research paper topics on postpartum depression

89 Postpartum Depression Essay Topic Ideas & Examples

🏆 best postpartum depression topic ideas & essay examples, 👍 most interesting postpartum depression topics to write about, ⭐ good research topics about postpartum depression, ❓ postpartum depression research questions.

• Activity During Pregnancy and Postpartum Depression Studies have shown that women’s mood and cardiorespiratory fitness improve when they engage in moderate-intensity physical activity in the weeks and months after giving birth to a child.
• Complementary Therapy for Postpartum Depression in Primary Care Thus, the woman faced frustration and sadness, preventing her from taking good care of the child, and the lack of support led to the emergence of concerns similar to those in the past. We will write a custom essay specifically for you by our professional experts 808 writers online Learn More
• Technology to Fight Postpartum Depression in African American Women I would like to introduce the app “Peanut” the social network designed to help and unite women exclusively, as a technology aimed at fighting postpartum depression in African American Women.
• The Postpartum Depression in Afro-Americans Policy The distribution of the funds is managed and administered on the state level. Minnesota and Maryland focused on passing the legislation regulating the adoption of Medicaid in 2013.
• Breastfeeding and Risk of Postpartum Depression The primary goal of the research conducted by Islam et al.was to analyze the correlation between exclusive breastfeeding and the risk of postpartum depression among new mothers.
• Postpartum Depression in African American Women As far as African American women are concerned, the issue becomes even more complex due to several reasons: the stigma associated with the mental health of African American women and the mental health complications that […]
• Postpartum Depression Among the Low-Income U.S. Mothers Mothers who take part in the programs develop skills and knowledge to use the existing social entities to ensure that they protect themselves from the undesirable consequences associated with the PPD and other related psychological […]
• In-Vitro Fertilization and Postpartum Depression The research was conducted through based on professional information sources and statistical data collected from the research study used to further validate the evidence and outcome of this study.
• Postpartum Depression and Its Impact on Infants The goal of this research was “to investigate the prevalence of maternal depressive symptoms at 5 and 9 months postpartum in a low-income and predominantly Hispanic sample, and evaluate the impact on infant weight gain, […]
• Postpartum Depression: Statistics and Methods of Diagnosis The incorporation of the screening tools into the existing electronic medical support system has proved to lead to positive outcomes for both mothers and children.
• Postpartum Psychosis: Impact on Family By the ties of kinship, the extended families of both parents are often intricately involved in the pregnancy and maybe major sources of support for the pregnant woman.
• Postpartum Depression: Treatment and Therapy It outlines the possible treatment and therapy methods, as well as the implications of the condition. A 28-year-old patient presented in the office three weeks after giving birth to her first son with the symptoms […]
• A Review of Postpartum Depression and Continued Post Birth Support In the first chapter – the introduction – the problem statement, background, purpose, and nature of the project are mentioned. The purpose of the project is to explain the significance of managing postpartum depression by […]
• Postpartum Depression: Understanding the Needs of Women This article also emphasizes the need to consider and assess the needs of the mother, infant as well as family members during the treatment of PPD.
• Postpartum Depression and Acute Depressive Symptoms It is hypothesized that the authors of the study wished to establish, with certainty, the effect of the proposed predictors for the development of PPD.
• Postpartum Depression and Its Peculiarities The major peculiarity of PPD in terms of its adverse effects is that it is detrimental to both the mother and the newborn child.
• Supporting the Health Needs of Patients With Parkinson’s, Preeclampsia, and Postpartum Depression The medical history of the patient will help the doctor to offer the best drug therapy. Members of the family might also be unable to cope with the disorder.
• Postpartum Depression and Comorbid Disorders For example, at a public hospital in Sydney, Australia, the psychiatrists used a Routine Comprehensive Psychosocial Assessment tool to study the chances of ‘low risk’ women developing the postpartum symptoms.
• Correlation Between Multiple Pregnancies and Postpartum Depression or Psychosis In recognition of the paucity of information on the relationship between multiple pregnancies and postpartum depression, the paper reviews the likely relationship by understanding the two variables, multiple pregnancies and postpartum depression, in terms of […]
• Acknowledging Postpartum Depression: Years Ago, There Was
• Postpartum Depression and Crime: The Case of Andrea Yates
• Baby Blues, Postpartum Depression, and Postpartum Psychosis
• Postpartum Depression and Parent-Child Relationships
• Cheryl Postpartum Depression Theory Analysis
• Cognitive Therapy for Postpartum Depression
• Postpartum Depression: An Important Issue in Women’s Health
• The Relationships Between Depression and Postpartum Depression
• Postpartum Depression: Causes and Treatments
• How Postpartum Depression Predicts Emotional and Cognitive Difficulties in 11-Year-Olds
• Economic and Health Predictors of National Postpartum Depression Prevalence
• Postpartum Depression (PPD): Symptoms, Causes, and Treatment
• Fathers Dealing With Postpartum Depression
• Postpartum Depression and the Birth of a New Baby
• Risk of Postpartum Depression in Women Without Depression in Pregnancy
• Intimate Partner Violence During Pregnancy and Postpartum Depression in Japan
• Managing Postpartum Depression Through Medications and Therapy
• Early Identification Essential to Treat Postpartum Depression
• Screening for Postpartum Depression and Associated Factors Among Women in China
• Postpartum Depression and Anxiety Disorders in Women
• Postpartum Depression and Child Development
• Association Between Family Members and Risk of Postpartum Depression in Japan
• Postpartum Depression and Its Effects on Mental Health
• Baby Blues, the Challenges of Postpartum Depression
• How Postpartum Depression Affects Employment
• Postpartum Depression During the Postpartum Period
• Evidence-Based Interventions of Postpartum Depression
• Proposed Policy for Postpartum Depression Screening and Treatment
• Sleep Deprivation and Postpartum Depression
• The Causes and Effects of Postpartum Depression
• The Main Facts About Postpartum Depression
• The Postpartum Depression and Crime Relations
• Sleep Quality and Mothers With Postpartum Depression
• Postpartum Depression and Its Effects on Early Brain
• Fetal Gender and Postpartum Depression in a Cohort of Chinese Women
• Postpartum Depression and Postnatal Depression Psychology
• The Problem of Postpartum Depression Among Canadian Women
• Postpartum Depression and Its Effect on the Family Experience
• Mothers With Postpartum Depression for Breastfeeding Success
• Postpartum Depression and Analysis of Treatments and Health Determinants
• How Are Neuroactive Steroids Related to Major Depressive Disorder and Postpartum Depression?
• What Are the Emotional and Behavioral Changes During Postpartum Depression?
• Does Postpartum Depression Affect the Child’s Development?
• When Does Postpartum Depression Lead to Psychosis?
• How to Recognize Postpartum Depression?
• What Is the Role of the Mother, Child, and Partner in Postpartum Depression?
• Is There an Association Between Family Members and the Risk of Postpartum Depression in Japan?
• What Are the Most Common Signs of Postpartum Depression?
• How Does Postpartum Depression Affect Parent-Child Relationships?
• What Type of Therapy Is Most Widely Used for a Person Suffering from Postpartum Depression?
• Can Postpartum Depression Cause Autism?
• What Is a Gender Perspective on Postpartum Depression and the Social Construction of Motherhood?
• How Are Postpartum Depression and Related Factors Screened Among Women in China?
• What Are the Economic and Medical Projections of the Prevalence of Postpartum Depression?
• Is There a Difference Between Postnatal and Postpartum Depression?
• What Is the Biggest Risk Factor for Postpartum Depression?
• How Are Fetal Gender and Postpartum Depression Related in a Cohort of Chinese Women?
• What Factors Contribute to the Development of Postpartum Depression?
• Is Postpartum Depression a Long-Term Disability?
• What Are the Causes and Consequences of Postpartum Depression?
• How Is Postpartum Depression Diagnosed?
• What Is Postpartum Depression and How Does It Affect Newborns and Mothers?
• Is Psychotherapy the Best Treatment for Postpartum Depression?
• What Should Be the Knowledge of Nurses in the Diagnosis of Postpartum Depression?
• How Does Postpartum Depression Affect the Family Experience?
• What Is the Relationship Between Sleep Quality and Postpartum Depression in Mothers?
• Can Postpartum Depression Be Managed with Medication and Therapy?
• What Treatment Options Are Available for People with Postpartum Depression?
• How Long After Childbirth Can Postpartum Depression Occur?
• Are Physical Activity Interventions Effective in the Treatment of Postpartum Depression?
• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2023, September 20). 89 Postpartum Depression Essay Topic Ideas & Examples. https://ivypanda.com/essays/topic/postpartum-depression-essay-topics/

"89 Postpartum Depression Essay Topic Ideas & Examples." IvyPanda , 20 Sept. 2023, ivypanda.com/essays/topic/postpartum-depression-essay-topics/.

IvyPanda . (2023) '89 Postpartum Depression Essay Topic Ideas & Examples'. 20 September.

IvyPanda . 2023. "89 Postpartum Depression Essay Topic Ideas & Examples." September 20, 2023. https://ivypanda.com/essays/topic/postpartum-depression-essay-topics/.

1. IvyPanda . "89 Postpartum Depression Essay Topic Ideas & Examples." September 20, 2023. https://ivypanda.com/essays/topic/postpartum-depression-essay-topics/.

Bibliography

IvyPanda . "89 Postpartum Depression Essay Topic Ideas & Examples." September 20, 2023. https://ivypanda.com/essays/topic/postpartum-depression-essay-topics/.

• Parenting Research Topics
• Motherhood Ideas
• Mental Health Essay Ideas
• Stress Titles
• Depression Essay Topics
• Suicide Topics
• Cognitive Therapy Essay Topics
• Mental Disorder Essay Topics
• Abnormal Psychology Paper Topics
• Childhood Essay Topics
• Family Problems Questions
• Cognitive Psychology Topics
• Infant Research Topics
• Gender Stereotypes Essay Titles
• Psychoanalysis Essay Topics

• Research article
• Open access
• Published: 27 January 2021

Postpartum depression symptoms in survey-based research: a structural equation analysis

• Che Wan Jasimah Bt Wan Mohamed Radzi 1 ,
• Hashem Salarzadeh Jenatabadi 1   na1 &
• Nadia Samsudin 1   na1  

BMC Public Health volume  21 , Article number:  27 ( 2021 ) Cite this article

18k Accesses

26 Citations

Metrics details

Since the last decade, postpartum depression (PPD) has been recognized as a significant public health problem, and several factors have been linked to PPD. Mothers at risk are rarely undetected and underdiagnosed. Our study aims to determine the factors leading to symptoms of depression using Structural Equation Modeling (SEM) analysis. In this research, we introduced a new framework for postpartum depression modeling for women.

We structured the model of this research to take into consideration the Malaysian culture in particular. A total of 387 postpartum women have completed the questionnaire. The symptoms of postpartum depression were examined using the Edinburgh Postnatal Depression Scale (EPDS), and they act as a dependent variable in this research model.

Four hundred fifty mothers were invited to participate in this research. 86% of the total distributed questionnaire received feedback. The majority of 79.6% of respondents were having depression symptoms. The highest coefficients of factor loading analysis obtained in every latent variable indicator were income (β = 0.77), screen time (β = 0.83), chips (β = 0.85), and anxiety (β = 0.88). Lifestyle, unhealthy food, and BMI variables were directly affected by the dependent variable. Based on the output, respondents with a high level of depression symptoms tended to consume more unhealthy food and had a high level of body mass indexes (BMI). The highest significant impact on depression level among postpartum women was unhealthy food consumption. Based on our model, the findings indicated that 76% of the variances stemmed from a variety of factors: socio-demographics, lifestyle, healthy food, unhealthy food, and BMI. The strength of the exogenous and endogenous variables in this research framework is strong.

The prevalence of postpartum women with depression symptoms in this study is considerably high. It is, therefore, imperative that postpartum women seek medical help to prevent postpartum depressive symptoms from worsening.

Peer Review reports

The number of people diagnosed with depression has been steadily increasing over the years. It affects the patient’s work performance, financial status, and interpersonal relationships [ 1 ]. Depression can be observed from the individual’s passive behavior such as loss of interest, feelings of guilt, low self-respect, sleep-deprived, poor appetite, constantly being unhappy, or showing signs of fatigue [ 2 , 3 ]. Living with depression causes a serious disability to the patient because it is associated with mental and behavioral disorders [ 4 ]. It is highly probable that this condition affects the patient’s physical well-being leading to increased morbidity and mortality [ 1 , 5 , 6 ]. In 2017, the World Health Organization (WHO) reported that over 300 million people suffered from depression [ 7 ]. However, previous studies showed that depression typically occurred among women, as opposed to men [ 8 ]. The primary reasons for depression among women were attributed to hormonal transition, such as puberty, pregnancy, and menopausal changes [ 1 ]. In particular, after giving birth, a woman needs extra care and should be given the right kind of health care priorities. Moreover, any unpleasant act can cause depression at this stage which will be devastating for the whole family [ 9 ]. Postpartum depression (PPD) was identified as the number one complication that plagued one in seven women [ 10 ]. It has been estimated that more than 20% of women globally suffer from PPD [ 11 ]. PPD usually occurs 6 to 8 weeks after childbirth, which may lead to a decrease in an individual’s daily performance [ 12 ]. Mothers are commonly faced with discomfort due to physical changes, poor sleeping quality, and various uncertainties related to their newborns in the postpartum stage [ 13 ].

Today, PPD has become a major worldwide health problem. Even so, many women with this mental illness were not medically diagnosed [ 12 ]. Several factors associated with PPD have been identified, although the specific causes remained unknown [ 11 ]. Previous studies have shown that depression and obesity were closely linked [ 14 , 15 ]. The risk of depression was increased by almost 37% due to obesity among women. It is quite common among women to gain excessive weight during pregnancy and the postpartum period [ 16 ]. Ertel et al. [ 17 ] and LaCoursiere et al. [ 18 ] also claimed that there was some evidence regarding pre-pregnancy obesity, which may lead to PPD. This claim was also supported by similar research conducted by Ruyak et al. [ 19 ]. Mgonja and Schoening [ 10 ] and Ezzeddin et al. [ 20 ] further placed emphasis on the issue by examining factors, aside from obesity, that could lead to the development of PPD; the factor included poor marital relationships, divorce, substance abuse, violence, other mental health diagnoses, low educational levels, unwanted or unexpected pregnancies, complicated labor, and a weak health care support system. This assertion by Mgonja and Schoening were reinforced by similar findings by Azale et al. [ 21 ], Zhao et al. [ 22 ], and Ukatu, Clare, and Brulja [ 11 ] who focused on factors leading to maternal depression. Hence, Bledsoe et al. [ 23 ] concluded that the negative outcomes from social, educational, health, and economic aspects tend to contribute a high possibility for the development of PPD among women. There is significant evidence that genetics and biochemical factors (brain chemistry), personality style, illness, and significant transitions in life, including adjusting to living with a new baby, may also contribute to PPD [ 24 , 25 ]. Postpartum depression has also been linked to women’s lifestyle choices, such as sleep quality [ 26 ], exercise [ 27 ], and prenatal smoking [ 28 ]. Dos Santos et al. [ 29 ] concluded that women who were diagnosed with maternal depression also experienced a higher risk of eating disorders during their pregnancies. Unhealthy eating habits developed among pregnant women because they were afraid to gain weight whenever they ate. Nevertheless, pregnant women with an eating disorder could have healthier food options, and some were concerned with their body shape rather than their body weight [ 30 ]. In other words, body dissatisfaction seemed to be a predictor of weight gain during pregnancy due to lifestyle factors (e.g., physical activity, diet, stress, and fatigue levels) [ 31 ]. In essence, a mother needs to have healthy food in order to supply the right kinds of nutrition to her unborn child [ 32 ].

Unfortunately, there were very few studies that investigated the impact of lifestyle and food intake by considering the body mass index (BMI), which may be associated with the PPD occurrence among women. Previous studies on PPD were infrequent; some utilized a modeling technique to measure the output and estimated the suggested indicators. Despite the contribution of these variables to PPD, a combined analysis of indicators involved in postpartum depression is surprisingly non-existent. A Structural Equation Modelling (SEM) analysis would allow the integration of variables such as demographic, lifestyle, and food intake in a conceptual model, which interrelates each of these variables to PPD. Therefore, in this research work, the authors aimed to analyze the factors, which contribute to PPD, and its relationship with socio-demographics, the lifestyle of postpartum women, healthy food intake, unhealthy food intake, and BMI range, which affects PPD by using SEM analysis.

Research framework

The authors designed a research framework that correlated to PPD, as shown in Fig.  1 . The conceptual framework of the research model includes an integrated model capable of providing an inclusive evaluation of the latent and observed variables within the SEM framework. The framework comprises socio-demographics as the initial independent variable and the depression level as the dependent variable. The remaining variables which acted as mediators were lifestyle, healthy food, unhealthy food, and BMI. As the BMI needed to be calculated based on the respondent’s weight and height, it was the only measured variable in our research framework. These variables have been taken into consideration the Malaysian culture because Malaysia is a multiracial country and have various ethnic groups [ 33 ]. Thus, we had chosen the variables wisely which were practical among Malaysian mothers.

The research framework provides a clear view of the study is carried out. By constructing the framework, it will lead this research to achieve its objective. This research framework was constructed using a combination of a theoretical framework with the addition of some new ideas to analyze the model. According to previous studies, socio-demographic variables played a significant role in establishing the relationship between the variables to postpartum women. These variables include with their lifestyles [ 34 ], healthy food intake [ 35 ], unhealthy food intake [ 36 ], BMI [ 37 ], and depression [ 38 ]. Lifestyle intervention during the postpartum period that give an impact on healthy food and unhealthy food intake [ 39 ], BMI [ 40 ], and depression [ 41 ] had also been investigated by other researchers.

Apart from that, food consumption among postpartum women has an interrelationship with BMI that claimed by research conducted by Kay et al. [ 42 ]. It was reported by Nathanson et al. [ 35 ] that healthy food intake closely associated with depression. Yet, Faria-Schutzer et al. [ 43 ] claimed that unhealthy food intake correlated with depression. Based on Ertel et al.’s [ 17 ] research, the postpartum BMI level can be affected by the depression level.

