Evidence map›Paper›PMID 33016883›Full record

ArticleJMIR formative research2020

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

Eric P Green, Yihuan Lai, Nicholas Pearson, Sathyanath Rajasekharan, Michiel Rauws, Angela Joerin, Edith Kwobah, Christine Musyimi, Rachel M Jones, Chaya Bhat and 2 more

Open access · goldAbstract read
In one paragraph

Article in JMIR formative research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 4 pooled it
3.8field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 4 syntheses or guidelines pooled it, 44 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Trial
  7. Article
  8. Review
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2023
    Article
  19. Health Chatbots in Africa: Scoping Review.Journal of medical Internet research · 2023
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 4 institutions in 2 countries.

Eric P GreenDuke Global Health Institute, Durham, NC, United States.ORCID https://orcid.org/0000-0003-2188-2244
Yihuan LaiDuke Global Health Institute, Durham, NC, United States.ORCID https://orcid.org/0000-0001-6225-141X
Nicholas PearsonDuke Global Health Institute, Durham, NC, United States.ORCID https://orcid.org/0000-0001-7114-8297
Sathyanath RajasekharanJacaranda Health, Nairobi, Kenya.ORCID https://orcid.org/0000-0001-6089-7196
Michiel RauwsX2AI, San Francisco, CA, United States.ORCID https://orcid.org/0000-0002-5931-7702
Angela JoerinX2AI, San Francisco, CA, United States.ORCID https://orcid.org/0000-0003-3667-9636
Edith KwobahMoi Teaching and Referral Hospital, Eldoret, Kenya.ORCID https://orcid.org/0000-0002-1754-2127
Christine MusyimiAfrica Mental Health Research and Training Foundation, Nairobi, Kenya.ORCID https://orcid.org/0000-0002-8828-2959
Rachel M JonesJacaranda Health, Nairobi, Kenya.ORCID https://orcid.org/0000-0002-4004-154X
Chaya BhatDuke Global Health Institute, Durham, NC, United States.ORCID https://orcid.org/0000-0002-1394-3871
Antonia MulingeJacaranda Health, Nairobi, Kenya.ORCID https://orcid.org/0000-0003-1739-9743
Eve S PufferDuke Global Health Institute, Durham, NC, United States.ORCID https://orcid.org/0000-0003-2281-821X
Duke Institute for Health Innovation · USAfrica Mental Health Foundation · KEDuke University · USMoi University · KE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDepression 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.

objectiveThis 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.

methodsWe 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.

resultsWe 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.

conclusionsZuri 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.

Indexed as

artificial intelligencedepressionKenyamental healthmobile phonetelemedicinetext messaging

Identifiers

PMID33016883
PMCPMC7573703
OpenAlexW3091632721

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.