Pregnancy, Physical Activity and Weight Control to Prevent Obesity and Future Chronic Disease Risk in Both Mother and Child
Bibliographic record
Abstract
Maternal obesity is accelerating world-wide and may be partly due to excessive gestational weight gain (GWG) and weight retention so that women begin a subsequent pregnancy with extra weight. Excessive GWG has been linked to chronic disease risk in the mother and also to an unhealthy foetal environment with downstream consequences for offspring health with risk for childhood obesity. Weight control during pregnancy and prevention of excessive GWG is an important issue for both mother and developing child. A healthy lifestyle through healthy eating and physical activity are key to prevention. Weight management for non-pregnant individuals has been evaluated for over 30 years, and lessons learned may assist in planning interventions for preventing excessive GWG. Many systematic reviews and meta-analyses analyzing the same studies on GWG report very different results. Recently, 10 intervention trials to prevent excessive GWG were published and only 6 of them were successful. Significant association between maternal exercise and GWG guidelines were reported, however, “one size does not fit all”. The failed trials did not have extra faceto- face sessions, were educational based and adherence was <50%. Accountability, face-to-face exercise sessions, and pedometers may motivate pregnant women to increase step counts to 10,000, which, when combined with nutrition control, prevents excessive GWG. Community walking programs that include family members and children may assist pregnant women of all body mass index categories to overcome potential barriers to promote a healthy lifestyle that will benefit them and their families for weight control and prevention of future chronic disease risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".