SynthesisJournal of medical Internet research2024
Chatbots That Deliver Contraceptive Support: Systematic Review.
Synthesis in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled 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.
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.
Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.
- Enhancing Interoperability for a Sustainable, Patient-Centric Health Care Value Chain: Systematic Review for Taxonomy Development.Journal of medical Internet research · 2025Pooled it
- Smart Contracts and Shared Platforms in Sustainable Health Care: Systematic Review.JMIR medical informatics · 2025Pooled it
- AI and Digital Tools in Dermatology: Addressing Access and Misinformation.JMIR dermatology · 2026Review
- Chatbots as frontline educators in sexual reproductive health rights: evidence, limitations, and ethical considerations.Frontiers in digital health · 2026Article
- Mixed-methods, cross-sectional assessment of a client-facing, family planning counseling chatbot in Côte d'Ivoire.Oxford open digital health · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA chatbot is a computer program that is designed to simulate conversation with humans. Chatbots may offer rapid, responsive, and private contraceptive information; counseling; and linkages to products and services, which could improve contraceptive knowledge, attitudes, and behaviors.
objectiveThis review aimed to systematically collate and interpret evidence to determine whether and how chatbots improve contraceptive knowledge, attitudes, and behaviors. Contraceptive knowledge, attitudes, and behaviors include access to contraceptive information, understanding of contraceptive information, access to contraceptive services, contraceptive uptake, contraceptive continuation, and contraceptive communication or negotiation skills. A secondary aim of the review is to identify and summarize best practice recommendations for chatbot development to improve contraceptive outcomes, including the cost-effectiveness of chatbots where evidence is available.
methodsWe systematically searched peer-reviewed and gray literature (2010-2022) for papers that evaluated chatbots offering contraceptive information and services. Sources were included if they featured a chatbot and addressed an element of contraception, for example, uptake of hormonal contraceptives. Literature was assessed for methodological quality using appropriate quality assessment tools. Data were extracted from the included sources using a data extraction framework. A narrative synthesis approach was used to collate qualitative evidence as quantitative evidence was too sparse for a quantitative synthesis to be carried out.
resultsWe identified 15 sources, including 8 original research papers and 7 gray literature papers. These sources included 16 unique chatbots. This review found the following evidence on the impact and efficacy of chatbots: a large, robust randomized controlled trial suggests that chatbots have no effect on intention to use contraception; a small, uncontrolled cohort study suggests increased uptake of contraception among adolescent girls; and a development report, using poor-quality methods, suggests no impact on improved access to services. There is also poor-quality evidence to suggest increased contraceptive knowledge from interacting with chatbot content. User engagement was mixed, with some chatbots reaching wide audiences and others reaching very small audiences. User feedback suggests that chatbots may be experienced as acceptable, convenient, anonymous, and private, but also as incompetent, inconvenient, and unsympathetic. The best practice guidance on the development of chatbots to improve contraceptive knowledge, attitudes, and behaviors is consistent with that in the literature on chatbots in other health care fields.
conclusionsWe found limited and conflicting evidence on chatbots to improve contraceptive knowledge, attitudes, and behaviors. Further research that examines the impact of chatbot interventions in comparison with alternative technologies, acknowledges the varied and changing nature of chatbot interventions, and seeks to identify key features associated with improved contraceptive outcomes is needed. The limitations of this review include the limited evidence available on this topic, the lack of formal evaluation of chatbots in this field, and the lack of standardized definition of what a chatbot is.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.