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. Author manuscript; available in PMC: 2025 Nov 15.
Published in final edited form as: J Affect Disord. 2024 Aug 12;365:56–64. doi: 10.1016/j.jad.2024.08.059

A Bioecological Longitudinal Study of Depressive Symptoms from Pregnancy to 36 Months Postpartum

Nicolette C Molina 1, Anna M Zhou 2, Parisa R Kaliush 3, Sarah E Maylott 3, Ashley E Pappal 2, Kira Wright 2, Dylan Neff 4, Jonathan E Butner 2, K Lee Raby 2, Elisabeth Conradt 2,5, Sheila E Crowell 1
PMCID: PMC11512642  NIHMSID: NIHMS2020156  PMID: 39142585

Abstract

Purpose

Depressive symptoms during the perinatal period have broad and enduring health implications for birthing parents and their offspring. Rising prevalence rates of perinatal depression highlight the need for research examining factors influencing depressive symptoms during pregnancy, and trajectories during the early postnatal period. Grounded in bioecological systems theory, this longitudinal multimethod study examined whether prenatal bioecological factors predict depressive symptoms from pregnancy to 36 months postpartum.

Methods

Participants were 162 pregnant individuals, oversampled for high emotion dysregulation, who completed a life stress interview and physiological assessment during the 3rd trimester and a self-report measure of depression at five time-points (3rd trimester, within 48 hours of birth, 7-, 18-, and 36-months postpartum). Multilevel models were used to test study aims.

Results

Participants exhibited the highest levels of depressive symptoms at 3rd trimester, and substantial variability in depressive symptom trajectories over time. Lower resting respiratory sinus arrhythmia (RSA), an index of parasympathetic nervous system functioning, in the 3rd trimester was associated with higher concurrent depressive symptoms. Higher levels of stress related to partner relationships, finances, and health were concurrently associated with more depressive symptoms during pregnancy and decreases in depressive symptoms over time. Specifically, depressive symptoms decreased only for individuals who reported high levels of stress during pregnancy.

Limitations

Although grounded in bioecological systems theory, this study did not assess the macrosystem.

Conclusions

Results of this study underscore the importance of multilevel predictors of perinatal health and highlights potential targets for preventing depression and promoting well-being during the perinatal transition.

Keywords: pregnancy, postpartum, depression, bioecological, RSA, life stress

Bioecological Study of Depressive Symptoms from Pregnancy to 36 Months Postpartum

Perinatal depression affects approximately 27% of people during pregnancy and 33% during postpartum, which has significant implications for maternal and offspring health (Davis et al., 2024; Mateus et al., 2022; Rogers et al., 2020). From 2008 to 2018, diagnoses of prenatal depression and suicidal ideation increased by 39% and 193%, respectively (Tabb et al., 2023). Biological shifts during pregnancy and childbirth, alongside the transition to parenthood, can initiate or exacerbate depressive symptoms (Kaliush et al., 2023; Motosko et al., 2017; Figueiredo et al., 2018). There is a need for research examining how both biological and ecological factors influence depressive symptom onset and trajectory in pregnancy and from pregnancy to postpartum (Tudge et al., 2009). Research identifying potential risk factors can lead to targeted preventive and therapeutic interventions during pregnancy.

Bioecological systems theory (Bronfenbrenner, 1979) can be used to understand how individual biology and environmental factors jointly influence perinatal depression. This framework emphasizes the role of multiple nested systems, including an individual’s biology (e.g., physiology), their immediate microsystem (e.g., support networks), and the broader exosystem (e.g., policy; Bronfenbrenner and Morris, 1998). Additionally, the biopsychosocial model can further enrich this understanding by explicitly acknowledging how biological predispositions and social environmental factors interact to shape mental health outcomes (Engel, 1977). These perspectives align with the developmental psychopathology perspective, which emphasizes that vulnerability arises from the convergence of both biological and contextual risks (Crowell et al., 2009; Zuckerman & Riskind, 2000).

Depression symptoms generally decrease over the course of the first postpartum years (Vliegen et al., 2014). However, the onset and trajectory of depression during the perinatal period can vary significantly between individuals (Putnam et al 2017; Roubinov et al., 2022; Simons et al., 2020; Yu et al., 2020). Longitudinal studies examining perinatal depression have found distinct trajectory types, suggesting that not all individuals decrease in symptoms over time (e.g., Gustafsson et al., 2021; Mughal et al., 2023). Stressful life events can influence these variable change patterns (Choi et al., 2022; Wajid et al., 2020; Zhang et al., 2022). Particularly, financial and partner relationship stress have been identified as meaningful predictors of postpartum depressive symptoms (Ward et al., 2017). Yet, the effect of health-related stress on perinatal depression has been less explored, with only two studies linking physical health stress to postpartum depression (Liu et al., 2013; Ward et al., 2017). Given the significant neurobiological transformations that occur from pregnancy to postpartum, health-related stress during this period warrants more attention.

Investigating physiological markers alongside life stressors can provide a deeper understanding of potential sources of perinatal depression. Respiratory sinus arrhythmia (RSA) is one physiological biomarker that has been associated with postpartum depressive symptoms (Beauchaine and Thayer 2015; Shea et al., 2008). RSA is an index of parasympathetic nervous system functioning, which is measured by assessing variability in heart rate across the respiratory cycle (Beauchaine 2001). Resting RSA is associated with emotion regulation capacity (Beauchaine 2001), and depressive symptoms in non-perinatal adults (Yaptangco et al., 2015). A prior cross-sectional study with our sample showed that higher resting RSA correlated with fewer depressive symptoms in the 3rd trimester (Lin et al., 2019). No studies to date have explored resting RSA during pregnancy as a predictor of perinatal depressive symptom trajectories. However, recent research exploring heart rate variability (HRV; Eriksson et al., 2024; Shah et al., 2020; Singh Solorzano et al., 2022) and other proxy indicators of parasympathetic activity (Singh Solorzano & Grano, 2023) find associations with peripartum depressive symptoms. For example, one recent study found that lower HRV at 3rd trimester was associated with depressive symptoms at 6 weeks postpartum (Eriksson et al., 2024). This literature supports the relevance of exploring physiological biomarkers, such as RSA, as biomarkers for depressive symptoms across the perinatal period.

