Efficacy of M-Health Solution on the Paternal Postpartum Depression Affecting the Relationship of the Couple: A Pilot Study in Four Hospitals of Karachi, Pakistan

Background Postpartum depression (PPD) significantly impacts mothers and fathers, with paternal PPD (PPPD) affecting 28.3% of fathers in Pakistan. PPPD exacerbates maternal symptoms and disrupts family dynamics but remains underdiagnosed due to cultural stigmas and gender-specific presentations, such as aggression and stress. Socio-economic challenges, marital dissatisfaction, and limited support further heighten PPPD risks. Mobile health (mHealth) technologies offer accessible, stigma-free solutions for mental health support. This study explores PPPD health needs and evaluates an mHealth intervention for improving detection, support, and relationship outcomes among postpartum couples in Pakistan. Methods This exploratory sequential mixed-methods study will be conducted in two phases. In Phase 0, fathers attending postnatal visits at four gynecology clinics in Karachi will be screened using the Gotland Male Depression Scale (GMDS). Those scoring 13–26 (indicating possible depression) will complete relationship assessments (Revised Dyadic Adjustment Scale [RDAS] and Relationship Dynamics Scale [RDS]) and participate in semi-structured interviews. In Phase 1, semi-structured interviews with purposively selected fathers (5 per clinic) will explore their postpartum health needs, experiences, and relationship dynamics. Thematic analysis of interview transcripts will identify key themes and categories, guiding the development of a tailored mHealth intervention to address PPPD and improve relationship quality. In Phase 2, a new cohort of fathers will use the mHealth intervention, with depression (GMDS) and relationship health (RDAS, RDS) scores assessed at baseline and every three months over six months. App engagement and changes in scores will be monitored throughout Data Analysis Qualitative data will be analyzed using thematic analysis to identify semantic units, which will be coded and refined into categories and themes, forming the basis for the mHealth intervention. Quantitative data will be analyzed with STATA (v15.1), using descriptive statistics for categorical and continuous variables. PPPD prevalence in Karachi will be estimated. Ordinal logistic regression will examine the relationship between PPPD, income, and relationship quality (RDS, RDAS), with odds ratios and 95% confidence intervals (p ≤ 0.05). Logistic regression will assess associations between PPPD and RDAS outcomes, with subgroup analysis for RDAS subscales if needed. The impact of the mHealth intervention will be evaluated through changes in GMDS, RDAS, RDS scores, and app engagement metrics.