Review
- Sadia Azmin Anisha1*, BBusCom, MBusInfSys ;
- Arkendu Sen1*, MBBS, GCHE, DipOphthal, DipForensMed, MSurg, PhD ;
- Chris Bain2*, MBBS, MInfoTech, MACS, PhD
1Jeffrey Cheah School of Medicine & Health Sciences, Monash University Malaysia, Bandar Sunway, Malaysia
2Faculty of Information Technology, Data Future Institutes, Monash University, Clayton, Australia
*all authors contributed equally
Corresponding Author:
Arkendu Sen, MBBS, GCHE, DipOphthal, DipForensMed, MSurg, PhD
Jeffrey Cheah School of Medicine & Health Sciences
Monash University Malaysia
Jalan Lagoon Selatan
Bandar Sunway, 47500
Malaysia
Phone: 60 3 551 46325
Fax:60 3 551 46323
Email: arkendu.sen@monash.edu
Abstract
Background: The rising prevalence of noncommunicable diseases (NCDs) worldwide and the high recent mortality rates (74.4%) associated with them, especially in low- and middle-income countries, is causing a substantial global burden of disease, necessitating innovative and sustainable long-term care solutions.
Objective: This scoping review aims to investigate the impact of artificial intelligence (AI)–based conversational agents (CAs)—including chatbots, voicebots, and anthropomorphic digital avatars—as human-like health caregivers in the remote management of NCDs as well as identify critical areas for future research and provide insights into how these technologies might be used effectively in health care to personalize NCD management strategies.
Methods: A broad literature search was conducted in July 2023 in 6 electronic databases—Ovid MEDLINE, Embase, PsycINFO, PubMed, CINAHL, and Web of Science—using the search terms “conversational agents,” “artificial intelligence,” and “noncommunicable diseases,” including their associated synonyms. We also manually searched gray literature using sources such as ProQuest Central, ResearchGate, ACM Digital Library, and Google Scholar. We included empirical studies published in English from January 2010 to July 2023 focusing solely on health care–oriented applications of CAs used for remote management of NCDs. The narrative synthesis approach was used to collate and summarize the relevant information extracted from the included studies.
Results: The literature search yielded a total of 43 studies that matched the inclusion criteria. Our review unveiled four significant findings: (1) higher user acceptance and compliance with anthropomorphic and avatar-based CAs for remote care; (2) an existing gap in the development of personalized, empathetic, and contextually aware CAs for effective emotional and social interaction with users, along with limited consideration of ethical concerns such as data privacy and patient safety; (3) inadequate evidence of the efficacy of CAs in NCD self-management despite a moderate to high level of optimism among health care professionals regarding CAs’ potential in remote health care; and (4) CAs primarily being used for supporting nonpharmacological interventions such as behavioral or lifestyle modifications and patient education for the self-management of NCDs.
Conclusions: This review makes a unique contribution to the field by not only providing a quantifiable impact analysis but also identifying the areas requiring imminent scholarly attention for the ethical, empathetic, and efficacious implementation of AI in NCD care. This serves as an academic cornerstone for future research in AI-assisted health care for NCD management.
Trial Registration: Open Science Framework; https://doi.org/10.17605/OSF.IO/GU5PX
doi:10.2196/56114
Keywords
Introduction
Burden of Noncommunicable Diseases
Noncommunicable diseases (NCDs), also known as chronic diseases, are medical conditions that are not primarily caused by infectious agents (eg, viruses, bacteria, fungi, or parasites) and cannot be transmitted from one individual to another through close contact [Noncommunicable diseases. World Health Organization. 2021. URL: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases [accessed 2024-04-29] 1]. NCDs, such as cancer, cardiovascular diseases (CVDs), chronic obstructive pulmonary diseases, chronic respiratory diseases, chronic kidney diseases, cognitive disorders, metabolic syndrome, diabetes, and hypertension, are rising worldwide, with significantly higher rates in low- and middle-income countries (LMICs) [Boutayeb A, Boutayeb S. The burden of non communicable diseases in developing countries. Int J Equity Health. Jan 14, 2005;4(1):2. [FREE Full text] [CrossRef] [Medline]2-Lee JS, Kang MA, Lee SK. Effects of the e-Motivate4Change program on metabolic syndrome in young adults using health apps and wearable devices: quasi-experimental study. J Med Internet Res. Jul 30, 2020;22(7):e17031. [FREE Full text] [CrossRef] [Medline]4]. In 2019, NCDs contributed to the highest proportion of total mortality (74.4%), accounting for 7.1 million additional deaths in 2019 as compared to 2009 [Qiao J, Lin X, Wu Y, Huang X, Pan X, Xu J, et al. Global burden of non-communicable diseases attributable to dietary risks in 1990-2019. J Hum Nutr Diet. Feb 2022;35(1):202-213. [CrossRef] [Medline]5]. Nearly half of the mortalities in Asia are attributable to NCDs, causing >80% of CVD and diabetes deaths, 90% of chronic obstructive pulmonary disease deaths, and two-thirds of all cancer deaths occurring in LMICs, resulting in 47% of the global burden of disease [Islam SM, Purnat TD, Phuong NT, Mwingira U, Schacht K, Fröschl G. Non-communicable diseases (NCDs) in developing countries: a symposium report. Global Health. Dec 11, 2014;10:81. [FREE Full text] [CrossRef] [Medline]3,Wagner KH, Brath H. A global view on the development of non communicable diseases. Prev Med. May 2012;54 Suppl:S38-S41. [CrossRef] [Medline]6]. Major risk factors of NCDs include unhealthy dietary habits; physical inactivity; stress; and consumption of drugs, tobacco, and alcohol, which are generally modifiable due to lifestyle choices [Qiao J, Lin X, Wu Y, Huang X, Pan X, Xu J, et al. Global burden of non-communicable diseases attributable to dietary risks in 1990-2019. J Hum Nutr Diet. Feb 2022;35(1):202-213. [CrossRef] [Medline]5,Budreviciute A, Damiati S, Sabir DK, Onder K, Schuller-Goetzburg P, Plakys G, et al. Management and prevention strategies for non-communicable diseases (NCDs) and their risk factors. Front Public Health. 2020;8:574111. [FREE Full text] [CrossRef] [Medline]7]. These chronic diseases are also major causes of long-term disabilities and prolonged costly treatment that may pose serious threats to a country’s health care resources and expenditure, especially among lower-income countries where health systems are not sufficiently equipped to tackle the escalating challenges [Islam SM, Purnat TD, Phuong NT, Mwingira U, Schacht K, Fröschl G. Non-communicable diseases (NCDs) in developing countries: a symposium report. Global Health. Dec 11, 2014;10:81. [FREE Full text] [CrossRef] [Medline]3,Lee JS, Kang MA, Lee SK. Effects of the e-Motivate4Change program on metabolic syndrome in young adults using health apps and wearable devices: quasi-experimental study. J Med Internet Res. Jul 30, 2020;22(7):e17031. [FREE Full text] [CrossRef] [Medline]4,Mohamud WN, Ismail AA, Sharifuddin A, Ismail IS, Musa KI, Kadir KA, et al. Prevalence of metabolic syndrome and its risk factors in adult Malaysians: results of a nationwide survey. Diabetes Res Clin Pract. Feb 2011;91(2):239-245. [CrossRef] [Medline]8].
In addition to regular visits to health care centers, NCDs require challenging self-care management, including compliance with medications, lifestyle modifications, and constant symptom monitoring to prevent disease progression; nonetheless, adherence to these procedures is generally low, especially among adults with limited health literacy who struggle to comprehend and follow instructions from their health care professionals [Bickmore T. Relational agents for chronic disease self-management. In: Hayes BM, Aspray W, editors. Health Informatics: A Patient-Centered Approach to Diabetes. Cambridge, MA. MIT Pres; 2010:181-204.9,Bickmore T, Pusateri A, Kimani EN, Paasche-Orlow MK, Trinh H, Magnani JW. Managing chronic conditions with a smartphone-based conversational virtual agent. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents. 2018. Presented at: IVA '18; November 5-8, 2018:119-124; Sydney, Australia. URL: https://dl.acm.org/doi/10.1145/3267851.3267908 [CrossRef]10]. Moreover, the shortage of health care providers and their limited time availability are substantial causes of patients’ being deprived of receiving adequate health education and support to make informed decisions required for the effective self-management of their chronic illnesses [Jennings A, Powell J, Armstrong N, Sturt J, Dale J. A virtual clinic for diabetes self-management: pilot study. J Med Internet Res. Mar 30, 2009;11(1):e10. [FREE Full text] [CrossRef] [Medline]11,Moriuchi E. Leveraging the science to understand factors influencing the use of AI-powered avatar in healthcare services. J Technol Behav Sci. Sep 09, 2022;7(4):588-602. [CrossRef]12]. In addition, access to proper health services can be limited in many underdeveloped areas and rural communities due to poor health care infrastructure and mobility facilities [Ahmed F. Beyond patient monitoring: conversational agents role in telemedicine and healthcare support for home-living elderly individuals. arXiv. Preprint posted online March 3, 2018. [FREE Full text]13].
The optimal prevention management strategy should incorporate elements of individual lifestyle management, societal health awareness management, national health policy decisions, and global health strategy [Budreviciute A, Damiati S, Sabir DK, Onder K, Schuller-Goetzburg P, Plakys G, et al. Management and prevention strategies for non-communicable diseases (NCDs) and their risk factors. Front Public Health. 2020;8:574111. [FREE Full text] [CrossRef] [Medline]7]. However, the escalating prevalence of NCDs worldwide reveals that traditional disease management techniques are not sufficiently effective, thereby indicating an urgent necessity to develop effective supplementary management strategies to mitigate the substantial financial burden imposed by NCDs on many households, particularly in LMICs [Boutayeb A, Boutayeb S. The burden of non communicable diseases in developing countries. Int J Equity Health. Jan 14, 2005;4(1):2. [FREE Full text] [CrossRef] [Medline]2,Islam SM, Purnat TD, Phuong NT, Mwingira U, Schacht K, Fröschl G. Non-communicable diseases (NCDs) in developing countries: a symposium report. Global Health. Dec 11, 2014;10:81. [FREE Full text] [CrossRef] [Medline]3,Kankeu HT, Saksena P, Xu K, Evans DB. The financial burden from non-communicable diseases in low- and middle-income countries: a literature review. Health Res Policy Syst. Aug 16, 2013;11:31. [FREE Full text] [CrossRef] [Medline]14].
The Role of Telehealth in NCD Management
Telehealth applications have the potential to improve patient self-care and disease-specific knowledge as well as minimize hospitalizations and mortality [Gingele AJ, Amin H, Vaassen A, Schnur I, Pearl C, Brunner-La Rocca HP, et al. Integrating avatar technology into a telemedicine application in heart failure patients : a pilot study. Wien Klin Wochenschr. Dec 2023;135(23-24):680-684. [FREE Full text] [CrossRef] [Medline]15]. There is evidence from several past studies suggesting the significant effectiveness of mobile-based telehealth apps in improving nutritional intake and physical activity with technology intervention, resulting in body weight loss and adoption of recommended lifestyle changes due to their convenience and accessibility [Toro-Ramos T, Lee DH, Kim Y, Michaelides A, Oh TJ, Kim KM, et al. Effectiveness of a smartphone application for the management of metabolic syndrome components focusing on weight loss: a preliminary study. Metab Syndr Relat Disord. Nov 2017;15(9):465-473. [CrossRef] [Medline]16]. This is because such apps, with the help of existing and emerging technologies, have the ability to assist patients in managing their chronic diseases more effectively by providing constant self-monitoring tools and promoting improved self-management of health problems [Jakobsen AS, Laursen LC, Rydahl-Hansen S, Østergaard B, Gerds TA, Emme C, et al. Home-based telehealth hospitalization for exacerbation of chronic obstructive pulmonary disease: findings from "the virtual hospital" trial. Telemed J E Health. May 2015;21(5):364-373. [FREE Full text] [CrossRef] [Medline]17]. Furthermore, the ever-growing ownership of mobile devices worldwide has greatly contributed to the shift toward digital health care services, including assessment, monitoring, and treatment of physical and mental health, thereby indicating the promising ability of mobile health apps in self-monitoring, assessment, and treatment of NCDs at a reduced cost [Gan SK, Koshy C, Nguyen PV, Haw YX. An overview of clinically and healthcare related apps in Google and Apple app stores: connecting patients, drugs, and clinicians. Sci Phone Appl Mob Devices. Jul 19, 2016;2(1). [CrossRef]18-Marshall JM, Dunstan DA, Bartik W. Clinical or gimmickal: the use and effectiveness of mobile mental health apps for treating anxiety and depression. Aust N Z J Psychiatry. Jan 2020;54(1):20-28. [CrossRef] [Medline]20]. Indeed, the COVID-19 pandemic has expedited the use of telehealth [Ladin K, Porteny T, Perugini JM, Gonzales KM, Aufort KE, Levine SK, et al. Perceptions of telehealth vs in-person visits among older adults with advanced kidney disease, care partners, and clinicians. JAMA Netw Open. Dec 01, 2021;4(12):e2137193. [FREE Full text] [CrossRef] [Medline]21,Matamala-Gomez M, Bottiroli S, Realdon O, Riva G, Galvagni L, Platz T, et al. Telemedicine and virtual reality at time of COVID-19 pandemic: an overview for future perspectives in neurorehabilitation. Front Neurol. 2021;12:646902. [FREE Full text] [CrossRef] [Medline]22], which has been proposed as a cost-effective method of delivering better health care services to people with chronic illnesses in a more flexible, personalized, transparent, dynamic, and accessible way [Ladin K, Porteny T, Perugini JM, Gonzales KM, Aufort KE, Levine SK, et al. Perceptions of telehealth vs in-person visits among older adults with advanced kidney disease, care partners, and clinicians. JAMA Netw Open. Dec 01, 2021;4(12):e2137193. [FREE Full text] [CrossRef] [Medline]21,Krausz M, Ward J, Ramsey D. From telehealth to an interactive virtual clinic. In: Mucic D, Hilty DM, editors. e-Mental Health. Cham, Switzerland. Springer; 2016:289-310.23].
Potential Enhancement of Existing Telehealth Apps With Artificial Intelligence
Although mobile-based telehealth apps offer an ideal platform for systems designed to help patients manage their chronic illnesses due to the smartphones’ computational power, connectivity, and consistent availability, many individuals struggle with complex user interfaces of existing digital health technologies [Bickmore T, Pusateri A, Kimani EN, Paasche-Orlow MK, Trinh H, Magnani JW. Managing chronic conditions with a smartphone-based conversational virtual agent. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents. 2018. Presented at: IVA '18; November 5-8, 2018:119-124; Sydney, Australia. URL: https://dl.acm.org/doi/10.1145/3267851.3267908 [CrossRef]10]. While the ubiquity of these apps reduces some acceptance barriers, most apps still overlook many other barriers, such as lack of motivational, psychological, and emotional support [Bickmore T, Pusateri A, Kimani EN, Paasche-Orlow MK, Trinh H, Magnani JW. Managing chronic conditions with a smartphone-based conversational virtual agent. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents. 2018. Presented at: IVA '18; November 5-8, 2018:119-124; Sydney, Australia. URL: https://dl.acm.org/doi/10.1145/3267851.3267908 [CrossRef]10,Gong E, Baptista S, Russell A, Scuffham P, Riddell M, Speight J, et al. My diabetes coach, a mobile app-based interactive conversational agent to support type 2 diabetes self-management: randomized effectiveness-implementation trial. J Med Internet Res. Nov 05, 2020;22(11):e20322. [FREE Full text] [CrossRef] [Medline]24]. Nonetheless, technological advancement involving artificial intelligence (AI) seems to have the potential to further upgrade existing mobile health apps with more user-friendly features that can support individual user needs [Viana Ó, Terroso M, Serejo C, Vilaça JL. Ascertaining the influence of style on the credibility and appeal of a digital health avatar. In: Proceedings of the 6th International Conference on Design and Digital Communication. Cham. Springer; 2022. Presented at: DIGICOM '22; November 3–5, 2022:27; Barcelos, Portugal. URL: https://link.springer.com/chapter/10.1007/978-3-031-20364-0_6 [CrossRef]25]. For instance, many patients lack the ability to effectively navigate conventional telehealth apps due to limited health-related or computer literacy and disabilities such as visual impairment; hence, such intelligent dialogue systems, specifically termed conversational agents (CAs), may help overcome these limitations and improve usability by providing an oral presentation of the apps’ contents in plain language [Gingele AJ, Amin H, Vaassen A, Schnur I, Pearl C, Brunner-La Rocca HP, et al. Integrating avatar technology into a telemedicine application in heart failure patients : a pilot study. Wien Klin Wochenschr. Dec 2023;135(23-24):680-684. [FREE Full text] [CrossRef] [Medline]15]. AI-powered CAs are computer systems that can communicate with humans through text, voice, and images on mobile, web-based, or audio-based platforms using AI techniques such as machine learning (ML; a statistical method of training models using data for making predictions based on a variety of features) and natural language processing (machines’ ability to detect and interpret humans’ verbal and written languages) [Milne-Ives M, de Cock C, Lim E, Shehadeh MH, de Pennington N, Mole G, et al. The effectiveness of artificial intelligence conversational agents in health care: systematic review. J Med Internet Res. Oct 22, 2020;22(10):e20346. [FREE Full text] [CrossRef] [Medline]26,Bin Sawad A, Narayan B, Alnefaie A, Maqbool A, Mckie I, Smith J, et al. A systematic review on healthcare artificial intelligent conversational agents for chronic conditions. Sensors (Basel). Mar 29, 2022;22(7):2625. [FREE Full text] [CrossRef] [Medline]27]. A CA may serve as an empathetic listener to understand patients’ problems as well as aid in monitoring a patient’s health 24/7 and notify physicians about an anticipated medical emergency [Ahmed F. Beyond patient monitoring: conversational agents role in telemedicine and healthcare support for home-living elderly individuals. arXiv. Preprint posted online March 3, 2018. [FREE Full text]13].
The popularity of CAs, especially those that use unrestrained natural language, has increased over the last decade as consumers can use their smartphones to interact with CAs for daily tasks [Bin Sawad A, Narayan B, Alnefaie A, Maqbool A, Mckie I, Smith J, et al. A systematic review on healthcare artificial intelligent conversational agents for chronic conditions. Sensors (Basel). Mar 29, 2022;22(7):2625. [FREE Full text] [CrossRef] [Medline]27]. When deployed on mobile devices, CAs have the potential to augment human intelligence and demonstrate multiple benefits such as delivering health education and behavior change for a range of chronic health conditions [Bickmore T, Pusateri A, Kimani EN, Paasche-Orlow MK, Trinh H, Magnani JW. Managing chronic conditions with a smartphone-based conversational virtual agent. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents. 2018. Presented at: IVA '18; November 5-8, 2018:119-124; Sydney, Australia. URL: https://dl.acm.org/doi/10.1145/3267851.3267908 [CrossRef]10]. Moreover, these AI-based CAs provide additional communication channels that are particularly effective for developing trust and therapeutic alliance to encourage adherence among users [Bickmore T, Pusateri A, Kimani EN, Paasche-Orlow MK, Trinh H, Magnani JW. Managing chronic conditions with a smartphone-based conversational virtual agent. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents. 2018. Presented at: IVA '18; November 5-8, 2018:119-124; Sydney, Australia. URL: https://dl.acm.org/doi/10.1145/3267851.3267908 [CrossRef]10]. The human-like conversational features of CAs due to advancements in natural language processing, voice recognition, and AI are increasingly substituting human employees in service encounters, including the health care industry to deliver personalized care and support to individuals with chronic health issues [Balsa J, Neves P, Félix I, Guerreiro MP, Alves P, Carmo MB, et al. Intelligent virtual assistant for promoting behaviour change in older people with T2D. In: Proceedings of the 19th EPIA Conference on Artificial Intelligence on Progress in Artificial Intelligence. 2019. Presented at: EPIA '19; September 3-6, 2019:372-383; Vila Real, Portugal. URL: https://link.springer.com/chapter/10.1007/978-3-030-30241-2_32 [CrossRef]28].
Types of CAs
In this paper, we broadly categorize intelligent CAs into 3 main types (Table 1): chatbots (text based or voice enabled—voicebots) without embodiment, computer-based embodied digital avatars, and physically embodied humanoid robots [Van Pinxteren MM, Pluymaekers M, Lemmink JG. Human-like communication in conversational agents: a literature review and research agenda. J Serv Manag. Jun 11, 2020;31(2):203-225. [CrossRef]29]. Although robots are beyond the scope of this paper, AI humanoid or social robots can also be classified as CAs that can be used as human-like health caregivers for managing NCDs. Despite the differences, all CAs, including humanoid robots, aim to enhance relational outcomes through human-like communication [Van Pinxteren MM, Pluymaekers M, Lemmink JG. Human-like communication in conversational agents: a literature review and research agenda. J Serv Manag. Jun 11, 2020;31(2):203-225. [CrossRef]29].