Present Study

In the present study, we modeled depressive symptoms trajectories across five time-points from 3rd trimester to 36 months postpartum among pregnant individuals oversampled for higher emotion dysregulation. We examined multiple bioecological factors during pregnancy theorized to influence depressive symptom trajectories, including resting RSA and three areas of stress: partner relationship, financial, and health. Consistent with prior literature, we hypothesized that, on average, depressive symptoms would peak in pregnancy and decrease from 3rd trimester to 36 months postpartum. We also hypothesized that higher partner, financial, and health stress in 3rd trimester would associate with higher concurrent depressive symptoms and increasing symptom trajectories over time. Additionally, we expected lower resting RSA during 3rd trimester would be concurrently associated with higher depressive symptoms and predict stability in high depressive symptoms over time.

Method

Participants

Data were collected from 162 pregnant individuals1. Nearly half identified as a racial/ethnic minority, average age was 29 years old (SD = 5.16), about 31% had some college education and another 32% received a bachelor’s degree, and median household income was between $50,000 and $79,999. Of the participants, 67 (41.4%) had carried one pregnancy to a viable gestational age, 39 (24.1%) had two, 20 (12.3%) had three, and 16 (9.9%) had four or more. See Table 1 for demographic data.

Table 1.

Demographic Data (N = 162)

Variable M (SD)
Age 28.99 (5.16)
Weight 177.30 (38.90)
N (%)
Race
  American Indian or Alaskan 5 (3.1%)
Native
  Asian 15 (9.3%)
  Hawaiian or Pacific Islander 2 (1.2%)
  Black/African American 2 (1.2%)
  White/Caucasian 128 (79.0%)
  Mixed 10 (6.2%)
Ethnicity
 Hispanic/Latina 44 (27.2%)
 Not Hispanic/Latina 118 (72.8%)
Education
 Less than high school diploma 5 (3.1%)
 High school graduate or equivalent 21 (13.0%)
 Some college or technical school 51 (31.5%)
 College graduate 51 (31.5%)
 Any post graduate school 31 (19.1%)
 Missing 3 (1.9%)
Household income in the last year
 Under $9,000 7 (4.3%)
  $9,000 - $19,999 16 (9.9%)
  $20,000 - $29,999 11 (6.8%)
  $30,000 - $39,999 16 (9.9%)
  $40,000 - $49,999 9 (5.6%)
  $50,000 - $79,999 48 (29.6%)
  $80,000 - $99,999 17 (10.5%)
  $100,000 or more 24 (14.8%)
  Missing 14 (8.7%)
Relationship status
 Married 123 (75.9%)
 Single and never married 27 (16.7%)
 Separated and divorced 10 (6.2%)
 Missing 2 (1.2%)
Other children
 Yes 63 (38.9%)
 No 28 (17.3%)
 Missing 71 (43.8%)
Parity
 1 67 (41.4%)
 2 39 (24.1%)
 3 20 (12.3%)
 ≥ 4 16 (9.9%)
 Psychotropic medication use 28 (17.3%)
Health insurance
 Yes 151 (93.2%)
 No 9 (5.6%)
 Missing 2 (1.2%)
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Note. Demographic information was self-reported during the 3rd trimester, except for parity—the number of pregnancies carried to a viable gestational age (≥20 weeks), including the current pregnancy—which was obtained from medical records.

Procedure

Individuals were recruited via email, community postings, and at University of Utah OBGYN clinics. Eligible participants were 18–40 years old, 25+ weeks pregnant, carrying singleton pregnancy, abstaining from substances/alcohol, and had no pregnancy-related complications at recruitment. Prospective participants were screened based on their scores from a validated measure of emotion dysregulation, specifically to achieve a uniform distribution across the spectrum of emotion dysregulation scores. This approach was adopted to align with the aims of the parent study, which investigated varying levels of emotion dysregulation and perinatal outcomes (see Lin et al., 2019). The Difficulties in Emotion Regulation Scale (DERS; Gratz & Roemer, 2004) was used to assess individuals’ typical levels of emotional control, awareness, and acceptance, providing a comprehensive evaluation of challenges with regulating emotions effectively.

At 3rd trimester, participants completed in-person assessments of life-stress and resting RSA, and an online self-report depression measure, repeated at birth, 7, 18, and 36 months postpartum. See Supplementary Material 1 and 3 for more information on retention. The University of Utah Institutional Review Board approved study procedures.

Measures

Depressive Symptoms.

The Center for Epidemiological Studies–Depression Scale (CES-D; Radloff, 1977) is a 20-item scale that closely mirrors criteria for depression, including poor appetite, loneliness, and sleep disturbance, and has demonstrated high reliability in perinatal populations (Stuart-Parrigon & Stuart, 2014). Responses are scored from 0 (rarely or none of the time) to 3 (most or all of the time), total scores range from 0 to 60, and scores of 16 or greater suggest risk for clinical depression (Lewinsohn et al., 1997). CES-D total scores were used as a continuous variable. Scores ranged from 0 to 59 across study time points and exhibited reliability of α = .75 to .94.

Areas of Life Stress.

The UCLA Life Stress Interview (LSI; Hammen et al., 1987) was administered at 3rd trimester to measure partner relationship, health, and financial stress over the prior six months. The LSI has strong convergent and construct validity (r = .97; Hammen et al., 1987). Trained personnel followed a semi-structured interview format and rated life stress domains on a 1 to 5 scale based on systematic scoring criteria. Scores on partner relationship, health, and financial stress domains ranged from 1 to 5, indicating conditions from positive (1)—stable relationships, exceptional health, and financial sufficiency—to negative (5)— negative relationship conditions, severe health issues, and financial hardship, respectively.

Resting Respiratory Sinus Arrhythmia (RSA).

Resting RSA data were collected during 3rd trimester during a 10-minute resting task using MindWare BioLab data acquisition software (version 3.1). In a laboratory setting, trained research assistants attached ECG electrodes on participants’ chest and abdomen. Participants were then instructed to relax for 10 minutes, during which resting RSA was recorded. This preparatory period was followed by a series of tasks designed to assess stress and regulation. HRV 3.1 software was used to clean and score data. Trained staff processed RSA as beat-to-beat variability in ECG R-peaks in the high-frequency power spectrum (0.12–0.60 Hz; Beauchaine 2015) and analyzed data in 60-second epochs averaged across the 10-minute task. Senior team members inspected values outside the expected range (i.e., 2–10) and excluded five cases.

Data Analysis

Missing Data.

We explored data missingness using the naniar package (Tierne and Cook 2023) in RStudio version 2.1.461 (R Core Team 2022). There were 19 missing data patterns. Little’s MCAR test indicated data likely were missing completely at random (χ2 = 188, p = .30), meaning there were not significant differences in the rate of depressive symptoms between participants retained versus missing. Therefore, we included the full sample of 162 participants in our multilevel models using maximum likelihood to handle missing data (Pinheiro, 2022).