Materials and measurements

In this research, socio-demographics were measured as the initial independent variable, which includes four indicators, i.e., age group, educational background, working experience, and income household per month. The age range was classified into four groups: 21 to 25 years old, 26 to 30 years old, 31 to 35 years old, and over 35 years old. The educational background of the respondents was categorized as “Less than high school”, “High school”, “Diploma”, “Bachelor’s degree” and “Master’s degree or Ph.D.”. The respondents were asked about their working experience, which was categorized as “no job experience”, “1 to 3 years”, “4 to 6 years”, “7 to 10 years”, and “more than 10 years”. The last question in the socio-demographic section was based on to the monthly household income in Ringgit Malaysia (RM) and the responses were classified as “Less than RM 2,000”, “RM 2,000-RM 3,000”, “More than RM 3,000 to RM 4,000”, “More than RM 4,000 to RM 5,000”, and “Over RM 5,000”.

Apart from that,the authors measured lifestyle based on Nakayama, Yamaguchi’s study [ 44 ] in which the authors selected a few indicators such as the average working hours per day, physical activity per week, and average sleeping hours per day. Besides, daily screen time (e.g., TV, smartphone, tablet, etc.) was added to measure the lifestyle of the respondents in terms of social media, which corresponded to Khajeheian et al.’s research [ 45 ]. Regarding the average working hours per day, the responses consisted of five categories denoted by “none”, “less than 7 hours”, “7 to 8 hours”, “8 to 9 hours” and “more than 9 hours”. As for the frequency of physical activity per week, this was indicated as “none”, “1 time”, “2 times”, “3 times”, “4 times”, and “more than 4 times”. The average screen time per day was denoted as “less than 1 hour”, “1 to 2 hours”, “2 to 3 hours”, “3 to 4 hours”, and “more than 4 hours”. The average sleeping hours per day were indicated as “less than 6 hours”, “6 to 7 hours”, “7 to 8 h”, “8 to 9 h”, and “more than 9 h”.

In addition to the mediators, the authors considered healthy and unhealthy food separately in this study. Fruits, vegetables, and whole grains were selected as ‘healthy food’ variables, whilst fast food such as sweets, chips, and soft drinks was categorized as ‘unhealthy food’ [ 42 , 46 ]. The respondents were asked about their healthy and unhealthy food intake, and the responses were based on a five-point scale (“never”, “rarely”, “sometimes”, “mostly”, and “always”) which have been used in the previous studies [ 47 ].

WHO defined BMI as a simple index of weight-to-height of an individual and calculated according to the formula, BMI = ((weight in kilograms)/ (height in meters) 2 ) [ 48 ]. There are four categories of BMI based on the BMI range including “underweight for less than 18.5 kg/m 2 ”, “normal for ranges between 18.5 to less than 25.0 kg/m 2 ”, “overweight for ranges between 25.0 kg/m 2 to less than 30.0 kg/m 2 ” and “obese for ranges between 30.0 kg/m 2 and above” [ 49 ].

For the dependent variable, the authors measured the depressive symptoms using the Edinburgh Postnatal Depression Scale (EPDS) questionnaire to validate prenatal and postpartum occurrences [ 50 , 51 , 52 , 53 ]. Moreover, previous studies have shown that EPDS had been validated in Malaysian samples as well [ 54 , 55 ]. EPDS was calculated using a four-point scale (0–4) for each item to measure the frequency of the depressive symptoms developed in the postpartum period. A total of 10 items was used in the EPDS to estimate the depressive symptoms of respondents that needed to be answered. The total score for the EPDS questions was then grouped into four categories with a different interpretation. A 0–9 score was categorized as “normal”, scores of 10–11 were categorized as “slightly increased risk”, scores of 12 to 15 as “increased risk” and those more than 15 were listed as “likely depression” [ 56 ].

Structural equation modeling (SEM)

The SEM technique was chosen to be used in this research as it was recognized as a suitable method that would most likely help a researcher to understand better the latent variables concepts and the interactions within the model. Several previous studies had used the structural equation methodology [ 57 , 58 ] in their studies due to its features. The features of SEM technique include being:

Capable of estimating and examining the direct and indirect interrelationships which exist among the variables in the research study [ 59 ].

Capable of showing the relationship among dependent variables, which helps indicate the simultaneous estimation of more than one exogenous and endogenous variable [ 60 ].

For sampling, we used a cross-sectional analysis. The survey data was collected from each subject at one point in time. Based on Hair et al. [ 61 ], the required sample size depended on the number of latent variables in the study, including the number of indicators. In other words, a)100 respondents were needed due to five or less latent variables, of which each of the latent variables included at least three indicators, b) 150 respondents were needed due to seven or less latent variables, of which each of the latent variables included at least three indicators, c) 300 respondents were needed since seven or less latent variables existed, of which some of the latent variables had less than three indicators, d) 500 respondents were needed due to the existence of more than seven latent variables, of which some of the latent variables had less than three indicators. In this research framework, the authors had five latent variables, to be precise. Thus, the authors were required to consider at least 150 respondents for a suitable sample size.

The respondents were selected randomly using proportionate stratified random sampling and the data were collected for almost 6 months. The questionnaires were self-administered and have been distributed online, by sending respondents the link. However, for respondents who don’t have access to the internet, they were given the printed questionnaire to fill up. The authors distributed 450 questionnaires to postpartum women who were living in Kuala Lumpur, the most highly populated city in Malaysia. We excluded the women who are not living in Kuala Lumpur from the analysis. A total of 387 completed questionnaires were received from the respondents. The data were collected from nine maternal and child health clinics around Kuala Lumpur. The maternal and child health clinics that we went for data collection were in the neighborhood area which most of the patients are living nearby the clinics. We chose to collect the data at the maternal and child health clinics because it was easy to recognize mothers in their one-year postpartum period as mothers went to the clinics for medical check-ups. The respondents were selected randomly as long as they met the main criteria, i.e., in the first postpartum year of their latest pregnancy. The survey was conducted under the backingsof the University of Malaya’s Research Ethics Committee approval (UM.TNC2/RC/H&E/UMREC 127) and with the grant obtained from the University of Malaya (Grant No.: GPF066B-2018andGC002C-17HNE).

Table  1 shows the descriptive statistics of this research. The respondents are made up of Malays (43.7%), Chinese (34.9%), and Indians (21.4%), who were mostly around 31 years of age and older. The majority of participants were educated and gained an income of over RM 3000 per month with 1 to 10 years of working experience. Based on the weight and height provided by the respondents, 38.0% of participants were obese, 28.7% were overweight, 24.8% were normal, and 8.5% were underweight. Regarding lifestyle, only 26.1% of respondents did not take part in any physical activities. The average sleeping hours of the respondents were around 7 to 9 h, coupled with 8 to 9 h working day. The mean screen time hours recorded were 4.08 (SD: 0.85 h) per day. In terms of the food intake among postpartum women, the majority of respondents mostly consumed fruits, vegetables, whole grains, fast food, and sweets. Apart from that, a large number of respondents always consumed chips and soft drinks. Based on the calculated EPDS score, only 20.4% of the respondents were normal. Depression levels for the rest of respondents were 25.3% (slightly increased risk), 32.6% (increased risk), and 21.7% (likely depression).

Fornell and Larcker [ 62 ] claimed that the validity and reliability of a survey needed to fit the requirements of the SEM analysis. The validity is supposed to be tested based on the Cronbach’s alpha coefficient. Every latent variable in the research framework should be equal to or higher than 0.7. The Cronbach’s alpha value in this research was more than 0.7, which aligned with the conditions required to validate this research. To examine the reliability of the research work, a loading factor higher than 0.7 needed to be obtained for the latent variable indicator (see Table  2 ). In Table  2 , several indicators obtain a factor loading coefficient of less than 0.7, which means that these indicators need to be eliminated from the SEM analysis.

The reliability of the research also needed to be fitted with another test after the elimination of these unfit indicators. All latent variables should obtain an equal or higher coefficient than 0.5 of the average variances extracted (AVE). AVE analysis for latent variables in this research achieved more than 0.5. Thus, in this research work, the validity and reliability features are fulfilled. The suitability of the research model was tested using the model fitting analysis. The comparative fit index (CFI), normed fit index (NFI), relative fit index (RFI), incremental fit index (IFI), the goodness of fit index (GFI), and Tucker-Lewis index (TLI) coefficient of this research were above 0.9, which means that the research data was acceptable. The structural model in the SEM analysis helped to recognize the connection between research variables and the considered conceptual model. Figure  2 shows the output of the structural model for postpartum women. From the pre-established 14 relationships between the research variables, only five relationships, represented by the dashed black arrow, were deemed not significant.

Final output of structural model

Figure 2 presents that R-square is equal to 0.76. which means that 76% of depression variations depend on BMI, healthy food intake, unhealthy food intake, lifestyle, and socio-demographics among postpartum women. The rest, 24% of depression variation belongs to other variables that were not involved inside the model. Moreover, from 14 relationships among research variables, nine of them have significant relationships. Among the four latent variables and one measurement variable, two of the latent variables i.e. unhealthy food and lifestyle, have a significant relationship with depression. Additionally, BMI as the only measurement variable has a significant relationship with depression. The highest relationships belong to unhealthy food intake → depression (0.84), lifestyle → depression (0.81), and BMI → depression (0.79). Socio-demographics, as the main independent variable has a significant relationship with healthy and unhealthy food intakes and there is no significant relationship with the lifestyle, BMI, and depression. However, socio-demographics has an indirect effect on depression through food intake mediators (socio-demographics →unhealthy food intake → BMI → depression) and (socio-demographics →unhealthy food intake → BMI → depression). As a result, socio-demographics has no direct effect on depression but have an indirect significant effect. Besides, socio-demographics also do not have a significant direct effect on BMI. It means that postpartum women with any spectrum of socio-demographics including age, education, income, and job experience has no significant effect on their BMI and depression. The correlation of these indicators as a latent variable indicates that their BMI and depression will be significantly affected through their food intake. Meanwhile, Table 3 presents the p -value of the final output obtained was significant (approximately p -value < 0.05).

This paper aimed to introduce a new postpartum depression model, which is designed based on factors associated with depression symptoms using the SEM technique. The depression levels of postpartum women were set as the dependent variable, and socio-demographics were maintained as an independent variable. In this research framework, lifestyle, healthy food, unhealthy food, and BMI acted as mediators. Based on previous theories and frameworks of postpartum depression, the authors designed an improved study model, as shown in Fig. 1 . The authors succeeded in gathering the questionnaires from 387 women diagnosed with postpartum. The respondents were into their first postnatal year, which matched the previous research conducted by Kubota et al. [ 51 ].

For this research model, 14 out of nine relationships among the variables were significant, with a positive coefficient. In Fig. 2 , we simplified our research model output. Thus, the significant impact of socio-demographics on healthy and unhealthy food is 0.39 and 0.56, respectively. It can be interpreted that respondents who have more money, good education backgrounds, and longer work experience tend to consume more unhealthy food than healthy food. Previous research [ 63 ] has reported that working mothers tend to feed the family with fast-food as it is the easiest and fastest way to prepare the meal. Yet, some research also mentioned that working mothers had bettereating practices [ 64 ]. These show a very contradictory output from the prior studies. On the other hand, it is claimed by Zagorsky and Smith [ 65 ] that adults from different levels of socio-demographicspreferred to consume fast food. This claim is supported by similar research done by Fryar, Hughes, Herrick, Ahluwalia [ 66 ]. Good educational background was linked with a greater frequency of fast food consumption among women as well [ 67 ]. In this research, we obtained a result showing that a high level of socio-demographics chose to eat unhealthy food more. It is proven before that the rationale for people consuming fast food due to convenience and wanted to socialize [ 68 ].

The age group indicator was eliminated from the SEM analysis, as the coefficient of factor loading did not achieve the required standard value. Additionally, the lifestyle variable is significant in terms of affecting the dependent variable in this research model. Referring to the factor loading analysis in Table 2 , the lifestyle indicators show that the average screen time hour has the highest loading factor, followed by the average working hour indicator. Previous studies have shown a positive correlation between smartphone addiction and depression [ 69 ]. The lifestyle factor had a significant impact on both food intake categories. An increase in terms of lifestyle will promote an increase in depression levels and food consumption, in particular, unhealthy food. Berk et al. [ 70 ] summarized that poor lifestyle and unhealthy diet contributed to depression.

Apart from that, healthy and unhealthy foods show a significant correlation with BMI in the structural model. In previous studies, it was reported that food consumption contributed to the BMI range [ 70 , 71 ]. To be exact, the quantity of the food that we consumed affected the BMI level. This will occur when you are eating healthy food but in a substantial amount, which will consequently lead to an increase in the BMI level. So, to apply healthy eating behavior, it is better to know the number of calories needed for the individual’s body. Based on the factor loading analysis in Table  2 , the chips indicator had the highest coefficient among the indicators of other food categories. Previous studies also claimed that snacks (i.e., chips) have an impact on the BMI of postpartum women [ 71 ]. When a comparison was made between the food categories’ impact on BMI, unhealthy food has a higher significant coefficient than healthy food. Therefore, an increase in unhealthy food intake will also increase the BMI levels of respondents. In Malaysia, unhealthy foods are easy to find and mostly cheaper than healthy food. As in Kuala Lumpur, a lot of 24-h restaurants are available, especially fast-food premises [ 72 ]. Thus, with the availability of easy food at any hours, people with high socio-economic backgrounds sometimes do choose to eat unhealthy food too. Even though people are well aware of the effect of eating unhealthy foods, it depends on an individual on what they choose to consume.

Based on Fig. 2 , unhealthy food and BMI have a significant impact on the depression levels, which seem to directly affect the dependent variables. The prevalence of overweight and obesity among postpartum women in the research sample is noted to be among the highest. From this research, the respondents who eat more unhealthy food and has a high level of BMI are considered to have a high level of depression. Several studies have claimed that depression has a link with maternal obesity [ 73 , 74 ]. Body dissatisfaction in terms of image, shape, or weight among women would probably affect their mental health.

In the EPDS section, the 10-item questions included anhedonia, self-blame, anxiety, fear or panic, inability to cope, sleeping difficulty, sadness, tearfulness, and self-harm ideas [ 75 ]. The descriptive output found that the majority of the respondents suffered from an increased risk of depression levels. The result of depression levels among mothers indeed raised concerns, where they needed help but did not get any. However, in the factor loading analysis, there are three indicators of depression, that have been eliminated from the SEM analysis- Q3, Q5, Q8 (self-blame, fear or panic, and sadness, respectively). Although the descriptive statistics data consider the total score of the EPDS for all 10-items of the depression level measurement, it had decided to remove these indicators, as it had been included in the process of the postpartum depression modeling. The highest loading factor of depression item concerning the anxiety issue (Q4), and the lowest is the anhedonia issue (Q1). For these issues, this research would be an effective platform for medical professionals to keep updated and act towards postpartum women who might feel ashamed or afraid to seek help in preventing them from depression.

Physical activity intervention plays a part in weight loss which happens to be an alternative for the prevention and treatment of the depression symptoms [ 76 ]. Moreover, poor sleep incite less motivation to do exercise that leads to weight gain and also obesity-related problemsas well as sleep disturbances [ 77 ]. Promotingphysical activity in an individual’s lifestyle can also benefit in averting the potentialenhancement of chronic diseases for which body weight is a risk factor [ 76 ]. Consistent with previous literature [ 78 ], excessive weight gain probably happen alliance with low physical activity. When ones living with obesity or overweight, their engagement to workout is so frustrating due to discomfort complaints in terms of musculoskeletal and sweating [ 77 ]. Prior literature proves that physical activity was correlated with lower BMI and depression levels [ 79 ].

Based on Fig. 2 , it is observed that the highest coefficient among the variables is the impact of unhealthy food on the depression levels. This corresponded with a previous study by Barker et al. [ 80 ], whereby the levels of depression symptoms were linked to unhealthy food consumption [ 81 , 82 ]. Regarding the research model output, the indirect impact of unhealthy food on the depression levels with the BMI level was identified. Previous studies reported that people who were obese and depressed consumed more unhealthy food [ 83 ]. The R-square (R 2 ) for the structural model in this research was 0.76. In relative terms, 76% of the variations in depression level were related to socio-demographics, lifestyle, healthy food, unhealthy food, and BMI. Only 24% of the variations correlated with other factors. Thus, it can be concluded that the strength of exogenous and endogenous variables in this research is strong.

However, this research had several limitations, as well. The respondent’s weight and height were self-reported in this study, despite previous research works which have also utilized this method, and although it is valid [ 84 , 85 , 86 ], it can be a possible limitation of the study. Furthermore, physical activity that we measured was defined as regular exercise (e.g., fast walking, jogging, cycling, swimming), which were mentioned in the questionnaire. Being a mother had change women’s lifestyle especially to engage in leisure-time especially physical activity [ 87 ]. Women seem to be a lack of doing any physical activity because of time constraints and managing their kids [ 88 ]. Mothers without husbands or partners were less physically active compared to married mothers [ 89 ]. Besides, some of them might work out in different places such as home, gym, park, etc. This indicator is not the main concern in this study. But it is a part of measuring how active the respondents were during the postpartum period.

While the dietary assessment was measured only by using a five-point Likert-type scale. Different BMI category needs different amounts of calories per day. As this research based on self-administrated questionnaires, the Likert-type scale seems to be the easiest way for respondents to report their dietary measurements. Not everyone knows how specific much of the food they consume every day. Yet, we believe that there a lot of ways to measure food intake. For example, the measurement would be in servings [ 90 ] or using the MooDFOOD dietary guidelines [ 91 ] which been used by recent studies.

Apart from that, the measurement of the depression levels using EPDS was not a substitute for a clinical diagnosis. EPDS was used in this research to determine depression symptoms, which the respondent might face as a form of risk. We acknowledge that many people who suffer from depression did not seek medical help [ 4 ]. Medical treatment programs for depression can be effective in reducing depression levels.

In this study, SEM with cross-sectional data could analyze the influence of lifestyle, healthy, and unhealthy food intake on depression. Nevertheless, our research framework, which was presented in Fig. 1 , is not capable of studying the vice versa effect of depression on lifestyle, healthy and unhealthy food intake. To overcome this matter, we recommend future studies to apply dynamic SEM with longitudinal data. Figure  3 illustrates an example of dynamic SEM pertaining to our research framework.

Dynamic SEM framework

The main framework of this study was prepared based on the combination of previous studies in obesity and depression model. However, calorie intake, genetics, and fiber intake are some of the variables that could be obesity indicators that might have been encompassed in our analysis. There were limitations to collect this type of data for this study, and it would have required a different research structure that could not be added in the current research framework. Hence, the analysis of these indicators in future studies is recommended.