AIa-based CA type (with examples) | Features and functionality | Interaction mode | Advantages | Challenges |
Chatbots (eg, ChatGPT) |
| Text based |
|
|
Voicebots (or voice assistants; eg, Amazon Alexa and Apple’s Siri) |
| Voice based (or text+voice enabled) |
|
|
Digital avatars (eg, Replika, Mitsuku, and Soul Machines) |
| Multimodal (text based, voice activated, and face-to-face) |
|
|
Humanoid (or anthropomorphic or social) robots (eg, Sophia and Ameca) |
| Physical embodiment |
|
|
aAI: artificial intelligence.
bCGI: computer-generated imagery.
cNLP: natural language processing.
dHRI: human-robot interaction.
eHCI: human-computer interaction.
Ethical Concerns of AI Agents
While AI agents have the capability to provide constant health surveillance support, challenges and risks associated with using AI-based CAs in health care remain. These include ethical concerns regarding data collection and interpretability of results, patient safety risks, biases encoded in algorithms, and cybersecurity [Garner TA, Powell WA, Carr V. Virtual carers for the elderly: a case study review of ethical responsibilities. Digit Health. 2016;2:2055207616681173. [FREE Full text] [CrossRef] [Medline]55,Kusal S, Patil S, Choudrie J, Kotecha K, Mishra S, Abraham A. AI-based conversational agents: a scoping review from technologies to future directions. IEEE Access. 2022;10:92337-92356. [CrossRef]56]. Furthermore, customers are often reluctant to engage with such AI-based CAs due to many factors such as trust, reliability, learning curve, usability, privacy, and data security that should be addressed in the design, deployment, and use of AI applications [Moriuchi E. Leveraging the science to understand factors influencing the use of AI-powered avatar in healthcare services. J Technol Behav Sci. Sep 09, 2022;7(4):588-602. [CrossRef]12,Van Pinxteren MM, Pluymaekers M, Lemmink JG. Human-like communication in conversational agents: a literature review and research agenda. J Serv Manag. Jun 11, 2020;31(2):203-225. [CrossRef]29,Yaghoubzadeh R, Kramer M, Pitsch K, Kopp S. Virtual agents as daily assistants for elderly or cognitively impaired people: studies on acceptance and interaction feasibility. In: Proceedings of the 13th International Conference on Intelligent Virtual Agents. 2013. Presented at: IVA '13; August 29-31, 2013:29-31; Edinburgh, UK. URL: https://link.springer.com/chapter/10.1007/978-3-642-40415-3_7 [CrossRef]57-Thakur N, Han CY. An approach to analyze the social acceptance of virtual assistants by elderly people. In: Proceedings of the 8th International Conference on the Internet of Things. 2018. Presented at: IOT '18; October 15-18, 2018:1-6; Santa Barbara, CA. URL: https://dl.acm.org/doi/10.1145/3277593.3277616 [CrossRef]59].
State-of-the-Art Summary
Our study is novel regarding CAs as human-like digital agents for managing NCDs remotely. As AI-based CAs are a relatively new area, limited research has been conducted on applying these emerging technologies in health care. While existing research on CAs as virtual caregivers for NCDs serves as a seminal foundation, such research has other critical limitations. To illustrate, most studies have primarily focused on applications of CAs for mental health conditions, overlooking broader NCDs such as CVDs, metabolic syndrome, or diabetes [Dingler T, Kwasnicka D, Wei J, Gong E, Oldenburg B. The use and promise of conversational agents in digital health. Yearb Med Inform. Aug 2021;30(1):191-199. [FREE Full text] [CrossRef] [Medline]60,Lyzwinski LN, Elgendi M, Menon C. Conversational agents and avatars for cardiometabolic risk factors and lifestyle-related behaviors: scoping review. JMIR Mhealth Uhealth. May 25, 2023;11:e39649. [FREE Full text] [CrossRef] [Medline]61]. A recent review [Lyzwinski LN, Elgendi M, Menon C. Conversational agents and avatars for cardiometabolic risk factors and lifestyle-related behaviors: scoping review. JMIR Mhealth Uhealth. May 25, 2023;11:e39649. [FREE Full text] [CrossRef] [Medline]61] that focused on a different set of research questions from ours concluded the following: “A future chatbot could be tailored to metabolic syndrome specifically, targeting all the areas covered in the literature, which would be novel.” Our research looks specifically into this gap.
Furthermore, despite some favorable anecdotal evidence, the effectiveness of CAs in NCD management is seldom explored in large-scale trials, particularly in older adults, who have the highest risk of developing NCDs [Tudor Car L, Dhinagaran DA, Kyaw BM, Kowatsch T, Joty S, Theng YL, et al. Conversational agents in health care: scoping review and conceptual analysis. J Med Internet Res. Aug 07, 2020;22(8):e17158. [FREE Full text] [CrossRef] [Medline]62-Guerreiro MP, Angelini L, Rafael Henriques H, El Kamali M, Baixinho C, Balsa J, et al. Conversational agents for health and well-being across the life course: protocol for an evidence map. JMIR Res Protoc. Sep 17, 2021;10(9):e26680. [FREE Full text] [CrossRef] [Medline]64]. Some recent previous studies have conducted systematic reviews [Bin Sawad A, Narayan B, Alnefaie A, Maqbool A, Mckie I, Smith J, et al. A systematic review on healthcare artificial intelligent conversational agents for chronic conditions. Sensors (Basel). Mar 29, 2022;22(7):2625. [FREE Full text] [CrossRef] [Medline]27,Xing Z, Yu F, Qanir YA, Guan T, Walker J, Song L. Intelligent conversational agents in patient self-management: a systematic survey using multi data sources. Stud Health Technol Inform. Aug 21, 2019;264:1813-1814. [CrossRef] [Medline]65,Griffin AC, Xing Z, Khairat S, Wang Y, Bailey S, Arguello J, et al. Conversational agents for chronic disease self-management: a systematic review. AMIA Annu Symp Proc. 2020;2020:504-513. [FREE Full text] [Medline]66] or scoping reviews [Lyzwinski LN, Elgendi M, Menon C. Conversational agents and avatars for cardiometabolic risk factors and lifestyle-related behaviors: scoping review. JMIR Mhealth Uhealth. May 25, 2023;11:e39649. [FREE Full text] [CrossRef] [Medline]61,Tudor Car L, Dhinagaran DA, Kyaw BM, Kowatsch T, Joty S, Theng YL, et al. Conversational agents in health care: scoping review and conceptual analysis. J Med Internet Res. Aug 07, 2020;22(8):e17158. [FREE Full text] [CrossRef] [Medline]62,Martinengo L, Jabir AI, Goh WW, Lo NY, Ho MR, Kowatsch T, et al. Conversational agents in health care: scoping review of their behavior change techniques and underpinning theory. J Med Internet Res. Oct 03, 2022;24(10):e39243. [FREE Full text] [CrossRef] [Medline]67] on CAs in chronic disease management; however, to the best of our knowledge, no review has been conducted yet on the application of AI-based CAs as human-like digital agents for managing NCDs remotely. Such limitations and gaps as highlighted previously present both challenges and opportunities for research to advance our understanding of how CAs can contribute to managing NCDs.
Aim
This scoping review aimed to provide an overview of the existing evidence and research on using assistive humanoid AI-based CAs in health and social care for managing NCDs. Our primary objective was to explore the impact of AI-based CAs, including embodied avatars, as human-like health carers for the self-management of chronic diseases. By examining the current literature on this topic, we hoped to identify key areas for future research and provide insights into how these technologies can be effectively used in health care and personalize NCD management strategies.
Research Objectives
Our research objectives were as follows: (1) to explore the current state of research on the use of AI-based CAs as human-like virtual health carers for managing NCDs, (2) to identify the potential benefits and challenges associated with the use of these technologies in the health care field, (3) to explore the efficacy of AI-based CAs in the remote management of NCDs, (4) to discover the specific target users primarily studied, and (5) to provide recommendations for future research in this area.
Research Questions
Our research questions were as follows: (1) what is the current state of research on using AI-based CAs as human-like health carers for managing NCDs? (2) What are the limitations or challenges associated with the use of these technologies in health care? (3) What are the potential benefits of using AI-based CAs in managing NCDs, and how can they be effectively used to improve health care delivery and reduce health care burden? (4) What is the efficacy of the CAs in the remote care of NCDs? (5) What are the frequently targeted user groups for such virtual agents (eg, specific age groups and individuals with special needs)?
Methods
Search Strategy
We followed the methodological frameworks proposed by Arksey and O’Malley [Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. Feb 2005;8(1):19-32. [CrossRef]68] and Levac et al [Levac D, Colquhoun H, O'Brien KK. Scoping studies: advancing the methodology. Implement Sci. Sep 20, 2010;5(1):69. [FREE Full text] [CrossRef] [Medline]69]. Initially, research objectives and questions were formulated, followed by a systematic literature search conducted on July 31, 2023. For primary searching, we used 6 electronic databases that are considered relevant to the research focus—Ovid MEDLINE, Embase, PsycINFO, PubMed, CINAHL, and Web of Science—applying the same set of keywords, such as “conversational agents,” “artificial intelligence,” and “noncommunicable diseases,” including their associated synonyms (as shown in Textbox 1). The Boolean operators “*” and “OR” were used to expand and ensure that different word combinations were included. The operator “AND” was used for combining the main search terms to identify articles focusing on AI-based CAs (as health carers) only applicable in the health care field, particularly for managing NCDs or chronic diseases.
Additional studies were identified by hand searching the reference lists of included studies and relevant review articles. Furthermore, a supplementary manual search was conducted to identify specific articles from diverse sources, including ProQuest Central, ResearchGate, ACM Digital Library, and Google Scholar. Some of these articles were identified through pilot hand searching and preselected as they were closely relevant but not retrieved from the selected databases used for primary searching, whereas others were discovered through manual searches targeting specific authors well known in this field. The search strategy underwent refinement following expert recommendations, including insights from a coauthor with digital health expertise, who advised excluding “robots” from the search terms, recognizing it as a distinct field. These methodological steps collectively ensured a comprehensive exploration of the relevant literature.
Example
- (“conversational agent*” OR “relational agent*” OR “virtual agent*” OR “dialogue agent*” OR “dialogue system*” OR “virtual assistant*” OR “chatbot*” OR “voice assistant*” OR “voicebot*” OR “voice-bot*” or “voice bot*” OR “humanoid *bot*” OR “social *bot*” OR “avatar*” OR “human-like avatar*” OR “anthropomorphic avatar*” OR “digital human*” OR “human digital twin*” OR “virtual human*”) AND (“intelligent” OR “artificial intelligence” OR “AI” OR “AI-based”) AND (“health” OR “healthcare” OR “caregiver” OR “self-management” OR “self-monitor*” OR “non-communicable disease*” OR “noncommunicable disease*” OR “chronic disease*”)
Eligibility Criteria
Our scoping review used comprehensive inclusion and exclusion criteria to ensure the selection of pertinent studies. We did not impose any limitations based on gender or age groups in the selection of articles. The search scope was confined to scholarly articles in English published between January 2010 and July 2023, aligning with the substantial rise in CAs after 2010 [Even C, Hammann T, Heyl V, Rietz C, Wahl HW, Zentel P, et al. Benefits and challenges of conversational agents in older adults : a scoping review. Z Gerontol Geriatr. Aug 2022;55(5):381-387. [CrossRef] [Medline]70], notably with the introduction of Apple’s Siri in 2011 [Grudin J. ChatGPT and chat history: challenges for the new wave. Computer. May 2023;56(5):94-100. [CrossRef]71]. Evidently, most of the selected papers were on recent studies conducted within the last 5 years due to the accelerated technological advancements and the latest evolution of AI. Specifically, we focused on empirical studies exploring CAs applied exclusively to human interaction within the health care context, emphasizing their role in the remote management of NCDs, ideally within home environments.
The exclusion criteria comprised the exclusion of conference abstracts, posters, reviews, protocols, position papers or viewpoints, and certain types of studies such as those involving noninteractive robotic devices. We also excluded studies related to CAs designed for medical education and non–patient-centered applications in hospital settings and those addressing specific health care domains such as surgery, dentistry, pregnancy or maternity, addiction or substance use disorders, and communicable diseases. Furthermore, we excluded studies centered solely on medical history data storage, telephone monitoring, data set construction methods, or user evaluations of commercial CAs for health care without their practical applications in the remote management of NCDs. Our criteria aimed to streamline the focus on patient-centered CAs contributing to the self-management and remote monitoring of NCDs, eliminating studies with a primary emphasis on clinical interviews, disease prediction, or decision support without a social interaction element.
Screening and Selection
In total, 2 of the authors independently searched each database. Titles and abstracts were screened for inclusion according to the aforementioned criteria, followed by an exclusion of duplicates, unrelated studies, and articles that could not be retrieved. The abstract screening yielded 264 articles eligible for full-text screening, of which 70 (26.5%) were review papers comparing different types of CAs in the health care field and 156 (59.1%) were empirical studies that explored different types of CAs used in the prevention, treatment, or rehabilitation of chronic diseases involving consumers, caregivers, and health care professionals.
Subsequently, the authors screened full texts of the remaining articles independently, and 40 full-text articles that met the inclusion criteria were selected for review. In addition, 10 [Bickmore T, Pusateri A, Kimani EN, Paasche-Orlow MK, Trinh H, Magnani JW. Managing chronic conditions with a smartphone-based conversational virtual agent. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents. 2018. Presented at: IVA '18; November 5-8, 2018:119-124; Sydney, Australia. URL: https://dl.acm.org/doi/10.1145/3267851.3267908 [CrossRef]10] specific articles were obtained from hand searches of other sources (Google Scholar, ProQuest, ACM Digital Library, and ResearchGate), of which 3 (30%) [Islam SM, Purnat TD, Phuong NT, Mwingira U, Schacht K, Fröschl G. Non-communicable diseases (NCDs) in developing countries: a symposium report. Global Health. Dec 11, 2014;10:81. [FREE Full text] [CrossRef] [Medline]3] relevant ones were selected upon full-text screening. It was an iterative process; any discrepancies were discussed among the authors. The search and selection process is illustrated in Figure 1.
Data Collation and Reporting
An Excel (Microsoft Corp) spreadsheet was initially created to aid the screening and selection process. Following the screening process, a total of 43 articles were eventually selected for synthesis. Quantitative and qualitative data from the included studies were extracted and summarized in a tabular format, including information such as intervention, type of CA, target population, number of participants with their age, methods, study duration, location, measures and outcomes, and limitations. The relevant extracted information was collated and summarized using the narrative synthesis approach, which was deemed appropriate for capturing the breadth of evidence in scoping reviews, identifying themes aligned with the research questions as well as patterns observed across the included studies and relevant reviews. The results were reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Given the divergent methodologies of the included studies, predominantly using mixed methods and qualitative approaches, no quantitative synthesis or meta-analysis was conducted.
Results
Characteristics of the Included Studies
Most of the included studies (22/43, 51%) were feasibility, acceptability, and usability studies (Table 2). These studies mostly found positive results in terms of feasibility and usability of the proposed CAs. Some studies (4/43, 9%) used the System Usability Scale questionnaire to measure the system’s usability score and predominantly found high usability, with System Usability Scale scores of >70 [Balsa J, Félix I, Cláudio AP, Carmo MB, Silva IC, Guerreiro A, et al. Usability of an intelligent virtual assistant for promoting behavior change and self-care in older people with type 2 diabetes. J Med Syst. Jun 13, 2020;44(7):130. [CrossRef] [Medline]72-Babington-Ashaye A, de Moerloose P, Diop S, Geissbuhler A. Design, development and usability of an educational AI chatbot for people with haemophilia in senegal. Haemophilia. Jul 2023;29(4):1063-1073. [CrossRef] [Medline]75]. Alternatively, multiple studies (10/43, 23%) used the subjective ratings of most of the participants to evaluate the feasibility and acceptability of CAs by applying indicators such as perceived usefulness [Stephens TN, Joerin A, Rauws M, Werk LN. Feasibility of pediatric obesity and prediabetes treatment support through Tess, the AI behavioral coaching chatbot. Transl Behav Med. May 16, 2019;9(3):440-447. [CrossRef] [Medline]76-Alhmiedat T, Alotaibi M. Employing social robots for managing diabetes among children: SARA. Wireless Pers Commun. Mar 14, 2023;130(1):449-468. [CrossRef]79], ease of use [Rahmanti AR, Yang HC, Bintoro BS, Nursetyo AA, Muhtar MS, Syed-Abdul S, et al. SlimMe, a chatbot with artificial empathy for personal weight management: system design and finding. Front Nutr. 2022;9:870775. [FREE Full text] [CrossRef] [Medline]78,Kimani E, Bickmore T, Trinh H, Ring L, Paasche-Orlow MK, Magnani JW. A smartphone-based virtual agent for atrial fibrillation education and counseling. In: Proceedings of the 16th International Conference on Intelligent Virtual Agents. 2016. Presented at: IVA '16; September 20-23, 2016:120-127; Los Angeles, CA. URL: https://link.springer.com/chapter/10.1007/978-3-319-47665-0_11 [CrossRef]80], user satisfaction [Kimani E, Bickmore T, Trinh H, Ring L, Paasche-Orlow MK, Magnani JW. A smartphone-based virtual agent for atrial fibrillation education and counseling. In: Proceedings of the 16th International Conference on Intelligent Virtual Agents. 2016. Presented at: IVA '16; September 20-23, 2016:120-127; Los Angeles, CA. URL: https://link.springer.com/chapter/10.1007/978-3-319-47665-0_11 [CrossRef]80,Kataoka Y, Takemura T, Sasajima M, Katoh N. Development and early feasibility of chatbots for educating patients with lung cancer and their caregivers in Japan: mixed methods study. JMIR Cancer. Mar 10, 2021;7(1):e26911. [FREE Full text] [CrossRef] [Medline]81], engagement rate [Kimani E, Bickmore T, Trinh H, Ring L, Paasche-Orlow MK, Magnani JW. A smartphone-based virtual agent for atrial fibrillation education and counseling. In: Proceedings of the 16th International Conference on Intelligent Virtual Agents. 2016. Presented at: IVA '16; September 20-23, 2016:120-127; Los Angeles, CA. URL: https://link.springer.com/chapter/10.1007/978-3-319-47665-0_11 [CrossRef]80,Krishnakumar A, Verma R, Chawla R, Sosale A, Saboo B, Joshi S, et al. Evaluating glycemic control in patients of South Asian origin with type 2 diabetes using a digital therapeutic platform: analysis of real-world data. J Med Internet Res. Mar 25, 2021;23(3):e17908. [FREE Full text] [CrossRef] [Medline]82,Maharjan R, Doherty K, Rohani DA, Bækgaard P, Bardram JE. Experiences of a speech-enabled conversational agent for the self-report of well-being among people living with affective disorders: an in-the-wild study. ACM Trans Interact Intell Syst. Jul 20, 2022;12(2):1-29. [CrossRef]83], perceived closeness [Stara V, Vera B, Bolliger D, Rossi L, Felici E, Di Rosa M, et al. Usability and acceptance of the embodied conversational agent Anne by people with dementia and their caregivers: exploratory study in home environment settings. JMIR Mhealth Uhealth. Jun 25, 2021;9(6):e25891. [FREE Full text] [CrossRef] [Medline]73], and Net Promoter Score [Calvo RA, Peters D, Moradbakhti L, Cook D, Rizos G, Schuller B, et al. Assessing the feasibility of a text-based conversational agent for asthma support: protocol for a mixed methods observational study. JMIR Res Protoc. Feb 02, 2023;12:e42965. [FREE Full text] [CrossRef] [Medline]84]. However, 2% (1/43) of the studies reported negative usability and acceptability outcomes, indicating unsatisfactory reliability, usability, and goal structure, which hindered health care professionals’ acceptance and trust [Neerincx MA, van Vught W, Blanson Henkemans O, Oleari E, Broekens J, Peters R, et al. Socio-cognitive engineering of a robotic partner for child's diabetes self-management. Front Robot AI. 2019;6:118. [FREE Full text] [CrossRef] [Medline]85].