Multilevel Models.

We used the nlme package (Pinheiro, 2022) in RStudio (R Core Team, 2022) to study longitudinal changes in depressive symptom with multilevel models, which accommodate hierarchical data (i.e., repeated measurements within participants) and separate within- and between-person variance (Luke, 2019). Parameters were estimated using maximum likelihood, and all predictors were grand-mean centered. Time was scaled by months since 3rd trimester (0 = 3rd trimester, 2 = birth, 9 = 7 months postpartum, 20 = 18 months postpartum, 38 = 36 months postpartum). To separate the variability in depression accounted for at the within-person level (i.e., depression scores at each time-point) versus between-person level, we calculated intraclass correlation coefficients (ICCs) with a random intercept model. Then, we conducted two multilevel models (i.e., fixed linear and random intercept) to test the statistical difference in fit between the two nested models using an anova.

To examine change in depressive symptoms over time, we conducted a multilevel model with time and a random intercept predicting depressive symptoms. Next, we tested grand-mean centered predictors (i.e., resting RSA, partner relationship stress, financial stress, health stress) as Level 2 predictors of symptoms of depression. Using a model-building approach, we conducted two multilevel models for each of our four predictors. The first model included time, each of the four variables in turn as a focal predictor of depression, and an interaction term between time and the focal variable. In the second model, we included all secondary variables as predictors to examine each of our four focal predictors and their interaction with time, while accounting for other bioecological risk factors, in line with Bronfenbrenner and Morris (1998). Finally, we tested significant cross-level interactions with simple slopes at ± 1 SD cut-offs.

Results

Preliminary Analyses

See Table 2 for descriptive statistics and bivariate correlations. As hypothesized, average depressive symptoms were highest at 3rd trimester (M = 14.53, SD = 10.85) and lowest at 36 months postpartum (M = 11.42, SD = 10.71). Depressive symptoms at all time-points correlated positively with one another. Resting RSA during pregnancy was negatively associated with depressive symptoms at 3rd trimester and 7 months. Prenatal partner relationship stress was positively associated with depressive symptoms at all time-points except 18 months. Prenatal financial and health stress were positively associated with depressive symptoms at all time-points.

Table 2.

Descriptive statistics and bivariate correlations

Variable M (SD) 1 2 3 4 5 6 7 8
Depressive Symptoms
 1. 3rd trimester 14.53 (10.85) -
 2. 48 hours after birth 11.80 (10.01) .71** [.62, .78] -
 3. 7 months postpartum 11.89 (10.17) .58** [.45, .68] .56** [.43, .67] -
 4. 18 months postpartum 13.49 (3.42) .28** [.10, .45] .34** [.16, .50] .21* [.02, .39] -
 5. 36 months postpartum 11.42 (10.71) .43** [.27, .57] .52** [.37, .65] .37** [.19, .53] .54** [.38, .67] -
3rd Trimester Bioecological Stress Factors
 6. Resting RSA 5.35 (1.06) −.23** [−.38, −.07] −.15 [−.31, .02] −.23* [−.39, −.05] .01 [−.19, .20] −.06 [−.25, .14] -
 7. Partner relationship stress 2.13 (.92) .49** [.36, .60] .40** [.25, .52] .43** [.28, .56] .09 [−.10, .27] .21* [.03, .38] .20* [−.35, −.03] -
 8. Financial stress 2.60 (.85) .48** [.35, .59] .42** [.28, .55] .38** [.23, .52] .32** [.14, .48] .35** [.17, .50] −.09 [−.25, .07] .53** [.41, .64] -
 9. Health stress 2.32 (.71) .44** [.31, .56] .37** [.23, .50] .22* [.05, .37] .36** [.19, .52] .33** [.16, .49] −.28** [−.42, −.12] .34** [.19, .47] .43** [.29, .55]
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Note. M and SD represent mean and standard deviation, respectively. Values in square brackets indicate the 95% confidence interval for each correlation.

*

Indicates p < .05.

**

indicates p < .01

Multilevel Models

Trajectory of Depressive Symptoms.

Results showed that a model with random intercept and linear effect of time fit significantly better than models with fixed terms; thus, we added a random linear effect of time to each model. See Supplementary Material 3 for multilevel model parameter estimates. Fifty percent of the variation in depressive symptoms was explained by between-person differences (ICC = .50), suggesting notable, variation within and between individuals’ depressive symptoms (see Figure 1). The intercept for the initial random intercept model (with time as the only predictor) indicated that the average depressive symptom score at 3rd trimester was 13.14 (t = 18.29, p < .001), and the effect of time was not statistically significant (b = −.03, t = −1.29, p = .19).

Figure 1.

Figure 1.

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Individual depressive symptom trajectories demonstrating the variability between- and within-participants in the study sample. The black line depicts the study sample’s mean trajectory. Time-points: prenatal = 3rd trimester, birth = ~48 hours after birth, 7 months = 7 months postpartum, 18 months = 18 months postpartum, and 36 months = 36 months postpartum.

Bioecological Stress Factors as Predictors of Depressive Symptoms Trajectories.

Before accounting for other bioecological stress factors, elevated life stressors were concurrently associated with higher depressive symptoms at 3rd trimester (see Table 3). Specifically, pregnant individuals who reported higher levels of stress in their partner relationships (B = 4.92, p < 0.001), finances (B = 5.20, p < 0.001), and health (B = 5.32, p < 0.001) reported higher depressive symptoms at 3rd trimester. Pregnant people with lower resting RSA during 3rd trimester displayed more depressive symptoms during 3rd trimester (B = −1.98, p = 0.005). Only partner relationship stress was associated with decrease depressive symptoms over time (B = −0.09, p = 0.001).

Table 3.