To conclude, this research examined the effects of depression levels in terms of socio-demographics, lifestyle, healthy food, unhealthy food, and BMI. Besides, the hypothesized model in the present study had been indicated as a suitable model for predicting the depression levels among postpartum women. Subsequently, depression levels affect people’s lives (e.g., personal matters, health, eating behavior), and it means clinical intervention is necessary to prevent depression symptoms from exacerbating. This research is the first study on postpartum women diagnosed with depression symptoms, which were carried out using SEM. The factors associated with depression were presented in the theoretical framework. The associated variables and theories were aligned with the Malaysian culture and the associated environment. Thereby, we believe that this research may be advantageous for future works on the postpartum depression modeling, particularly among public health and life science research scholars.

Availability of data and materials

The data are not publicly available due to the University of Malaya Research Ethics Committee rules and regulations. The data that support the findings of this research are available upon reasonable request from the corresponding author and with permission of the University of Malaya Research Ethics Committee.

Abbreviations

Postpartum depression

Structural Equation Modeling

Body mass index (BMI)

Edinburgh Postnatal Depression Scale

Ringgit Malaysia

Comparative fit index

Normed fit index

Relative fit index

Incremental fit index

Goodness of fit index

Tucker-Lewis index

Muharam R, Setiawan MW, Ikhsan M, Rizkinya HE, Sumapraja K. Depression and its link to other symptoms in menopausal transition. Middle East Fertility Society J. 2018;23(1):27–30.

Article   Google Scholar  

El-Gilany AH, Elkhawaga GO, Sarraf BB. Depression and its associated factors among elderly: a community-based study in Egypt. Arch Gerontol Geriatr. 2018;77:103–7.

Article   PubMed   Google Scholar  

Zeng N, Pope Z, Lee J, Gao Z. Virtual reality exercise for anxiety and depression: a preliminary review of current research in an emerging field. J Clin Med. 2018;7(3):42.

Article   PubMed Central   Google Scholar  

Archana PS, Das S, Philip S, Philip RR, Joseph J, Punnoose VP, et al. Prevalence of depression among middle aged women in the rural area of Kerala. Asian J Psychiatr. 2017;29:154–9.

Elamoshy R, Bird Y, Thorpe L, Moraros J. Risk of depression and suicidality among diabetic patients: a systematic review and meta-analysis. J Clin Med. 2018;7(11):445.

Jia H, Zack MM, Gottesman II, Thompson W. Associations of smoking, physical inactivity, heavy drinking, and obesity with quality-adjusted life expectancy among US adults with depression. Value Health. 2018;21(3):364–71.

Organization WH. World Health Organization. https://www.who.int/mental_health/management/depression/en/ . Accessed 20 Dec 2018.

Jin QG, Mori E, Sakajo A. Risk factors, cross-cultural stressors and postpartum depression among immigrant Chinese women in Japan. Int J Nurs Pract. 2016;22:38–47.

Correa MSM, Feliciano KVD, Pedrosa EN, de Souza AI. Postpartum follow-up of women's health. Cadernos De Saude Publica. 2017;33(3):1–11.

Google Scholar  

Mgonja S, Schoening A. Postpartum depression screening at well-child appointments: a quality improvement project. J Pediatr Health Care. 2017;31(2):178–83.

Ukatu N, Clare CA, Brulja M. Postpartum depression screening tools: a review. Psychosomatics. 2018;59(3):211–9.

Rogathi JJ, Manongi R, Mushi D, Rasch V, Sigalla GN, Gammeltoft T, et al. Postpartum depression among women who have experienced intimate partner violence: a prospective cohort study at Moshi, Tanzania. J Affect Disord. 2017;218:238–45.

Thein-Nissenbaum J. The postpartum triathlete. Physical Therapy Sport. 2016;21:95–106.

Keshavarz SA, Mostafavi S-A, Akhondzadeh S, Mohammadi MR, Hosseini S, Eshraghian MR, et al. Omega-3 supplementation effects on body weight and depression among dieter women with co-morbidity of depression and obesity compared with the placebo: a randomized clinical trial. Clin Nutrition ESPEN. 2018;25:37–43.

Rogan SC, Payne JL, Meltzer-Brody S. Relationship Between Depressive Mood and Maternal Obesity: Implications for Postpartum Depression. Obesity During Pregnancy in Clinical Practice: Springer; 2014. p. 99–120.

Sumithran P, Houlihan C, Shub A, Churilov L, Pritchard N, Price S, et al. How common is substantial weight gain after pregnancy? Obesity Research Clin Practice. 2018;12(2):139–45.

Ertel KA, Huang T, Rifas-Shiman SL, Kleinman K, Rich-Edwards J, Oken E, et al. Perinatal weight and risk of prenatal and postpartum depressive symptoms. Annals Epidemiol. 2017;27(11):695–700 e1.

LaCoursiere DY, Barrett-Connor E, O'Hara MW, Hutton A, Varner MW. The association between prepregnancy obesity and screening positive for postpartum depression. Bjog-an Int J Obstetrics Gynaecol. 2010;117(8):1011–8.

Article   CAS   Google Scholar  

Ruyak SL, Lowe NK, Corwin EJ, Neu M, Boursaw B. Prepregnancy obesity and a biobehavioral predictive model for postpartum depression. Jognn-J Obstetric Gynecol Neonatal Nurs. 2016;45(3):326–38.

Ezzeddin N, Jahanihashemi H, Zavoshy R, Noroozi M. The prevalence of postpartum depression and its association with food insecurity among mothers referring to community health centers. Iran J Psychiatry. 2018;13(4):280–7.

PubMed   PubMed Central   Google Scholar  

Azale T, Fekadu A, Medhin G, Hanlon C. Coping strategies of women with postpartum depression symptoms in rural Ethiopia: a cross-sectional community study. Bmc Psychiatry. 2018;18:1–13.

Zhao Y, Munro-Kramer ML, Shi S, Wang J, Zhu X. A longitudinal study of perinatal depression among Chinese high-risk pregnant women. Women Birth. 2018;31(6):e395–e402.

Bledsoe SE, Rizo CF, Wike TL, Killian-Farrell C, Wessel J, Bellows A-MO, et al. Pregnant adolescent women’s perceptions of depression and psychiatric services in the United States. Women Birth. 2017;30(5):e248–e57.

Article   PubMed   PubMed Central   Google Scholar  

Fan Q, Long Q, De Silva V, Gunarathna N, Jayathilaka U, Dabrera T, et al. Prevalence and risk factors for postpartum depression in Sri Lanka: a population-based study. Asian J Psychiatr. 2020;47:101855.

Ghaedrahmati M, Kazemi A, Kheirabadi G, Ebrahimi A, Bahrami M. Postpartum depression risk factors: a narrative review. J Education Health Promotion. 2017;6:1–24.

Chung T-C, Chung C-H, Peng H-J, Tsao C-H, Chien W-C, Sun H-F. An analysis of whether sleep disorder will result in postpartum depression. Oncotarget. 2018;9(38):25304.

Chen CM, Kuo SF, Chou YH, Chen HC. Postpartum Taiwanese women: their postpartum depression, social support and health-promoting lifestyle profiles. J Clin Nurs. 2007;16(8):1550–60.

Chen H-L, Cai J-Y, Zha M-L, Shen W-Q. Prenatal smoking and postpartum depression: a meta-analysis. J Psychosom Obstet Gynecol. 2018;40(2):1–9.

dos Santos AM, Benute GRG, dos Santos NO, Nomura RMY, de Lucia MCS, Francisco RPV. Presence of eating disorders and its relationship to anxiety and depression in pregnant women. Midwifery. 2017;51:12–5.

Harris AA. Practical advice for caring for women with eating disorders during the perinatal period. J Midwifery Women's Health. 2010;55(6):579–86.

Fuller-Tyszkiewicz M, Broadbent J, Richardson B, Watson B, Klas A, Skouteris H. A network analysis comparison of central determinants of body dissatisfaction among pregnant and non-pregnant women. Body Image. 2020;32:111–20.

Köhler R, Lambert C, Biesalski HK. Animal-based food taboos during pregnancy and the postpartum period of southeast Asian women–a review of literature. Food Res Int. 2019;115:480–6.

Omar SR, Omar SN. Malaysian heritage food (MHF): a review on its unique food culture, tradition and present lifestyle. Int J Heritage Art Multimedia. 2018;1(3):01–15.

Aluş Tokat M, Sancı M, Girgeç S, Kulhan NG, Özcan ÇY. Postpartum education and lifestyle changes for preventing type 2 diabetes in Turkish women with previous gestational diabetes: a retrospective study. Int J Nurs Pract. 2016;22(5):427–35.

Nathanson R, Hill B, Skouteris H, Bailey C. Antenatal diet and postpartum depressive symptoms: a prospective study. Midwifery. 2018;62:69–76.

Fadzil F, Shamsuddin K, Puteh SEW, Tamil AM, Ahmad S, Hayi NSA, et al. Predictors of postpartum weight retention among urban Malaysian mothers: a prospective cohort study. Obes Res Clin Pract. 2018;12(6):493–9.

Tong M, Jin L, Liu J, Li Z, Li H, Zhang L, et al. Association between gestational weight gain and exclusive breast-feeding for the first 6 months postpartum in Chinese women. Public Health Nutr. 2019;22(11):1–7.

Fisher SD, Sit DK, Yang A, Ciolino JD, Gollan JK, Wisner KL. Four maternal characteristics determine the 12-month course of chronic severe postpartum depressive symptoms. Depression Anxiety. 2019;36(4):375–83.

Moran LJ, Flynn AC, Louise J, Deussen AR, Dodd JM. The effect of a lifestyle intervention on pregnancy and postpartum dietary patterns determined by factor analysis. Obesity. 2017;25(6):1022–32.

Huvinen E, Koivusalo SB, Meinilä J, Valkama A, Tiitinen A, Rönö K, et al. Effects of a lifestyle intervention during pregnancy and first postpartum year: findings from the RADIEL study. J Clin Endocrinol Metabolism. 2018;103(4):1669–77.

Okun ML, Mancuso RA, Hobel CJ, Schetter CD, Coussons-Read M. Poor sleep quality increases symptoms of depression and anxiety in postpartum women. J Behav Med. 2018;41(5):703–10.

Kay MC, Wasser H, Adair LS, Thompson AL, Siega-Riz AM, Suchindran CM, et al. Consumption of key food groups during the postpartum period in low-income, non-Hispanic black mothers. Appetite. 2017;117:161–7.

Faria-Schutzer DB, Surita FG, Rodrigues L, Turato ER. Eating Behaviors in Postpartum: A Qualitative Study of Women with Obesity. Nutrients. 2018;10(7)1–11.

Nakayama K, Yamaguchi K, Maruyama S, Morimoto K. The relationship of lifestyle factors, personal character, and mental health status of employees of a major Japanese electrical manufacturer. Environ Health Prev Med. 2001;5(4):144–9.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Khajeheian D, Colabi AM, Shah N, Radzi C, Jenatabadi HS. Effect of Social Media on Child Obesity: Application of Structural Equation Modeling with the Taguchi Method. Int J Environ Res Public Health. 2018;15(7):1–22.

Kroller K, Warschburger P. Maternal feeding strategies and child's food intake: considering weight and demographic influences using structural equation modeling. Int J Behav Nutr Phys Act. 2009;6:1–9.

Radzi WM, Jasimah CW, Salarzadeh Jenatabadi H, Alanzi AR, Mokhtar MI, Mamat MZ, et al. Analysis of obesity among Malaysian University students: a combination study with the application of Bayesian structural equation Modelling and Pearson correlation. Int J Environ Res Public Health. 2019;16(3):492.

Orgnization WH. World Health Organization. http://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight . Accessed 23 Oct 2018.

Leigh L, Byles JE, Jagger C. BMI and healthy life expectancy in old and very old women. Br J Nutr. 2016;116(4):692–9.

Article   CAS   PubMed   Google Scholar  

Di Florio A, Putnam K, Altemus M, Apter G, Bergink V, Bilszta J, et al. The impact of education, country, race and ethnicity on the self-report of postpartum depression using the Edinburgh postnatal depression scale. Psychol Med. 2017;47(5):787–99.

Kubota C, Inada T, Nakamura Y, Shiino T, Ando M, Aleksic B, et al. Stable factor structure of the Edinburgh postnatal depression scale during the whole peripartum period: results from a Japanese prospective cohort study. Sci Rep. 2018;8:1–7.

Bozzatello P, Brignolo E, De Grandi E, Bellino S. Supplementation with omega-3 fatty acids in psychiatric disorders: a review of literature data. J Clin Med. 2016;5(8):67.

Article   PubMed Central   CAS   Google Scholar  

Koh Y, Lee A, Chan C, Fong D, Lee C, Leung K, et al. Survey on examining prevalence of paternal anxiety and its risk factors in perinatal period in Hong Kong: a longitudinal study. BMC Public Health. 2015;15(1):1131.

Mohamad Yusuff AS, Tang L, Binns CW, Lee AH. Prevalence of antenatal depressive symptoms among women in Sabah, Malaysia. J Matern Fetal Neonatal Med. 2016;29(7):1170–4.

Rashid A, Mohd R. Poor social support as a risk factor for antenatal depressive symptoms among women attending public antennal clinics in Penang, Malaysia. Reproductive Health. 2017;14(1):144.

Yawn BP, Pace W, Wollan PC, Bertram S, Kurland M, Graham D, et al. Concordance of Edinburgh postnatal depression scale (EPDS) and patient health questionnaire (PHQ-9) to assess increased risk of depression among postpartum women. J Am Board Family Med. 2009;22(5):483–91.

Shimamoto K, Gipson JD. Examining the mechanisms by which women’s status and empowerment affect skilled birth attendant use in Senegal: a structural equation modeling approach. Bmc Pregnancy and Childbirth. 2017;17(2):341.

Kananura RM, Wamala R, Ekirapa-Kiracho E, Tetui M, Kiwanuka SN, Waiswa P, et al. A structural equation analysis on the relationship between maternal health services utilization and newborn health outcomes: a cross-sectional study in eastern Uganda. Bmc Pregnancy Childbirth. 2017;17(1):98.

Gefen D, Straub D, Boudreau M-C. Structural equation modeling and regression: guidelines for research practice. Commun Assoc Inf Syst. 2000;4(1):7.

Jeon J. The strengths and limitations of the statistical modeling of complex social phenomenon: focusing on SEM, path analysis, or multiple regression models. Int J Social Behavioral Educ Econ Business Industrial Eng. 2015;9(5):1559–67.

Hair J, Black W, Babin B, Anderson R. Multivariate data analysis: Pearson new international edition. New Jersey: Pearson/Prentice Hall; 2014.

Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18(1):39–50.

Ahmad A, Zulaily N, Shahril MR, Abdullah EFHS, Ahmed A. Association between socioeconomic status and obesity among 12-year-old Malaysian adolescents. PloS one. 2018;13(7):1–11.

Raza L, Ali TM, Hasnain A. Comparison of dietary practices and body mass index among educated housewives and working women in Karachi. J Ayub Medical College Abbottabad. 2017;29(2):293–7.

Zagorsky JL, Smith PK. The association between socioeconomic status and adult fast-food consumption in the US. Economics Human Biol. 2017;27:12–25.

Fryar CD, Hughes JP, Herrick KA, Ahluwalia N. Fast food consumption among adults in the United States, 2013–2016; 2018.

Hidaka BH, Hester CM, Bridges KM, Daley CM, Greiner KA. Fast food consumption is associated with higher education in women, but not men, among older adults in urban safety-net clinics: a cross-sectional survey. Prev Med Rep. 2018;12:148–51.

Garza KB, Ding M, Owensby JK, Zizza CA. Impulsivity and fast-food consumption: a cross-sectional study among working adults. J Acad Nutr Diet. 2016;116(1):61–8.

Kim E, Cho I, Kim EJ. Structural equation model of smartphone addiction based on adult attachment theory: mediating effects of loneliness and depression. Asian Nurs Res. 2017;11(2):92–7.

Berk M, Sarris J, Coulson CE, Jacka FN. Lifestyle management of unipolar depression. Acta Psychiatr Scand. 2013;127:38–54.

Paans NP, Gibson-Smith D, Bot M, van Strien T, Brouwer IA, Visser M, et al. Depression and eating styles are independently associated with dietary intake. Appetite. 2019;134:103–10.

Ali N, Abdullah MA. The food consumption and eating behaviour of Malaysian urbanites: Issues and concerns. Geografia Malays J Soc Space. 2017;8(6):157–65.

Nagl M, Linde K, Stepan H, Kersting A. Obesity and anxiety during pregnancy and postpartum: a systematic review. J Affect Disord. 2015;186:293–305.

Abdollahi F, Agajani-Delavar M, Zarghami M, Lye MS. Postpartum Mental Health in First-Time Mothers: A Cohort Study. Iranian J Psychiatry Behavioral Sciences. 2016;10(1):1–7.

Bhusal BR, Bhandari N, Chapagai M, Gavidia T. Validating the Edinburgh postnatal depression scale as a screening tool for postpartum depression in Kathmandu, Nepal. Int J Ment Heal Syst. 2016;10:1–7.

Quezada AD, Macías-Waldman N, Salmerón J, Swigart T, Gallegos-Carrillo K. Physical activity and calorie intake mediate the relationship from depression to body fat mass among female Mexican health workers. Int J Behav Nutr Phys Act. 2017;14(1):160.

Kishida M, Elavsky S. An intensive longitudinal examination of daily physical activity and sleep in midlife women. Sleep Health. 2016;2(1):42–8.

Staiano AE, Marker AM, Martin CK, Katzmarzyk PT. Physical activity, mental health, and weight gain in a longitudinal observational cohort of nonobese young adults. Obesity. 2016;24(9):1969–75.

Lincoln KD. Social stress, obesity, and depression among women: clarifying the role of physical activity. Ethnicity Health. 2019;24(6):662–78.

Barker ED, Kirkham N, Ng J, Jensen SK. Prenatal maternal depression symptoms and nutrition, and child cognitive function. Br J Psychiatry. 2013;203(6):417–21.

Jacka FN, Berk M. Depression, diet and exercise. Med J Aust. 2013;199(6):21–3.

Pina-Camacho L, Jensen S, Gaysina D, Barker E. Maternal depression symptoms, unhealthy diet and child emotional–behavioural dysregulation. Psychol Med. 2015;45(9):1851–60.

Privitera GJ, King-Shepard QW, Cuifolo KN, Doraiswamy PM. Differential food intake and food choice by depression and body mass index levels following a mood manipulation in a buffet-style setting. J Health Psychol. 2016;1359105316650508:1–11.

