PregnancyNCT07883993
Comparing AI and Standard Pregnancy Chatbots
A study testing Scripted maternal health chatbot and MATRI retrieval-augmented maternal health chatbot in people with Pregnancy and Prenatal Care, run by Nivi, Inc. at 1 site in India.
Trial summary
Many pregnant women in India use mobile phones to get health information. Nivi runs a free WhatsApp service called askNivi that answers questions about pregnancy and sends reminders about prenatal care visits. The current version of askNivi replies using a set of prepared answers. Read more about this study
- Who is it for?
- Ages 18 years and older, women onlyHealthy volunteers can take part
- Conditions the study is for
- Pregnancy, Prenatal Care, Maternal Health
- What is being tested?
- Scripted maternal health chatbot and MATRI retrieval-augmented maternal health chatbot
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 2,500 participants
- Where
- 1 siteIndia
- Study dates
- 30 Oct 2026to 31 Aug 2028
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Women only
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Age 18 years or older
- Currently pregnant with gestational age between 10 and 20 weeks, by self report
- Located in India
- Self-reported female gender
- Regular access to a smartphone with WhatsApp
- Able to communicate in English, Hindi, or Assamese
- New user of the askNivi chatbot, with study enrollment occurring during onboarding
- Consents to participate and completes the baseline survey
Who cannot join
- Age under 18 years
- Not currently pregnant
- Gestational age under 10 weeks or over 20 weeks at enrollment
- Located outside India
- Self-reported male gender or gender diverse identity, as the chatbot is designed specifically for women
- No regular access to a smartphone, or unable to use WhatsApp
Show all 10Show fewer
- Unable to communicate in Hindi, English, or Assamese
- Self-reported high-risk pregnancy requiring specialized care beyond the scope of the chatbot
- Previous user of the askNivi chatbot
- Healthcare provider or researcher involved in maternal health
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
India1 site
AskNivi Private Limited
Kalyān, Maharashtra, 421201
Study Coordinator, 240-481-7476, ben@nivi.io
About this study
Official title: Evaluating the Impact of Generative AI on Pregnancy Chatbot User Engagement and Health Behaviors
Many pregnant women in India use mobile phones to get health information. Nivi runs a free WhatsApp service called askNivi that answers questions about pregnancy and sends reminders about prenatal care visits.
The current version of askNivi replies using a set of prepared answers. This study tests a new version that uses artificial intelligence to write replies. The new version looks up information in a reviewed collection of maternal health materials before answering, so its replies stay grounded in checked sources. It is also designed to recognize symptoms that may signal a pregnancy complication and to tell the user to seek care right away.
Read more
Women who join the study are assigned at random to one of the two versions. Neither version replaces medical care, and both send the same weekly messages and clinic reminders. Women can ask questions at any time throughout their pregnancy.
The study will look at how much women use each version, how useful they find it, how they decide when to seek care, and whether they attend prenatal care visits. About 2,500 pregnant women in Assam, Madhya Pradesh, and Uttar Pradesh will take part. Participation lasts from enrollment, between 10 and 20 weeks of pregnancy, until delivery.
Read the detailed description
Background
Maternal health remains a critical public health challenge in India, with maternal mortality ratios at 96 per 100,000 live births as of 2020. While this represents a significant improvement from 254 per 100,000 in 2006, partly attributed to community health workers (Accredited Social Health Activists or ASHAs) using WhatsApp for health communication, substantial gaps remain in providing timely, accurate maternal health information to pregnant women.
Digital health interventions, particularly chatbots, offer promising solutions for delivering scalable, cost-effective maternal health support. Chatbots can provide 24/7 access to health information, appointment reminders, and emotional support without requiring additional healthcare infrastructure. However, current evidence reveals a critical limitation: engagement with health chatbots typically declines dramatically over time, with retention rates falling from 72% at 2 months to 31% by 8 months.
