Healthy behavior of pregnant women: review and integrated model conceptualization.

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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Pregnancy Childbirth . 2026 Mar 6;26:406. doi: 10.1186/s12884-026-08761-7 Search in PMC Search in PubMed View in NLM Catalog Add to search Healthy behavior of pregnant women: review and integrated model conceptualization Erry Yudhya Mulyani Erry Yudhya Mulyani 1 Department of Nutritional Science, Faculty of Health Sciences, Universitas Esa Unggul, Jl Arjuna Utara Tol Tomang, Kebon Jeruk Jakarta Barat, Jakarta, 11510 Indonesia Find articles by Erry Yudhya Mulyani 1, ✉ , Tri Rakhmawati Tri Rakhmawati 4 Quality Management Research Group, Research Center for Testing Technology and Standards, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia Find articles by Tri Rakhmawati 4 , Sih Damayanti Sih Damayanti 4 Quality Management Research Group, Research Center for Testing Technology and Standards, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia Find articles by Sih Damayanti 4 , Agung Mulyo Widodo Agung Mulyo Widodo 2 Department of Computer Science, Faculty of Computer Sciences, Universitas Esa Unggul, Jakarta, Indonesia Find articles by Agung Mulyo Widodo 2 , Anugrah Novianti Anugrah Novianti 1 Department of Nutritional Science, Faculty of Health Sciences, Universitas Esa Unggul, Jl Arjuna Utara Tol Tomang, Kebon Jeruk Jakarta Barat, Jakarta, 11510 Indonesia Find articles by Anugrah Novianti 1 , Harlinda Syofyan Harlinda Syofyan 3 Department of Elementary School Teacher Education, Faculty of Teacher Training and Education, Universitas Esa Unggul, Jakarta, Indonesia Find articles by Harlinda Syofyan 3 , Idrus Jus’at Idrus Jus’at 1 Department of Nutritional Science, Faculty of Health Sciences, Universitas Esa Unggul, Jl Arjuna Utara Tol Tomang, Kebon Jeruk Jakarta Barat, Jakarta, 11510 Indonesia 5 Indonesian Nutrition Foundation for Food Fortification, Jakarta, Indonesia Find articles by Idrus Jus’at 1, 5 , Sik Sumaedi Sik Sumaedi 4 Quality Management Research Group, Research Center for Testing Technology and Standards, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia Find articles by Sik Sumaedi 4 Author information Article notes Copyright and License information 1 Department of Nutritional Science, Faculty of Health Sciences, Universitas Esa Unggul, Jl Arjuna Utara Tol Tomang, Kebon Jeruk Jakarta Barat, Jakarta, 11510 Indonesia 2 Department of Computer Science, Faculty of Computer Sciences, Universitas Esa Unggul, Jakarta, Indonesia 3 Department of Elementary School Teacher Education, Faculty of Teacher Training and Education, Universitas Esa Unggul, Jakarta, Indonesia 4 Quality Management Research Group, Research Center for Testing Technology and Standards, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia 5 Indonesian Nutrition Foundation for Food Fortification, Jakarta, Indonesia ✉ Corresponding author. Received 2025 Jun 4; Accepted 2026 Feb 2; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13078101  PMID: 41792655 Abstract Background Pregnancy is a specific period that requires important nutrients for fetal growth and development. This study aims to review previous empirical studies that examine the determinants of healthy behavior during pregnancy, especially the studies that use HBM, SCT, or TPB framework. More specifically, this study develops a comprehensive conceptual model based on findings from previous studies to obtain a model with high predictive ability. Methods This study reviews 61 selected journal articles obtained from the Scopus and Web of Science databases and selected using the PRISMA method. The analysis includes trend analysis and analysis of findings. The analysis methods used are bibliometric analysis and content analysis. Results The analysis shows that research on this topic began in 1999. Although relatively low, there has been a growth in researchers’ interest in this topic. Most of the previous research used survey designs and cross-sectional data collection. Compared to HBM and SCT, TPB or its extension is more widely used to explain healthy behavior during pregnancy. Furthermore, the healthy behaviors that focus on researchers’ attention are quite diverse, which can then be categorized into six types of behavior. Analysis of previous research findings shows that healthy behavior in pregnant women is influenced by many factors. Conclusions A conceptual model to explain these behaviors has been developed based on these findings involving 11 variables. Originality/value This study is the first to integrate the three models (HBM, SCT, and TPB) to explain healthy behaviors during pregnancy. Implications This study can be used to develop intervention strategies to increase awareness and actively encourage pregnant women to adopt healthy lifestyles and habits for their health and that of their babies. However, this model needs to be tested empirically in various contexts to ensure its predictive power and stability. Keywords: Pregnancy, Health behavior, Social cognitive theory, Health belief model, Theory of planned behavior, Pregnant women Introduction Background Pregnancy represents a vital stage in a woman’s life, marked by profound physiological and psychological transformations [ 1 – 3 ]. These changes can affect mood and emotions, reducing a woman’s quality of life [ 4 – 6 ]. The health of a mother during this period significantly influences fetal development and birth outcomes [ 1 , 2 , 7 ]. Pre-existing health conditions and inadequate health behaviors before and during pregnancy are key contributors to birth complications and pregnancy-related mortality [ 8 – 10 ]. Alarmingly, in 2017, 810 women worldwide die each day from preventable complications related to pregnancy or childbirth [ 11 ]. To reduce these risks, pregnant women need to adopt a healthy lifestyle. A nutritious diet during pregnancy is a reliable way to promote a healthy pregnancy and successful childbirth [ 12 ]. Regular exercise has been shown to prevent excessive weight gain, prevent premature birth, manage gestational diabetes, reduce the risk of hypertensive disorders and preeclampsia, enhance overall well-being during pregnancy, and improve stamina during delivery [ 13 – 15 ]. Moreover, maintaining adequate sleep during pregnancy is crucial for normal fetal growth and development [ 16 ]. Additionally, avoiding harmful substances is essential to prevent birth defects, mental disabilities, and neurodevelopmental disorders [ 10 ]. Conversely, poor dietary quality before and during pregnancy is associated with serious complications, including gestational diabetes, hypertension, and postpartum depression [ 17 ]. Inadequate maternal nutrition can impair fetal growth, leading to low birth weight, which has long-term negative impacts on child survival and economic productivity [ 18 , 19 ]. Furthermore, lack of physical activity during pregnancy is linked to an increased risk of gestational diabetes mellitus and excessive weight gain [ 20 ]. Despite the well-known benefits of healthy behaviors during pregnancy, many pregnant women still engage in unhealthy behaviors [ 10 , 21 , 22 ]. This situation underscores the need for targeted interventions that increase awareness and actively encourage the implementation of healthy behaviors during pregnancy. Understanding the factors that influence health behaviors is essential to developing such effective interventions [ 23 , 24 ]. By addressing the underlying factors, interventions can be more effective in improving maternal and fetal health outcomes, ultimately leading to healthier pregnancies and better long-term health for both mothers and children. Research gaps and objectives The Health Belief Model (HBM), Social Cognitive Theory (SCT), and Theory of Planned Behavior (TPB) are three of the most commonly used health behavior theories and models [ 25 , 26 ]. The various components of the three provide useful frameworks for examining health behavior. Extensive research has applied those three to understand health-related behaviors in diverse contexts. They were applied in their original forms or extended forms. For instance, the HBM has been used to identify determinants of preventive behavior against COVID-19 among secondary school students in Iran [ 27 ], the TPB to assess factors influencing the practice of unsafe abortion among postnatal mothers in Tanzania [ 28 ], SCT to investigate physical activity behaviors in adults with Crohn’s Disease [ 29 ], a combination of the Risk Perception Attitude (RPA) framework with TPB to study mental health promotion behaviors in young Singaporeans [ 30 ], and integration of TPB and HBM to explore tourists’ health risk preventive behaviors and travel satisfaction in Tibet [ 31 ]. Although the HBM, SCT, and TPB have been widely applied and empirically proven to explain health behavior, there are still significant gaps in their application to maternal health behavior. No study has integrated components from the three frameworks to produce a comprehensive behavioral model with high predictive ability. Most studies have relied on just one or two frameworks at a time, and none have simultaneously integrated all three theories or specifically focused on pregnancy-related health behaviors. Furthermore, the few studies that have tested these theories in the context of pregnant women’s health behaviors have produced inconsistent results, highlighting the need for further research in this area. For instance, discrepancies have been observed in studies examining vaccination intentions, where perceived susceptibility and