Psychological and Behavioral Determinants of Dietary Patterns in Postpartum Women

Psychological and Behavioral Determinants of Dietary Patterns in Postpartum Women | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Psychological and Behavioral Determinants of Dietary Patterns in Postpartum Women Ye Shengyao, Nadia Samsudin, Hashem Salarzadeh Jenatabadi, Ye Jianqiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6620245/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Malaysia was ranked first among ASEAN countries for obesity prevalence, raising significant public health concerns. Postpartum women are particularly vulnerable to obesity and related psychological challenges, yet the complex interplay between postpartum depression, body mass index (BMI), lifestyle factors, and dietary behaviors remains insufficiently understood. This study introduces an innovative framework to examine how personal characteristics, lifestyle behaviors, BMI, and depression collectively shape healthy and unhealthy food consumption during the postpartum period. A structural equation modeling (SEM) approach, using maximum likelihood estimation, was applied to data from 623 postpartum women within their first year after childbirth. Depression emerged as the strongest predictor of unhealthy food consumption, with higher depressive symptoms corresponding to poorer dietary choices. Higher BMI was also significantly associated with increased intake of unhealthy foods. The model explained 75% of the variance in unhealthy food consumption and 61% in healthy food consumption. This study highlights the critical roles of depression and BMI in shaping postpartum dietary behaviors and provides important new insights into the behavioral mechanisms contributing to obesity among postpartum women. Given Malaysia's high obesity rates, these findings offer valuable directions for developing targeted interventions to promote healthier eating habits during the postpartum period. women’s health public health psychological well-being health risk non-communicable disease Figures Figure 1 Figure 2 Background The postpartum period represents a profound physiological, psychological, and social transition in women's lives, yet it is increasingly characterized by the emergence of complex and interrelated health challenges ( 1 ). Among these, obesity and depression stand out as global public health concerns with far-reaching consequences for individual well-being, family functioning, and societal health systems ( 2 ). Recent data from the World Health Organization (WHO) report that 16% of adults globally were classified as obese in 2022, with women demonstrating disproportionately higher rates than men ( 3 ). The reproductive and postpartum periods, spanning the ages of 25 to 34 years, represent a window of heightened vulnerability, where pregnancy-related weight gain and postpartum weight retention cumulatively elevate long-term obesity risk. Empirical evidence highlights that 50–80% of postpartum women retain between 1.4 and 5 kilograms of pregnancy-related weight within the first year following childbirth, while 20–50% retain weight gains exceeding 5 kilograms ( 4 , 5 ). This persistent weight retention is not simply a personal health issue; it signals broader systemic risks for chronic diseases, including type 2 diabetes mellitus, cardiovascular conditions, hypertension, and increasingly, psychological disorders such as depression ( 6 , 7 ). Moreover, postpartum weight trajectories are not merely shaped by biological factors. They reflect deeply embedded social, economic, and behavioral forces, including access to nutritional resources, structural inequalities, and the psychosocial stressors inherent to early motherhood ( 8 ). Dietary behavior during the postpartum period is a particularly potent, yet underexamined, determinant of health outcomes. Evidence consistently demonstrates a shift toward less nutritious, energy-dense diets following childbirth, with reduced intake of fruits, vegetables, and whole grains, and increased consumption of fast foods and sugar-sweetened beverages ( 9 ). These changes are rarely voluntary; they emerge at the nexus of psychological exhaustion, social expectations of motherhood, reduced self-care, and limited systemic support for maternal nutrition ( 10 ). Simultaneously, the postpartum period marks a time of profound psychological vulnerability. Postpartum depression (PPD), affecting approximately 10–15% of new mothers worldwide, manifests as a constellation of affective, cognitive, and behavioral symptoms including sadness, anxiety, anhedonia, impaired concentration, and disrupted eating patterns ( 1 , 11 ). Despite the prevalence and gravity of PPD, it remains frequently underrecognized and undertreated, exacerbating risks for poor maternal-infant attachment, family dysfunction, and intergenerational health consequences ( 12 ). Recent research suggests a reciprocal, cyclical relationship between obesity and depression: elevated BMI increases susceptibility to depressive symptoms, while depression, in turn, fosters maladaptive lifestyle behaviors, creating a self-perpetuating loop of physical and psychological risk. Yet despite recognition of these individual relationships, few studies have interrogated the complex, integrated pathways linking lifestyle factors, depression, obesity, and dietary behaviors in postpartum women. Even fewer have applied advanced analytical methodologies capable of capturing the intricate web of direct and indirect effects that characterize real-world behavioral dynamics ( 13 ). Structural Equation Modeling (SEM), a powerful tool for modeling complex latent constructs and multiple pathways simultaneously, offers an opportunity to advance understanding of postpartum health beyond siloed variables toward a systemic, human-centered perspective ( 14 ). Addressing this critical gap, the present study employs SEM to investigate how lifestyle behaviors, depression, and body mass index (BMI) collectively influence dietary choices among postpartum women. Uniquely, it differentiates between healthy and unhealthy food consumption patterns, recognizing that the determinants of these behaviors may diverge significantly. By adopting a multidimensional, systems-based analytical approach, the study illuminates how psychological well-being, physiological status, and social behaviors intersect to shape health-relevant dietary decisions during a pivotal life stage. This research has implications that extend beyond the clinical management of postpartum women. It challenges prevailing individualistic narratives of "personal responsibility" in postpartum health, instead highlighting the structural and psychological complexities that inform behavior. Moreover, it contributes to broader discussions on how societal systems (from maternity care practices to food environments) can be restructured to support healthier, more equitable trajectories for women after childbirth. By integrating perspectives from behavioral science, nutrition, public health, and psychology, this study advances a more holistic understanding of maternal health during the postpartum period. It aligns with growing calls within human behavioral science to move beyond reductionist models and toward integrative frameworks capable of capturing the complex realities of human lived experience ( 12 , 15 ). This study is conceptually grounded in the Biopsychosocial Model and Self-Determination Theory (SDT), two complementary frameworks that provide a comprehensive lens for understanding the complex interplay between physical health, psychological well-being, and behavioral outcomes among postpartum women. The Biopsychosocial Model posits that health and illness arise from a dynamic interaction among biological, psychological, and social factors, rather than being attributable to a single cause ( 16 , 17 ). Within the postpartum context, body weight status, depressive symptoms, and dietary behaviors are deeply interwoven with physiological changes, emotional adaptations, and environmental conditions ( 18 ). This model supports the study’s systems-level approach to investigating how personal characteristics, lifestyle behaviors, mental health, and diet interact to influence postpartum health trajectories. SDT further elucidates the motivational processes underlying postpartum women’s dietary behaviors ( 19 ). According to SDT, the fulfillment of basic psychological needs (autonomy, competence, and relatedness) is critical for promoting intrinsic motivation toward healthful behaviors ( 20 ). The postpartum period, characterized by shifts in personal identity, increased caregiving demands, and social role changes, may disrupt these psychological needs, influencing dietary choices and health-related behaviors ( 21 ). Depressive symptoms may exacerbate motivational deficits, contributing to maladaptive eating patterns and further complicating postpartum weight management ( 9 , 22 ). By integrating these frameworks, the present study situates postpartum dietary behaviors within a multidimensional context that acknowledges the biological, emotional, and social complexities of early motherhood. The application of SEM enables a rigorous examination of the direct and indirect pathways linking these factors, advancing a more nuanced understanding of postpartum health that transcends reductionist models. Methods Research framework Figure 1 illustrates the proposed research framework, which integrates five latent variables (personal characteristics, lifestyle behaviors, depression, healthy food intake, and unhealthy food intake) and one observed variable, BMI. In this model, "personal characteristics" serve as the initial independent variable, while "healthy food" and "unhealthy food" represent two distinct dependent variables. The relationships between the independent and dependent variables are hypothesized to be mediated by three constructs: lifestyle, BMI, and depression. Guided by the primary objectives of this study, two central mediation hypotheses underpin the framework: (i) depression mediates the relationship between lifestyle behaviors and food intake, and (ii) BMI mediates the relationship between lifestyle behaviors and food intake. SEM was selected as the analytical approach due to its superior capacity for handling complex models involving latent constructs, as well as its ability to simultaneously estimate and test multiple direct and indirect pathways ( 23 ). Unlike conventional regression-based methods, SEM allows for the modeling of interdependencies among multiple dependent variables within a unified, coherent framework, thereby offering a more nuanced and holistic understanding of the interrelationships among the study variables ( 18 ). The application of SEM further enables the simultaneous assessment of exogenous and endogenous variables, enhancing the explanatory power and theoretical robustness of the model ( 14 ). Measure of Variables The questionnaire used in this study was developed specifically for this research by integrating validated measurement items from multiple previously published sources. Instead of relying on a single existing questionnaire, this study carefully selected and adapted specific indicators from different established scales to create a tailored instrument. This approach was chosen to comprehensively capture the unique combination of variables relevant to this study, including personal characteristics, lifestyle, BMI, depression and food intake. Each measurement item was selected based on its relevance, established validity, and reliability as demonstrated in prior literature. By integrating these validated indicators, the questionnaire aimed to accurately reflect the constructs of interest, ensuring robust and meaningful data collection for subsequent analysis. Personal characteristics were assessed using four indicators: age group, education level, working experience, and household income. Age was classified into four categories: 21–25 years, 26–30 years, 31–35 years, and above 35 years. Education level was categorized into five tiers: less than high school, high school, diploma, bachelor's degree, and master's or Ph.D. qualification. Working experience was divided into five groups: no job experience, 1–3 years, 4–6 years, 7–10 years, and more than 10 years. Household income, measured in Malaysian Ringgit (RM), was classified into five income brackets: less than RM 2,000, RM 2,000–3,000, RM 3,000–4,000, RM 4,000–5,000, and above RM 5,000. Lifestyle behaviors were measured based on indicators adapted from previous study ( 14 ), including average working hours per day, frequency of physical activity per week, average sleeping hours per day, and screen time per day (television, smartphone, tablet usage). Physical activity frequency was categorized as none, 1 time, 2 times, 3 times, 4 times, or more than 4 times per week. Screen time was grouped into less than 1 hour, 1–2 hours, 2–3 hours, 3–4 hours, and more than 4 hours daily. Sleep duration was classified as