Designing, validation and evaluation of an mHealth app for prenatal health promotion based on the unified theory of acceptance and use of technology: A mixed methods study protocol

Design of the mixed methods study

This study employs an exploratory sequential mixed-methods design, guided by the taxonomy development model. The research begins with a qualitative phase, followed by a quantitative phase, allowing for the integration of in-depth contextual understanding with generalizable testing. This design enables a systematic exploration of users’ perspectives to inform the development, validation, and evaluation of a culturally tailored mHealth application for prenatal health promotion. The study protocol has undergone peer review as part of the funding process and received ethical approval from the Research Ethics Committee of Mashhad University of Medical Sciences, Iran.

To ensure methodological rigor, the qualitative phase adheres to the 10-step guide by Kaba et al.²⁸and the quantitative phase (a randomized controlled trial) follows the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) checklist²⁹.

In Phase I (Qualitative study), a directed content analysis using the Unified Theory of Acceptance and Use of Technology (UTAUT) will be conducted to understand pregnant women’s perceptions and experiences regarding health-promoting behaviors and mHealth app use. The findings from this phase—combined with a review of prenatal care guidelines, relevant literature, and existing apps—will inform the app’s design and ensure theoretical alignment.

In Phase II (App Design and Validation), the app will be developed using the ADDIE instructional design model. Feedback from both pregnant women and field experts will be integrated iteratively to validate the app’s content, usability, and functionality.

In Phase III (Quantitative Evaluation), the app will be evaluated via a randomized controlled trial to determine its effectiveness in improving health-promoting behaviors, as well as user acceptance and continued engagement with the mHealth intervention.

Integration of qualitative and quantitative data will occur at multiple stages—design, implementation, and interpretation—to ensure that user needs and cultural context are embedded throughout the study. A joint display will be used to connect qualitative themes with quantitative outcomes based on the UTAUT model (Fig. 2).

Phase I. Qualitative research study design

In the qualitative phase, a directed content analysis based on the UTAUT will be employed to examine pregnant women’s perceptions and experiences regarding health-promoting behaviors and the use of mHealth applications. Directed content analysis is a qualitative research method employed to validate or extend an existing theoretical framework or theory. This method is particularly effective in studies aiming to test pre-established theories or model³⁰ In Iran, mHealth technology, especially in the realm of pregnancy care, is relatively new, presenting a unique cultural and social setting for investigating pregnant women’s acceptance and usage behaviors. The adoption of technology in Iran is influenced by specific societal values and practices, necessitating a thorough understanding of these factors for effective and sustainable mHealth implementation. The novelty of mHealth in Iran presents an opportunity to evaluate the UTAUT model within this emerging context, providing insights into its cross-cultural applicability. Utilizing directed content analysis with the UTAUT framework enables a structured yet adaptable exploration, ensuring that the analysis remains theory-driven while incorporating new cultural perspectives. This approach enhances the credibility and transferability of the study, offering a comprehensive understanding of the research problem. Insights from this phase will inform the design and validation of a smartphone application tailored to the specific needs of pregnant women.

Qualitative research setting

In this study, the research setting will encompass health centers, midwifery clinics affiliated with Mashhad University of Medical Sciences, midwifery counseling clinics, and the private clinics of midwives and gynecologists. These locations will be used for identifying participants in the first phase of the study.

Sample size and its justification

In the qualitative phase, sample size is determined by the principle of data saturation, where interviews continue until no new information emerges. Based on evidence from a systematic review of empirical studies on data saturation in qualitative research, thematic saturation is commonly achieved within 12 to 20 interviews³¹. Accordingly, we anticipate conducting approximately 15 to 20 interviews with pregnant women to ensure adequate depth and breadth of thematic exploration in this study. This range is considered sufficient to achieve saturation in theory-driven qualitative research using the UTAUT framework. Saturation will be determined when no new codes, themes, or perspectives emerge from at least two consecutive interviews. The research team will regularly review transcripts and coding patterns during data collection to monitor saturation.

In addition, 12 stakeholders from various disciplines will be interviewed to gather diverse professional insights that inform app design, cultural tailoring, and implementation feasibility. This multidisciplinary input is expected to enhance the content validity and contextual relevance of the mHealth intervention.

Inclusion and exclusion criteria

Inclusion criteria for pregnant women will include:

• Age between 18 and 45 years.

• Singleton pregnancy.

• Gestational age between 12 and 28 weeks (to ensure adequate time for participation).

• Ownership of a smartphone and basic proficiency in using mobile applications.

• Minimum education level of high school diploma or equivalent.

• Ability and willingness to provide informed consent and participate in an interview conducted in Persian.

Exclusion criteria for pregnant women will include:

• Diagnosis of a high-risk pregnancy requiring specialized medical attention.

• Known cognitive or psychological disorders that may impair participation.

• Inability to provide informed consent.

Inclusion criteria for professional stakeholders will include:

• Midwives or obstetricians with at least six months of experience in maternal care within clinical or community settings (e.g., hospitals, clinics, or health centers).

• Medical informatics experts with experience in the design, development, or implementation of mHealth technologies.

• Social science researchers with qualitative research experience in digital health, health literacy, or maternal health behavior.

Sampling method

Participants will be selected through purposive sampling with maximum variation to ensure a broad and diverse representation of experiences. This approach will take into account factors such as age, socio-economic status, educational background, occupation, and pregnancy history to capture a wide array of viewpoints. After receiving approval from Mashhad University of Medical Sciences, participants will be invited to join the study based on their direct experience with mobile health applications during pregnancy.