severity yielded mixed results [ 32 , 33 ]. These inconsistencies suggest the need for a more comprehensive approach to understanding the factors that influence health behaviors during pregnancy. To address the identified research gaps, this study aims to develop a comprehensive conceptual model by integrating the Health Belief Model (HBM), Social Cognitive Theory (SCT), and Theory of Planned Behavior (TPB). This model will be designed to explain the various factors influencing the health behaviors of pregnant women. The conceptual framework will be constructed by synthesizing empirical findings from previous research on these theories within the context of pregnancy health behaviors. The ultimate goal is to enhance understanding of the determinants of health behaviors during pregnancy, which is crucial for designing and planning effective intervention programs. Additionally, this study will include a literature review focusing on the annual publication count, the most relevant journals, leading authors, research methods used, the framework used, and the types of healthy behaviors studied among pregnant women. This review will serve as a foundational step for our empirical research to identify and address the factors influencing health behaviors during pregnancy. Literature review Health behavior Health behavior is a broad concept encompassing a wide range of actions and habits that can significantly impact an individual’s health, either positively or negatively [ 11 ]. In essence, it includes behaviors that promote health and those that undermine it. Health promotion behavior is a complex model that involves an individual’s perception and self-initiated practices aimed at enhancing well-being [ 34 , 35 ]. Maintaining a health-promoting lifestyle requires careful management and selection of daily behaviors that contribute to overall health [ 34 , 36 ]. Kasl and Cobb categorized health behavior into three types [ 37 ]: Preventive behavior: Activities undertaken by individuals who believe they are healthy, aimed at preventing or detecting disease in an asymptomatic state. Illness behavior: Activities carried out by individuals who perceive themselves as ill to define their health condition and to seek appropriate treatment. Sick-role behavior: Activities performed by individuals who consider themselves sick to recover. During pregnancy, women need to make behavioral changes because women experience changes in anatomy, physiology, biochemistry, and hormones. They are at risk of experiencing birth complications. They should change or stop risky behaviors and develop or maintain healthy behaviors [ 10 ], such as taking supplements (e.g., folic acid, various vitamins); diet and healthy eating (e.g., fiber intake, hydration); physical activity; stopping alcohol consumption, smoking, use of other substances; medication; screening tests; preparation for labor/pain management; self-monitoring (e.g., monitoring for symptoms of preeclampsia, depression, introduction of active labor); and infant feeding [ 38 ]. Theoretical frameworks The Health Belief Model (HBM) The Health Belief Model (HBM) is a widely used framework for explaining and predicting preventive health behaviors [ 33 ]. The HBM focuses on two key aspects of individuals’ perceptions of health and health behavior: threat perception and behavioral evaluation [ 39 ] (see Fig. 1 ). Threat perception comprises two primary beliefs: perceived susceptibility to illness or health problems and anticipated severity of the consequences of those illnesses. Behavioral evaluation also consists of two distinct sets of beliefs: concerning the benefits or efficacy of a recommended health behavior and the costs of, or barriers to, enacting the behavior. Threat perception, behavioral evaluation, and health motivation drive health behaviors. In detail, the HBM posits that an individual’s participation in health-promoting behaviors is influenced by their beliefs about the health issue, the benefits of the action, the barriers to taking action, and their health motivation [ 40 ]. The HBM also suggested that cues to action trigger behavior when appropriate beliefs are held [ 40 ]. Cues to action can include internal stimuli (physical symptoms) or external stimuli such as social influences and health education campaigns [ 39 ]. The relationship between health beliefs and sociodemographic variables such as socioeconomic status and other demographic indices such as gender, ethnicity, and age are another important feature of the HBM [ 40 ]. The HBM proposes that modifying factors such as patient characteristics, demographics, and specific knowledge directly influence individual beliefs, shaping personal intentions [ 41 ]. Table 1 describes the components of the HBM and their definitions. Fig. 1. Open in a new tab The Health Belief Model Source: (Orbell et al., 2020) [ 40 ] Table 1. Key concepts and definitions of the health belief model Concepts Definition Perceived susceptibility The belief about one’s likelihood of getting a disease [ 33 , 42 , 43 ]. Perceived severity The belief about the seriousness and negative consequences of disease [ 33 , 43 , 44 ]. Perceived benefits The belief in the effectiveness or usefulness of a specific health behavior [ 42 ]. It includes the belief that the health behavior will reduce the risk or seriousness of the disease threat. Perceived barriers The belief in the negative aspects of taking action, such as cost, physical discomfort, psychological concerns, or logistical challenges, which may hinder engagement in health behaviors [ 42 , 44 ]. Cues to action Internal or external stimuli that motivate specific behaviors [ 42 , 44 ]. Health motivation Drives that influence health behavior [ 45 ]. Open in a new tab Social Cognitive Theory (SCT) In addition to HBM, Social Cognitive Theory (SCT) is also a widely used theory as a basis for explaining and predicting preventive health behaviors (e.g. Abdollahi et al., 2024; Ariyani et al., 2022; Mahdizadeh et al., 2021; Moravejjifar et al., 2021) [ 46 – 49 ]. SCT provides a framework for understanding, predicting, and changing human behavior [ 50 ]. SCT, first introduced by Albert Bandura in 1986, is a behavior change model that explains human behavior based on the triangular causality of behavioral, social/environmental, and personal factors (see Fig. 2 ) [ 46 ]. SCT explains how individuals learn and maintain behaviors and proposes that behavioral, personal, and environmental factors determine behavioral outcomes [ 51 ]. SCT assumes that people learn by observing the behavior of others and that behavior is learned in a social context [ 52 ]. Fig. 2. Open in a new tab Social Cognitive Theory Source: (Schunk and Usher, 2019) [ 53 ] SCT has been widely applied to many fields across contexts and settings. In relation to interventions to change behavior, this theory is also the main approach to guide those interventions [ 52 ]. The concept of mutual determinism, in which individual factors (e.g., self-efficacy, behavioral responses) and environmental factors (e.g., facilitating conditions) are proposed to affect each other reciprocally, is emphasized in this theory. Self-efficacy and outcome expectancies are central constructs in this theory. Furthermore, these constructs operate in conjunction with goals, socio-structural barriers, and facilitators in determining behavior [ 52 ]. The first construct of SCT, self-efficacy, represents people’s beliefs in their capabilities to perform an action required to attain the desired outcome [ 52 ]. Outcome expectations are referred to as predicting the possible consequence [ 12 , 52 ]. In the health sector, Bandura (2004) specifically specifies the core determinants of health promotion and disease prevention based on social cognitive theory, including knowledge, perceived self-efficacy, outcome expectations, goals, and sociostructural factors [ 51 ]. Knowledge, in this case, is knowledge about the health risks and benefits of various health practices. Perceived self-efficacy is a person’s ability to control one’s health habits. Outcome expectations are about the expected costs and benefits of different health habits. Goals refer to the health goals people set and the concrete plans and strategies for realizing them. Sociostructural factors relate to facilitators and perceived social and structural barriers to the changes they seek. Theory of Planned Behavior (TPB) Fishbein and Ajzen proposed the Theory of Planned Behavior (TPB) in 1991 in response to criticisms and limitations of the Theory of Reasoned Action (TRA) [ 54 ]. In TPB, a person’s actual behavior in performing various types of behavior is influenced by intention and perceived behavioral control (PBC) [ 54 ]. Furthermore, intention to perform various types of behavior can be predicted from attitudes, subjective norms, and PBC [ 54 ]. In this case, the better attitudes and subjective norms towards a behavior, the greater the PBC, the stronger a person’s intention to perform the behavior [ 54 ] (See in Fig. 3 ). Fig. 3. Open in a new tab The Theory of Planned Behavior Source: (Ajzen, 1991) [ 54 ] Intention indicates how hard individuals are willing to try and how much effort they plan to put into a particular behavior [ 54 ]. In other words, intention describes how committed to performing a particular behavior [ 55 ]. Attitudes refers to the degree to which a person has a favorable or unfavorable evaluation or appraisal of the behavior [ 54 ]. It represents positive or negative feelings regarding the behavior [ 55 ]. Subjective norms refer to the perceived social pressure to perform or not to perform