less than 6 hours, 6–7 hours, 7–8 hours, 8–9 hours, or more than 9 hours per day. Average working hours were categorized as none, less than 7 hours, 7–8 hours, 8–9 hours, or more than 9 hours per day. BMI was calculated using the standard formula: weight (kg) divided by height squared (m²) ( 24 ). Following WHO classifications ( 25 ), BMI was categorized as underweight (< 18.5 kg/m²), normal weight (18.5–24.9 kg/m²), overweight (25.0–29.9 kg/m²), and obese (≥ 30.0 kg/m²). Depression levels were assessed using the Edinburgh Postnatal Depression Scale (EPDS) ( 26 , 27 ), a validated instrument widely utilized to screen for postpartum depressive symptoms. The EPDS comprises ten items rated on a four-point Likert scale (0–3), reflecting the frequency of depressive symptoms over the past week. Total scores were classified into four categories: normal (0–9), slightly increased risk ( 10 – 11 ), increased risk ( 12 – 15 ), and likely depression (≥ 16) ( 28 ). Dietary behavior was measured through two distinct constructs: healthy food consumption and unhealthy food consumption. Consistent with prior frameworks ( 29 , 30 ), nine indicators were selected to capture dietary patterns: intake of whole grains (grams/day), fruits (grams/day), vegetables (grams/day), sweets (grams/day), chips (grams/day), soft drinks (milliliters/day), and fast food (grams/day). Healthy food intake was operationalized through the consumption of whole grains, fruits, and vegetables, whereas unhealthy food intake encompassed sweets, chips, soft drinks, and fast food. Sampling The minimum sample size required in structural equation modeling should be determined based on the number of latent variables and the number of indicators per construct ( 14 , 31 ). Specifically, studies with five or fewer latent variables, each measured by at least three indicators, require a minimum of 100 respondents. When there are up to seven latent variables, a sample size of at least 150 is recommended if each latent variable has three or more indicators. In cases where latent variables are measured by fewer than three indicators, larger samples—300 to 500 respondents—are recommended to ensure statistical power and model stability. Given that this study involves five latent variables, each measured by multiple indicators, a minimum sample size of 100 respondents was deemed sufficient. To enhance the generalizability and robustness of the findings, an online survey link was disseminated to postpartum women residing in Kuala Lumpur, Malaysia. A total of 623 completed questionnaires were collected, substantially exceeding the minimum sample size requirement. The study was conducted in accordance with relevant ethical guidelines and institutional regulations. All participants were informed about the study's objectives, and written informed consent was obtained prior to participation. Confidentiality and anonymity were maintained throughout the data collection process. Results Descriptive Statistics Analysis Table 1 presents the demographic, lifestyle, and health-related characteristics of the study sample (N = 623). The respondents comprised Malays (42.2%), Chinese (35.8%), and Indians (22.0%), broadly reflecting the ethnic distribution of the urban population in Kuala Lumpur. Regarding personal characteristics, the majority of participants were aged between 31 and 35 years and held a Bachelor's degree as their highest educational qualification. In terms of socioeconomic status, 39.2% of respondents reported a monthly household income between RM 4,000 and RM 5,000. In terms of employment history, while 13.5% of respondents reported no prior work experience, a substantial proportion (29.2%) had accumulated between 7 and 10 years of working experience. Lifestyle behaviors demonstrated considerable variability. Physical activity levels were moderate: 25.4% of respondents engaged in physical activity twice per week, while 23.3% reported no engagement in physical activities. Only 7.2% of respondents reported exercising more than four times per week. Patterns of screen time use indicated high digital engagement, with 38.5% of respondents reporting an average of four hours of screen exposure daily, followed by 33.9% reporting two hours and 25.0% reporting three hours per day. Sleep patterns were relatively healthy, with the majority sleeping between seven and eight hours per night; only 5.9% of respondents reported sleeping fewer than six hours per day. Most participants (52.2%) reported average working hours of eight to nine hours daily. Based on BMI classifications, 10.9% of the respondents were categorized as underweight, 26.0% as normal weight, 28.7% as overweight, and 34.3% as obese. Depression levels, assessed using the EPDS, revealed that 19.7% of the respondents fell within the normal range, while 22.6% exhibited a slightly increased risk, 33.4% showed an increased risk, and 24.2% were likely to experience postpartum depression. These findings highlight a diverse sample characterized by moderate physical activity engagement, significant screen time exposure, a high prevalence of overweight and obesity, and a concerning proportion of respondents at elevated risk for postpartum depression. The distribution of food consumption is presented in Table 2. Whole grains (e.g. bread, rice, pasta, noodles, breakfast cereals) is equal to 240.94 ± 86.84 grams per day. Table 2 Descriptive statistics of food intake Food consumption (Mean ± Std) Whole Grains (grams/ day) 240.94 ± 86.84 Fruits (grams/ day) 399.64 ± 118.47 Vegetables (grams/ day) 326.22 ± 99.48 Sweets (grams/ day) 130.40 ± 29.31 Chips (grams/ day) 91.15 ± 34.81 Soft Drinks (milliliter/ day) 379.48 ± 128.26 Fast Food (grams/ day) 212.37 ± 98.59 Validity and Reliability Certain criteria must be met to assess the validity and reliability of survey instruments within SEM. One of these essential conditions is that the Cronbach’s alpha for each latent variable should be at least 0.7, indicating acceptable internal consistency and validity (32). As illustrated in Table 3, the Cronbach’s alpha values for all latent variables in this study exceed the recommended threshold, thereby confirming the reliability and validity of the measurement scales used in the analysis. To assess the reliability of the research instrument, the factor loading value for each indicator within the latent variables should be above 0.7 (33). As presented in Table 4, several indicators related to respondents’ personal characteristics and lifestyle, specifically age, income, and work experience, showed factor loading values below the recommended threshold of 0.7. Additionally, two indicators measuring respondents’ depression levels did not meet this criterion. Consequently, these indicators were excluded from further analysis in the SEM, ensuring the robustness and reliability of the final measurement model. After the removal of indicators that did not meet reliability requirements, another essential criterion was evaluated to confirm the reliability of the research instrument. Specifically, each latent variable should demonstrate an Average Variance Extracted (AVE) value of at least 0.5, signifying acceptable internal consistency and reliability (14). As shown in Table 5, the AVE values obtained in this study for all latent variables clearly exceeded the recommended threshold of 0.5. Meeting this criterion further strengthens the reliability of the measurement model used in this research, confirming the robustness and accuracy of the findings. Model Fitting For a structural model to be considered acceptable, standard guidelines recommend that model-fit indices should generally exceed a threshold of 0.9 (30). As presented in Table 6, the values of the comparative fit index (CFI), normed fit index (NFI), relative fit index (RFI), incremental fit index (IFI), goodness of fit index (GFI), and Tucker-Lewis index (TLI) in this study all met or exceeded this standard. These findings indicate that the collected data align closely with the proposed theoretical model, confirming an appropriate fit and further validating the overall structural framework used in this research. Table 6 Model fitting analysis Chi-square (df) 156.41 (38) CFI 0.902 NFI 0.938 RFI 0.921 IFI 0.945 GFI 0.963 TLI 0.921 Structural Model Figure 2 presents the final structural model. Significant relationships between variables are indicated with solid arrows, while non-significant paths are represented with dashed arrows. Out of fourteen hypothesized relationships, eleven were found to be statistically significant. Only three paths namely, the impact of personal characteristics on unhealthy food consumption, the impact of lifestyle behaviors on healthy food consumption, and the impact of depression on healthy food consumption, were not statistically significant. Among the significant pathways, the strongest observed effect was the positive association between depression and unhealthy food consumption (standardized coefficient = 0.66), suggesting a substantial influence of depressive symptoms on maladaptive dietary behaviors. In contrast, the weakest significant relationship was identified between personal characteristics and healthy food consumption (standardized coefficient = 0.16). These findings underscore the dominant role of psychological factors, particularly depression, in shaping unhealthy dietary behaviors among postpartum women, relative to demographic or lifestyle factors. This pattern emphasizes the need for postpartum health interventions to prioritize psychological well-being as a key determinant of dietary outcomes. Table 7 Model parameter Standardized Estimate Standard Error Unstandardized Estimate 95% Confidence Interval Correlation Personal Characteristics → Lifestyle 0.27 0.87 1.44 (0.11, 0.56) Personal Characteristics → Depression 0.22 1.22 2.34 (0.16, 0.34) Personal Characteristics → BMI 0.24 0.35 0.64 (0.11, 0.63) Personal Characteristics → Healthy Food 0.16 1.89 2.07 (0.09, 0.21) Lifestyle → Depression 0.31 0.36 0.67 (0.21, 0.44) Lifestyle → BMI 0.39 0.66 1.22 (0.31, 0.53) Lifestyle → Healthy Food 0.32 1.07 3.55 (0.17, 0.48) BMI → Healthy Food 0.19 0.97 0.62 (0.05, 0.33) BMI → Unhealthy Food 0.42 1.69 1.06 (0.29, 0.63) BMI → Depression 0.36 1.11 0.48 (0.22, 0.57) Depression → Unhealthy Food 0.66 0.83 3.66 (0.46, 0.82) Table 7 presents the parameters for the research model. According to Fig. 2, the results indicate that: i) BMI has a significant impact on healthy food; ii) BMI has a significant impact on unhealthy food; iii) depression significantly influences unhealthy food consumption; and iv) lifestyle significantly affects unhealthy food consumption. Mediation Analysis Mediation analysis has been widely applied across the social and behavioral sciences to elucidate the mechanisms through which independent variables influence dependent outcomes (34). More recently, mediation approaches have been further advanced by the adoption of the counterfactual framework, which provides greater precision in estimating causal pathways (35, 36). In the present study, mediation analysis was employed to investigate the mechanisms linking the primary indicators (personal characteristics and lifestyle) to dietary behaviors, focusing on the role of three mediators: lifestyle behaviors, BMI, and depression. The primary aim of the mediation analysis was to examine both direct and indirect relationships among five latent variables and one observed variable within the proposed structural model. Table 8 summarizes the mediation effects observed between the independent variables, the mediators, and the dependent variables. In this context, relationships were classified as exhibiting an indirect effect, partial mediation, or full mediation, depending on the strength and significance of the mediated pathways. By applying mediation analysis within a structural equation modeling framework, this study provides a nuanced understanding of how lifestyle, BMI, and depression operate as key intermediary mechanisms linking personal characteristics to postpartum dietary behaviors. Mediation analysis followed the established two-step procedure. First, the effects of the independent variable (lifestyle behaviors) on the mediators (depression and BMI) and subsequently on the dependent variables (healthy and unhealthy food intake) were assessed for statistical significance. Only if these direct paths were significant was the presence of a mediating effect further evaluated. The nature of mediation was determined by comparing direct, indirect, and total effects, thereby distinguishing between partial and full mediation pathways. Table 9 presents the estimates for direct, indirect, and total effects involving depression and BMI as mediators of food intake behaviors. As shown in Fig. 2 and Table 9, the direct effect of lifestyle behaviors on healthy food intake was not statistically significant. Consequently, neither depression nor BMI mediated the relationship between lifestyle behaviors and healthy food intake. Conversely, for unhealthy food intake, both