Evidence map›Paper›PMID 38050083›Full record

ArticleGlobal health, science and practice2023

Using a Responsive Feedback Approach to Develop and Pilot a Counseling Chatbot to Strengthen Child Nutrition in Rural India.

Namrata Tomar, Sriya Srikrishnan, Neal Lesh, Brian DeRenzi

Abstract read
In one paragraph

Article in Global health, science and practice, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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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

4 authors.

Namrata TomarDimagi India, New Delhi, India. ntomar@dimagi.com.
Sriya SrikrishnanDimagi India, Mumbai, India.
Neal LeshDimagi USA, Boston, MA, USA.
Brian DeRenziDimagi South Africa, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionNearly half of all deaths in children in India aged younger than 5 years are attributable to undernutrition. Reinforcing the caregiver's positive behaviors through multiple channels can lead to improved child nutrition outcomes. We describe the development and piloting of a chatbot to improve nutrition outcomes for children aged 0-12 months. POSHAN DIDI CHATBOT DEVELOPMENT PROCESS: We engaged key stakeholders to understand existing interventions to improve nutrition outcomes and developed a theory of change that included an intervention that provides nutrition-related information directly to beneficiaries. A chatbot, Poshan Didi, was developed to provide individual counseling to mothers with children aged 0-12 months on age-appropriate, nutrition-related topics. The chatbot was piloted in Katni district from February 2019 to October 2019 in 2 phases to investigate the acceptability and feasibility of the chatbot (Phase 1, n=10 mothers) and to assess whether users would continue to be engaged and would engage with both the automated content as well as through free form questions (Phase 2, n=100 mothers). Quantitative and qualitative data were collected in focus group discussions with health care workers and mothers (n=4) and interviews with mothers (n=26).

resultsMothers viewed the chatbot as being a private channel to discuss topics and referred to Poshan Didi's persona as being knowledgeable. Eighty percent of users replied at least once to a chatbot-generated message. Sixty-four percent engaged beyond the standard chatbot content by discussing at least 1 issue with a nurse who responded to escalated messages.

conclusionThis work demonstrates how chatbots could create multiple interaction touchpoints between caregivers and health care workers to increase caregivers' access to age-appropriate nutrition counseling and information. The study shows the value of using the responsive feedback approach in the chatbot design and implementation to improve the efficacy of the digital tool.

Indexed as

CounselingMalnutritionChildFeedbackFemaleHumansMothersNutritional Status

Identifiers

PMID38050083
PMCPMC10727465

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.