McVay MA, Yancy WS, Bennett GG, Jung S-H, Voils CI. Perceived barriers and facilitators of initiation of behavioral weight loss interventions among adults with obesity: a qualitative study. BMC Public Health. 2018;18(1):854.

Beleigoli AM, de Andrade AQ, Diniz MdFH, Alvares RS, Ribeiro AL. Online platform for healthy weight loss in adults with overweight and obesity-the “POEmaS” project: a randomized controlled trial. BMC Public Health. 2018;18(1):945.

Noh J-W, Kwon YD, Yang Y, Cheon J, Kim J. Relationship between body image and weight status in east Asian countries: comparison between South Korea and Taiwan. BMC Public Health. 2018;18(1):814.

Miller YD, Trost SG, Brown WJ. Mediators of physical activity behavior change among women with young children. Am J Prev Med. 2002;23(2):98–103.

Groth SW, David T. New mothers’ views of weight and exercise. MCN Am J Maternal Child Nurs. 2008;33(6):364.

Dlugonski D, Motl RW. Marital status and motherhood: implications for physical activity. Women Health. 2013;53(2):203–15.

Purdue-Smithe AC, Whitcomb BW, Manson JE, Hankinson SE, Rosner BA, Troy LM, et al. A prospective study of dairy-food intake and early menopause. Am J Epidemiol. 2019;188(1):188–96.

Grasso AC, Olthof MR, van Dooren C, Roca M, Gili M, Visser M, et al. Effect of food-related behavioral activation therapy on food intake and the environmental impact of the diet: results from the MooDFOOD prevention trial. Eur J Nutr. 2019:1–13.

Download references

Acknowledgements

The authors would like to express gratitude to all participants for their cooperation during the research.

This research was supported by University of Malaya, Malaysia (Grant No.: GPF066B-2018andGC002C-17HNE). The funders had no role in study design, data collection, and analysis, decision to publish or preparation of the manuscript. No additional external funding was received for this study.

Author information

Hashem Salarzadeh Jenatabadi and Nadia Samsudin contributed equally to this work.

Authors and Affiliations

Department of Science and Technology Studies, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia

Che Wan Jasimah Bt Wan Mohamed Radzi, Hashem Salarzadeh Jenatabadi & Nadia Samsudin

You can also search for this author in PubMed   Google Scholar

Contributions

Conceived of and designed the study: H.S.J and N.S. Performed the methodology: H.S.J and N.S. Analyzed and interpreted the data: H.S.J. Wrote the manuscript text: H.S.J., C.W.J.W.M.R., and N.S. All authors reviewed the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Hashem Salarzadeh Jenatabadi .

Ethics declarations

Ethics approval and consent to participate.

The survey was conducted with the University of Malaya Research Ethics Committee approval (UM.TNC2/RC/H&E/UMREC 127). The research methods were performed in accordance with the relevant guidelines and regulations. Participants of the study were informed about the purpose, objectives, and their right to participate, decline participation, or withdraw their participation in the research activities by verbal. Respondents have been notified that the information given was private and confidential which only going to use for academic purposes only. Written informed consent was obtained from all respondents.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Wan Mohamed Radzi, C.W.J.B., Salarzadeh Jenatabadi, H. & Samsudin, N. Postpartum depression symptoms in survey-based research: a structural equation analysis. BMC Public Health 21 , 27 (2021). https://doi.org/10.1186/s12889-020-09999-2

Download citation

Received : 07 August 2019

Accepted : 01 December 2020

Published : 27 January 2021

DOI : https://doi.org/10.1186/s12889-020-09999-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Postpartum depression symptoms
• Structural equation modeling
• Edinburgh postnatal depression scale

BMC Public Health

ISSN: 1471-2458

• Submission enquiries: [email protected]
• General enquiries: [email protected]

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 12 April 2021

Predicting women with depressive symptoms postpartum with machine learning methods

• Sam Andersson 1 ,
• Deepti R. Bathula 2 ,
• Stavros I. Iliadis 1 ,
• Martin Walter 3 , 4 , 5 &
• Alkistis Skalkidou 1  

Scientific Reports volume  11 , Article number:  7877 ( 2021 ) Cite this article

11k Accesses

47 Citations

2 Altmetric

Metrics details

• Machine learning
• Risk factors

Postpartum depression (PPD) is a detrimental health condition that affects 12% of new mothers. Despite negative effects on mothers’ and children’s health, many women do not receive adequate care. Preventive interventions are cost-efficient among high-risk women, but our ability to identify these is poor. We leveraged the power of clinical, demographic, and psychometric data to assess if machine learning methods can make accurate predictions of postpartum depression. Data were obtained from a population-based prospective cohort study in Uppsala, Sweden, collected between 2009 and 2018 (BASIC study, n = 4313). Sub-analyses among women without previous depression were performed. The extremely randomized trees method provided robust performance with highest accuracy and well-balanced sensitivity and specificity (accuracy 73%, sensitivity 72%, specificity 75%, positive predictive value 33%, negative predictive value 94%, area under the curve 81%). Among women without earlier mental health issues, the accuracy was 64%. The variables setting women at most risk for PPD were depression and anxiety during pregnancy, as well as variables related to resilience and personality. Future clinical models that could be implemented directly after delivery might consider including these variables in order to identify women at high risk for postpartum depression to facilitate individualized follow-up and cost-effectiveness.

Similar content being viewed by others

Causal machine learning for predicting treatment outcomes

Development and validation of a new algorithm for improved cardiovascular risk prediction

Major depressive disorder: hypothesis, mechanism, prevention and treatment

Introduction.

Postpartum depression (PPD), defined as having an episode of minor or major depression during pregnancy or up to one year after giving birth, is a relatively common condition that affects 8–15% of new mothers in Sweden every year 1 , 2 . The etiology of PPD is not well understood, but the condition likely arises from a combination of psychological, psychosocial and biological factors 3 , 4 . The most well documented biological risk factors for PPD are hypothalamic–pituitary–adrenal axis dysregulation, inflammatory processes, genetic vulnerability, and allopregnanolone withdrawal 4 . The strongest psychosocial factors are previous depression, severe life events, some forms of chronic stress and relationship struggles 4 , 5 . The role of resilience and personality have been lately also gaining attention 6 , 7 .

PPD is a condition that can have devastating effects on the mothers, as well as their children 8 , 9 . Mothers may experience persistent doubts about their ability to care for the child, have difficulties bonding with their child, and also have thoughts about hurting the child 2 . Moreover, PPD can affect a child’s development by interfering with the mother-infant relationship 10 , 11 . For instance, children of mothers with PPD have greater cognitive, behavioral and interpersonal problems compared to children of mothers without PPD 12 , 13 . Despite PPD being a detrimental health condition for many women, numerous affected women fail to receive adequate care 14 . There exist several effective treatments and interventions for PPD 14 , 15 , 16 , but they are only cost-effective among high-risk women. The idea of prenatal prediction of PPD has existed for several years and early studies using more traditional methods attempted to predict women at risk by prenatal assessment of critical variables 17 . However, to date, there has been no effective way to predict women at risk for the development of depressive symptoms postpartum.

Traditional statistical methods allow researchers to estimate risks by sequentially analysing the associations mainly between two variables, often controlling for the effect of others. Further, machine learning (ML) methods enable researchers to iteratively and simultaneously analyse multiple interacting associations between variables 18 as well as to devise data-driven predictive models that then can be evaluated by quantifying the performance metrics across all models in order to find the best predictive model. The power of ML allows for the analysis of complex non-linear relationships and even the integration and pooling of multiple different data-types from several sources 19 , 20 , 21 . Over the last decade, there has been a steady increase in the use of ML in medicine and its effects can be observed in many fields including oncology 22 , 23 , 24 , 25 , cardiology and hematology 26 , 27 , critical care 28 , 29 , and psychiatry 30 , 31 , 32 , 33 , 34 , 35 . Importantly, PPD represents a unique case in which a moderately high chance to develop a serious psychiatric condition is coupled with a very precise temporal prediction of when such symptoms are to be expected. As such, and considering PPDs substantial societal burden, ML-based risk classification can be applied in an ideal situation with high expected societal benefit. With approximately 120,000 annual births in Sweden and the typical prevalence of PPD at 12% among women who nearly in their entirety present with a multitude of adaptations after childbirth, close monitoring of the whole population for early depressive sentinels after childbirth seems hardly feasible in reality. In contrast, close follow-up among high risk groups during midwife or nurse-led postpartum assessments may strongly contribute to more tailored and cost-efficient maternal perinatal mental care services.

However, despite promising results in other fields, relatively few studies have been performed using ML in the field of perinatal mental health. An early study in the field could predict PPD with an accuracy of 84% by use of multilayer perceptrons and assessment of 16 variables 36 . A recent pilot study used ML algorithms applied to data extracted from electronic health records to show that ML models can be utilized to predict PPD and identify critical variables that conform with known risk variables such as race, demographics, threatened abortion, prenatal mental disorder, anxiety, and an earlier episode of major depression 34 . Another study also developed models to predict PPD, which were then integrated into a mobile application platform to be used by pregnant women 37 , while a recently published study compared four PPD prediction models that comprised demographic, social and mental health data 38 . In the latter study, psychological resilience was pointed out as an important predictive factor. However, these studies have been limited by either sample size or richness of data. Finally, in a recently published study, Zhang et al. proposed a machine learning based framework for PPD risk prediction in pregnancy, using electronic health record data 39 .

To date, our study is the first using a population-based, large and rich dataset, including a wide range of clinical and psychometric self-report and medical journal-derived variables and evaluating a range of different ML algorithms against each other, and also after stratification for earlier or pregnancy depression, to provide a robust screening tool, at discharge from the delivery ward, for predicting women at risk for developing depressive symptoms later in the postpartum period.

Hence, we aim to predict women at risk for depressive symptoms at 6 weeks postpartum, from clinical, demographic, and psychometric questionnaire data available after childbirth, by use of machine learning methods.

Descriptive statistics

Table 1 shows summary statistics of the study population by depressive symptom status at 6 weeks postpartum. Results are presented as frequencies and relative frequencies within EPDS status [N (%)] or median (interquartile range) for sociodemographic, clinical and questionnaire variables. Of the 4313 participants in the study, 577 had depressive symptoms at 6 weeks postpartum. The mean age for both groups was 31 years. Differences were seen among women with depressive symptoms and women without depressive symptoms across sociodemographic variables like education, employment, and country of origin, as well as many other variables known as risk factors for postpartum depression. A greater proportion of women with depressive symptoms postpartum did not receive adequate support from their partner and were not breastfeeding.

Classification graphs

To evaluate whether ML can predict women with depressive symptoms, two datasets were used, namely the BP variables and the combined dataset, that includes the BP variables and three psychometric questionnaires (RS, SOC, and VPSQ). Performance of different ML models was first evaluated for the BP data (Fig.  1 ). The performance metrics for Ridge Regression, LASSO Regression, Gradient Boosting Machines, Distributed Radom Forests (DRF), Extreme Randomized Forests (XRT), Naïve Bayes and Stacked Ensembles models are shown. Balanced accuracy, NPV and AUC were quite similar across the models, with accuracy reaching 72% and AUC 79% for XRT. NPV was over 92% for all models. Sensitivity was quite low and together with specificity and PPV, they varied between the models. Sensitivity was highest for DRF at 84%, while only 65% for XRT; DRF had though the lowest specificity and PPV. The highest PPV was observed for Ridge Regression and Stacked Ensemble, at 41%.

Evaluation of model performance in the dataset containing only background, medical and pregnancy-related variables (n = 4277 women). The models tested were Ridge Regression, LASSO Regression, Distributed Random Forest, Extremely Randomized Trees, Gradient Boosted Machines, Stacked Ensemble, and Naïve Bayes. Models were assessed for accuracy (ACC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC), the outcome being depressive symptoms at 6 weeks postpartum. The bars represent the level of performance measures (in percent) and the table below the bar plot presents the exact numerical values. Error bars represent one standard deviation from the mean.

Performance of different ML models was then evaluated for the combined dataset, even including psychometric measures (Fig.  2 ). Performance metrics for the same models showed that NPV was still over 90% for all models, but otherwise, similar levels of accuracy and AUC were observed. More variability among the models was observed for sensitivity, specificity and PPV. XTR had the highest accuracy (at 73%) and AUC (at 81%) among all models, with a balance in sensitivity at 72% and specificity at 75%; PPV was at 33% and NPV at 94%. As this balancing act is an essential attribute of predictive models based on imbalanced datasets the subsequent experimental analysis was provided using only XRT.

Evaluation of model performance in the total combined dataset (n = 2385 women). The combined dataset contained the background, medical and pregnancy-related variables, as well as answers to the questionnaires Resilience-14, Sense of Coherence-29 and Vulnerable Personality Scale Questionnaire. The models tested were Ridge Regression, LASSO Regression, Distributed Random Forest, Extremely Randomized Trees, Gradient Boosted Machines, Stacked Ensemble, and Naïve Bayes. Models were assessed for accuracy (ACC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC), the outcome being depressive symptoms at 6 weeks postpartum. The bars represent the level of performance measures (in percent) and the table below the bar plot presents the exact numerical values. Error bars represent one standard deviation from the mean.

Comparative performance of the XRT model using all variables, the top 50%, and the top 25% variables, for both the BP and the combined dataset is shown in Fig.  3 . There was an apparent trade-off between model sensitivity and specificity, which were both affected by dataset used and percent of variables included (Fig.  3 ). Sensitivity was highest with use of only 25% of the combined dataset, while specificity was highest with the use of the top 50% of the BP dataset. None among the other measures were greatly affected by either dataset used or percent of variables included (a trend to lower PPV when 25% of variables used was noted). The AUC curves corresponding to Figs. 2 and 3 are available in the supplementary material (Supplementary Figure 1 ).

Comparative performance of the dataset containing only background, medical history and pregnancy-related variables (BP) and the combined dataset (BP + RS + SOC + VPSQ). The Extremely Randomized Trees (XRT) algorithm was used to compare the performance of the two datasets for predicting depression at 6 weeks postpartum. Models were assessed for accuracy (ACC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC). The variable selection procedure shows results when All (100%), Top 50%, and Top 25% of variables were retained, ranked according to Mean Decrease in Impurity (MDI) relevance.

The results for the performance of the XRT models after stratification for previous depression are shown in Fig.  4 . For all women, XRT achieved a balanced accuracy of 73%, a sensitivity of 72%, a specificity of 75%, a positive predictive value of 33%, a negative predictive value of 94% and an AUC of 81%. For women with depression in pregnancy or earlier in life, XRT achieved a balanced accuracy of 69%, a sensitivity of 76%, a specificity of 61%, a positive predictive value of 44%, a negative predictive value of 87% and an AUC of 77%. For women without any previous depressive episode, balanced accuracy was 64%, sensitivity 52%, specificity 76%, positive predictive value of 13%, negative predictive value 97% and AUC of 73% (Fig.  4 ). Among the results from analyses of the individual questionnaires, no single one achieved an accuracy of more than 70% (Supplementary Figure 2 ).

Stratified classification graphs for Extreme Randomized Forest (XRT) model, by pregnancy/previous depression status. Results presented for all women (All, n = 2385, of which 14% had postpartum depression, PPD), women with depression during current pregnancy or earlier in life (With Previous Depression, n = 971, of which 27% had PPD), and women without any previous depression episode (Without Previous Depression, n = 1414, of which 6% had PPD). For each category, models were assessed for accuracy (ACC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC).

Variable importance

The 25 most important variables by MDI based on Distributed Random Forests (DRF) models, considering the women with different previous depression status are shown in Fig.  5 . For all women, Anxiety During Pregnancy and Depressive During Pregnancy stand out as the two most important variables (importance level above 0.7) (Fig.  5 A). The variables following in importance were questions included in the psychometric instruments, except for history of depression. Similarly, for women with previous depression, Anxiety During Pregnancy and Depressive During Pregnancy stand out as important variables for the presence of depression postpartum (importance level above 0.9) (Fig.  5 B). Finally, for women without depression, Anxiety During Pregnancy was the absolutely most important variable (importance level of 1) (Fig.  5 C). Even here, variables relating to resilience, sense of coherence and personality followed, but interestingly, variables such as breastfeeding, BMI, traumatic events in childhood, mode of delivery, hypoxia in the newborn and age place among the top 25 variables.

Ranked importance of the assessed variables using the Extremely Randomized Trees (XRT) models in the combined dataset, considering the women with different previous depression status. Results presented for all women ( A ), All women (n = 2385), ( B ) women with depression during current pregnancy or earlier in life (Previous/pregnancy depression, n = 971), and ( C ) women without any previous depression episode (No previous depression, n = 1414). The graphs depict the variable importance as a relative measure that is scaled to a maximum of 1.0. The x-axis represents the relative contribution to the classification algorithm of the corresponding feature on the y-axis.

The 25 most important variables based only on BP variables for all women (n = 4313) can be found in Fig.  6 . The two variables that have an importance level above 0.9 are again Depression During Pregnancy and Anxiety During Pregnancy. The next variable with an importance level above 0.3 is Depression History, while the remaining rate below 0.2.

Ranked importance of the assessed background, medical history and pregnancy variables for all women (n = 4277) using Extremely Randomized Trees (XRT) models. The top 25% of the variables are reported . The x-axis represents the relative contribution of the corresponding variable to the classification algorithm.

Including only the top 20 variables, the AUC is only reduced by 1% to 0.79, including just 10 variables reduced the AUC by 2% to ~ 0.78, while after including just 5 variables reduced the AUC by 3% to ~ 0.77. For the previously non-depressed group, including 10 variables gives an AUC of 0.72, and 5 variables an AUC of 0.71.

In this study, we evaluated a range of different machine learning (ML) methods to predict pregnant women at risk for postpartum depressive (PPD) symptoms. The classification performance of the chosen ML algorithms was not significantly different in regard to accuracy, NPV, AUC measures. However, variations were more pronounced in regard to sensitivity, specificity and PPV. In general, as expected, an inverse relationship is observed in performance with respect to sensitivity and specificity. Furthermore, PPV is considerably lower than NPV due to low prevalence of PPD, as expected.

Overall, XRT provides robust performance with highest accuracy and well-balanced sensitivity and specificity. Addition of resilience and personality self-reported variables to the background, medical history and pregnancy-related variables provides marginal improvement in both accuracy and AUC. It is nevertheless of note that these extra variables boost the sensitivity of the XRT model substantially for only a slight drop in specificity. As this does not depend on the lower sample size used for the second step of analyses involving personality and resilience measures, it could be hypothesized that there is either a certain redundancy between variables, e.g. that low resilience is a core feature among depressed patients during pregnancy, or that anxiety and depression measures, available for all patients, have such a strong predictive value that the further addition of variables does not greatly improve accuracy.