Multilevel model results with focal bioecological stress variables

Resting RSA
Predictors		 Estimates SE p
(Intercept) 12.93 0.70 <.001
Time −0.03 0.02 0.234
Resting RSA −1.37 0.65 0.035
Time*Resting RSA 0.02 0.02 0.432
Partner Relationship Stress
Predictors Estimates SE p
(Intercept) 13.11 0.64 <.001
Time −0.03 0.02 .195
Partner relationship stress 4.92 0.69 <.001
Time*Partner relationship stress −0.09 0.03 <.001
Financial Stress
Predictors Estimates SE p
(Intercept) 13.20 0.64 <.001
Time −0.03 0.02 .195
Financial stress 5.20 0.75 <.001
Time*Financial stress −0.06 0.03 .055
Health Stress
Predictors Estimates SE p
(Intercept) 13.15 0.65 <.001
Time −0.03 0.02 .192
Health stress 5.33 0.93 <.001
Time*Health stress −0.05 0.03 .112
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Note. SE = standard error

After accounting for other bioecological stress factors, elevated life stressors also associated with higher depression symptoms at 3rd trimester (see Table 4 and Figure 2). Consistent with model one, after accounting for resting RSA, health stress, and financial stress, individuals who reported higher levels of prenatal partner relationship stress exhibited a significant decrease in depressive symptom scores each month from 3rd trimester to 36 months postpartum (B = −0.09, p = 0.001). Only after accounting for resting RSA and other life stressors, prenatal financial stress (B = −0.08, p = 0.008) and health stress (B = −0.08, p = 0.02) each predicted a significant decrease in depressive symptom scores each month. However, the interaction between time and each life stressor was significant only at +1 SD above the mean (partner relationship stress, p = 0.001; financial stress, p = 0.003; health stress, p = 0.008), indicating that there was a decrease in depressive symptoms over time only for those individuals who experienced higher levels of life stress during pregnancy. Resting RSA did not predict changes in depressive symptoms over time (p = .060), even after accounting for other domains of stress.

Table 4.

Multilevel model results accounting for additional bioecological stress variables

Resting RSA
Predictors		 Estimates SE p
(Intercept) 13.09 0.66 <.001
Time −0.04 0.03 .139
Resting RSA −1.08 0.62 .087
Financial stress 1.78 0.77 .021
Partner relationship stress 2.20 0.67 .001
Health stress 1.97 0.83 .017
Time*Resting RSA 0.05 0.02 .055
Partner Relationship Stress
Predictors Estimates SE p
(Intercept) 13.04 0.64 <.001
Time −0.04 0.03 .145
Partner relationship stress 3.51 0.78 <.001
Health stress 2.07 0.82 .013
Financial stress 1.69 0.76 .028
Resting RSA −0.34 0.49 .495
Time*Partner relationship stress −0.09 0.03 <.001
Financial Stress
Predictors Estimates SE p
(Intercept) 13.13 0.64 <.001
Time −0.04 0.02 .095
Financial stress 2.96 0.88 <.001
Health stress 2.10 0.82 .011
Partner relationship stress 2.16 0.67 .002
Resting RSA −0.36 0.49 .466
Time*Financial stress −0.08 0.03 .008
Health Stress
Predictors Estimates SE p
(Intercept) 13.13 0.64 <.001
Time −0.04 0.02 .112
Health stress 3.23 0.99 .002
Financial stress 1.81 0.77 .020
Partner relationship stress 2.22 0.68 <.001
Resting RSA −0.36 0.49 .474
Time*Health stress −0.08 0.04 .023
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Note. SE = standard error

Figure 2.

Figure 2.

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Interactions between risk factors and time were significant for each life stressor only at +1 standard deviation (SD) above the mean—partner relationship stress (p = 0.001), financial stress (p = 0.003), and health stress (p = 0.008)—with decreases in depressive symptoms from the 3rd trimester to 36 months postpartum. Time-points analyzed included the 3rd trimester, ~48 hours post-birth, and 7, 18, and 36 months postpartum. *p < 0.05; **p < 0.01; ***p < 0.001.

Discussion

The present study provides insight into how prenatal bioecological stressors influence depressive symptom trajectories from pregnancy to 36 months postpartum. Several of our findings aligned with our hypotheses and strengthened our understanding of factors associated with depressive symptoms during pregnancy. First, higher life stress in each area (i.e., partner relationship, health, and finances) at 3rd trimester was associated with higher depressive symptoms at the same time-point in pregnancy. These findings are consistent with existing research on these areas of life stress (Chow et al., 2019; Wajid et al., 2020; Zhang et al., 2022). Second, life stressors during the 3rd trimester predicted depression symptom trajectories that decreased over time. Associations between our predictors and symptom trajectory were significant even after accounting for other bioecological factors. Nonetheless, contrary to our hypotheses, pregnant individuals reporting high levels of stress across various domains (i.e., partner, finances, and health) showed significant decreases in depressive symptoms, ultimately ending up with average symptom levels closer to those who reported lower levels of stress prenatally. This pattern may be explained by regression toward the mean, where participants with particularly high initial depressive symptom scores were likely to regress toward the overall sample mean. This effect, possibly intensified by the challenges of pregnancy, appeared to dissipate by 36 months postpartum. Additionally, while depressive symptoms tended to remain elevated up to 18 months postpartum for those initially depressed, they appeared to diminish between 18 months and 3 years, suggesting a potential fading of pregnancy-related effects over time. This finding was unexpected, particularly in light of relevant stress theories, such as stress proliferation theory, which posits that initial stressors may trigger a cascade of additional stressors, potentially worsening depressive symptoms over time (Pearlin et al., 1997). While some of these theories may apply better to other populations, making this a surprising finding, more research is needed to test these theories in perinatal populations to confirm what mechanisms may be at play. One possible explanation for this unexpected finding is that stressors concurrent with each assessment point may be more influential on depressive symptoms than those experienced during pregnancy. Another potential explanation is that higher initial stress might prompt an adaptive response and increase resilience against new stressors during the perinatal period. This interpretation aligns with emerging research suggesting that the perinatal period may serve as a developmental window where maternal stress response systems are particularly malleable, adapting rapidly to changing conditions (Glynn et al., 2018; Howland, 2023). This reduction in symptoms highlights the complexity of stress impacts and underscores the need for further research to elucidate the mechanisms by which perinatal stressors influence depressive symptoms.

In line with existing research, our findings show that prenatal stressful life events increase the risk of depressive symptoms during pregnancy, with their impact diminishing over the postnatal period (Brown et al., 2021; Ding et al., 2023; Gustafsson et al., 2021). Although some participants scored above the clinical threshold of 16, the overall mild to moderate symptoms across time-points and the modest sample size may have limited our ability to detect variations in depressive symptoms. That said, the literature presents conflicting findings on perinatal depressive symptom trajectories. Some studies show varying depressive trajectories, with individuals experiencing increasing or persistently high symptoms (Mughal et al., 2023), while others find more stability from pregnancy to postpartum than expected (Kee et al., 2023).

