Dietary Intake and Breastfeeding Practices Differ Between Women Who Gain Versus Lose Muscle Mass from 3 to 9 Months Postpartum
Notice bibliographique
Résumé
Background Changes in postpartum weight and fat mass have previously been explored while little attention has been given to muscle mass (MM). Higher MM is associated with better indicators of cardio‐metabolic and musculoskeletal health, and factors that may contribute to the gain or loss of MM could help to guide interventions during this dynamic physiological period. This study 1) characterised changes in MM, and 2) examined the contributions of energy intake, physical activity and breastfeeding practices to MM changes from 3 to 9 months postpartum. Methods This was a longitudinal observational study with measurements taken at 3 and 9 months postpartum. Women self‐reported pre‐pregnancy weight and highest weight during pregnancy. MM (via Dual energy x‐ray absorptiometry) and current weight along with information about dietary intake (3‐day food records), physical activity (Baecke questionnaire) and breastfeeding practices (3‐day breastfeeding diary including 1 day of infant test weighing) were measured at each time point. Differences in weight, MM, dietary intake and physical activity from 3 to 9 months postpartum were determined using paired t‐tests. Women were categorized according to whether they gained or lost MM; independent t‐tests were used to examine differences between these two groups. Binary logistic regression was used to examine whether dietary intake, physical activity or breastfeeding practices were associated with MM gain or loss at 9 months postpartum. Results On average, women completing the study (n=49) were 32.9 ± 3.8 years, had a pre‐pregnancy BMI of 25.6 ± 5.2 kg/m 2 and gestational weight gain of 15.4 ± 5.0 kg. Most (88%) breastfed for at least 3 months, and 57% continued breastfeeding until 9 months postpartum. Women experienced significant weight loss over the postpartum period (−10.8 ± 4.5 kg, p <0.001; range −2.7 kg to −22.0 kg). Changes in MM from 3 to 9 months postpartum varied from −2.51 kg to + 2.50 kg with 29 women gaining MM (+1.1 ± 0.7 kg, p< 0.001) and 20 women losing MM (−0.9 ± 0.8 kg, p <0.001). Body weight did not differ between those who gained or lost MM at either time (3 Months: Gained MM = 72.1 ± 15.1 kg, Lost MM = 78.2 ± 16.4 kg, p = 0.193; 9 months: Gained MM = 70.9 ± 16.9 kg, Lost MM = 75.3 ± 16.9 kg, p = 0.377). Energy intake (32 ± 10 kcal/kg vs. 26 ± 8 kcal/kg, p = 0.019) and % kcal from fat at 3 months postpartum was higher in women gained vs. those who lost MM at 9 months postpartum (Gained MM = 34 ± 5 % kcal, Lost MM = 29 ± 4 % kcal, p = 0.002). Women who gained MM reported breastfeeding their infants more frequently (Gained MM = 8 ± 3, Lost MM = 5 ± 1 feeds/day, p = 0.014) and for more time per day (Gained MM = 115 ± 78 mins/day, Lost MM = 59 ± 34 mins/day, p = 0.016) at 9 but not 3 months postpartum. Energy intake (32 ± 10 kcal/kg vs. 28 ± 10 kcal/kg, p = 0.437) and physical activity scores at 9 months postpartum (8.5 ± 1.2 vs. 8.2 ± 1.2, p = 0.390) did not differ between those who gained or lost MM. Energy intake and % kcal from fat at 3 months were significant predictors of MM gain (β [SE] = 0.08 [0.04] and 0.24 [0.09], respectively). Support or Funding Information The ENRICH Project is funded through the Alberta Innovates ‐ Health Solutions (AIHS), Collaborative Research and Innovation Opportunity team grant. Additional funding for this project was provided through the Muttart Diabetes Research and Training Centre as well as the University of Alberta/Faculty of ALES Food and Health Innovation Initiative.
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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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».