depression and BMI served as significant mediators. Specifically, the total indirect effect of lifestyle behaviors through depression was 0.5246, and through BMI was 0.4838. These results confirm that depression and BMI partially mediate the association between lifestyle behaviors and unhealthy food consumption among postpartum women. Table 9 Mediation test of research model. Outcome Input Standardized Estimates Direct Indirect Total Healthy Food Lifestyle (from Depression) N.S. N.S. - Lifestyle (from BMI) N.S. 0.0741 0.0741 R 2 = 0.61 Unhealthy Food Lifestyle (from Depression) 0.32 0.2046 0.5246 Lifestyle (from BMI) 0.32 0.1638 0.4838 R 2 = 0.76 Discussion This study proposed and validated a new postpartum food intake model that examines how personal characteristics, lifestyle behaviors, BMI, and depressive symptoms interact to influence healthy and unhealthy food consumption among postpartum women. Using SEM, the research illuminated critical pathways that connect psychological, physiological, and behavioral factors to dietary outcomes during the first year after childbirth. Out of fourteen hypothesized relationships, eleven were statistically significant. Personal characteristics, particularly education level and work experience, were positively associated with healthy food consumption. Respondents with higher educational attainment and longer work experience tended to make better dietary choices, which is consistent with previous research findings ( 37 ). Lifestyle behaviors showed significant effects on BMI, depression, and unhealthy food consumption. Among all lifestyle indicators, average daily screen time emerged as the strongest predictor, suggesting that high levels of digital engagement are linked to higher BMI, increased depressive symptoms, and greater consumption of unhealthy foods. This finding supports earlier studies that have emphasized the negative consequences of sedentary behavior and excessive smartphone usage on mental health and diet quality ( 38 , 39 ). BMI was closely related to both depression levels and food consumption patterns. Although higher BMI was associated with greater consumption of both healthy and unhealthy foods, the relationship with unhealthy food intake was notably stronger. Respondents with higher BMI reported consuming more soft drinks and energy-dense foods, consistent with research linking sugary beverage consumption to postpartum weight gain. Depression demonstrated the strongest influence on unhealthy food consumption among all variables in the model. This reinforces existing evidence that depressive symptoms significantly affect eating behaviors, often encouraging the consumption of high-fat and high-sugar foods. Although two indicators, namely anxiety and sleep difficulty, were removed during SEM due to low factor loadings, the total score from the EPDS still provided a reliable measure of depressive symptomatology among respondents. The model explained 61% of the variance in healthy food consumption and 75% of the variance in unhealthy food consumption. This substantial explanatory power highlights the central role that psychological and lifestyle factors play in shaping postpartum dietary behaviors. Overall, postpartum women appeared more inclined to consume unhealthy food compared to healthy food, signaling an urgent need for targeted health interventions. Implications for Practice and Policy These findings emphasize that postpartum dietary behaviors are influenced not only by knowledge and socioeconomic status but also by psychological well-being and daily lifestyle habits. Effective interventions should address screen time management, mental health support, and body image concerns in addition to promoting nutritional education. Given Malaysia's high and rising prevalence of obesity, prioritizing postpartum women in public health strategies could lead to meaningful long-term benefits for maternal and child health ( 40 ). Programs that combine mental health screening, lifestyle counseling, and nutritional guidance during the postpartum period could help curb the intergenerational transmission of poor health outcomes ( 41 ). Furthermore, since maternal eating behaviors strongly influence children's future health, supporting postpartum women to adopt healthier lifestyles could play an essential role in reducing childhood obesity rates across Malaysia and the broader ASEAN region. Several limitations of this study should be considered when interpreting the findings. First, the use of self-reported weight and height to calculate BMI might introduce measurement bias due to potential inaccuracies in respondents' self-assessments. Second, depressive symptoms were measured using the EPDS, which serves as a screening instrument rather than a clinical diagnostic tool. Therefore, the findings related to depressive symptoms should be interpreted cautiously and not equated with clinical diagnoses. Third, certain influential variables such as breastfeeding status, parity, and pre-pregnancy weight were not assessed in this research. Prior studies have consistently demonstrated that these factors play crucial roles in postpartum weight management and mental health ( 42 ). Therefore, future research should incorporate these variables to achieve a more comprehensive understanding of postpartum health outcomes. Additionally, other unmeasured factors, such as engagement in therapy, use of medications, or participation in health-promoting interventions, may have influenced respondents' BMI and mental health but were not captured by the current study. Acknowledging and including these additional factors in future research could enhance the robustness and depth of findings in postpartum health studies. Future studies should differentiate between lactating and non-lactating mothers and between women who were overweight or obese before pregnancy and those who became overweight postpartum ( 27 ). Including clinical assessments of depression and detailed dietary intake records would further strengthen the validity of future models. Longitudinal research following postpartum women over time could provide deeper insights into the causal pathways linking lifestyle, mental health, and dietary behaviors. Intervention studies focusing on promoting both psychological well-being and healthier eating habits in postpartum women are urgently needed. Conclusions The postpartum period represents a critical stage in a woman's life, characterized by significant physical, emotional, and psychological adjustments ( 43 ). It is also a period during which mothers are particularly vulnerable to depression ( 44 ). This study explored the eating habits of postpartum women by examining the relationships among personal characteristics, lifestyle behaviors, BMI, and depressive symptoms. Utilizing a SEM approach, this study provides a fresh and comprehensive understanding of the complex determinants shaping postpartum dietary behaviors among Malaysian women. To date, this is the first research employing SEM to examine postpartum food consumption patterns explicitly within Malaysia's unique cultural context. The conceptual framework established in this research thoughtfully integrates demographic, lifestyle, and psychological variables directly relevant to Malaysian women's lived experiences, offering an insightful and culturally attuned analysis of postpartum health. The findings clearly demonstrate the interconnected roles of personal, lifestyle, and psychological factors in influencing dietary decisions during the postpartum period. Understanding these nutritional behaviors sheds critical light on the broader implications for postpartum health, especially concerning weight management and mental well-being ( 11 ). This work emphasizes the necessity of developing comprehensive postpartum care strategies that simultaneously support physical recovery, psychological resilience, and healthier lifestyle choices ( 10 ). Given the pivotal nature of the postpartum period, enhancing dietary practices and promoting mental health can significantly improve health outcomes for mothers ( 45 ). Such efforts are likely to have positive cascading effects on their families and subsequent generations, emphasizing the broader societal impact of targeted postpartum interventions. The results presented here strongly advocate for integrative healthcare approaches that address the multifaceted needs of postpartum women, laying a foundation for sustained health and well-being in the community. Abbreviations SEM Structural Equation Modeling BMI Body Mass Index RM Ringgit Malaysia R² R-square CFI Comparative Fit Index NFI Normed Fit Index RFI Relative Fit Index IFI Incremental Fit Index GFI Goodness of Fit Index TLI Tucker Lewis Index AVE Average Variance Extracted Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of UCSI University (protocol code IEC-2023-FOSSLA-0202 on 9 April 2024). Informed consent was obtained from all subjects involved in the study. 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. Availability of data and materials The data are not publicly available due to the Institutional Ethics Committee of UCSI University 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 Institutional Ethics Committee of UCSI University. Competing interests The authors declare no competing interests. Funding This research received no external funding Authors' contributions Conceptualization: Y.S., N.S. & H.S.J., Methodology: H.S.J. & N.S. Writing – original draft: Y.S., N.S., H.S.J. & Y.J., Writing – review & editing: Y.S. & N.S. Acknowledgements All the authors appreciate and thank the participants for their cooperation with this project. References Arnold L, Volkel M, Rosendahl J, Rost M. A multi-level meta-analysis of the relationship between decision-making during birth and postpartum mental health. Health Psychol Behav Med. 2025;13(1):2456032. Baik HU, Seo BK, Kim GR, Ku JE. A Keyword Analysis Study on Postpartum Obesity Using Big Data. Int J Environ Res Public Health. 2021;18(16). Organization WH. Obesity and overweight: World Health Organization; 2024 [Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight. Bello JK, Bauer V, Plunkett BA, Poston L, Solomonides A, Endres L. Pregnancy weight gain, postpartum weight retention, and obesity. Current Cardiovascular Risk Reports. 2016;10:1-12. Meyer D, Gjika E, Raab R, Michel SK, Hauner H. How does gestational weight gain influence short‐and long‐term postpartum weight retention? An updated systematic review and meta‐analysis. Obesity Reviews. 2024;25(4):e13679. Khadka N, Fassett MJ, Oyelese Y, Mensah NA, Chiu VY, Yeh M, et al. Trends in Postpartum Depression by Race, Ethnicity, and Prepregnancy Body Mass Index. JAMA Netw Open. 2024;7(11):e2446486. Valkama AJ, Meinila JM, Koivusalo SB, Lindstrom J, Rono K, Stach-Lempinen B, et al. Diet quality as assessed by the Healthy Food Intake Index and relationship with serum lipoprotein particles and serum fatty acids in pregnant women at increased risk for gestational diabetes. Br J Nutr. 2018;120(8):914-24. Green TL, Son YK, Simuzingili M, Mezuk B, Bodas M, Hagiwara N. Pregnancy-Related Weight and Postpartum Depressive Symptoms: Do the Relationships Differ by Race/Ethnicity? J Womens Health (Larchmt). 2021;30(6):816-28. Silva DF, et al. Maternal Dietary Patterns and Risk of Postpartum Depression. Nutrients. 2023;15(17):3853. Liu P, et al. Postpartum Depression Is Associated With Maternal Dietary Patterns: A Cross-Sectional Study in China. Nutrients. 2023;15(17):3853. Stein SF, Rios JM, Gearhardt AN, Nuttall AK, Riley HO, Kaciroti N, et al. Food addiction and dietary restraint in postpartum women: The role of childhood trauma exposure and postpartum depression. Appetite. 2023;187:106589. Holmes L, Bitew T, Haile A, Van Lith LM, Burgess S, Vandermark J, et al. Mothers Time: A Cluster Randomized Controlled Trial of the Effects of a Community-Based Cognitive Behavioral Therapy Intervention on Postpartum Mental Health and Family Planning in Northwest Ethiopia. Stud Fam Plann. 2025;56(1):9-39. Miranda AR, Cortez MV, Scotta AV, Soria EA. Caffeinated non-alcoholic beverages on the postpartum mental health related to the COVID-19 pandemic by a cross-sectional study in Argentina. Hum Nutr Metab. 2023;33:200198. Wan Mohamed Radzi C, Salarzadeh Jenatabadi H, Samsudin N. Postpartum depression symptoms in survey-based research: a structural equation analysis. BMC Public Health. 2021;21(1):27. Shea L, Sadowsky M, Tao S, Rast J, Schendel D, Chesnokova A, et al. Perinatal and Postpartum Health Among People With Intellectual and Developmental Disabilities. JAMA Netw Open. 2024;7(8):e2428067. Qi W, Wang Y, Wang Y, Huang S, Li C, Jin H, et al. Prediction of postpartum depression in women: development and validation of multiple machine learning models. J Transl Med. 2025;23(1):291. Starzec-Proserpio M, Rejano-Campo M, Szymanska A, Szymanski J, Baranowska B. The Association between Postpartum Pelvic Girdle Pain and Pelvic Floor Muscle Function, Diastasis Recti and Psychological Factors-A Matched Case-Control Study. Int J Environ Res Public Health. 