Application of Ecological momentary assessment (EMA) in assessing the relationship between affect and movement behaviors among people with mood disorders: a scoping review
Bibliographic record
Abstract
Ecological momentary assessment (EMA) enables the generation of intensive longitudinal data to examine dynamic relationships between variables. This study aims to describe the use of EMA in assessing dynamic associations between movement behaviors (physical activity, sedentary behavior, and sleep) and affective experiences among people with affective disorders. This scoping review searched peer-reviewed journal articles in eight electronic databases in both June 2022 and October 2023. Twenty-two studies were identified. Affective constructs were inconsistently implemented conceptually and operationally. Most studies (4/5) comparing compliance rates between mood-disordered participants and healthy controls found no significant differences, supporting EMA feasibility for individuals with affective disorders. Sleep quality was consistently linked to higher positive affect, lower negative affect, and mood enhancements. Physical activity (6/8 studies) was consistently associated with mood enhancements or improved positive affect, but not negative affect (2/3 studies). One study investigated affect and an indicator of sedentary behavior. Our review highlights EMA feasibility for investigating movement behaviors and affective experiences among people with affective disorders. Understanding these associations may contribute to informing clinical management of affective disorders and developing behavioral interventions such as just-in-time adaptive interventions. However, enhancing EMA methodology design and reporting is necessary to improve study reliability and validity.
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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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".