Understanding pregnant women’s intention to use mobile health apps and its determinants: applying the UTAUT model in a mixed-methods study

Abstract

Background

Prenatal care is vital for ensuring healthy pregnancies, yet many women face barriers such as geographic distance, socioeconomic limitations, and lack of transportation. Mobile health (mHealth) technologies offer a promising approach to improving access to prenatal care information. However, the motivations, barriers, and behaviors related to mHealth app use, particularly within diverse cultural and sociodemographic contexts, remain underexplored.

Objective

This study aimed to investigate Iranian pregnant women’s intention to use mobile health apps and identify its determinants using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework.

Methods

A sequential exploratory mixed-methods design was employed, comprising qualitative content analysis followed by a quantitative survey. In the qualitative phase, semi-structured interviews were conducted with 14 pregnant women and 7 healthcare professionals, guided by the UTAUT model. Directed content analysis was used to explore participants’ experiences and perceptions. In the quantitative phase, a cross-sectional survey based on the UTAUT framework was administered to 60 pregnant women. Inclusion criteria included being currently pregnant, having access to a smartphone, and using an mHealth app for prenatal care. Participants were recruited via email and social media platforms. Data were analyzed using SPSS version 29. A concurrent triangulation approach was used to integrate qualitative and quantitative findings.

Results

Qualitative findings indicated that performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC) shaped behavioral intentions to use mHealth apps. Participants appreciated features such as appointment reminders and symptom trackers, but also raised concerns regarding information accuracy and app usability. Social influences from peers and healthcare providers were especially influential. Quantitative results confirmed that PE (B = 0.47, p < .001), EE (B = 0.35, p = .009), and SI (B = 0.28, p = .049) were significant predictors of behavioral intention to use mHealth apps. FC (B = 0.23, p = .131), however, did not have a statistically significant direct effect.

Conclusion

The integration of qualitative and quantitative findings offers a comprehensive understanding of the factors influencing pregnant women’s behavioral intentions to use mHealth apps. To enhance adoption and effectiveness, mHealth app design should prioritize usability, credibility, and support mechanisms tailored to prenatal care needs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12875-025-02950-7.

Introduction

Prenatal care is essential for promoting healthy pregnancies and achieving positive birth outcomes [1]. Inadequate prenatal care is associated with severe consequences, including elevated risks of low birth weight, preterm birth, labor complications, cesarean deliveries, and adverse maternal and neonatal health outcomes [2, 3]. Delivering comprehensive and culturally sensitive prenatal care is essential for safeguarding maternal and fetal health [4]. However, consistent access to healthcare services in Iran is often hindered by factors such as geographical isolation, socioeconomic disparities, and transportation limitations—challenges that are especially pronounced in rural and underserved regions [5, 6]. In addition to infrastructural barriers, sociocultural factors such as traditional beliefs, limited digital literacy, and linguistic diversity significantly influence the adoption of mHealth technologies [7].

Against this backdrop, mHealth technologies have emerged as a promising solution to bridge gaps in prenatal care access and deliver critical information and support [8]. Research demonstrates that mHealth applications can empower pregnant women by providing accessible resources on fetal development, dietary guidance, and self-care practices [9–11]. A systematic review and meta-analysis of six trials from low- and middle-income countries, encompassing 7,886 participants, reported that pregnant women using mHealth interventions were significantly more likely (RR = 1.66, 95% CI = 1.07–2.58) to attend antenatal care (ANC) check-ups compared to non-users [12]. Despite these benefits, the motivations, barriers, and behavioral patterns influencing mHealth adoption remain underexplored [13]. Cultural and socio-demographic factors play a pivotal role in shaping pregnant women’s intentions to adopt and engage with mHealth technologies [4]. In Iran, traditional practices—such as reliance on familial advice, community-based decision-making, and gender-specific healthcare preferences—profoundly influence health-related behaviors. Cultural norms in many Iranian communities may favor traditional sources of health information and pose challenges to the acceptance of digital health tools. Concerns about privacy, autonomy, and trust in technology can also affect users’ willingness to engage with mHealth platforms [8, 14].

To ensure equitable and effective implementation of mHealth interventions, it is crucial to address both structural and sociocultural barriers. Tailoring digital solutions to local values, enhancing user trust, and improving digital literacy are key strategies for promoting mHealth adoption among pregnant women across diverse contexts.

To understand the determinants of mHealth adoption among pregnant women, this study draws on the Unified Theory of Acceptance and Use of Technology (UTAUT) developed by Venkatesh et al. (2003) [15]. UTAUT integrates elements from multiple acceptance models and identifies four key constructs that influence behavioral intention and usage behavior: PE (the perceived usefulness of the technology), EE (the perceived ease of use), SI (the perceived pressure or support from others), and FC (the availability of resources and support for use). UTAUT has been widely applied in healthcare and mHealth research, demonstrating strong predictive power in explaining technology acceptance across different populations, including patients and healthcare professionals [16, 17]. Its relevance to this study lies in its ability to systematically explore both individual factors—such as perceived usefulness and ease of use, captured through the constructs of Performance Expectancy and Effort Expectancy—and contextual influences, including cultural norms and infrastructural challenges, which are addressed through the constructs of Social Influence and Facilitating Conditions, respectively. This makes UTAUT particularly suitable for examining pregnant women’s intention to use mHealth applications in the Iranian setting. This study seeks to address the research gap surrounding mHealth applications in prenatal care by examining the behavioral intention to use such technologies among pregnant women in Iran, employing the UTAUT. The investigation focuses on identifying key factors influencing mHealth adoption in this population, including motivations, perceived ease of use, and culturally specific barriers. While cross-cultural comparisons are beyond the scope of this study, the emphasis on Iran’s unique socio-cultural landscape provides a foundation for understanding how cultural factors shape technology acceptance. These insights can inform the design of culturally appropriate mHealth interventions and guide future research into cross-cultural variations in digital health adoption.

