Background:
Menopause is a major life transition associated with diverse physical, psychological, and social symptoms. Up to 70% of women experience musculoskeletal pain, described as the musculoskeletal syndrome of menopause (Wright et al., 2024). These symptoms are often dismissed as a normal consequence of ageing rather than recognized as a treatable condition, leading to underdiagnosis and limited management. There is a need to better understand how musculoskeletal pain in peri- and post-menopausal women is addressed, particularly through digital health interventions. Mobile health (mHealth) applications are increasingly used to provide education, symptom tracking, and self-management support for menopausal women (Sillence et al., 2025). However, despite the proliferation of menopause-related apps, evidence regarding their quality, usability, clinical validation, and effectiveness remains fragmented (Paripoorani et al., 2023).
Aims and Hypotheses:
This project will map existing evidence on mHealth applications for menopausal women, emphasizing their purpose, functionality, evaluation outcomes, and the extent to which they address musculoskeletal symptoms. It is hypothesized that few apps include assessment or management features targeting musculoskeletal pain, and that higher-quality apps are more likely to incorporate such components.
Objectives:
Identify mHealth applications currently available for menopause management.
Characterize their key features, target users, and intended outcomes.
Determine the extent to which these mHealth applications include assessment or management of musculoskeletal symptoms of menopause.
Explore the relationship between app quality and inclusion of musculoskeletal symptom assessment or management.
Methods:
A scoping review will be conducted following Joanna Briggs Institute (JBI) methodology and reported in accordance with PRISMA-ScR guidelines. Systematic searches will be performed across PubMed/MEDLINE, Cochrane Library, PsycINFO, and CINAHL, supplemented by app-store and web searches. Screening and data extraction will be completed in Covidence, and app usage assessed using the Technology Acceptance Model (TAM). Findings will be synthesized narratively and presented descriptively using frequency tables and thematic summaries.