Study, year | Intervention and study duration | CAa type and delivery platform | Target population, sample size, and participants’ age | Methods and location | Measures and outcomes | Limitations |
Watson et al [Watson A, Bickmore T, Cange A, Kulshreshtha A, Kvedar J. An internet-based virtual coach to promote physical activity adherence in overweight adults: randomized controlled trial. J Med Internet Res. Jan 26, 2012;14(1):e1. [FREE Full text] [CrossRef] [Medline]86], 2012 |
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Kimani et al [Kimani E, Bickmore T, Trinh H, Ring L, Paasche-Orlow MK, Magnani JW. A smartphone-based virtual agent for atrial fibrillation education and counseling. In: Proceedings of the 16th International Conference on Intelligent Virtual Agents. 2016. Presented at: IVA '16; September 20-23, 2016:120-127; Los Angeles, CA. URL: https://link.springer.com/chapter/10.1007/978-3-319-47665-0_11 [CrossRef]80], 2016 |
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Shamekhi et al [Shamekhi A, Bickmore T, Lestoquoy A, Gardiner P. Augmenting group medical visits with conversational agents for stress management behavior change. In: Proceedings of the 12th International Conference on Persuasive Technology: Development and Implementation of Personalized Technologies to Change Attitudes and Behaviors. 2017. Presented at: PERSUASIVE '17; April 4-6, 2017:55-67; Amsterdam, the Netherlands. URL: https://link.springer.com/chapter/10.1007/978-3-319-55134-0_587], 2017 |
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Bickmore et al [Bickmore T, Pusateri A, Kimani EN, Paasche-Orlow MK, Trinh H, Magnani JW. Managing chronic conditions with a smartphone-based conversational virtual agent. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents. 2018. Presented at: IVA '18; November 5-8, 2018:119-124; Sydney, Australia. URL: https://dl.acm.org/doi/10.1145/3267851.3267908 [CrossRef]10], 2018 |
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Cheng et al [Cheng A, Raghavaraju V, Kanugo J, Handrianto YP, Yi S. Development and evaluation of a healthy coping voice interface application using the Google home for elderly patients with type 2 diabetes. In: Proceedings of the 15th IEEE Annual Consumer Communications & Networking Conference. 2018. Presented at: CCNC '18; January 12-15, 2018:1-5; Las Vegas, NV. URL: https://ieeexplore.ieee.org/document/8319283 [CrossRef]58], 2018 |
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Suganuma et al [Suganuma S, Sakamoto D, Shimoyama H. An embodied conversational agent for unguided internet-based cognitive behavior therapy in preventative mental health: feasibility and acceptability pilot trial. JMIR Ment Health. Jul 31, 2018;5(3):e10454. [FREE Full text] [CrossRef] [Medline]88], 2018 |
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Hussain and Athula [Hussain S, Athula G. Extending a conventional chatbot knowledge base to external knowledge source and introducing user based sessions for diabetes education. In: Proceedings of the 32nd International Conference on Advanced Information Networking and Applications Workshops. 2018. Presented at: WAINA '18; May 16-18, 2018:698-703; Krakow, Poland. URL: https://ieeexplore.ieee.org/document/8418155 [CrossRef]89], 2018 |
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Inkster et al [Inkster B, Sarda S, Subramanian V. An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: real-world data evaluation mixed-methods study. JMIR Mhealth Uhealth. Nov 23, 2018;6(11):e12106. [FREE Full text] [CrossRef] [Medline]90], 2018 |
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Neerincx et al [Neerincx MA, van Vught W, Blanson Henkemans O, Oleari E, Broekens J, Peters R, et al. Socio-cognitive engineering of a robotic partner for child's diabetes self-management. Front Robot AI. 2019;6:118. [FREE Full text] [CrossRef] [Medline]85], 2019 |
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Easton et al [Easton K, Potter S, Bec R, Bennion M, Christensen H, Grindell C, et al. A virtual agent to support individuals living with physical and mental comorbidities: co-design and acceptability testing. J Med Internet Res. May 30, 2019;21(5):e12996. [FREE Full text] [CrossRef] [Medline]91], 2019 |
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Chaix et al [Chaix B, Bibault JE, Pienkowski A, Delamon G, Guillemassé A, Nectoux P, et al. When chatbots meet patients: one-year prospective study of conversations between patients with breast cancer and a chatbot. JMIR Cancer. May 02, 2019;5(1):e12856. [FREE Full text] [CrossRef] [Medline]92], 2019 |
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Stephens et al [Stephens TN, Joerin A, Rauws M, Werk LN. Feasibility of pediatric obesity and prediabetes treatment support through Tess, the AI behavioral coaching chatbot. Transl Behav Med. May 16, 2019;9(3):440-447. [CrossRef] [Medline]76], 2019 |
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Balsa et al [Balsa J, Félix I, Cláudio AP, Carmo MB, Silva IC, Guerreiro A, et al. Usability of an intelligent virtual assistant for promoting behavior change and self-care in older people with type 2 diabetes. J Med Syst. Jun 13, 2020;44(7):130. [CrossRef] [Medline]72], 2020 |
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Gong et al [Gong E, Baptista S, Russell A, Scuffham P, Riddell M, Speight J, et al. My diabetes coach, a mobile app-based interactive conversational agent to support type 2 diabetes self-management: randomized effectiveness-implementation trial. J Med Internet Res. Nov 05, 2020;22(11):e20322. [FREE Full text] [CrossRef] [Medline]24], 2020 |
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Issom et al [Issom DZ, Rochat J, Hartvigsen G, Lovis C. Preliminary evaluation of a mHealth coaching conversational artificial intelligence for the self-care management of people with sickle-cell disease. Stud Health Technol Inform. Jun 16, 2020;270:1361-1362. [CrossRef] [Medline]77], 2020 |
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Anastasiadou et al [Anastasiadou M, Alexiadis A, Polychronidou E, Votis K, Tzovaras D. A prototype educational virtual assistant for diabetes management. In: Proceedings of the 20th International Conference on Bioinformatics and Bioengineering. 2020. Presented at: BIBE '20; October 26-28, 2020:999-1004; Cincinnati, OH. URL: https://ieeexplore.ieee.org/document/9288129 [CrossRef]93], 2020 |
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Roca et al [Roca S, Lozano ML, García J, Alesanco Á. Validation of a virtual assistant for improving medication adherence in patients with comorbid type 2 diabetes mellitus and depressive disorder. Int J Environ Res Public Health. Nov 17, 2021;18(22):12056. [FREE Full text] [CrossRef] [Medline]94], 2021 |
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Yao et al [Yao K, Wong KK, Yu X, Volpi J, Wong ST. An intelligent augmented lifelike avatar app for virtual physical examination of suspected strokes. Annu Int Conf IEEE Eng Med Biol Soc. Nov 2021;2021:1727-1730. [CrossRef] [Medline]95], 2021 |
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Krishnakumar et al [Krishnakumar A, Verma R, Chawla R, Sosale A, Saboo B, Joshi S, et al. Evaluating glycemic control in patients of South Asian origin with type 2 diabetes using a digital therapeutic platform: analysis of real-world data. J Med Internet Res. Mar 25, 2021;23(3):e17908. [FREE Full text] [CrossRef] [Medline]82], 2021 |
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Egede et al [Egede JO, Price D, Krishnan DB, Jaiswal S, Elliott N, Morriss R. Design and evaluation of virtual human mediated tasks for assessment of depression and anxiety. In: Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents. 2021. Presented at: IVA '21; September 14-17, 2021:52-59; Virtual Event. URL: https://dl.acm.org/doi/10.1145/3472306.3478361 [CrossRef]96], 2021 |
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Romanovskyi et al [Romanovskyi O, Pidbutska N, Knysh A. Elomia chatbot: the effectiveness of artificial intelligence in the fight for mental health. In: Proceedings of the 5th International Conference on Computational Linguistics and Intelligent Systems. 2021. Presented at: COLINS '21; April 22-23, 2021:1-10; Kharkiv, Ukraine. URL: https://ceur-ws.org/Vol-2870/paper89.pdf97], 2021 |
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Anan et al [Anan T, Kajiki S, Oka H, Fujii T, Kawamata K, Mori K, et al. Effects of an artificial intelligence-assisted health program on workers with neck/shoulder pain/stiffness and low back pain: randomized controlled trial. JMIR Mhealth Uhealth. Sep 24, 2021;9(9):e27535. [FREE Full text] [CrossRef] [Medline]98], 2021 |
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Zisis et al [Zisis G, Carrington MJ, Oldenburg B, Whitmore K, Lay M, Huynh Q, et al. An m-Health intervention to improve education, self-management, and outcomes in patients admitted for acute decompensated heart failure: barriers to effective implementation. Eur Heart J Digit Health. Dec 2021;2(4):649-657. [FREE Full text] [CrossRef] [Medline]99], 2021 |
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Stara et al [Stara V, Vera B, Bolliger D, Rossi L, Felici E, Di Rosa M, et al. Usability and acceptance of the embodied conversational agent Anne by people with dementia and their caregivers: exploratory study in home environment settings. JMIR Mhealth Uhealth. Jun 25, 2021;9(6):e25891. [FREE Full text] [CrossRef] [Medline]73], 2021 |
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Nguyen et al [Nguyen TT, Sim K, Kuen AT, O'Donnell RR, Lim ST, Wang W, et al. Designing AI-based conversational agent for diabetes care in a multilingual context. arXiv. Preprint posted online May 20, 2021. [FREE Full text]74], 2021 |
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Apergi et al [Apergi LA, Bjarnadottir MV, Baras JS, Golden BL, Anderson KM, Chou J, et al. Voice interface technology adoption by patients with heart failure: pilot comparison study. JMIR Mhealth Uhealth. Apr 01, 2021;9(4):e24646. [FREE Full text] [CrossRef] [Medline]100], 2021 |
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Kataoka et al [Kataoka Y, Takemura T, Sasajima M, Katoh N. Development and early feasibility of chatbots for educating patients with lung cancer and their caregivers in Japan: mixed methods study. JMIR Cancer. Mar 10, 2021;7(1):e26911. [FREE Full text] [CrossRef] [Medline]81], 2021 |
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Rathnayaka et al [Rathnayaka P, Mills N, Burnett D, De Silva D, Alahakoon D, Gray R. A mental health chatbot with cognitive skills for personalised behavioural activation and remote health monitoring. Sensors (Basel). May 11, 2022;22(10):3653. [FREE Full text] [CrossRef] [Medline]101], 2022 |
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Kannampallil et al [Kannampallil T, Ronneberg CR, Wittels NE, Kumar V, Lv N, Smyth JM, et al. Design and formative evaluation of a virtual voice-based coach for problem-solving treatment: observational study. JMIR Form Res. Aug 12, 2022;6(8):e38092. [FREE Full text] [CrossRef] [Medline]102], 2022 |
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Rahmanti et al [Rahmanti AR, Yang HC, Bintoro BS, Nursetyo AA, Muhtar MS, Syed-Abdul S, et al. SlimMe, a chatbot with artificial empathy for personal weight management: system design and finding. Front Nutr. 2022;9:870775. [FREE Full text] [CrossRef] [Medline]78], 2022 |
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Alturaiki et al [Alturaiki AM, Banjar HR, Barefah AS, Alnajjar SA, Hindawi S. A smart chatbot for interactive management in beta thalassemia patients. Int J Telemed Appl. 2022;2022:9734518. [FREE Full text] [CrossRef] [Medline]103], 2022 |
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Zahedi et al [Zahedi FM, Zhao H, Sanvanson P, Walia N, Jain H, Shaker R. My real avatar has a doctor appointment in the Wepital: a system for persistent, efficient, and ubiquitous medical care. Inf Manag. Dec 2022;59(8):103706. [CrossRef]104], 2022 |
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Eagle et al [Eagle T, Blau C, Bales S, Desai N, Li V, Whittaker S. “I don’t know what you mean by `I am anxious'”: a new method for evaluating conversational agent responses to standardized mental health inputs for anxiety and depression. ACM Trans Interact Intell Syst. Jul 20, 2022;12(2):1-23. [CrossRef]37], 2022 |
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Maharjan et al [Maharjan R, Doherty K, Rohani DA, Bækgaard P, Bardram JE. Experiences of a speech-enabled conversational agent for the self-report of well-being among people living with affective disorders: an in-the-wild study. ACM Trans Interact Intell Syst. Jul 20, 2022;12(2):1-29. [CrossRef]83], 2022 |
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Meheli et al [Meheli S, Sinha C, Kadaba M. Understanding people with chronic pain who use a cognitive behavioral therapy-based artificial intelligence mental health app (Wysa): mixed methods retrospective observational study. JMIR Hum Factors. Apr 27, 2022;9(2):e35671. [FREE Full text] [CrossRef] [Medline]105], 2022 |
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Henson et al [Henson JB, Glissen Brown JR, Lee JP, Patel A, Leiman DA. Evaluation of the potential utility of an artificial intelligence chatbot in gastroesophageal reflux disease management. Am J Gastroenterol. Dec 01, 2023;118(12):2276-2279. [CrossRef] [Medline]106], 2022 |
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Babington‐Ashaye et al [Babington-Ashaye A, de Moerloose P, Diop S, Geissbuhler A. Design, development and usability of an educational AI chatbot for people with haemophilia in senegal. Haemophilia. Jul 2023;29(4):1063-1073. [CrossRef] [Medline]75], 2023 |
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Alhmiedat and Alotaibi [Alhmiedat T, Alotaibi M. Employing social robots for managing diabetes among children: SARA. Wireless Pers Commun. Mar 14, 2023;130(1):449-468. [CrossRef]79], 2023 |
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Calvo et al [Calvo RA, Peters D, Moradbakhti L, Cook D, Rizos G, Schuller B, et al. Assessing the feasibility of a text-based conversational agent for asthma support: protocol for a mixed methods observational study. JMIR Res Protoc. Feb 02, 2023;12:e42965. [FREE Full text] [CrossRef] [Medline]84], 2023 |
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Epalte et al [Epalte K, Tomsone S, Vētra A, Bērziņa G. Patient experience using digital therapy "Vigo" for stroke patient recovery: a qualitative descriptive study. Disabil Rehabil Assist Technol. Feb 2023;18(2):175-184. [CrossRef] [Medline]107], 2023 |
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LeRouge et al [LeRouge C, Dickhut K, Lisetti C, Sangameswaran S, Malasanos T. Engaging adolescents in a computer-based weight management program: avatars and virtual coaches could help. J Am Med Inform Assoc. Jan 2016;23(1):19-28. [FREE Full text] [CrossRef] [Medline]108], 2023 |
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Park et al [Park G, Chung J, Lee S. Human vs. machine-like representation in chatbot mental health counseling: the serial mediation of psychological distance and trust on compliance intention. Curr Psychol. Apr 20, 2023:1-12. [FREE Full text] [CrossRef] [Medline]109], 2023 |
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Boggiss et al [Boggiss A, Consedine N, Hopkins S, Silvester C, Jefferies C, Hofman P, et al. Improving the well-being of adolescents with type 1 diabetes during the COVID-19 pandemic: qualitative study exploring acceptability and clinical usability of a self-compassion chatbot. JMIR Diabetes. May 05, 2023;8:e40641. [FREE Full text] [CrossRef] [Medline]110], 2023 |
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aCA: conversational agent.
bAI: artificial intelligence.
cRCT: randomized controlled trial.
dAF: atrial fibrillation.
eAFEQT: AF Effect on Quality of Life questionnaire.
fT2DM: type 2 diabetes mellitus.
gML: machine learning.
hmHealth: mobile health.
iAADE: American Association of Diabetes Educators.
jWHO-5: 5-item World Health Organization Well-Being Index.
kK10: Kessler Psychological Distress Scale.
lBADS: Behavioral Activation for Depression Scale.
mBADS-AC: Behavioral Activation for Depression Scale-Activation.
nBADS-AR: Behavioral Activation for Depression Scale-Avoidance/Rumination.
oBADS-WS: Behavioral Activation for Depression Scale-Work/School Impairment.
pBADS-SI: Behavioral Activation for Depression Scale-Social Impairment.
qVDMS: virtual diabetes management system.
rPHQ-9: 9-item Patient Health Questionnaire.
sGAD-7: 7-item Generalized Anxiety Disorder Scale.
tT1DM: type 1 diabetes mellitus.
uPAL: personal assistant for a healthy lifestyle.
vSUS: System Usability Scale.
wBCT: behavior change theory.
xHRQoL: health-related quality of life.
yAQoL-8D: Assessment of quality of life-8 dimensions.
zHbA1c: glycated hemoglobin.
aaHADS: Hospital Anxiety and Depression Scale.
abGP: general practitioner.
acSCD: sickle cell disease.
adEVA: Education virtual assistant.
aeFBG: fasting blood glucose.
afPPBG: postprandial blood glucose.
agVH: virtual human.
ahUX: user experience.
aiUEQ: User Experience Questionnaire.
ajTAM: technology acceptance model.
akNLP: natural language processing.
alCONSORT-EHEALTH: Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth.
amOR: odds ratio.
anRMDQ: Roland-Morris Disability Questionnaire.
aoHF: heart failure.
apMoCA: Montreal Cognitive Assessment.
aqDHFKS: Dutch Heart Failure Knowledge Scale.
arQoL-AD: Quality of Life in Alzheimer Disease scale.
asTTS: Text-to-Speech.
atFAQ: frequently asked question.
auWAI-TECH: Working Alliance Inventory–Technology Version.
avPST: problem-solving treatment.
awGIF: graphics interchange format.
axGI: gastrointestinal.
ayGERD: gastrointestinal reflux disease.
azPPI: proton-pump inhibitor.
baNLU: Natural Language Understanding.
A few studies (6/43, 14%) focused on evaluating the effectiveness of the CAs used for the self-management of certain chronic illnesses, such as atrial fibrillation [Bickmore T, Pusateri A, Kimani EN, Paasche-Orlow MK, Trinh H, Magnani JW. Managing chronic conditions with a smartphone-based conversational virtual agent. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents. 2018. Presented at: IVA '18; November 5-8, 2018:119-124; Sydney, Australia. URL: https://dl.acm.org/doi/10.1145/3267851.3267908 [CrossRef]10], type 2 diabetes mellitus [Gong E, Baptista S, Russell A, Scuffham P, Riddell M, Speight J, et al. My diabetes coach, a mobile app-based interactive conversational agent to support type 2 diabetes self-management: randomized effectiveness-implementation trial. J Med Internet Res. Nov 05, 2020;22(11):e20322. [FREE Full text] [CrossRef] [Medline]24,Krishnakumar A, Verma R, Chawla R, Sosale A, Saboo B, Joshi S, et al. Evaluating glycemic control in patients of South Asian origin with type 2 diabetes using a digital therapeutic platform: analysis of real-world data. J Med Internet Res. Mar 25, 2021;23(3):e17908. [FREE Full text] [CrossRef] [Medline]82], and mental health conditions such as depression and anxiety [Inkster B, Sarda S, Subramanian V. An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: real-world data evaluation mixed-methods study. JMIR Mhealth Uhealth. Nov 23, 2018;6(11):e12106. [FREE Full text] [CrossRef] [Medline]90,Egede JO, Price D, Krishnan DB, Jaiswal S, Elliott N, Morriss R. Design and evaluation of virtual human mediated tasks for assessment of depression and anxiety. In: Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents. 2021. Presented at: IVA '21; September 14-17, 2021:52-59; Virtual Event. URL: https://dl.acm.org/doi/10.1145/3472306.3478361 [CrossRef]96,Romanovskyi O, Pidbutska N, Knysh A. Elomia chatbot: the effectiveness of artificial intelligence in the fight for mental health. In: Proceedings of the 5th International Conference on Computational Linguistics and Intelligent Systems. 2021. Presented at: COLINS '21; April 22-23, 2021:1-10; Kharkiv, Ukraine. URL: https://ceur-ws.org/Vol-2870/paper89.pdf97]. All these studies found improvement in various parameters in management, such as significant reduction in glycated hemoglobin level (P<.001), as well as BMI, and weight (P<.001) in patients with type 2 diabetes mellitus [Krishnakumar A, Verma R, Chawla R, Sosale A, Saboo B, Joshi S, et al. Evaluating glycemic control in patients of South Asian origin with type 2 diabetes using a digital therapeutic platform: analysis of real-world data. J Med Internet Res. Mar 25, 2021;23(3):e17908. [FREE Full text] [CrossRef] [Medline]82]; notable improvement in quality of life measured using the Atrial Fibrillation Effect on Quality of Life questionnaire score (P<.05) or health-related quality of life utility score (P=.007) in patients with atrial fibrillation [Bickmore T, Pusateri A, Kimani EN, Paasche-Orlow MK, Trinh H, Magnani JW. Managing chronic conditions with a smartphone-based conversational virtual agent. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents. 2018. Presented at: IVA '18; November 5-8, 2018:119-124; Sydney, Australia. URL: https://dl.acm.org/doi/10.1145/3267851.3267908 [CrossRef]10,Gong E, Baptista S, Russell A, Scuffham P, Riddell M, Speight J, et al. My diabetes coach, a mobile app-based interactive conversational agent to support type 2 diabetes self-management: randomized effectiveness-implementation trial. J Med Internet Res. Nov 05, 2020;22(11):e20322. [FREE Full text] [CrossRef] [Medline]24]; and substantial reduction in depression (9-item Patient Health Questionnaire score) and anxiety (7-item Generalized Anxiety Disorder Scale score) symptoms [Inkster B, Sarda S, Subramanian V. An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: real-world data evaluation mixed-methods study. JMIR Mhealth Uhealth. Nov 23, 2018;6(11):e12106. [FREE Full text] [CrossRef] [Medline]90,Romanovskyi O, Pidbutska N, Knysh A. Elomia chatbot: the effectiveness of artificial intelligence in the fight for mental health. In: Proceedings of the 5th International Conference on Computational Linguistics and Intelligent Systems. 2021. Presented at: COLINS '21; April 22-23, 2021:1-10; Kharkiv, Ukraine. URL: https://ceur-ws.org/Vol-2870/paper89.pdf97,Meheli S, Sinha C, Kadaba M. Understanding people with chronic pain who use a cognitive behavioral therapy-based artificial intelligence mental health app (Wysa): mixed methods retrospective observational study. JMIR Hum Factors. Apr 27, 2022;9(2):e35671. [FREE Full text] [CrossRef] [Medline]105].
