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

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.