Methods

This research employs a sequential exploratory mixed-methods design, comprising qualitative and quantitative phases to comprehensively investigate the behavioral intention and its determinants among Iranian pregnant women to use mHealth applications, guided by the UTAUT.

Qualitative phase

Directed Content Analysis Guided by the UTAUT Framework

A directed content analysis approach, grounded in the UTAUT framework, was utilized to explore participants’ experiences and perceptions regarding mHealth adoption during pregnancy. Directed content analysis is a qualitative method suited for validating or extending pre-existing theoretical models [18]. This approach is particularly valuable when testing established frameworks like UTAUT in novel contexts [15]. This study leverages directed content analysis to explore the applicability of UTAUT in prenatal mHealth adoption among Iranian women, while also capturing culturally specific insights that can enhance the framework’s relevance across diverse populations.

Theoretical background

In this directed content analysis, the UTAUT, developed by Venkatesh et al. (2003), was used as the theoretical framework. UTAUT integrates key constructs from established technology acceptance models like TAM (Technology Acceptance Model) and TPB (Theory of Planned Behavior) to predict user behavior towards technology adoption and use. UTAUT posits four core determinants of user intention to use a technology [19]. Figure 1 illustrates the model of UTAUT with all its constructs and moderating variables.

Fig. 1.

The UTAUT model

In this theory, Performance Expectancy means having belief that using the technology will enhance performance [15]. For mHealth in pregnancy, this includes the degree to which pregnant women believe that mHealth apps will be useful and can improve their health-promoting behaviors during pregnancy. Effort Expectancy denotes the perceived ease or difficulty of using the technology [15]. For mHealth apps, EE relates to refers to the perceived ease of use of mHealth apps by pregnant women. Social Influence signifies the perceived pressure from significant others to use the technology [15]. For pregnant women, SI is the extent to which pregnant women perceive that they have the support of healthcare providers, spouses, family, and friends to recommend mHealth apps to patients. Facilitating Conditions indicates the perception of having the resources and support needed to use the technology [15]. In the context of mHealth for pregnancy, FC is the degree to which pregnant women believe that the organizational infrastructure is in place to support the use of mHealth apps.

Data collection procedures

Qualitative Phase

Study Setting and Participants

The qualitative phase was conducted at healthcare centers affiliated with Mashhad University of Medical Sciences and private women’s health specialist offices in Mashhad, Iran. We employed purposive sampling with a maximum variation strategy to capture a broad range of perspectives across different ages, education levels, and pregnancy histories. To further enhance participant diversity and enrich the data, we also utilized snowball sampling. Fourteen pregnant women were initially recruited using purposive sampling with maximum variation to ensure representation across varying ages, parity, educational level, and technology usage. To enrich the dataset and capture a wide range of professional insights, seven additional stakeholders were purposively selected using snowball and expert sampling methods. These individuals included women’s health specialists, midwives, reproductive health experts, medical informatics professionals, and social scientists—each offering a distinct perspective on mHealth adoption in pregnancy. Their inclusion enabled the exploration of the phenomenon from clinical, technological, and sociocultural angles. For example, while clinicians emphasized usability and patient engagement, informatics professionals addressed system design and privacy issues, and social scientists highlighted cultural norms and digital equity. This multidisciplinary approach strengthened the qualitative analysis by enhancing conceptual depth and supporting a more comprehensive interpretation of the findings. All individuals approached consented to participate, and no dropouts occurred.

Sampling Strategy

Participant recruitment employed purposeful sampling to ensure alignment with the study’s objectives, specifically targeting pregnant women with experience using mHealth technologies for self-care. This enabled the inclusion of diverse individuals varying in age, social class, education, occupation, parity, gestational age, and digital proficiency.To further broaden sample diversity, snowball sampling was used—initial participants referred other eligible individuals. Additionally, convenience sampling was applied to recruit accessible participants who met the criteria.

Eligibility Criteria

Inclusion criteria for pregnant women were: (1) be currently pregnant, (2) own a smartphone, (3) having prior experience using mHealth apps for pregnancy-related purposes, (4) being agree to face-to-face interviews, and (5) obtaining score ≥20 (50%) on the Electronic Health Literacy Scale (eHEALS), a threshold previously used to indicate moderate to adequate digital health literacy for engaging with online health technologies [20, 21]. The eHEALS, a validated 8-item instrument, assesses individuals’ ability to locate, comprehend, and apply electronic health information, with scores ranging from 8 to 40 based on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree) [22]. A minimum threshold of ≥20 ensured sufficient digital literacy for mHealth navigation. Exclusion criteria included clinically diagnosed high-risk pregnancies requiring specialized care (e.g., due to preeclampsia, gestational diabetes, or multiple gestation), as confirmed by the participants’ medical records or healthcare providers, and complete unfamiliarity with mHealth applications. For stakeholders, inclusion criteria comprised: (1) at least five years of experience in maternal or reproductive health (for healthcare professionals), (2) direct involvement in supporting pregnant women’s mHealth use, and (3) willingness to participate in interviews. Exclusion criteria were: (1) no experience in maternal/reproductive healthcare, (2) lack of prior mHealth engagement, or (3) unwillingness to participate.

Interview Procedures

A semi-structured interview guide, rooted in the UTAUT model [15], was developed to probe the five core constructs: PE, EE, SI, FC, and Behavioral Intention. The core guide was tailored for pregnant women, while a slightly adapted version—with professionally oriented wording—was used for stakeholder interviews to ensure contextual relevance. Questions were informed by prior qualitative studies on UTAUT applications in healthcare and mHealth adoption. To validate and refine the guide, a pilot test was conducted with one pregnant woman and one healthcare professional, with adjustments made based on feedback to enhance clarity and alignment with study aims. The final guide is provided in English as Supplementary File S1.Interviews were conducted face-to-face by the first author, a female PhD student in Reproductive Health with extensive training in qualitative methods, particularly in maternal health and digital technologies. Sessions occurred in private settings, lasting 45–60 minutes, with no additional individuals present. The researcher’s expertise includes participation in workshops on interviewing pregnant women in sensitive health contexts. To maintain transparency, potential bias stemming from her reproductive health background and interest in advancing mHealth was disclosed to participants at the outset. Participants were informed of her professional qualifications and the study’s goal—to explore their mHealth experiences during pregnancy and improve technology access. No prior relationships existed with participants, though rapport-building preceded each interview to foster comfort. With written informed consent, interviews were audio-recorded and transcribed verbatim. Data saturation was achieved after 12 interviews with pregnant women, when no new themes or insights emerged; this was further confirmed by the consistency of responses in the final two interviews.