the behavior [ 54 ]. The pressure comes from other people, such as friends, colleagues, and family members. Perceived behavioral control (PBC) refers to the perceived ease or difficulty of performing the behavior, and it is assumed to reflect experience as well as anticipated impediments and obstacles [ 54 ]. Methods Data collection This study followed the Preferred Reporting Items for Systematic Reviews (PRISMA). The process of searching and selecting articles on factors affecting the healthy behavior of pregnant women was carried out through three main stages: identification, checking and removing duplicates, and content screening. These stages were carried out to ensure that the articles to be reviewed were relevant to this study and could answer the research questions. Identification At this stage, relevant articles were searched in two databases, namely Scopus and Web of Science (WoS). These two databases were chosen to complement each other. Scopus covers more journals and articles than WoS, covering both fundamental and applied research, and WoS covers articles detailing fundamental research [ 56 ]. More importantly, many related articles are published in journals indexed in both databases. The terms used for searching articles include pregnancy (and its equivalent), health (and its equivalent), behavior (and its equivalent), as well as HBM, SCT, and TPB (and its extension). The search query strings used in the Scopus and WoS databases are illustrated in Fig. 4 . Using those queries, 154 documents were obtained from Scopus and 84 from WoS. Criteria such as document type, source type, language, and publication stage are applied to ensure relevance. Specifically, the study focuses on peer-reviewed journal articles published in English and at the final publication stage. We focus on peer-reviewed journals to ensure that the quality of publications aligns with the standards of the scientific community, thereby reducing the risk of bias in the manuscript’s content. Using the filters provided by each database, 129 documents from Scopus and 81 documents from WoS met the criteria. This identification process was completed on August 22, 2024. The identification stage, including the selection of search terms and the article search strategy, was discussed and agreed upon by all team members. Fig. 4. Open in a new tab Data search and selection process Duplication checking and removal After the initial identification, the next step is to check and remove duplicate entries. As the same article can appear in both Scopus and WoS, it is important to remove these duplicates to streamline the dataset. This stage prevents redundancy. RStudio was used to merge datasets from Scopus and WoS and remove duplications. The duplication check resulted in 141 unique documents ready to proceed to the next stage. Content screening Once duplicates have been removed, the content screening stage begins. Here, researchers review the titles, abstracts, and sometimes the full texts of the identified articles to determine their relevance to the research question. This screening process is guided by predefined inclusion and exclusion criteria, which help to filter out articles that do not directly contribute to the research objectives. For this study, the inclusion criteria focus on articles that address healthy behaviors of pregnant women or healthy behaviors during pregnancy, empirically examine the factors influencing these behaviors, involve pregnant women as research subjects, and use the original or extended Theory of Planned Behavior (TPB), Social Cognitive Theory (SCT), or Health Belief Model (HBM) as a framework. Articles that do not meet these criteria are excluded. In this study, 61 out of 141 documents met the inclusion criteria. Figure 4 outlines a systematic approach to the article search and selection process, detailing each stage from identification to content screening. Two members of the research team were responsible for the content screening process. One member conducted the screening, while the other validated the results. Data analysis Data analysis was conducted to address the research questions. In this study, the analysis includes: Publication Trends Analysis: This includes examining the annual publication count, most relevant journals, most relevant authors, research methods used, and theoretical framework employed. A bibliometric analysis facilitated by Biblioshiny, was used to identify the annual publication count, most relevant journals, and most relevant authors. Through a thorough review of the articles, content analysis is conducted to determine the research methods and frameworks used. Analysis of Previous Research Findings: This involves reviewing existing studies to identify the healthy behaviors of pregnant women and the significant factors influencing these behaviors. A content analysis of the selected articles was conducted to pinpoint these behaviors and the key factors that have been shown to significantly impact them, either directly or indirectly. From this process, both the influencing factors and the relationships between them were identified. The results of this analysis were tabulated using Microsoft Word as shown in Appendix 1. Content analysis in this study was conducted by one researcher and cross-checked by another to ensure accuracy and reliability. Results Overview of previous research Figure 5 shows the annual scientific production, namely the number of peer-reviewed journal articles published per year that empirically examine factors that influence the health-related behavior of pregnant women using the HBM, SCT, and TPB frameworks from 1999 to 2024. From 1999 to 2014, the number of annual publications on this topic was relatively low, between 0 and 3 articles. From 2015, the number of annual publications began to grow. Although in 2019 there was a 50% decrease in publications, in the following year, the number of publications increased again with the highest number in 2020–2022 of 7 articles per year. The number of annual publications for 2015–2024 was in the range of 2–7. In general, the annual growth rate was 2.81% (see Table 2 ). The number of annual publications and this growth indicate the increasing attention and interest of academics towards pregnant women and maternal health; the HBM, SCT, and TPB frameworks, and their applications to explain healthy behaviors of pregnant women. Fig. 5. Open in a new tab Annual Scientific Production Table 2. Overview of previous research Description Results Main Information about Data Timespan 1999:2024 Sources (Journals) 54 Documents 61 Annual Growth Rate % 2.81 Average citations per doc 14.49 Authors Authors 237 Authors Collaboration Single-authored docs 0 Co-Authors per Doc 4.26 International co-authorships % 1.639 Document Types Article 61 Open in a new tab Furthermore, research on this topic has been published across 54 different sources (see Table 2 ). Figure 6 shows the most relevant sources (journals) where research on the health behavior of pregnant women using frameworks like TPB, SCT, or HBM has been published. The graph shows that articles on this topic have been published in a variety of journals, indicating that this topic has received attention from various fields such as health, services, nursing, nutrition, environment, dentistry, and education. However, the number of articles for each field is still relatively few (1 or 2 articles), indicating opportunities for conducting further research in each area. BMC Health Services Research, International Journal of Environmental Research and Public Health, Journal of Education and Health Promotion, Journal of International Society of Preventive and Community Dentistry, Journal of Obstetrics and Gynecology, Midwifery, and Plos One are the journals with the most articles on this topic with 2 articles each. Fig. 6. Open in a new tab Most Relevant Journals 237 authors (see Table 2 ) have contributed to this body of research, showing significant collaboration and involvement from the academic community. Of the 61 articles, none were written by a single author. All were the result of cooperation by at least two authors. The average number of co-authors per article was 4.26, with an international co-authorship of 1.639%. Figure 7 shows the chart that ranks authors based on the number of published documents. Downs D and Jalambadani Z are the most prolific authors, each with 4 published documents. This suggests they are leading contributors in this research area. Beni N, Hausenblas H, and Kazemi A each have 3 published documents, making them significant contributors as well. Downs D has been involved in this topic relatively earlier than other authors since 2003, together with Hausenblas H (see Fig. 8 ). Their first article examined the determinants of pregnant women’s exercise intentions and behavior using the framework of the Theory of Planned Behavior. Furthermore, although Jalambadani Z is relatively new to the field (since 2018), her productivity on this topic exceeds that of her predecessors, such as Beni N, Hausenblas H, and Kazemi A (see Fig. 8 ). Fig. 7. Open in a new tab Most Relevant Authors Fig. 8. Open in a new tab Authors’ Production over Time Previous empirical studies on health-related behaviors among pregnant women have predominantly utilized survey and experimental designs (see Table 3 ), both aimed at testing causal relationships between specific variables. The primary distinction between these approaches lies in their methodologies. Experimental research involves the manipulation of one or more independent variables to observe their effects on dependent