Postpartum Metabolism: How Does It Change from Pregnancy and What are the Potential Implications?

Tinius, Rachel A.; Yoho, Kristin; Blankenship, Maire M.; Maples, Jill M.

doi:10.2147/ijwh.s314469

Cited by 10 publications

(5 citation statements)

References 37 publications

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“…The negative relationship in the context of pregnancy/PPD can likely be derived from the metabolic changes during pregnancy, resulting in an increased basal metabolic rate 35 . In the context of postpartum/PPD, we speculate that the negative relationship is because the median duration between the delivery date and PPD diagnosis is 83 days, where patients may not have fully returned to their pre-pregnancy physiological or behavioral patterns, which can take up to six months 36 . As a result, the relationship between Shapley values and actual values of calories BMR may reflect this transitional period and the ongoing postpartum changes experienced by women.…”

Section: Discussionmentioning

confidence: 99%

“…This trend is likely a result of the common occurrence of an increased basal metabolic rate during pregnancy 35 . In the context of the postpartum/PPD-equivalent time periods, our utilization of an index date set at 83 days after delivery – the median number of days after delivery for PPD diagnosis in the PPD cohort – implies that women likely have not fully returned to their pre-pregnancy physiological baseline 36 . This aligns with the parallel observation seen during the pregnancy/PPD-equivalent time periods, reaffirming the persisting metabolic effect postpartum.…”

Section: Discussionmentioning

confidence: 99%

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Harnessing consumer wearable digital biomarkers for individualized recognition of postpartum depression using theAll of UsResearch Program dataset

Hurwitz,

Butzin-Dozier,

Master

et al. 2023

Preprint

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Postpartum depression (PPD), afflicting one in seven women, poses a major challenge in maternal health. Existing approaches to detect PPD heavily depend on in-person postpartum visits, leading to cases of the condition being overlooked and untreated. We explored the potential of consumer wearable-derived digital biomarkers for PPD recognition to address this gap. Our study demonstrated that intra-individual machine learning (ML) models developed using these digital biomarkers can discern between pre-pregnancy, pregnancy, postpartum without depression, and postpartum with depression time periods (i.e., PPD diagnosis). When evaluating variable importance, calories burned from the basal metabolic rate (calories BMR) emerged as the digital biomarker most predictive of PPD. To confirm the specificity of our method, we demonstrated that models developed in women without PPD could not accurately classify the PPD-equivalent phase. Prior depression history did not alter model efficacy for PPD recognition. Furthermore, the individualized models demonstrated superior performance compared to a conventional cohort-based model for the detection of PPD, underscoring the effectiveness of our individualized ML approach. This work establishes consumer wearables as a promising avenue for PPD identification. More importantly, it also emphasizes the utility of individualized ML model methodology, potentially transforming early disease detection strategies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Harnessing consumer wearable digital biomarkers for individualized recognition of postpartum depression using theAll of UsResearch Program dataset

Hurwitz,

Butzin-Dozier,

Master

et al. 2023

Preprint

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“…(2) poor psychological endurance; (3) poor adaptability; (4) psychological imbalance; (5) emotional disorder; (6) anxiety; (7) depression; (8) hostility; (9) stubbornly biased; and (10) somatization. Each subscale consists of eight items.…”

Section: Methodsmentioning

confidence: 99%

“…Both living environment and social situations contribute to anxiety and depression during pregnancy ( 5 ). Gestation of life requires the mother to constantly adjust the internal environment for the embryo ( 6 ), including rising body weight and dramatically changing hormone and metabolic levels ( 7 – 9 ). Foremost among these, the perinatal status is a susceptible period with a high incidence of antenatal depression, which is related to maternal mortality, in particular suicide ( 10 ).…”

Section: Introductionmentioning

confidence: 99%

Factors associated with the mental health status of pregnant women in China: A latent class analysis