Most existing maternal health chatbots use scripted, rule-based approaches with predetermined responses. While these systems are reliable and predictable, they often fail to address users' specific questions, leading to frustration and disengagement. Recent advances in Artificial Intelligence (AI), specifically Retrieval-Augmented Generation (RAG) technology, offer a potential solution. RAG enhances Large Language Models (LLMs) by grounding their responses in retrieved information from validated knowledge bases, potentially providing more relevant, personalized responses while reducing the risk of AI hallucinations.
Despite the emergence of RAG technology, no published studies have directly compared RAG-enhanced chatbots with scripted versions in any health context, let alone maternal health. This represents a critical knowledge gap, as RAG systems could potentially maintain higher engagement through more contextually relevant responses while ensuring medical accuracy through grounded information retrieval.
The askNivi platform provides an ideal setting for this comparison. As an established WhatsApp-based chatbot serving pregnant women across India, askNivi has demonstrated feasibility and acceptability. WhatsApp's penetration in India exceeds 500 million users, making it an optimal platform for reaching diverse populations, including those in rural and resource-limited settings.
Design
Two-arm, parallel-group, individually randomized controlled trial conducted on the askNivi WhatsApp platform in India. Allocation is 1:1 using computer-generated permuted block randomization with variable block sizes of 4, 6, and 8, stratified by language preference (Hindi, English, or Assamese). Allocation is automated following consent and completion of the baseline survey. Target enrollment incorporates 20 percent expected attrition.
Arms
Control: the scripted askNivi chatbot currently in deployment. Responses are drawn from prepared content selected by keyword matching, within fixed conversation flows covering predetermined topics.
Intervention: a retrieval-augmented chatbot that classifies incoming user messages, escalates messages indicating a possible clinical emergency to urgent care-seeking guidance, and generates responses to other messages from a curated maternal health corpus. Safety guardrails constrain generated content.
Both arms receive identical weekly template messages and antenatal care visit reminders.
Emergency detection
The intervention arm screens user messages for symptoms that may indicate a pregnancy complication, including severe or persistent headache, unusual swelling of the hands or face, vaginal bleeding, reduced fetal movement, and signs of preeclampsia. On detection, the chatbot advises the user to seek care immediately at her enrolling facility. The chatbot does not make facility appointments or contact providers directly. The control arm provides general health information without active symptom screening.
Setting
Three states where askNivi is deployed through the e-SAATHI initiative: Assam (six districts, with Assamese, Hindi, and English available), Madhya Pradesh (multiple blocks, Hindi and English), and Uttar Pradesh (Varanasi district, Hindi and English). Participation is entirely remote. Data are held on servers in the Mumbai region.
Pilot phase
A pilot precedes full enrollment, testing the randomization mechanism, consent and survey administration over WhatsApp, and system performance across all three languages. Pilot participants are drawn from the same population and pilot data are excluded from the analyses described here.
Analysis
The primary analysis follows intention to treat. Engagement outcomes are measured weekly within participant and analyzed with methods appropriate to repeated measures, accounting for correlation within participant. Secondary analyses adjust for baseline characteristics. An interim review at 50 percent enrollment addresses safety and futility only, with no efficacy stopping rule. Further detail is given in the statistical analysis plan.
- Conditions
- Pregnancy, Prenatal Care, Maternal Health
- Being tested
- Scripted maternal health chatbot, MATRI retrieval-augmented maternal health chatbot
- Keywords
- Chatbot, Artificial Intelligence, Retrieval-Augmented Generation, Digital Health, mHealth, WhatsApp, Antenatal Care, India
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Scripted maternal health chatbotBehavioural
WhatsApp-delivered chatbot providing maternal health information through prepared responses selected by keyword matching, weekly template messages, and antenatal care visit reminders. Available in Hindi, English, and Assamese.