2022;19(10). Catala P, Suso-Ribera C, Marin D, Penacoba C. Predicting postpartum post-traumatic stress and depressive symptoms in low-risk women from distal and proximal factors: a biopsychosocial prospective study using structural equation modeling. Arch Gynecol Obstet. 2021;303(6):1415-23. Snyder K, Pelster AK, Dinkel D. Healthy eating and physical activity among breastfeeding women: the role of misinformation. BMC Pregnancy Childbirth. 2020;20(1):470. Reut N, Kanat-Maymon Y. Spouses' prenatal autonomous motivation to have a child and postpartum depression symptoms. J Clin Psychol. 2018;74(10):1808-19. Gallagher D, Spyreli E, Anderson AS, Bridges S, Cardwell CR, Coulman E, et al. Effectiveness and cost-effectiveness of a 12-month automated text message intervention for weight management in postpartum women with overweight or obesity: protocol for the Supporting MumS (SMS) multisite, parallel-group, randomised controlled trial. BMJ Open. 2024;14(5):e084075. Awoke MA, Earnest A, Skouteris H, Moran LJ, Wycherley TP. Modeling the effect of diet and physical activity on body mass index in prepregnant and postpartum women. Nutrition. 2023;111:112026. Miranda AR, Scotta AV, Cortez MV, Soria EA. Two-years mothering into the pandemic: Impact of the three COVID-19 waves in the Argentinian postpartum women's mental health. PLoS One. 2025;20(3):e0294220. Van Hulst A, Zheng S, Argiropoulos N, Ybarra M, Ball GDC, Kakinami L. Overweight and obesity in early childhood and obesity at 10 years of age: a comparison of World Health Organization definitions. Eur J Pediatr. 2025;184(4):270. Organization WH. Malnutrition in women n.d. [Available from: https://www.who.int/data/nutrition/nlis/info/malnutrition-in-women. Abdollahi F, Etemadinezhad S, Lye MS. Postpartum mental health in relation to sociocultural practices. Taiwan J Obstet Gynecol. 2016;55(1):76-80. Ether ST, Afrin S, Habib NN, Akter F, Chowdhury AT, Sayeed A, et al. Managing pre and postpartum mental health issues of refugee women from fragile and conflict-affected countries: A systematic review. Public Health Pract (Oxf). 2025;9:100573. Goyal D, Dol J, Huynh J, Anand S, Dennis CL. Postpartum Mental Health and Perceptions of Discrimination Among Asian Fathers During the COVID-19 Pandemic. MCN Am J Matern Child Nurs. 2024;49(2):88-94. Salarzadeh Jenatabadi H, Bt Wan Mohamed Radzi CWJ, Samsudin N. Associations of Body Mass Index with Demographics, Lifestyle, Food Intake, and Mental Health among Postpartum Women: A Structural Equation Approach. Int J Environ Res Public Health. 2020;17(14). Bt Wan Mohamed Radzi CWJ, Salarzadeh Jenatabadi H, Samsudin N. mHealth Apps Assessment among Postpartum Women with Obesity and Depression. Healthcare (Basel). 2020;8(2). Somers JA. Dyadic resilience after postpartum depression: The protective role of mother-infant respiratory sinus arrhythmia synchrony during play for maternal and child mental health across early childhood. Dev Psychopathol. 2025:1-17. Cheung GW, Cooper-Thomas HD, Lau RS, Wang LC. Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management. 2024;41(2):745-83. Reichenheim ME, Moraes CL, Lopes CS, Lobato G. The role of intimate partner violence and other health-related social factors on postpartum common mental disorders: a survey-based structural equation modeling analysis. BMC Public Health. 2014;14:427. Mor S, Sela Y, Lev-Ari S. Postpartum Mothers' Mental Health in a Conflict-Affected Region: A Cross-Sectional Study of Emotion Regulation and Social Support. J Clin Med. 2025;14(4). Sayed SH. The mediating role of emotional intelligence in the relationship between learning motivation and academic outcomes: Conditional indirect effect of gender. J Educ Health Promot. 2024;13:123. McKetta S, Chakraborty P, Gimbrone C, Soled KRS, Hoatson T, Beccia AL, et al. Restrictive abortion legislation and adverse mental health during pregnancy and postpartum. Ann Epidemiol. 2024;92:47-54. Baker MT, Lu P, Parrella JA, Leggette HR. Consumer acceptance toward functional foods: A scoping review. International Journal of Environmental Research and Public Health. 2022;19(3):1217. Apostolopoulos M, Hnatiuk JA, Maple J-L, Olander EK, Brennan L, van der Pligt P, et al. Influences on physical activity and screen time amongst postpartum women with heightened depressive symptoms: a qualitative study. BMC Pregnancy and Childbirth. 2021;21(1):376. Wilcox S, Liu J, Sevoyan M, Parker-Brown J, Turner-McGrievy GM. Effects of a behavioral intervention on physical activity, diet, and health-related quality of life in postpartum women with elevated weight: results of the HIPP randomized controlled trial. BMC Pregnancy Childbirth. 2024;24(1):808. Kamarudin SS, Idris IB, Ahmad N, Sharip S. Exploring Asian maternal experiences and mHealth needs for postpartum mental health care. Digit Health. 2024;10:20552076241292679. Swami V, Barron D, Smith L, Furnham A. Mental health literacy of maternal and paternal postnatal (postpartum) depression in British adults. J Ment Health. 2020;29(2):217-24. Papadopoulos A, Nichols ES, Mohsenzadeh Y, Giroux I, Mottola MF, Van Lieshout RJ, et al. Prenatal and postpartum maternal mental health and neonatal motor outcomes during the COVID-19 pandemic. J Affect Disord Rep. 2022;10:100387. McAlister K, Baez L, Huberty J, Kerppola M. Chatbot to Support the Mental Health Needs of Pregnant and Postpartum Women (Moment for Parents): Design and Pilot Study. JMIR Form Res. 2025;9:e72469. Kulkarni M, Fielding-Singh P. Mothers' Experiences of Institutional Betrayal During Childbirth and their Postpartum Mental Health Outcomes: Evidence From a Survey of New Mothers in the United States. J Midwifery Womens Health. 2025;70(2):292-300. Chang MW, et al. Empirically Derived Dietary Patterns and Postpartum Depression. BMC Psychiatry. 2023;23:393. Table 1,3,4,5,8 Table 1,3,4,5,8 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table13458.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6620245","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466723082,"identity":"1eb8a9c9-5e51-4df3-928d-fcbdb0d0cab4","order_by":0,"name":"Ye Shengyao","email":"","orcid":"","institution":"Wenzhou Vocational College of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Shengyao","suffix":""},{"id":466723083,"identity":"83aed202-a7d8-4997-9109-d6ad08d7078d","order_by":1,"name":"Nadia Samsudin","email":"data:image/png;base64,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","orcid":"","institution":"UCSI University","correspondingAuthor":true,"prefix":"","firstName":"Nadia","middleName":"","lastName":"Samsudin","suffix":""},{"id":466723084,"identity":"d373c6ca-a591-43b8-b2cb-1ee2f9555444","order_by":2,"name":"Hashem Salarzadeh Jenatabadi","email":"","orcid":"","institution":"Monash University","correspondingAuthor":false,"prefix":"","firstName":"Hashem","middleName":"Salarzadeh","lastName":"Jenatabadi","suffix":""},{"id":466723085,"identity":"09f3e76f-b340-4d2e-9686-62ac75804957","order_by":3,"name":"Ye Jianqiang","email":"","orcid":"","institution":"Wenzhou Vocational College of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Jianqiang","suffix":""}],"badges":[],"createdAt":"2025-05-08 11:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6620245/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6620245/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84091230,"identity":"aa427c8c-5196-4de7-b294-b02d75912356","added_by":"auto","created_at":"2025-06-06 16:20:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":30055,"visible":true,"origin":"","legend":"\u003cp\u003eA graphical research model for SEM analysis\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6620245/v1/fe239372f71ba697010735f3.png"},{"id":84091231,"identity":"e837848e-7eb7-48ac-8306-bceb9393fb2e","added_by":"auto","created_at":"2025-06-06 16:20:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43797,"visible":true,"origin":"","legend":"\u003cp\u003eThe structural model output\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6620245/v1/aa19081642bd294727642ed2.png"},{"id":102337351,"identity":"472be856-2530-43e5-81df-b38b22663b72","added_by":"auto","created_at":"2026-02-10 16:12:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":810567,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6620245/v1/db04bff5-19b9-475a-b041-dc3c4ab356d6.pdf"},{"id":84091229,"identity":"a394ca29-1341-44ef-bc31-a30f532b1632","added_by":"auto","created_at":"2025-06-06 16:20:43","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":59812,"visible":true,"origin":"","legend":"","description":"","filename":"Table13458.docx","url":"https://assets-eu.researchsquare.com/files/rs-6620245/v1/56381930f9c86a0db2a174aa.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Psychological and Behavioral Determinants of Dietary Patterns in Postpartum Women","fulltext":[{"header":"Background","content":"\u003cp\u003eThe postpartum period represents a profound physiological, psychological, and social transition in women's lives, yet it is increasingly characterized by the emergence of complex and interrelated health challenges (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Among these, obesity and depression stand out as global public health concerns with far-reaching consequences for individual well-being, family functioning, and societal health systems (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Recent data from the World Health Organization (WHO) report that 16% of adults globally were classified as obese in 2022, with women demonstrating disproportionately higher rates than men (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The reproductive and postpartum periods, spanning the ages of 25 to 34 years, represent a window of heightened vulnerability, where pregnancy-related weight gain and postpartum weight retention cumulatively elevate long-term obesity risk. Empirical evidence highlights that 50\u0026ndash;80% of postpartum women retain between 1.4 and 5 kilograms of pregnancy-related weight within the first year following childbirth, while 20\u0026ndash;50% retain weight gains exceeding 5 kilograms (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). This persistent weight retention is not simply a personal health issue; it signals broader systemic risks for chronic diseases, including type 2 diabetes mellitus, cardiovascular conditions, hypertension, and increasingly, psychological disorders such as depression (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Moreover, postpartum weight trajectories are not merely shaped by biological factors. They reflect deeply embedded social, economic, and behavioral forces, including access to nutritional resources, structural inequalities, and the psychosocial stressors inherent to early motherhood (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDietary behavior during the postpartum period is a particularly potent, yet underexamined, determinant of health outcomes. Evidence consistently demonstrates a shift toward less nutritious, energy-dense diets following childbirth, with reduced intake of fruits, vegetables, and whole grains, and increased consumption of fast foods and sugar-sweetened beverages (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). These changes are rarely voluntary; they emerge at the nexus of psychological exhaustion, social expectations of motherhood, reduced self-care, and limited systemic support for maternal nutrition (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Simultaneously, the postpartum period marks a time of profound psychological vulnerability. Postpartum depression (PPD), affecting approximately 10\u0026ndash;15% of new mothers worldwide, manifests as a constellation of affective, cognitive, and behavioral symptoms including sadness, anxiety, anhedonia, impaired concentration, and disrupted eating patterns (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Despite the prevalence and gravity of PPD, it remains frequently underrecognized and undertreated, exacerbating risks for poor maternal-infant attachment, family dysfunction, and intergenerational health consequences (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Recent research suggests a reciprocal, cyclical relationship between obesity and depression: elevated BMI increases susceptibility to depressive symptoms, while depression, in turn, fosters maladaptive lifestyle behaviors, creating a self-perpetuating loop of physical and psychological risk.