The Effectiveness of eHealth Technologies on Weight Management in Pregnant and Postpartum Women: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The emergence and utilization of electronic health (eHealth) technologies has increased in a variety of health interventions. Exploiting the real-time advantages offered by mobile technologies during and after pregnancy has the potential to empower women and encourage behaviors that may improve maternal and child health. OBJECTIVE: The objective of this study was to assess the effectiveness of eHealth technologies for weight management during pregnancy and the postpartum period and to review the efficacy of eHealth technologies on health behaviors, specifically nutrition and physical activity. METHODS: A systematic search was conducted of the following databases: MEDLINE, EMBASE, Cochrane database of systematic reviews (CDSR), Cochrane central register of controlled trials (CENTRAL), CINAHL (Cumulative Index to Nursing and Allied Health Literature), and PsycINFO. The search included studies published from 1990 to July 5, 2016. All relevant primary studies that involved randomized controlled trials (RCTs), non-RCTs, before-and-after studies, historically controlled studies, and pilot studies were included. The study population was adult women of childbearing age either during pregnancy or the postpartum period. eHealth weight management intervention studies targeting physical activity, nutrition, or both, over a minimum 3-month period were included. Titles and abstracts, as well as full-text screening were conducted. Study quality was assessed using Cochrane's risk of bias tool. Data extraction was completed by a single reviewer, which was then verified by a second independent reviewer. Results were meta-analyzed to calculate pooled estimates of the effect, wherever possible. RESULTS: Overall, 1787 and 176 citations were reviewed at the abstract and full-text screening stages, respectively. A total of 10 studies met the inclusion criteria ranging from high to low risk of bias. Pooled estimates from studies of the effect for postpartum women resulted in a significant reduction in weight (-2.55 kg, 95% CI -3.81 to -1.28) after 3 to 12 months and six studies found a nonsignificant reduction in weight gain for pregnant women (-1.62 kg, 95% CI -3.57 to 0.33) at approximately 40 weeks. CONCLUSIONS: This review found evidence for benefits of eHealth technologies on weight management in postpartum women only. Further research is still needed regarding the use of these technologies during and after pregnancy.
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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 | 0.023 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.034 |
| Bibliometrics | 0.009 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".