Types of AI-Based CAs in the Included Studies
Of the 47 AI-based CAs representing digital health assistants identified from the included studies, 9 (19%) were existing commercial CAs available as marketed products, which included voice assistants—Google Assistant, Amazon Alexa, Microsoft Cortana, and Apple’s Siri, as well as social chatbots—ChatGPT and Replika (a voice-enabled, avatar-based chatbot), and health care chatbots—Vik, Wysa, and Tess. The remaining 38 CAs were proposed designs or prototypes, where 19 (50%) of them were chatbots, 2 (5%) were voice assistants or voicebots [Cheng A, Raghavaraju V, Kanugo J, Handrianto YP, Yi S. Development and evaluation of a healthy coping voice interface application using the Google home for elderly patients with type 2 diabetes. In: Proceedings of the 15th IEEE Annual Consumer Communications & Networking Conference. 2018. Presented at: CCNC '18; January 12-15, 2018:1-5; Las Vegas, NV. URL: https://ieeexplore.ieee.org/document/8319283 [CrossRef]58,Kannampallil T, Ronneberg CR, Wittels NE, Kumar V, Lv N, Smyth JM, et al. Design and formative evaluation of a virtual voice-based coach for problem-solving treatment: observational study. JMIR Form Res. Aug 12, 2022;6(8):e38092. [FREE Full text] [CrossRef] [Medline]102], 13 (34%) were human-like digital avatars, 1 (3%) was a digital avatar version of a humanoid social robot [Neerincx MA, van Vught W, Blanson Henkemans O, Oleari E, Broekens J, Peters R, et al. Socio-cognitive engineering of a robotic partner for child's diabetes self-management. Front Robot AI. 2019;6:118. [FREE Full text] [CrossRef] [Medline]85], 1 (3%) was a text-based voice-enabled bot [Nguyen TT, Sim K, Kuen AT, O'Donnell RR, Lim ST, Wang W, et al. Designing AI-based conversational agent for diabetes care in a multilingual context. arXiv. Preprint posted online May 20, 2021. [FREE Full text]74], and 2 (5%) were avatar-based chatbots [Easton K, Potter S, Bec R, Bennion M, Christensen H, Grindell C, et al. A virtual agent to support individuals living with physical and mental comorbidities: co-design and acceptability testing. J Med Internet Res. May 30, 2019;21(5):e12996. [FREE Full text] [CrossRef] [Medline]91,Boggiss A, Consedine N, Hopkins S, Silvester C, Jefferies C, Hofman P, et al. Improving the well-being of adolescents with type 1 diabetes during the COVID-19 pandemic: qualitative study exploring acceptability and clinical usability of a self-compassion chatbot. JMIR Diabetes. May 05, 2023;8:e40641. [FREE Full text] [CrossRef] [Medline]110]. In addition, 5% (2/38) were CAs as humanoid social robots [Alhmiedat T, Alotaibi M. Employing social robots for managing diabetes among children: SARA. Wireless Pers Commun. Mar 14, 2023;130(1):449-468. [CrossRef]79,Neerincx MA, van Vught W, Blanson Henkemans O, Oleari E, Broekens J, Peters R, et al. Socio-cognitive engineering of a robotic partner for child's diabetes self-management. Front Robot AI. 2019;6:118. [FREE Full text] [CrossRef] [Medline]85], which were included as they particularly emphasized managing NCDs such as diabetes (although the robots topic was beyond the scope of this study).
Intelligence Level of the AI-Based CAs
Of the 38 CA prototypes presented, 27 (71%) were found to have indicated their AI algorithm implementation methods to generate appropriate health-related responses. A total of 30% (8/27) of them were implemented using rule-based algorithms [Balsa J, Félix I, Cláudio AP, Carmo MB, Silva IC, Guerreiro A, et al. Usability of an intelligent virtual assistant for promoting behavior change and self-care in older people with type 2 diabetes. J Med Syst. Jun 13, 2020;44(7):130. [CrossRef] [Medline]72,Kataoka Y, Takemura T, Sasajima M, Katoh N. Development and early feasibility of chatbots for educating patients with lung cancer and their caregivers in Japan: mixed methods study. JMIR Cancer. Mar 10, 2021;7(1):e26911. [FREE Full text] [CrossRef] [Medline]81,Maharjan R, Doherty K, Rohani DA, Bækgaard P, Bardram JE. Experiences of a speech-enabled conversational agent for the self-report of well-being among people living with affective disorders: an in-the-wild study. ACM Trans Interact Intell Syst. Jul 20, 2022;12(2):1-29. [CrossRef]83,Calvo RA, Peters D, Moradbakhti L, Cook D, Rizos G, Schuller B, et al. Assessing the feasibility of a text-based conversational agent for asthma support: protocol for a mixed methods observational study. JMIR Res Protoc. Feb 02, 2023;12:e42965. [FREE Full text] [CrossRef] [Medline]84,Watson A, Bickmore T, Cange A, Kulshreshtha A, Kvedar J. An internet-based virtual coach to promote physical activity adherence in overweight adults: randomized controlled trial. J Med Internet Res. Jan 26, 2012;14(1):e1. [FREE Full text] [CrossRef] [Medline]86-Suganuma S, Sakamoto D, Shimoyama H. An embodied conversational agent for unguided internet-based cognitive behavior therapy in preventative mental health: feasibility and acceptability pilot trial. JMIR Ment Health. Jul 31, 2018;5(3):e10454. [FREE Full text] [CrossRef] [Medline]88,Alturaiki AM, Banjar HR, Barefah AS, Alnajjar SA, Hindawi S. A smart chatbot for interactive management in beta thalassemia patients. Int J Telemed Appl. 2022;2022:9734518. [FREE Full text] [CrossRef] [Medline]103], 7% (2/27) of the CAs were developed using network-based scripted algorithms [Bickmore T, Pusateri A, Kimani EN, Paasche-Orlow MK, Trinh H, Magnani JW. Managing chronic conditions with a smartphone-based conversational virtual agent. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents. 2018. Presented at: IVA '18; November 5-8, 2018:119-124; Sydney, Australia. URL: https://dl.acm.org/doi/10.1145/3267851.3267908 [CrossRef]10,Kimani E, Bickmore T, Trinh H, Ring L, Paasche-Orlow MK, Magnani JW. A smartphone-based virtual agent for atrial fibrillation education and counseling. In: Proceedings of the 16th International Conference on Intelligent Virtual Agents. 2016. Presented at: IVA '16; September 20-23, 2016:120-127; Los Angeles, CA. URL: https://link.springer.com/chapter/10.1007/978-3-319-47665-0_11 [CrossRef]80], and 4% (1/27) of the CAs were implemented using prescripted algorithms based on behavior change theory, whereas 33% (9/27) of the CAs were implemented using ML approaches [Cheng A, Raghavaraju V, Kanugo J, Handrianto YP, Yi S. Development and evaluation of a healthy coping voice interface application using the Google home for elderly patients with type 2 diabetes. In: Proceedings of the 15th IEEE Annual Consumer Communications & Networking Conference. 2018. Presented at: CCNC '18; January 12-15, 2018:1-5; Las Vegas, NV. URL: https://ieeexplore.ieee.org/document/8319283 [CrossRef]58,Nguyen TT, Sim K, Kuen AT, O'Donnell RR, Lim ST, Wang W, et al. Designing AI-based conversational agent for diabetes care in a multilingual context. arXiv. Preprint posted online May 20, 2021. [FREE Full text]74,Babington-Ashaye A, de Moerloose P, Diop S, Geissbuhler A. Design, development and usability of an educational AI chatbot for people with haemophilia in senegal. Haemophilia. Jul 2023;29(4):1063-1073. [CrossRef] [Medline]75,Hussain S, Athula G. Extending a conventional chatbot knowledge base to external knowledge source and introducing user based sessions for diabetes education. In: Proceedings of the 32nd International Conference on Advanced Information Networking and Applications Workshops. 2018. Presented at: WAINA '18; May 16-18, 2018:698-703; Krakow, Poland. URL: https://ieeexplore.ieee.org/document/8418155 [CrossRef]89,Anastasiadou M, Alexiadis A, Polychronidou E, Votis K, Tzovaras D. A prototype educational virtual assistant for diabetes management. In: Proceedings of the 20th International Conference on Bioinformatics and Bioengineering. 2020. Presented at: BIBE '20; October 26-28, 2020:999-1004; Cincinnati, OH. URL: https://ieeexplore.ieee.org/document/9288129 [CrossRef]93-Yao K, Wong KK, Yu X, Volpi J, Wong ST. An intelligent augmented lifelike avatar app for virtual physical examination of suspected strokes. Annu Int Conf IEEE Eng Med Biol Soc. Nov 2021;2021:1727-1730. [CrossRef] [Medline]95,Romanovskyi O, Pidbutska N, Knysh A. Elomia chatbot: the effectiveness of artificial intelligence in the fight for mental health. In: Proceedings of the 5th International Conference on Computational Linguistics and Intelligent Systems. 2021. Presented at: COLINS '21; April 22-23, 2021:1-10; Kharkiv, Ukraine. URL: https://ceur-ws.org/Vol-2870/paper89.pdf97,Zisis G, Carrington MJ, Oldenburg B, Whitmore K, Lay M, Huynh Q, et al. An m-Health intervention to improve education, self-management, and outcomes in patients admitted for acute decompensated heart failure: barriers to effective implementation. Eur Heart J Digit Health. Dec 2021;2(4):649-657. [FREE Full text] [CrossRef] [Medline]99] and 15% (4/27) of the CAs were developed using a combination of rule-based and ML techniques [Rahmanti AR, Yang HC, Bintoro BS, Nursetyo AA, Muhtar MS, Syed-Abdul S, et al. SlimMe, a chatbot with artificial empathy for personal weight management: system design and finding. Front Nutr. 2022;9:870775. [FREE Full text] [CrossRef] [Medline]78,Neerincx MA, van Vught W, Blanson Henkemans O, Oleari E, Broekens J, Peters R, et al. Socio-cognitive engineering of a robotic partner for child's diabetes self-management. Front Robot AI. 2019;6:118. [FREE Full text] [CrossRef] [Medline]85,Rathnayaka P, Mills N, Burnett D, De Silva D, Alahakoon D, Gray R. A mental health chatbot with cognitive skills for personalised behavioural activation and remote health monitoring. Sensors (Basel). May 11, 2022;22(10):3653. [FREE Full text] [CrossRef] [Medline]101,Epalte K, Tomsone S, Vētra A, Bērziņa G. Patient experience using digital therapy "Vigo" for stroke patient recovery: a qualitative descriptive study. Disabil Rehabil Assist Technol. Feb 2023;18(2):175-184. [CrossRef] [Medline]107]. However, one of the CAs identified from a study was a Wizard-of-Oz human-like AI avatar with prescripted text and human-controlled responses to evaluate the potential effectiveness of visually embodied avatars in the assessment of anxiety and depression through users’ behavioral expressiveness [Egede JO, Price D, Krishnan DB, Jaiswal S, Elliott N, Morriss R. Design and evaluation of virtual human mediated tasks for assessment of depression and anxiety. In: Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents. 2021. Presented at: IVA '21; September 14-17, 2021:52-59; Virtual Event. URL: https://dl.acm.org/doi/10.1145/3472306.3478361 [CrossRef]96].
Types of Health Interventions and Study Duration
Most included studies (24/43, 56%) focused on behavioral or lifestyle interventions and patient education for improved self-management of physical NCDs such as CVDs [Bickmore T, Pusateri A, Kimani EN, Paasche-Orlow MK, Trinh H, Magnani JW. Managing chronic conditions with a smartphone-based conversational virtual agent. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents. 2018. Presented at: IVA '18; November 5-8, 2018:119-124; Sydney, Australia. URL: https://dl.acm.org/doi/10.1145/3267851.3267908 [CrossRef]10,Kimani E, Bickmore T, Trinh H, Ring L, Paasche-Orlow MK, Magnani JW. A smartphone-based virtual agent for atrial fibrillation education and counseling. In: Proceedings of the 16th International Conference on Intelligent Virtual Agents. 2016. Presented at: IVA '16; September 20-23, 2016:120-127; Los Angeles, CA. URL: https://link.springer.com/chapter/10.1007/978-3-319-47665-0_11 [CrossRef]80,Zisis G, Carrington MJ, Oldenburg B, Whitmore K, Lay M, Huynh Q, et al. An m-Health intervention to improve education, self-management, and outcomes in patients admitted for acute decompensated heart failure: barriers to effective implementation. Eur Heart J Digit Health. Dec 2021;2(4):649-657. [FREE Full text] [CrossRef] [Medline]99,Apergi LA, Bjarnadottir MV, Baras JS, Golden BL, Anderson KM, Chou J, et al. Voice interface technology adoption by patients with heart failure: pilot comparison study. JMIR Mhealth Uhealth. Apr 01, 2021;9(4):e24646. [FREE Full text] [CrossRef] [Medline]100], diabetes [Gong E, Baptista S, Russell A, Scuffham P, Riddell M, Speight J, et al. My diabetes coach, a mobile app-based interactive conversational agent to support type 2 diabetes self-management: randomized effectiveness-implementation trial. J Med Internet Res. Nov 05, 2020;22(11):e20322. [FREE Full text] [CrossRef] [Medline]24,Balsa J, Félix I, Cláudio AP, Carmo MB, Silva IC, Guerreiro A, et al. Usability of an intelligent virtual assistant for promoting behavior change and self-care in older people with type 2 diabetes. J Med Syst. Jun 13, 2020;44(7):130. [CrossRef] [Medline]72,Nguyen TT, Sim K, Kuen AT, O'Donnell RR, Lim ST, Wang W, et al. Designing AI-based conversational agent for diabetes care in a multilingual context. arXiv. Preprint posted online May 20, 2021. [FREE Full text]74,Alhmiedat T, Alotaibi M. Employing social robots for managing diabetes among children: SARA. Wireless Pers Commun. Mar 14, 2023;130(1):449-468. [CrossRef]79,Krishnakumar A, Verma R, Chawla R, Sosale A, Saboo B, Joshi S, et al. Evaluating glycemic control in patients of South Asian origin with type 2 diabetes using a digital therapeutic platform: analysis of real-world data. J Med Internet Res. Mar 25, 2021;23(3):e17908. [FREE Full text] [CrossRef] [Medline]82,Neerincx MA, van Vught W, Blanson Henkemans O, Oleari E, Broekens J, Peters R, et al. Socio-cognitive engineering of a robotic partner for child's diabetes self-management. Front Robot AI. 2019;6:118. [FREE Full text] [CrossRef] [Medline]85,Hussain S, Athula G. Extending a conventional chatbot knowledge base to external knowledge source and introducing user based sessions for diabetes education. In: Proceedings of the 32nd International Conference on Advanced Information Networking and Applications Workshops. 2018. Presented at: WAINA '18; May 16-18, 2018:698-703; Krakow, Poland. URL: https://ieeexplore.ieee.org/document/8418155 [CrossRef]89,Anastasiadou M, Alexiadis A, Polychronidou E, Votis K, Tzovaras D. A prototype educational virtual assistant for diabetes management. In: Proceedings of the 20th International Conference on Bioinformatics and Bioengineering. 2020. Presented at: BIBE '20; October 26-28, 2020:999-1004; Cincinnati, OH. URL: https://ieeexplore.ieee.org/document/9288129 [CrossRef]93] or prediabetes [Stephens TN, Joerin A, Rauws M, Werk LN. Feasibility of pediatric obesity and prediabetes treatment support through Tess, the AI behavioral coaching chatbot. Transl Behav Med. May 16, 2019;9(3):440-447. [CrossRef] [Medline]76], cancer [Kataoka Y, Takemura T, Sasajima M, Katoh N. Development and early feasibility of chatbots for educating patients with lung cancer and their caregivers in Japan: mixed methods study. JMIR Cancer. Mar 10, 2021;7(1):e26911. [FREE Full text] [CrossRef] [Medline]81,Chaix B, Bibault JE, Pienkowski A, Delamon G, Guillemassé A, Nectoux P, et al. When chatbots meet patients: one-year prospective study of conversations between patients with breast cancer and a chatbot. JMIR Cancer. May 02, 2019;5(1):e12856. [FREE Full text] [CrossRef] [Medline]92], gastrointestinal diseases [Zahedi FM, Zhao H, Sanvanson P, Walia N, Jain H, Shaker R. My real avatar has a doctor appointment in the Wepital: a system for persistent, efficient, and ubiquitous medical care. Inf Manag. Dec 2022;59(8):103706. [CrossRef]104,Henson JB, Glissen Brown JR, Lee JP, Patel A, Leiman DA. Evaluation of the potential utility of an artificial intelligence chatbot in gastroesophageal reflux disease management. Am J Gastroenterol. Dec 01, 2023;118(12):2276-2279. [CrossRef] [Medline]106], and obesity [Stephens TN, Joerin A, Rauws M, Werk LN. Feasibility of pediatric obesity and prediabetes treatment support through Tess, the AI behavioral coaching chatbot. Transl Behav Med. May 16, 2019;9(3):440-447. [CrossRef] [Medline]76,Watson A, Bickmore T, Cange A, Kulshreshtha A, Kvedar J. An internet-based virtual coach to promote physical activity adherence in overweight adults: randomized controlled trial. J Med Internet Res. Jan 26, 2012;14(1):e1. [FREE Full text] [CrossRef] [Medline]86,LeRouge C, Dickhut K, Lisetti C, Sangameswaran S, Malasanos T. Engaging adolescents in a computer-based weight management program: avatars and virtual coaches could help. J Am Med Inform Assoc. Jan 2016;23(1):19-28. [FREE Full text] [CrossRef] [Medline]108].
A reasonable number of studies (13/43, 30%) focused on cognitive behavioral interventions related to the self-management of chronic cognitive impairments (eg, dementia and poststroke) [Stara V, Vera B, Bolliger D, Rossi L, Felici E, Di Rosa M, et al. Usability and acceptance of the embodied conversational agent Anne by people with dementia and their caregivers: exploratory study in home environment settings. JMIR Mhealth Uhealth. Jun 25, 2021;9(6):e25891. [FREE Full text] [CrossRef] [Medline]73,Epalte K, Tomsone S, Vētra A, Bērziņa G. Patient experience using digital therapy "Vigo" for stroke patient recovery: a qualitative descriptive study. Disabil Rehabil Assist Technol. Feb 2023;18(2):175-184. [CrossRef] [Medline]107], including chronic mental health conditions such as anxiety, depression, psychological distress, and bipolar disorders [Maharjan R, Doherty K, Rohani DA, Bækgaard P, Bardram JE. Experiences of a speech-enabled conversational agent for the self-report of well-being among people living with affective disorders: an in-the-wild study. ACM Trans Interact Intell Syst. Jul 20, 2022;12(2):1-29. [CrossRef]83,Shamekhi A, Bickmore T, Lestoquoy A, Gardiner P. Augmenting group medical visits with conversational agents for stress management behavior change. In: Proceedings of the 12th International Conference on Persuasive Technology: Development and Implementation of Personalized Technologies to Change Attitudes and Behaviors. 2017. Presented at: PERSUASIVE '17; April 4-6, 2017:55-67; Amsterdam, the Netherlands. URL: https://link.springer.com/chapter/10.1007/978-3-319-55134-0_587,Suganuma S, Sakamoto D, Shimoyama H. An embodied conversational agent for unguided internet-based cognitive behavior therapy in preventative mental health: feasibility and acceptability pilot trial. JMIR Ment Health. Jul 31, 2018;5(3):e10454. [FREE Full text] [CrossRef] [Medline]88,Inkster B, Sarda S, Subramanian V. An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: real-world data evaluation mixed-methods study. JMIR Mhealth Uhealth. Nov 23, 2018;6(11):e12106. [FREE Full text] [CrossRef] [Medline]90,Egede JO, Price D, Krishnan DB, Jaiswal S, Elliott N, Morriss R. Design and evaluation of virtual human mediated tasks for assessment of depression and anxiety. In: Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents. 2021. Presented at: IVA '21; September 14-17, 2021:52-59; Virtual Event. URL: https://dl.acm.org/doi/10.1145/3472306.3478361 [CrossRef]96,Romanovskyi O, Pidbutska N, Knysh A. Elomia chatbot: the effectiveness of artificial intelligence in the fight for mental health. In: Proceedings of the 5th International Conference on Computational Linguistics and Intelligent Systems. 2021. Presented at: COLINS '21; April 22-23, 2021:1-10; Kharkiv, Ukraine. URL: https://ceur-ws.org/Vol-2870/paper89.pdf97,Rathnayaka P, Mills N, Burnett D, De Silva D, Alahakoon D, Gray R. A mental health chatbot with cognitive skills for personalised behavioural activation and remote health monitoring. Sensors (Basel). May 11, 2022;22(10):3653. [FREE Full text] [CrossRef] [Medline]101,Kannampallil T, Ronneberg CR, Wittels NE, Kumar V, Lv N, Smyth JM, et al. Design and formative evaluation of a virtual voice-based coach for problem-solving treatment: observational study. JMIR Form Res. Aug 12, 2022;6(8):e38092. [FREE Full text] [CrossRef] [Medline]102].