Data Analysis Procedures

Data were analyzed using directed content analysis, following Elo and Kyngäs’s (2008) three-stage process: preparation, organization, and reporting [19]. In the preparation stage, interview transcripts (in Farsi, translated to English for analysis) were reviewed to identify units of meaning aligned with UTAUT constructs, focusing on factors influencing mHealth adoption rather than unrelated events. The organization stage involved coding data into a categorization matrix based on predefined UTAUT categories (e.g., PE), supplemented by emergent themes (e.g., cultural barriers). Two researchers independently coded the transcripts, achieving consensus through discussion; no surveys were included in this phase (prior mention was an error). The reporting stage linked findings to UTAUT hypotheses, assessing their relevance in the Iranian prenatal context.

Trustworthiness

This study adhered to Lincoln and Guba's (1985) four criteria of rigor in qualitative research: credibility, dependability, confirmability, and transferability [23]. To enhance credibility, multiple strategies were employed, including prolonged engagement with the data, participant validation (member checking), and expert peer review. Specifically, member checking was conducted with three participants (two pregnant women and one provider), who were invited to review summarized themes and preliminary interpretations extracted from their interviews. Their feedback helped refine category labels and confirm the accuracy of researchers’ interpretations.Dependability was enhanced through the involvement of an external auditor, a qualitative research expert affiliated with Mashhad University of Medical Sciences, Mashhad, Iran. The auditor reviewed a sample of coded transcripts (n=4), along with the initial coding framework and thematic structure developed by the primary research team. Although the auditor was not blinded to the researchers’ preliminary codes, their role was to critically appraise consistency, clarity, and alignment with the study’s objectives. Based on the auditor’s feedback, several theme definitions were refined, and two overlapping subcategories were merged to improve coherence and reduce redundancy in the coding schema.Confirmability was supported by sharing anonymized transcripts and coded excerpts with a peer researcher (a PhD candidate in health informatics), whose feedback was incorporated into the refinement of codes and categories.To enhance transferability, the research provided a rich description of participant demographics, contexts, and variations in experience. Additionally, preliminary findings were presented to five non-participant pregnant women at a local clinic, who confirmed that the themes resonated with their own experiences of using mHealth during pregnancy.

Quantitative phase

This study utilized a cross-sectional survey design to assess the behavioral intention of pregnant women to adopt mHealth applications, guided by the UTAUT model.

A power analysis was conducted using G*Power software [24] to determine the required sample size based on a medium effect size (f² = 0.15), consistent with prior UTAUT studies in healthcare technology adoption [24, 25]. With a statistical power of 0.80 and an alpha level of 0.05, a minimum of 53 participants was needed to detect significant relationships between UTAUT constructs and behavioral intention. To account for potential attrition, the target sample size was increased to 60 pregnant women. Although a larger sample would enhance generalizability, the nascent adoption of mHealth apps in Iran constrained the pool of eligible participants.

Eligibility criteria

Participants were included if they: (1) were currently pregnant (2), owned a smartphone (3), had experience using mHealth apps for prenatal care (4), were aged ≥ 18 years, and (5) scored ≥ 20 on the Electronic Health Literacy Scale (eHEALS) to ensure adequate digital health literacy. Individuals who participated in the qualitative phase were excluded from the quantitative phase to avoid bias. Incomplete questionnaires were also excluded to uphold data integrity and reliability.

Sampling method

Participants in the quantitative phase were recruited using convenience sampling from prenatal care clinics affiliated with Mashhad University of Medical Sciences and private women’s health centers in Mashhad, Iran. This method was selected to facilitate timely recruitment given the limited number of pregnant women with prior experience using mHealth applications in this setting. Convenience sampling enabled efficient access to eligible participants while aligning with the study’s objectives to assess behavioral intention to use mHealth during pregnancy.

Recruitment and consent procedures

Participants were recruited via an email-based survey disseminated through targeted emails and private messages on popular social media platforms among Iranian pregnant women, including Instagram, Telegram, and WhatsApp. Recruitment messages, written in Farsi, were posted within pregnancy-related groups and channels to reach the intended population. The recruitment notice clearly outlined the study’s purpose, voluntary participation, estimated survey completion time (10–15 min), and assurance of confidentiality. An example message stated:

“We are conducting research to understand how pregnant women in Iran use mobile health apps during pregnancy. Your input will enhance mHealth tools and prenatal care access. The survey takes approximately 10–15 minutes, with all responses kept confidential. Participation is voluntary. Click below to join.”

No incentives were offered. Interested participants accessed a secure webpage containing detailed study information, including objectives, significance, and procedures. Electronic informed consent was required prior to survey access, ensuring participants understood and agreed to participate voluntarily. Confidentiality was maintained throughout the process.

Survey design and data collection

Data were collected from March 20, 2024, to July 21, 2024, using a secure, encrypted online platform designed to protect participant privacy. The survey spanned eight pages for readability and engagement, with 5–7 items per page addressing distinct constructs of the UTAUT model.

The survey consisted of two main sections:

• Demographic Questionnaire: Collected information on participants’ age, education level, employment status, and gestational age.

• UTAUT Model Questionnaire: Included 20 items across five constructs — PE (4 items), EE (4 items), SI (4 items), FC (4 items), and Behavioral Intention (4 items) — adapted to the Iranian mHealth context and translated into Farsi. Each item employed a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree).

