Perinatal anxiety and depression are common, often co-morbid, and occur more frequently than outside pregnancy. Preventative measures can reduce the risk of poor mental and physical health outcomes and improve coping self-efficacy and the perception of social support. The antenatal period is an optimal time for intervention. Efficacious interventions include engaging pregnant people in cognitive-behavioral therapy and increasing the pregnant person's perception of social support -particularly partner support. Despite the evidence that partners can buffer stressors and, more generally, that perceived social support is protective, there are few interventions to reduce symptoms of anxiety or depression that involve partners. Online interventions can effectively manage mental health, increase knowledge and access to resources, and improve social connectivity. Online interventions have decreased perinatal anxiety, depression, and perception of stress, increased awareness about mental health risk factors and protective strategies, and improved coping self-efficacy successfully. Although perceived partner support is protective against poor mental health for pregnant people, partners' potential roles in online interventions is an understudied area of intervention innovation. Expanding the number of effective and equitably accessible online interventions is essential, given structural and social barriers to care.

Engaging the pregnant person and her partner as a dyad in perinatal studies complicates recruitment, retention, adherence, and longitudinally measuring change. This pilot study engaged pregnant couples experiencing elevated symptoms of maternal anxiety or depression with an existing online psychoeducation intervention, the Online Mothers and Babies Course (eMB). The study had three primary aims to assess overall feasibility; 1) explore the feasibility of delivering eMB to couples by assessing recruitment, retainment, and adherence, 2) examine eMB's preliminary efficacy for reducing PMAD symptoms, and 3) describe participants' satisfaction and perceptions about eMB acceptability.

Analytic Approach The approach follows Whitehead et al. (2016) guidance on sample size to complete a pilot feasibility trial; a sample size of 30 people for each condition was necessary. (1) The study uses univariate analysis to calculate descriptive statistics describing the sample and feasibility outcomes.

Given the study's aims, the study hypothesized that it would be feasible to deliver the program to pregnant couples and there would be a significant reduction in anxiety and depression symptoms in favor of the intervention group. A factorial Analysis of Variance (ANOVA) with repeated measures on the dyadic role and survey time was used to test the study hypotheses. The analysis used an intention-to-treat analysis approach and a significance level of α = 0.05 or less. Repeated measures were recorded three times for each participant and each outcome of interest to evaluate the effect of the eMB course. Based on the minimum Akaike Information Criterion (AIC), researchers used an optimal residual covariance structure for each outcome. When factor interactions were significant, posthoc comparisons were used. The study retained data when available and dropped cases missing at times two or three. Data missing in this study are considered missing at random and analyzed with maximum likelihood estimation. Analyses were conducted using SPSS software.

To assess the degree of program satisfaction, item responses were summed, and the average satisfaction score was calculated for the intervention group. Interview data were analyzed using an inductive thematic approach. Both authors are PhD-trained researchers with extensive qualitative experience and contributions to various health and social science studies. The analysis occurred in stages: preparing transcripts after Zoom download, becoming familiar with the data, creating memos, coding (i.e., applying labels to text segments), searching for patterns in the coding, defining and naming themes from patterns, and writing up results. Analysis was conducted in Dedoose. The study investigator completed each stage first, and a member check was conducted by the second researcher.