MATRI retrieval-augmented maternal health chatbotBehavioural
MATRI (Maternal-health Agent for Triage and Retrieval-grounded Interaction) is a WhatsApp-delivered chatbot that classifies incoming user messages, escalates messages indicating a possible clinical emergency to urgent care-seeking guidance, and generates responses to other messages from a curated maternal health corpus. Safety guardrails constrain generated content. Delivers the same weekly template messages and antenatal care visit reminders as the comparator. Available in Hindi, English, and Assamese.
| Study group | Role | What they receive |
|---|---|---|
| Scripted chatbot | Active comparator | Participants receive the askNivi chatbot version currently in deployment. Responses are drawn from prepared content selected by keyword matching, within fixed conversation flows covering predetermined topics. Participants receive weekly template messages and antenatal care visit reminders. Receives: Behavioral: Scripted maternal health chatbot |
| Retrieval-augmented chatbot | Experimental | Participants receive a retrieval-augmented version of the askNivi chatbot that generates responses in real time from a curated maternal health corpus, can address questions outside predetermined topics, screens messages for symptoms indicating a possible pregnancy complication, and advises urgent care-seeking when such symptoms are detected. Participants receive the same weekly template messages and antenatal care visit reminders as the comparator arm. Receives: Behavioral: MATRI retrieval-augmented maternal health chatbot |
What is being measured
The primary outcome is the main question the study is designed to answer.
Percentage of weeks with any user interaction
Binary measure of whether the user gave any response during the specified week. Measured from platform interaction logs.
Measured: Weekly from enrollment through delivery or gestational week 41, up to 31 weeks
Mean conversational turns per week
Number of user-initiated messages sent to the chatbot in a given week, averaged across the participant's follow-up weeks. Measured from platform interaction logs.
Measured: Weekly from enrollment through delivery or gestational week 41, up to 31 weeks
Mean number of substantive user queries per week
Number of substantive queries sent by the participant in a given week, averaged across her follow-up weeks. A substantive query is a user-initiated free-text message, excluding greetings, acknowledgments, single-word replies, emoji-only messages, system strings, and blanks. The same classification procedure is applied in both arms. Measured from platform interaction logs.
Measured: Weekly from enrollment through delivery or gestational week 41, up to 31 weeks
Percentage of weeks with any substantive user query
Binary measure of whether a participant gave any substantive query during the given week, defined as above. Measured from platform interaction logs.
Measured: Weekly from enrollment through delivery or gestational week 41, up to 31 weeks
Secondary outcomes (8)
Percentage of weeks with user response to weekly template message
Binary measure of whether the user responded to the template message of the specified week
Measured: Weekly from enrollment through delivery or gestational week 41, up to 31 weeks
Discernment index from user responses to clinical scenarios
Score from 0 to 2 across a paired scenario battery. Each participant sees one high-severity and one low-severity scenario at baseline and the other pair at endline, so no scenario is answered twice. Each scenario scores 1 for the keyed action and 0 otherwise. High-severity and low-severity accuracy are also reported separately.
Measured: Baseline and gestational weeks 38 to 41, up to 31 weeks
Self-reported prenatal care attendance and health-seeking behaviors
Total antenatal care visits during the pregnancy, self-reported by the participant. Visits since enrollment are reported at midline and total visits for the pregnancy at endline.
Measured: From enrollment through delivery or gestational week 41, up to 31 weeks
Self-reported acute care-seeking
The share of reported worrying symptoms for which the participant sought care the same day. Collected through between-survey pulse checks and the midline questionnaire.
Measured: Every 6 to 8 weeks from enrollment through delivery or gestational week 41, up to 31 weeks
Usability of the chatbot, adapted System Usability Scale
Mean score across a 5-item usability instrument adapted from the System Usability Scale, with items rated from 1 (strongly disagree) to 5 (strongly agree). Possible range 1 to 5. Higher scores indicate better perceived usability. Negatively worded items are reverse-scored before averaging.
Measured: Gestational weeks 28 to 30, up to 20 weeks after enrollment
Net Promoter Score
Single survey item asking how likely the participant is to recommend the chatbot to a pregnant friend or family member, rated from 0 (not at all likely) to 10 (extremely likely). Higher scores indicate greater likelihood to recommend.
Measured: Gestational weeks 38 to 41 or within one week of delivery, up to 31 weeks
Change in knowledge self-efficacy
Single survey item, "I feel knowledgeable about my pregnancy," rated from 1 (strongly disagree) to 5 (strongly agree). Higher scores indicate greater self-efficacy. Analyzed as change from baseline.