Mandatory response fields ensured completeness, and a “Back” button allowed participants to review and revise their answers before final submission. Unique personalized links prevented duplicate responses, which were deactivated upon submission to protect privacy without relying on cookies or IP tracking.

The Persian UTAUT questionnaire, validated by Mohammadian et al. (2021), demonstrated construct and content validity through expert review and pilot testing, with a Cronbach’s alpha of 0.86 indicating high reliability [26]. For this study, five faculty members in Reproductive Health and Medical Informatics reviewed the adapted questionnaire to confirm its alignment with research objectives. The survey adhered to the CHERRIES guidelines [27] for transparent reporting of internet-based surveys.

Data security & ethical considerations

Data were stored in a password-protected database, accessible only to authorized researchers, with regular backups to prevent loss. Ethical standards ensured participant confidentiality and regulatory compliance, with electronic consent detailing study aims, data use, and privacy protections.

Data analysis procedures

Analyses were performed using SPSS version 29. Descriptive statistics summarized demographic characteristics and UTAUT construct responses. Multiple linear regression examined the influence of PE, EE, SI, and FC (independent variables) on Behavioral Intention (dependent variable). All independent variables were entered simultaneously using a forced-entry approach to test the full UTAUT model. No covariates were included, as the focus was on direct UTAUT relationships in this exploratory context, rather than controlling for external factors. This approach was chosen over simpler bivariate analyses (e.g., correlation) to assess the combined predictive power of UTAUT constructs, consistent with the model’s theoretical framework.

Integration of qualitative and quantitative data

Following the separate analysis of qualitative and quantitative data, a triangulated integration was conducted using a convergent comparison approach [28]. This involved systematically comparing thematic codes derived from interviews with statistical patterns observed in the survey data. A joint display matrix was constructed to align qualitative themes with corresponding UTAUT constructs and quantitative findings, enabling the identification of converging (confirmatory), complementary (enriching), or divergent (contradictory) insights. This integrative process allowed for a comprehensive and multi-dimensional understanding of pregnant women’s acceptance and utilization of mHealth applications for prenatal care, ensuring that both contextual narratives and numerical data obtained through measurable behavioral patterns informed the study’s interpretations

Results

This mixed methods study aimed to investigate the factors influencing pregnant women’s acceptance and utilization of mobile health apps for prenatal care. To achieve a comprehensive understanding, the study employed a two-phase approach. The first phase utilized a qualitative directed content analysis of semi-structured interviews with pregnant women and healthcare professionals to explore the determinants of behavioral intention to use of mobile health apps among Iranian pregnant women adopting the Unified Theory of Acceptance and Use of Technology. The second phase employed a quantitative survey based on the UTAUT model, administered to pregnant women with experience using mHealth apps, to assess the factors influencing their acceptance and continued use of these technologies.

Qualitative findings

Demographics of participants

A total of 14 pregnant woman and seven specialists were interviewed. The mean age of the pregnant women was 29.5 years (SD = 6.1), with ages ranging from 18 to 40 years. The majority had at least a bachelor’s degree, The gestational age of participants ranged from 18 to 33 weeks, with a mean of 24.7 weeks (SD = 5.3). eHEALS scores ranged from 21 to 38 (out of a maximum of 40), with a mean score of 30.1 (SD = 4.6), indicating moderate to high levels of perceived digital health literacy. The average interview duration was 60 min (SD = 28.3), conducted in diverse locations, including homes, clinics, and public places (Table 1).

Table 1.

Characteristics of pregnant women participants in the qualitative phase

Participant Code	Age Range (Years)	Education Level	Marriage Duration (Years)	Pregnancy History	Gestational Age (Weeks)	Occupation Category	Use of mHealth Apps	Interview Duration (Minutes)
PC1	25–29	Bachelor’s degree	8	Multigravida	25	Homemaker	Yes	45
PC2	30–34	Bachelor’s degree	9	Primigravida	27	Private sector employee	Yes	90
PC3	30–34	Master’s degree	6	Multigravida	19	Homemaker	Yes	110
PC4	30–34	Bachelor’s degree	12	Multigravida	26	Homemaker	Yes	70
PC5	18–24	Bachelor’s degree	3	Primigravida	18	Healthcare worker	Yes	40
PC6	35–39	High school diploma	10	Multigravida	30	Private sector employee	No	100
PC7	30–34	High school diploma	12	Multigravida	20	Homemaker	No	40
PC8	18–24	Associate degree	3	Multigravida	26	Homemaker	No	35
PC9	18–24	High school diploma	5	Multigravida	29	Private sector employee	Yes	40
PC10	40+	Middle school	4	Multigravida	33	Private sector employee	No	30
PC11	30–34	Bachelor’s degree	6	Multigravida	20	Public sector employee	Yes	40
PC12	18–24	High school diploma	1	Primigravida	19	Homemaker	Yes	35
PC13	25–29	Associate degree	8	Multigravida	31	Homemaker	No	60
PC14	25–29	Bachelor’s degree	1	Primigravida	21	Private sector employee	No	45

Age, education, occupation, and pregnancy history have been generalized to protect participant anonymity

Pregnancy History: “Primigravida” first pregnancy; “Multigravida” previous pregnancies

Occupation Categories: “Private sector employee” includes retail, clerical, and other non-government jobs; “Healthcare worker” includes clinical staff; “Public sector employee” includes educators and administrative staff, generalized into categories such as clinic, university, hospital, or home

The expert professionals ranged in age from 26 to 52 years, with a mean age of 39.1 years (SD = 8.9). Their experience in their respective fields ranged from 2 to 30 years. The average interview duration was 53.1 min (SD = 14.1), conducted in various professional settings, including clinics, hospitals, and universities. One interview (PC17) was conducted in the participant’s own home, who voluntarily selected it as a comfortable and convenient setting. The interviewer ensured that the environment was private and suitable for audio recording. All ethical protocols—including written informed consent, confidentiality, and the right to withdraw at any time—were rigorously followed (Table 2).

Table 2.

