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

Abstract

Abstract Premenstrual symptoms are common, with premenstrual syndrome and premenstrual dysphoric disorder associated with decreased wellbeing and suicidality. High-quality 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. A online survey was delivered. Structural equation modelling (SEM) explored HBM constructs. Data from 530 participants were analysed. Symptom monitoring (74.72%, n = 396) and psychoeducation (57.92%, n = 307) were sought after, with 52.64% (n = 279) indicating unwillingness to pay. Satorra Bentler-scaled fit statistics indicated a good model fit (χ2(254) = 565.91, p < .001; CFI = .939, RMSEA = .048, SRMR = .058). HBM constructs explained 58.22% of intention to use, driven by cues to action (β = .49, p < .001), perceived barriers (β=-.22, p < .001), perceived severity (β = .16, P = .012), and perceived benefits (β = .10, p = .035). Results indicate that app developers should engage in co-design, secure endorsement from healthcare professionals, highlight therapeutic benefits, and address barriers like digital discomfort, privacy concerns, and quality.