The duration of these studies was reasonably divergent, ranging from 5 days to 1 year. However, a substantial number of studies (14/43, 33%) could be considered long term, such as 8 weeks to 2 months [Neerincx MA, van Vught W, Blanson Henkemans O, Oleari E, Broekens J, Peters R, et al. Socio-cognitive engineering of a robotic partner for child's diabetes self-management. Front Robot AI. 2019;6:118. [FREE Full text] [CrossRef] [Medline]85,Suganuma S, Sakamoto D, Shimoyama H. An embodied conversational agent for unguided internet-based cognitive behavior therapy in preventative mental health: feasibility and acceptability pilot trial. JMIR Ment Health. Jul 31, 2018;5(3):e10454. [FREE Full text] [CrossRef] [Medline]88,Inkster B, Sarda S, Subramanian V. An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: real-world data evaluation mixed-methods study. JMIR Mhealth Uhealth. Nov 23, 2018;6(11):e12106. [FREE Full text] [CrossRef] [Medline]90,Zisis G, Carrington MJ, Oldenburg B, Whitmore K, Lay M, Huynh Q, et al. An m-Health intervention to improve education, self-management, and outcomes in patients admitted for acute decompensated heart failure: barriers to effective implementation. Eur Heart J Digit Health. Dec 2021;2(4):649-657. [FREE Full text] [CrossRef] [Medline]99,Rathnayaka P, Mills N, Burnett D, De Silva D, Alahakoon D, Gray R. A mental health chatbot with cognitive skills for personalised behavioural activation and remote health monitoring. Sensors (Basel). May 11, 2022;22(10):3653. [FREE Full text] [CrossRef] [Medline]101], 12 weeks to 90 days [Watson A, Bickmore T, Cange A, Kulshreshtha A, Kvedar J. An internet-based virtual coach to promote physical activity adherence in overweight adults: randomized controlled trial. J Med Internet Res. Jan 26, 2012;14(1):e1. [FREE Full text] [CrossRef] [Medline]86,Anan T, Kajiki S, Oka H, Fujii T, Kawamata K, Mori K, et al. Effects of an artificial intelligence-assisted health program on workers with neck/shoulder pain/stiffness and low back pain: randomized controlled trial. JMIR Mhealth Uhealth. Sep 24, 2021;9(9):e27535. [FREE Full text] [CrossRef] [Medline]98,Apergi LA, Bjarnadottir MV, Baras JS, Golden BL, Anderson KM, Chou J, et al. Voice interface technology adoption by patients with heart failure: pilot comparison study. JMIR Mhealth Uhealth. Apr 01, 2021;9(4):e24646. [FREE Full text] [CrossRef] [Medline]100], 16 weeks to 6 months [Stephens TN, Joerin A, Rauws M, Werk LN. Feasibility of pediatric obesity and prediabetes treatment support through Tess, the AI behavioral coaching chatbot. Transl Behav Med. May 16, 2019;9(3):440-447. [CrossRef] [Medline]76,Krishnakumar A, Verma R, Chawla R, Sosale A, Saboo B, Joshi S, et al. Evaluating glycemic control in patients of South Asian origin with type 2 diabetes using a digital therapeutic platform: analysis of real-world data. J Med Internet Res. Mar 25, 2021;23(3):e17908. [FREE Full text] [CrossRef] [Medline]82,Shamekhi A, Bickmore T, Lestoquoy A, Gardiner P. Augmenting group medical visits with conversational agents for stress management behavior change. In: Proceedings of the 12th International Conference on Persuasive Technology: Development and Implementation of Personalized Technologies to Change Attitudes and Behaviors. 2017. Presented at: PERSUASIVE '17; April 4-6, 2017:55-67; Amsterdam, the Netherlands. URL: https://link.springer.com/chapter/10.1007/978-3-319-55134-0_587,Anastasiadou M, Alexiadis A, Polychronidou E, Votis K, Tzovaras D. A prototype educational virtual assistant for diabetes management. In: Proceedings of the 20th International Conference on Bioinformatics and Bioengineering. 2020. Presented at: BIBE '20; October 26-28, 2020:999-1004; Cincinnati, OH. URL: https://ieeexplore.ieee.org/document/9288129 [CrossRef]93], 9 months [Roca S, Lozano ML, García J, Alesanco Á. Validation of a virtual assistant for improving medication adherence in patients with comorbid type 2 diabetes mellitus and depressive disorder. Int J Environ Res Public Health. Nov 17, 2021;18(22):12056. [FREE Full text] [CrossRef] [Medline]94], and 12 months or 1 year [Gong E, Baptista S, Russell A, Scuffham P, Riddell M, Speight J, et al. My diabetes coach, a mobile app-based interactive conversational agent to support type 2 diabetes self-management: randomized effectiveness-implementation trial. J Med Internet Res. Nov 05, 2020;22(11):e20322. [FREE Full text] [CrossRef] [Medline]24,Chaix B, Bibault JE, Pienkowski A, Delamon G, Guillemassé A, Nectoux P, et al. When chatbots meet patients: one-year prospective study of conversations between patients with breast cancer and a chatbot. JMIR Cancer. May 02, 2019;5(1):e12856. [FREE Full text] [CrossRef] [Medline]92]. A total of 26% (11/43) of the studies conducted trials for approximately ≤1 month [Bickmore T, Pusateri A, Kimani EN, Paasche-Orlow MK, Trinh H, Magnani JW. Managing chronic conditions with a smartphone-based conversational virtual agent. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents. 2018. Presented at: IVA '18; November 5-8, 2018:119-124; Sydney, Australia. URL: https://dl.acm.org/doi/10.1145/3267851.3267908 [CrossRef]10,Stara V, Vera B, Bolliger D, Rossi L, Felici E, Di Rosa M, et al. Usability and acceptance of the embodied conversational agent Anne by people with dementia and their caregivers: exploratory study in home environment settings. JMIR Mhealth Uhealth. Jun 25, 2021;9(6):e25891. [FREE Full text] [CrossRef] [Medline]73,Kataoka Y, Takemura T, Sasajima M, Katoh N. Development and early feasibility of chatbots for educating patients with lung cancer and their caregivers in Japan: mixed methods study. JMIR Cancer. Mar 10, 2021;7(1):e26911. [FREE Full text] [CrossRef] [Medline]81,Maharjan R, Doherty K, Rohani DA, Bækgaard P, Bardram JE. Experiences of a speech-enabled conversational agent for the self-report of well-being among people living with affective disorders: an in-the-wild study. ACM Trans Interact Intell Syst. Jul 20, 2022;12(2):1-29. [CrossRef]83,Calvo RA, Peters D, Moradbakhti L, Cook D, Rizos G, Schuller B, et al. Assessing the feasibility of a text-based conversational agent for asthma support: protocol for a mixed methods observational study. JMIR Res Protoc. Feb 02, 2023;12:e42965. [FREE Full text] [CrossRef] [Medline]84,Suganuma S, Sakamoto D, Shimoyama H. An embodied conversational agent for unguided internet-based cognitive behavior therapy in preventative mental health: feasibility and acceptability pilot trial. JMIR Ment Health. Jul 31, 2018;5(3):e10454. [FREE Full text] [CrossRef] [Medline]88,Romanovskyi O, Pidbutska N, Knysh A. Elomia chatbot: the effectiveness of artificial intelligence in the fight for mental health. In: Proceedings of the 5th International Conference on Computational Linguistics and Intelligent Systems. 2021. Presented at: COLINS '21; April 22-23, 2021:1-10; Kharkiv, Ukraine. URL: https://ceur-ws.org/Vol-2870/paper89.pdf97,Epalte K, Tomsone S, Vētra A, Bērziņa G. Patient experience using digital therapy "Vigo" for stroke patient recovery: a qualitative descriptive study. Disabil Rehabil Assist Technol. Feb 2023;18(2):175-184. [CrossRef] [Medline]107] (ie, within 5-10 days) [Balsa J, Félix I, Cláudio AP, Carmo MB, Silva IC, Guerreiro A, et al. Usability of an intelligent virtual assistant for promoting behavior change and self-care in older people with type 2 diabetes. J Med Syst. Jun 13, 2020;44(7):130. [CrossRef] [Medline]72,Rahmanti AR, Yang HC, Bintoro BS, Nursetyo AA, Muhtar MS, Syed-Abdul S, et al. SlimMe, a chatbot with artificial empathy for personal weight management: system design and finding. Front Nutr. 2022;9:870775. [FREE Full text] [CrossRef] [Medline]78,Alhmiedat T, Alotaibi M. Employing social robots for managing diabetes among children: SARA. Wireless Pers Commun. Mar 14, 2023;130(1):449-468. [CrossRef]79]. A total of 42% (18/43) of the studies did not mention the study duration.
Study Population and Location
Almost half (20/43, 47%) of the included studies did not specify the target population other than patients with a particular chronic disease that the health intervention was meant for. The studies that specified the target population or their age group (24/43, 56%) were relatively heterogeneous, with older adults [Bickmore T, Pusateri A, Kimani EN, Paasche-Orlow MK, Trinh H, Magnani JW. Managing chronic conditions with a smartphone-based conversational virtual agent. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents. 2018. Presented at: IVA '18; November 5-8, 2018:119-124; Sydney, Australia. URL: https://dl.acm.org/doi/10.1145/3267851.3267908 [CrossRef]10,Cheng A, Raghavaraju V, Kanugo J, Handrianto YP, Yi S. Development and evaluation of a healthy coping voice interface application using the Google home for elderly patients with type 2 diabetes. In: Proceedings of the 15th IEEE Annual Consumer Communications & Networking Conference. 2018. Presented at: CCNC '18; January 12-15, 2018:1-5; Las Vegas, NV. URL: https://ieeexplore.ieee.org/document/8319283 [CrossRef]58,Balsa J, Félix I, Cláudio AP, Carmo MB, Silva IC, Guerreiro A, et al. Usability of an intelligent virtual assistant for promoting behavior change and self-care in older people with type 2 diabetes. J Med Syst. Jun 13, 2020;44(7):130. [CrossRef] [Medline]72,Stara V, Vera B, Bolliger D, Rossi L, Felici E, Di Rosa M, et al. Usability and acceptance of the embodied conversational agent Anne by people with dementia and their caregivers: exploratory study in home environment settings. JMIR Mhealth Uhealth. Jun 25, 2021;9(6):e25891. [FREE Full text] [CrossRef] [Medline]73,Easton K, Potter S, Bec R, Bennion M, Christensen H, Grindell C, et al. A virtual agent to support individuals living with physical and mental comorbidities: co-design and acceptability testing. J Med Internet Res. May 30, 2019;21(5):e12996. [FREE Full text] [CrossRef] [Medline]91,Yao K, Wong KK, Yu X, Volpi J, Wong ST. An intelligent augmented lifelike avatar app for virtual physical examination of suspected strokes. Annu Int Conf IEEE Eng Med Biol Soc. Nov 2021;2021:1727-1730. [CrossRef] [Medline]95] and middle-aged adults [Gong E, Baptista S, Russell A, Scuffham P, Riddell M, Speight J, et al. My diabetes coach, a mobile app-based interactive conversational agent to support type 2 diabetes self-management: randomized effectiveness-implementation trial. J Med Internet Res. Nov 05, 2020;22(11):e20322. [FREE Full text] [CrossRef] [Medline]24,Zisis G, Carrington MJ, Oldenburg B, Whitmore K, Lay M, Huynh Q, et al. An m-Health intervention to improve education, self-management, and outcomes in patients admitted for acute decompensated heart failure: barriers to effective implementation. Eur Heart J Digit Health. Dec 2021;2(4):649-657. [FREE Full text] [CrossRef] [Medline]99] being the most frequently targeted user group (8/43, 19%). However, some studies (6/43, 14%) also focused on younger adults [Romanovskyi O, Pidbutska N, Knysh A. Elomia chatbot: the effectiveness of artificial intelligence in the fight for mental health. In: Proceedings of the 5th International Conference on Computational Linguistics and Intelligent Systems. 2021. Presented at: COLINS '21; April 22-23, 2021:1-10; Kharkiv, Ukraine. URL: https://ceur-ws.org/Vol-2870/paper89.pdf97], adolescents [Stephens TN, Joerin A, Rauws M, Werk LN. Feasibility of pediatric obesity and prediabetes treatment support through Tess, the AI behavioral coaching chatbot. Transl Behav Med. May 16, 2019;9(3):440-447. [CrossRef] [Medline]76,LeRouge C, Dickhut K, Lisetti C, Sangameswaran S, Malasanos T. Engaging adolescents in a computer-based weight management program: avatars and virtual coaches could help. J Am Med Inform Assoc. Jan 2016;23(1):19-28. [FREE Full text] [CrossRef] [Medline]108,Boggiss A, Consedine N, Hopkins S, Silvester C, Jefferies C, Hofman P, et al. Improving the well-being of adolescents with type 1 diabetes during the COVID-19 pandemic: qualitative study exploring acceptability and clinical usability of a self-compassion chatbot. JMIR Diabetes. May 05, 2023;8:e40641. [FREE Full text] [CrossRef] [Medline]110], and children [Alhmiedat T, Alotaibi M. Employing social robots for managing diabetes among children: SARA. Wireless Pers Commun. Mar 14, 2023;130(1):449-468. [CrossRef]79,Neerincx MA, van Vught W, Blanson Henkemans O, Oleari E, Broekens J, Peters R, et al. Socio-cognitive engineering of a robotic partner for child's diabetes self-management. Front Robot AI. 2019;6:118. [FREE Full text] [CrossRef] [Medline]85].
The locations of the studies conducted were largely diverse, but most (27/43, 63%) were conducted in higher-income countries. A total of 9 studies were completed in Europe, where 2 (22%) of them were conducted in the United Kingdom [Calvo RA, Peters D, Moradbakhti L, Cook D, Rizos G, Schuller B, et al. Assessing the feasibility of a text-based conversational agent for asthma support: protocol for a mixed methods observational study. JMIR Res Protoc. Feb 02, 2023;12:e42965. [FREE Full text] [CrossRef] [Medline]84,Easton K, Potter S, Bec R, Bennion M, Christensen H, Grindell C, et al. A virtual agent to support individuals living with physical and mental comorbidities: co-design and acceptability testing. J Med Internet Res. May 30, 2019;21(5):e12996. [FREE Full text] [CrossRef] [Medline]91], 2 (22%) studies were conducted in Italy and the Netherlands [Stara V, Vera B, Bolliger D, Rossi L, Felici E, Di Rosa M, et al. Usability and acceptance of the embodied conversational agent Anne by people with dementia and their caregivers: exploratory study in home environment settings. JMIR Mhealth Uhealth. Jun 25, 2021;9(6):e25891. [FREE Full text] [CrossRef] [Medline]73,Neerincx MA, van Vught W, Blanson Henkemans O, Oleari E, Broekens J, Peters R, et al. Socio-cognitive engineering of a robotic partner for child's diabetes self-management. Front Robot AI. 2019;6:118. [FREE Full text] [CrossRef] [Medline]85], 1 (11%) was conducted in Portugal [Balsa J, Félix I, Cláudio AP, Carmo MB, Silva IC, Guerreiro A, et al. Usability of an intelligent virtual assistant for promoting behavior change and self-care in older people with type 2 diabetes. J Med Syst. Jun 13, 2020;44(7):130. [CrossRef] [Medline]72], 1 (11%) was conducted in France [Issom DZ, Rochat J, Hartvigsen G, Lovis C. Preliminary evaluation of a mHealth coaching conversational artificial intelligence for the self-care management of people with sickle-cell disease. Stud Health Technol Inform. Jun 16, 2020;270:1361-1362. [CrossRef] [Medline]77], 1 (11%) was conducted in Greece [Anastasiadou M, Alexiadis A, Polychronidou E, Votis K, Tzovaras D. A prototype educational virtual assistant for diabetes management. In: Proceedings of the 20th International Conference on Bioinformatics and Bioengineering. 2020. Presented at: BIBE '20; October 26-28, 2020:999-1004; Cincinnati, OH. URL: https://ieeexplore.ieee.org/document/9288129 [CrossRef]93], 1 (11%) was conducted in Spain [Roca S, Lozano ML, García J, Alesanco Á. Validation of a virtual assistant for improving medication adherence in patients with comorbid type 2 diabetes mellitus and depressive disorder. Int J Environ Res Public Health. Nov 17, 2021;18(22):12056. [FREE Full text] [CrossRef] [Medline]94], and 1 (11%) was conducted in Ukraine [Romanovskyi O, Pidbutska N, Knysh A. Elomia chatbot: the effectiveness of artificial intelligence in the fight for mental health. In: Proceedings of the 5th International Conference on Computational Linguistics and Intelligent Systems. 2021. Presented at: COLINS '21; April 22-23, 2021:1-10; Kharkiv, Ukraine. URL: https://ceur-ws.org/Vol-2870/paper89.pdf97]. A total of 16% (7/43) of the studies were conducted in the United States [Bickmore T, Pusateri A, Kimani EN, Paasche-Orlow MK, Trinh H, Magnani JW. Managing chronic conditions with a smartphone-based conversational virtual agent. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents. 2018. Presented at: IVA '18; November 5-8, 2018:119-124; Sydney, Australia. URL: https://dl.acm.org/doi/10.1145/3267851.3267908 [CrossRef]10,Eagle T, Blau C, Bales S, Desai N, Li V, Whittaker S. “I don’t know what you mean by `I am anxious'”: a new method for evaluating conversational agent responses to standardized mental health inputs for anxiety and depression. ACM Trans Interact Intell Syst. Jul 20, 2022;12(2):1-23. [CrossRef]37,Stephens TN, Joerin A, Rauws M, Werk LN. Feasibility of pediatric obesity and prediabetes treatment support through Tess, the AI behavioral coaching chatbot. Transl Behav Med. May 16, 2019;9(3):440-447. [CrossRef] [Medline]76,Watson A, Bickmore T, Cange A, Kulshreshtha A, Kvedar J. An internet-based virtual coach to promote physical activity adherence in overweight adults: randomized controlled trial. J Med Internet Res. Jan 26, 2012;14(1):e1. [FREE Full text] [CrossRef] [Medline]86,Shamekhi A, Bickmore T, Lestoquoy A, Gardiner P. Augmenting group medical visits with conversational agents for stress management behavior change. In: Proceedings of the 12th International Conference on Persuasive Technology: Development and Implementation of Personalized Technologies to Change Attitudes and Behaviors. 2017. Presented at: PERSUASIVE '17; April 4-6, 2017:55-67; Amsterdam, the Netherlands. URL: https://link.springer.com/chapter/10.1007/978-3-319-55134-0_587,LeRouge C, Dickhut K, Lisetti C, Sangameswaran S, Malasanos T. Engaging adolescents in a computer-based weight management program: avatars and virtual coaches could help. J Am Med Inform Assoc. Jan 2016;23(1):19-28. [FREE Full text] [CrossRef] [Medline]108,Park G, Chung J, Lee S. Human vs. machine-like representation in chatbot mental health counseling: the serial mediation of psychological distance and trust on compliance intention. Curr Psychol. Apr 20, 2023:1-12. [FREE Full text] [CrossRef] [Medline]109]. In total, 4 of the studies were carried out in the Australasia region—3 (75%) in Australia [Gong E, Baptista S, Russell A, Scuffham P, Riddell M, Speight J, et al. My diabetes coach, a mobile app-based interactive conversational agent to support type 2 diabetes self-management: randomized effectiveness-implementation trial. J Med Internet Res. Nov 05, 2020;22(11):e20322. [FREE Full text] [CrossRef] [Medline]24,Zisis G, Carrington MJ, Oldenburg B, Whitmore K, Lay M, Huynh Q, et al. An m-Health intervention to improve education, self-management, and outcomes in patients admitted for acute decompensated heart failure: barriers to effective implementation. Eur Heart J Digit Health. Dec 2021;2(4):649-657. [FREE Full text] [CrossRef] [Medline]99,Rathnayaka P, Mills N, Burnett D, De Silva D, Alahakoon D, Gray R. A mental health chatbot with cognitive skills for personalised behavioural activation and remote health monitoring. Sensors (Basel). May 11, 2022;22(10):3653. [FREE Full text] [CrossRef] [Medline]101] and 1 (25%) in New Zealand [Boggiss A, Consedine N, Hopkins S, Silvester C, Jefferies C, Hofman P, et al. Improving the well-being of adolescents with type 1 diabetes during the COVID-19 pandemic: qualitative study exploring acceptability and clinical usability of a self-compassion chatbot. JMIR Diabetes. May 05, 2023;8:e40641. [FREE Full text] [CrossRef] [Medline]110]. A total of 7 studies were conducted in Asia, where 4 (57%) of them were conducted in Japan [Kataoka Y, Takemura T, Sasajima M, Katoh N. Development and early feasibility of chatbots for educating patients with lung cancer and their caregivers in Japan: mixed methods study. JMIR Cancer. Mar 10, 2021;7(1):e26911. [FREE Full text] [CrossRef] [Medline]81,Suganuma S, Sakamoto D, Shimoyama H. An embodied conversational agent for unguided internet-based cognitive behavior therapy in preventative mental health: feasibility and acceptability pilot trial. JMIR Ment Health. Jul 31, 2018;5(3):e10454. [FREE Full text] [CrossRef] [Medline]88,Egede JO, Price D, Krishnan DB, Jaiswal S, Elliott N, Morriss R. Design and evaluation of virtual human mediated tasks for assessment of depression and anxiety. In: Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents. 2021. Presented at: IVA '21; September 14-17, 2021:52-59; Virtual Event. URL: https://dl.acm.org/doi/10.1145/3472306.3478361 [CrossRef]96,Anan T, Kajiki S, Oka H, Fujii T, Kawamata K, Mori K, et al. Effects of an artificial intelligence-assisted health program on workers with neck/shoulder pain/stiffness and low back pain: randomized controlled trial. JMIR Mhealth Uhealth. Sep 24, 2021;9(9):e27535. [FREE Full text] [CrossRef] [Medline]98], 1 (14%) was conducted in Singapore [Nguyen TT, Sim K, Kuen AT, O'Donnell RR, Lim ST, Wang W, et al. Designing AI-based conversational agent for diabetes care in a multilingual context. arXiv. Preprint posted online May 20, 2021. [FREE Full text]74], 1 (14%) was conducted in Taiwan [Rahmanti AR, Yang HC, Bintoro BS, Nursetyo AA, Muhtar MS, Syed-Abdul S, et al. SlimMe, a chatbot with artificial empathy for personal weight management: system design and finding. Front Nutr. 2022;9:870775. [FREE Full text] [CrossRef] [Medline]78], and 1 (14%) was conducted in India [Krishnakumar A, Verma R, Chawla R, Sosale A, Saboo B, Joshi S, et al. Evaluating glycemic control in patients of South Asian origin with type 2 diabetes using a digital therapeutic platform: analysis of real-world data. J Med Internet Res. Mar 25, 2021;23(3):e17908. [FREE Full text] [CrossRef] [Medline]82]. A total of 5% (2/43) of the studies were conducted in Saudi Arabia [Alhmiedat T, Alotaibi M. Employing social robots for managing diabetes among children: SARA. Wireless Pers Commun. Mar 14, 2023;130(1):449-468. [CrossRef]79] and Senegal in West Africa [Babington-Ashaye A, de Moerloose P, Diop S, Geissbuhler A. Design, development and usability of an educational AI chatbot for people with haemophilia in senegal. Haemophilia. Jul 2023;29(4):1063-1073. [CrossRef] [Medline]75]. The remaining 33% (14/43) of the studies did not specify the study location.
