The Application of Machine Learning in Postpartum Low Back Pain

Abstract

Postpartum low back pain (LBP) is a common health issue that significantly impacts women’s quality of life; however, traditional rehabilitation models often provide generalized guidance, making it difficult to address the individual differences in patients’ physiological, psychological, and lifestyle factors with precision. With the development of smart healthcare, machine learning (ML) provides the technological foundation for personalized interventions. This study employs a scoping review methodology to systematically analyze the current state of ML applications in postpartum low back pain and related fields between 2015 and 2025, aiming to provide a theoretical framework for the design of future personalized postpartum rehabilitation service systems.The study found that although ML has made significant progress in precise diagnosis and personalized recommendations in adjacent fields such as non-specific low back pain, its application to postpartum LBP remains in its infancy. This paper identifies key design challenges, including multi-dimensional data integration, model interpretability, and the motivation for participation among specific populations. From a design science perspective, the study proposes that future efforts should focus on integrating multimodal data—such as physiological indicators and psychosocial factors—to construct a personalized rehabilitation service system capable of real-time monitoring and dynamic adjustment, thereby meeting the complex needs of postpartum women. This research not only distils a transferable interdisciplinary methodology but also provides directional guidance for the design of smarter postpartum health services.

Keywords: Machine Learning, Postpartum Low Back Pain, Personalized Intervention

DOI: 10.54941/ahfe1007497