These results suggest a possible benefit of using ML to screen new mothers at discharge from the delivery ward in order to identify those at high risk for postpartum depressive symptoms. However, because of the low PPV across all models, due to the relatively low prevalence of PPD at 12%, one would expect that many women identified at high risk would in the end not get depressed. On the other hand, these methods may nevertheless permit the identification of a high-risk group, to which preventive interventions would be offered in a cost-effective way, mainly by avoiding large costs related to full-blown depressive episodes postpartum. These could include the provision of extra support as well as more focused and longitudinal assessments in these mothers. Furthermore, the variables included in the BASIC study refer to easily acquired web-based self-reports, which support their use for screening purposes. Because of the high NPV, we would not expect many women not identified as high risk to develop depression postpartum. As such, the application of our classification algorithms would boost cost-effectiveness, allowing for a tailored resource allocation towards the mothers initially identified at risk versus a more widespread follow up of all mothers; in the low-risk group, assessments could be limited to single timepoints, as is praxis today. As PPD affects more than 16,000 families every year in Sweden alone, with high associated costs, estimated at $30,000 per mother-infant pair for untreated peripartum mood disorders, preventive efforts would have substantial societal benefits 40 .

It is interesting that performance metrics, especially accuracy and AUC, remain stable even when the number of variables used in the models is reduced from 100 to 50% and even to 25% of all variables available, and AUC is relatively stable even at 5–10 variables. As discussed above, this is in line with the thought that there is some redundancy when it comes to the variables included, with depression and anxiety during pregnancy being highly correlated with some background and medical history variables, and possibly mediating their association with PPD. It is thus intriguing to observe that only among non-previously depressed, variables such as breastfeeding, BMI, traumatic interpersonal events in childhood, mode of delivery, infant hypoxia and age are emerging as important for prediction, along with resilience and personality variables, which are otherwise more prominent among those earlier depressed. This is important to have in mind when developing screening strategies; the variables used might need to be adjusted for the group of women with previous depression. Anxiety during pregnancy continues to be very predictive in both groups. The stability of the performance measures however, indicates that an abbreviated survey can be used to screen without significantly affecting predictive power.

Among possible explanations for the somewhat lower accuracy in both the depressed group (earlier or during pregnancy) (n = 971, accuracy = 69%) and never-depressed subgroups (n = 1414, accuracy = 64%) are the lower sample sizes as well as a relatively decreased variability in the data (the algorithms did not have a big number of examples of alternatives to learn from). Sensitivity is the same in the earlier depressed group, but drops to 52% in the never depressed group, underlining the difficulty in identifying women at high risk for having their first ever depressive episode after childbirth. In general, the high NPV figure in the never earlier depressed group means that women with a negative screening in that group do not need tighter follow-up; NPV nonetheless drops to 86% in the earlier depressed group, suggesting that further screening in the postpartum period might still benefit this high-risk group of women.

Our study showed a slightly higher AUC than most earlier studies’ best prediction models (79% by Wang et al. and 78% by Zhang et al.), though our accuracy of 73% is lower than the 84% reported by Tortajada et al. 34 , 36 , 38 . However, in the latter study, the main outcome was depression at 32 weeks and not at 6 weeks postpartum, genetic data was included and the study sample was more homogeneous since it consisted of SSRI-free Caucasian women. Moreover, a lower EPDS cut-off was used followed by clinical interviews, possibly reducing the risk of misclassification of study cases and controls. Nevertheless, in our study, a clinical evaluation was not possible for practical reasons, due to the much larger study population. Finally, in addition to clinical and environmental variables, information on related gene polymorphisms was also utilized in that study.

Furthermore, Wang et al. identified race, obesity, anxiety, depression, different types of pain, and antidepressant and anti-inflammatory drug use during pregnancy as the most important variables for their prediction models 34 . These variables differed somewhat from the ones we identified as being most important with the caveat that our model also indicated that anxiety during pregnancy and depression history or depressive symptoms during pregnancy were overwhelmingly the most significant predictors for PPD. It has to be noted that we included many psychometric measures, which followed in importance, e.g. the question 19 on the SOC scale “Do you have very mixed up feelings and ideas?” and question 4 on RS, which measures self-regard (“I am friends with myself”). The population in the BASIC study is quite homogeneous, most participants having a high education, are quite healthy and born in the Nordic countries. Further, the BASIC dataset has no information on race. BMI was also identified in our study as an important variable, both in the BP dataset analysis and the sub-analysis among women without previous depression. Rates of antidepressant use are low. Differences in the analytical approach might also account for some differences in the results.

These findings further illuminate the difficulties in predicting which women will go on to develop postpartum depressive symptoms after childbirth. From the variable importance plots, the most predictive variables for postpartum depressive symptoms, available at the time of discharge from the delivery ward, is to either have anxiety or depressive symptoms during pregnancy. In fact, these two variables are by far the most predictive, along nevertheless with distinct variables related to resilience, sense of coherence and personality. The predictive algorithms reach an accuracy for the whole group of 73% and AUC of 81%, which is at the limit for possible use in clinical settings. The algorithms might need to be different according to whether women had experience depression before in life. Further studies, possibly using more advanced methods and bigger samples, are warranted.

Very recently, Zhang et al. also proposed a machine learning based framework for PPD risk prediction using electronic health record (EHR) data 39 . While the techniques employed are comparable to our study with similar processing pipeline, they report higher AUC. This increment can be attributed majorly to the substantially larger cohort used in their study. Several ML studies have demonstrated that large datasets lead to lower estimation variance and hence provide better predictive performance. Furthermore, the top predictors also differ between our study due to differences in data sources. Additionally, a PPV higher than that reported in our study would significantly increase the clinical utility of our proposed framework. However, PPV is directly related to the prevalence of PPD in the population studied, which is only about 12%. While the classification threshold of the model can be adjusted to improve PPV, it does not ensure the expected benefit as other evaluation metrics, like sensitivity, specificity and NPV, would be adversely affected. Even Zhang et al. that reported higher AUC values, only report a PPV of ~ 27% for the validation site with prevalence of 6.5%, highlighting the issue 39 .

The lack of effective ways that would allow for early prediction of women at risk for depressive symptoms in the postpartum period has been addressed in the Introduction. In fact, the Edinburgh Postnatal Depression Scale is nowadays used as a screening tool for current depression 41 . National guidelines in several countries recommend screening for PPD at 6 to 8 weeks postpartum; however, the suggested target groups of women to be screened vary between countries 42 , 43 , 44 . Also, the use of the EPDS at this time is used to screen for concurrent depression. In contrast, the role of EPDS in pregnancy, in combination with other variables, for early identification of women at risk for development of depressive symptoms later in the postpartum period has not been studied. In our study we do show that high EPDS scores in pregnancy are highly predictive of postpartum depression.

This study had numerous strengths. First, it addresses a novel field, as there are very few studies in the area, none from the Nordic countries, and none of earlier algorithms is being widely used in clinical practice. The large sample size allowed us to train a robust range of different ML algorithms. The richness of the BASIC dataset provided us with the opportunity to investigate the predictive power of a large number of background, medical history, pregnancy and delivery related variables, as well as psychometric questionnaires; the last ones both as total scores but also at individual item level. A key novelty feature of the study in the inclusion of many resilience and personality-related variables, that have been identified in the literature but not included in previous models. We also explore the importance of variables in terms of their predictive power of PPD, an effort directed towards to designing a compact survey to screen for PPD. Finally, the analysis of clinically relevant sub-groups such as women with previous depression or depression during pregnancy gave clinically useful insights.

Some limitations of the study include the non-representative sample in that women born in Scandinavia, with a high education and cohabitating with the child’s father were over-represented in the cohort, which makes the findings difficult to generalize to the background population. Sources of selection bias are the exclusion of non-Swedish speaking women as the questionnaires were only offered in the Swedish language, and the fact that more healthy women are more prone to participate in studies of this kind. Not all women self-reported on all variables, but we addressed this problem of missing values with exclusions and imputations where appropriate. Class imbalance in the outcome made the training stages of the algorithms challenging but were also addressed appropriately. Lastly, theoretically, some items from the scales on personality (SSP), and attachment (ASQ) might have had a more prominent role in prediction if they would have been available for a larger proportion of the women in this study. The study by Zhang et al., published after our study was conducted, reported higher AUC and included some predictors lacking in our study 39 . Future studies should make sure to include these important predictive variables for further evaluation.

Depressive symptoms and anxiety during pregnancy are highly predictive factors for women who go on and develop postpartum depressive symptoms, while variables relating to resilience, sense of coherence and personality also play a modest role. The predictive algorithms have relatively good accuracy and AUC, with XRT performing best.

Data sources

Data for the development of the prediction models were obtained from the “Biology, Affect, Stress, Imaging and Cognition during Pregnancy and the Puerperium” (BASIC) study. BASIC is a population-based prospective cohort study at the Department of Obstetrics and Gynaecology at Uppsala University Hospital, Uppsala, Sweden 7 . Between September 2009 and November 2018 all pregnant women who were 18 years of age or older, did not have their identities concealed, had sufficient ability to read and understand Swedish and did not have known bloodborne infections and/or non-viable pregnancy as diagnosed by routine ultrasound were invited to participate in the study 45 . Data acquisition in the BASIC study was mainly based on online surveys and questionnaires that the women were asked to fill out during pregnancy at the 17th and 32nd gestational week and at 6 weeks, 6 months and 12 months postpartum. The surveys included questions about background characteristics, such as sociodemographic variables, psychological measures, medical information, information on reproductive history, lifestyle and sleep. All questionnaires were self-reported and web-based. Data are also retrieved from the medical journals. The participation rate for the study was 20% but the cohort had a relatively low attrition rate, with 71% of the participants remaining in the study at 12 months follow-up 45 .

This study focuses on two subsets of variables from the BASIC study: and (i) background, medical history and pregnancy/delivery variables (BP) and (ii) further psychometric questionnaires (information on exact assessment methods and coding is provided in Table 1 for the background variables and Supplementary Table 1 for the exact questions in the different questionnaires). The BP variables consisted of sociodemographic and lifestyle information, self-reported health, medical history and variables relating to pregnancy and childbirth. This dataset included even information on depression and anxiety symptoms during pregnancy. Depression symptoms were assessed by a score of 12 or more on the Edinburg Postnatal Depression Scale (EPDS) in pregnancy weeks 17, 32 or 38, while anxiety during pregnancy was defined as ratings in the highest quartile on either the State Trait Anxiety Inventory (STAI) 46 , the Beck Anxiety Inventory or the anxiety subscale of the EPDS (EPDS-3A). These variables were available for the majority of the BASIC participants. The total number of interpersonal and non-interpersonal events in the Lifetime Instances of Traumatic Events Scale (LITE) 47 was also included among BP variables. The BP variables consisted of continuous, discrete, nominal and ordinal categorical variables, measured at various time points during the study.

The extra psychometric scales used were the Attachment Style Questionnaire (ASQ) 48 , the Resilience-14 scale (RS) 49 , 50 , the Sense of Coherence Scale-29 (SOC) 51 , the Vulnerable Personality Style Questionnaire (VPSQ) 52 , 53 , and the Swedish Scale of Personalities (SSP) 54 . ASQ, RS, SOC, VPSQ, and SSP were filled out at gestational week 17 or 32, VPSQ and LITE assessments were conducted at 12 months postpartum. All variables were assessed on a Likert scale and coded as ordinal variables. These scales were used for only specific period of time during the course of the BASIC project, different for each scale, and are thus available for different number of women (Table 1 ) 45 .

Additionally, the participants of BASIC study were also asked to fill out the EPDS at different time-points during and after pregnancy. The outcome in this study was EPDS score at 6 weeks postpartum, assessing the degree of self-reported depressive symptoms in the early postpartum period. The discrete scores for this timepoint were then aggregated and a cut-off of a score of 12 or higher was used to indicate women with depressive symptoms, in accordance to validation studies for the Swedish population 55 . The number of women in the BASIC study who had completed the EPDS at 6 weeks postpartum and were thus included was 4313.

Ethics declarations

The study has been approved by the Research Ethics Board in Uppsala (Dnr 2009/171, with amendments). All participating women gave written informed consent before being included in the study. All methods were carried out in accordance with relevant guidelines and regulations.

Data pre-processing

The pre-processing consisted of splitting the original BASIC dataset into different subsets. Two subsets were retained for our study, i.e. background & pregnancy (BP) data and psychometric questionnaire data. Data for twins and women with multiple pregnancies were removed from the dataset, as these are relatively rare, are followed very closely during and after childbirth, and are associated with higher risk for PPD 56 , 57 . Explorative data analyses were conducted on individual variables to check their distributions and to identify and remove outliers that were assessed to be non-informative. Psychometric questionnaires and BP variables that contained information about the women after the time point of the outcome, namely 6 weeks postpartum, were also excluded to avoid inadvertent biases of the results.

SSP was omitted from the analysis due to large number of missing observations, as this survey was used only for few years during recruitment for the BASIC study 45 . Its inclusion would have resulted in a much smaller sample size for the final analysis.

The dataset consists of continuous, nominal and ordinal variables. As continuous variables in the dataset have varying scales, normalization is performed to transform all the variables to a common range from 0 to 1. Furthermore, nominal and ordinal variables that represent non-numerical values are encoded using binary numerical representations for improving the performance of the ML algorithms.

Data imputation

As missing values can drastically impact the performance of ML models, a conservative approach was adopted to handle them. Firstly, samples (rows, corresponding to one pregnancy) with more than 50% missing values in the included variables were eliminated, and the final number of pregnancies in the ML analyses was 4277. Next, variables (columns, corresponding to a distinct variable) with more than 25% missing data were also eliminated. Finally, the remaining missing values were imputed from the available data. While continuous variables were imputed using multivariate imputation by chained equations (MICE) 58 , categorical and ordinal variables were imputed with K nearest neighbors’ imputation 59 .

Classification techniques

With ML algorithms, there is no one-size-fits-all solution, making it imperative to try multiple alternatives. Consequently, this study explored different ML algorithms for supervised classification that modeled data in different ways. In order to present a comprehensive comparison, the following algorithms were implemented: Ridge Regression, LASSO Regression, Gradient Boosting Machines, Distributed Radom Forests, Extreme Randomized Forest, Naïve Bayes, and Stacked Ensembles. Ridge Regression specializes in analysing multiple regression data with multicollinearity, while LASSO Regression is a type of linear regression that shrinks data values towards a central point, and results in simple, sparse models (i.e. models with fewer parameters). Gradient Boosting Machines (GBM) and Random Forests are ensemble learners. In Distributed Radom Forests (DRF), a subset of features is used to determine the most discriminative thresholds to split the trees on. However, unlike DRF, where one builds an ensemble of deep independent trees, in GBM, we specify an ensemble of weak, shallow successive trees, where each tree is learning and improving on the previous tree. In Extremely Randomized Trees (XRT), instead of using the most discriminative thresholds for the splits, thresholds are drawn at random for each feature and the best of these random thresholds are used as the splitting rule, resulting in lower variance but more bias. XRT are similar to DRF with the caveat of more randomness. Naïve Bayes (NB) is a probabilistic classifier based on Bayes’ Theorem. The NB works under the assumption that the presence of any particular feature for a certain outcome is unrelated to the presence of any other feature for that outcome. Thus, despite if the features depend on each other or upon the existence of other features, the NB assumes that all of the features independently contribute to the outcome probability. Stacked Ensemble learns a new model by combining predictions of existing models. Stacked Ensembles are a class of supervised learning algorithms that work by training a meta-learner to find the optimal combination of base learners. Unlike bagging and boosting were the goal is to stack a number of weak learners together, the goal is to stack a number of diverse and strong learners together to optimize learning 60 .

For all the classification algorithms, the outcome measure was the participants’ EPDS score at 6 weeks postpartum represented as a binary variable with 12 as cut-off, while predictor variables included the BP variables and psychometric data described above.

Class imbalance

The BASIC dataset, as a population-based sample and in accordance to clinical situations, is predominantly composed of data from women who did not experience PPD at 6 weeks postpartum (less than 10% of the women representing PPD cases), consequently leading to extreme data class imbalance. ML classifiers trained on such imbalanced datasets usually generate biased results. To mitigate this imbalance, the minority class consisting of women with PPD was oversampled during ML training. Unlike under sampling of majority class consisting of women without PPD, this approach avoids loss of information and leverages all the samples from both classes.

Evaluation metrics

The performance of model prediction of the ML classification algorithms was evaluated using a variety of performance metrics. The performance of each classification model was captured by the Confusion Matrix that formed the basis for other metrics. In addition to the most commonly used classification accuracy, sensitivity (true positive rate) and specificity (false positive rate) are also reported. The positive predictive value (PPV) and negative predictive value (NPV) are also reported. Additionally, a Receiver Operating Characteristic (ROC) curve was specified for each classification to show the relation between the true positive rate and false positive rate. The performance of the classifiers was then summarized by the total area under the ROC curve (AUC), with the higher the AUC (between 0 and 1) indicating a better performance of the classification.

Variable (feature) importance/selection

The success of a ML algorithm does not only depend on good predictive performance but also on generalizability and easy interpretability. Identifying variables that have significant impact on the outcome is valuable, especially in the medical domain. Variable importance using Random Forests models can be calculated using Gini Importance or Mean Decrease in Impurity (MDI) 61 . The MDI relevance of a variable is obtained by calculating how effective the variable is at reducing the uncertainty when creating decision trees. The variable that is most effective and used the most will be ranked as most important.

Analytic strategy

The analytical strategy consisted of breaking the analysis down into steps and iteratively building towards a final classification model, all the while being cognizant of any potential biases introduced by the approach. The workflow is presented in Fig.  7 . First, the raw data was split into the BP and the different psychometric questionnaires datasets in order to build predictive models independently on each psychometric questionnaire and to identify the ones with the highest accuracy for classification of PPD. Second, the psychometric questionnaires that yielded the highest accuracies were combined with the BP dataset. Predictions were then performed with the aggregate data (combined dataset). Additional models were trained with reduced datasets resulting from variable selection. Top 50% and top 25% variables with MDI were used to train separate classification models to determine the relative contribution of those variables to the prediction. Additionally, stratified analyses were performed, where participants were stratified by a previous history of depression (defined as earlier depression, earlier contact with psychiatrist/psychologist, or depression during pregnancy).

