Measuring 24-hour Movement Profiles During Pregnancy: Protocol for the 24MOVE Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Physical activity (PA) during the waking period and sleep during pregnancy may mitigate the increased risk for adverse pregnancy outcomes posed by gestational diabetes mellitus (GDM) and excessive gestational weight gain (GWG) in pregnant individuals with pre-pregnancy overweight and obesity. Recent studies have conceptualized PA, sedentary behavior, and sleep as part of a 24-hour movement framework; however, there is a gap in the knowledge about the relationship between 24-hour movement and pregnancy outcomes. OBJECTIVE: This paper describes the study protocol for the 24MOVE study, a prospective cohort study that examines associations between 24-hour movement profiles across pregnancy and maternal glucose tolerance, GWG, and infant birthweight. METHODS: Participants (N=306) were recruited from a large, integrated health care delivery system at 10 weeks' gestation. In early (10-12 weeks), mid- (20-22 weeks), and late (33-35 weeks) pregnancy, all eligible individuals with a pre-pregnancy BMI of ≥25 kg/m2 completed online surveys collecting information about sociodemographic characteristics and pregnancy symptoms and behaviors, including sleep quality. Participants concurrently wore a research-grade accelerometer for 24 hours per day for 7 consecutive days to capture movement, sedentary behavior, and sleep data. Data from accelerometry will be processed to create 24-hour movement profiles. Pregnancy outcomes will be ascertained from electronic health records (EHRs). We will use compositional data analysis (CoDA) methods, modeling associations of reallocations of time from one component behavior to another at various timepoints with outcomes. RESULTS: Recruitment began on March 19, 2023, and ended on September 11, 2024. Enrollment was completed on September 19, 2024. Data collection was completed in April 2025. Over an 18-month recruitment period, 2035 individuals were invited to participate, and of those, 306 (15%) eligible participants were enrolled in this study. The enrolled cohort had a median age of 33 (quintile 1 [Q1]-quintile 3 [Q3] 30-36) years and a median BMI of 28.8 (Q1-Q3 26.9-32.7) kg/m². Most participants had private insurance (n=266, 86.9%) and were multiparous (n=232, 75.8%). Analyses are in progress. CONCLUSIONS: The 24MOVE study is designed to address gaps in our knowledge of the impact of 24-hour movement during pregnancy on maternal glucose metabolism, GWG, and other risk factors for childhood obesity, such as a high birthweight. Data from this study will also serve as a rich resource for future investigations of 24-hour movement profiles and behavior substitutions and other perinatal mental and physical health outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/72828.
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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 | 0.032 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".