Measured: Baseline, gestational weeks 28 to 30, and gestational weeks 38 to 41, up to 31 weeks
Retention assessed as time to last user interaction
Number of weeks from enrollment to the participant's last interaction with the chatbot. Disengagement is defined as no user interaction of any type for four or more consecutive weeks, with the event dated to the last week containing an interaction. Participants are censored at delivery where the delivery date is known, and at gestational week 41 otherwise.
Measured: From enrollment to gestational week 41, up to 31 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Oct 2026
- Study start30 Oct 2026 (estimated)
- Primary completion31 May 2028 (estimated)
- Study completion31 Aug 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for PregnancyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 2,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Nivi, Inc.
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- Carnegie Mellon University
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07883993
- Sponsor's protocol ID
- NIVI-2026-01
- Other IDs
- IIS-2229881, PCIIRB\2025-2026\022
- Status last verified
- Sep 2026
- First submitted
- 20 Sep 2026
- Last updated
- 20 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: The Principal Investigator will make all raw individual participant data files, together with statistical analysis protocol descriptions and the information needed for proper analysis, available for download a minimum of one year and a maximum of three years after publication of the corresponding manuscripts. All human subjects data will be anonymized prior to analysis, so participant confidentiality will not be compromised by this plan. All hypotheses, methods, and planned analyses are preregistered on ClinicalTrials.gov and AsPredicted.
Study at a glance
From the registry record, last updated by the sponsor on 20 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluating the Impact of Generative AI on Pregnancy Chatbot User Engagement and Health Behaviors
- ClinicalTrials.gov ID
- NCT07883993
- Acronym / sponsor ID
- NIVI-2026-01
- Conditions
- Pregnancy, Prenatal Care, Maternal Health
- Keywords
- Chatbot, Artificial Intelligence, Retrieval-Augmented Generation, Digital Health, mHealth, WhatsApp, Antenatal Care, India
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 2,500 participants (estimated)
- Lead sponsor
- Nivi, Inc.Industry (pharma, biotech, devices)
- Collaborators
- Carnegie Mellon University
- Study start
- 30 Oct 2026 (estimated)
- Primary completion
- 31 May 2028 (estimated)
- Study completion
- 31 Aug 2028 (estimated)
- First posted
- 20 Oct 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Many pregnant women in India use mobile phones to get health information. Nivi runs a free WhatsApp service called askNivi that answers questions about pregnancy and sends reminders about prenatal care visits.
The current version of askNivi replies using a set of prepared answers. This study tests a new version that uses artificial intelligence to write replies. The new version looks up information in a reviewed collection of maternal health materials before answering, so its replies stay grounded in checked sources. It is also designed to recognize symptoms that may signal a pregnancy complication and to tell the user to seek care right away.
Women who join the study are assigned at random to one of the two versions. Neither version replaces medical care, and both send the same weekly messages and clinic reminders. Women can ask questions at any time throughout their pregnancy.
The study will look at how much women use each version, how useful they find it, how they decide when to seek care, and whether they attend prenatal care visits. About 2,500 pregnant women in Assam, Madhya Pradesh, and Uttar Pradesh will take part. Participation lasts from enrollment, between 10 and 20 weeks of pregnancy, until delivery.
Detailed description
Background
Maternal health remains a critical public health challenge in India, with maternal mortality ratios at 96 per 100,000 live births as of 2020. While this represents a significant improvement from 254 per 100,000 in 2006, partly attributed to community health workers (Accredited Social Health Activists or ASHAs) using WhatsApp for health communication, substantial gaps remain in providing timely, accurate maternal health information to pregnant women.
Digital health interventions, particularly chatbots, offer promising solutions for delivering scalable, cost-effective maternal health support. Chatbots can provide 24/7 access to health information, appointment reminders, and emotional support without requiring additional healthcare infrastructure. However, current evidence reveals a critical limitation: engagement with health chatbots typically declines dramatically over time, with retention rates falling from 72% at 2 months to 31% by 8 months.