\u003c/p\u003e \u003cp\u003eYet despite recognition of these individual relationships, few studies have interrogated the complex, integrated pathways linking lifestyle factors, depression, obesity, and dietary behaviors in postpartum women. Even fewer have applied advanced analytical methodologies capable of capturing the intricate web of direct and indirect effects that characterize real-world behavioral dynamics (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Structural Equation Modeling (SEM), a powerful tool for modeling complex latent constructs and multiple pathways simultaneously, offers an opportunity to advance understanding of postpartum health beyond siloed variables toward a systemic, human-centered perspective (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Addressing this critical gap, the present study employs SEM to investigate how lifestyle behaviors, depression, and body mass index (BMI) collectively influence dietary choices among postpartum women. Uniquely, it differentiates between healthy and unhealthy food consumption patterns, recognizing that the determinants of these behaviors may diverge significantly. By adopting a multidimensional, systems-based analytical approach, the study illuminates how psychological well-being, physiological status, and social behaviors intersect to shape health-relevant dietary decisions during a pivotal life stage.\u003c/p\u003e \u003cp\u003eThis research has implications that extend beyond the clinical management of postpartum women. It challenges prevailing individualistic narratives of \"personal responsibility\" in postpartum health, instead highlighting the structural and psychological complexities that inform behavior. Moreover, it contributes to broader discussions on how societal systems (from maternity care practices to food environments) can be restructured to support healthier, more equitable trajectories for women after childbirth. By integrating perspectives from behavioral science, nutrition, public health, and psychology, this study advances a more holistic understanding of maternal health during the postpartum period. It aligns with growing calls within human behavioral science to move beyond reductionist models and toward integrative frameworks capable of capturing the complex realities of human lived experience (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study is conceptually grounded in the Biopsychosocial Model and Self-Determination Theory (SDT), two complementary frameworks that provide a comprehensive lens for understanding the complex interplay between physical health, psychological well-being, and behavioral outcomes among postpartum women. The Biopsychosocial Model posits that health and illness arise from a dynamic interaction among biological, psychological, and social factors, rather than being attributable to a single cause (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Within the postpartum context, body weight status, depressive symptoms, and dietary behaviors are deeply interwoven with physiological changes, emotional adaptations, and environmental conditions (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). This model supports the study\u0026rsquo;s systems-level approach to investigating how personal characteristics, lifestyle behaviors, mental health, and diet interact to influence postpartum health trajectories.\u003c/p\u003e \u003cp\u003eSDT further elucidates the motivational processes underlying postpartum women\u0026rsquo;s dietary behaviors (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). According to SDT, the fulfillment of basic psychological needs (autonomy, competence, and relatedness) is critical for promoting intrinsic motivation toward healthful behaviors (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The postpartum period, characterized by shifts in personal identity, increased caregiving demands, and social role changes, may disrupt these psychological needs, influencing dietary choices and health-related behaviors (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Depressive symptoms may exacerbate motivational deficits, contributing to maladaptive eating patterns and further complicating postpartum weight management (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy integrating these frameworks, the present study situates postpartum dietary behaviors within a multidimensional context that acknowledges the biological, emotional, and social complexities of early motherhood. The application of SEM enables a rigorous examination of the direct and indirect pathways linking these factors, advancing a more nuanced understanding of postpartum health that transcends reductionist models.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch framework\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the proposed research framework, which integrates five latent variables (personal characteristics, lifestyle behaviors, depression, healthy food intake, and unhealthy food intake) and one observed variable, BMI. In this model, \"personal characteristics\" serve as the initial independent variable, while \"healthy food\" and \"unhealthy food\" represent two distinct dependent variables. The relationships between the independent and dependent variables are hypothesized to be mediated by three constructs: lifestyle, BMI, and depression. Guided by the primary objectives of this study, two central mediation hypotheses underpin the framework: (i) depression mediates the relationship between lifestyle behaviors and food intake, and (ii) BMI mediates the relationship between lifestyle behaviors and food intake. SEM was selected as the analytical approach due to its superior capacity for handling complex models involving latent constructs, as well as its ability to simultaneously estimate and test multiple direct and indirect pathways (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Unlike conventional regression-based methods, SEM allows for the modeling of interdependencies among multiple dependent variables within a unified, coherent framework, thereby offering a more nuanced and holistic understanding of the interrelationships among the study variables (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The application of SEM further enables the simultaneous assessment of exogenous and endogenous variables, enhancing the explanatory power and theoretical robustness of the model (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasure of Variables\u003c/h3\u003e\n\u003cp\u003eThe questionnaire used in this study was developed specifically for this research by integrating validated measurement items from multiple previously published sources. Instead of relying on a single existing questionnaire, this study carefully selected and adapted specific indicators from different established scales to create a tailored instrument. This approach was chosen to comprehensively capture the unique combination of variables relevant to this study, including personal characteristics, lifestyle, BMI, depression and food intake. Each measurement item was selected based on its relevance, established validity, and reliability as demonstrated in prior literature. By integrating these validated indicators, the questionnaire aimed to accurately reflect the constructs of interest, ensuring robust and meaningful data collection for subsequent analysis.\u003c/p\u003e \u003cp\u003ePersonal characteristics were assessed using four indicators: age group, education level, working experience, and household income. Age was classified into four categories: 21\u0026ndash;25 years, 26\u0026ndash;30 years, 31\u0026ndash;35 years, and above 35 years. Education level was categorized into five tiers: less than high school, high school, diploma, bachelor's degree, and master's or Ph.D. qualification. Working experience was divided into five groups: no job experience, 1\u0026ndash;3 years, 4\u0026ndash;6 years, 7\u0026ndash;10 years, and more than 10 years. Household income, measured in Malaysian Ringgit (RM), was classified into five income brackets: less than RM 2,000, RM 2,000\u0026ndash;3,000, RM 3,000\u0026ndash;4,000, RM 4,000\u0026ndash;5,000, and above RM 5,000.\u003c/p\u003e \u003cp\u003eLifestyle behaviors were measured based on indicators adapted from previous study (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), including average working hours per day, frequency of physical activity per week, average sleeping hours per day, and screen time per day (television, smartphone, tablet usage). Physical activity frequency was categorized as none, 1 time, 2 times, 3 times, 4 times, or more than 4 times per week. Screen time was grouped into less than 1 hour, 1\u0026ndash;2 hours, 2\u0026ndash;3 hours, 3\u0026ndash;4 hours, and more than 4 hours daily. Sleep duration was classified as less than 6 hours, 6\u0026ndash;7 hours, 7\u0026ndash;8 hours, 8\u0026ndash;9 hours, or more than 9 hours per day. Average working hours were categorized as none, less than 7 hours, 7\u0026ndash;8 hours, 8\u0026ndash;9 hours, or more than 9 hours per day.\u003c/p\u003e \u003cp\u003eBMI was calculated using the standard formula: weight (kg) divided by height squared (m\u0026sup2;) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Following WHO classifications (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), BMI was categorized as underweight (\u0026lt;\u0026thinsp;18.5 kg/m\u0026sup2;), normal weight (18.5\u0026ndash;24.9 kg/m\u0026sup2;), overweight (25.0\u0026ndash;29.9 kg/m\u0026sup2;), and obese (\u0026ge;\u0026thinsp;30.0 kg/m\u0026sup2;).\u003c/p\u003e \u003cp\u003eDepression levels were assessed using the Edinburgh Postnatal Depression Scale (EPDS) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), a validated instrument widely utilized to screen for postpartum depressive symptoms. The EPDS comprises ten items rated on a four-point Likert scale (0\u0026ndash;3), reflecting the frequency of depressive symptoms over the past week. Total scores were classified into four categories: normal (0\u0026ndash;9), slightly increased risk (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), increased risk (\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), and likely depression (\u0026ge;\u0026thinsp;16) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDietary behavior was measured through two distinct constructs: healthy food consumption and unhealthy food consumption. Consistent with prior frameworks (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), nine indicators were selected to capture dietary patterns: intake of whole grains (grams/day), fruits (grams/day), vegetables (grams/day), sweets (grams/day), chips (grams/day), soft drinks (milliliters/day), and fast food (grams/day). Healthy food intake was operationalized through the consumption of whole grains, fruits, and vegetables, whereas unhealthy food intake encompassed sweets, chips, soft drinks, and fast food.\u003c/p\u003e\n\u003ch3\u003eSampling\u003c/h3\u003e\n\u003cp\u003eThe minimum sample size required in structural equation modeling should be determined based on the number of latent variables and the number of indicators per construct (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Specifically, studies with five or fewer latent variables, each measured by at least three indicators, require a minimum of 100 respondents. When there are up to seven latent variables, a sample size of at least 150 is recommended if each latent variable has three or more indicators. In cases where latent variables are measured by fewer than three indicators, larger samples\u0026mdash;300 to 500 respondents\u0026mdash;are recommended to ensure statistical power and model stability. Given that this study involves five latent variables, each measured by multiple indicators, a minimum sample size of 100 respondents was deemed sufficient. To enhance the generalizability and robustness of the findings, an online survey link was disseminated to postpartum women residing in Kuala Lumpur, Malaysia. A total of 623 completed questionnaires were collected, substantially exceeding the minimum sample size requirement. The study was conducted in accordance with relevant ethical guidelines and institutional regulations. All participants were informed about the study's objectives, and written informed consent was obtained prior to participation. Confidentiality and anonymity were maintained throughout the data collection process.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003eDescriptive Statistics Analysis\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;1 presents the demographic, lifestyle, and health-related characteristics of the study sample (N\u0026thinsp;=\u0026thinsp;623). The respondents comprised Malays (42.2%), Chinese (35.8%), and Indians (22.0%), broadly reflecting the ethnic distribution of the urban population in Kuala Lumpur. Regarding personal characteristics, the majority of participants were aged between 31 and 35 years and held a Bachelor\u0026apos;s degree as their highest educational qualification. In terms of socioeconomic status, 39.2% of respondents reported a monthly household income between RM 4,000 and RM 5,000. In terms of employment history, while 13.5% of respondents reported no prior work experience, a substantial proportion (29.2%) had accumulated between 7 and 10 years of working experience.