Data collection will continue until thematic saturation is achieved, meaning that no new information or themes will emerge from additional interviews. This will ensure that the study thoroughly explores the research questions while adhering to the principles of qualitative research.

Qualitative data collection

Data will be collected through semi-structured, in-depth interviews to allow participants to freely express their experiences and views. An initial interview guide will be developed based on the constructs of the UTAUT framework, including performance expectancy, effort expectancy, social influence, and facilitating conditions.

In the context of this study, each UTAUT construct will be operationalized as follows:

• Performance Expectancy: Belief that the app will improve maternal health. Addressed through personalized education, health tracking, and motivational feedback; assessed via interviews and a validated UTAUT-based questionnaire.

• Effort Expectancy: Perceived ease of use. App features include intuitive navigation, visual icons, voice guidance, and offline access. Evaluated through user feedback on interface and accessibility.

• Social Influence: Perceived support from significant others. Operationalized through features like family-sharing, joint decision-making prompts, and partner-involved content.

• Facilitating Conditions: Perceived availability of support and infrastructure. Addressed via in-app guidance, technical support, and considerations of users’ digital access and literacy.

These constructs will guide both the qualitative exploration (Phase 1) and the app evaluation (Phase 3) to ensure the intervention is grounded in relevant adoption determinants.

The guide will include open-ended questions and prompts aligned with the UTAUT framework. It will be reviewed by experts in reproductive health, medical informatics, and qualitative research to ensure clarity and content validity. A pilot test with 2–3 pregnant women will be conducted to assess flow and comprehensibility, with final adjustments made before formal data collection.

Interviews will be audio-recorded, transcribed verbatim, and conducted in participant-convenient settings. Sessions will last approximately 30–90 min, and non-verbal cues will be documented. All interviews will be conducted by the first author, a trained midwife and qualitative researcher. Demographic data will also be collected to contextualize findings.

Recruitment will continue until data saturation, with an estimated sample size of ~ 20 participants, based on comparable qualitative studies. This approach aims to yield a rich understanding of women’s needs, expectations, and behaviors related to mHealth during pregnancy.

Qualitative data analysis

The data analysis process was grounded in the deductive approach of directed qualitative content analysis as outlined by Elo and Kyngäs (2008)³²encompassing three phases of preparation, organization, and reporting.

In the preparation phase, the unit of analysis will be entire interview transcripts. Researchers will immerse themselves in the data by reading transcripts repeatedly to gain contextual understanding and will document reflective notes on speaker roles, situational context, and potential influences.

In the organization phase, a structured categorization matrix will be developed based on the four core UTAUT constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions. Each construct will be defined operationally, informed by the UTAUT framework and relevant literature. For example, performance expectancy will refer to the perceived usefulness of the mHealth app in supporting maternal health.

A deductive coding approach will be used to assign meaning units to predefined categories. When data do not fit the initial framework, inductive open coding will be applied to generate emergent subcategories. These will be reviewed by the research team for relevance and potential expansion of the theoretical framework³³.

To ensure coding reliability, two independent researchers will initially code a sample of transcripts. Cohen’s Kappa will be used to assess inter-coder agreement, with discrepancies resolved through discussion with a senior qualitative expert. A coding manual will be developed and iteratively refined. Audit trails, memo-writing, and peer debriefings will support analytic transparency. Member checks with selected participants will be conducted to confirm interpretive accuracy.

In the reporting phase, findings will be presented under each UTAUT construct, including both confirming and disconfirming data. Relationships among categories will be discussed to explore how pregnant women perceive and engage with mHealth tools (32). The combined use of a theory-driven matrix and inductive openness enhances both theoretical rigor and contextual relevance.

Trustworthiness

The reliability of the research will be ensured by adhering to the Lincoln and Guba’s (1985) four standards for rigor in qualitative studies, which includes credibility, dependability, confirmability and transferability³⁴. To enhance the credibility of the study, the researcher will completely immerse in the data and return the coded texts to the participants for their confirmation of the researcher’s understanding. Additionally, the interviews and coding will be reviewed by supervising professor and expert reviewers to further bolster data credibility. Also, the researcher’s academic coursework, several years of experience as a midwife, and practical experience in providing care to pregnant women will contribute to increased theoretical sensitivity and research credibility.

Dependability refers to the stability and reliability of the findings over time and under different conditions³⁵. In this study, an external auditor will be asked to examine discrepancies and similarities of findings with the researcher’s interpretation. Efforts will be made to avoid prolonged data collection period as much as possible, and all participants will be asked about a specific topic to ensure consistency.

Confirmability indicates the extent to which the data are linked to the sources and the interpretations drawn³⁵. To achieve this criterion, a comprehensive description of the research processes, including data collection, analysis, and theme development, will be provided to allow for auditability by readers. Furthermore, the text of some interviews, along with extracted codes and categories, will be made available so that external reviewers and non-participating individuals can assess the confirmability of the data.

Transferability reflects the extent to which the findings derived from the data can be applied to other settings or groups³². In this study, the researcher will provide a detailed description of the demographic characteristics and experiences of the participants and the research setting. The results will also be reported to individuals not involved in the study to allow them to judge the similarity between the research findings and their own experiences.