Study workflow and analytical strategy. Data were obtained from the “Biology, Affect, Stress, Imaging and Cognition during Pregnancy and the Puerperium” (BASIC) study, a population-based prospective cohort study in Uppsala, Sweden. Data included in our study comprised (i) background, medical history and pregnancy-related variables (BP) from women, and (ii) further psychometric questionnaires, available at discharge from the delivery ward. The data were processed and either were used to test models or train the machine learning algorithms, to predict depressive symptoms at 6 weeks postpartum.

Based on preliminary analyses, SSP and ASQ did not provide any information gain relative to BP data. Hence, only RS, SOC and VPSQ variables that provided predictive performances comparable to BP variables were included in the aggregate analysis.

Data availability

The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request and after data transfer agreements are in place, according to current regulations.

American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM-5 5th edn. (American Psychiatric Association, 2013).

Book   Google Scholar  

Fitelson, E., Kim, S., Baker, A. S. & Leight, K. Treatment of postpartum depression: clinical, psychological and pharmacological options. Int. J. Womens Health 3 , 1–14. https://doi.org/10.2147/IJWH.S6938 (2010).

Article   PubMed   PubMed Central   Google Scholar  

Patel, M. et al. Postpartum depression: a review. J. Health Care Poor Underserved 23 , 534–542. https://doi.org/10.1353/hpu.2012.0037 (2012).

Article   PubMed   Google Scholar  

Yim, I. S., Tanner Stapleton, L. R., Guardino, C. M., Hahn-Holbrook, J. & Dunkel Schetter, C. Biological and psychosocial predictors of postpartum depression: systematic review and call for integration. Annu. Rev. Clin. Psychol. 11 , 99–137. https://doi.org/10.1146/annurev-clinpsy-101414-020426 (2015).

Bloch, M., Daly, R. C. & Rubinow, D. R. Endocrine factors in the etiology of postpartum depression. Compr. Psychiatry 44 , 234–246. https://doi.org/10.1016/S0010-440X(03)00034-8 (2003).

Asif, S. et al. Severe obstetric lacerations associated with postpartum depression among women with low resilience—a Swedish birth cohort study. BJOG https://doi.org/10.1111/1471-0528.16271 (2020).

Iliadis, S. I. et al. Personality and risk for postpartum depressive symptoms. Arch. Womens Ment. Health 18 , 539–546. https://doi.org/10.1007/s00737-014-0478-8 (2015).

Article   CAS   PubMed   Google Scholar  

Committee on Obstetric Practice. American College of Obstetricians and Gynecologists Committee opinion no. 630: screening for perinatal depression. Obstet. Gynecol. 125 , 1268–1271 (2015).

Article   Google Scholar  

Ko, J. Y., Rockhill, K. M., Tong, V. T., Morrow, B. & Farr, S. L. Trends in postpartum depressive symptoms—27 States, 2004, 2008, and 2012. MMWR Morb. Mortal Wkly. Rep. 66 , 153–158. https://doi.org/10.15585/mmwr.mm6606a1 (2017).

Dennis, C. L. & McQueen, K. The relationship between infant-feeding outcomes and postpartum depression: a qualitative systematic review. Pediatrics 123 , e736–e751. https://doi.org/10.1542/peds.2008-1629 (2009).

Slomian, J., Honvo, G., Emonts, P., Reginster, J.-Y. & Bruyère, O. Consequences of maternal postpartum depression: a systematic review of maternal and infant outcomes. Womens Health 15 , 174550651984404. https://doi.org/10.1177/1745506519844044 (2019).

Article   CAS   Google Scholar  

Moore Simas, T. A. et al. Matched cohort study of healthcare resource utilization and costs in young children of mothers with postpartum depression in the United States. J. Med. Econ. 23 , 174–183. https://doi.org/10.1080/13696998.2019.1679157 (2020).

Murray, L., Woolgar, M., Cooper, P. & Hipwell, A. Cognitive vulnerability to depression in 5-year-old children of depressed mothers. J. Child Psychol. Psychiatry 42 , 891–899. https://doi.org/10.1111/1469-7610.00785 (2001).

Nguyen, J. A literature review of alternative therapies for postpartum depression. Nurs. Womens Health 21 , 348–359. https://doi.org/10.1016/j.nwh.2017.07.003 (2017).

The Management of Depression During Pregnancy. A report from the American Psychiatric Association and the American College of Obstetricians and Gynecologists. Obstet. Gynecol. 114 , 703–713. https://doi.org/10.1097/AOG.0b013e3181ba0632 (2009).

Dennis, C.-L. & Hodnett, E. D. Psychosocial and psychological interventions for treating postpartum depression. Cochrane Database Syst. Rev. https://doi.org/10.1002/14651858.CD006116.pub2 (2007).

Righetti-Veltema, M., Conne-Perréard, E., Bousquet, A. & Manzano, J. Risk factors and predictive signs of postpartum depression. J. Affect. Disord. 49 , 167–180. https://doi.org/10.1016/s0165-0327(97)00110-9 (1998).

Lee, Y. et al. Applications of machine learning algorithms to predict therapeutic outcomes in depression: a meta-analysis and systematic review. J. Affect. Disord. 241 , 519–532. https://doi.org/10.1016/j.jad.2018.08.073 (2018).

Bzdok, D. & Meyer-Lindenberg, A. Machine learning for precision psychiatry: opportunities and challenges. Biol. Psychiatry Cognit. Neurosci. Neuroimaging 3 , 223–230. https://doi.org/10.1016/j.bpsc.2017.11.007 (2018).

Friston, K. J., Redish, A. D. & Gordon, J. A. Computational nosology and precision psychiatry. Comput. Psychiatry 1 , 2–23. https://doi.org/10.1162/CPSY_a_00001 (2017).

Rohart, F., Gautier, B., Singh, A. & Lê Cao, K.-A. mixOmics: an R package for ‘omics feature selection and multiple data integration. PLOS Comput. Biol. 13 , e1005752. https://doi.org/10.1371/journal.pcbi.1005752 (2017).

Article   CAS   PubMed   PubMed Central   ADS   Google Scholar  

Ahmed, F. E. Artificial neural networks for diagnosis and survival prediction in colon cancer. Mol. Cancer 4 , 29 (2005).

Anagnostou, T., Remzi, M., Lykourinas, M. & Djavan, B. Artificial neural networks for decision-making in urologic oncology. Eur. Urol. 43 , 596–603 (2003).

Jerez, J. et al. Improvement of breast cancer relapse prediction in high risk intervals using artificial neural networks. Breast Cancer Res. Treat. 94 , 265–272 (2005).

Suzuki, K., Li, F., Sone, S. & Doi, K. Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network. IEEE Trans. Med. Imaging 24 , 1138–1150 (2005).

Baxt, W. G., Shofer, F. S., Sites, F. D. & Hollander, J. E. A neural computational aid to the diagnosis of acute myocardial infarction. Ann. Emerg. Med. 39 , 366–373 (2002).

Zini, G. Artificial intelligence in hematology. Hematology 10 , 393–400 (2005).

Bent, P. et al. Early and intensive continuous hemofiltration for severe renal failure after cardiac surgery. Ann. Thorac. Surg. 71 , 832–837 (2001).

Huang, L., Yu, P., Ju, F. & Cheng, J. Prediction of response to incision using the mutual information of electroencephalograms during anaesthesia. Med. Eng. Phys. 25 , 321–327 (2003).

Choi, J., Choi, J. & Jung, H.-T. Applying machine-learning techniques to build self-reported depression prediction models. CIN Comput. Inform. Nurs. 36 , 317–321. https://doi.org/10.1097/CIN.0000000000000463 (2018).

Gao, S., Calhoun, V. D. & Sui, J. Machine learning in major depression: from classification to treatment outcome prediction. CNS Neurosci. Ther. 24 , 1037–1052. https://doi.org/10.1111/cns.13048 (2018).

Graham, S. et al. Artificial intelligence for mental health and mental illnesses: an overview. Curr. Psychiatry Rep. 21 , 1–18. https://doi.org/10.1007/s11920-019-1094-0 (2019).

Helbich, M., Hagenauer, J. & Roberts, H. Relative importance of perceived physical and social neighborhood characteristics for depression: a machine learning approach. Soc. Psychiatry Psychiatr. Epidemiol. https://doi.org/10.1007/s00127-019-01808-5 (2019).

Wang, S., Pathak, J. & Zhang, Y. Using electronic health records and machine learning to predict postpartum depression. Stud. Health Technol. Inform. 264 , 888–892. https://doi.org/10.3233/SHTI190351 (2019).

Tai, A. M. Y. et al. Machine learning and big data: implications for disease modeling and therapeutic discovery in psychiatry. Artif. Intell. Med. 99 , 101704. https://doi.org/10.1016/j.artmed.2019.101704 (2019).

Tortajada, S. et al. Prediction of postpartum depression using multilayer perceptrons and pruning. Methods Inf. Med. 48 , 291–298. https://doi.org/10.3414/ME0562 (2009).

Jiménez-Serrano, S., Tortajada, S. & García-Gómez, J. M. A mobile health application to predict postpartum depression based on machine learning. Telemed. e-Health 21 , 567–574. https://doi.org/10.1089/tmj.2014.0113 (2015).

Zhang, W., Liu, H., Silenzio, V. M. B., Qiu, P. & Gong, W. Machine learning models for the prediction of postpartum depression: application and comparison based on a cohort study. JMIR Med. Inform. 8 , e15516. https://doi.org/10.2196/15516 (2020).

Zhang, Y., Wang, S., Hermann, A., Joly, R. & Pathak, J. Development and validation of a machine learning algorithm for predicting the risk of postpartum depression among pregnant women. J. Affect. Disord. 279 , 1–8. https://doi.org/10.1016/j.jad.2020.09.113 (2021).

Luca, D. L., Garlow, N., Staatz, C., Margiotta, C. & Zivin, K. Societal Costs of Untreated Perinatal Mood and Anxiety Disorders in the United States (Mathematica Policy Research, 2019).

Google Scholar  

Levis, B., Negeri, Z., Sun, Y., Benedetti, A. & Thombs, B. D. Accuracy of the Edinburgh Postnatal Depression Scale (EPDS) for screening to detect major depression among pregnant and postpartum women: systematic review and meta-analysis of individual participant data. BMJ 371 , m4022. https://doi.org/10.1136/bmj.m4022 (2020).

Siu, A. L. & Force, A. T. U. P. S. T. Screening for depression in adults: US Preventive Services Task Force recommendation statement. JAMA 315 , 380–387. https://doi.org/10.1001/jama.2015.18392 (2016).

National Collaborating Centre for Mental Health (UK). Antenatal and Postnatal Mental Health: Clinical Management and Service Guidance: Updated Edition (NICE Clinical Guidelines, No. 192. 5, CASE IDENTIFICATION AND ASSESSMENT, British Psychological Society, 2014).

Austin, M. P., Highet, N. & Group, E. W. Mental Health Care in the Perinatal Period: Australian Clinical Practice Guideline (Centre of Perinatal Excellence, 2017).

Axfors, C. et al. Cohort profile: the Biology, Affect, Stress, Imaging and Cognition (BASIC) study on perinatal depression in a population-based Swedish cohort. BMJ Open 9 , e031514. https://doi.org/10.1136/bmjopen-2019-031514 (2019).

Spielberger, C. D. State‐trait anxiety inventory. In The Corsini Encyclopedia of Psychology , 1. https://doi.org/10.1002/9780470479216.corpsy0943 (2010).

Greenwald, R. & Rubin, A. Assessment of posttraumatic symptoms in children: development and preliminary validation of parent and child scales. Res. Soc. Work Pract. 9 , 61–75. https://doi.org/10.1177/104973159900900105 (1999).

Feeney, J., Noller, P. & Hanrahan, M. Assessing adult attachment. In Attachment in Adults: Clinical and Developmental Perspectives (eds Sperling, M. B. & Berman, W. H.) 128–151 (The Guilford Press, New York, 1994).

Aiena, B. J., Baczwaski, B. J., Schulenberg, S. E. & Buchanan, E. M. Measuring resilience with the RS–14: a tale of two samples. J. Pers. Assess. 97 , 291–300 (2015).

Wagnild, G. M. & Young, H. M. Development and psychometric evaluation of the resilience scale. J. Nurs. Meas. 1 , 165–178 (1993).

CAS   PubMed   Google Scholar  

Antonovsky, A. The structure and properties of the sense of coherence scale. Soc. Sci. Med. 36 , 725–733. https://doi.org/10.1016/0277-9536(93)90033-Z (1993).

Boyce, P., Hickey, A., Gilchrist, J. & Talley, N. J. The development of a brief personality scale to measure vulnerability to postnatal depression. Arch. Womens Ment. Health 3 , 147–153. https://doi.org/10.1007/s007370170012 (2001).

Gelabert, E. et al. The vulnerable personality style questionnaire: psychometric properties in Spanish postpartum women. Arch. Womens Ment. Health 14 , 115–124 (2011).

Gustavsson, J. P. et al. Swedish universities Scales of Personality (SSP): construction, internal consistency and normative data. Acta Psychiatr. Scand. 102 , 217–225. https://doi.org/10.1034/j.1600-0447.2000.102003217.x (2000).

Wickberg, B. & Hwang, C. P. The Edinburgh Postnatal Depression Scale: validation on a Swedish community sample. Acta Psychiatr. Scand. 94 , 181–184. https://doi.org/10.1111/j.1600-0447.1996.tb09845.x (1996).

Vilska, S. et al. Mental health of mothers and fathers of twins conceived via assisted reproduction treatment: a 1-year prospective study. Hum. Reprod. 24 , 367–377. https://doi.org/10.1093/humrep/den427 (2009).

Wenze, S. J., Battle, C. L. & Tezanos, K. M. Raising multiples: mental health of mothers and fathers in early parenthood. Arch. Womens Ment. Health 18 , 163–176. https://doi.org/10.1007/s00737-014-0484-x (2015).

Azur, M. J., Stuart, E. A., Frangakis, C. & Leaf, P. J. Multiple imputation by chained equations: what is it and how does it work?: Multiple imputation by chained equations. Int. J. Methods Psychiatr. Res. 20 , 40–49. https://doi.org/10.1002/mpr.329 (2011).

Beretta, L. & Santaniello, A. Nearest neighbor imputation algorithms: a critical evaluation. BMC Med. Inform. Decis. Mak. 16 (Suppl 3), 74. https://doi.org/10.1186/s12911-016-0318-z (2016).

van der Laan, M. J., Polley, E. C. & Hubbard, A. E. Super learner. Stat. Appl. Genet. Mol. Biol. https://doi.org/10.2202/1544-6115.1309 (2007).

Article   MathSciNet   PubMed   PubMed Central   MATH   Google Scholar  

Hastie, T., Tibshirani, R. & Friedman, J. H. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd edn. (Springer, 2009).

Download references

Acknowledgements

The authors would like to acknowledge Anastasia Kollia, Hanna Henriksson and Emma Bränn for valuable insights and help with data collection and management in the BASIC study. Marina Krylova, Nils Kroemer, and Hamidreza Jamalabadi for valuable insights and assistance in the initial phase of the analyses planning. Dr. Narayanan Chatapuram Krishnan for his excellent machine learning course. Subhranil Bagchi for time spent aiding in coding and students at IIT Ropar for their insights and help with coding and theoretical discussions. Prof. Inger Sundström Poromaa, Ass. Prof. Fotios Papadopoulos and all colleagues working in the BASIC research group for their contribution with critical comments and discussions. Finally, the authors would like to sincerely thank Dr. Diem Nguyen for language editing and comments.

Open access funding provided by Uppsala University. This study has been supported by the municipality of Uppsala and Akademiska University Hospital in Sweden, the Swedish Research foundation (523-2014-2342 and 523-2014-07605), Marianne and Marcus Wallenberg foundation and the Swedish Medical Association.

Author information

Authors and affiliations.

Department of Women’s and Children’s Health, Uppsala University, 751 85, Uppsala, Sweden

Sam Andersson, Stavros I. Iliadis & Alkistis Skalkidou

Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, 140001, India

Deepti R. Bathula

Department of Psychiatry and Psychotherapy, University Hospital Jena, Jena, Germany

Martin Walter

Department of Psychiatry and Psychotherapy, Eberhardt Karls University, Tübingen, Germany

Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany

You can also search for this author in PubMed   Google Scholar

Contributions

A.S. conceived and designed the study. The analysis plan was decided on with the contribution of all authors. Analyses were performed by D.R.B., S.A., and S.I.I. S.A., D.R.B., and S.I.I. prepared the figures and tables. All authors made substantial contributions to the interpretation of results. S.A. wrote the first draft and all authors critically revised the manuscript and approved the final version.

Corresponding author

Correspondence to Alkistis Skalkidou .

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary informations., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Andersson, S., Bathula, D.R., Iliadis, S.I. et al. Predicting women with depressive symptoms postpartum with machine learning methods. Sci Rep 11 , 7877 (2021). https://doi.org/10.1038/s41598-021-86368-y

Download citation

Received : 16 September 2020

Accepted : 15 March 2021

Published : 12 April 2021

DOI : https://doi.org/10.1038/s41598-021-86368-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Enhancing explainability in predicting mental health disorders using human–machine interaction.

• Inderpreet Kaur
• Umesh Gupta

Multimedia Tools and Applications (2024)

Unveiling the prevalence and risk factors of early stage postpartum depression: a hybrid deep learning approach

• Umesh Kumar Lilhore
• Surjeet Dalal
• Arshad Hashmi

Wife-Mother Role Conflict at the Critical Child-Rearing Stage: A Machine-Learning Approach to Identify What and How Matters in Maternal Depression Symptoms in China

• Liuzhi Hong

Prevention Science (2023)

Prenatal and Childbirth Risk Factors of Postpartum Pain and Depression: A Machine Learning Approach

• McClain Sampson

Maternal and Child Health Journal (2023)

Postpartum depression: a developed and validated model predicting individual risk in new mothers

• Trine Munk-Olsen
• Xiaoqin Liu
• Merete L. Maegbaek

Translational Psychiatry (2022)

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Postpartum Depression: Pathophysiology, Treatment, and Emerging Therapeutics

Affiliations.

