Perceived insufficient milk among primiparous, fully breastfeeding women: Is infant crying important?
Lisa M MohebatiPeter HilpertSarah C BathMargaret Philomena RaymanMonique M RaatsHomero MartinezLaura E CaulfieldPublished in: Maternal & child nutrition (2021)
Breastfeeding mothers often report perceived insufficient milk (PIM) believing their infant is crying too much, which leads to introducing formula and the early abandonment of breastfeeding. We sought to determine if infant crying was associated with reported PIM (yes/no) and number of problems associated with lactation (lactation problem score [LPS] 6-point Likert scale) before formula introduction. Primiparous breastfeeding mothers were recruited at birth and visited at 1, 2 and 4 weeks. Among those fully breastfeeding at 1 week (N = 230), infant crying variables based on maternal reports were not associated with PIM at 1 week, but LPS was. However, a mother's expectation that her infant would cry more than other infants was associated with increased odds of reporting PIM at 2 and 4 weeks, as were delayed onset of lactation and previous LPS. At 1 week, crying variables (frequency, difficulty in soothing) were associated with LPS along with percent weight change. Delayed onset of lactation, infant care style, number of breastfeeds and previous LPS were longitudinally associated with change in LPS from 1 to 2 weeks and 2 to 4 weeks. Our data suggest that reported infant crying is associated with PIM and LPS in the first 4 weeks of life. Guidance on what to expect in crying behaviour and the impact of infant care style may be beneficial in reducing PIM and LPS in the first month.
Keyphrases
- inflammatory response
- anti inflammatory
- human milk
- preterm infants
- gestational age
- mental health
- healthcare
- dairy cows
- physical activity
- palliative care
- low birth weight
- depressive symptoms
- randomized controlled trial
- quality improvement
- type diabetes
- chronic pain
- pregnant women
- pregnancy outcomes
- adipose tissue
- machine learning
- deep learning
- data analysis
- health insurance
- drug induced