Dietary Intake and Breastfeeding Practices Differ Between Women Who Gain Versus Lose Muscle Mass from 3 to 9 Months Postpartum
Bibliographic record
Abstract
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.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".