Study Strengths

A novel contribution of this study is the exploration of physiology, particularly lower resting RSA at 3rd trimester, as predictive of concurrent depressive symptoms, consistent with findings in non-pregnant samples (Yaptangco et al., 2015). Lower resting RSA is one marker of reduced stress-related emotion regulation capacity, suggesting that enhancing emotion regulation during pregnancy could alleviate symptoms. However, resting RSA did not predict depressive symptom trajectory postnatally, possibly due to parallel physiological and psychological shifts over the perinatal period. Resting RSA did not predict the trajectory of depressive symptoms postnatally, which may be attributed to the complex physiological and psychological changes occurring during the perinatal period. We measured RSA during the third trimester to forecast depressive symptoms up to 36 months postpartum—a period characterized by significant and abrupt biological changes, including childbirth (Davis & Narayan, 2020; Glynn et al., 2018; Kaliush et al., 2023). Interestingly, while RSA measured in the 3rd trimester did not significantly predict depressive symptoms across the full postpartum trajectory, it was correlated with depressive symptoms at 7 months postpartum. Therefore, while RSA provides valuable physiological insights, its predictive value for long-term depressive outcomes in perinatal populations should be approached with caution. Measurements of RSA closer to the times of depressive symptom assessments might associate more clearly with depressive outcomes. Further research is necessary to understand how RSA and other proxy indicators of parasympathetic activity may be used to understand perinatal mental health.

The present study also had several methodological strengths, including use of longitudinal data extending up to 36 months postpartum. Further, this study measured depressive symptoms 48 hours after birth, a critical time-point lacking attention in the current longitudinal perinatal depression literature (Kaliush et al., 2023). Additionally, our study includes an examination of stressors spanning various levels of the bioecological model. To that end, we used an interview measure of life stress, building upon findings from self-report questionnaires commonly used in existing studies in this area. Our multi-method (i.e., self-report, physiology, and interview) approach reduces potential inflation of associations with self-reported depressive symptoms (e.g., due to shared method variance; Johnson et al., 2011).

Study Limitations and Future Directions

Our findings, aligned with existing research, highlight the critical need to focus on depressive symptoms during pregnancy. Although many scholars are rightly interested in postpartum depression, findings across various studies show that depressive symptoms are often highest during pregnancy and, on average, decrease across the perinatal period (Vliegen et al., 2014; Yu et al., 2020). A future direction of research should include an exploration of depressive symptoms in the months leading up to and across pregnancy. With the framing of the perinatal period as a period of stress recalibration (Howland, 2023), it will be important to measure proximal bioecological stress factors along with depressive symptoms to understand patterns in their association over time. While a linear model fit our data well, investigating non-linear trajectories in future studies with more frequent assessments could deepen insights into longitudinal changes. It is also important to note that our study did not include social support, which may serve as a protective buffer against mental health challenges and stressors during the perinatal period (Morikawa et al., 2015; Sufredini et al., 2022). Future research should consider incorporating social support to provide a more comprehensive understanding of risk factors and known protective factors.

Our results emphasize the need to capture multiple levels of the bioecological model to understand mental health during pregnancy. That said, our study did not account for the macrosystem, or the broader social-contextual landscape, where pregnant individuals live. Future studies should consider the impact of racism (Garland McKinney and Meinersmann, 2022), discrimination (Noroña-Zhou et al., 2022), expectations of motherhood (Westgate et al., 2023), and structural barriers that inhibit access to fundamental resources such as rest, safety, or mental and reproductive health care (Chow et al., 2019; Coleman et al., 2023). Our results may not capture the breadth of experiences among all pregnant individuals given limited sample sizes within various gender, racial, and ethnic groups. However, our study did include a diverse population, with nearly half of the participants identifying as members of historically underrepresented groups and had a range of scores on emotion dysregulation. Despite most participants reporting mild to moderate depressive symptoms postnatally, the study’s transdiagnostic approach captures changes in depressive symptoms across a relatively broad spectrum of emotion regulation functioning.

Conclusion

Our study has several implications for research, practice, and policy. Our findings highlight the variability in depressive symptom trajectories between people, challenging the traditional one-size-fits-all approach to perinatal depression, and advocating for care that acknowledges this heterogeneity. Our findings also support the importance of buffering pregnant individuals from prenatal stress. Systems-based programs offer a promising approach for practice and policy, providing continuous, tailored support from prenatal to postpartum, through early stress management and holistic care. This approach not only buffers prenatal stress but also enhances the well-being of pregnant individuals and their children (Campbell, 2019; Lugo-Candelas and Monk, 2024). These programs align with our findings, which suggest that depressive symptoms, associated with high levels of stress, may peak during pregnancy for some individuals. Lastly, the observed peak in depressive symptoms during pregnancy, followed by an average decrease postpartum, underscores the need for enhanced support for families navigating the transition to parenthood, reinforcing the value of intervention prenatally.

Supplementary Material

1

Highlights.

  • Depressive symptoms peaked during third trimester of pregnancy

  • Bioecological factors during pregnancy were associated with trajectory of depressive symptoms

  • Lower RSA during pregnancy was associated with higher depressive symptoms during pregnancy

  • Higher life-stress during pregnancy was associated with higher depressive symptoms during pregnancy

  • For women with high stress during pregnancy, depressive symptoms decreased over perinatal period

Acknowledgements:

The authors would like to thank the families involved with the study and the larger research team for their contributions to the study.

Funding:

Data for this study is supported by grants from National Institutes of Health awarded to Crowell and Conradt (R01MH119070 and R21MH109777).

Footnotes

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Author Statements

Author Contributions: Nicolette C. Molina: Conceptualization, methodology, formal analysis, writing – original draft, writing – review & editing, visualization. Anna M. Zhou: Methodology, formal analysis, writing – review & editing, visualization. Parisa R. Kaliush: Conceptualization, writing – review & editing. Sarah E. Maylott: Methodology, formal analysis, writing – review & editing, visualization. Ashley E. Pappal: writing – review & editing. Kira Wright: Project administration, data curation, writing – review & editing. Dylan Neff: Project administration, data curation, writing – review & editing. Jonathan E. Butner: Methodology, writing – review & editing. K. Lee Raby: Writing – review & editing. Elisabeth Conradt: Writing – review & editing, funding acquisition. Sheila E. Crowell: Conceptualization, methodology, writing – review & editing, funding acquisition.

Statements and Declarations

Disclosure Statement: The authors declare they have no conflicts of interest.

1

While most of our participants (161 out of 162) identified as female, one participant did not endorse a female gender identity. To ensure inclusivity and accuracy in our research, we have used the term ‘pregnant individuals’ throughout this manuscript. This approach aligns with recent recommendations to avoid erasure of gender diversity and to acknowledge the experiences of all pregnant individuals (Rioux et al., 2023).

Data availability:

Data pertaining to R21MH109777 (MPIs Crowell & Conradt) and that support these findings are uploaded to the National Institutes of Mental Health (NIMH) repository.