Most existing maternal health chatbots use scripted, rule-based approaches with predetermined responses. While these systems are reliable and predictable, they often fail to address users' specific questions, leading to frustration and disengagement. Recent advances in Artificial Intelligence (AI), specifically Retrieval-Augmented Generation (RAG) technology, offer a potential solution. RAG enhances Large Language Models (LLMs) by grounding their responses in retrieved information from validated knowledge bases, potentially providing more relevant, personalized responses while reducing the risk of AI hallucinations.
Despite the emergence of RAG technology, no published studies have directly compared RAG-enhanced chatbots with scripted versions in any health context, let alone maternal health. This represents a critical knowledge gap, as RAG systems could potentially maintain higher engagement through more contextually relevant responses while ensuring medical accuracy through grounded information retrieval.
The askNivi platform provides an ideal setting for this comparison. As an established WhatsApp-based chatbot serving pregnant women across India, askNivi has demonstrated feasibility and acceptability. WhatsApp's penetration in India exceeds 500 million users, making it an optimal platform for reaching diverse populations, including those in rural and resource-limited settings.
Design
Two-arm, parallel-group, individually randomized controlled trial conducted on the askNivi WhatsApp platform in India. Allocation is 1:1 using computer-generated permuted block randomization with variable block sizes of 4, 6, and 8, stratified by language preference (Hindi, English, or Assamese). Allocation is automated following consent and completion of the baseline survey. Target enrollment incorporates 20 percent expected attrition.
Arms
Control: the scripted askNivi chatbot currently in deployment. Responses are drawn from prepared content selected by keyword matching, within fixed conversation flows covering predetermined topics.
Intervention: a retrieval-augmented chatbot that classifies incoming user messages, escalates messages indicating a possible clinical emergency to urgent care-seeking guidance, and generates responses to other messages from a curated maternal health corpus. Safety guardrails constrain generated content.
Both arms receive identical weekly template messages and antenatal care visit reminders.
Emergency detection
The intervention arm screens user messages for symptoms that may indicate a pregnancy complication, including severe or persistent headache, unusual swelling of the hands or face, vaginal bleeding, reduced fetal movement, and signs of preeclampsia. On detection, the chatbot advises the user to seek care immediately at her enrolling facility. The chatbot does not make facility appointments or contact providers directly. The control arm provides general health information without active symptom screening.
Setting
Three states where askNivi is deployed through the e-SAATHI initiative: Assam (six districts, with Assamese, Hindi, and English available), Madhya Pradesh (multiple blocks, Hindi and English), and Uttar Pradesh (Varanasi district, Hindi and English). Participation is entirely remote. Data are held on servers in the Mumbai region.
Pilot phase
A pilot precedes full enrollment, testing the randomization mechanism, consent and survey administration over WhatsApp, and system performance across all three languages. Pilot participants are drawn from the same population and pilot data are excluded from the analyses described here.
Analysis
The primary analysis follows intention to treat. Engagement outcomes are measured weekly within participant and analyzed with methods appropriate to repeated measures, accounting for correlation within participant. Secondary analyses adjust for baseline characteristics. An interim review at 50 percent enrollment addresses safety and futility only, with no efficacy stopping rule. Further detail is given in the statistical analysis plan.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Scripted chatbot | Active comparator | Behavioral: Scripted maternal health chatbotParticipants receive the askNivi chatbot version currently in deployment. Responses are drawn from prepared content selected by keyword matching, within fixed conversation flows covering predetermined topics. Participants receive weekly template messages and antenatal care visit reminders. |
| Retrieval-augmented chatbot | Experimental | Behavioral: MATRI retrieval-augmented maternal health chatbotParticipants receive a retrieval-augmented version of the askNivi chatbot that generates responses in real time from a curated maternal health corpus, can address questions outside predetermined topics, screens messages for symptoms indicating a possible pregnancy complication, and advises urgent care-seeking when such symptoms are detected. Participants receive the same weekly template messages and antenatal care visit reminders as the comparator arm. |
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (8 listed)
- Age 18 years or older
- Currently pregnant with gestational age between 10 and 20 weeks, by self report
- Located in India
- Self-reported female gender
- Regular access to a smartphone with WhatsApp
Key exclusion criteria (10 listed)
- Age under 18 years
- Not currently pregnant
- Gestational age under 10 weeks or over 20 weeks at enrollment
- Located outside India
- Self-reported male gender or gender diverse identity, as the chatbot is designed specifically for women
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Scripted maternal health chatbotWhatsApp-delivered chatbot providing maternal health information through prepared responses selected by keyword matching, weekly template messages, and antenatal care visit reminders. Available in Hindi, English, and Assamese. | Behavioural | Scripted chatbot |
| MATRI retrieval-augmented maternal health chatbotMATRI (Maternal-health Agent for Triage and Retrieval-grounded Interaction) is a WhatsApp-delivered chatbot that classifies incoming user messages, escalates messages indicating a possible clinical emergency to urgent care-seeking guidance, and generates responses to other messages from a curated maternal health corpus. Safety guardrails constrain generated content. Delivers the same weekly template messages and antenatal care visit reminders as the comparator. Available in Hindi, English, and Assamese. | Behavioural | Retrieval-augmented chatbot |
Primary outcome
Percentage of weeks with any user interaction
Time frame: Weekly from enrollment through delivery or gestational week 41, up to 31 weeks
Binary measure of whether the user gave any response during the specified week. Measured from platform interaction logs.
