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Accepted for/Published in: JMIR Formative Research

Date Submitted: Jan 20, 2020
Date Accepted: Sep 1, 2020

The final, peer-reviewed published version of this preprint can be found here:

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

Green EP, Pearson N, Rajasekharan S, Rauws M, Joerin A, Kwobah E, Musyimi C, Jones RM, Bhat C, Lai Y, Mulinge A, Puffer ES

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

JMIR Form Res 2020;4(10):e17895

DOI: 10.2196/17895

PMID: 33016883

PMCID: 7573703

Expanding Access to Perinatal Depression Treatment in Kenya Through Automated Psychological Support: Stage 2 Registered Report

  • Eric P. Green; 
  • Nicholas Pearson; 
  • Sathyanath Rajasekharan; 
  • Michiel Rauws; 
  • Angela Joerin; 
  • Edith Kwobah; 
  • Christine Musyimi; 
  • Rachel M Jones; 
  • Chaya Bhat; 
  • Yihuan Lai; 
  • Antonia Mulinge; 
  • Eve S. Puffer

ABSTRACT

Background:

Depression during pregnancy and in the postpartum period is associated with a number of 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 great potential in feasibility and efficacy trials as a strategy for expanding access to treatment in low-resource settings, but there are significant barriers to scale-up. We are addressing 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:

The primary objective of this pre-pilot study was to gather preliminary data on the Healthy Moms perinatal depression intervention to learn how to build and test a more robust service. We did this through a single-case experimental design with pregnant women and new mothers recruited from public hospitals outside of Nairobi, Kenya.

Methods:

We invited 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 short message service every 3 days during the baseline and intervention periods, and we used this 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. IRRID: DERR1-10.2196/11800.

Results:

We invited 647 women to learn more about Zuri. Of those invited, 86 completed our automated SMS screening, and 41 enrolled in the study. Most of the enrolled women submitted at least 3 mood ratings (75.6%) and sent at least 1 message to Zuri (65.9%). A third of the sample engaged beyond registration (34.1%). The average woman who engaged with Zuri post-registration started and completed 3.4 (SD=3.2) and 3.1 (SD=2.9) Healthy Moms sessions, respectively. Most interviewees who had tried Zuri had a very positive attitude towards the service and expressed that they could trust Zuri. They also attributed positive life changes to the intervention. We estimated that using this alpha version of Zuri 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 pre-pilot will serve as a baseline for future studies in terms of recruitment, data collection, and outcomes. The next step in Zuri’s development is to refine the intervention content and add Swahili language support. Conversational agents like Zuri have great potential to address the large treatment gap that exists in many low-resource settings, both as a new channel of treatment and as an adjunct to traditional and task-shifting approaches.


 Citation

Please cite as:

Green EP, Pearson N, Rajasekharan S, Rauws M, Joerin A, Kwobah E, Musyimi C, Jones RM, Bhat C, Lai Y, Mulinge A, Puffer ES

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

JMIR Form Res 2020;4(10):e17895

DOI: 10.2196/17895

PMID: 33016883

PMCID: 7573703

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