References

  1. Beauchaine TP, 2001. Vagal tone, development, and Gray’s motivational theory: toward an integrated model of autonomic nervous system functioning in psychopathology. Development and Psychopathology, 13, 183–214. 10.1017/S0954579401002012 [DOI] [PubMed] [Google Scholar]
  2. Beauchaine TP, 2015a. Respiratory sinus arrhythmia: a transdiagnostic biomarker of emotion dysregulation and psychopathology. Current Opinion in Psychology, 3, 43–47. 10.1016/j.copsyc.2015.01.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Beauchaine TP, Thayer JF, 2015b. Heart rate variability as a transdiagnostic biomarker of psychopathology. International Journal of Psychophysiology, 98, 338–350. 10.1016/j.ijpsycho.2015.08.004 [DOI] [PubMed] [Google Scholar]
  4. Bronfenbrenner U, 1979a. The Ecology of Human Development: Experiments by Nature and Design. Harvard University Press, Cambridge, Mass. [Google Scholar]
  5. Bronfenbrenner U, 1979b. Contexts of child rearing: problems and prospects. The American Psychologist, 34, 844–850. 10.1037/0003-066X.34.10.844 [DOI] [Google Scholar]
  6. Bronfenbrenner U, Morris PA, 1998. The ecology of developmental processes. In: Damon W, Lerner RM (Eds.), Handbook of child psychology: theoretical models of human development, 5th Edition. John Wiley & Sons Inc, New Jersey, pp 993–1028 [Google Scholar]
  7. Brown RL, Fagundes CP, Thayer JF, Christian LM, 2021. Longitudinal changes in HRV across pregnancy and postpartum: effect of negative partner relationship qualities. Psychoneuroendocrinology, 129, 105216. 10.1016/j.psyneuen.2021.105216 [DOI] [PubMed] [Google Scholar]
  8. Campbell M, 2019. Family leave: comparing the United States’ family and medical leave act with Sweden’s parental leave policy notes. Notre Dame Journal of International & Comparative Law 9, 116–142. [Google Scholar]
  9. Choi C, Mersky JP, Janczewski CE, Goyal D, 2022. Advancing research on perinatal depression trajectories: evidence from a longitudinal study of low-income women. Journal of Affective Disorders, 301, 44–51. 10.1016/j.jad.2022.01.026 [DOI] [PubMed] [Google Scholar]
  10. Chow A, Dharma C, Chen E, Mandhane PJ, Turvey SE, Elliott SJ, Becker AB, Subbarao P, Sears MR, Kozyrskyj AL, 2019. Trajectories of depressive symptoms and perceived stress from pregnancy to the postnatal period among Canadian women: impact of employment and immigration. American Journal of Public Health, 109, S197–S204. 10.2105/AJPH.2018.304624 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Coleman JN, Hellberg SN, Hopkins TA, Thompson KA, Bruening AB, Jones AC, 2023. Situating reproductive coercion in the sociocultural context: an ecological model to inform research, practice, and policy in the United States. Journal of Trauma & Dissociation, 24, 471–488. 10.1080/15299732.2023.2212403 [DOI] [PubMed] [Google Scholar]
  12. Crowell SE, Beauchaine TP, Linehan MM, 2009. A biosocial developmental model of borderline personality: elaborating and extending Linehan’s theory. Psychological Bulletin, 135, 495–510. 10.1037/a0015616 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Cox J, Holden J, & Sagovsky R (1987). Detection of postnatal depression: Development of the 10-item Edinburgh Postnatal Depression Scale. British Journal of Psychiatry, 150, 782–786. 10.1192/bjp.150.6.782 [DOI] [PubMed] [Google Scholar]
  14. Davis EP, Demers CH, Deer L, Gallop RJ, Hoffman MC, Grote N, Hankin BL, 2024. Impact of prenatal maternal depression on gestational length: Post hoc analysis of a randomized clinical trial. Clinical Medicine, 72, 102601. 10.1016/j.eclinm.2024.102601 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Davis EP, & Narayan AJ (2020). Pregnancy as a period of risk, adaptation, and resilience for mothers and infants. Development and Psychopathology, 32(5), 1625–1639. 10.1017/S0954579420001121 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Ding X, Liang M, Wang H, Song Q, Guo X, Su W, Li N, Liu H, Ma S, Zhou X, Sun Y, 2023. Prenatal stressful life events increase the prevalence of postpartum depression: Evidence from prospective cohort studies. Journal of Psychiatric Research, 160, 263–271. 10.1016/j.jpsychires.2023.02.036 [DOI] [PubMed] [Google Scholar]
  17. Engel GL (1977). The need for a new medical model: a challenge for biomedicine. Science, 196(4286), 129–136. [DOI] [PubMed] [Google Scholar]
  18. Eriksson A, Kimmel MC, Furmark T, Wikman A, Grueschow M, Skalkidou A, Frick A, & Fransson E (2024). Investigating heart rate variability measures during pregnancy as predictors of postpartum depression and anxiety: An exploratory study. Translational Psychiatry, 14(1), 1–10. 10.1038/s41398-024-02909-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Figueiredo B, Canário C, Tendais I, Pinto TM, Kenny DA, Field T, 2018. Couples’ relationship affects mothers’ and fathers’ anxiety and depression trajectories over the transition to parenthood. Journal of Affective Disorders, 238, 204–212. 10.1016/j.jad.2018.05.064 [DOI] [PubMed] [Google Scholar]
  20. Garland McKinney JL, Meinersmann LM, 2022. The cost of intersectionality: motherhood, mental health, and the state of the country. Journal of Social Issues, 79, 596–616. 10.1111/josi.12539 [DOI] [Google Scholar]
  21. Gao M, Saenz C, Neff D, Santana ML, Amici J, Butner J, Raby KL, Crowell SE, Conradt E, 2022. Bringing the laboratory into the home: a protocol for remote biobehavioral data collection in pregnant women with emotion dysregulation and their infants. Journal of Health Psychology, 27, 2644–2667. 10.1177/13591053211064984 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Glynn LM, Howland MA, Fox M, 2018. Maternal programming: Application of a developmental psychopathology perspective. Development and Psychopathology, 30, 905–919. 10.1017/S0954579418000524 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Gratz KL, Roemer L, 2004. Multidimensional assessment of emotion regulation and dysregulation: Development, factor structure, and initial validation of the Difficulties in Emotion Regulation Scale. Journal of Psychopathology and Behavioral Assessment, 30, 315–315. 10.1007/s10862-008-9102-4 [DOI] [Google Scholar]