Data Collection Methods of the Included Studies
The included studies mainly used quantitative (17/43, 40%) or mixed methods (17/43, 40%) approaches. A substantial proportion of these studies (11/43, 26%) were pretest-posttest intervention studies to observe the differences before and after the intervention within the same group of participants (single armed) or between intervention and control groups (2 armed). The study designs of the quantitative and mixed methods studies included observational experiments using 2-armed randomized controlled trials (7/34, 21%) [Bickmore T, Pusateri A, Kimani EN, Paasche-Orlow MK, Trinh H, Magnani JW. Managing chronic conditions with a smartphone-based conversational virtual agent. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents. 2018. Presented at: IVA '18; November 5-8, 2018:119-124; Sydney, Australia. URL: https://dl.acm.org/doi/10.1145/3267851.3267908 [CrossRef]10,Gong E, Baptista S, Russell A, Scuffham P, Riddell M, Speight J, et al. My diabetes coach, a mobile app-based interactive conversational agent to support type 2 diabetes self-management: randomized effectiveness-implementation trial. J Med Internet Res. Nov 05, 2020;22(11):e20322. [FREE Full text] [CrossRef] [Medline]24,Watson A, Bickmore T, Cange A, Kulshreshtha A, Kvedar J. An internet-based virtual coach to promote physical activity adherence in overweight adults: randomized controlled trial. J Med Internet Res. Jan 26, 2012;14(1):e1. [FREE Full text] [CrossRef] [Medline]86,Shamekhi A, Bickmore T, Lestoquoy A, Gardiner P. Augmenting group medical visits with conversational agents for stress management behavior change. In: Proceedings of the 12th International Conference on Persuasive Technology: Development and Implementation of Personalized Technologies to Change Attitudes and Behaviors. 2017. Presented at: PERSUASIVE '17; April 4-6, 2017:55-67; Amsterdam, the Netherlands. URL: https://link.springer.com/chapter/10.1007/978-3-319-55134-0_587,Anan T, Kajiki S, Oka H, Fujii T, Kawamata K, Mori K, et al. Effects of an artificial intelligence-assisted health program on workers with neck/shoulder pain/stiffness and low back pain: randomized controlled trial. JMIR Mhealth Uhealth. Sep 24, 2021;9(9):e27535. [FREE Full text] [CrossRef] [Medline]98,Zisis G, Carrington MJ, Oldenburg B, Whitmore K, Lay M, Huynh Q, et al. An m-Health intervention to improve education, self-management, and outcomes in patients admitted for acute decompensated heart failure: barriers to effective implementation. Eur Heart J Digit Health. Dec 2021;2(4):649-657. [FREE Full text] [CrossRef] [Medline]99,Kannampallil T, Ronneberg CR, Wittels NE, Kumar V, Lv N, Smyth JM, et al. Design and formative evaluation of a virtual voice-based coach for problem-solving treatment: observational study. JMIR Form Res. Aug 12, 2022;6(8):e38092. [FREE Full text] [CrossRef] [Medline]102], a nonrandomized prospective study (1/34, 3%) [Suganuma S, Sakamoto D, Shimoyama H. An embodied conversational agent for unguided internet-based cognitive behavior therapy in preventative mental health: feasibility and acceptability pilot trial. JMIR Ment Health. Jul 31, 2018;5(3):e10454. [FREE Full text] [CrossRef] [Medline]88], retrospective studies (2/34, 6%) [Krishnakumar A, Verma R, Chawla R, Sosale A, Saboo B, Joshi S, et al. Evaluating glycemic control in patients of South Asian origin with type 2 diabetes using a digital therapeutic platform: analysis of real-world data. J Med Internet Res. Mar 25, 2021;23(3):e17908. [FREE Full text] [CrossRef] [Medline]82,Meheli S, Sinha C, Kadaba M. Understanding people with chronic pain who use a cognitive behavioral therapy-based artificial intelligence mental health app (Wysa): mixed methods retrospective observational study. JMIR Hum Factors. Apr 27, 2022;9(2):e35671. [FREE Full text] [CrossRef] [Medline]105], quasi-experimental studies (4/34, 12%) [Bickmore T, Pusateri A, Kimani EN, Paasche-Orlow MK, Trinh H, Magnani JW. Managing chronic conditions with a smartphone-based conversational virtual agent. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents. 2018. Presented at: IVA '18; November 5-8, 2018:119-124; Sydney, Australia. URL: https://dl.acm.org/doi/10.1145/3267851.3267908 [CrossRef]10,Inkster B, Sarda S, Subramanian V. An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: real-world data evaluation mixed-methods study. JMIR Mhealth Uhealth. Nov 23, 2018;6(11):e12106. [FREE Full text] [CrossRef] [Medline]90,Rathnayaka P, Mills N, Burnett D, De Silva D, Alahakoon D, Gray R. A mental health chatbot with cognitive skills for personalised behavioural activation and remote health monitoring. Sensors (Basel). May 11, 2022;22(10):3653. [FREE Full text] [CrossRef] [Medline]101,Park G, Chung J, Lee S. Human vs. machine-like representation in chatbot mental health counseling: the serial mediation of psychological distance and trust on compliance intention. Curr Psychol. Apr 20, 2023:1-12. [FREE Full text] [CrossRef] [Medline]109], a single-armed pilot trial [Roca S, Lozano ML, García J, Alesanco Á. Validation of a virtual assistant for improving medication adherence in patients with comorbid type 2 diabetes mellitus and depressive disorder. Int J Environ Res Public Health. Nov 17, 2021;18(22):12056. [FREE Full text] [CrossRef] [Medline]94], and comparative evaluation studies (3/34, 9%) [Hussain S, Athula G. Extending a conventional chatbot knowledge base to external knowledge source and introducing user based sessions for diabetes education. In: Proceedings of the 32nd International Conference on Advanced Information Networking and Applications Workshops. 2018. Presented at: WAINA '18; May 16-18, 2018:698-703; Krakow, Poland. URL: https://ieeexplore.ieee.org/document/8418155 [CrossRef]89,Egede JO, Price D, Krishnan DB, Jaiswal S, Elliott N, Morriss R. Design and evaluation of virtual human mediated tasks for assessment of depression and anxiety. In: Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents. 2021. Presented at: IVA '21; September 14-17, 2021:52-59; Virtual Event. URL: https://dl.acm.org/doi/10.1145/3472306.3478361 [CrossRef]96,Apergi LA, Bjarnadottir MV, Baras JS, Golden BL, Anderson KM, Chou J, et al. Voice interface technology adoption by patients with heart failure: pilot comparison study. JMIR Mhealth Uhealth. Apr 01, 2021;9(4):e24646. [FREE Full text] [CrossRef] [Medline]100].
The qualitative studies (9/43, 21%) were mostly usability and acceptability assessments of prototypes through qualitative posttest surveys [Alturaiki AM, Banjar HR, Barefah AS, Alnajjar SA, Hindawi S. A smart chatbot for interactive management in beta thalassemia patients. Int J Telemed Appl. 2022;2022:9734518. [FREE Full text] [CrossRef] [Medline]103], postintervention interviews [Epalte K, Tomsone S, Vētra A, Bērziņa G. Patient experience using digital therapy "Vigo" for stroke patient recovery: a qualitative descriptive study. Disabil Rehabil Assist Technol. Feb 2023;18(2):175-184. [CrossRef] [Medline]107], focus groups [Boggiss A, Consedine N, Hopkins S, Silvester C, Jefferies C, Hofman P, et al. Improving the well-being of adolescents with type 1 diabetes during the COVID-19 pandemic: qualitative study exploring acceptability and clinical usability of a self-compassion chatbot. JMIR Diabetes. May 05, 2023;8:e40641. [FREE Full text] [CrossRef] [Medline]110], a participatory design approach with focus groups and interviews [LeRouge C, Dickhut K, Lisetti C, Sangameswaran S, Malasanos T. Engaging adolescents in a computer-based weight management program: avatars and virtual coaches could help. J Am Med Inform Assoc. Jan 2016;23(1):19-28. [FREE Full text] [CrossRef] [Medline]108], and workshops [Easton K, Potter S, Bec R, Bennion M, Christensen H, Grindell C, et al. A virtual agent to support individuals living with physical and mental comorbidities: co-design and acceptability testing. J Med Internet Res. May 30, 2019;21(5):e12996. [FREE Full text] [CrossRef] [Medline]91]. A total of 22% (2/9) of the qualitative studies compared the proposed prototypes against similar existing applications [Cheng A, Raghavaraju V, Kanugo J, Handrianto YP, Yi S. Development and evaluation of a healthy coping voice interface application using the Google home for elderly patients with type 2 diabetes. In: Proceedings of the 15th IEEE Annual Consumer Communications & Networking Conference. 2018. Presented at: CCNC '18; January 12-15, 2018:1-5; Las Vegas, NV. URL: https://ieeexplore.ieee.org/document/8319283 [CrossRef]58,Hussain S, Athula G. Extending a conventional chatbot knowledge base to external knowledge source and introducing user based sessions for diabetes education. In: Proceedings of the 32nd International Conference on Advanced Information Networking and Applications Workshops. 2018. Presented at: WAINA '18; May 16-18, 2018:698-703; Krakow, Poland. URL: https://ieeexplore.ieee.org/document/8418155 [CrossRef]89].
Discussion
Principal Findings
The discussion of this scoping review synthesizes the findings in light of the overarching themes emergent from each research question, elucidating the interplay between the current state, challenges, benefits, and the targeted users of AI-based CAs within the scope of the examined literature.
Current State of AI-Powered CAs
Regarding the current state of research on AI-powered CAs as human-like health carers for managing NCDs, empirical studies have revealed the limited ability of existing commercial CAs to provide appropriate lifestyle advice and health recommendations [Eagle T, Blau C, Bales S, Desai N, Li V, Whittaker S. “I don’t know what you mean by `I am anxious'”: a new method for evaluating conversational agent responses to standardized mental health inputs for anxiety and depression. ACM Trans Interact Intell Syst. Jul 20, 2022;12(2):1-23. [CrossRef]37,Kocaballi AB, Quiroz JC, Rezazadegan D, Berkovsky S, Magrabi F, Coiera E, et al. Responses of conversational agents to health and lifestyle prompts: investigation of appropriateness and presentation structures. J Med Internet Res. Feb 09, 2020;22(2):e15823. [FREE Full text] [CrossRef] [Medline]111]. Perhaps this is due to the lack of specified data training for these commercial tools in the medical context, which is currently restricting their integration in clinical and remote care [Henson JB, Glissen Brown JR, Lee JP, Patel A, Leiman DA. Evaluation of the potential utility of an artificial intelligence chatbot in gastroesophageal reflux disease management. Am J Gastroenterol. Dec 01, 2023;118(12):2276-2279. [CrossRef] [Medline]106].
Functionality-wise, commercial chatbots (eg, Wysa and Replika) tend to slightly outperform voice agents (eg, Google Assistant, Siri, Alexa, and Cortana) with improved response quality due to the former’s better dialogue capabilities and empathy support. At the same time, the latter are more susceptible to speech recognition errors, including greater dependency on simple web searches to generate advice [Eagle T, Blau C, Bales S, Desai N, Li V, Whittaker S. “I don’t know what you mean by `I am anxious'”: a new method for evaluating conversational agent responses to standardized mental health inputs for anxiety and depression. ACM Trans Interact Intell Syst. Jul 20, 2022;12(2):1-23. [CrossRef]37]. Nonetheless, voice-enabled CAs offer hands-free communication, making them preferable over text-based chatbots for older adult users, as suggested by a recent scoping review conducted by Even et al [Even C, Hammann T, Heyl V, Rietz C, Wahl HW, Zentel P, et al. Benefits and challenges of conversational agents in older adults : a scoping review. Z Gerontol Geriatr. Aug 2022;55(5):381-387. [CrossRef] [Medline]70] focusing on the benefits and challenges of CAs in the older adult population (Table 1). In addition, another study revealed that older adults preferred voice-based communications over SMS text messages or free-text entry as voice is regarded as a powerful mode for promoting motivation [Wiratunga N, Cooper K, Wijekoon A, Palihawadana C, Mendham V, Reiter E, et al. FitChat: conversational artificial intelligence interventions for encouraging physical activity in older adults. arXiv. Preprint posted online April 29, 2020. [FREE Full text] [CrossRef]112]. In addition, voice agents may also be more accessible compared to digital avatars due to their lower bandwidth requirements, making them suitable for locations with lower bandwidth connectivity or devices with limited computing power [Yaqoob I, Salah K, Jayaraman R, Omar M. Metaverse applications in smart cities: enabling technologies, opportunities, challenges, and future directions. Internet Things. Oct 2023;23:100884. [CrossRef]48]. Conversely, digital avatars are generally more likely to be favored by users over other types of CAs for their widely accepted face-to-face conversation format [Bickmore T, Pusateri A, Kimani EN, Paasche-Orlow MK, Trinh H, Magnani JW. Managing chronic conditions with a smartphone-based conversational virtual agent. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents. 2018. Presented at: IVA '18; November 5-8, 2018:119-124; Sydney, Australia. URL: https://dl.acm.org/doi/10.1145/3267851.3267908 [CrossRef]10] as they can support both text and audio outputs [Shubham K, Mukherjee A, Jayagopi DB. Review of realistic behavior and appearance generation in embodied conversational agents: a comparison between traditional and modern approaches. In: Proceedings of the 2022 International Conference on Multimodal Interaction. 2022. Presented at: ICMI '22; November 7-11, 2022:191-197; Bengaluru, India. URL: https://dl.acm.org/doi/10.1145/3536221.3556592 [CrossRef]47,Denecke K, Tschanz M, Dorner TL, May R. Intelligent conversational agents in healthcare: hype or hope? Stud Health Technol Inform. 2019;259:77-84. [Medline]113] and, therefore, can serve as a valuable alternative complement for remote care assistance [Kim K, Norouzi N, Losekamp T, Bruder G, Anderson M, Welch G. Effects of patient care assistant embodiment and computer mediation on user experience. In: Proceedings of the 2019 IEEE International Conference on Artificial Intelligence and Virtual Reality. 2019. Presented at: AIVR '19; December 9-11, 2019:17-177; San Diego, CA. URL: https://ieeexplore.ieee.org/document/8942267 [CrossRef]114]. This suggests that digital avatars remain a popular choice for their versatility and interactive capabilities, driving the rapid progression toward the anthropomorphism era as they represent realistic human-like traits in behaviors and appearances for enhanced interaction with the users [Sen A, Liew SH. Augmented reality and its use in education. In: Tatnall A, editor. Encyclopedia of Education and Information Technologies. Cham, Switzerland. Springer; 2018:1719-1726.115].
Moreover, anthropomorphic CAs are more preferred over nonanthropomorphic CAs, as shown by Park et al [Park G, Chung J, Lee S. Human vs. machine-like representation in chatbot mental health counseling: the serial mediation of psychological distance and trust on compliance intention. Curr Psychol. Apr 20, 2023:1-12. [FREE Full text] [CrossRef] [Medline]109], indicating that the human-like representations of a chatbot yielded a higher likelihood of intervention compliance than the machinelike chatbot because the former increased closeness and trust. Similarly, another study demonstrated that an augmented human-like embodiment of a digital avatar resulted in greater social closeness among the participants [Kim K, Boelling L, Haesler S, Bailenson J, Bruder G, Welch GF. Does a digital assistant need a body? The influence of visual embodiment and social behavior on the perception of intelligent virtual agents in AR. In: Proceedings of the 2018 IEEE International Symposium on Mixed and Augmented Reality. 2018. Presented at: ISMAR '18; October 16-20, 2018:105-115; Munich, Germany. URL: https://ieeexplore.ieee.org/document/8613756 [CrossRef]116]. Correspondingly, strong social presence and emotional closeness are regarded as key facilitators of favorable attitudinal and behavioral outcomes among users [Li Q, Luximon Y, Zhang J. The influence of anthropomorphic cues on patients' perceived anthropomorphism, social presence, trust building, and acceptance of health care conversational agents: within-subject web-based experiment. J Med Internet Res. Aug 10, 2023;25:e44479. [FREE Full text] [CrossRef] [Medline]117]. Hence, anthropomorphism positively correlates to users’ perceived trust and acceptance of a virtual agent [Moriuchi E. Leveraging the science to understand factors influencing the use of AI-powered avatar in healthcare services. J Technol Behav Sci. Sep 09, 2022;7(4):588-602. [CrossRef]12,Li Q, Luximon Y, Zhang J. The influence of anthropomorphic cues on patients' perceived anthropomorphism, social presence, trust building, and acceptance of health care conversational agents: within-subject web-based experiment. J Med Internet Res. Aug 10, 2023;25:e44479. [FREE Full text] [CrossRef] [Medline]117]. Indeed, digital human faces as intelligent CAs are considerably promising for achieving a human-like presence to enhance human-machine interaction [Seymour M, Yuan L, Dennis AR, Riemer K. Facing the artificial: understanding affinity, trustworthiness, and preference for more realistic digital humans. In: Proceedings of the 53rd Hawaii International Conference on System Sciences. 2020. Presented at: HICSS '20; January 7-10, 2020:4673-4682; Maui, HI. URL: https://scholarspace.manoa.hawaii.edu/server/api/core/bitstreams/07da3db0-4dec-40ae-9bd8-550de5636593/content [CrossRef]42]. Consequently, the application of AI-based conversational avatars as digital human-like health carers has been gaining attention recently, leading patients to prefer digital human avatars as medical professionals over chatbots due to the former’s perceived human-like and interactive nature, which is essential for maintaining the professionalism and emotional aspects in physician-patient relationships [Sestino A, D'Angelo A. My doctor is an avatar! the effect of anthropomorphism and emotional receptivity on individuals' intention to use digital-based healthcare services. Technol Forecast Soc Change. Jun 2023;191:122505. [CrossRef]39].
