Expanding Access to Perinatal Depression Treatment in Kenya Through Automated Psychological Support: Development and Usability Study

Abstract

Background: Depression during pregnancy and in the postpartum period is associated with poor outcomes for women and their children. Although effective interventions exist for common mental disorders that occur during pregnancy and the postpartum period, most cases in low- and middle-income countries go untreated because of a lack of trained professionals. Task-sharing models such as the Thinking Healthy Program have shown potential in feasibility and efficacy trials as a strategy for expanding access to treatment in low-resource settings; however, there are significant barriers to scale-up. We address this gap by adapting Thinking Healthy for automated delivery via a mobile phone. This new intervention, Healthy Moms, uses an existing artificial intelligence system called Tess (Zuri in Kenya) to drive conversations with users.

Objective: This prepilot study aims to gather preliminary data on the Healthy Moms perinatal depression intervention to learn how to build and test a more robust service.

Methods: We conducted a single-case experimental design with pregnant women and new mothers recruited from public hospitals outside of Nairobi, Kenya. We invited these women to complete a brief, automated screening delivered via text messages to determine their eligibility. Enrolled participants were randomized to a 1- or 2-week baseline period and then invited to begin using Zuri. We prompted participants to rate their mood via SMS text messaging every 3 days during the baseline and intervention periods, and we used these preliminary repeated measures data to fit a linear mixed-effects model of response to treatment. We also reviewed system logs and conducted in-depth interviews with participants to study engagement with the intervention, feasibility, and acceptability.

Results: We invited 647 women to learn more about Zuri: 86 completed our automated SMS screening and 41 enrolled in the study. Most of the enrolled women submitted at least 3 mood ratings (31/41, 76%) and sent at least 1 message to Zuri (27/41, 66%). A third of the sample engaged beyond registration (14/41, 34%). On average, women who engaged post registration started 3.4 (SD 3.2) Healthy Moms sessions and completed 3.1 (SD 2.9) of the sessions they started. Most interviewees who tried Zuri reported having a positive attitude toward the service and expressed trust in Zuri. They also attributed positive life changes to the intervention. We estimated that using this alpha version of Zuri may have led to a 7% improvement in mood.

Conclusions: Zuri is feasible to deliver via SMS and was acceptable to this sample of pregnant women and new mothers. The results of this prepilot study will serve as a baseline for future studies in terms of recruitment, data collection, and outcomes.

International registered report identifier (irrid): RR2-10.2196/11800.

Keywords: Kenya; artificial intelligence; depression; mental health; mobile phone; telemedicine; text messaging.

Figure 1

Study flow diagram.

Figure 2

Time series of 705 mood ratings among 31 participants who submitted at least three ratings.

Figure 3

Distribution of number of days engaged and number of incoming messages sent among 14 women who engaged with Zuri beyond registration.

Figure 4

Distribution of incoming messages by free chat conversation module and maternity status.

Figure 5

Engagement pattern for Participant 3. Dates shifted to maintain anonymity but pattern preserved.

Figure 6

Results of a Bayesian linear regression model of incoming messages on participants’ characteristics measured at baseline (N=40; 1 participant missing required data). The plot shows the Markov Chain Monte Carlo draws from the posterior distribution of the parameters. PHQ-9: Patient Health Questionnaire-9.

Figure 7

Time series of 432 mood ratings by participant (N=12) and period. Days engaged with Zuri indicated by vertical lines.

Figure 8

Estimates from a Bayesian linear mixed-effects model of repeated measures data on self-reported mood throughout the study period (432 observations among 12 participants). Uncertainty intervals computed from posterior Markov chain Monte Carlo draws.