  24. Gustafsson HC, Young AS, Doyle O, Nagel BJ, Mackiewicz Seghete K, Nigg JT, Sullivan EL, Graham AM, 2021. Trajectories of perinatal depressive symptoms in the context of the COVID-19 pandemic. Child Development, 92, e749–e763. 10.1111/cdev.13656 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Hammen C, Adrian C, Gordon D, Burge D, Jaenicke C, Hiroto D, 1987. Children of depressed mothers: maternal strain and symptom predictors of dysfunction. Journal of Abnormal Psychology, 96, 190–198. 10.1037/0021-843X.96.3.190 [DOI] [PubMed] [Google Scholar]
  26. Howland MA, 2023. Recalibration of the stress response system over adult development: is there a perinatal recalibration period? Development and Psychopathology, 35, 2315–2337. 10.1017/S0954579423000998 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Johnson RE, Rosen CC, Djurdjevic E, 2011. Assessing the impact of common method variance on higher order multidimensional constructs. Journal of Applied Psychology, 96, 744–761. 10.1037/a0021504 [DOI] [PubMed] [Google Scholar]
  28. Kaliush P, Conradt E, Kerig P, Williams P, & Crowell S (2023). A multilevel developmental psychopathology model of childbirth and the perinatal transition. Development and Psychopathology, 1–12. 10.1017/S0954579422001389 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Kaliush PR, Kerig PK, Raby KL, Maylott SE, Neff D, Speck B, Molina NC, Pappal AE, Parameswaran UD, Conradt E, Crowell SE, 2023. Examining implications of the developmental timing of maternal trauma for prenatal and newborn outcomes. Infant Behavior and Development, 72, 101861. 10.1016/j.infbeh.2023.101861 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Kee MZL, Cremaschi A, De Iorio M, Chen H, Montreuil T, Nguyen TV, Côté SM, O’Donnell KJ, Giesbrecht GF, Letourneau N, Chan SY, Meaney MJ, 2023. Perinatal trajectories of maternal depressive symptoms in prospective, community-based cohorts across 3 continents. JAMA Network Open 6, e2339942. 10.1001/jamanetworkopen.2023.39942 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Kerig PK, 2020. Emotion dysregulation and childhood trauma. In: Beauchaine TP, Crowell SE (Eds.) The Oxford Handbook of Emotion Dysregulation. Oxford Library of Psychology, Online, pp. 264–282. [Google Scholar]
  32. Lewinsohn Seeley JR, Allen NB, Roberts RE, 1997. Center for Epidemiologic Studies Depression Scale (CES-D) as a screening instrument for depression among community-residing older adults. Psychology and Aging, 12, 277–287. 10.1037/0882-7974.12.2.277 [DOI] [PubMed] [Google Scholar]
  33. Lin B, Kaliush PR, Conradt E, Terrell S, Neff D, Allen AK, Smid MC, Monk C, Crowell SE, 2019. Intergenerational transmission of emotion dysregulation: Part I. psychopathology, self-injury, and parasympathetic responsivity among pregnant women. Development and Psychopathology, 31, 817–831. 10.1017/S0954579419000336 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Lugo-Candelas C, Monk C, 2024. System-level approaches to perinatal depression. The Lancet Public Health, 9, 4–5. 10.1016/S2468-2667(23)00304-3 [DOI] [PubMed] [Google Scholar]
  35. Luke DA, 2019. Multilevel modeling. SAGE Publications, London. [Google Scholar]
  36. Mateus V, Cruz S, Costa R, Mesquita A, Christoforou A, Wilson CA, Vousoura E, Dikmen-Yildiz P, Bina R, Dominguez-Salas S, Contreras-García Y, Motrico E, Osório A, 2022. Rates of depressive and anxiety symptoms in the perinatal period during the COVID-19 pandemic: comparisons between countries and with pre-pandemic data. Journal of Affective Disorders, 316, 245–253. 10.1016/j.jad.2022.08.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Morikawa M, Okada T, Ando M, Aleksic B, Kunimoto S, Nakamura Y, Kubota C, Uno Y, Tamaji A, Hayakawa N, Furumura K, Shiino T, Morita T, Ishikawa N, Ohoka H, Usui H, Banno N, Murase S, Goto S, Ozaki N (2015). Relationship between social support during pregnancy and postpartum depressive state: A prospective cohort study. Scientific Reports, 5(1), 10520. 10.1038/srep10520 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Motosko CC, Bieber AK, Pomeranz MK, Stein JA, & Martires KJ (2017). Physiologic changes of pregnancy: A review of the literature. International Journal of Women’s Dermatology, 3(4), 219–224. 10.1016/j.ijwd.2017.09.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Mughal MK, Giallo R, Arshad M, Arnold PD, Bright K, Charrois EM, Rai B, Wajid A, Kingston D, 2023. Trajectories of maternal depressive symptoms from pregnancy to 11 years postpartum: Findings from Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Journal of Affective Disorders. 10.1016/j.jad.2023.02.023 [DOI] [PubMed] [Google Scholar]
  40. Noroña-Zhou A, Aran Ö, Garcia SE, Haraden D, Perzow SED, Demers CH, Hennessey E-MP, Melgar Donis S, Kurtz M, Hankin BL, & Davis EP (2022). Experiences of Discrimination and Depression Trajectories over Pregnancy. Women’s Health Issues, 32(2), 147–155. 10.1016/j.whi.2021.10.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Pearlin LI, Aneshensel CS, & Leblanc AJ (1997). The Forms and Mechanisms of Stress Proliferation: The Case of AIDS Caregivers. Journal of Health and Social Behavior, 38(3), 223–236. 10.2307/2955368 [DOI] [PubMed] [Google Scholar]
  42. Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team, 2022. nlme: Linear and nonlinear mixed effects models. R package version, 3.1–160. [Google Scholar]
  43. Putnam KT, Wilcox M, Robertson-Blackmore E, Sharkey K, Bergink V, Munk-Olsen T, Deligiannidis KM, Payne J, Altemus M, Newport J, Apter G, Devouche E, Viktorin A, Magnusson P, Penninx B, Buist A, Bilszta J, O’Hara M, Stuart S, Brock R, Roza S, Tiemeier H, Guille C, Epperson CN, Kim D, Schmidt P, Martinez P, Di Florio A, Wisner KL, Stowe Z, Jones I, Sullivan PF, Rubinow D, Wildenhaus K, Meltzer-Brody S, 2017. Clinical phenotypes of perinatal depression and time of symptom onset: analysis of data from an international consortium. The Lancet Psychiatry, 4, 477–485. 10.1016/S2215-0366(17)30136-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Radloff LS, 1977. The CES-D Scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1, 385–401. 10.1177/014662167700100306 [DOI] [Google Scholar]
