Application of an Ecological Momentary Assessment Protocol in a Workplace Intervention: Assessing Compliance, Criterion Validity, and Reactivity
Bibliographic record
Abstract
BACKGROUND: Ecological momentary assessment (EMA) is a method of collecting behavioral data in real time. The purpose of this study was to examine EMA compliance, identify factors predicting compliance, assess criterion validity of, and reactivity to, using EMA in a workplace intervention study. METHODS: Forty-five adults (91.1% female, 39.7 [9.6] y) were recruited for a workplace standing desk intervention. Participants received 5 surveys each day for 5 workdays via smartphone application. EMA items assessed current position (sitting/standing/stepping). EMA responses were time matched to objectively measured time in each position before and after each prompt. Multilevel logistic regression models estimated factors influencing EMA response. Cohen kappa measured interrater agreement between EMA-reported and device-measured position. Reactivity was assessed by comparing objectively measured sitting/standing/stepping in the 15 minutes before and after each EMA prompt using multilevel repeated-measures models. RESULTS: Participants answered 81.4% of EMA prompts. Differences in compliance differed by position. There was substantial agreement between EMA-reported and device-measured position (κ = .713; P < .001). Following the EMA prompt, participants sat 0.87 minutes more than before the prompt (P < .01). CONCLUSION: The use of EMA is a valid assessment of position when used in an intervention to reduce occupational sitting and did not appear to disrupt sitting in favor of the targeted outcome.
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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 | 0.041 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".