Nonetheless, this evolution may apparently raise perceived uncanniness, which is considered a genuine ethical concern with realistic anthropomorphic avatars, yet it does not appear to be a universal problem for the avatar appearance or limit users’ engagement with the virtual environment [Garner TA, Powell WA, Carr V. Virtual carers for the elderly: a case study review of ethical responsibilities. Digit Health. 2016;2:2055207616681173. [FREE Full text] [CrossRef] [Medline]55]. Realistic humanoid avatars representing virtual carers can appear more trustworthy and increase compliance among users [Garner TA, Powell WA, Carr V. Virtual carers for the elderly: a case study review of ethical responsibilities. Digit Health. 2016;2:2055207616681173. [FREE Full text] [CrossRef] [Medline]55,Park G, Chung J, Lee S. Human vs. machine-like representation in chatbot mental health counseling: the serial mediation of psychological distance and trust on compliance intention. Curr Psychol. Apr 20, 2023:1-12. [FREE Full text] [CrossRef] [Medline]109]. However, striking a balance between realistic and stylized avatar appearances can be worthwhile to avoid visual overloading and discomfort among users while representing the required elements accurately to maximize perceived trust, closeness, and acceptability [Viana Ó, Terroso M, Serejo C, Vilaça JL. Ascertaining the influence of style on the credibility and appeal of a digital health avatar. In: Proceedings of the 6th International Conference on Design and Digital Communication. Cham. Springer; 2022. Presented at: DIGICOM '22; November 3–5, 2022:27; Barcelos, Portugal. URL: https://link.springer.com/chapter/10.1007/978-3-031-20364-0_6 [CrossRef]25].
Challenges and Limitations of CAs
The following challenges with CAs, including limitations in intelligence level, empathy, data privacy, and ensuring patient safety, highlight significant barriers to their adoption and effectiveness in health care.
Intelligence Level of CAs
Most of the proposed AI-based CAs were rule-based or scripted systems with a predetermined set of rules and conditions, which are easier to design and implement but have limited capability to handle complex conversations (unlike the automated ML-based systems) [Singh S, Beniwal H. A survey on near-human conversational agents. J King Saud Univ Comput Inf Sci. Nov 2022;34(10):8852-8866. [CrossRef]33,Kusal S, Patil S, Choudrie J, Kotecha K, Mishra S, Abraham A. AI-based conversational agents: a scoping review from technologies to future directions. IEEE Access. 2022;10:92337-92356. [CrossRef]56,Grunitz M. Rule-based AI vs machine learning: what’s the difference? WeAreBrain. Sep 2021. URL: https://wearebrain.com/blog/rule-based-ai-vs-machine-learning-whats-the-difference/ [accessed 2024-04-29] 118] and, therefore, cannot generate personalized responses in a more human-like manner, which the users would mostly prefer [Calvo RA, Peters D, Moradbakhti L, Cook D, Rizos G, Schuller B, et al. Assessing the feasibility of a text-based conversational agent for asthma support: protocol for a mixed methods observational study. JMIR Res Protoc. Feb 02, 2023;12:e42965. [FREE Full text] [CrossRef] [Medline]84,Easton K, Potter S, Bec R, Bennion M, Christensen H, Grindell C, et al. A virtual agent to support individuals living with physical and mental comorbidities: co-design and acceptability testing. J Med Internet Res. May 30, 2019;21(5):e12996. [FREE Full text] [CrossRef] [Medline]91,LeRouge C, Dickhut K, Lisetti C, Sangameswaran S, Malasanos T. Engaging adolescents in a computer-based weight management program: avatars and virtual coaches could help. J Am Med Inform Assoc. Jan 2016;23(1):19-28. [FREE Full text] [CrossRef] [Medline]108-Boggiss A, Consedine N, Hopkins S, Silvester C, Jefferies C, Hofman P, et al. Improving the well-being of adolescents with type 1 diabetes during the COVID-19 pandemic: qualitative study exploring acceptability and clinical usability of a self-compassion chatbot. JMIR Diabetes. May 05, 2023;8:e40641. [FREE Full text] [CrossRef] [Medline]110]. Furthermore, few studies proposed integrating personality-tailored behavior in CAs, precisely known as personality-adaptive CAs, which can automatically recognize and adapt to the users’ distinctive personality traits accordingly to be able to support patients in a personalized and authentic human-like manner [Ahmad R, Siemon D, Gnewuch U, Robra-Bissantz S. The benefits and caveats of personality-adaptive conversational agents in mental health care. In: Proceedings of the 27th annual Americas Conference on Information Systems. 2021. Presented at: AMCIS '21; August 9-13, 2021; Virtual Event. URL: https://web.archive.org/web/20220804131608id_/https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1225&context=amcis2021119,Siemon D, Ahmad R, Harms H, de Vreede T. Requirements and solution approaches to personality-adaptive conversational agents in mental health care. Sustainability. 2022;14(7):3832. [FREE Full text]120] as well as effectively persuade users to alter their attitudes and behaviors for healthy lifestyle habits [Kang Y, Tan AH, Miao C. An adaptive computational model for personalized persuasion. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence. 2015. Presented at: IJCAI '15; July 25–31, 2015:61-67; Buenos Aires, Argentina. URL: https://www.ijcai.org/Proceedings/15/Papers/016.pdf121].
Empathy of CAs
Studies have emphasized the importance of including the “empathy” element in the design of CAs [Faddoul G. The virtual diabetician: a virtual platform providing diabetes treatment information through storytelling and virtual companions. Center of Information Systems and Technology Claremont Graduate University. 2018. URL: https://www.proquest.com/openview/6b73f74fdd97109ee35723b289ea2b96/1?pq-origsite=gscholar&cbl=18750 [accessed 2024-04-29] 122,Tironi A, Mainetti R, Pezzera M, Borghese NA. An empathic virtual caregiver for assistance in exer-game-based rehabilitation therapie. In: Proceedings of the IEEE 7th International Conference on Serious Games and Applications for Health. 2019. Presented at: SeGAH '19; August 5-7, 2019:1-6; Kyoto, Japan. URL: https://ieeexplore.ieee.org/document/8882477123], yet only one of the included studies notably aimed to incorporate “empathy” in the proposed chatbot by applying text-based emotion analysis to recognize the user’s emotion and respond accordingly using emojis, stickers, and Graphics Interchange Format images [Rahmanti AR, Yang HC, Bintoro BS, Nursetyo AA, Muhtar MS, Syed-Abdul S, et al. SlimMe, a chatbot with artificial empathy for personal weight management: system design and finding. Front Nutr. 2022;9:870775. [FREE Full text] [CrossRef] [Medline]78]. Furthermore, a scoping review on AI-based CAs conducted by Kusal et al [Kusal S, Patil S, Choudrie J, Kotecha K, Mishra S, Abraham A. AI-based conversational agents: a scoping review from technologies to future directions. IEEE Access. 2022;10:92337-92356. [CrossRef]56] has suggested that the major shortcoming of CAs is their lack of competence to make communication with humans seem natural with the aid of empathy and sentiments, mainly due to their failure to comprehend the human context and the users’ emotions as their responses are restricted to the queries they are trained for, which can lead to a frustrating user experience. Indeed, a study conducted by Casas et al [Casas J, Spring T, Daher K, Mugellini E, Khaled OA, Cudre-Mauroux P. Enhancing conversational agents with empathic abilities. In: Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents. 2021. Presented at: IVA '21; September 14-17, 2021:41-47; Virtual Event. URL: https://dl.acm.org/doi/10.1145/3472306.3478344 [CrossRef]124] determined the capability of an empathetic chatbot to surpass a benchmark bot or even humans in predicting the emotional state of textual messages. Therefore, CAs should be able to model their context and select important information to remember as communication depends on past messages [Kusal S, Patil S, Choudrie J, Kotecha K, Mishra S, Abraham A. AI-based conversational agents: a scoping review from technologies to future directions. IEEE Access. 2022;10:92337-92356. [CrossRef]56].
Data Privacy
Surprisingly, the included studies demonstrated limited emphasis on the data privacy of the AI-based CAs while handling patient data, which is a common challenge faced in the design and evaluation of CAs [Kocaballi AB, Quiroz JC, Rezazadegan D, Berkovsky S, Magrabi F, Coiera E, et al. Responses of conversational agents to health and lifestyle prompts: investigation of appropriateness and presentation structures. J Med Internet Res. Feb 09, 2020;22(2):e15823. [FREE Full text] [CrossRef] [Medline]111,May R, Denecke K. Security, privacy, and healthcare-related conversational agents: a scoping review. Inform Health Soc Care. Apr 03, 2022;47(2):194-210. [CrossRef] [Medline]125]. Hence, it is recommended to prioritize data privacy and confidentiality factors during the development of such technologies to gain users’ trust and acceptance [Monaco A, Palmer K, Holm Ravn Faber N, Kohler I, Silva M, Vatland A, et al. Digital health tools for managing noncommunicable diseases during and after the COVID-19 pandemic: perspectives of patients and caregivers. J Med Internet Res. Jan 29, 2021;23(1):e25652. [FREE Full text] [CrossRef] [Medline]63].
Patient Safety
Another area that needs to be focused on is patient safety [Laranjo L, Dunn AG, Tong HL, Kocaballi AB, Chen J, Bashir R, et al. Conversational agents in healthcare: a systematic review. J Am Med Inform Assoc. Sep 01, 2018;25(9):1248-1258. [FREE Full text] [CrossRef] [Medline]126], which was also not highlighted in the included studies. For instance, the inability of digital health tools to recognize the urgency of the patient’s health condition may generate inappropriate health advice or lead to delayed treatment, increasing the risk of further complications. In addition, limited validation and medical training for existing CAs (as mentioned previously) poses risk of inaccuracy and raises concerns about patient safety. Therefore, assessing the validation status of these digital health tools is crucial for patient safety and optimal health care outcomes.
Uses and Benefits of AI-Based CAs in Managing NCDs
This review suggests that CAs are primarily used for supporting nonpharmacological interventions such as behavioral or lifestyle medication for managing NCDs such as CVDs, diabetes, obesity, cancer, and gastrointestinal diseases, as well as preventive measures for healthy living, indicating their potential ability to complementarily assist in the long-term remote management of NCDs [Kim K, Norouzi N, Losekamp T, Bruder G, Anderson M, Welch G. Effects of patient care assistant embodiment and computer mediation on user experience. In: Proceedings of the 2019 IEEE International Conference on Artificial Intelligence and Virtual Reality. 2019. Presented at: AIVR '19; December 9-11, 2019:17-177; San Diego, CA. URL: https://ieeexplore.ieee.org/document/8942267 [CrossRef]114,Ranieri A, Ruggiero A. Complementary role of conversational agents in e-health services. In: Proceedings of the 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering. 2022. Presented at: MetroXRAINE '22; October 26-28, 2022:528-533; Rome, Italy. URL: https://ieeexplore.ieee.org/document/9967603 [CrossRef]127,Altamimi I, Altamimi A, Alhumimidi AS, Altamimi A, Temsah MH. Artificial intelligence (AI) chatbots in medicine: a supplement, not a substitute. Cureus. Jun 2023;15(6):e40922. [FREE Full text] [CrossRef] [Medline]128]. The ubiquitous self-care support through constant symptom monitoring, along with the personalized behavioral modifications provided by CAs, can possibly rescue patients from minor health concerns and prevent the progression of chronic diseases [Bickmore T, Pusateri A, Kimani EN, Paasche-Orlow MK, Trinh H, Magnani JW. Managing chronic conditions with a smartphone-based conversational virtual agent. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents. 2018. Presented at: IVA '18; November 5-8, 2018:119-124; Sydney, Australia. URL: https://dl.acm.org/doi/10.1145/3267851.3267908 [CrossRef]10,Zahedi FM, Zhao H, Sanvanson P, Walia N, Jain H, Shaker R. My real avatar has a doctor appointment in the Wepital: a system for persistent, efficient, and ubiquitous medical care. Inf Manag. Dec 2022;59(8):103706. [CrossRef]104]. Health care professionals are optimistic about CAs’ potential ability to save patients time and money that they usually spend on physical clinic visits [Alturaiki AM, Banjar HR, Barefah AS, Alnajjar SA, Hindawi S. A smart chatbot for interactive management in beta thalassemia patients. Int J Telemed Appl. 2022;2022:9734518. [FREE Full text] [CrossRef] [Medline]103], thereby reducing the health care cost and burden and increasing accessibility [Easton K, Potter S, Bec R, Bennion M, Christensen H, Grindell C, et al. A virtual agent to support individuals living with physical and mental comorbidities: co-design and acceptability testing. J Med Internet Res. May 30, 2019;21(5):e12996. [FREE Full text] [CrossRef] [Medline]91,Zahedi FM, Zhao H, Sanvanson P, Walia N, Jain H, Shaker R. My real avatar has a doctor appointment in the Wepital: a system for persistent, efficient, and ubiquitous medical care. Inf Manag. Dec 2022;59(8):103706. [CrossRef]104]. Augmented reality applications could enhance the training of health care professionals and even patients in using these new technologies effectively [Sen A, Liew SH. Augmented reality and its use in education. In: Tatnall A, editor. Encyclopedia of Education and Information Technologies. Cham, Switzerland. Springer; 2018:1719-1726.115,Sen A, Chuen CL, Hta AC. Toward smart learning environments: affordances and design architecture of augmented reality (AR) applications in medical education. In: Proceedings of 1st International Conference on Smart System, Innovations and Computing. 2019. Presented at: SSIC '17; April 14-16, 2017:843-861; Jaipur, India. URL: https://link.springer.com/chapter/10.1007/978-981-10-5828-8_80 [CrossRef]129,Sen A, Leong C. Technology-enhanced learning. In: Tatnall A, editor. Encyclopedia of Education and Information Technologies. Cham, Switzerland. Springer; 2019:1-8.130].
Efficacy
The efficacy evaluation of CAs for remote care is still not thoroughly explored and well understood. According to Monaco et al [Monaco A, Palmer K, Holm Ravn Faber N, Kohler I, Silva M, Vatland A, et al. Digital health tools for managing noncommunicable diseases during and after the COVID-19 pandemic: perspectives of patients and caregivers. J Med Internet Res. Jan 29, 2021;23(1):e25652. [FREE Full text] [CrossRef] [Medline]63], inadequate evidence regarding the efficacy of digital health tools may have hindered the potential beneficial outcomes of using such resources to control NCDs. Nonetheless, the few included studies that aimed to identify the effectiveness of CAs in managing the target NCDs found favorable outcomes [Bickmore T, Pusateri A, Kimani EN, Paasche-Orlow MK, Trinh H, Magnani JW. Managing chronic conditions with a smartphone-based conversational virtual agent. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents. 2018. Presented at: IVA '18; November 5-8, 2018:119-124; Sydney, Australia. URL: https://dl.acm.org/doi/10.1145/3267851.3267908 [CrossRef]10,Gong E, Baptista S, Russell A, Scuffham P, Riddell M, Speight J, et al. My diabetes coach, a mobile app-based interactive conversational agent to support type 2 diabetes self-management: randomized effectiveness-implementation trial. J Med Internet Res. Nov 05, 2020;22(11):e20322. [FREE Full text] [CrossRef] [Medline]24,Krishnakumar A, Verma R, Chawla R, Sosale A, Saboo B, Joshi S, et al. Evaluating glycemic control in patients of South Asian origin with type 2 diabetes using a digital therapeutic platform: analysis of real-world data. J Med Internet Res. Mar 25, 2021;23(3):e17908. [FREE Full text] [CrossRef] [Medline]82,Inkster B, Sarda S, Subramanian V. An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: real-world data evaluation mixed-methods study. JMIR Mhealth Uhealth. Nov 23, 2018;6(11):e12106. [FREE Full text] [CrossRef] [Medline]90,Egede JO, Price D, Krishnan DB, Jaiswal S, Elliott N, Morriss R. Design and evaluation of virtual human mediated tasks for assessment of depression and anxiety. In: Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents. 2021. Presented at: IVA '21; September 14-17, 2021:52-59; Virtual Event. URL: https://dl.acm.org/doi/10.1145/3472306.3478361 [CrossRef]96,Romanovskyi O, Pidbutska N, Knysh A. Elomia chatbot: the effectiveness of artificial intelligence in the fight for mental health. In: Proceedings of the 5th International Conference on Computational Linguistics and Intelligent Systems. 2021. Presented at: COLINS '21; April 22-23, 2021:1-10; Kharkiv, Ukraine. URL: https://ceur-ws.org/Vol-2870/paper89.pdf97,Meheli S, Sinha C, Kadaba M. Understanding people with chronic pain who use a cognitive behavioral therapy-based artificial intelligence mental health app (Wysa): mixed methods retrospective observational study. JMIR Hum Factors. Apr 27, 2022;9(2):e35671. [FREE Full text] [CrossRef] [Medline]105]. Moreover, many reviews have emphasized the efficacy of CAs in health care support. For instance, a systematic review conducted by Milne-Ives et al [Milne-Ives M, de Cock C, Lim E, Shehadeh MH, de Pennington N, Mole G, et al. The effectiveness of artificial intelligence conversational agents in health care: systematic review. J Med Internet Res. Oct 22, 2020;22(10):e20346. [FREE Full text] [CrossRef] [Medline]26] investigated the effectiveness and usability of AI-based CAs designed for health care and found them generally effective, with positive or mixed evidence, whereas a narrative review by Dingler et al [Dingler T, Kwasnicka D, Wei J, Gong E, Oldenburg B. The use and promise of conversational agents in digital health. Yearb Med Inform. Aug 2021;30(1):191-199. [FREE Full text] [CrossRef] [Medline]60] used context-aware voice assistants (eg, Google Home and Amazon Echo), demonstrating the utility of speech-based CAs in providing personalized remote health care support. However, a systematic review conducted by Hossain et al [Hossain MM, Tasnim S, Sharma R, Sultana A, Shaik AF, Faizah F, et al. Digital interventions for people living with non-communicable diseases in India: a systematic review of intervention studies and recommendations for future research and development. Digit Health. 2019;5:2055207619896153. [FREE Full text] [CrossRef] [Medline]131] exhibited low positive outcomes of digital health interventions among people with NCDs in India, requiring further exploration of advanced technologies for the equitable and sustainable development of digital health tools.
Targeted Users
Most of the target user groups were based on chronic disease conditions rather than any specific age group. A fair proportion of studies (6/43, 14%) focused on the application of CAs in the older adult population, exhibiting that this vulnerable user group may be most in need of such technologies as older adults have greater risk of developing serious NCDs [Balsa J, Neves P, Félix I, Guerreiro MP, Alves P, Carmo MB, et al. Intelligent virtual assistant for promoting behaviour change in older people with T2D. In: Proceedings of the 19th EPIA Conference on Artificial Intelligence on Progress in Artificial Intelligence. 2019. Presented at: EPIA '19; September 3-6, 2019:372-383; Vila Real, Portugal. URL: https://link.springer.com/chapter/10.1007/978-3-030-30241-2_32 [CrossRef]28,Cheng A, Raghavaraju V, Kanugo J, Handrianto YP, Yi S. Development and evaluation of a healthy coping voice interface application using the Google home for elderly patients with type 2 diabetes. In: Proceedings of the 15th IEEE Annual Consumer Communications & Networking Conference. 2018. Presented at: CCNC '18; January 12-15, 2018:1-5; Las Vegas, NV. URL: https://ieeexplore.ieee.org/document/8319283 [CrossRef]58,Lyzwinski LN, Elgendi M, Menon C. Conversational agents and avatars for cardiometabolic risk factors and lifestyle-related behaviors: scoping review. JMIR Mhealth Uhealth. May 25, 2023;11:e39649. [FREE Full text] [CrossRef] [Medline]61] and many older adult patients lead an isolated, lonely life and, therefore, require constant remote monitoring as well as a virtual companion for healthy living [Maher CA, Davis CR, Curtis RG, Short CE, Murphy KJ. A physical activity and diet program delivered by artificially intelligent virtual health coach: proof-of-concept study. JMIR Mhealth Uhealth. Jul 10, 2020;8(7):e17558. [FREE Full text] [CrossRef] [Medline]132,Jegundo AL, Dantas C, Quintas J, Dutra J, Almeida AL, Caravau H, et al. Perceived usefulness, satisfaction, ease of use and potential of a virtual companion to support the care provision for older adults. Technologies. Jul 25, 2020;8(3):42. [FREE Full text] [CrossRef]133]. Hence, in other words, the selected user groups were mostly based on targeted chronic diseases that may be more prevalent in older adults. However, we did not encounter any significant study that modified the CAs to enhance accessibility for older adults, but social networking sites could potentially enhance engagement with such CAs among this user group [Nagarajan A, Sen A. Can Bloom’s higher order thinking skills be achieved by gamified learning through social networking sites (SNS) like Facebook? Interact Des Archit. 2022;(53):144-160. [FREE Full text]134].