Pei

Chen

Zhang

et al. 2023

Front. Public Health

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BackgroundPrenatal mental health is a neglected public health issue that places pregnant women at a higher risk for mental disorders. The purpose of this study was to investigate the influencing factors of prenatal mental disorders and provide a scientific basis to guide and promote the mental health of pregnant women.MethodsThe study sample comprised 973 women in their first pregnancy, who were in their second trimester and third trimester, who underwent obstetric outpatient checkups at the Maternal and Child Health Hospital in Huai'an, who were recruited in the survey that was conducted from July to December 2017. The Chinese mental health scale (CMHS) was used to assess the mental health of pregnant women. The present study uses the chi-square test to compare the rates of class with different demographic variables, a latent class analysis to identify psychological symptoms, and multiple logistic regression analysis to examine whether the demographics predicted class membership.ResultsThe chi-square test results showed that participants who reported feeling different in the perinatal period (χ2 = 6.35, P = 0.04), having marital satisfaction (χ2 = 15.8, P < 0.001), with an in-law relationship (χ2 = 29.43, P < 0.001), with a friend relationship (χ2 = 24.81, P < 0.001), with basic diseases (χ2 = 8.04, P = 0.02), and taking birth control pills (χ2 = 8.97, P = 0.01) have different probabilities of being classified. Three latent classes were identified: the high symptoms group (6.89%), the moderate symptoms group (20.56%), and the low symptoms group (72.56%). Pregnant women in the third trimester [odds ratio (OR) = 1.83, 95% confidence interval (CI): 1.04–3.25, P = 0.04], with a poor in-law relationship (OR = 2.82, 95% CI:1.45–5.51, P = 0.002), with a bad friend relationship (OR = 3.17, 95% CI: 1.31–7.71, P = 0.01), and who had basic diseases (OR = 1.70, 95% CI: 1.00–2.90, P = 0.04) tended to be classified under the high symptoms group than under the low symptoms group. Pregnant women with a bad friend relationship (OR = 2.15, 95% CI: 1.08–4.28, P = 0.03) and taking birth control pills (OR = 1.51, 95% CI: 1.08–2.11, P = 0.02) were more likely to be placed under the moderate symptoms group than under the low symptoms group.ConclusionsA pregnant woman's mental health status factors include feeling different in the perinatal period, those with marital satisfaction, those with an in-law relationship, those with a friend relationship, those with basic diseases, and those taking birth control pills. To ensure a smooth progress of pregnancy and promote the physical and mental health of pregnant women, psychological screening and psychological intervention measures should be strengthened.

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“…Data were collected as part of a larger study among postpartum people in southcentral Kentucky [5]. Recruitment for the present study took place between 32 and 34 weeks of gestation.…”

Section: Methodsmentioning

confidence: 99%

Modifiable Maternal Factors and Their Relationship to Postpartum Depression

Howard

Maples

Tinius

2022

IJERPH

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The purpose of the study was to examine how modifiable maternal factors (body mass index (BMI), household income, fatigue, sleep, breastfeeding status, diet, and physical activity) relate to postpartum depression (PPD) at 6 and 12 months postpartum. Participants (n = 26) participated in two study visits (6 and 12 months postpartum) where vitals, weight, body composition (skinfold anthropometrics), and physical activity levels (Actigraph GTX9 accelerometer) were assessed. Validated instruments (BRUMS-32, Subjective Exercise Experience Scale, Pittsburg Sleep Quality index, NIH breastfeeding survey, NIH Dietary History Questionnaire, and Edinburg Postnatal Depression Scale) assessed lifestyle and demographic factors of interest. PPD at six months was correlated to PPD at 12 months (r = 0.926, p < 0.001). At six months postpartum, PPD was positively correlated to BMI (r = 0.473, p = 0.020) and fatigue (r = 0.701, p < 0.001), and negatively correlated to household income (r = −0.442, p = 0.035). Mothers who were breastfeeding had lower PPD scores (breastfeeding 3.9 ± 3.5 vs. not breastfeeding 7.6 ± 4.8, p = 0.048). At 12 months, PPD was positively correlated to sleep scores (where a higher score indicates poorer sleep quality) (r = 0.752, p < 0.001) and fatigue (r = 0.680, p = 0.004). When analyzed collectively via regression analyses, household income and fatigue appeared to be the strongest predictors of PPD at six months postpartum.

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Postpartum Metabolism: How Does It Change from Pregnancy and What are the Potential Implications?

Cited by 10 publications

References 37 publications

Harnessing consumer wearable digital biomarkers for individualized recognition of postpartum depression using theAll of UsResearch Program dataset

Harnessing consumer wearable digital biomarkers for individualized recognition of postpartum depression using theAll of UsResearch Program dataset

Factors associated with the mental health status of pregnant women in China: A latent class analysis

Modifiable Maternal Factors and Their Relationship to Postpartum Depression

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