variables, typically using longitudinal data collected before and after the intervention (in this topic, the majority of interventions are in the form of education, training, or counseling based on the TPB, HBM, or SCT) (e.g., Demilew et al., 2020; Ebrahimipour et al., 2016; Ziyenda Katenga-Kaunda et al., 2020) [ 1 , 19 , 57 ]. This approach often includes both experimental and control groups for comparison. In contrast, survey research focuses on collecting data without manipulating variables, with most surveys being conducted cross-sectionally or at a single point in time (e.g., Goldani Moghaddam et al., 2023; Santi et al., 2023; Zhu et al., 2020) [ 58 – 60 ]. Table 3. Articles profile Category n % Research Design Survey/online survey 37 60.65 Experimental 22 36.07 Mixed 2 3.28 Types of Data Cross-sectional 35 57.38 Longitudinal/multiple cross-sections 25 40.98 Mixed 1 1.64 Framework HBM/extended HBM 15 24.59 SCT/extended SCT 5 8.20 TPB/extended TPB 36 59.02 HBM & TPB 5 8.20 Open in a new tab The analysis of frameworks used in the research on health behaviors of pregnant women reveals that the TPB or its extended versions are the most prevalent, appearing in 59.02% of the studies. The HBM or its extended versions is utilized in 24.59% of the research, while SCT or its extended forms is used in only 8.20% of the studies. Additionally, 8.20% of the studies integrate both HBM and TPB. This distribution indicates a strong preference for TPB in examining health behaviors, with less frequent use of SCT and a moderate application of HBM. Results of previous research The content analysis of the articles found that the health-related behaviors performed by pregnant women and studied varied, such as physical activity, nutritional behavior/healthy eating, making a smoke-free home, maintaining oral and dental health, vaccination, and prevention of urinary tract infection. This study grouped these various health-related behaviors into six categories. Table 4 shows the categorization and examples of previous research for each category. Most articles [ 58 ] focused on one type of health behavior. Three articles discussed more than one healthy behavior (i.e., Jing et al., 2015; Whitaker et al., 2016; Wilcox et al., 2022) [ 61 – 63 ]. Table 4. Category of healthy behaviors of pregnant women Health Behaviors and Definition No of Studies Examples of Previous studies DIETARY BEHAVIORS Includes all practices related to food consumption, such as choosing the type of food, food diversity, choosing and consuming the right vitamins and supplements, wise consumption of certain types of food, compliance with recommendations regarding eating, balanced diet. 21 (Beressa et al., 2024; Demilew et al., 2020; Diddana et al., 2018; Khani Jeihooni et al., 2021; Khoramabadi et al., 2015; Ziyenda Katenga-Kaunda et al., 2020) [ 19 , 57 , 64 – 67 ]. PHYSICAL ACTIVITY Includes all forms of exercise and movement that contribute to physical fitness. 13 (Black et al., 2007; Downs and Hausenblas, 2003; Hausenblas et al., 2008; Newham et al., 2016; De Vivo and Mills, 2021; Whitford and Jones, 2011; Yu et al., 2022) [ 18 , 68 – 73 ]. ORAL HEALTH BEHAVIOR Includes behaviors that are specifically focused on maintaining dental and oral health. 4 (Ebrahimipour et al., 2016; Jeihooni et al., 2017; Mohammadkhah et al., 2023; Poshtamsary et al., 2020) [ 1 , 74 – 76 ]. SELF-MONITORING BEHAVIOR Includes behaviors in which individuals track and monitor their health-related metrics. 4 (Delanoë et al., 2016; Phadungyotee et al., 2024; Santi et al., 2023) [ 59 , 77 , 78 ]. PREVENTIVE BEHAVIOR Includes all actions taken to prevent diseases or health problems before they arise, such as vaccinations, hygiene practices, stopping smoking, adopting urinary tract infection prevention behavior, and so on. 15 (Fletcher et al., 2023; Kazemi et al., 2011; Mirkuzie et al., 2011; Moradpour et al., 2023; Nankya-Mutyoba et al., 2019; Noronha et al., 2013; Saleh and Halperin, 2022) [ 24 , 79 – 84 ]. SICK-ROLE BEHAVIOR Includes behaviors that individuals engage in when they are ill, to recover. 1 (Pratiwi et al., 2023) [ 85 ]. Open in a new tab In addition to identifying health-related behavior among pregnant women, this study further identifies factors that influence it both directly and indirectly through a review of findings from previous studies. These factors and their relationships are presented in Appendix 1. These findings are important for constructing a comprehensive and valid conceptual model. The proposed conceptual model Literature has shown that human behavior is influenced by many factors. Social, emotional, and cognitive factors play a role in health behavior change(Schwarzer, 2008) [ 86 ]. In this study, researchers develop a conceptual model that can provide the best explanation of the health-related behavior of pregnant women based on findings from previous empirical studies using HBM, SCT, and TPB, which are the most widely used models or theories to explain health behavior. This conceptual model involves 11 variables and 19 relationships (see Fig. 9 ). The following sections discuss the variables and propositions in detail. Fig. 9. Open in a new tab The Proposed Conceptual Model Behavioral intention Behavioral intention generally shows an individual’s motivation to perform a behavior and the amount of effort that will be expended to perform the behavior [ 54 , 87 ]. Intention is a direct antecedent and the strongest predictor of behavior [ 88 ]. The stronger the intention, the higher the likelihood of the behavior being performed. Conversely, the weaker the intention, the smaller the likelihood of the behavior being performed. In the context of health behaviors during pregnancy, many studies have shown that behavioral intentions directly influence various health-related behaviors of pregnant women positively (see Appendix 1). When pregnant women have strong intentions to engage in certain health behaviors, they are more likely to do so. Conversely, weak intentions are often insufficient to drive the desired behavior. For example, strong exercise intentions drive the actual exercise behavior of pregnant women [ 68 , 69 ]. Furthermore, Malek et al. (2017) found that strong healthy eating intentions led to better adherence to food group recommendations [ 89 ]. Other studies conducted by Phadungyotee et al. (2024), Goldani Moghaddam et al. (2023), De Vivo and Mills (2021), andMoradpour et al. (2021) also showed similar relationships [ 58 , 71 , 78 , 81 ]. Based on the findings discussed above, the first proposition of this study is as follows: Proposition 1: Behavioral intention has a positive and significant impact on the health-related behavior of pregnant women Personal agency Personal agency is linked to a person’s confidence to perform the behavior, encompassing personal control and self-efficacy [ 90 ]. Perceived control and self-efficacy are related but distinct constructs [ 77 ]. Perceived behavioral control (PBC) is a person’s perception of the control they have over a particular behavior and the barriers that can prevent the performance of the behavior [ 23 , 91 – 94 ]. In other words, PBC is an individual’s perception of the ease/difficulty of carrying out a behavior based on existing resources and barriers [ 95 ]. Meanwhile, self-efficacy shows a person’s belief in their ability to perform a behavior [ 73 , 96 ]. Both PBC and self-efficacy stem from control beliefs—beliefs about the factors that can either facilitate or hinder behavior performance [ 88 ]. Although intention is the immediate antecedent of behavior, unavailability of resources, such as time, money, or other resources, required skills, and many other factors may prevent someone from turning intention into actual behavior [ 88 ]. How well they can overcome those barriers, and the facilitating factors influence an individual’s control over the behavior [ 88 ]. Therefore, TPB postulates that behavioral control moderates the effect of intention on behavior. The greater the perceived behavioral control (PBC), the more likely the intention will be translated into behavior [ 88 ]. Research on healthy behaviors among pregnant women shows that higher PBC is associated with stronger pregnant women’s intentions to engage in health-promoting activities (see Appendix 1). For example, perceived control over folic acid supplementation influences pregnant women’s intentions to take this supplement [ 97 ]. Higher PBC was associated with stronger intentions to take folic acid. Similarly, De Vivo and Mills (2021) found that pregnant women’s perceived control over physical activity influences their intention to maintain it [ 71 ]. As postulated by TPB, for the context of pregnant women’s health behavior, PBC influences intentions and directly impacts actual behavior [ 54 ]. Perceived control influences pregnant women’s salt consumption behavior [ 58 ]. and their behaviors to prevent urinary tract infection [ 81 ]. Self-efficacy also plays a significant role in shaping intentions. If a pregnant woman feels confident about her ability to maintain healthy behavior, she is more likely to intend to do so. Higher self-efficacy is linked to stronger intentions to engage in behaviors that prevent hepatitis B virus (HBV) transmission [ 82 ]. Self-efficacy was also evident to influence the intention to adhere to a high folate diet permanently [ 98 ]. Apart from significantly influencing behavioral intentions, self-efficacy also affects behaviors directly. Another study showed that self-efficacy perceptions influence the behavior of pregnant women regarding detecting warning signs of preeclampsia during pregnancy [ 59 ]. Furthermore, a significant positive impact of self-efficacy on pregnant women’s behavior was also found in the context of nutritional behavior [ 12 ]. The role of personal agency (PBC and self-efficacy) in driving behavior is also explained in Social Cognitive Theory (SCT). According to this theory, an action is taken if the individual feels they have control over the outcome, if there are few external obstacles, and if the individual has strong self-efficacy [ 99 ]. Therefore, the following propositions are proposed: Proposition 2: Personal agency has a positive and significant impact on the health-related behavioral intention of pregnant women Proposition 3: Personal agency has a positive and significant impact on the health-related behaviors of pregnant women Attitude and subjective norm Attitude refers to an individual’s evaluation of a particular behavior, which is rooted in their beliefs about the behavior and its possible outcomes [ 26 , 90 ]. In essence, attitude is shaped by behavioral beliefs about the likely consequences and experiences resulting from the performance of the behavior [ 88 ] and the perceived benefits of the behavior effectiveness or usefulness [ 42 ]. It entails evaluating the behavior as positive or negative (e.g., good vs. bad; pleasant vs. unpleasant) [ 23 , 92 , 94 ]. When individuals believe and perceive behavior’s benefits, they are likely to develop a positive attitude toward it. The greater the perceived benefits, the more positive the attitude. Subjective norm pertains to the perceived social pressure to perform or avoid a specific behavior [ 88 ]. It is shaped by normative beliefs, which involve perceptions of what significant social referents expect or how they behave [ 88 ], or the belief about whether most people approve or disapprove of a particular behavior [ 100 , 101 ]. According to the Theory of Planned Behavior (TPB), attitude and subjective norms significantly impact the individual’s intention to perform or avoid certain behaviors [ 54 ]. If someone believes that behavior will provide benefits, they are likely to have a positive attitude toward it and be more inclined to engage in that behavior. Conversely, if they perceive the behavior as having no or negative benefits, they will likely have a negative attitude and be less inclined to perform it [ 54 ]. Furthermore, when individuals believe that certain health behaviors are endorsed and supported by family, friends, healthcare professionals, and other significant people, they are more likely to be motivated to engage in those behaviors. Conversely, they may be less inclined to engage in the behavior if they perceive disapproval [ 54 ]. Empirical research in the context of healthy behavior of pregnant women supports the relationship between attitudes and subjective norms with behavioral intention (see Appendix 1). If a pregnant woman believes that health-related behaviors will benefit her and her baby, she will hold a positive attitude toward those behaviors. As a result, she is more likely to form the intention to engage in these behaviors. On the contrary, if she does not believe that these behaviors are beneficial, she may hold a negative attitude and be less motivated to adopt them. Additionally, if she perceives support from significant people in her life for these health behaviors, she is more likely to form intentions to adopt them. Malek et al. (2017) examined how the TPB constructs—attitude, subjective norm, and perceived behavioral control (PBC)—along with psychosocial factors like perceived stress, health value, and self-identity as a healthy eater, explain pregnant women’s intentions to follow a healthy diet and actual food consumption behavior during pregnancy [ 89 ]. They found that significant predictors of stronger intentions to eat healthy were higher PBC, subjective norm, positive attitude, and a stronger self-identity as a healthy eater. Zhu et al. (2020) explored the intention of Chinese pregnant women to engage in physical activity (PA) using TPB [ 60 ]. They revealed that behavioral attitudes, subjective norms, and perceived behavioral control (PBC) directly influenced the intention to perform PA, thereby impacting the behavior itself. Jalambadani & Hosseini (2022b) found similar findings where attitudes, subjective norms, and PBC can predict 65% of the variance in the intention to consume vitamin D3 among pregnant women [ 102 ]. Therefore, the next two propositions of this study are as follows: Proposition 4: Attitude toward behavior has a positive and significant impact on the health-related behavioral intention of pregnant women Proposition 5: Subjective norm has a positive and significant impact on the health-related behavioral intention of pregnant women Perceived threat HBM suggests that perceived threat is a factor that drives prevention efforts [ 25 ]. It involves two components: perceived susceptibility and perceived severity [ 39 ]. These two components shape individual’s sense of threat regarding a particular health problem. The greater the individual’s perception of susceptibility and severity, the more likely they are to feel at risk or threatened by the disease. Individuals who believe that they are at risk for a particular health problem and that the impact or consequences of that problem are serious are more likely to engage in behaviors to prevent it or reduce the seriousness of the impacts. Pregnancy is a significant period that is susceptible to complications. Pregnant women who believe they are at risk of complications are more likely to engage in health-promoting behaviors during their pregnancy, as suggested in previous research (see Appendix 1). Perceived susceptibility and severity were found to have a direct impact on the early detection of preeclampsia pregnancy warning signs [ 59 ]. Pregnant women who feel the threat of preeclampsia pregnancy tend to do early detection. Similarly, Saleh & Halperin (2022) demonstrated that receiving the influenza vaccination during pregnancy was associated with higher perceived susceptibility, higher perceived severity, lower barriers, higher perceived benefits, more cues for action, and higher trust in healthcare providers [ 84 ]. In addition to directly influencing behavior, studies have found that perceived threat (susceptibility and severity) indirectly affect behavior through intention [ 101 ]. In the context of HIV detection during pregnancy, it was found that perceived susceptibility to HIV, perceived benefits of screening, perceived severity of the illness, health motivation, and normative beliefs predicted women’s intentions to undergo HIV screening [ 101 ]. Based on past research findings, this study develops a proposition: Proposition 6: Perceived threat has a positive and significant impact on the health-related behavioral intention of pregnant women Proposition 7: Perceived threat has a positive and significant impact on the health-related behavior of pregnant women Past behavior (experience) Previous literature suggests that future behavior is influenced by past behavior [ 103 ]. The influence of past behavior (experience) on intention has been explored in several studies. The inclusion of past behavior into the behavioral model has been shown to increase the explanatory power of the model to predict behavior and intention [ 71 , 72 ]. Furthermore, it was found that past behavior (performing pelvic floor exercises before pregnancy) significantly predicted intentions to continue performing these exercises during pregnancy [ 72 ]. In addition, pregnant women who are accustomed to regular physical activity are more likely to intend to continue physical activity during pregnancy [ 71 ]. Another evidence shows that previous experience with health services influences pregnant women’s intention to use institutional delivery services. Pregnant women who had experience giving birth at an institutional delivery service had a greater intention to give birth there compared to those who had no previous experience [ 104 ]. Based on the findings discussed above, the next proposition of this study is as follows: Proposition 8: Past behavior (experience) has a positive and significant impact on the health-related behavioral intentions of pregnant women Cues to action Cues to action, as postulated in the Health Belief Model (HBM), is one of the important components that influence individuals to perform health actions. It can be defined as internal or external stimuli that motivate an individual to perform certain behaviors related to their health [ 42 , 44 ]. These ‘cues’ cover various triggers, including individual’s perception of symptoms, social influences, and health education campaigns [ 39 ]. Information received from outside, such as television advertisements, text messages, and or reminder letters, are examples of external stimuli [ 40 ]. Moreover, internal stimuli can be perceived as physical symptoms [ 40 ]. Health conditions, as internal stimuli, affect changes in individual health behavior. Having certain health conditions encourages people to improve their health behavior. In the context of health-related behavior of pregnant women, some empirical studies have proven the influence of cues to action on behavior (see Appendix 1). The greater the cues to action, the more likely the behavior will be performed, and vice versa. Saleh & Halperin (2022) demonstrated that receiving the influenza vaccination during pregnancy was associated with higher perceived susceptibility, higher perceived severity, lower barriers, higher perceived benefits, more cues for action, and higher trust in healthcare providers [ 84 ]. Similar findings were found by Santi et al. (2023) in their study analyzing factors affecting pregnant women’s behaviors in the early detection of warning signs of preeclampsia (PE) based on the health belief model [ 59 ]. They found cues to action had direct and indirect pathways toward detection behaviors of the PE warning signs. Based on the above findings, the next proposition of this study is as follows: Proposition 9: Cues to action have a positive and significant impact on the health-related behavior of pregnant women Anticipated regret Regret management theory suggests that people act to reduce their perceived or potential regret, which is an unpleasant cognitive emotion experienced when realizing or imagining that a situation could have been better if a different decision had been made [ 105 ]. Evidence suggests that anticipated regret can drive people’s actions. Regretting from taking action can prevent a particular behavior, while anticipated regret from not acting can drive action. In the context of pregnancy, anticipated regret significantly impacts health-related behaviors. For instance, in a study exploring the influence of psychosocial factors on pregnant women’s intention to use a Decision Aid (DA) for prenatal screening for Down syndrome (DS), (Delanoë et al., 2016) defined anticipated regret as an estimation of the regret that would result from not adopting Decision Aid (DA) for prenatal screening for Down syndrome (DS) [ 77 ]. They found that intention was shaped by attitude, moral norm, descriptive norm, and anticipated regret. Specifically, the potential regret of not using DA (anticipated regret) was significantly associated with a strong intention to use it. Based on these findings, this study proposes that: Proposition 10: Anticipated regret has a positive and significant impact on the health-related behavioral intention of pregnant women Self-identity Self-identity is a role identity, defined as a set of characteristics or expectations that are determined by a social position within the community and become a component of the individual’s self [ 106 , 107 ]. Self-identity influences individual behavior. Individuals tend to choose behaviors that are in line with their self-concept, and self-identity develops based on these behaviors [ 108 ]. During pregnancy, self-identity influences health-related behaviors. Malek et al. (2017) found that a strong self-identity as a healthy eater was a significant predictor of pregnant women’s intention to maintain healthy eating habits [ 89 ]. They will implement a healthy balanced diet during pregnancy. Therefore, pregnant women who identify as health-conscious will tend to implement a healthy lifestyle, including during pregnancy. Based on these findings, this study proposes that: Proposition 11: Self-identity has a positive and significant impact on the health-related behavioral intention of pregnant women Health motivation Health motivation is a factor found to be an important predictor of health behavior [ 109 ]. In the Health Belief Model (HBM), individuals find themselves motivated, or not, to act in part by health motivation (see Fig. 1 ). Threat perception, behavioral evaluation, and health motivation drive health behaviors. Individuals who perform a certain healthy behavior because they understand the value of the behavior will be motivated to do so [ 99 ]. In the context of pregnancy and HIV, Ben Natan & Kutygaro (2015) found health motivation as a predictor of the intention to be HIV screened [ 101 ]. Pregnant women have higher intentions to undergo screening if they are motivated to maintain their health. Based on relevant theories and study findings, this study proposes that: Proposition 12: Health motivation has a positive and significant impact on the health-related behavioral intention of pregnant women Socio-demographic In several studies, socio-demographic characteristics are one of the variables that shape behavior among pregnant women. The study of dietary diversity found that having received the intervention, being literate, and having a high level of wealth significantly improved maternal dietary diversity [ 64 ]. Education provides more job opportunities and wealth to pregnant women which can increase consumption of a variety of foods. Pregnant women with high wealth are more likely to consume foods with adequate diversity. Furthermore, related to HIV testing, Mirkuzie et al. (2011) found that pregnant women with more than 8th-grade education were less likely to undergo HIV testing compared to women with lower levels of education [ 80 ]. In addition to directly affecting behavior, socio-demographics indirectly influence behavior through intention [ 70 , 84 , 104 , 110 ]. A study by Saleh & Halperin (2022) showed a significant relationship between background variables and willingness to receive the COVID-19 vaccination in which willingness to receive vaccination was higher among Jewish women than Arab women, academically educated compared to those without academic education, employed compared to unemployed, having higher economic status compared to low, urban women compared to rural women, and non-religious compared to religious [ 84 ]. Furthermore, Newham et al. (2016) identified that sociodemographic characteristics such as race and education level were associated with intention scores for resting [ 70 ]. Being Caucasian and having a higher education level were associated with lower intention scores for resting. Another study by Ayana et al. (2021) also shows how the number of children and place of residence are significantly associated with using institutional delivery services [ 104 ]. Pregnant women living in rural areas were less likely to use institutional delivery services than those living in urban areas. Pregnant women with parity 1–3 and 4 or more were less likely to use institutional delivery services compared to pregnant women with parity 0 (never given birth). Another study found that the type of residence influenced health behavioral intention, especially for antiretroviral adherence intention [ 110 ]. For the context of pregnant women’s behavior in early detection of preeclampsia warning signs, Santi et al. (2023) demonstrated that sociodemographic factors have an indirect pathway toward the detection of behavior through perceived susceptibility/severity and perceived barrier [ 59 ]. Sociodemographics are significantly related to the susceptibility perception experienced by pregnant women related to their pregnancy conditions (perceived susceptibility and severity). In addition, Nankya-Mutyoba et al. (2019) showed that differences in region and religion are related to differences in perceived self-efficacy for hepatitis B prevention [ 82 ]. Based on these findings, we propose the following propositions: Proposition 13: Socio-demographic characteristics have a significant impact on the health-related behavioral intention of pregnant women Proposition 14: Socio-demographic characteristics have a significant impact on the health-related behavior of pregnant women Proposition 15: Socio-demographic characteristics have a significant impact on perceived threat Proposition 16: Socio-demographic characteristics have a significant impact on personal agency Education/counseling/training/campaign Research related to healthy behavior during pregnancy discusses a lot about how education/counseling/training/campaign affects the behavior of pregnant women. The study used HBM, SCT, or TPB as a guide in education/counseling/training/campaign to encourage healthier behavior from pregnant women. The effectiveness of this education has been proven. The educational intervention was proven to affect attitude [ 1 , 66 , 75 ], subjective norm [ 64 , 66 ], PBC [ 65 , 111 ] and self-efficacy [ 12 , 34 , 76 ]. The information provided is also a form of external stimuli that can encourage action. Education significantly causes positive attitudes toward healthy behavior and increases subjective norms, PBC, and self-efficacy. Therefore, the study proposes the following propositions: Proposition 17: Education has a positive and significant impact on attitude Proposition 18: Education has a positive and significant impact on subjective norm Proposition 19: Education has a positive and significant impact on personal agency Conclusion Pregnancy is an important period for women. During this period, women have increased needs for macro- and micronutrients, energy, folate, iodine, and iron. Data shows that childbirth complications and pregnancy-related deaths are high. Therefore, pregnant women are required to make lifestyle changes to ensure their health and that of their fetuses. Several previous studies have identified various factors influencing women’s health-related behavior during pregnancy. Most of these studies used the original or extended HBM, SCT, or TPB frameworks to explain these behaviors. However, there is still limited evidence regarding integrating components from the three frameworks in explaining healthy behaviors during pregnancy. This integration is expected to improve the predictive ability of behavioral models. A comprehensive understanding of the factors influencing these behaviors is essential for developing effective intervention strategies. Based on the findings of relevant empirical studies, this study has developed a conceptual model to explain the health-related behavior of pregnant women. Specifically, the model explains what factors influence pregnant women to do or not do certain behaviors that can directly and indirectly improve their health. This model involves 11 variables and 19 