EDs after completing an EMA study, with the goal of elucidating potential positive and negative effects of EMA methodology. METHOD: A heterogeneous sample of participants with EDs (N = 192) completed clinical interviews, questionnaires, and an EMA protocol (five surveys/day for 14 days). A subsample of these participants (n = 16) completed a qualitative interview exploring their experience participating in the study. A reflexive thematic analysis was conducted using Nvivo software. RESULTS: The following themes were identified: (1) Self-awareness, mindfulness, and reflection; (2) Behavioral change; (3) Rewarding aspects of the study; (4) Challenging aspects of the study; (5) Study design (including facilitators and barriers to participating); and (6) Suggestions for future studies. DISCUSSION: Although participants reported some challenging aspects of the study, most described their experience as positive (or at least neutral), and many noted direct benefits of participating. Future EMA research may benefit from integrating the perspectives of those with lived experience into study design, potentially reducing participant burden, improving the quantity and quality of data collected, and increasing benefits for participants.
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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 | 0.119 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".