Trauma-informed mHealth intervention for improving childbirth self-efficacy following perinatal loss: a mixed-methods development and pilot validation study

Background: Pregnancy after perinatal loss may involve fear of recurrence, emotional ambivalence, and difficulty engaging with pregnancy-related information. This study developed and preliminarily evaluated a trauma-informed mobile health (mHealth) intervention to support childbirth self-efficacy among pregnant women with a history of perinatal loss. Methods: A sequential mixed-methods design comprised three phases: qualitative needs assessment, trauma-informed prototype development, and pilot quasi-experimental evaluation. The evaluation included 100 pregnant women at 10 primary health centres in Muna and West Muna Regencies, Indonesia, with 50 receiving the intervention and 50 receiving routine antenatal care. The four-week intervention included a Trauma-Filter Switch, Manual Kick Counter, Safe Space Journal, Midwife Direct Chat, and optional Moderated Peer Support. The primary outcome was change in total score on an adapted Childbirth Self-Efficacy Inventory (CBSEI). Results: Mean adapted CBSEI scores increased from 2.392 ± 1.198 to 3.631 ± 0.912 in the intervention group and from 2.291 ± 0.904 to 2.523 ± 0.901 in the routine-care group. The between-group difference in mean change was 1.007 points (independent-samples t(98)=3.25, p = 0.002 on change scores; Cohen's d = 0.65). The intervention group showed significant within-group improvement (t(49)=5.27, p < 0.001), whereas the routine-care group did not (t(49)=1.15, p = 0.256). The adapted CBSEI demonstrated excellent internal consistency (α=0.94, 95% CI 0.91–0.95 at baseline; α=0.92, 95% CI 0.89–0.94 post-intervention). Conclusions: The trauma-informed mHealth intervention was associated with substantial improvement in childbirth self-efficacy in this pilot evaluation. Findings support integrating emotional safety, choice, control, and empowerment into digital antenatal support following perinatal loss. Larger randomized trials with longer follow-up and a conventional mHealth comparator are warranted.