Demographics of expert professionals

Participant Code	Age Range (Years)	Highest Education Level	Years of Experience	Professional Role	Interview Duration (Minutes)	Interview Location (Generalized)
PC15	40–44	PhD candidate	15	Midwifery services manager	41	Clinic
PC16	40–44	Medical doctorate (OB/GYN)	12	OB/GYN, private/hospital practice	35	Clinic
PC17	50+	Bachelor’s degree	30	Retired midwife, private practice	60	Participant’s Home
PC18	25–29	Bachelor’s degree	2	Midwife in public health center	70	Health center
PC19	35–39	Medical doctorate (OB/GYN)	7	OB/GYN, private/hospital practice	45	Hospital
PC20	50+	PhD	21	University faculty (social science)	75	University
PC21	35–39	PhD	4	University faculty (informatics)	50	Clinical research center

Age, education, profession, and interview locations have been generalized to preserve participant confidentiality

Professional Role: Aggregated where necessary to avoid specific identifiability (e.g., field and function only)

Interview locations generalized into categories such as clinic, university, hospital, or home

Emergent categories

This section synthesizes key themes from semi-structured interviews with 14 pregnant women and 7 healthcare providers, examining their perspectives on mHealth app use in prenatal care through the UTAUT framework. The qualitative data were analyzed using directed content analysis, yielding categories aligned with UTAUT constructs: PE, EE, SI, and FC. Table 3 presents representative quotes, corresponding codes, and emergent categories, providing a structured overview of the findings detailed below.

Table 3.

Qualitative analysis of mHealth app utilization: categories and subcategories within the UTAUT framework

Subcategory	Category	UTAUT Construct
Practical Utility	Perceived Benefits	Performance Expectancy
Remote Accessibility
Provision of Innovative and Timely Training	Education and Resources
Promotion of Interactive Learning Experiences
User-Friendly Interfaces	Perceived Ease of Use	Effort Expectancy
Clear Instructions and Tutorials
Overwhelming Features	Perceived Complexity
Need for Simplified Options
User Testimonials and Peer Recommendations	Recommendations from trusted sources	Social Influence
Impact of Healthcare Professionals
Fear of social comparison and unrealistic expectations	Addressing social comparison anxiety and peer pressure
Peer Pressure Concerns
Reliable internet connectivity	Resource Access	Facilitating Conditions
Sufficient smartphone storage
Essential Technical Support from Developers	Technical Assistance
Timely Issue Resolution for Enhanced User Experience
Constraints of Limited Data Plans	Barriers to Access
Requirement for Device Compatibility
Options for Offline Functionality	Accessibility Enhancements
Data-Efficient Features for Limited Internet Users

Performance expectancy

Participants expressed mixed views on the perceived usefulness of mHealth apps. Pregnant women valued practical features like appointment reminders, fetal movement tracking, and educational resources on nutrition and prenatal care, noting their potential to enhance accessibility, particularly in constrained circumstances such as the COVID-19 pandemic. One participant stated:

“Considering the circumstances with the coronavirus, I was unable to attend prenatal classes. If there is an application available that is credible and up-to-date with adequate teachings and explanations, many of the in-person cares can be provided remotely. It wouldn’t necessarily require physical attendance.” (Pc1).

Another highlighted organizational benefits:

“It reminds me of my check-ups, so I don’t miss them. It also allows me to jot down questions beforehand and feel more prepared for my doctor’s consultation.” (Pc2).

Participants emphasized the importance of receiving training that was not only relevant but also tailored to their unique experiences as pregnant women. One participant expressed:

“I really value when the training is both new and specific to my journey. It helps me feel more connected and informed.” (Pc7).

Furthermore, the need for interactive and multimedia elements in training sessions was clearly articulated by many participants. One noted:

“I love when training includes videos or hands-on activities. It makes learning so much more enjoyable and effective.” (Pc3).

Another participant highlighted the significance of informed decision-making when selecting biomedical applications:

“I really want to know what I’m getting into. Making educated choices is key to feeling secure in my pregnancy.” (Pc14).

Effort expectancy

EE refers to the perceived ease or difficulty of using mHealth apps. Participants’ experiences with app complexity varied. Some appreciated user-friendly interfaces and features that integrated seamlessly with existing health apps. Others found the app overwhelming due to excessive features or a complex sign-up process.

A participant highlights the importance of having a user-friendly and straightforward interface. Clear instructions and helpful tutorials make the app accessible to users who may not be very familiar with technology, promoting wider adoption and user satisfaction.

“The app interface was user-friendly and straightforward, even for someone not very tech-savvy. It offered clear instructions and helpful tutorials for getting started.” (PC4).

Conversely, complexity deterred others:

"It took too long to set up, making it overwhelming to navigate and find what I needed. It would be helpful to have a simplified version for first-time users.” (PC10).

These categories and subcategories highlight the crucial factors influencing EE and user interactions with mHealth applications.

Social influence

SI refers to the perceived pressure from significant others to use mHealth apps. SI played a role in some participants’ decisions. Positive experiences shared by friends or healthcare professionals encouraged exploration of mHealth apps. However, some women expressed concerns about peer pressure or unrealistic expectations associated with app usage.

A participant highlights how positive testimonials from friends can serve as a powerful motivator for exploring mHealth apps. The shared experiences provide a sense of trust and reliability, making individuals more open to trying these tools.

“Seeing my friends who shared their positive experiences using pregnancy apps for monitoring fetal movement or mood tracking encouraged me to explore these tools as well.” (PC9).

One participant emphasized the influence of medical advice, stating,

“My doctor suggested it, so I tried it."(PC8).

However, some potential negative aspects of SI are existed too. One participant noted the fear of social comparison and unrealistic expectations can deter individuals from using mHealth apps, indicating the need for addressing these concerns to foster a supportive and pressure-free environment.

"Seeing others’ updates made me feel behind when my friends were using pregnancy apps to share their experiences, but I wasn’t sure if it was right for me. I worried about the potential for social comparison and unrealistic expectations.” (PC12).