  45. Rogers A, Obst S, Teague SJ, Rossen L, Spry EA, Macdonald JA, Sunderland M, Olsson CA, Youssef G, Hutchinson D, 2020. Association Between Maternal Perinatal Depression and Anxiety and Child and Adolescent Development: a meta-analysis. JAMA Pediatrics, 174, 1082–1092. 10.1001/jamapediatrics.2020.2910 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Roubinov D, Musci R, Hipwell A, Wu G, Santos H Jr, Felder J, Faleschini S, Conradt E, McEvoy C, Lester B, Buss C, Elliott A, Cordero J, Stroustrup A, Bush N, 2022. Trajectories of depressive symptoms among mothers of preterm and full-term infants in a national sample. Archives of Women’s Mental Health, 1–11. 10.1007/s00737-022-01245-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Salm Ward T, Kanu FA, Robb SW, 2017. Prevalence of stressful life events during pregnancy and its association with postpartum depressive symptoms. Archives of Women’s Mental Health, 20, 161–171. 10.1007/s00737-016-0689-2 [DOI] [PubMed] [Google Scholar]
  48. Shah Z, Pal P, Pal GK, Papa D, & Bharadwaj B (2020). Assessment of the association of heart rate variability and baroreflex sensitivity with depressive symptoms and stress experienced by women in pregnancy. Journal of Affective Disorders, 277, 503–509. 10.1016/j.jad.2020.08.039 [DOI] [PubMed] [Google Scholar]
  49. Shea AK, Kamath MV, Fleming A, Streiner DL, Redmond K, Steiner M, 2008. The effect of depression on heart rate variability during pregnancy. Clinical Autonomic Research, 18, 203–212. 10.1007/s10286-008-0480-1 [DOI] [PubMed] [Google Scholar]
  50. Simons HR, Thorpe LE, Jones HE, Lewis JB, Tobin JN, Ickovics JR, 2020. Perinatal depressive symptom trajectories among adolescent women in New York City. Journal of Adolescent Health 67, 84–92. 10.1016/j.jadohealth.2019.12.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Singh Solorzano C, & Grano C (2023). Predicting postpartum depressive symptoms by evaluating self-report autonomic nervous system reactivity during pregnancy. Journal of Psychosomatic Research, 174, 111484. 10.1016/j.jpsychores.2023.111484 [DOI] [PubMed] [Google Scholar]
  52. Singh Solorzano C, Violani C, & Grano C (2022). Pre-partum HRV as a predictor of postpartum depression: The potential use of a smartphone application for physiological recordings. Journal of Affective Disorders, 319, 172–180. 10.1016/j.jad.2022.09.056 [DOI] [PubMed] [Google Scholar]
  53. Stuart-Parrigon K, & Stuart S (2014). Perinatal Depression: An Update and Overview. Current Psychiatry Reports, 16(9), 468. 10.1007/s11920-014-0468-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Sufredini F, Catling C, Zugai J, & Chang S (2022). The effects of social support on depression and anxiety in the perinatal period: A mixed-methods systematic review. Journal of Affective Disorders, 319, 119–141. 10.1016/j.jad.2022.09.005 [DOI] [PubMed] [Google Scholar]
  55. Tabb KM, Dalton VK, Tilea A, Kolenic GE, Admon LK, Hall SV, Zhang X, Ryckman KK, Zivin K, 2023. Trends in antenatal depression and suicidal ideation diagnoses among commercially insured childbearing individuals in the United States, 2008–2018. Journal of Affective Disorders, 320, 263–267. 10.1016/j.jad.2022.09.120 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Tierney N, Cook D, 2023. Expanding tidy data principles to facilitate missing data exploration, visualization and assessment of imputations. Journal of Statistical Software, 105, 1–31. 10.18637/jss.v105.i0736798141 [DOI] [Google Scholar]
  57. Tudge JRH, Mokrova I, Hatfield BE, Karnik RB, 2009. Uses and misuses of Bronfenbrenner’s bioecological theory of human development. Journal of Family Theory & Review, 1, 198–210. 10.1111/j.1756-2589.2009.00026.x [DOI] [Google Scholar]
  58. Vliegen N, Casalin S, Luyten P, 2014. The course of postpartum depression: a review of longitudinal studies. Harvard Review of Psychiaty, 22, 1–22. 10.1097/HRP.0000000000000013 [DOI] [PubMed] [Google Scholar]
  59. Wajid A, Kingston D, Bright KS, Kashif Mughal M, Charrois EM, Giallo R, 2020. Psychosocial factors associated with trajectories of maternal psychological distress over a 10-year period from the first year postpartum: an Australian population-based study. Journal of Affective Disorders, 263, 31–38. 10.1016/j.jad.2019.11.138 [DOI] [PubMed] [Google Scholar]
  60. Westgate V, Manchanda T, Maxwell M, 2023. Women’s experiences of care and treatment preferences for perinatal depression: a systematic review. Archive of Women’s Mental Health, 26, 311–319. 10.1007/s00737-023-01318-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Yaptangco M, Crowell SE, Baucom BR, Bride DL, Hansen EJ, 2015. Examining the relation between respiratory sinus arrhythmia and depressive symptoms in emerging adults: a longitudinal study. Biological Psychology 110, 34–41. 10.1016/j.biopsycho.2015.06.004 [DOI] [PubMed] [Google Scholar]
  62. Yu M, Li H, Xu DR, Wu Y, Liu H, Gong W, 2020. Trajectories of perinatal depressive symptoms from early pregnancy to six weeks postpartum and their risk factors—a longitudinal study. Journal of Affective Disorders, 275, 149–156. 10.1016/j.jad.2020.07.005 [DOI] [PubMed] [Google Scholar]
  63. Zhang X, Zhang Y, Vasilenko SA, 2022. The longitudinal relationships among poverty, material hardship, and maternal depression in the USA: a latent growth mediation model. Archive of Women’s Mental Health, 25, 763–770. 10.1007/s00737-022-01238-4 [DOI] [PubMed] [Google Scholar]
  64. Zuckerman M, Riskind JH, 2000. Vulnerability to psychopathology: a biosocial model. Journal of Cognitive Psychotherapy 14, 407–408. 10.1891/0889-8391.14.4.407 [DOI] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS2020156-supplement-1.docx (1.5MB, docx)

Data Availability Statement

Data pertaining to R21MH109777 (MPIs Crowell & Conradt) and that support these findings are uploaded to the National Institutes of Mental Health (NIMH) repository.

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