relationships that have been arranged into 19 propositions. The contributions of this study are fourfold. This study applies HBM, SCT, and TPB in a behavioral context (healthy behavior during pregnancy), which has received less attention than other healthy behaviors. This study integrates three established theories that have been tested to explain individual behavior. This study provides insights into how widely HBM, SCT, and TPB are used in the context of healthy behaviors during pregnancy. This study provides insight into the research trends of healthy behaviors during pregnancy, including the development of publications and research methods used. Future research directions This study developed a conceptual model that has not been empirically tested. Therefore, this model needs to be tested empirically in further studies to determine which factors have a major impact on behavior and how these factors are interrelated. In addition, testing should be conducted across cultures/countries to assess its predictive power and stability. Furthermore, a review of previous articles found that the distribution of research locations on this topic is uneven (see Fig. 10 ). There are 17 countries as research locations, with most of the research on this topic concentrated in Iran (22 articles). Furthermore, the US ranks second as a research location with 9 articles. Several other countries in Asia (i.e., Israel, Indonesia, China, Thailand, Taiwan, Philippines, India), Australia, Europe (i.e., Poland), America (i.e., Canada), and Africa (i.e., Ethiopia, Zambia, Uganda, Malawi) also show contributions to this topic. Fig. 10. Open in a new tab Distribution of Research Locations The distribution of research locations opens opportunities for further research. Each country has different social, cultural, and economic characteristics as well as health policies. In one country, maternal and child health is a national priority, but in another country, it may be less of a priority. Furthermore, in one country, religion may influence the behavior of pregnant women regarding preventive vaccination, but in another country, religion is not a factor in determining vaccination behavior. This topic is interesting to study from a socio-cultural perspective. Appendix 1 Influence Relationship Context (Health Behavior/Intention) Previous Empirical Studies Attitude➔Intention Glycemic control (Phadungyotee et al., 2024) Exercise/physical activity (Hausenblas et al., 2008) (Downs and Hausenblas, 2003) (Hausenblas and Symons Downs, 2004) (De Vivo and Mills, 2021) (Zhu et al., 2020) Healthy eating (Malek et al., 2017) (Chitsaz et al., 2017) The aspirin adherence (Lin et al., 2018) Pearl vitamin D3 consumption (Jalambadani and Hosseini, 2022b) Folic acid consumption (Ben Natan et al., 2018) Alcohol use (Fletcher et al., 2023) Institutional delivery service use (Ayana et al., 2021) Salt consumption (Goldani Moghaddam et al., 2023) The use of a decision aid for prenatal screening for Down syndrome (Delanoë et al., 2016) Influenza vaccination (Greyson et al., 2021) Stop smoking (Ben Natan et al., 2010) Self-medication (Karimian et al., 2021) Maintain better mental health (Jalambadani and Hosseini, 2022a) Urinary Tract Infection Prevention (Moradpour et al., 2023) HIV testing (Mirkuzie et al., 2011) Perceived benefits➔Intention Exercise (Black et al., 2007) Permanently high folate diet (Kloeblen, 1999) HIV screening (Ben Natan and Kutygaro, 2015) Behavioral beliefs➔Intention Folic acid consumption (Ben Natan et al., 2018) Stop smoking (Ben Natan et al., 2010) Attitude➔Behavior Salt consumption (Goldani Moghaddam et al., 2023) Dietary diversity (Ziyenda Katenga-Kaunda et al., 2020) Perceived benefits➔Behavior Detection of preeclampsia pregnancy warning signs (Santi et al., 2023) Influenza vaccination compliance (Saleh and Halperin, 2022) Subjective norm➔Intention Glycemic control (Phadungyotee et al., 2024) Exercise intention/physical activity (Downs and Hausenblas, 2003) (Lee et al., 2016) (Hausenblas and Symons Downs, 2004) (De Vivo and Mills, 2021) (Zhu et al., 2020) Healthy eating (Malek et al., 2017) (Chitsaz et al., 2017) Alcohol use (Fletcher et al., 2023) Salt consumption (Goldani Moghaddam et al., 2023) Stop smoking (Ben Natan et al., 2010) Influenza vaccination (Greyson et al., 2021) Pearl vitamin D3 consumption (Jalambadani and Hosseini, 2022b) Self-medication (Karimian et al., 2021) Maintain better mental health (Jalambadani and Hosseini, 2022a) Urinary tract infection prevention (Moradpour et al., 2023) HIV testing (Mirkuzie et al., 2011) Oral health behavior (Ebrahimipour et al., 2016) Descriptive norm➔Intention The use of a decision aid for prenatal screening for Down syndrome (Delanoë et al., 2016) HIV testing (Mirkuzie et al., 2011) Moral norm➔Intention The use of a decision aid for prenatal screening for Down syndrome (Delanoë et al., 2016) Normative beliefs➔Intention Folic acid consumption (Ben Natan et al., 2018) Stop smoking (Ben Natan et al., 2010) HIV screening (Ben Natan and Kutygaro, 2015) Subjective norm➔Behavior Self-medication (Karimian et al., 2021) Dietary diversity (Ziyenda Katenga-Kaunda et al., 2020) Oral health behavior (Ebrahimipour et al., 2016) Subjective norm➔Relationship with husband The aspirin adherence (Lin et al., 2018) Belief in self-control behavior /PBC➔Intention Glycemic control (Phadungyotee et al., 2024) Exercise/physical activity (Hausenblas et al., 2008) (Downs and Hausenblas, 2003) (Lee et al., 2016) (De Vivo and Mills, 2021) (Zhu et al., 2020) Healthy eating intention (Malek et al., 2017) (Chitsaz et al., 2017) Folic acid consumption (Ben Natan et al., 2018) The aspirin adherence (Lin et al., 2018) Pearl vitamin D3 consumption (Jalambadani and Hosseini, 2022b) Institutional delivery service use (Ayana et al., 2021) Salt consumption (Goldani Moghaddam et al., 2023) Stop smoking (Ben Natan et al., 2010) Self-medication (Karimian et al., 2021) Maintain better mental health (Jalambadani and Hosseini, 2022a) Urinary Tract Infection Prevention (Moradpour et al., 2023) Oral health behaviors (Ebrahimipour et al., 2016) Perceived barriers➔Intention Permanently high folate diet (Kloeblen, 1999) Ability to overcome environmental barriers➔Intention Exercise (Black et al., 2007) Ability to overcome personal barriers➔Intention Exercise (Black et al., 2007) Perceived self-efficacy➔Intention HBV prevention (Nankya-Mutyoba et al., 2019b) Permanently high folate diet (Kloeblen, 1999) PBC➔Behavior Salt consumption (Goldani Moghaddam et al., 2023) Urinary tract infection prevention (Moradpour et al., 2021) Healthy eating (Chitsaz et al., 2017) The aspirin adherence (Lin et al., 2018) Exercise (Hausenblas and Symons Downs, 2004) Dietary diversity (Ziyenda Katenga-Kaunda et al., 2020) Oral health behaviors (Ebrahimipour et al., 2016) Perceived barriers➔Behavior Detection of preeclampsia pregnancy warning signs (Santi et al., 2023) Influenza vaccination compliance (Saleh and Halperin, 2022) Oral and dental health behavior (Poshtamsary et al., 2020) HIV testing (Mirkuzie et al., 2011) Reducing anxiety (Pratiwi et al., 2023) Perceived self-efficacy➔Behavior Detection of preeclampsia pregnancy warning signs (Santi et al., 2023) Influenza vaccination compliance (Saleh and Halperin, 2022) Nutritional behavior/healthy eating behavior (Arefi et al., 2024) Reducing anxiety (Pratiwi et al., 2023) PBC➔Action planning The aspirin adherence (Lin et al., 2018) PBC➔Coping planning The aspirin adherence (Lin et al., 2018) Past behavior➔Intention Exercise/physical activity (Whitford and Jones, 2011) (De Vivo and Mills, 2021) Influenza vaccination (Greyson et al., 2021) Past experience➔Intention Institutional delivery service use (Ayana et al., 2021) Intention➔Behavior Glycemic control (Phadungyotee et al., 2024) Exercise/physical activity (Hausenblas et al., 2008) (Downs and Hausenblas, 2003) (De Vivo and Mills, 2021) Salt consumption (Goldani Moghaddam et al., 2023) Urinary tract infection prevention (Moradpour et al., 2021) (Moradpour et al., 2023) Adherence to food group recommendations (Malek et al., 2017) Healthy eating (Chitsaz et al., 2017) The aspirin adherence (Lin et al., 2018) Influenza vaccination (Greyson et al., 2021) Oral health behaviors (Ebrahimipour et al., 2016) HIV testing (Mirkuzie et al., 2011) Intention➔Action planning The aspirin adherence (Lin et al., 2018) Intention➔Coping planning The aspirin adherence (Lin et al., 2018) Perceived susceptibility/perceived risk➔Intention HIV screening (Ben Natan and Kutygaro, 2015) Perceived severity➔Intention HIV screening (Ben Natan and Kutygaro, 2015) Perceived threat➔Behavior Detection of preeclampsia pregnancy warning signs (Santi et al., 2023) Influenza vaccination compliance (Saleh and Halperin, 2022) Perceived susceptibility/sensitive➔Behavior Detection of preeclampsia pregnancy warning signs (Santi et al., 2023) Oral and dental health behavior (Poshtamsary et al., 2020) Reducing anxiety (Pratiwi et al., 2023) Perceived severity➔Behavior Detection of preeclampsia pregnancy warning signs (Santi et al., 2023) Information seeking➔Intention Influenza vaccination (Greyson et al., 2021) Self-identity➔Intention Healthy eating (Malek et al., 2017) Cues to action➔Perceived self-efficacy Detection of preeclampsia pregnancy warning signs (Santi et al., 2023) Cues to action➔Behavior Detection of preeclampsia pregnancy warning signs (Santi et al., 2023) Influenza vaccination compliance (Saleh and Halperin, 2022) Health motivation➔Intention HIV screening (Ben Natan and Kutygaro, 2015) Anticipated regret➔Intention The use of a decision aid for prenatal screening for Down syndrome (Delanoë et al., 2016) Knowledge➔behavior Urinary tract infection prevention (Moradpour et al., 2021) Oral and Dental Health Behavior (Poshtamsary et al., 2020) Knowledge➔Intention HIV testing (Mirkuzie et al., 2011) Antiretroviral adherence (Nutor et al., 2021) Action planning➔behavior The aspirin adherence (Lin et al., 2018) Coping planning➔behavior The aspirin adherence (Lin et al., 2018) Self-regulation➔behavior Nutritional behavior/healthy eating behavior (Torkan et al., 2018) (Arefi et al., 2024) Relationship with husband➔behavior The aspirin adherence (Lin et al., 2018) Relationship with husband➔Intention The aspirin adherence (Lin et al., 2018) Venturesomeness➔Intention Alcohol use (Fletcher et al., 2023) Similarity➔Intention Alcohol use (Fletcher et al., 2023) Willingness➔Intention Alcohol use (Fletcher et al., 2023) Campaign➔Knowledge Consumption of iron folic acid tablets and iron-rich foods (Gamboa et al., 2020) Educational intervention➔Knowledge Oral health behavior/oral and dental health behaviors (Ebrahimipour et al., 2016) (Jeihooni et al., 2017) (Mohammadkhah et al., 2023) Nutritional behavior/healthy eating behavior (Khani Jeihooni et al., 2021) (Arefi et al., 2024) Dietary Practice (Diddana et al., 2018) (Khoramabadi et al., 2015) Folic acid consumption (Jalambadani et al., 2020) Anaemia prevention (Noronha et al., 2013) Iron consumption (Jalambadani et al., 2018) The intention of pregnant women to optimize nutrition (Setia et al., 2020) Educational intervention➔Attitude Oral health behavior/oral and dental health behaviors (Ebrahimipour et al., 2016) (Mohammadkhah et al., 2023) Nutritional behaviors for preventing Anemia (Khani Jeihooni et al., 2021) Dietary diversity (Beressa et al., 2024) Iron consumption (Jalambadani et al., 2018) The intention of pregnant women to optimize nutrition (Setia et al., 2020) Campaign➔Attitude/intention Consumption of iron folic acid tablets and iron-rich foods (Gamboa et al., 2020) Educational intervention➔perceived benefit Oral and dental hygiene behaviors (Jeihooni et al., 2017) Dietary Practice (Diddana et al., 2018) Educational intervention➔perceived barriers Dietary Practice/ Dietary diversity (Beressa et al., 2024) (Khoramabadi et al., 2015) (Diddana et al., 2018) Oral and dental health behavior (Jeihooni et al., 2017) Educational intervention➔Subjective norm Oral health behavior/oral and dental health behaviors (Ebrahimipour et al., 2016) (Mohammadkhah et al., 2023) Nutritional behaviors for preventing Anemia (Khani Jeihooni et al., 2021) Dietary diversity (Beressa et al., 2024) Educational intervention➔PBC Oral health behavior/oral and dental health behaviors (Ebrahimipour et al., 2016) (Mohammadkhah et al., 2023) Nutritional behaviors for preventing Anemia (Khani Jeihooni et al., 2021) Dietary diversity (Beressa et al., 2024) Folic acid consumption (Jalambadani et al., 2020) Iron consumption (Jalambadani et al., 2018) Educational intervention➔Self-efficacy Dietary Practice/ Dietary diversity (Beressa et al., 2024) (Diddana et al., 2018) Health-promoting behaviors (Ghahremani et al., 2017) Oral and dental health behavior (Poshtamsary et al., 2020) (Jeihooni et al., 2017) Nutritional behavior/healthy eating behavior (Arefi et al., 2024) Educational intervention➔Intention Oral health behaviors (Ebrahimipour et al., 2016) Nutritional behaviors for preventing Anemia (Khani Jeihooni et al., 2021) Dietary diversity (Beressa et al., 2024) Folic acid consumption (Jalambadani et al., 2020) Iron consumption (Jalambadani et al., 2018) Educational intervention➔Behavior Oral health behaviors/oral and dental health behaviors (Ebrahimipour et al., 2016) (Jeihooni et al., 2017) (Mohammadkhah et al., 2023) Nutritional behaviors for preventing Anemia (Khani Jeihooni et al., 2021) Dietary Practice/ Dietary diversity (Diddana et al., 2018) (Khoramabadi et al., 2015) (Beressa et al., 2024) (Ziyenda Katenga-Kaunda et al., 2020) (Demilew et al., 2020) (Diddana et al., 2018) (Wilcox et al., 2022) Folic acid consumption (Jalambadani et al., 2020) Health promoting behaviors (Ghahremani et al., 2017) Nutritional behavior/healthy eating behavior (Arefi et al., 2024) The intention of pregnant women to optimize nutrition (Setia et al., 2020) Preventive health behaviors (tobacco cessation, condom use for disease prevention, nutrition optimization, seat belt use, breastfeeding) (Moniz et al., 2015) Personalized interventions➔Behavior Dietary behavior and physical activity (Jing et al., 2015) Educational intervention➔food selection ability (health promoting behavior to prevent anaemia) Anaemia prevention (Noronha et al., 2013) Educational intervention➔Perceived susceptibility Dietary Praffctice/ Dietary diversity (Beressa et al., 2024) (Diddana et al., 2018) Making smoke-free home (Kazemi et al., 2011b) Oral and dental hygiene behaviors (Jeihooni et al., 2017) Educational intervention➔Perceived severity Dietary Practice/ Dietary diversity (Beressa et al., 2024) (Khoramabadi et al., 2015) (Diddana et al., 2018) Making smoke-free home (Kazemi et al., 2011b) Oral and dental hygiene behaviors (Jeihooni et al., 2017) Educational intervention➔Cues to action Oral and dental health behavior (Poshtamsary et al., 2020) (Jeihooni et al., 2017) Educational intervention➔Outcome expectation Nutritional behavior/healthy eating behavior (Arefi et al., 2024) Educational intervention➔Outcome value Nutritional behavior/healthy eating behavior (Arefi et al., 2024) Educational intervention➔Physical activity Health-promoting behaviors (Ghahremani et al., 2017) Educational intervention➔Health responsibility Health-promoting behaviors (Ghahremani et al., 2017) Educational intervention➔Stress management Health-promoting behaviors (Ghahremani et al., 2017) Educational intervention➔weekly environmental tobacco smoke (ETS) exposure Making smoke-free home (Kazemi et al., 2011b) pre-test counselling➔behavior HIV testing (Mirkuzie et al., 2011) Mutual determinants➔Behavior nutritional behavior/healthy eating behavior (Arefi et al., 2024) Maternal education (literacy)➔Behavior Dietary diversity (Beressa et al., 2024) Number of ANC visit➔Intention intention to use institutional delivery service (Ayana et al., 2021) Parity➔Intention intention to use institutional delivery service (Ayana et al., 2021) Trimester➔Intention intention for being physically active (-) (Newham et al., 2016) intention for resting (+) (Newham et al., 2016) Health condition➔Intention intention for being physically active (Newham et al., 2016) Toilet type➔Intention antiretroviral adherence intention (Nutor et al., 2021) Sociodemographic➔Perceived susceptibility detection behaviours of preeclampsia pregnancy warning signs (Santi et al., 2023) Sociodemographic➔Perceived barrier detection behaviours of preeclampsia pregnancy warning signs (Santi et al., 2023) Sociodemographic➔Intention willingness to receive the COVID-19 vaccination (Saleh and Halperin, 2022) Etnic➔Intention intention for resting (+) (Newham et al., 2016) Education➔Intention intention for resting (+) (Newham et al., 2016) the residence of respondents➔Intention intention to use institutional delivery service (Ayana et al., 2021) antiretroviral adherence intention (Nutor et al., 2021) Primiparous➔Intention intention for resting (+) (Newham et al., 2016) Income➔Intention antiretroviral adherence intention (Nutor et al., 2021) Region➔Perceived self-efficacy HBV prevention (Nankya-Mutyoba et al., 2019b) Religion➔Perceived self-efficacy HBV prevention (Nankya-Mutyoba et al., 2019b) Wealth➔Behavior Dietary diversity (Beressa et al., 2024) Education➔behavior HIV testing (Mirkuzie et al., 2011) Open in a new tab Acknowledgements We would like to thank the Ministry of Research, Technology and Higher Education of the Republic of Indonesia [grant numbers 105/E5/PG.02.00.PL/2024; 794/LL3/AL.04/2024], Universitas Esa Unggul [grant number 019/SP-PT/LPPM/VI/2024] was supported this study and the National Research and Innovation Agency (BRIN), Indonesia, for collaborating in this research. Authors’ contributions EYM Project Administration, Funding Acquisition, Supervision, Conceptualization, Methodology, Formal Analysis, Writing-Original Draft Preparation, Review & Editing; TR, SD Conceptualization, Methodology, Data Collection, Data Curation, Software, Formal Analysis, Visualization, Writing-Original Draft Preparation; SS Funding Acquisition, Supervision, Conceptualization, Methodology, Formal Analysis, Review & Editing; AMW, AN, HS, IJ Project Administration, Supervision, Review. EYM had primary responsibility for final content. All authors read and approved the final manuscript. Funding This work was supported by the Ministry of Research, Technology and Higher Education of the Republic of Indonesia [grant numbers 105/E5/PG.02.00.PL/2024; 794/LL3/AL.04/2024]; The APC was partially funded by Universitas Esa Unggul [grant number 019/SP-PT/LPPM/VI/2024]. Data availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Declarations Ethics approval and consent to participate Since this paper did not involve human subjects and/or the use of harmful materials, a formal ethical clearance process was not required for this research. Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. Ebrahimipour S, Ebrahimipoiur H, Alibakhshian F, Mohamadzadeh M Effect of education based on the theory of planned behavior on adoption of oral health behaviors of pregnant women referred to health centers of Birjand in 2016. J Int Soc Prev Community Dent. 2016;6(6):584–89. 10.4103/2231-0762.195514. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. 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