Sense of Weight Control Prior to Pregnancy Could Help Predict Women at Risk for Excessive Gestational Weight Gain
Notice bibliographique
Résumé
Introduction Gaining weight in excess of the Institute of Medicine gestational weight gain (GWG) guidelines is associated with adverse health outcomes for mother and child. It is estimated that 49% of women in Alberta gain excess gestational weight. Weight gain in pregnancy is multifactorial and there is a need to better support women during pregnancy to gain weight within the guidelines. A study in Edmonton, AB, aimed to understand if differing amounts of lifestyle support provided by a Registered Dietitian (RD) throughout pregnancy could help women achieve appropriate GWG. The purpose of these analyses is to understand if there was any difference in GWG between the three groups and to explore the gestational weight related perspectives of the women within the study. Methods Seventy low‐risk pregnant women, <20 weeks gestation, were randomized to an active control (AC) or intervention (INT) group where they saw a RD for two visits in pregnancy. The two visits occurred at < 24 weeks and 30 weeks gestation. Women completed lifestyle questionnaires throughout pregnancy and a postpartum questionnaire that asked about their prenatal experiences. A passive control group (PC) of women ≤12 months postpartum (n=55) was recruited to only complete the postpartum questionnaire. This group models standard prenatal care in Alberta (no RD visits in pregnancy). GWG information was collected from obstetrical charts and adherence to GWG recommendations was defined in accordance with IOM guidelines. Data were analyzed using chi‐square, t‐test, or one‐way ANOVA. Results Pre‐pregnancy BMI, ethnicity, education, marital status, household income and parity did not differ between INT (n=33), AC (n=37), and PC (n=55) groups. However, maternal age was different between the groups with the mean age in the PC group as 32.1 compared to 34.4 years for both the INT and AC groups (p=0.022). When asked about GWG perspectives, 78% (n=81) of women stated the amount of weight they gained was important to them with the top 3 reasons being: “To have a healthy pregnancy”, “To deliver a healthy baby” and “For my health”. Intentional changes were made by 68% of the women to help manage their weight in pregnancy, with ~45% changing the type of foods they ate (n=57) and 28% increasing their physical activity (n=35). Total GWG, rate of weight gain and adherence to GWG guidelines did not differ between the three groups. Overall, 50%, 42% and 8% of the participants exceeded, met and fell below the guidelines, respectively. Guideline concordance differed by pre‐pregnancy BMI category (p=0.024), with 78% of the overweight and 67% of the obese women exceeding the guidelines compared with 34% of women within a normal BMI. Women who agreed to feeling in control of their weight prior to pregnancy were more likely to gain weight within the guidelines during pregnancy (p=0.008). Conclusion Most women understand why weight gain in pregnancy is important, yet half exceed the guidelines. Understanding a woman's sense of weight control prior to pregnancy may help aid healthcare providers understand which women who may be at increased risk for excess GWG. Support or Funding Information The Danone Institute of Canada, Alberta Innovates Health Solutions
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».