Capturing Movement Behaviors in Latinas: Feasibility, Validity, and Acceptability Study of an Ecological Momentary Assessment Protocol
Bibliographic record
Abstract
Background: Latinas are one of the largest and fastest-growing female ethnic groups in the United States and have high levels of physical inactivity and sedentary behavior (SB), contributing to a disproportionate burden of chronic health conditions. An ecological momentary assessment (EMA) involves the use of smartphone-based data collected in real time to assess health behaviors and outcomes. Objective: We examined the feasibility, validity, and acceptability of an EMA protocol assessing physical activity (PA) and SB in Latina adults. Methods: For 7 days, 67 Latinas (average age 39 years, SD = 13.6; n=37, 55.2% earning less than US $50,000/year; n=53, 79.1% foreign-born; and n=49, 73.1% of Mexican or Mexican American origin) completed a signal-contingent EMA protocol with 3 prompts per day and wore an ActiGraph GT3X accelerometer to measure levels of PA and SB. EMA prompts inquired about current behavior, feelings, beliefs, social conditions, and contexts. Results: Latinas completed 69.7% (892/1279) of EMA prompts. They were more likely to respond to EMA prompts when engaged in more SB (odds ratio [OR] 1.04, 95% CI 1.01-1.06) and less light-intensity PA (OR 0.97, 95% CI 0.94-0.99) in the 30 minutes around the prompt. Accelerometer data validated self-reported occasions of PA and SB via EMA. The majority of participants (>70%) were satisfied with the protocol and expressed interest in participating in future studies. Conclusions: EMA is a feasible, valid, and acceptable methodology for capturing movement behaviors among Latinas, which can provide insights into the antecedents and consequences of these behaviors in their daily lives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".