AI Chatbots Can Efficiently Promote Healthy Lifestyles

Chatbots with artificial intelligence (AI) are able to mimic human interactions using oral, written or verbal communication with the user. AI chatbots can deliver important health-related information and services, ultimately leading to promising technology-enabled interventions.

Study: Artificial intelligence (AI)-based chatbots in promoting behavioral change in health: a systematic review. Image Credit: TippaPatt/

AI chatbots in healthcare

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

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

AI chatbots are thus able to promote diverse health behaviors by delivering effective interventions. In addition, this technology may provide additional benefits for changes in health behaviors by integrating into embodied functions.

Most previous research on AI chatbots has focused on improving mental health outcomes. By comparison, recent studies have increasingly focused on using AI chatbots to instigate behavioral changes in health.

However, a systematic review on the impact of AI chatbots on lifestyle modification came with several limitations. These include the authors’ inability to distinguish AI chatbots from other chatbots. In addition, this study only focused on a limited number of behaviors and did not discuss all potential platforms that AI chatbots could use.

A new systematic review published on the preprint server medRxiv* discusses the results of previous studies on the features, functionality, and components of AI chatbot intervention, 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. In it, three authors searched seven bibliographic databases, including IEEE Xplore, PubMed, JMIR publications, EMBASE, ACM Digital Library, Web of Science and PsychINFO.

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

The inclusion criteria for the search were intervention studies focused on health behaviors, studies developed on existing AI platforms or AI algorithms, empirical studies using chatbots, English articles published between 1980 and 2022, and studies reporting quantitative or qualitative intervention results. All data was extracted from these studies and has undergone a 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 spread across developed countries. The median sample size was 116 participants, while the mean was 7,200 participants.

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

The targeted health behaviors included smoking cessation, promotion of a healthy lifestyle, reduction of substance abuse, and adherence or treatment. In addition, only four studies were reported to use randomized control trials (RCTs), while others used a quasi-experimental design.

The risk of reporting outcomes and bias in the randomization process was low, the risk of bias from intended interventions was low to moderate, the risk of bias in outcome measurement was moderate, and the risk of outcomes from unintended sources was high. All factors for the description of AI components were sufficient except for the processing of 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 security. Additionally, 11 studies reported usability in terms of content usability, chatbot ease of use, user-initiated conversation, non-judgmental safe space, and outside support. Acceptability and engagement were reported in 12 surveys in terms of satisfaction, retention rate, technical issues and contract duration.

An increase in physical activity was reported in six studies and an improvement in nutrition through chatbot-based interventions in three studies. Smoking cessation was reported in the four studies reviewed, while one study reported a reduction in substance use and two studies reported an increase in adherence or medication from chatbot use.

Several behavioral change theories were integrated into the chatbots, including the transtheoretical model (TTM), cognitive behavioral therapy (CBT), social cognitive theory (SCT), habit formation model, motivational interviewing, Mohr’s Model of Supportive Accountability and emotionally focused therapy to provide motivational support and monitoring the behavior of participants. Most studies focused on behavioral goal setting, used behavior monitoring, and provided behavior-related information, while four studies also provided emotional support.

Most studies used different AI techniques such as ML, NLP, Hybrid Health Recommender Systems (HHRS), hybrid techniques (ML and NLP), and face tracking technology to provide personalized interventions. The chatbots mainly used text-based communication and were either integrated into pre-existing platforms or delivered as independent platforms. In addition, most chatbots required data about users’ background information, their goals, and behavioral performance feedback to ensure the delivery of personalized services.


Taken together, AI chatbots can efficiently promote healthy lifestyles, smoking cessation, and adherence or medication. In addition, the current study found that AI chatbots showed significant usability, feasibility and acceptability.

Overall, AI chatbots are capable of providing personalized interventions and can be scalable for diverse and large populations. However, further studies are needed to get an accurate description of AI-related processes, as AI chatbot interventions are still in their nascent stage.


The current study did not include a meta-analysis and focused only on three behavioral outcomes. In addition, 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 patient safety information was limited.

*Important announcement

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

Share is Love^^