Effects of digital health counseling and behavioral interventions on weight management during pregnancy and postpartum: A systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
OBJECTIVES: This systematic review aimed to evaluate the effects of digital health counseling or behavioral weight management interventions for preventing excessive gestational weight gain (GWG) among pregnant individuals of all body mass index (BMI) categories, compared to routine care. METHODS: We searched MEDLINE, Embase, CINAHL, ProQuest Dissertations/Theses, PsycINFO, and the Cochrane Central Register of Controlled Trials, up to February 2024. We included randomized controlled trials (RCTs) wherein pregnant women received counseling or behavioral interventions through digital health compared to routine care. Pairs of reviewers independently screened titles and abstracts and extracted data from eligible RCTs. Data were pooled using inverse-variance random-effects meta-analyses. We applied the Cochrane Risk of Bias 2.0 tool and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to assess the magnitude and certainty of the effects. RESULTS: We included 18 trials. Moderate certainty evidence showed 0.78 kg lower GWG in the weight management digital health intervention group, compared with routine care (95% CI: -1.40 to -0.16 kg). This reduction was higher in individuals with BMI ≥ 25 kg/m2. Digital health interventions likely reduce the risk of excessive GWG (RR = 0.80; 95% CI: 0.68 to 0.95) and may result in little to no difference in the rate of cesarean birth (CB) (RR = 1.09; 95% CI: 0.81 to 1.48). Low-certainty evidence suggested that digital health weight management interventions may reduce the risk of gestational diabetes mellitus (GDM) (RR = 0.80; 95% CI: 0.57 to 1.12), pre-eclampsia (RR = 0.82; 95% CI: 0.51 to 1.33), and preterm birth (RR = 0.83; 95% CI: 0.53 to 1.28). High-certainty evidence showed that digital health weight management interventions have little to no effect on birthweight (MD = 0.00; 95% CI: -0.08 to 0.08). CONCLUSIONS: Digital health interventions are effective in reducing GWG and excessive GWG based on BMI. Additionally, evidence suggests that these interventions may lower the risk of GDM, pre-eclampsia, and preterm birth. However, their impact on birthweight, GWG across all BMI categories, and the risk of CB is trivial.
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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 | 0.024 | 0.076 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.033 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".