AI chatbots can efficiently promote a healthy lifestyle

Artificial intelligence (AI) chatbots are able to mimic human interactions with the help of oral, written or verbal communication with the user. AI chatbots can provide important health-related information and services, ultimately leading to promising technology-facilitated interventions.

Study: Artificial intelligence (AI)-based chatbots in promoting health behavior change: A systematic review. Image credit: TippaPatt /

AI chatbots in healthcare

Current digital telehealth and therapeutic interventions are associated with several challenges, including instability, low adherence, and inflexibility. AI chatbots are able to overcome these challenges and provide on-demand personalized support, higher interaction and higher durability.

AI chatbots use input from various sources, which is followed by data analysis that is completed through natural language processing (NLP) and machine learning (ML). The data output then helps users achieve their health behavior goals.

Thus, AI chatbots are able to promote various health behaviors by effectively delivering interventions. Additionally, this technology may provide additional benefits for health behavior change by being integrated into embodied functions.

Most previous studies conducted on AI chatbots aimed to improve mental health outcomes. In contrast, recent studies have increasingly focused on the use of AI chatbots to drive health behavior change.

However, a systematic review on the impact of AI chatbots on lifestyle modification was accompanied by some limitations. These include the authors’ inability to distinguish AI chatbots from other chatbots. Furthermore, this study only targeted a limited set of behaviors and did not discuss all possible platforms that AI chatbots could use.

A new systematic review published on the preprint server medRxiv* discusses the results of previous studies on AI chatbot characteristics, functionality, and intervention components, as well as their impact on a wide range of health behaviors.

About the study

The current study was conducted in June 2022 and followed PRISMA guidelines. Here, three authors searched seven bibliographic databases including IEEE Xplore, PubMed, JMIR publications, EMBASE, ACM Digital Library, Web of Science, and PsychINFO.

The search included a combination of keywords belonging to the three categories. The first category includes keywords related to AI-based chatbots, the second includes keywords related to health behaviors, and the third focuses on interventions.

Inclusion criteria for the search were studies that included intervention research focused on health behaviors, those that were developed on existing AI platforms or AI algorithms, empirical studies that used chatbots, articles in English that were published between 1980 and 2022, as well as studies that reported quantitative or qualitative intervention outcomes. All data were extracted from these studies and subjected to quality assessment according to the National Institutes of Health (NIH) Quality Assessment Tool.

Study findings

A total of 15 studies met the inclusion criteria, most of which were distributed across developed countries. The average sample size was 116 participants, while the median was 7,200 participants.

Most of the studies included adult participants, while only two included participants under 18 years of age. All study participants had pre-existing conditions and included individuals with lower physical activity, obesity, smokers, substance abusers, breast cancer patients and Medicare recipients.

Targeted health behaviors included smoking cessation, promoting a healthy lifestyle, reducing substance abuse, and adhering to medication or treatment. Furthermore, only four studies were reported to use randomized control trials (RCTs), while the others used a quasi-experimental design.

The risk of outcome reporting and randomization process bias was low, the risk of bias from targeted interventions was low to moderate, the risk of bias in outcome measurement was moderate, and the risk of bias from unintended sources was high. All factors for describing the AI ​​components were sufficient, except for handling unavailable input data and input data characteristics.

Of the 15 studies, six reported feasibility in terms of the average number of messages exchanged with the chatbot per month and safety. Additionally, 11 studies reported usability in terms of content usability, chatbot ease of use, user-initiated conversation, safe non-judgmental space, and out-of-office support. Acceptability and engagement were reported in 12 studies in terms of satisfaction, retention rate, technical issues and duration of engagement.

An increase in physical activity was reported in six studies, along with an improvement in diet in three studies through chatbot-based interventions. Smoking cessation was reported in the four studies evaluated, while one study reported a reduction in substance use and two studies reported an increase in adherence to treatment or medication through the use of chatbots.

Several behavior change theories were integrated into the chatbot including the transtheoretical model (TTM), cognitive behavioral therapy (CBT), social cognitive theory (SCT), habit formation model, motivational interviewing, Mohr’s Supportive Responsiveness Model, and therapy emotionally focused to provide motivational support and behavioral tracking of participants. Most studies aimed to set behavioral goals, used behavioral monitoring, and provided behaviorally related information, while four studies also provided emotional support.

Most of the studies used various AI techniques such as ML, NLP, Hybrid Health Recommender Systems (HHRS), hybrid techniques (ML and NLP) and face tracking technology to deliver personalized interventions. Chatbots used mostly text-based communication and were either integrated into pre-existing platforms or distributed as stand-alone platforms. In addition, most chatbots required data on users’ background information, their intentions, and feedback on behavioral performance to ensure the provision of personalized services.


Taken together, AI chatbots can effectively promote a healthy lifestyle, smoking cessation, and adherence to treatment or medications. Additionally, the current study found that AI chatbots demonstrated significant usability, feasibility, and acceptability.

Taken together, AI chatbots are capable of providing personalized interventions and can be scaled to diverse and large populations. However, further studies are needed to acquire an accurate description of AI-related processes, as AI chatbot interventions are still at a nascent stage.


The current study did not include a meta-analysis and focused on only three behavioral outcomes. Additionally, articles from unselected databases, articles in other languages, gray literature, and unpublished articles were not included in the study.

An additional limitation was that the interventions could not provide a clear description of excluded AI chatbots. The study also lacked generalizability and information on patient safety was limited.


medRxiv publishes preliminary scientific reports that have not been peer-reviewed and, therefore, should not be considered definitive, guide clinical practice/health-related behavior, or be treated as established information.

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