Moreover, it is crucial to recognize that the vast majority (41/43, 95%) of the included studies were conducted in high-income countries, underscoring the necessity of further research among underserved populations, particularly in LMICs where individuals may be more vulnerable to developing NCDs due to limited health awareness and access to health care services.
Limitations
Although this is an emerging field of research and we conducted an extensive literature search strategy, the possibility of noninclusion of relevant studies may still exist (eg, missing certain keywords or search terms, studies in languages other than English, studies not listed in the searched databases, and unidentified studies from gray literature). Similarly, our stringent inclusion and exclusion criteria were implemented to refine our focus on patient-centered AI-based CAs tailored for the remote management of NCDs, aiming to ensure the relevancy and applicability of our review to this particular domain of interest. However, this approach may have inadvertently narrowed the scope, potentially overlooking other relevant studies that could provide wider insights into our findings. Despite this, we strived to conduct a fair selection of studies meeting our inclusion criteria to mitigate selection bias and ensure a balanced representation of the literature. Nonetheless, it is important to acknowledge that other potential biases, such as publication bias, may still exist, which are beyond our control. In addition, new articles may have appeared after our search deadline (July 31, 2023), which may cause additional differentiations.
Furthermore, some common limitations were also identified within the included studies that may impact the overall findings of this review: (1) limited sample sizes (a vast majority of the studies had relatively small sample sizes, which could limit the generalizability of the findings), (2) short-term interventions or lack of long-term outcome measures (the duration of interventions in most studies was relatively short [Kimani E, Bickmore T, Trinh H, Ring L, Paasche-Orlow MK, Magnani JW. A smartphone-based virtual agent for atrial fibrillation education and counseling. In: Proceedings of the 16th International Conference on Intelligent Virtual Agents. 2016. Presented at: IVA '16; September 20-23, 2016:120-127; Los Angeles, CA. URL: https://link.springer.com/chapter/10.1007/978-3-319-47665-0_11 [CrossRef]80,Krishnakumar A, Verma R, Chawla R, Sosale A, Saboo B, Joshi S, et al. Evaluating glycemic control in patients of South Asian origin with type 2 diabetes using a digital therapeutic platform: analysis of real-world data. J Med Internet Res. Mar 25, 2021;23(3):e17908. [FREE Full text] [CrossRef] [Medline]82,Suganuma S, Sakamoto D, Shimoyama H. An embodied conversational agent for unguided internet-based cognitive behavior therapy in preventative mental health: feasibility and acceptability pilot trial. JMIR Ment Health. Jul 31, 2018;5(3):e10454. [FREE Full text] [CrossRef] [Medline]88,Rathnayaka P, Mills N, Burnett D, De Silva D, Alahakoon D, Gray R. A mental health chatbot with cognitive skills for personalised behavioural activation and remote health monitoring. Sensors (Basel). May 11, 2022;22(10):3653. [FREE Full text] [CrossRef] [Medline]101], potentially overlooking long-term user interactions and health outcomes essential for a comprehensive evaluation of CAs’ ability to sustainably assist in NCD management [Bickmore T, Pusateri A, Kimani EN, Paasche-Orlow MK, Trinh H, Magnani JW. Managing chronic conditions with a smartphone-based conversational virtual agent. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents. 2018. Presented at: IVA '18; November 5-8, 2018:119-124; Sydney, Australia. URL: https://dl.acm.org/doi/10.1145/3267851.3267908 [CrossRef]10,Eagle T, Blau C, Bales S, Desai N, Li V, Whittaker S. “I don’t know what you mean by `I am anxious'”: a new method for evaluating conversational agent responses to standardized mental health inputs for anxiety and depression. ACM Trans Interact Intell Syst. Jul 20, 2022;12(2):1-23. [CrossRef]37,Issom DZ, Rochat J, Hartvigsen G, Lovis C. Preliminary evaluation of a mHealth coaching conversational artificial intelligence for the self-care management of people with sickle-cell disease. Stud Health Technol Inform. Jun 16, 2020;270:1361-1362. [CrossRef] [Medline]77,Neerincx MA, van Vught W, Blanson Henkemans O, Oleari E, Broekens J, Peters R, et al. Socio-cognitive engineering of a robotic partner for child's diabetes self-management. Front Robot AI. 2019;6:118. [FREE Full text] [CrossRef] [Medline]85,Easton K, Potter S, Bec R, Bennion M, Christensen H, Grindell C, et al. A virtual agent to support individuals living with physical and mental comorbidities: co-design and acceptability testing. J Med Internet Res. May 30, 2019;21(5):e12996. [FREE Full text] [CrossRef] [Medline]91-Anastasiadou M, Alexiadis A, Polychronidou E, Votis K, Tzovaras D. A prototype educational virtual assistant for diabetes management. In: Proceedings of the 20th International Conference on Bioinformatics and Bioengineering. 2020. Presented at: BIBE '20; October 26-28, 2020:999-1004; Cincinnati, OH. URL: https://ieeexplore.ieee.org/document/9288129 [CrossRef]93,Egede JO, Price D, Krishnan DB, Jaiswal S, Elliott N, Morriss R. Design and evaluation of virtual human mediated tasks for assessment of depression and anxiety. In: Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents. 2021. Presented at: IVA '21; September 14-17, 2021:52-59; Virtual Event. URL: https://dl.acm.org/doi/10.1145/3472306.3478361 [CrossRef]96-Anan T, Kajiki S, Oka H, Fujii T, Kawamata K, Mori K, et al. Effects of an artificial intelligence-assisted health program on workers with neck/shoulder pain/stiffness and low back pain: randomized controlled trial. JMIR Mhealth Uhealth. Sep 24, 2021;9(9):e27535. [FREE Full text] [CrossRef] [Medline]98,Alturaiki AM, Banjar HR, Barefah AS, Alnajjar SA, Hindawi S. A smart chatbot for interactive management in beta thalassemia patients. Int J Telemed Appl. 2022;2022:9734518. [FREE Full text] [CrossRef] [Medline]103,Park G, Chung J, Lee S. Human vs. machine-like representation in chatbot mental health counseling: the serial mediation of psychological distance and trust on compliance intention. Curr Psychol. Apr 20, 2023:1-12. [FREE Full text] [CrossRef] [Medline]109]), (3) limited demographic data (some studies lacked detailed demographic information of the participants [Eagle T, Blau C, Bales S, Desai N, Li V, Whittaker S. “I don’t know what you mean by `I am anxious'”: a new method for evaluating conversational agent responses to standardized mental health inputs for anxiety and depression. ACM Trans Interact Intell Syst. Jul 20, 2022;12(2):1-23. [CrossRef]37,Anastasiadou M, Alexiadis A, Polychronidou E, Votis K, Tzovaras D. A prototype educational virtual assistant for diabetes management. In: Proceedings of the 20th International Conference on Bioinformatics and Bioengineering. 2020. Presented at: BIBE '20; October 26-28, 2020:999-1004; Cincinnati, OH. URL: https://ieeexplore.ieee.org/document/9288129 [CrossRef]93,Park G, Chung J, Lee S. Human vs. machine-like representation in chatbot mental health counseling: the serial mediation of psychological distance and trust on compliance intention. Curr Psychol. Apr 20, 2023:1-12. [FREE Full text] [CrossRef] [Medline]109], which may potentially limit the analysis of how demographic factors can impact the feasibility or effectiveness of the CAs), (4) potential bias (many studies had potential biases resulting from self-reported user data [Eagle T, Blau C, Bales S, Desai N, Li V, Whittaker S. “I don’t know what you mean by `I am anxious'”: a new method for evaluating conversational agent responses to standardized mental health inputs for anxiety and depression. ACM Trans Interact Intell Syst. Jul 20, 2022;12(2):1-23. [CrossRef]37,Issom DZ, Rochat J, Hartvigsen G, Lovis C. Preliminary evaluation of a mHealth coaching conversational artificial intelligence for the self-care management of people with sickle-cell disease. Stud Health Technol Inform. Jun 16, 2020;270:1361-1362. [CrossRef] [Medline]77,Rahmanti AR, Yang HC, Bintoro BS, Nursetyo AA, Muhtar MS, Syed-Abdul S, et al. SlimMe, a chatbot with artificial empathy for personal weight management: system design and finding. Front Nutr. 2022;9:870775. [FREE Full text] [CrossRef] [Medline]78,Rathnayaka P, Mills N, Burnett D, De Silva D, Alahakoon D, Gray R. A mental health chatbot with cognitive skills for personalised behavioural activation and remote health monitoring. Sensors (Basel). May 11, 2022;22(10):3653. [FREE Full text] [CrossRef] [Medline]101,Park G, Chung J, Lee S. Human vs. machine-like representation in chatbot mental health counseling: the serial mediation of psychological distance and trust on compliance intention. Curr Psychol. Apr 20, 2023:1-12. [FREE Full text] [CrossRef] [Medline]109], sample bias [Babington-Ashaye A, de Moerloose P, Diop S, Geissbuhler A. Design, development and usability of an educational AI chatbot for people with haemophilia in senegal. Haemophilia. Jul 2023;29(4):1063-1073. [CrossRef] [Medline]75,Krishnakumar A, Verma R, Chawla R, Sosale A, Saboo B, Joshi S, et al. Evaluating glycemic control in patients of South Asian origin with type 2 diabetes using a digital therapeutic platform: analysis of real-world data. J Med Internet Res. Mar 25, 2021;23(3):e17908. [FREE Full text] [CrossRef] [Medline]82,Meheli S, Sinha C, Kadaba M. Understanding people with chronic pain who use a cognitive behavioral therapy-based artificial intelligence mental health app (Wysa): mixed methods retrospective observational study. JMIR Hum Factors. Apr 27, 2022;9(2):e35671. [FREE Full text] [CrossRef] [Medline]105], coding bias [LeRouge C, Dickhut K, Lisetti C, Sangameswaran S, Malasanos T. Engaging adolescents in a computer-based weight management program: avatars and virtual coaches could help. J Am Med Inform Assoc. Jan 2016;23(1):19-28. [FREE Full text] [CrossRef] [Medline]108], and exclusion of variables or unaccounted confounding factors in analyses [Apergi LA, Bjarnadottir MV, Baras JS, Golden BL, Anderson KM, Chou J, et al. Voice interface technology adoption by patients with heart failure: pilot comparison study. JMIR Mhealth Uhealth. Apr 01, 2021;9(4):e24646. [FREE Full text] [CrossRef] [Medline]100], which could impact the validity of the results), and (5) unaddressed technical aspects (some studies did not discuss the technical aspects of the proposed CAs, including their potential technical limitations or challenges [Neerincx MA, van Vught W, Blanson Henkemans O, Oleari E, Broekens J, Peters R, et al. Socio-cognitive engineering of a robotic partner for child's diabetes self-management. Front Robot AI. 2019;6:118. [FREE Full text] [CrossRef] [Medline]85,Chaix B, Bibault JE, Pienkowski A, Delamon G, Guillemassé A, Nectoux P, et al. When chatbots meet patients: one-year prospective study of conversations between patients with breast cancer and a chatbot. JMIR Cancer. May 02, 2019;5(1):e12856. [FREE Full text] [CrossRef] [Medline]92,Anastasiadou M, Alexiadis A, Polychronidou E, Votis K, Tzovaras D. A prototype educational virtual assistant for diabetes management. In: Proceedings of the 20th International Conference on Bioinformatics and Bioengineering. 2020. Presented at: BIBE '20; October 26-28, 2020:999-1004; Cincinnati, OH. URL: https://ieeexplore.ieee.org/document/9288129 [CrossRef]93]). Addressing these issues could contribute to the enhancement of the design, development, and usability of CAs in the future [Sen A, Richardson S. Some controversies relating to the causes and preventive management of computer vision syndrome. In: Proceeding of the 2002 conference on CybErg. 2002. Presented at: CybErg '02; September 2-4, 2002:123-130; Johannesburg, South Africa. URL: https://research.monash.edu/en/publications/some-controversies-relating-to-the-causes-and-preventive-manageme [CrossRef]135].
Implications and Future Recommendations
As CAs strive to facilitate personalized and empathetic human-like conversations in the health care field [Denecke K, Tschanz M, Dorner TL, May R. Intelligent conversational agents in healthcare: hype or hope? Stud Health Technol Inform. 2019;259:77-84. [Medline]113], the necessity for context-oriented and domain-specific training emerges as a crucial consideration for improved accuracy and relevance [Yang S, Zhao H, Zhu S, Zhou G, Xu H, Jia Y, et al. Zhongjing: enhancing the chinese medical capabilities of large language model through expert feedback and real-world multi-turn dialogue. arXiv. Preprint posted online August 7, 2023. [FREE Full text]136]. Although advanced AI systems such as large language models trained on vast textual data (eg, the generative pretrained transformer series and LLaMA) have proven transformative in various sectors such as marketing, education, and customer service, their application in the health care sector remains underexplored, primarily due to a scarcity of relevant high-quality medical data sets [Wang J, Yao Z, Yang Z, Zhou H, Li R, Wang X, et al. NoteChat: a dataset of synthetic doctor-patient conversations conditioned on clinical notes. arXiv. Preprint posted online October 24, 2023. [FREE Full text]137]. In enhancing the AI-based CAs for health care, only a few recent studies have aimed to investigate real-world interactions between patients and health care professionals [Yang S, Zhao H, Zhu S, Zhou G, Xu H, Jia Y, et al. Zhongjing: enhancing the chinese medical capabilities of large language model through expert feedback and real-world multi-turn dialogue. arXiv. Preprint posted online August 7, 2023. [FREE Full text]136-Huang CW, Wu BC, Nguyen PA, Wang HH, Kao CC, Lee PC, et al. Emotion recognition in doctor-patient interactions from real-world clinical video database: initial development of artificial empathy. Comput Methods Programs Biomed. May 2023;233:107480. [CrossRef] [Medline]139]. While recognizing the dynamic and nonstandardized nature of historical human interactions in health care, our recommendation to study real-world communications between patients and health caregivers stems from the objective of training CAs to be contextually relevant, particularly in addressing the unique challenges associated with specific chronic diseases. This approach may seek to empower CAs to navigate the complexities of diverse health conditions with greater intelligence and empathy, thereby improving their adaptability through the use of high-quality data sets acquired from real-world scenarios [Yang S, Zhao H, Zhu S, Zhou G, Xu H, Jia Y, et al. Zhongjing: enhancing the chinese medical capabilities of large language model through expert feedback and real-world multi-turn dialogue. arXiv. Preprint posted online August 7, 2023. [FREE Full text]136] given the substantial amount of data and extensive knowledge base required for domain-specific contextual training [Kusal S, Patil S, Choudrie J, Kotecha K, Mishra S, Abraham A. AI-based conversational agents: a scoping review from technologies to future directions. IEEE Access. 2022;10:92337-92356. [CrossRef]56]. However, to ensure effective training of AI agents in health care, it is essential to define their roles (eg, nurse, health assistant, nutritionist, and pharmacist), which can determine the appropriate level of training required, allowing for the selection of specific data sets tailored to the medical training and evaluation of CAs.
Findings from the literature suggest an overall higher user preference for realistic human-like representations in CAs, specifically in the form of anthropomorphic digital avatars, which can be accessed through mobile devices (unlike physical humanoid robots) [Linders GM, Vaitonytė J, Alimardani M, Mitev KO, Louwerse MM. A realistic, multimodal virtual agent for the healthcare domain. In: Proceedings of the 22nd ACM International Conference on Intelligent Virtual Agents. 2022. Presented at: IVA '22; September 6-9, 2022:1-3; Faro, Portugal. URL: https://dl.acm.org/doi/abs/10.1145/3514197.3551250 [CrossRef]52,Kaptein F, Kiefer B, Cully A, Celiktutan O, Bierman B, Rijgersberg-peters R, et al. A cloud-based robot system for long-term interaction: principles, implementation, lessons learned. J Hum Robot Interact. Oct 18, 2021;11(1):1-27. [CrossRef]54], thus accommodating a broad user base including older adult users [Cheng A, Raghavaraju V, Kanugo J, Handrianto YP, Yi S. Development and evaluation of a healthy coping voice interface application using the Google home for elderly patients with type 2 diabetes. In: Proceedings of the 15th IEEE Annual Consumer Communications & Networking Conference. 2018. Presented at: CCNC '18; January 12-15, 2018:1-5; Las Vegas, NV. URL: https://ieeexplore.ieee.org/document/8319283 [CrossRef]58,Lewis L. Avatars and robots as social companions in healthcare: requirements, engineering, adoption and ethics. Int J Enterp Inf Syst. 2014;10(2):21-39. [CrossRef]140]. Therefore, future research should prioritize investigating the impact of anthropomorphic avatars or virtual humans in managing NCDs, including their potential ethical concerns (eg, uncanny valley) that should be addressed in the design considerations to increase acceptance of CAs for self-care [Viana Ó, Terroso M, Serejo C, Vilaça JL. Ascertaining the influence of style on the credibility and appeal of a digital health avatar. In: Proceedings of the 6th International Conference on Design and Digital Communication. Cham. Springer; 2022. Presented at: DIGICOM '22; November 3–5, 2022:27; Barcelos, Portugal. URL: https://link.springer.com/chapter/10.1007/978-3-031-20364-0_6 [CrossRef]25,Lucas GM, Gratch J, King A, Morency LP. It’s only a computer: virtual humans increase willingness to disclose. Comput Human Behav. Aug 2014;37:94-100. [CrossRef]49,Tironi A, Mainetti R, Pezzera M, Borghese NA. An empathic virtual caregiver for assistance in exer-game-based rehabilitation therapie. In: Proceedings of the IEEE 7th International Conference on Serious Games and Applications for Health. 2019. Presented at: SeGAH '19; August 5-7, 2019:1-6; Kyoto, Japan. URL: https://ieeexplore.ieee.org/document/8882477123].
Moreover, user preferences related to CA types and features may vary notably among different user groups. For instance, older adults and users with disabilities may require voice-activated CAs over text-based CAs due to their typing inabilities, and therefore, enhanced speech recognition functionality is required to incorporate into CAs to accommodate voice conversations in multiple languages [Even C, Hammann T, Heyl V, Rietz C, Wahl HW, Zentel P, et al. Benefits and challenges of conversational agents in older adults : a scoping review. Z Gerontol Geriatr. Aug 2022;55(5):381-387. [CrossRef] [Medline]70]. In addition to language requirements, factors such as culture, ethnicity, and individual personality traits may also play a significant role in influencing the adoption of AI dialogue systems for remote care purposes. Indeed, the adoption of CAs can be impeded among populations traditionally experiencing health inequities due to the limited availability of such tools with localized and culturally tailored features. These populations, often at higher risk of NCDs, could greatly benefit from more culturally sensitive health care technologies, which could improve health care access and outcomes, thereby necessitating careful consideration in future work.
Conclusions
The review highlighted the promising acceptance of CAs by users for the self-management of chronic conditions, with feedback indicating helpfulness, satisfaction, and ease of use in most of the included studies. AI-based CAs present opportunities for communication and interaction in health care settings. However, understanding and optimizing the communication channels between humans and such CAs is crucial for enhancing their capabilities and potential benefits in health care. While our study confirmed the increasing role of CAs in augmenting self-care and potentially reducing health care costs, it also exposed critical limitations, particularly concerning conversational depth and emotional intelligence. This review also emphasized the lack of reliable and comparable evidence to determine the efficacy of AI-enabled CAs for chronic health conditions. Therefore, while user feedback and acceptance were positive, there is a need for more rigorous studies and standardized reporting to evaluate the effectiveness of AI-based CAs as human-like health carers for managing NCDs.
Acknowledgments
We acknowledge the article processing fee (APF) sponsorship provided by the Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia.
This study would not have been possible without the project’s support provided by the Jeffrey Cheah School of Medicine and Health Sciences (Seed grant I-M010-SED-000158).
All authors declared that they had insufficient funding to support open access publication of this manuscript, including from affiliated organizations or institutions, funding agencies, or other organizations. JMIR Publications provided article processing fee (APF) support for the publication of this paper.
Data Availability
The data sets generated during and analyzed during this study are available from the corresponding author on reasonable request.
Conflicts of Interest
None declared.
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Abbreviations
AI: artificial intelligence |
CA: conversational agent |
CVD: cardiovascular disease |
LMIC: low- and middle-income country |
ML: machine learning |
NCD: noncommunicable disease |
PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews |
Edited by T de Azevedo Cardoso; submitted 08.01.24; peer-reviewed by M Kim, T Su, E Hekler, DLT Steven; comments to author 05.02.24; revised version received 06.03.24; accepted 25.03.24; published 16.07.24.
Copyright©Sadia Azmin Anisha, Arkendu Sen, Chris Bain. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.07.2024.
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