• 1 Department of Psychiatry, University of Toronto, Toronto, Ontario M5G 2C4, Canada; email: [email protected].
• 2 Department of Obstetrics and Gynecology, University of Toronto, Toronto, Ontario M5G 2C4, Canada.
• 3 Toronto General Hospital Research Institute, Toronto, Ontario M5G 2C4, Canada.
• 4 University Health Network Centre for Mental Health, Toronto, Ontario M5G 2C4, Canada.
• 5 Women's College Research Institute, Women's College Hospital, Toronto, Ontario M5G 2C4, Canada; email: [email protected].
• PMID: 30691372
• DOI: 10.1146/annurev-med-041217-011106

Postpartum depression (PPD) is common, disabling, and treatable. The strongest risk factor is a history of mood or anxiety disorder, especially having active symptoms during pregnancy. As PPD is one of the most common complications of childbirth, it is vital to identify best treatments for optimal maternal, infant, and family outcomes. New understanding of PPD pathophysiology and emerging therapeutics offer the potential for new ways to add to current medications, somatic treatments, and evidence-based psychotherapy. The benefits and potential harms of treatment, including during breastfeeding, are presented.

Keywords: allopregnanolone; emerging therapies; genetic aspects; pathophysiology; postpartum depression.

Publication types

• Antidepressive Agents / therapeutic use*
• Depression, Postpartum / epidemiology
• Depression, Postpartum / physiopathology*
• Depression, Postpartum / therapy*
• Psychotherapy / methods*
• Risk Assessment
• Severity of Illness Index
• Treatment Outcome
• Antidepressive Agents

Featured Clinical Reviews

• Screening for Atrial Fibrillation: US Preventive Services Task Force Recommendation Statement JAMA Recommendation Statement January 25, 2022
• Evaluating the Patient With a Pulmonary Nodule: A Review JAMA Review January 18, 2022

Select Your Interests

Customize your JAMA Network experience by selecting one or more topics from the list below.

• Academic Medicine
• Acid Base, Electrolytes, Fluids
• Allergy and Clinical Immunology
• American Indian or Alaska Natives
• Anesthesiology
• Anticoagulation
• Art and Images in Psychiatry
• Artificial Intelligence
• Assisted Reproduction
• Bleeding and Transfusion
• Caring for the Critically Ill Patient
• Challenges in Clinical Electrocardiography
• Climate and Health
• Climate Change
• Clinical Challenge
• Clinical Decision Support
• Clinical Implications of Basic Neuroscience
• Clinical Pharmacy and Pharmacology
• Complementary and Alternative Medicine
• Consensus Statements
• Coronavirus (COVID-19)
• Critical Care Medicine
• Cultural Competency
• Dental Medicine
• Dermatology
• Diabetes and Endocrinology
• Diagnostic Test Interpretation
• Drug Development
• Electronic Health Records
• Emergency Medicine
• End of Life, Hospice, Palliative Care
• Environmental Health
• Equity, Diversity, and Inclusion
• Facial Plastic Surgery
• Gastroenterology and Hepatology
• Genetics and Genomics
• Genomics and Precision Health
• Global Health
• Guide to Statistics and Methods
• Hair Disorders
• Health Care Delivery Models
• Health Care Economics, Insurance, Payment
• Health Care Quality
• Health Care Reform
• Health Care Safety
• Health Care Workforce
• Health Disparities
• Health Inequities
• Health Policy
• Health Systems Science
• History of Medicine
• Hypertension
• Images in Neurology
• Implementation Science
• Infectious Diseases
• Innovations in Health Care Delivery
• JAMA Infographic
• Law and Medicine
• Leading Change
• Less is More
• LGBTQIA Medicine
• Lifestyle Behaviors
• Medical Coding
• Medical Devices and Equipment
• Medical Education
• Medical Education and Training
• Medical Journals and Publishing
• Mobile Health and Telemedicine
• Narrative Medicine
• Neuroscience and Psychiatry
• Notable Notes
• Nutrition, Obesity, Exercise
• Obstetrics and Gynecology
• Occupational Health
• Ophthalmology
• Orthopedics
• Otolaryngology
• Pain Medicine
• Palliative Care
• Pathology and Laboratory Medicine
• Patient Care
• Patient Information
• Performance Improvement
• Performance Measures
• Perioperative Care and Consultation
• Pharmacoeconomics
• Pharmacoepidemiology
• Pharmacogenetics
• Pharmacy and Clinical Pharmacology
• Physical Medicine and Rehabilitation
• Physical Therapy
• Physician Leadership
• Population Health
• Primary Care
• Professional Well-being
• Professionalism
• Psychiatry and Behavioral Health
• Public Health
• Pulmonary Medicine
• Regulatory Agencies
• Reproductive Health
• Research, Methods, Statistics
• Resuscitation
• Rheumatology
• Risk Management
• Scientific Discovery and the Future of Medicine
• Shared Decision Making and Communication
• Sleep Medicine
• Sports Medicine
• Stem Cell Transplantation
• Substance Use and Addiction Medicine
• Surgical Innovation
• Surgical Pearls
• Teachable Moment
• Technology and Finance
• The Art of JAMA
• The Arts and Medicine
• The Rational Clinical Examination
• Tobacco and e-Cigarettes
• Translational Medicine
• Trauma and Injury
• Treatment Adherence
• Ultrasonography
• Users' Guide to the Medical Literature
• Vaccination
• Venous Thromboembolism
• Veterans Health
• Women's Health
• Workflow and Process
• Wound Care, Infection, Healing
• Download PDF
• CME & MOC
• Share X Facebook Email LinkedIn
• Permissions

Postpartum Depression—New Screening Recommendations and Treatments

• 1 University of Massachusetts Chan Medical School, Worcester
• 2 UMass Memorial Health, Worcester, Massachusetts
• Medical News & Perspectives What to Know About the First Pill Approved for Postpartum Depression Rita Rubin, MA JAMA
• Comment & Response Screening Recommendations and Treatments for Postpartum Depression—Reply Tiffany A. Moore Simas, MD, MPH, MEd; Anna Whelan, MD; Nancy Byatt, DO, MS, MBA JAMA
• Comment & Response Screening Recommendations and Treatments for Postpartum Depression Itamar Nitzan, MD; Raylene Philips, MD; Robert D. White, MD JAMA

Perinatal mental health conditions are those that occur during pregnancy and the year following childbirth, whether onset of the condition(s) predates pregnancy or occurs in the perinatal period. Perinatal mental health conditions are the leading cause of overall and preventable maternal mortality and include a wide array of mental health conditions including anxiety, depression, and substance use disorders. 1 , 2 Perinatal depression specifically affects 1 in 7 perinatal individuals. 3 While commonly referred to as postpartum depression, it is more accurately called perinatal depression because its onset corresponds with prepregnancy (27%), pregnancy (33%), and postpartum (40%) time frames. 3

Two-thirds of individuals with perinatal depression have 1 or more comorbid psychiatric conditions, mainly anxiety disorders (83%), including generalized anxiety disorder (52%), panic disorder (14%), social phobia (12%), and obsessive-compulsive disorder (11%). 3 Untreated perinatal depression is associated with short- and long-term negative consequences for affected individuals and their offspring, partners, families, and society. 4 Perinatal depression remains underdetected and undertreated; more than 75% of those who screen positive receive no treatment. 5

In June 2023, the American College of Obstetricians and Gynecologists released new recommendations to minimally screen for depression at least twice in pregnancy (initial prenatal visit and later) and again at postpartum visits, using validated instruments. 4 Additionally, depression screening is recommended at pediatric well-infant/child visits and well-woman visits. Numerous depression screening instruments exist. The 2 most widely studied and used are the Patient Health Questionnaire (PHQ-9, 9 questions) and the Edinburgh Postnatal Depression Screen (EPDS, 10 questions). They are self-administered, easy to score, include self-harm questions, and are validated in numerous languages. An overall score of 10 or higher is commonly considered positive. In addition to the overall score, the clinician should separately and intentionally review the response to the self-harm item, which is the last question for both. Any response other than “never” (EPDS) or “not at all” (PHQ-9) for the self-harm item merits further assessment.

Although PHQ-9 and EPDS total scores correlate with depression severity (10-14 mild, 15-19 moderate, and >19 severe), they are screening and not diagnostic tools. A diagnosis of depression requires at least 5 symptoms present during the same 2-week period, as a change from previous functioning, and at least 1 symptom must be a depressed mood or loss of interest or pleasure (anhedonia). The diagnostic criteria for depression are not different in the perinatal period compared with other times and are consistent with the Diagnostic and Statistical Manual of Mental Disorders, Text Revision (Fifth Edition). 6

As part of a diagnostic assessment, clinicians should query symptom type, frequency, severity, and duration, and how symptoms impact daily functioning, in addition to stressors and supports. Relevant information about personal and family psychiatric history, previous psychiatric treatments including medication trials, psychotherapy, hospitalization, and prior or recent suicidal ideation or attempts are important to elucidate. Prior to diagnosing a patient with perinatal depression, additional history and/or laboratory evaluation may be needed to evaluate other potential etiologies including medical conditions (eg, anemia, thyroid dysfunction), substance use (eg, alcohol, opioids), or medications that can mimic, cause, or exacerbate symptoms. Laboratory evaluation may include measuring hemoglobin or hematocrit, folate, vitamin B 12 , iron, and thyrotropin. Screening for comorbid substance use disorders via other screening instruments (eg, 4Ps or NIDA Quick Screen) is also recommended. 4 Once a depression diagnosis is established, including for patients whose depression predates pregnancy, screening instruments such as the EPDS and PHQ-9 may be used for symptom monitoring. In conjunction with clinical assessments, such serial symptom monitoring can be used to inform and guide treatment. 7

Prior to initiating treatment, particularly pharmacotherapy, bipolar disorder must be considered. Up to 1 in 5 persons who screen positive for perinatal depression may have bipolar disorder rather than unipolar depression. 3 Bipolar disorder is treated with mood stabilizers rather than antidepressants. Treating bipolar disorder with antidepressant monotherapy can precipitate mania, mixed states, rapid-cycling, or psychosis, which can increase suicide and infanticide risk. Given this, screening for bipolar disorder is recommended before initiating pharmacotherapy. 4 Validated screening instruments for bipolar disorder include the Mood Disorder Questionnaire (MDQ) and the 3-question Composite International Diagnostic Interview (CIDI). 4

Psychotherapy is the first-line treatment for mild depression and individuals should be referred for psychotherapy regardless of symptom severity. Pharmacotherapy is often indicated for individuals with moderate and severe symptoms, those who have needed pharmacotherapy previously for depression, or for those who may not have access to psychotherapy. 7

Patients, and some clinicians, may be hesitant to continue or initiate pharmacotherapy in pregnancy or when breast/chestfeeding due to concern regarding effects on the fetus or infant. However, undertreatment or no treatment of perinatal depression is associated with preterm birth, low-birth-weight neonates, preeclampsia, impaired infant attachment affecting neurodevelopment, challenges with partner and social support systems, and suicide, among other negative consequences. 7 Evaluating for and discussing whether pharmacotherapy is indicated needs to invoke a shared decision-making model and take into account the risks of untreated illness and the risk and benefits of medication treatment for the perinatal individual and their infant. When pharmacotherapy is indicated for depression, the risks of no treatment typically outweigh the risks of pharmacotherapy.

Selective serotonin reuptake inhibitors (SSRIs) and serotonin/norepinephrine reuptake inhibitors (SNRIs) are the most commonly prescribed medications for perinatal depression. While they are highly effective, depression symptom improvement can take 4 to 6 weeks. 7 The most common adverse effects include nausea, dry mouth, insomnia, diarrhea, headache, dizziness, agitation, sexual dysfunction, or drowsiness. 7 SSRIs/SNRIs should be started at the lowest dose and then up-titrated with adjustments over 4 to 10 days to therapeutic range. 7 SSRIs in particular are some of the best studied medications during pregnancy and are considered reasonable first-line pharmacotherapy during all trimesters and lactation. The benefits of treatment with SSRIs to the perinatal individual and their infant are generally thought to outweigh known risks. 7

In 2019, a novel treatment of postpartum depression was approved. Brexanolone (Zulresso) is a synthetic neuroactive steroid (allopregnanolone) that is an allosteric modulator of GABA A receptors. 7 It is indicated for individuals with onset of moderate to severe depression in the third trimester or within 4 weeks postpartum. Clinical trials demonstrated significant improvement in depression symptoms within 24 hours of initiation. 7 Brexanolone is administered via 60-hour intravenous infusion, requires inpatient admission, and costs more than $34 000 per patient treated. 7 Due to limited data, breastfeeding is not recommended during the infusion or for 4 days afterward. Therefore, despite initial excitement, there has been markedly limited access and uptake.

On August 4, 2023, the FDA approved an oral synthetic allopregnanolone, zuranolone (Zurzuvae), for the treatment of postpartum depression. 8 The new formulation has similar indications to brexanolone; however, it is administered as a nightly oral pill taken following a fatty meal, for 14 days. In 2 phase 3 placebo-controlled randomized trials, participants experienced symptom improvement as early as day 3 with effects persisting until day 45 (study end). 8 Common adverse effects include headache, somnolence, dizziness, and sedation. The sedating effects are significant enough that activities like driving are precluded for 12 hours after dosing. Dosages may require adjustment in the context of hepatic and kidney impairment, concomitant use of medications that are strong CYP3A4 inhibitors (CYP3A4 inducers should be avoided), and central nervous system (CNS) depressant effects. Other CNS depressants (eg, opioids, alcohol, benzodiazepines) should be avoided. Zuranolone may be used alone or as adjunct to other antidepressants like SSRIs/SNRIs. Given lack of relevant data, pregnancy should be avoided, and lactation decisions should be made through shared decision-making (relative infant dose lower than SSRIs). Information on zuranolone access, follow-up needs, long-term remission, and insurance coverage is not yet available.

There is a new and now widely available model specifically designed to increase the capacity of all clinicians caring for preconception, pregnant, postpartum, and lactating persons to address perinatal mental health conditions and substance use disorder. 4 , 7 Perinatal Psychiatry Access Programs work directly with clinicians through the core components of (1) training and toolkits, (2) clinician-to-clinician telephone psychiatric consultation and, in some cases, face-to-face consultation with patients, (3) linkages to community-based mental health resources, and (4) technical assistance to facilitate practices integrating mental health care into their workflow. 4 , 7 Access programs are generally available on nonholiday Mondays through Fridays during standard work hours and have demonstrated acceptability, efficacy, sustainability, and reproducibility. There currently exists 22 state programs and 2 national programs supported through various funding mechanisms. 4 , 7 Additional programs are anticipated with support from the Consolidated Appropriations Act of 2023 as well as the Into the Light for Maternal Mental Health and Substance Use Disorders Act. 4 , 7

Conclusions

Perinatal mental health conditions are the most common pregnancy complications and the leading cause of overall and preventable maternal mortality. New recommendations to screen for perinatal depression multiple times during pregnancy and postpartum aim to assist with early detection, diagnosis, and treatment. Novel rapidly acting treatments exist for depression onset in late pregnancy and early postpartum. Innovative and equitable approaches to increasing timely access to treatment need to be developed. Collectively, the effectiveness and impact of perinatal mental health treatments are directly proportional to perinatal individuals’ ability to access them. State-based and national access programs are available to help clinicians address perinatal mental health and increase access to care.

Corresponding Author: Tiffany A. Moore Simas, MD, MPH, MEd, UMass Memorial Health, 119 Belmont St, Jaquith 2.008, Worcester, MA 01605 ( [email protected] ).

Published Online: November 27, 2023. doi:10.1001/jama.2023.21311

Conflict of Interest Disclosures: Dr Moore Simas reported consulting fees from MCPAP for Moms; serving as medical director for Lifeline for Moms; receiving fees from ACOG, AIM, and IHI; grants from NIMH, PCORI, CDC, ACOG, and PMH; and personal fees from Miller Medical Communications. Dr Byatt reported consulting fees from The Kinetix Group, Colorado Perinatal Mental Health Access Program, Daymark Foundation, Venture Well, and JBS International; honoraria from Global Learning Collaborative; serving as medical director of MCPAP for Moms and executive director of Lifeline for Families and Lifeline for Moms outside the submitted work. In addition, Drs Moore Simas and Byatt have developed, copyrighted, and licensed to ACOG, with no payment, multiple resources available on the ACOG website including (1) Perinatal Mental Health toolkit, (2) Perinatal Mental Health e-modules (for CME), and (3) Guide to Integration of Obstetric and Mental Health Care. No other disclosures were reported.

See More About

Moore Simas TA , Whelan A , Byatt N. Postpartum Depression—New Screening Recommendations and Treatments. JAMA. 2023;330(23):2295–2296. doi:10.1001/jama.2023.21311

Manage citations:

© 2024

Artificial Intelligence Resource Center

Cardiology in JAMA : Read the Latest

Browse and subscribe to JAMA Network podcasts!

Others Also Liked

• Register for email alerts with links to free full-text articles
• Access PDFs of free articles
• Manage your interests
• Save searches and receive search alerts

Research Topic Starters

• Aggressive/Distracted Driving
• Climate Change
• Depression & Postpartum Depression
• E-Cigarettes and Vaping
• Freedom of Speech
• Gender Equity
• Gun Control
• Prescription Medication Abuse
• Psychological Disorders
• Systemic Racism
• Trigger Warnings

NEW: All STCC students, faculty & staff are now automatically registered in the library system!

>help with accessing library resources., ask a librarian..., >email:  [email protected].

>Call a librarian:  (413)755-4549  

Staff available during  library hours

• Academic Search Complete This link opens in a new window A good starting point for a database search. - provides full text articles to many journals and magazines, including peer-reviewed or scholarly titles
• MedlinePlus This link opens in a new window Produced by the National Library of Medicine, it brings you information about diseases, conditions, and wellness issues in language you can understand.
• Psychology & Behavioral Sciences Collection This link opens in a new window More advanced database - covering topics in emotional and behavioral characteristics, psychiatry & psychology, mental processes.

Resources on Depression & Postpartum Depression

• Search Terms
• Citation Aids

Depression is a serious medical illness. It's more than just a feeling of being sad or "blue" for a few days. If you are one of the more than 19 million teens and adults in the United States who have depression, the feelings do not go away. They persist and interfere with your everyday life. Symptoms can include

• Feeling sad or "empty"
• Loss of interest in favorite activities
• Overeating, or not wanting to eat at all
• Not being able to sleep, or sleeping too much
• Feeling very tired
• Feeling hopeless, irritable, anxious, or guilty
• Aches or pains, headaches, cramps, or digestive problems
• Thoughts of death or  suicide

Depression is a disorder of the brain. There are a variety of causes, including genetic, biological, environmental, and psychological factors.

>Taken from MedlinePlus  and produced by the National Institute of Health, a United States Government Agency.  

