Preferences for and intention to use an app for premenstrual mental health symptoms using the Health Behaviour Model (HBM)

Abstract

Premenstrual symptoms are common, with premenstrual syndrome and premenstrual dysphoric disorder associated with decreased wellbeing and increased suicidality. Apps can offer convenient support for premenstrual mental health symptoms. We aimed to understand app preferences and Health Belief Model (HBM) constructs driving app use intention. An online survey was delivered. Structural equation modelling (SEM) explored HBM constructs. Data from 530 United Kingdom based participants who reported their mental health was impacted by their menstrual cycle (mean age = 35.85, SD = 7.28) were analysed. In terms of preferred app features, results indicated that symptom monitoring (74.72%, n = 396) and psychoeducation (57.92%, n = 307) were sought after, with 52.64% (n = 279) indicating unwillingness to pay for an app for mental health symptoms related to the menstrual cycle. Regarding HBM results, Satorra–Bentler-scaled fit statistics indicated a good model fit (χ²(254) = 565.91, p < 0.001; CFI = 0.939, RMSEA = 0.048, SRMR = 0.058). HBM constructs explained 58.22% of intention to use, driven by cues to action (β = 0.49, p < 0.001), perceived barriers (β = −0.22, p < 0.001), perceived severity (β = 0.16, P = 0.012), and perceived benefits (β = 0.10, p = 0.035). Results indicate that app developers should undertake co-design, secure healthcare professional endorsement, highlight therapeutic benefits, and address barriers like digital discomfort, privacy concerns, and quality.