\u003c/p\u003e\n \u003cp\u003eLifestyle behaviors demonstrated considerable variability. Physical activity levels were moderate: 25.4% of respondents engaged in physical activity twice per week, while 23.3% reported no engagement in physical activities. Only 7.2% of respondents reported exercising more than four times per week. Patterns of screen time use indicated high digital engagement, with 38.5% of respondents reporting an average of four hours of screen exposure daily, followed by 33.9% reporting two hours and 25.0% reporting three hours per day. Sleep patterns were relatively healthy, with the majority sleeping between seven and eight hours per night; only 5.9% of respondents reported sleeping fewer than six hours per day. Most participants (52.2%) reported average working hours of eight to nine hours daily. Based on BMI classifications, 10.9% of the respondents were categorized as underweight, 26.0% as normal weight, 28.7% as overweight, and 34.3% as obese. Depression levels, assessed using the EPDS, revealed that 19.7% of the respondents fell within the normal range, while 22.6% exhibited a slightly increased risk, 33.4% showed an increased risk, and 24.2% were likely to experience postpartum depression.\u003c/p\u003e\n \u003cp\u003eThese findings highlight a diverse sample characterized by moderate physical activity engagement, significant screen time exposure, a high prevalence of overweight and obesity, and a concerning proportion of respondents at elevated risk for postpartum depression.\u003c/p\u003e\n \u003cdiv\u003eThe distribution of food consumption is presented in Table 2. Whole grains (e.g. bread, rice, pasta, noodles, breakfast cereals) is equal to 240.94\u0026thinsp;\u0026plusmn;\u0026thinsp;86.84 grams per day.\u003c/div\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDescriptive statistics of food intake\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFood consumption\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;Std)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhole Grains (grams/ day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240.94\u0026thinsp;\u0026plusmn;\u0026thinsp;86.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFruits (grams/ day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e399.64\u0026thinsp;\u0026plusmn;\u0026thinsp;118.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetables (grams/ day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e326.22\u0026thinsp;\u0026plusmn;\u0026thinsp;99.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSweets (grams/ day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130.40\u0026thinsp;\u0026plusmn;\u0026thinsp;29.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChips (grams/ day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.15\u0026thinsp;\u0026plusmn;\u0026thinsp;34.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoft Drinks (milliliter/ day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e379.48\u0026thinsp;\u0026plusmn;\u0026thinsp;128.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFast Food (grams/ day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e212.37\u0026thinsp;\u0026plusmn;\u0026thinsp;98.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eValidity and Reliability\u003c/h2\u003e\n \u003cp\u003eCertain criteria must be met to assess the validity and reliability of survey instruments within SEM. One of these essential conditions is that the Cronbach\u0026rsquo;s alpha for each latent variable should be at least 0.7, indicating acceptable internal consistency and validity (32). As illustrated in Table\u0026nbsp;3, the Cronbach\u0026rsquo;s alpha values for all latent variables in this study exceed the recommended threshold, thereby confirming the reliability and validity of the measurement scales used in the analysis.\u003c/p\u003e\n \u003cdiv\u003eTo assess the reliability of the research instrument, the factor loading value for each indicator within the latent variables should be above 0.7 (33). As presented in Table 4, several indicators related to respondents\u0026rsquo; personal characteristics and lifestyle, specifically age, income, and work experience, showed factor loading values below the recommended threshold of 0.7. Additionally, two indicators measuring respondents\u0026rsquo; depression levels did not meet this criterion. Consequently, these indicators were excluded from further analysis in the SEM, ensuring the robustness and reliability of the final measurement model.\u003c/div\u003e\n \u003cdiv\u003eAfter the removal of indicators that did not meet reliability requirements, another essential criterion was evaluated to confirm the reliability of the research instrument. Specifically, each latent variable should demonstrate an Average Variance Extracted (AVE) value of at least 0.5, signifying acceptable internal consistency and reliability (14). As shown in Table 5, the AVE values obtained in this study for all latent variables clearly exceeded the recommended threshold of 0.5. Meeting this criterion further strengthens the reliability of the measurement model used in this research, confirming the robustness and accuracy of the findings.\u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eModel Fitting\u003c/h3\u003e\n\u003cp\u003eFor a structural model to be considered acceptable, standard guidelines recommend that model-fit indices should generally exceed a threshold of 0.9 (30). As presented in Table\u0026nbsp;6, the values of the comparative fit index (CFI), normed fit index (NFI), relative fit index (RFI), incremental fit index (IFI), goodness of fit index (GFI), and Tucker-Lewis index (TLI) in this study all met or exceeded this standard. These findings indicate that the collected data align closely with the proposed theoretical model, confirming an appropriate fit and further validating the overall structural framework used in this research.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 6\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eModel fitting analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChi-square (df)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e156.41 (38)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.902\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eStructural Model\u003c/h3\u003e\n\u003cp\u003eFigure 2 presents the final structural model. Significant relationships between variables are indicated with solid arrows, while non-significant paths are represented with dashed arrows. Out of fourteen hypothesized relationships, eleven were found to be statistically significant. Only three paths namely, the impact of personal characteristics on unhealthy food consumption, the impact of lifestyle behaviors on healthy food consumption, and the impact of depression on healthy food consumption, were not statistically significant.\u003c/p\u003e\n\u003cp\u003eAmong the significant pathways, the strongest observed effect was the positive association between depression and unhealthy food consumption (standardized coefficient\u0026thinsp;=\u0026thinsp;0.66), suggesting a substantial influence of depressive symptoms on maladaptive dietary behaviors. In contrast, the weakest significant relationship was identified between personal characteristics and healthy food consumption (standardized coefficient\u0026thinsp;=\u0026thinsp;0.16). These findings underscore the dominant role of psychological factors, particularly depression, in shaping unhealthy dietary behaviors among postpartum women, relative to demographic or lifestyle factors. This pattern emphasizes the need for postpartum health interventions to prioritize psychological well-being as a key determinant of dietary outcomes.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 7\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eModel parameter\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eStandardized\u003c/p\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eStandard\u003c/p\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eUnstandardized\u003c/p\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e95% Confidence\u003c/p\u003e\n \u003cp\u003eInterval\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCorrelation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonal Characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026rarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLifestyle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.11, 0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonal Characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026rarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.16, 0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonal Characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026rarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.11, 0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonal Characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026rarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealthy Food\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.09, 0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLifestyle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026rarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.21, 0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLifestyle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026rarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.31, 0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLifestyle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026rarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealthy Food\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.17, 0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026rarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealthy Food\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.05, 0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026rarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnhealthy Food\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.29, 0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026rarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.22, 0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026rarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnhealthy Food\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.46, 0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;7 presents the parameters for the research model. According to Fig.\u0026nbsp;2, the results indicate that: i) BMI has a significant impact on healthy food; ii) BMI has a significant impact on unhealthy food; iii) depression significantly influences unhealthy food consumption; and iv) lifestyle significantly affects unhealthy food consumption.\u003c/p\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eMediation Analysis\u003c/h2\u003e\n \u003cp\u003eMediation analysis has been widely applied across the social and behavioral sciences to elucidate the mechanisms through which independent variables influence dependent outcomes (34). More recently, mediation approaches have been further advanced by the adoption of the counterfactual framework, which provides greater precision in estimating causal pathways (35, 36). In the present study, mediation analysis was employed to investigate the mechanisms linking the primary indicators (personal characteristics and lifestyle) to dietary behaviors, focusing on the role of three mediators: lifestyle behaviors, BMI, and depression. The primary aim of the mediation analysis was to examine both direct and indirect relationships among five latent variables and one observed variable within the proposed structural model. Table\u0026nbsp;8 summarizes the mediation effects observed between the independent variables, the mediators, and the dependent variables. In this context, relationships were classified as exhibiting an indirect effect, partial mediation, or full mediation, depending on the strength and significance of the mediated pathways. By applying mediation analysis within a structural equation modeling framework, this study provides a nuanced understanding of how lifestyle, BMI, and depression operate as key intermediary mechanisms linking personal characteristics to postpartum dietary behaviors.\u003c/p\u003e\n \u003cdiv\u003eMediation analysis followed the established two-step procedure. First, the effects of the independent variable (lifestyle behaviors) on the mediators (depression and BMI) and subsequently on the dependent variables (healthy and unhealthy food intake) were assessed for statistical significance. Only if these direct paths were significant was the presence of a mediating effect further evaluated. The nature of mediation was determined by comparing direct, indirect, and total effects, thereby distinguishing between partial and full mediation pathways.\u003c/div\u003e\n \u003cp\u003eTable\u0026nbsp;9 presents the estimates for direct, indirect, and total effects involving depression and BMI as mediators of food intake behaviors. As shown in Fig.\u0026nbsp;2 and Table\u0026nbsp;9, the direct effect of lifestyle behaviors on healthy food intake was not statistically significant. Consequently, neither depression nor BMI mediated the relationship between lifestyle behaviors and healthy food intake.