Background Chatbots empowered by artificial intelligence (AI) can increasingly engage in natural conversations and build relationships with users. Applying AI chatbots to lifestyle modification programs is one of the promising areas to develop cost-effective and feasible behavior interventions to promote physical activity and a healthy diet. Objective The purposes of this perspective paper are to present a brief literature review of chatbot use in promoting physical activity and a healthy diet, describe the AI chatbot behavior change model our research team developed based on extensive interdisciplinary research, and discuss ethical principles and considerations. Methods We conducted a preliminary search of studies reporting chatbots for improving physical activity and/or diet in four databases in July 2020. We summarized the characteristics of the chatbot studies and reviewed recent developments in human-AI communication research and innovations in natural language processing. Based on the identified gaps and opportunities, as well as our own clinical and research experience and findings, we propose an AI chatbot behavior change model. Results Our review found a lack of understanding around theoretical guidance and practical recommendations on designing AI chatbots for lifestyle modification programs. The proposed AI chatbot behavior change model consists of the following four components to provide such guidance: (1) designing chatbot characteristics and understanding user background; (2) building relational capacity; (3) building persuasive conversational capacity; and (4) evaluating mechanisms and outcomes. The rationale and evidence supporting the design and evaluation choices for this model are presented in this paper. Conclusions As AI chatbots become increasingly integrated into various digital communications, our proposed theoretical framework is the first step to conceptualize the scope of utilization in health behavior change domains and to synthesize all possible dimensions of chatbot features to inform intervention design and evaluation. There is a need for more interdisciplinary work to continue developing AI techniques to improve a chatbot’s relational and persuasive capacities to change physical activity and diet behaviors with strong ethical principles.

Background Coronavirus disease (COVID-19) has affected more than 200 countries and territories worldwide. This disease poses an extraordinary challenge for public health systems because screening and surveillance capacity is often severely limited, especially during the beginning of the outbreak; this can fuel the outbreak, as many patients can unknowingly infect other people. Objective The aim of this study was to collect and analyze posts related to COVID-19 on Weibo, a popular Twitter-like social media site in China. To our knowledge, this infoveillance study employs the largest, most comprehensive, and most fine-grained social media data to date to predict COVID-19 case counts in mainland China. Methods We built a Weibo user pool of 250 million people, approximately half the entire monthly active Weibo user population. Using a comprehensive list of 167 keywords, we retrieved and analyzed around 15 million COVID-19–related posts from our user pool from November 1, 2019 to March 31, 2020. We developed a machine learning classifier to identify “sick posts,” in which users report their own or other people’s symptoms and diagnoses related to COVID-19. Using officially reported case counts as the outcome, we then estimated the Granger causality of sick posts and other COVID-19 posts on daily case counts. For a subset of geotagged posts (3.10% of all retrieved posts), we also ran separate predictive models for Hubei province, the epicenter of the initial outbreak, and the rest of mainland China. Results We found that reports of symptoms and diagnosis of COVID-19 significantly predicted daily case counts up to 14 days ahead of official statistics, whereas other COVID-19 posts did not have similar predictive power. For the subset of geotagged posts, we found that the predictive pattern held true for both Hubei province and the rest of mainland China regardless of the unequal distribution of health care resources and the outbreak timeline. Conclusions Public social media data can be usefully harnessed to predict infection cases and inform timely responses. Researchers and disease control agencies should pay close attention to the social media infosphere regarding COVID-19. In addition to monitoring overall search and posting activities, leveraging machine learning approaches and theoretical understanding of information sharing behaviors is a promising approach to identify true disease signals and improve the effectiveness of infoveillance.

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Bias in clinical practice, in particular in relation to race and gender, is a persistent cause of healthcare disparities. We investigated the potential of a peer-network approach to reduce bias in medical treatment decisions within an experimental setting. We created “egalitarian” information exchange networks among practicing clinicians who provided recommendations for the clinical management of patient scenarios, presented via standardized patient videos of actors portraying patients with cardiac chest pain. The videos, which were standardized for relevant clinical factors, presented either a white male actor or Black female actor of similar age, wearing the same attire and in the same clinical setting, portraying a patient with clinically significant chest pain symptoms. We found significant disparities in the treatment recommendations given to the white male patient-actor and Black female patient-actor, which when translated into real clinical scenarios would result in the Black female patient being significantly more likely to receive unsafe undertreatment, rather than the guideline-recommended treatment. In the experimental control group, clinicians who were asked to independently reflect on the standardized patient videos did not show any significant reduction in bias. However, clinicians who exchanged real-time information in structured peer networks significantly improved their clinical accuracy and showed no bias in their final recommendations. The findings indicate that clinician network interventions might be used in healthcare settings to reduce significant disparities in patient treatment.

Scant research has investigated whether health promotion interventions have sustained effects in increasing physical activity and healthful diet among adolescents in sub-Saharan Africa, which is experiencing an epidemiological transition from infectious diseases to noncommunicable diseases as leading causes of mortality. We examined whether an intervention increased adherence to 5-a-day diet and physical activity guidelines during a 54-month postintervention period among South African adolescents and whether its effects weakened at long-term (42 and 54 months postintervention) compared with short-term (3, 6, and 12 months postintervention) follow-up. Methods: We randomized 18 randomly selected schools serving grade 6 learners (mean age ¼ 12.6) in a township and a semirural area in Eastern Cape Province, South Africa, to one of the two 12-hour interventions: health promotion, targeting healthful diet and physical activity; attentionmatched control, targeting sexual risk behaviors. We tested the intervention's effects on adherence to 5-a-day diet and physical activity guidelines using generalized estimating equations logistic regression models adjusting for baseline behavior and clustering within schools. Results: Health promotion intervention participants had higher odds of meeting 5-a-day diet and physical activity guidelines than control participants. The effect on 5-a-day diet did not weaken at long-term compared with short-term follow-up, but the effect on physical activity guidelines was weaker at long-term follow-up, mainly because of a reduced effect on muscle-strengthening physical activity. The intervention also increased health promotion attitude and intention and health knowledge and reduced binge drinking compared with the control group.

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Objective: We sought to test whether participation in an online group including IUD users influenced IUD-related knowledge, attitudes, and behavior among IUD non-users, as a proof-ofconcept evaluation of information dissemination for less commonly used or novel contraceptives. Study design: We conducted a blinded, randomized controlled trial on the effect of online communication with IUD users within an online program called Birth Control Connect. Participants were women age 18-45 living in the United States who had never used an IUD. We invited participants randomized to the intervention to join two-week, nine-member discussion groups including four satisfied IUD users and five IUD non-users; we invited control participants to groups including nine IUD non-users. We performed chi-squared tests on IUD knowledge, information-seeking, informational support and use in immediate post-surveys, and t-tests comparing change in IUD attitudes and frequency of logins to discussion groups. Results: We invited 488 IUD non-users and enrolled them into 70 groups between October 2015 and April 2016. We found increased positive attitudes towards the IUD in the intervention arm (0.65-point increase between pre-and post-surveys, versus 0.05 mean change for control arm, p = 0.03 for hormonal IUD, with a trend in the same direction for the non-hormonal IUD). Informational support also increased, with 70.3% of intervention arm participants self-reporting that they gained a better idea of what the IUD would be like, compared to 51.