Healthcare providers reinforced this:

“Recommendations from healthcare professionals can significantly boost the credibility and adoption of mHealth apps.” (PC16).

The midwife’s perspective highlights the positive impact of peer experiences on confidence in using mHealth apps, while also emphasizing the need to reassure women about the individuality of their pregnancy journeys to reduce pressure.

“From my experience, pregnant women are more confident in using mHealth apps when they see their peers having positive outcomes. It’s important to reassure them that every pregnancy journey is different and that these tools should complement their care, not create pressure.” (PC17).

Recommendations from trusted sources like doctors or friends can positively influence mHealth app adoption. Addressing potential anxieties around social comparison and unrealistic expectations is important to promote healthy app usage.

Facilitating conditions

Participants’ responses highlight the critical role of having adequate resources and support to facilitate the effective use of mHealth apps. Access to reliable internet, sufficient storage on smartphones, and technical support are essential for optimal app usage. Barriers such as limited data plans and outdated phone models need to be addressed to ensure broader accessibility and inclusivity. For example, a participant emphasizes the importance of having reliable resources, such as internet connectivity and smartphone storage, for effective app usage. Technical support from developers also plays a crucial role in resolving issues and enhancing user experience.

“Having a reliable internet connection and a smartphone with enough storage is crucial for using the app effectively. Without good internet, it’s useless. Access to technical support from the app developer would be also helpful.” (PC16).

Also, one of the specialist highlights how limited data plans can restrict the use of essential app features, such as video consultations and educational resource downloads. Addressing data constraints is vital for ensuring that users can fully benefit from all app functionalities.

“Limited data plans can restrict app usage, especially for features like video consultations or downloading educational resources.” (PC21).

One other expert perspective underscores the need for app compatibility with a wide range of devices and operating systems. Offering offline use and data-efficient features can significantly improve accessibility, particularly for users with limited internet access.

“Ensuring app compatibility with various devices and operating systems is fundamental. Additionally, providing options for offline use and data-efficient features can enhance accessibility for users with limited internet access.” (PC20).

The perspective from a midwife emphasizes the significance of reliable technical support in facilitating continuous and effective use of mHealth apps. Prompt resolution of issues enhances user confidence and satisfaction.

“Access to reliable technical support is crucial for pregnant women using mHealth apps. It helps address any issues promptly, ensuring continuous and effective use of the app.” (PC18).

Quantitative findings

Response rate and data integrity

Of 78 distributed questionnaires, 60 were fully completed, yielding a 76.92% response rate. Eighteen incomplete responses—lacking answers to ≥ 20% of items (predominantly UTAUT constructs)—were excluded to ensure robust data quality. No time restrictions were imposed for completion.

Data description

The study sample comprised 60 pregnant women with diverse backgrounds. Based on Table 4, participants’ ages ranged from 19 to over 40 years, with the majority between 26 and 30 years and 31–35 years. Regarding education, most participants held a Bachelor’s degree, followed by those with a Diploma. In terms of employment, the majority were employees, with others being self-employed or housewives. The gestational age of participants ranged from 10 to over 40 weeks, with an average of 28 weeks. Most participants were in the 20–29 weeks range, followed by 30–39 weeks.

Table 4.

Descriptive statistics for participants’ demographics

Variable	Frequency	Percentage
Age (years)
19–25	10	16.7%
26–30	20	33.3%
31–35	20	33.3%
36–40	8	13.3%
> 40	2	3.3%
Education
Diploma	12	20%
Bachelor’s degree	30	50%
Master’s degree	12	20%
Ph.D.	6	10%
Career
Employee	35	58.3%
Self-employed	17	28.3%
Housewife	8	13.3%
Gestational Age (weeks)
10–19	12	20%
20–29	24	40%
30–39	18

This study examined factors influencing pregnant women’s behavioral intention to use mHealth applications, employing the UTAUT model. Participants’ responses to the UTAUT constructs, measured on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree) as detailed in the “Methods - Quantitative” section, indicated moderate to high agreement across key dimensions. Specifically, PE yielded a mean of 4.17 (SD = 0.44), EE a mean of 4.32 (SD = 0.33), SI a mean of 4.11 (SD = 0.61), and FC a mean of 4.07 (SD = 0.32). These scores, ranging from 1 to 5, reflect participants’ generally positive perceptions, with higher means indicating stronger agreement.

Multiple linear regression analysis, revealed significant predictors of Behavioral Intention to use mHealth apps. The model yielded an R² of 0.412, indicating that 41.2% of the variance in Behavioral Intention was explained by the predictors, with an adjusted R² of 0.351 accounting for the number of variables. The model’s statistical significance was confirmed by an F-value of 10.12 (df = 4, 55, p <.001), demonstrating that the UTAUT constructs collectively influenced Behavioral Intention.

Individual construct effects were as follows: PE showed a strong positive association with Behavioral Intention (B = 0.47, SE = 0.12, t = 3.92, p <.001), suggesting that women who perceived mHealth apps as beneficial to their prenatal care were significantly more likely to intend to use them. EE also exhibited a significant positive relationship (B = 0.35, SE = 0.13, t = 2.69, p =.009), indicating that perceived ease of use enhanced usage intentions. SI had a modest but significant effect (B = 0.28, SE = 0.14, t = 2.00, p =.049), implying that encouragement from healthcare providers, family, or peers increased intent. Conversely, FC did not significantly predict Behavioral Intention (B = 0.23, SE = 0.15, t = 1.53, p =.131), suggesting that resource availability (e.g., internet access, technical support) was less critical in this sample. These findings are detailed in Table 5.

Table 5.

Relationships of UTAUT model constructs with behavioral intention

Variable	Coefficient (B)	Standard Error (SE)	t-statistic	p-value	Significance
PE	0.47	0.12	3.92	< 0.001	Significant
EE	0.35	0.13	2.69	0.009	Significant
SI	0.28	0.14	2.00	0.049	Significant
FC	0.23	0.15	1.53	0.131	Not Significant

Discussion

This mixed-methods study elucidates the factors influencing pregnant women’s behavioral intention to adopt mHealth applications for prenatal care in Iran, employing the UTAUT framework. By integrating qualitative interviews and quantitative survey data, the findings reveal how PE, EE, SI, and FC interact with Iran’s structural and cultural context, offering insights that extend beyond generic technology acceptance models.