\u003c/p\u003e\n \u003cp\u003eConversely, for unhealthy food intake, both depression and BMI served as significant mediators. Specifically, the total indirect effect of lifestyle behaviors through depression was 0.5246, and through BMI was 0.4838. These results confirm that depression and BMI partially mediate the association between lifestyle behaviors and unhealthy food consumption among postpartum women.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 9\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMediation test of research model.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eInput\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eStandardized Estimates\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDirect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndirect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHealthy Food\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLifestyle (from Depression)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN.S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN.S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLifestyle (from BMI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN.S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0741\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUnhealthy Food\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLifestyle (from Depression)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLifestyle (from BMI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4838\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study proposed and validated a new postpartum food intake model that examines how personal characteristics, lifestyle behaviors, BMI, and depressive symptoms interact to influence healthy and unhealthy food consumption among postpartum women. Using SEM, the research illuminated critical pathways that connect psychological, physiological, and behavioral factors to dietary outcomes during the first year after childbirth. Out of fourteen hypothesized relationships, eleven were statistically significant. Personal characteristics, particularly education level and work experience, were positively associated with healthy food consumption. Respondents with higher educational attainment and longer work experience tended to make better dietary choices, which is consistent with previous research findings (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLifestyle behaviors showed significant effects on BMI, depression, and unhealthy food consumption. Among all lifestyle indicators, average daily screen time emerged as the strongest predictor, suggesting that high levels of digital engagement are linked to higher BMI, increased depressive symptoms, and greater consumption of unhealthy foods. This finding supports earlier studies that have emphasized the negative consequences of sedentary behavior and excessive smartphone usage on mental health and diet quality (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). BMI was closely related to both depression levels and food consumption patterns. Although higher BMI was associated with greater consumption of both healthy and unhealthy foods, the relationship with unhealthy food intake was notably stronger. Respondents with higher BMI reported consuming more soft drinks and energy-dense foods, consistent with research linking sugary beverage consumption to postpartum weight gain.\u003c/p\u003e \u003cp\u003eDepression demonstrated the strongest influence on unhealthy food consumption among all variables in the model. This reinforces existing evidence that depressive symptoms significantly affect eating behaviors, often encouraging the consumption of high-fat and high-sugar foods. Although two indicators, namely anxiety and sleep difficulty, were removed during SEM due to low factor loadings, the total score from the EPDS still provided a reliable measure of depressive symptomatology among respondents. The model explained 61% of the variance in healthy food consumption and 75% of the variance in unhealthy food consumption. This substantial explanatory power highlights the central role that psychological and lifestyle factors play in shaping postpartum dietary behaviors. Overall, postpartum women appeared more inclined to consume unhealthy food compared to healthy food, signaling an urgent need for targeted health interventions.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImplications for Practice and Policy\u003c/h2\u003e \u003cp\u003eThese findings emphasize that postpartum dietary behaviors are influenced not only by knowledge and socioeconomic status but also by psychological well-being and daily lifestyle habits. Effective interventions should address screen time management, mental health support, and body image concerns in addition to promoting nutritional education. Given Malaysia's high and rising prevalence of obesity, prioritizing postpartum women in public health strategies could lead to meaningful long-term benefits for maternal and child health (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Programs that combine mental health screening, lifestyle counseling, and nutritional guidance during the postpartum period could help curb the intergenerational transmission of poor health outcomes (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Furthermore, since maternal eating behaviors strongly influence children's future health, supporting postpartum women to adopt healthier lifestyles could play an essential role in reducing childhood obesity rates across Malaysia and the broader ASEAN region.\u003c/p\u003e \u003cp\u003eSeveral limitations of this study should be considered when interpreting the findings. First, the use of self-reported weight and height to calculate BMI might introduce measurement bias due to potential inaccuracies in respondents' self-assessments. Second, depressive symptoms were measured using the EPDS, which serves as a screening instrument rather than a clinical diagnostic tool. Therefore, the findings related to depressive symptoms should be interpreted cautiously and not equated with clinical diagnoses. Third, certain influential variables such as breastfeeding status, parity, and pre-pregnancy weight were not assessed in this research. Prior studies have consistently demonstrated that these factors play crucial roles in postpartum weight management and mental health (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Therefore, future research should incorporate these variables to achieve a more comprehensive understanding of postpartum health outcomes. Additionally, other unmeasured factors, such as engagement in therapy, use of medications, or participation in health-promoting interventions, may have influenced respondents' BMI and mental health but were not captured by the current study. Acknowledging and including these additional factors in future research could enhance the robustness and depth of findings in postpartum health studies.\u003c/p\u003e \u003cp\u003eFuture studies should differentiate between lactating and non-lactating mothers and between women who were overweight or obese before pregnancy and those who became overweight postpartum (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Including clinical assessments of depression and detailed dietary intake records would further strengthen the validity of future models. Longitudinal research following postpartum women over time could provide deeper insights into the causal pathways linking lifestyle, mental health, and dietary behaviors. Intervention studies focusing on promoting both psychological well-being and healthier eating habits in postpartum women are urgently needed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe postpartum period represents a critical stage in a woman's life, characterized by significant physical, emotional, and psychological adjustments (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). It is also a period during which mothers are particularly vulnerable to depression (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). This study explored the eating habits of postpartum women by examining the relationships among personal characteristics, lifestyle behaviors, BMI, and depressive symptoms. Utilizing a SEM approach, this study provides a fresh and comprehensive understanding of the complex determinants shaping postpartum dietary behaviors among Malaysian women. To date, this is the first research employing SEM to examine postpartum food consumption patterns explicitly within Malaysia's unique cultural context. The conceptual framework established in this research thoughtfully integrates demographic, lifestyle, and psychological variables directly relevant to Malaysian women's lived experiences, offering an insightful and culturally attuned analysis of postpartum health.\u003c/p\u003e \u003cp\u003eThe findings clearly demonstrate the interconnected roles of personal, lifestyle, and psychological factors in influencing dietary decisions during the postpartum period. Understanding these nutritional behaviors sheds critical light on the broader implications for postpartum health, especially concerning weight management and mental well-being (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). This work emphasizes the necessity of developing comprehensive postpartum care strategies that simultaneously support physical recovery, psychological resilience, and healthier lifestyle choices (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the pivotal nature of the postpartum period, enhancing dietary practices and promoting mental health can significantly improve health outcomes for mothers (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Such efforts are likely to have positive cascading effects on their families and subsequent generations, emphasizing the broader societal impact of targeted postpartum interventions. The results presented here strongly advocate for integrative healthcare approaches that address the multifaceted needs of postpartum women, laying a foundation for sustained health and well-being in the community.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.0759%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSEM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88.9241%;\"\u003e\n \u003cp\u003eStructural Equation Modeling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.0759%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88.9241%;\"\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.0759%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88.9241%;\"\u003e\n \u003cp\u003eRinggit Malaysia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.0759%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88.9241%;\"\u003e\n \u003cp\u003eR-square\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.0759%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88.9241%;\"\u003e\n \u003cp\u003eComparative Fit Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.0759%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88.9241%;\"\u003e\n \u003cp\u003eNormed Fit Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.0759%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88.9241%;\"\u003e\n \u003cp\u003eRelative Fit Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.0759%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88.9241%;\"\u003e\n \u003cp\u003eIncremental Fit Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.0759%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88.9241%;\"\u003e\n \u003cp\u003eGoodness of Fit Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.0759%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88.9241%;\"\u003e\n \u003cp\u003eTucker Lewis Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.0759%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAVE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88.9241%;\"\u003e\n \u003cp\u003eAverage Variance Extracted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of UCSI University (protocol code IEC-2023-FOSSLA-0202 on 9 April 2024). Informed consent was obtained from all subjects involved in the study. 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data are not publicly available due to the Institutional Ethics Committee of UCSI University 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 Institutional Ethics Committee of UCSI University.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Y.S., N.S. \u0026amp; H.S.J., Methodology: H.S.J. \u0026amp; N.S. Writing \u0026ndash; original draft:\u0026nbsp;Y.S., N.S., H.S.J. \u0026amp; Y.J.,\u0026nbsp;Writing \u0026ndash; review \u0026amp; editing: Y.S. \u0026amp; N.S.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors appreciate and thank the participants for their cooperation with this project.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArnold L, Volkel M, Rosendahl J, Rost M. A multi-level meta-analysis of the relationship between decision-making during birth and postpartum mental health. Health Psychol Behav Med. 2025;13(1):2456032.