Background: Chronic diseases represent a significant global burden of mortality, exacerbated by behavioral risk factors. Artificial intelligence (AI) has transformed health promotion and disease reduction through improved early detection, encouraging healthy lifestyle modifications, and mitigating the economic strain on health systems. Objective: The aim of this study is to investigate how AI contributes to health promotion and disease reduction among Organization for Economic Co-operation and Development countries. Methods: We conducted a rapid review of the literature to identify the latest evidence on how AI is used in health promotion and disease reduction. We applied comprehensive search strategies formulated for MEDLINE (OVID) and CINAHL to locate studies published between 2019 and 2024. A pair of reviewers independently applied the inclusion and exclusion criteria to screen the titles and abstracts, assess the full texts, and extract the data. We synthesized extracted data from the study characteristics, intervention characteristics, and intervention purpose using structured narrative summaries of main themes, giving a portrait of the current scope of available AI initiatives used in promoting healthy activities and preventing disease. Results: We included 22 studies in this review (out of 3442 publications screened), most of which were conducted in the United States (10/22, 45%) and focused on health promotion by targeting lifestyle dimensions, such as dietary behavior (10/22, 45%), smoking cessation (6/22, 27%), physical activity (4/22, 18%), and mental health (3/22, 14%). Three studies targeted disease reduction related to metabolic health (eg, obesity, diabetes, hypertension). Most AI initiatives were AI-powered mobile apps. Overall, positive results were reported for process outcomes (eg, acceptability, engagement), cognitive and behavioral outcomes (eg, confidence, step count), and health outcomes (eg, glycemia, blood pressure). We categorized the challenges, benefits, and suggestions identified in the studies using a Strengths, Weaknesses, Opportunities, and Threats analysis to inform future developments. Key recommendations include conducting further investigations, taking into account the needs of end users, improving the technical aspect of the technology, and allocating resources. Conclusions: These findings offer critical insights into the effective implementation of AI for health promotion and disease prevention, potentially guiding policymakers and health care practitioners in optimizing the use of AI technologies in supporting health promotion and disease reduction.

Background: Digital health literacy (DHL), recognized as a key determinant of health, can influence health and well-being, improve health equity, and reduce health disparities. However, DHL is often limited among forced migrant populations, who usually lack the skills to understand and evaluate health information or to access and use digital health resources appropriately. Objective: We aimed to (1) identify effective interventions designed to improve DHL among forced migrant populations and (2) categorize and describe the characteristics of interventions that aim to improve the abilities of forced migrants or adapt digital health services to meet the needs and expectations of forced migrant populations limited by low levels of DHL. Methods: We conducted a mixed methods systematic review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, involving an iterative process among the authors. A medical information specialist assisted in developing a search strategy for the 6 most relevant databases (MEDLINE, Embase, CINAHL, Web of Science, Academic Search Premier, and PsycINFO) and the Google Scholar search engine, covering studies published between 2000 and 2022. Pairs of reviewers selected, individually and independently, titles, abstracts, and then full texts. Data extraction and quality assessment were performed by 2 reviewers and validated by a senior researcher. We used narrative synthesis to provide a comprehensive overview of effective DHL interventions for forced migrant populations, highlighting their success factors. Results: We identified 1845 studies, of which only 6 (0.33%) were finally selected for narrative synthesis. Studies were excluded due to irrelevance, lack of primary data, or low methodological quality. The analysis revealed a diverse methodological landscape with a predominance of qualitative approaches aimed at understanding the challenges and needs of forced migrants concerning DHL. The main challenges were associated with cultural, linguistic, and practical contexts. Interventions targeted various groups, including older adults, individuals with low literacy or education, and those with limited digital experience. We identified 4 effective educational intervention categories to enhance DHL among forced migrants: education and training; education and social support; enabling and education; and social, educational, technological, and infrastructural support. Overall, most of the studies (5/6, 83%) reported positive results in terms of improving DHL among forced migrants. Conclusions: This systematic review highlights the importance of improving DHL among forced migrant populations to promote their health and well-being. In addition, it provides comprehensive knowledge about effective interventions conducted with these groups. These findings can inform stakeholders, particularly policy makers, of the need to address low DHL among forced migrant populations. Going forward, these stakeholders need to develop innovative initiatives that rely on holistic approaches and are based on the specific needs of forced migrants to improve equity and health outcomes. Trial Registration: PROSPERO CRD42022373448; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022373448 International Registered Report Identifier (IRRID): RR2-10.2196/50798