The strong influence of PE aligns with global UTAUT studies in Western settings [15, 29], but takes on distinct significance in Iran. Qualitative data highlighted valued features like symptom tracking and appointment reminders, reflecting a need for accessible prenatal resources amid Iran’s overburdened healthcare system, where rural women often face limited clinic access However, concerns about information accuracy (e.g., “Can I trust this app?”) resonate with Iran’s cultural emphasis on authoritative medical knowledge, rooted in a hierarchical healthcare tradition [7, 30]. Studies like Rahimi et al. (2018) on Iranian mHealth adoption note similar trust issues, attributing them to low health literacy and skepticism toward unregulated digital tools [31]. This suggests that while PE drives adoption, cultural expectations of credibility—shaped by Iran’s reliance on physician-led care—necessitate apps vetted by local health authorities to enhance uptake.

EE’s significant role underscores usability’s importance, consistent with UTAUT research in developing contexts like Hoque and Sorwar (2017) in Bangladesh [32]. the significance of EE can be linked to the principle of cognitive load theory, developed by Sweller (1988), posits that the human brain has a limited capacity for processing information [33]. Therefore, individuals are more likely to adopt technologies that minimize cognitive effort and are easy to use [34, 35]. In the context of mHealth apps, a user-friendly interface that simplifies navigation and reduces complexity is likely to be more appealing to pregnant women, who may already be managing multiple stressors and responsibilities. In designing mHealth apps, developers should aim to minimize extraneous load by presenting information clearly and concisely and optimizing the user interface to enhance usability. Culturally, the preference for simplicity may tie to traditional reliance on oral health advice from family, reducing tolerance for cognitive load—a dynamic less pronounced in Western UTAUT studies [15]. Developers must thus prioritize intuitive designs tailored to Iran’s variable digital skills.

Participants cited peer experiences and healthcare provider endorsements as motivators, consistent with the quantitative link between SI (M = 4.11, SD = 0.61) and Behavioral Intention (B = 0.28, p =.049). This reflects the Theory of Reasoned Action, where subjective norms shape intentions [36]. From a sociological perspective, the findings highlight the importance of social networks and community influence in shaping health behaviors. From a sociological perspective, the findings highlight the importance of social networks and community influence in shaping health behaviors. Positive peer experiences and endorsements from healthcare professionals serve as powerful motivators for adopting mHealth apps. Our findings align with those of Alam, et al. (2020), who found that recommendations from healthcare professionals and positive peer experiences significantly impact mHealth app usage intentions [37]. healthcare professionals’ attitudes towards technology, including their perceptions of usefulness, ease of use, and barriers, can significantly impact patient compliance [38], their own intentions to adopt digital health practices [39], and how they integrate technology into healthcare education and delivery [40]. Social networks, as defined by Wasserman and Faust (1994), are structures composed of individuals or organizations connected by one or more specific types of interdependency [41].

However, anxieties about social comparison (e.g., “I feel behind others”) align with Lupton’s (2016) digital health critiques [42] and specific studies, which note social media’s role in heightening maternal stress [43]. This dual nature of SI—promoting adoption yet fostering pressure—suggests culturally tailored interventions, such as community-based app promotion, to leverage Iran’s social networks while mitigating comparison risks.

The non-significant quantitative link between FC and Behavioral Intention contrasts with qualitative emphasis on resources like internet access. Research indicates significant disparities in broadband internet access between urban and rural areas across various countries. Urban centers generally enjoy better infrastructure and connectivity, while rural regions struggle with unreliable and limited access [44, 45]. This discrepancy, also noted in Rahimi et al. (2018), reflects Iran’s infrastructural disparities: urban areas enjoy 80% internet coverage, while rural regions struggle with unreliable connectivity [31]. Culturally, the assumption of FC availability among urban participants may mask its critical role for rural women, a pattern less evident in high-infrastructure settings [54]. Sen’s capability approach [46] frames FC as essential for enabling health agency, suggesting that Iran’s structural gaps—exacerbated by economic sanctions—limit mHealth’s reach [47]. Future tools must address these inequities, perhaps via offline functionalities, to align with Iran’s realities.

The study focused on UTAUT constructs rather than explicitly measuring cultural values, given the exploratory nature of mHealth adoption in Iran. While qualitative data noted cultural influences (e.g., familial decision-making), PE, EE, and SI emerged as stronger predictors of Behavioral Intention in quantitative analyses, suggesting these factors may outweigh cultural barriers in this sample. However, this finding is preliminary and context-specific, requiring further research to directly compare cultural influences against UTAUT constructs, as cultural norms may vary across Iranian subpopulations.

These results suggest that emphasizing PE and EE, alongside leveraging SI, could effectively promote mHealth app adoption among pregnant women. While FC may play a role in broader contexts, their non-significant effect here indicates they were not a primary driver of usage intentions in this Iranian sample.

Integration and interpretation of findings

This mixed methods study explored factors influencing pregnant women’s behavioral intention to use of mHealth apps for prenatal care. The qualitative data, consisting of interviews with pregnant women and healthcare professionals, provided rich insights into user experiences and perspectives related to the UTAUT model constructs. In the following, a breakdown of how the qualitative and quantitative findings complement and strengthen each other, highlighting areas of convergence and divergence, is presented:

• PE: Qualitative findings underscored the perceived benefits of mHealth apps, with participants citing features like appointment reminders and symptom tracking as key motivators (e.g., “It reminds me of my check-ups, so I don’t miss them”). This aligns with the quantitative results, where PE (M = 4.17, SD = 0.44) strongly predicted behavioral intention (B = 0.47, p <.001). However, qualitative data also revealed concerns about information accuracy and overreliance (e.g., “I worry if the app’s advice is correct”), suggesting developers should enhance content credibility and position apps as supplements to professional care.

