The association between multidimensional sleep health and gestational weight gain

Marquis S. Hawkins*, Darya Y. Pokutnaya, Lisa M. Bodnar, Michele D. Levine, Daniel J. Buysse, Esa M. Davis, Meredith L. Wallace, Phyllis C. Zee, William A. Grobman, Kathryn J. Reid, Francesca L. Facco

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Background: Although poor sleep health is associated with weight gain and obesity in the non-pregnant population, research on the impact of sleep health on weight change among pregnant people using a multidimensional sleep health framework is needed. Objectives: This secondary data analysis of the Nulliparous Pregnancy Outcome Study: Monitoring Mothers-to-be Sleep Duration and Continuity Study (n = 745) examined associations between mid-pregnancy sleep health indicators, multidimensional sleep health and gestational weight gain (GWG). Methods: Sleep domains (i.e. regularity, nap duration, timing, efficiency and duration) were assessed via actigraphy between 16 and 21 weeks of gestation. We defined ‘healthy’ sleep in each domain with empirical thresholds. Multidimensional sleep health was based on sleep profiles derived from latent class analysis and composite score defined as the sum of healthy sleep domains. Total GWG, the difference between self-reported pre-pregnancy weight and the last measured weight before delivery, was converted to z-scores using gestational age- and BMI-specific charts. GWG was defined as low (<−1 SD), moderate (−1 or +1 SD) and high (>+1 SD). Results: Nearly 50% of the participants had a healthy sleep profile (i.e. healthy sleep in most domains), whereas others had a sleep profile defined as having varying degrees of unhealthy sleep in each domain. The individual sleep domains were associated with a 20%–30% lower risk of low or high GWG. Each additional healthy sleep indicator was associated with a 10% lower risk of low (vs. moderate), but not high, GWG. Participants with late timing, long duration and low efficiency (vs. healthy) profiles had the strongest risk of low GWG (relative risk 1.5, 95% confidence interval 0.9, 2.4). Probabilistic bias analysis suggested that most associations between individual sleep health indicators, sleep health profiles and GWG were biased towards the null. Conclusions: Future research should determine whether sleep health is an intervention target for healthy GWG.

Original languageEnglish (US)
JournalPaediatric and Perinatal Epidemiology
StateAccepted/In press - 2023


  • actigraphy
  • gestational weight gain
  • pregnancy
  • prospective
  • sleep

ASJC Scopus subject areas

  • Epidemiology
  • Pediatrics, Perinatology, and Child Health


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