Participant characteristics in the effectiveness of lifestyle interventions to optimize gestational weight gain: a systematic review and meta-analysis
Bibliographic record
Abstract
Precision prevention involves tailoring interventions to the unique characteristics of a group or individual to maximize their effectiveness. In this study, we examined the role of participant characteristics in the effectiveness of lifestyle interventions to optimize gestational weight gain (GWG). We searched Medline, Embase, and PubMed, from inception up to March 2025, to identify randomized and non-randomized controlled trials of lifestyle interventions (diet, physical activity, or combined) commencing before or during pregnancy. Participant characteristics, including age, race/ethnicity, body mass index (BMI), employment status, fasting low- and high-density lipoprotein cholesterol (HDL-C) were assessed. Mean differences (MD) in GWG were pooled using the random-effect model. Meta-regression and subgroup analysis were conducted by participant characteristics (e.g., BMI). A total of 86 studies with 28,270 participants were included in this systematic review and meta-analysis. All lifestyle intervention types significantly reduced GWG. Combined lifestyle interventions initiated at first (MD −0.68; 95% confidence interval [CI]: −1.28, −0.07) and early second (13–17 weeks) trimester (MD −0.83; 95% CI: −1.46, −0.20) provide better effectiveness in optimizing GWG. Diet-only interventions significantly reduced GWG only in participants with normal BMI (MD −1.33 kg; CI: −1.75, −1.91) compared to the other BMI categories. Combined diet and physical activity interventions reduce excessive GWG in women with higher baseline HDL-C (β −0.04; 95% CI −0.06, −0.01). Lifestyle interventions reduced excessive GWG, with possible differential effects by intervention initiation time, BMI, and HDL-C. Future studies should consider physiological as well as social characteristics, in line with a holistic framework for precision medicine. A growing body of evidence underscores the pivotal role of lifestyle intervention in reducing the risk of excessive weight gain during pregnancy and associated maternal and child health complications. However, instead of a one-size-fits-all approach, further research is needed to help differentiate how to optimize the effectiveness of these interventions based on individual physiological and social determinants. This study found that lifestyle interventions reduce excessive weight gain during pregnancy, with greater benefits for certain women, including those with a normal body mass index and higher high-density lipoprotein cholesterol (good cholesterol) levels at the beginning of lifestyle interventions. Non-stratified data reporting prevented us from examining other pertinent participant characteristics, and future studies are required to inform precision intervention approaches that benefit all women. Grieger, Takele, Vesco, et al. perform a systematic review and meta-analysis of gestational weight gain interventions. Findings indicate lifestyle interventions that reduce excessive gestational weight gain provide greater benefits for women with a normal BMI and higher HDL cholesterol levels at the initiation of interventions.
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 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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".