Step and weight tracking with targets and coaching interventions in gestational diabetes: A randomized factorial feasibility trial
Bibliographic record
Abstract
BACKGROUND: Pregnancy guidelines recommend moderate to vigorous physical activity of ≥150 min/week (7000 steps/day) and weight gain specific to prepregnancy weight category. We aimed to assess step and weight changes, with tracking to achieve individualized targets and with coaching conversations on physical activity and eating. The overarching goal was to identify interventions warranting integration and evaluation through a larger trial assessing perinatal outcomes. METHODS: In this feasibility trial, we randomized 227 participants (GDM clinics, 5 Canadian cities) to 'track & target,' 'coaching,' 'both,' or 'neither' arms. 'Track & target' participants monitored steps/day (counter) and weight (scale). We delivered weekly targets, applying algorithms that incorporated step and weight data and nudged towards recommendations. Coaching participants conversed weekly with a coach, who applied motivational communication methods. We examined changes in steps/day and weight, between trial entry and 37 weeks' gestation. FINDINGS: Weight change was guideline-concordant across arms. Steps/day averaged 6385 (SD 3406) at baselinue and were stable in the 'track & target' arm (change -59, 95 %CI -749 to 630). The other arms declined (coaching: -915, 95 %CI -1605 to -225; both -1183, 95 %CI -1979 to -386; neither -1456, 95 %CI -2193 to -718). INTERPRETATION: Our algorithm-driven step target strategy prevents step count decline, meriting study for perinatal impact.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".