Background: Digital health literacy is considered a health determinant that can influence improved health and well-being, health equity, and the reduction of social health inequalities. Therefore, it serves as an asset for individuals to promote their health. However, low digital health literacy is a major problem among forced migrant populations. They do not always have the capacity and skills to access digital health resources and use them appropriately. To our knowledge, no studies are currently available to examine effective interventions for improving digital health literacy among forced migrant populations. Objective: This paper presents the protocol for a systematic review that aims to assess the effectiveness of digital health literacy interventions among forced migrant populations. With this review, our objectives are as follows: (1) identify interventions designed to improve digital health literacy among forced migrant populations, including interventions aimed at creating enabling conditions or environments that cater to the needs and expectations of forced migrants limited by low levels of digital health literacy, with the goal of facilitating their access to and use of eHealth resources; (2) define the categories and describe the characteristics of these interventions, which are designed to enhance the abilities of forced migrants or adapt digital health services to meet the needs and expectations of forced migrant populations. Methods: A mixed methods systematic review will be conducted according to the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) checklist. The research will be conducted in an iterative process among the different authors. With the help of a medical information specialist, a specific search strategy will be formulated for the 6 most relevant databases (ie, MEDLINE, Embase, CINAHL, Web of Science, Academic Search Premier, PsycINFO, and the Google Scholar search engine). A literature search covering studies published between 2000 and 2022 has already been conducted. Two reviewers then proceeded, individually and independently, to conduct a double selection of titles, abstracts, and then full texts. Data extraction will be conducted by a reviewer and validated by a senior researcher. We will use the narrative synthesis method (ie, structured narrative summaries of key themes) to present a comprehensive picture of effective digital health literacy interventions among forced migrant populations and the success factors of these interventions. Results: The search strategy and literature search were completed in December 2022. A total of 1232 articles were identified. The first selection was completed in July 2023. The second selection is still in progress. The publication of the systematic review is scheduled for December 2023. Conclusions: This mixed methods systematic review will provide comprehensive knowledge on effective interventions for digital literacy among forced migrant populations. The evidence generated will further inform stakeholders and aid decision makers in promoting equitable access to and use of digital health resources for forced migrant populations and the general population in host countries. International Registered Report Identifier (IRRID): DERR1-10.2196/50798

Interactive conversational agents, also known as chatbots, have the potential to increase the success rate of digital technology interventions to promote healthy behaviors. However, due to their newness and limited use, little is known about their integration, usefulness, and effectiveness in promoting smoking and vaping cessation. The aim of this mixed-methods systematic review was to assess the effectiveness and characteristics of current interactive conversational agents in promoting and supporting smoking and vaping cessation. A mixed-methods systematic review was conducted to identify studies published in the last 20 years in five relevant databases. Eight studies, including seven on smoking cessation and one on smoking and vaping cessation, were included. The results showed that, compared to other smoking cessation methods, chatbots can lead to better engagement in treatment, resulting in higher rates of sustained abstinence and improved quality of life. In addition, chatbots can be perceived as empathetic and establish a decent therapeutic alliance thanks to their communication skills. This knowledge could be useful for the development of interactive conversational agents to support smoking and/or vaping cessation. Alternative intervention tools targeting younger generations, such as chatbots, may offer an additional way for public health professionals to reach them.

Background: Interactive conversational agents, also known as “chatbots,” are computer programs that use natural language processing to engage in conversations with humans to provide or collect information. Although the literature on the development and use of chatbots for health interventions is growing, important knowledge gaps remain, such as identifying design aspects relevant to health care and functions to offer transparency in decision-making automation. Objective: This paper presents the protocol for a scoping review that aims to identify and categorize the interactive conversational agents currently used in health care. Methods: A mixed methods systematic scoping review will be conducted according to the Arksey and O’Malley framework and the guidance of Peters et al for systematic scoping reviews. A specific search strategy will be formulated for 5 of the most relevant databases to identify studies published in the last 20 years. Two reviewers will independently apply the inclusion criteria using the full texts and extract data. We will use structured narrative summaries of main themes to present a portrait of the current scope of available interactive conversational agents targeting health promotion, prevention, and care. We will also summarize the differences and similarities between these conversational agents. Results: The search strategy and screening steps were completed in March 2022. Data extraction and analysis started in May 2022, and the results are expected to be published in October 2022. Conclusions: This fundamental knowledge will be useful for the development of interactive conversational agents adapted to specific groups in vulnerable situations in health care and community settings. International Registered Report Identifier (IRRID): DERR1-10.2196/40265

This study aimed to systematically identify, synthesize, and evaluate measurement properties of patient-reported outcome measures (PROMs) of eHL in adult p