Mean conversational turns per week
Time frame: Weekly from enrollment through delivery or gestational week 41, up to 31 weeks
Number of user-initiated messages sent to the chatbot in a given week, averaged across the participant's follow-up weeks. Measured from platform interaction logs.
Mean number of substantive user queries per week
Time frame: Weekly from enrollment through delivery or gestational week 41, up to 31 weeks
Number of substantive queries sent by the participant in a given week, averaged across her follow-up weeks. A substantive query is a user-initiated free-text message, excluding greetings, acknowledgments, single-word replies, emoji-only messages, system strings, and blanks. The same classification procedure is applied in both arms. Measured from platform interaction logs.
Percentage of weeks with any substantive user query
Time frame: Weekly from enrollment through delivery or gestational week 41, up to 31 weeks
Binary measure of whether a participant gave any substantive query during the given week, defined as above. Measured from platform interaction logs.
8 secondary outcomes
Percentage of weeks with user response to weekly template message
Time frame: Weekly from enrollment through delivery or gestational week 41, up to 31 weeks
Discernment index from user responses to clinical scenarios
Time frame: Baseline and gestational weeks 38 to 41, up to 31 weeks
Self-reported prenatal care attendance and health-seeking behaviors
Time frame: From enrollment through delivery or gestational week 41, up to 31 weeks
Self-reported acute care-seeking
Time frame: Every 6 to 8 weeks from enrollment through delivery or gestational week 41, up to 31 weeks
Usability of the chatbot, adapted System Usability Scale
Time frame: Gestational weeks 28 to 30, up to 20 weeks after enrollment
Net Promoter Score
Time frame: Gestational weeks 38 to 41 or within one week of delivery, up to 31 weeks
Change in knowledge self-efficacy
Time frame: Baseline, gestational weeks 28 to 30, and gestational weeks 38 to 41, up to 31 weeks
Retention assessed as time to last user interaction
Time frame: From enrollment to gestational week 41, up to 31 weeks
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Oct 2026.
- Individual participant dataSharing planned: Yes. The Principal Investigator will make all raw individual participant data files, together with statistical analysis protocol descriptions and the information needed for proper analysis, available for download a minimum of one year and a maximum of three years after publication of the corresponding manuscripts. All human subjects data will be anonymized prior to analysis, so participant confidentiality will not be compromised by this plan. All hypotheses, methods, and planned analyses are preregistered on ClinicalTrials.gov and AsPredicted.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Scripted maternal health chatbot and MATRI retrieval-augmented maternal health chatbot in people with Pregnancy and Prenatal Care, run by Nivi, Inc. at 1 site in India.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 8 inclusion and 10 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 20 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in India. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Ben Bellows, PhD (240-481-7476, ben@nivi.io), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Percentage of weeks with any user interaction (time frame: Weekly from enrollment through delivery or gestational week 41, up to 31 weeks). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Oct 2026.