______________________________________________________________________________________________

Postpartum Depression :  The birth of a baby can trigger a jumble of powerful emotions, from excitement and joy to fear and anxiety. But it can also result in something you might not expect — depression.

Most new moms experience postpartum "baby blues" after childbirth, which commonly include mood swings, crying spells, anxiety and difficulty sleeping. Baby blues typically begin within the first two to three days after delivery, and may last for up to two weeks.

Postpartum depression isn't a character flaw or a weakness. If you have postpartum depression, prompt treatment can help you manage your symptoms and help you bond with your baby.

>Taken from the Mayo Clinic Website , a prominent health care provider.

Some search terms to try when searching databases for Depression:

Mental Depression

Clinical Depression

Major Depressive Disorder (MDD)

Unipolar Depression

Bipolar Depression

Antidepressants

Persistent depressive disorder

Note:  Using just the search term 'depression' in non-medical databases can lead to returns on an economic depression.  It is best to use more than one search term to optimize search returns.

Some search terms to try when searching databases for Postpartum Depression:

Postpartum Depression (PPD)

Baby Blues S yndrome

Postpartum Anxiety

Postpartum Psychosis

Postnatal Complications,

Note It is best to use more than one search term to optimize search returns

General Background - Depression

• Depression - also called: Clinical depression, Dysthymic disorder, Major depressive disorder, Unipolar depression From MedlinePlus. Good starting point and introduction to the brain disease.
• Depression Basics Background information on Depression from the National Institute of Mental Health, A United States Government agency..

General Background - Postpartum Depression

• Postpartum Depression- also called: Post-pregnancy depression Starting point article from MedlinePlus - Many women have the baby blues after childbirth and may have mood swings, feel sad, anxious or overwhelmed, have crying spells, lose your appetite, or have trouble sleeping.
• Postpartum Depression Introduction from the Mayo Clinic. Good background and starting point to begin your research.
• Postpartum depression. - Research Starter Magill’s Medical Guide (Online Edition), 2019

Scholarly Peer Reviewed Articles on Depression

• Major Depression Disorder in Adults: A Review of Antidepressants. Major Depressive Disorder (MDD) is the "most common mood disorder having at least one single major depressive episode." The purpose of this paper is to discuss and review current drugs and treatment for MDD.
• General practitioners' perspectives on barriers to depression care: development and validation of a questionnaire. General practitioners (GPs) regularly feel challenged by the care of depressed patients and may encounter several barriers in providing best management. The aim of this study was to develop and validate a questionnaire assessing barriers to depression care
• The Association of Diet and Depression: An Analysis of Dietary Measures in Depressed, Non-depressed, and Healthy Youth The authors designed this study to assess the association between dietary patterns and depression using four dietary measures previously studied in children and adolescents.

Scholarly Peer Reviewed Articles on Postpartum Depression

• The Effect of Social Support on Pregnancy and Postpartum Depression. Introduction: Recent researches show us the given social support to the mother during pregnancy, birth and in postpartum peroid effects positively the adaptation to the role of motherhood, increases sensitivity to the baby and helps to relations with the relatives.
• Postpartum depression screening in primary care: How to make it a success. The article discusses the conduct of postpartum depression (PPD) screening in primary care to identify, support, and refer mothers for appropriate mental health treatment in 2019.
• Tracking Postpartum Depression In Young Women Objective: to track postpartum depression among young women who are in the second week and in the sixth month postpartum.

• Diagnosing Depression Signs and symptoms of depression are spelled out, and multimodal treatment through psychotherapy, medication, support groups, and aerobic exercise is discussed
• Understanding Depression Dr. Andrew Leuchter, Director of Adult Psychiatry at UCLA, explains that depression is an illness not a weakness, and that real, physical changes in brain neurochemistry or in horm...
• Women and Depression Clinical depression affects 19 million Americans, of whom two-thirds are women. This program from The Doctor Is In addresses the good news about depression: it is a diagnosable and treatable illness. From Films on Demand. Login Required

STCC Library Citation Handouts

• 2020 APA Citation Handout 7th edition
• 2020 MLA Citation Handout

MyBib Plug in your source information and MyBib will create a citation for you!

• << Previous: Climate Change
• Next: E-Cigarettes and Vaping >>
• Last Updated: Jan 19, 2024 11:23 AM
• URL: https://libguides.stcc.edu/ResearchStarter

Postpartum Depression

Overcoming postpartum depression with supportive traditions, learn an integrative approach to treating postpartum depression and anxiety..

Posted May 2, 2024 | Reviewed by Ray Parker

• Find a therapist to overcome depression
• Postpartum depression and anxiety are common and can disrupt a new mother's emotional well-being.
• Integrative approaches for postpartum depression include a range of interventions tailored to mothers' needs.
• La cuarentena, a Mexican tradition, offers support during postpartum, promoting bonding and reducing stress.

Postpartum depression and anxiety are common conditions following the birth of a child. They are characterized by feelings of disconnection, challenges with bonding , overwhelming worry, doubt, fear , sorrow, rage , worthlessness, and even suicidality . The drastic drop in estrogen and progesterone after childbirth contributes to mood changes, which are aggravated by the sleep deprivation associated with caring for a newborn infant.

Fatigue and worry are normal for new mothers. Still, postpartum depression and anxiety are diagnosed when these feelings interfere with interpersonal relationships and the mother’s ability to care for her child.

There is some evidence that a low level of vitamin D antepartum is a risk factor in postpartum depression, suggesting both sun exposure and supplemental vitamin D are important. Physical exercise antepartum also reduces the risk of depression. Fatigue is a risk factor for depression for two years postpartum, which suggests that having extended family and friends provide childcare and household relief is beneficial.

Herbal remedies for depression and anxiety should be carefully considered if the mother is nursing and could pass herbal constituents to the infant through breast milk. During this “fourth trimester,” she should continue to work closely with her health team to get support and advice and consider an integrative protocol tailored to her specific needs.

What is essential is to treat depression and anxiety in order to safeguard the well-being of the mother and infant. This may include bioidentical hormones , psychotherapy , vitamins, antioxidants, and herbs that stabilize or boost mood and reduce anxiety.

You may be interested in this study that explores the role of tryptophan and tyrosine, both antidepressant amino acids, in lessening the severity of postpartum blues.

La Cuarentena: A Mexican Postpartum Tradition

When I first arrived in Mexico, la cuarentena was widely practiced. La cuarentena literally translates as “quarantine” and is the forty-day period when a woman rests after giving birth. She abstains from sex , eats lots of nourishing foods and herbs, and is cared for by her female friends and relatives who cook, clean, and support almost all of her needs.

La cuarentena is being practiced less frequently due to the demands of modern life. Even among rural women, la cuarentena is disappearing. Yet, there is so much wisdom in providing women with unfettered time to bond with their newborns while being relieved of all the pressures of caring for others. What a marvelous gift to give the new mother, to whatever degree possible, so she is the focus of care and support.

How We Can Use This Tradition To Support New Mothers

Organize a group of friends and family members to review the new mother's needs. These may include food preparation, cleaning with herbal products and aromatherapy, pitching in to serve as a nanny, a wet nurse or lactation consultant, or someone to sleep over to help soothe the crying baby or arranging for laundry, house cleaning, and food preparation.

Provide helpful gifts for self-care, like an all-natural skin brush with instructions on how to dry brush skin daily toward the heart. This will keep the immune system strong and also speed the release of swelling fluids collected during pregnancy .

Leslie Korn, Ph.D. , is a clinician specializing in Integrative Mental Health, Nutrition and Traumatic Stress.

• Find a Therapist
• Find a Treatment Center
• Find a Psychiatrist
• Find a Support Group
• Find Online Therapy
• United States
• Brooklyn, NY
• Chicago, IL
• Houston, TX
• Los Angeles, CA
• New York, NY
• Portland, OR
• San Diego, CA
• San Francisco, CA
• Seattle, WA
• Washington, DC
• Asperger's
• Bipolar Disorder
• Chronic Pain
• Eating Disorders
• Passive Aggression
• Personality
• Goal Setting
• Positive Psychology
• Stopping Smoking
• Low Sexual Desire
• Relationships
• Child Development
• Therapy Center NEW
• Diagnosis Dictionary
• Types of Therapy

Understanding what emotional intelligence looks like and the steps needed to improve it could light a path to a more emotionally adept world.

• Emotional Intelligence
• Gaslighting
• Affective Forecasting
• Neuroscience

We Screen Postpartum Women for Depression. But What About PTSD?

— we're not asking all of the right questions.

by Julia Zukerberg May 5, 2024

While scrolling through TikTok, I've seen many women share their traumatic birth experiences: an emergency C-section followed by significant blood loss; a home birth turned into an ambulance ride to the nearest hospital; and a seemingly perfect delivery followed by an extremely rare medical condition that resulted in the patient being in a coma for a month.

To some medical providers with years of experience under their belts, these may be familiar stories. In fact, some practitioners may be numb to the complications that can accompany childbirth. But as a medical student, when I hear these stories I can't help but wonder: who is checking in on these women? Is anyone?

Childbirth-related post-traumatic stress disorder (CB-PTSD), the development of PTSD after childbirth, is a recognized phenomenon affecting 4.6%-to-6.3% of women postpartum. It is associated with multiple negative maternal and child health outcomes, including decreased maternal attachment. This isn't too surprising; it's understandable to feel distant from the source of trauma, in this case the baby.

In fact, that's something that makes CB-PTSD so unique. There is a constant trigger in the patient's life: the newborn. Also, society tends to romanticize childbirth, celebrating the arrival of a patient's precious bundle of joy. As a result, the challenges a mother faces when bringing a child into the world are often overlooked.

In routine clinical practice, postpartum patients are screened for postpartum depression at ob/gyn visits that take place in the weeks following childbirth. But what about PTSD, specifically CB-PTSD? Sure, the depression screening questionnaire may capture some of these patients who are experiencing CB-PTSD, but not all of them.

For the few patients who may actually be given a standard PTSD screening questionnaire to fill out, they answer questions based on well-recognized traumatic events, including wars, fires, and physical or sexual assault. The standard PTSD screening questionnaire fails to recognize childbirth as a traumatic event, leaving most patients who suffer from CB-PTSD going undetected and untreated.

We're not being specific. We're not asking if these patients are having recurrent nightmares about their birth experiences or flashbacks to the moments in which they had to quickly make critical life-or-death decisions. Instead, we're just letting them continue with their lives, prioritizing their newest addition to the family while suppressing unwanted memories of their traumatic birth experiences.

After a patient delivers her baby and enters the postpartum period, she's seen in the hospital by a medical team for the first few days. We ask the standard questions: How are you feeling? Are you in any pain? Are you bleeding? How's the baby doing? We check to make sure she is physically doing well, but not necessarily mentally. Everyone experiences childbirth differently, and we're missing the opportunity to identify early the high-risk patients who may have found their birth to be traumatic. The sooner we identify these patients, the sooner we can provide help before their PTSD symptoms worsen.

If these patients can be identified in their first few days postpartum, great. If not, we aim to see them at a routine 6-week postpartum visit where we get the opportunity for another assessment. We already screen these patients for postpartum depression. What harm could come from an extra sheet of paper with a more detailed PTSD questionnaire that recognizes childbirth as a potential traumatic trigger? And what about collaborating with our psychiatric colleagues to ensure that these patients are connected with appropriate and tailored therapy, as well as medications if deemed necessary?

As an aspiring ob/gyn, I urge providers to keep CB-PTSD in the back of their minds when seeing postpartum patients. There is so much to cover in clinical visits, making it challenging to add something else to the slew of things to discuss. However, addressing CB-PTSD could make a huge difference in how patients process their experiences and ultimately heal. Instead of having to turn to social media as an outlet, our patients should be able to turn to us.

Julia Zukerberg is a fourth-year medical student in the combined MD/Master's in Public Health Program at the University of Miami Miller School of Medicine.

An official website of the United States government

Here’s how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( A locked padlock ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

• Heart-Healthy Living
• High Blood Pressure
• Sickle Cell Disease
• Sleep Apnea
• Information & Resources on COVID-19
• The Heart Truth®
• Learn More Breathe Better®
• Blood Diseases and Disorders Education Program
• Publications and Resources
• Blood Disorders and Blood Safety
• Sleep Science and Sleep Disorders
• Lung Diseases
• Health Disparities and Inequities
• Heart and Vascular Diseases
• Precision Medicine Activities
• Obesity, Nutrition, and Physical Activity
• Population and Epidemiology Studies
• Women’s Health
• Research Topics
• Clinical Trials
• All Science A-Z
• Grants and Training Home
• Policies and Guidelines
• Funding Opportunities and Contacts
• Training and Career Development
• Email Alerts
• NHLBI in the Press
• Research Features
• Past Events
• Upcoming Events
• Mission and Strategic Vision
• Divisions, Offices and Centers
• Advisory Committees
• Budget and Legislative Information
• Jobs and Working at the NHLBI
• Contact and FAQs
• NIH Sleep Research Plan
• News and Events
• < Back To All News

Moving more for a healthy pregnancy

When Noelia M. Zork, M.D., was pregnant, she wanted to do everything she could to avoid having gestational diabetes — high blood sugar that typically develops between the second and third trimester. Diabetes runs in her family, and because her blood sugar levels were borderline high during her pregnancy, Zork, a maternal-fetal medicine specialist and researcher at Columbia University Irving Medical Center, followed the advice she gives patients: Find ways to stay active and follow a heart-healthy diet. 

“If you can do small things, they add up,” she explained. This worked to offset her risks. In addition to eating well, she focused on simple activities she could do at home, like a pregnancy-focused yoga video a few times a week. After having her second child, she walked to the park with her family and they danced to free children’s Zumba videos. “It’s about batching in activity while you’re entertaining the child,” she said. “Anything that gets the heart rate going counts.”

Studies confirm it. For years regular physical activity has been linked to healthier pregnancies and better outcomes for new moms and babies. Several studies, including those from the NIH-supported nuMoM2b and nuMoM2b Heart Health Study network, have found that people who can stay physically active throughout pregnancy not only have a reduced likelihood for developing gestational diabetes, but are more likely to have lower blood pressure during and after pregnancy. Other studies have found that regular physical activity supports a healthy body weight during and after pregnancy, while reducing symptoms of postpartum depression. 

It’s why researchers continue to study the positive effects of physical activity and how to help people move more throughout their lives, including during pregnancy.  

All the powerful perks

“Exercise helps you sleep better at night, lowers anxiety, and reduces blood pressure for up to 24 hours,” said Bethany Barone Gibbs, Ph.D., a nuMoM2b network researcher and principal investigator of the Gibbs Physical Activity and Sedentary Behavior Research Lab at West Virginia University. “This happens every time you exercise.”

The long-term benefits are just as important. Regular physical activity supports bone health, the brain, a normal body weight, metabolic and physical functions, and offsets risks that can lead to a heart attack, stroke, diabetes, certain forms of cancer, and dementia. 

This is why the Department of Health and Human Services recommends that adults without underlying health complications get at least 150 minutes each week of moderate-intense physical activity, like brisk walking or easy cycling. The recommendations can also be met with 75 minutes of weekly vigorous exercise, like taking a fast-paced aerobics class. Adults should also incorporate at least two weekly strength-based sessions, which can include body-weight exercises, into their routine. 

The guidelines for physical activity during pregnancy are the same but encourage women who haven’t participated in vigorous activities to focus on moderate-intense activity . People who are pregnant should avoid exercising in extreme heat, contact sports that increase risks for falling, and doing exercises while lying on their spine after the first trimester. 

“Even committing to 10 minutes at a time, three days a week, is going to make a huge difference,” said Zork. For people new to exercise, she and others recommend a gradual increase in physical activity. Think of working up to 10 minutes of marching in place a few times each day. 

According to research from the nuMoM2b study, first-time moms who increased their physical activity throughout the first half of pregnancy were less likely to develop gestational diabetes compared to those who were the least active. Among the more than 10,000 study participants, 5.7% of those who did not participate in physical activity developed gestational diabetes. This dropped to 3.8% for those who gradually increased their activity levels and to 3.1% for the most active participants. 

“Taking steps to support heart-healthy living, such as by moving more and knowing numbers for a healthy heart, supports individual health and well-being and helps create a healthy environment for the baby to grow and develop,” said Jasmina Varagic, M.D., Ph.D., program director of the Vascular Biology and Hypertension Branch in the Division of Cardiovascular Sciences at NHLBI. These outcomes — positive pregnancy experiences and optimal indicators of heart health — have also been linked to better health outcomes years later. 

A new research frontier

As investigators study the many benefits of physical activity, they are also conducting research to specify how to “sit less and move more” each day. 

Gibbs is studying these effects during and after pregnancy. Her research in this area started with a pilot study with 120 women during their pregnancy. She and researchers measured how many times participants stood, sat, and stepped each day. They also tracked moderate-intense activities and sedentary behavior, or time spent sitting, reclining, or lying down. At the end the study, 19 participants experienced at least one adverse pregnancy outcome, such as gestational diabetes, complications related to high blood pressure, or a preterm birth.

Among those who experienced complications, 16 fell into a “high-sitting” category, a pattern that included sitting or reclining for about 11 hours a day and getting around 5,000 daily steps. Those who sat for less than 9 hours a day logged about 7,000 steps and were largely protected from the same risks. The associations between pregnancy complications and “high-sitting/low steps” were also independent from moderate-intense activities. 

Gibbs and her colleague Kara M. Whitaker, Ph.D., M.P.H., at the University of Iowa are now studying these patterns among 500 women through an NHLBI-supported study called Pregnancy 24/7 . “Even if you are getting 30 minutes a day of physical activity — that’s only 3% of the time you are awake,” she said. “We want to understand what’s happening the rest of that time and how it relates to a healthy pregnancy.”

To study these patterns after pregnancy, Gibbs and other researchers are partnering with women in the nuMoM2b Heart Health Study to assess how daily movement overlaps with cardiovascular health. In the meantime, Gibbs encourages people, especially busy moms, to try to work physical activity into their day, however and whenever they can. 

“Women do a lot,” said Gibbs. “It doesn’t have to take a lot of time, but caring for yourself with physical activity is really important and can have so many benefits.” 

About the National Heart, Lung, and Blood Institute (NHLBI):  NHLBI is the global leader in conducting and supporting research in heart, lung, and blood diseases and sleep disorders that advances scientific knowledge, improves public health, and saves lives. For more information, visit  www.nhlbi.nih.gov .

About the National Institutes of Health (NIH):  NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit  www.nih.gov .

More Information

Related health topics, health education, related news.