• EE: Both datasets emphasized the importance of usability. Qualitative interviews highlighted frustrations with complex features or lengthy sign-up processes (e.g., “It took too long to set up”), corroborating the quantitative finding that EE (M = 4.32, SD = 0.33) significantly influenced behavioral intention (B = 0.35, p =.009). This convergence underscores the need for streamlined, user-friendly interfaces to bolster app acceptance.

• SI: The role of social influence emerged consistently across methods. Qualitative data illustrated how positive peer experiences and healthcare provider endorsements encouraged app use (e.g., “My doctor suggested it, so I tried it”), aligning with the quantitative result that SI (M = 4.11, SD = 0.61) positively affected behavioral intention (B = 0.28, p =.049). However, qualitative insights also noted potential drawbacks, such as anxieties from social comparison (e.g., “Seeing others’ updates made me feel behind”), indicating a need for interventions promoting balanced app use alongside social encouragement.

• FC: Qualitative findings emphasized the critical role of resources like smartphone access, internet connectivity, and technical support (e.g., “Without good internet, it’s useless”), yet the quantitative analysis found no significant link between FC (M = 4.07, SD = 0.32) and behavioral intention (B = 0.23, p =.131). This divergence suggests that while structural support is valued, it may not directly drive usage intentions in this context, possibly due to participants’ baseline access to technology.

By weaving together these qualitative and quantitative strands, this study provides a robust, multi-faceted understanding of how UTAUT constructs shape pregnant women’s acceptance and sustained use of mHealth apps for prenatal care in Iran, highlighting both drivers of adoption and areas for refinement.

Strengths and limitations

The study’s strength lies in its mixed-methods design, which allows for triangulation, enriching the data interpretation by combining rich qualitative insights with quantitative rigor. This design not only enhances the reliability of the findings but also provides a deeper understanding of mHealth adoption in Iran, as emphasized by Creswell and Plano Clark (2017) [48]. Furthermore, by utilizing a culturally relevant UTAUT framework, we addressed specific cultural nuances that influence technology acceptance, thereby strengthening our conclusions and suggesting tailored strategies for mHealth implementation that resonate with local values and practices, as highlighted by Venkatesh et al. (2003) [15]. Also, this research situates mHealth adoption within Iran’s unique constraints, advancing knowledge in the field of digital health by contributing valuable insights into the challenges faced in a diverse cultural context. This contextualization allows future researchers to draw parallels and contrasts with other settings, enhancing the understanding of cultural influences on technology adoption, a point noted by Masimba et al. (2019) [49].

However, we are aware of the limitations of our study. Th cross-sectional design limits the ability to infer causal relationships or assess longitudinal trends. Also, small sample size restricts statistical power and generalizability, particularly given Iran’s diverse ethnic and geographic profile. The reliance on self-reports presents a risk of social desirability bias, particularly in a cultural context that values conformity. Moreover, our dominant urban focus overlooks the structural challenges present in rural areas. Additionally, the lack of direct measurement of cultural variables restricts our conclusions regarding their relative influence, which remains a gap not fully addressed by merely applying a Western-derived model like UTAUT. These shortcomings highlight the need for critical reflection on the scope and assumptions of the study, urging caution in interpreting results as broadly representative.

To address the limitations identified in our study, future research should utilize larger, stratified samples to better represent Iran’s diverse population, as recommended by Etikan et al. (2016) [50]. Incorporating multiple data collection methods, such as direct observations, can mitigate social desirability bias in self-reported data [51]. Additionally, employing longitudinal designs will allow researchers to track trends and establish causal relationships over time [52].

Conclusion

This mixed methods study provided valuable insights into the factors influencing pregnant women’s intension to use of mHealth apps for prenatal care. By integrating qualitative and quantitative findings, we highlighted the importance of PE, EE, SI, and FC in promoting mHealth app adoption. It is noteworthy that the integration and synthesis of the provided research findings have shed light on the importance of developing user-friendly, accessible, and socially influential mHealth apps that enhance communication, education, and self-management during pregnancy. Addressing data limitations and promoting inclusion are key factors that need to be considered to ensure the widespread adoption and effectiveness of these apps. Future research should focus on addressing knowledge gaps and developing regulatory frameworks to guide the development and implementation of mHealth apps for pregnancy.

Abbreviations

ANC

Antenatal care

EE

Effort expectancy

FC

Facilitating conditions

mHealth

Mobile health

PE

Performance expectancy

SI

Social influence

TAM

Technology acceptance model

TPB

Theory of planned behavior

TRA

Theory of reasoned action

UTAUT

Unified theory of acceptance and use of technology

Authors’ contributions

FA and RLR contributed substantially in the conception and design of the study. FA carried out the data collection. FA and SEZ analyzed the qualitative data. FA and SEZ analyzed the quantitative data. RLR supervised both qualitative and quantitative data analysis, interpretation and integration. FA and RLR drafted the manuscript. RLR and SEZ reviewed the manuscript critically for important intellectual content. All authors read and approved the final manuscript and agreed to be accountable for all aspects of the work.

Funding

This study is part of the first author’s (FA) doctoral thesis in Reproductive Health, funded by Vice Dean for Research, Mashhad University of Medical Sciences, Mashhad, Iran (Grant number: 4000201).

Data availability

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Declarations

Ethics approval and consent to participate

The study protocol was approved by the Research Ethics Committee of Mashhad University of Medical Sciences, Mashhad, Iran (IR.MUMS.REC.1400.179), in accordance with the ethical standards outlined in the Declaration of Helsinki. Written informed consent was obtained from all participants prior to their involvement in the study. Participation was voluntary, and participants were informed of their right to withdraw at any time without any consequences. Confidentiality and anonymity were rigorously protected by removing all personal identifiers and anonymizing the data. All data were stored securely using encrypted systems, with access limited to the research team.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.