\u003c/li\u003e\n\u003cli\u003eBaik HU, Seo BK, Kim GR, Ku JE. A Keyword Analysis Study on Postpartum Obesity Using Big Data. Int J Environ Res Public Health. 2021;18(16).\u003c/li\u003e\n\u003cli\u003eOrganization WH. Obesity and overweight: World Health Organization; 2024 [Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight.\u003c/li\u003e\n\u003cli\u003eBello JK, Bauer V, Plunkett BA, Poston L, Solomonides A, Endres L. Pregnancy weight gain, postpartum weight retention, and obesity. Current Cardiovascular Risk Reports. 2016;10:1-12.\u003c/li\u003e\n\u003cli\u003eMeyer D, Gjika E, Raab R, Michel SK, Hauner H. How does gestational weight gain influence short‐and long‐term postpartum weight retention? An updated systematic review and meta‐analysis. Obesity Reviews. 2024;25(4):e13679.\u003c/li\u003e\n\u003cli\u003eKhadka N, Fassett MJ, Oyelese Y, Mensah NA, Chiu VY, Yeh M, et al. Trends in Postpartum Depression by Race, Ethnicity, and Prepregnancy Body Mass Index. JAMA Netw Open. 2024;7(11):e2446486.\u003c/li\u003e\n\u003cli\u003eValkama AJ, Meinila JM, Koivusalo SB, Lindstrom J, Rono K, Stach-Lempinen B, et al. Diet quality as assessed by the Healthy Food Intake Index and relationship with serum lipoprotein particles and serum fatty acids in pregnant women at increased risk for gestational diabetes. Br J Nutr. 2018;120(8):914-24.\u003c/li\u003e\n\u003cli\u003eGreen TL, Son YK, Simuzingili M, Mezuk B, Bodas M, Hagiwara N. Pregnancy-Related Weight and Postpartum Depressive Symptoms: Do the Relationships Differ by Race/Ethnicity? J Womens Health (Larchmt). 2021;30(6):816-28.\u003c/li\u003e\n\u003cli\u003eSilva DF, et al. Maternal Dietary Patterns and Risk of Postpartum Depression. Nutrients. 2023;15(17):3853.\u003c/li\u003e\n\u003cli\u003eLiu P, et al. Postpartum Depression Is Associated With Maternal Dietary Patterns: A Cross-Sectional Study in China. Nutrients. 2023;15(17):3853.\u003c/li\u003e\n\u003cli\u003eStein SF, Rios JM, Gearhardt AN, Nuttall AK, Riley HO, Kaciroti N, et al. Food addiction and dietary restraint in postpartum women: The role of childhood trauma exposure and postpartum depression. Appetite. 2023;187:106589.\u003c/li\u003e\n\u003cli\u003eHolmes L, Bitew T, Haile A, Van Lith LM, Burgess S, Vandermark J, et al. Mothers Time: A Cluster Randomized Controlled Trial of the Effects of a Community-Based Cognitive Behavioral Therapy Intervention on Postpartum Mental Health and Family Planning in Northwest Ethiopia. Stud Fam Plann. 2025;56(1):9-39.\u003c/li\u003e\n\u003cli\u003eMiranda AR, Cortez MV, Scotta AV, Soria EA. Caffeinated non-alcoholic beverages on the postpartum mental health related to the COVID-19 pandemic by a cross-sectional study in Argentina. Hum Nutr Metab. 2023;33:200198.\u003c/li\u003e\n\u003cli\u003eWan Mohamed Radzi C, Salarzadeh Jenatabadi H, Samsudin N. Postpartum depression symptoms in survey-based research: a structural equation analysis. BMC Public Health. 2021;21(1):27.\u003c/li\u003e\n\u003cli\u003eShea L, Sadowsky M, Tao S, Rast J, Schendel D, Chesnokova A, et al. Perinatal and Postpartum Health Among People With Intellectual and Developmental Disabilities. JAMA Netw Open. 2024;7(8):e2428067.\u003c/li\u003e\n\u003cli\u003eQi W, Wang Y, Wang Y, Huang S, Li C, Jin H, et al. Prediction of postpartum depression in women: development and validation of multiple machine learning models. J Transl Med. 2025;23(1):291.\u003c/li\u003e\n\u003cli\u003eStarzec-Proserpio M, Rejano-Campo M, Szymanska A, Szymanski J, Baranowska B. The Association between Postpartum Pelvic Girdle Pain and Pelvic Floor Muscle Function, Diastasis Recti and Psychological Factors-A Matched Case-Control Study. Int J Environ Res Public Health. 2022;19(10).\u003c/li\u003e\n\u003cli\u003eCatala P, Suso-Ribera C, Marin D, Penacoba C. Predicting postpartum post-traumatic stress and depressive symptoms in low-risk women from distal and proximal factors: a biopsychosocial prospective study using structural equation modeling. Arch Gynecol Obstet. 2021;303(6):1415-23.\u003c/li\u003e\n\u003cli\u003eSnyder K, Pelster AK, Dinkel D. Healthy eating and physical activity among breastfeeding women: the role of misinformation. BMC Pregnancy Childbirth. 2020;20(1):470.\u003c/li\u003e\n\u003cli\u003eReut N, Kanat-Maymon Y. Spouses\u0026apos; prenatal autonomous motivation to have a child and postpartum depression symptoms. J Clin Psychol. 2018;74(10):1808-19.\u003c/li\u003e\n\u003cli\u003eGallagher D, Spyreli E, Anderson AS, Bridges S, Cardwell CR, Coulman E, et al. Effectiveness and cost-effectiveness of a 12-month automated text message intervention for weight management in postpartum women with overweight or obesity: protocol for the Supporting MumS (SMS) multisite, parallel-group, randomised controlled trial. BMJ Open. 2024;14(5):e084075.\u003c/li\u003e\n\u003cli\u003eAwoke MA, Earnest A, Skouteris H, Moran LJ, Wycherley TP. Modeling the effect of diet and physical activity on body mass index in prepregnant and postpartum women. Nutrition. 2023;111:112026.\u003c/li\u003e\n\u003cli\u003eMiranda AR, Scotta AV, Cortez MV, Soria EA. Two-years mothering into the pandemic: Impact of the three COVID-19 waves in the Argentinian postpartum women\u0026apos;s mental health. PLoS One. 2025;20(3):e0294220.\u003c/li\u003e\n\u003cli\u003eVan Hulst A, Zheng S, Argiropoulos N, Ybarra M, Ball GDC, Kakinami L. Overweight and obesity in early childhood and obesity at 10 years of age: a comparison of World Health Organization definitions. Eur J Pediatr. 2025;184(4):270.\u003c/li\u003e\n\u003cli\u003eOrganization WH. Malnutrition in women n.d. [Available from: https://www.who.int/data/nutrition/nlis/info/malnutrition-in-women.\u003c/li\u003e\n\u003cli\u003eAbdollahi F, Etemadinezhad S, Lye MS. Postpartum mental health in relation to sociocultural practices. Taiwan J Obstet Gynecol. 2016;55(1):76-80.\u003c/li\u003e\n\u003cli\u003eEther ST, Afrin S, Habib NN, Akter F, Chowdhury AT, Sayeed A, et al. Managing pre and postpartum mental health issues of refugee women from fragile and conflict-affected countries: A systematic review. Public Health Pract (Oxf). 2025;9:100573.\u003c/li\u003e\n\u003cli\u003eGoyal D, Dol J, Huynh J, Anand S, Dennis CL. Postpartum Mental Health and Perceptions of Discrimination Among Asian Fathers During the COVID-19 Pandemic. MCN Am J Matern Child Nurs. 2024;49(2):88-94.\u003c/li\u003e\n\u003cli\u003eSalarzadeh Jenatabadi H, Bt Wan Mohamed Radzi CWJ, Samsudin N. Associations of Body Mass Index with Demographics, Lifestyle, Food Intake, and Mental Health among Postpartum Women: A Structural Equation Approach. Int J Environ Res Public Health. 2020;17(14).\u003c/li\u003e\n\u003cli\u003eBt Wan Mohamed Radzi CWJ, Salarzadeh Jenatabadi H, Samsudin N. mHealth Apps Assessment among Postpartum Women with Obesity and Depression. Healthcare (Basel). 2020;8(2).\u003c/li\u003e\n\u003cli\u003eSomers JA. Dyadic resilience after postpartum depression: The protective role of mother-infant respiratory sinus arrhythmia synchrony during play for maternal and child mental health across early childhood. Dev Psychopathol. 2025:1-17.\u003c/li\u003e\n\u003cli\u003eCheung GW, Cooper-Thomas HD, Lau RS, Wang LC. Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management. 2024;41(2):745-83.\u003c/li\u003e\n\u003cli\u003eReichenheim ME, Moraes CL, Lopes CS, Lobato G. The role of intimate partner violence and other health-related social factors on postpartum common mental disorders: a survey-based structural equation modeling analysis. BMC Public Health. 2014;14:427.\u003c/li\u003e\n\u003cli\u003eMor S, Sela Y, Lev-Ari S. Postpartum Mothers\u0026apos; Mental Health in a Conflict-Affected Region: A Cross-Sectional Study of Emotion Regulation and Social Support. J Clin Med. 2025;14(4).\u003c/li\u003e\n\u003cli\u003eSayed SH. The mediating role of emotional intelligence in the relationship between learning motivation and academic outcomes: Conditional indirect effect of gender. J Educ Health Promot. 2024;13:123.\u003c/li\u003e\n\u003cli\u003eMcKetta S, Chakraborty P, Gimbrone C, Soled KRS, Hoatson T, Beccia AL, et al. Restrictive abortion legislation and adverse mental health during pregnancy and postpartum. Ann Epidemiol. 2024;92:47-54.\u003c/li\u003e\n\u003cli\u003eBaker MT, Lu P, Parrella JA, Leggette HR. Consumer acceptance toward functional foods: A scoping review. International Journal of Environmental Research and Public Health. 2022;19(3):1217.\u003c/li\u003e\n\u003cli\u003eApostolopoulos M, Hnatiuk JA, Maple J-L, Olander EK, Brennan L, van der Pligt P, et al. Influences on physical activity and screen time amongst postpartum women with heightened depressive symptoms: a qualitative study. BMC Pregnancy and Childbirth. 2021;21(1):376.\u003c/li\u003e\n\u003cli\u003eWilcox S, Liu J, Sevoyan M, Parker-Brown J, Turner-McGrievy GM. Effects of a behavioral intervention on physical activity, diet, and health-related quality of life in postpartum women with elevated weight: results of the HIPP randomized controlled trial. BMC Pregnancy Childbirth. 2024;24(1):808.\u003c/li\u003e\n\u003cli\u003eKamarudin SS, Idris IB, Ahmad N, Sharip S. Exploring Asian maternal experiences and mHealth needs for postpartum mental health care. Digit Health. 2024;10:20552076241292679.\u003c/li\u003e\n\u003cli\u003eSwami V, Barron D, Smith L, Furnham A. Mental health literacy of maternal and paternal postnatal (postpartum) depression in British adults. J Ment Health. 2020;29(2):217-24.\u003c/li\u003e\n\u003cli\u003ePapadopoulos A, Nichols ES, Mohsenzadeh Y, Giroux I, Mottola MF, Van Lieshout RJ, et al. Prenatal and postpartum maternal mental health and neonatal motor outcomes during the COVID-19 pandemic. J Affect Disord Rep. 2022;10:100387.\u003c/li\u003e\n\u003cli\u003eMcAlister K, Baez L, Huberty J, Kerppola M. Chatbot to Support the Mental Health Needs of Pregnant and Postpartum Women (Moment for Parents): Design and Pilot Study. JMIR Form Res. 2025;9:e72469.\u003c/li\u003e\n\u003cli\u003eKulkarni M, Fielding-Singh P. Mothers\u0026apos; Experiences of Institutional Betrayal During Childbirth and their Postpartum Mental Health Outcomes: Evidence From a Survey of New Mothers in the United States. J Midwifery Womens Health. 2025;70(2):292-300.\u003c/li\u003e\n\u003cli\u003eChang MW, et al. Empirically Derived Dietary Patterns and Postpartum Depression. BMC Psychiatry. 2023;23:393.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1,3,4,5,8","content":"\u003cp\u003eTable 1,3,4,5,8 are available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"women’s health, public health, psychological well-being, health risk, non-communicable disease","lastPublishedDoi":"10.21203/rs.3.rs-6620245/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6620245/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMalaysia was ranked first among ASEAN countries for obesity prevalence, raising significant public health concerns. Postpartum women are particularly vulnerable to obesity and related psychological challenges, yet the complex interplay between postpartum depression, body mass index (BMI), lifestyle factors, and dietary behaviors remains insufficiently understood. This study introduces an innovative framework to examine how personal characteristics, lifestyle behaviors, BMI, and depression collectively shape healthy and unhealthy food consumption during the postpartum period.\u003c/p\u003e \u003cp\u003eA structural equation modeling (SEM) approach, using maximum likelihood estimation, was applied to data from 623 postpartum women within their first year after childbirth. Depression emerged as the strongest predictor of unhealthy food consumption, with higher depressive symptoms corresponding to poorer dietary choices. Higher BMI was also significantly associated with increased intake of unhealthy foods. The model explained 75% of the variance in unhealthy food consumption and 61% in healthy food consumption. This study highlights the critical roles of depression and BMI in shaping postpartum dietary behaviors and provides important new insights into the behavioral mechanisms contributing to obesity among postpartum women. Given Malaysia's high obesity rates, these findings offer valuable directions for developing targeted interventions to promote healthier eating habits during the postpartum period.\u003c/p\u003e","manuscriptTitle":"Psychological and Behavioral Determinants of Dietary Patterns in Postpartum Women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-06 16:20:39","doi":"10.21203/rs.3.rs-6620245/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4f80a4ac-762e-449a-b5e5-88b57f670eb1","owner":[],"postedDate":"June 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-10T16:11:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-06 16:20:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6620245","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6620245","identity":"rs-6620245","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}