MRAGDATASET/citation_with_pmcid · Datasets at Hugging Face

source_paper string	source_section string	premise string	target_doi string	target_paper string
PMC10007007	Results	The chatbots that targeted healthy lifestyles (5/11, 45%; HLCC, Paola [22], HCAI [28], Ida [32], and Chat1 [33]) enabled behavioral monitoring by consistently providing feedback through performance content and pictures, weekly check-ins, and data-based inputs on performance.	10.2196/diabetes.8590	PMC6238835
PMC10007007	Results	The chatbots that targeted healthy lifestyles (5/11, 45%; HLCC, Paola [22], HCAI [28], Ida [32], and Chat1 [33]) enabled behavioral monitoring by consistently providing feedback through performance content and pictures, weekly check-ins, and data-based inputs on performance.	10.2196/28577	PMC8665384
PMC10007007	Results	The chatbots that targeted healthy lifestyles (5/11, 45%; HLCC, Paola [22], HCAI [28], Ida [32], and Chat1 [33]) enabled behavioral monitoring by consistently providing feedback through performance content and pictures, weekly check-ins, and data-based inputs on performance.	10.1016/j.pec.2013.05.011	PMC3727973
PMC10007007	Results	The chatbots that targeted smoking cessation (3/11, 27%; DigiQuit [23], SFA [24], and SMAG [27]) offered data-driven feedback on health indicators through web-based diaries and graphs.	10.2196/17530	PMC7215523
PMC10007007	Results	The chatbots that targeted smoking cessation (3/11, 27%; DigiQuit [23], SFA [24], and SMAG [27]) offered data-driven feedback on health indicators through web-based diaries and graphs.	10.1177/2055207619880676	PMC6775545
PMC10007007	Results	The chatbots that targeted medication or treatment adherence (2/11, 18%; Vik [26] and mPulse [30]) offered timely reminders to take medications or refill medicines.	10.2196/12856	PMC6521209
PMC10007007	Results	The chatbots that targeted medication or treatment adherence (2/11, 18%; Vik [26] and mPulse [30]) offered timely reminders to take medications or refill medicines.	10.2196/15771	PMC6887813
PMC10007007	Results	The chatbot that targeted the reduction in substance misuse performed mood tracking and regular check-ins to maintain accountability (1/11, 9%; Woebot [31]).	10.2196/24850	PMC8074987
PMC10007007	Results	The chatbots that targeted healthy lifestyles (3/8, 38%) offered educational sessions on the benefits of physical activity (Ida [32]) and healthy diet (Paola [22]) and information on sex, drugs, and alcohol (Bzz [29]).	10.2196/28577	PMC8665384
PMC10007007	Results	The chatbots that targeted healthy lifestyles (3/8, 38%) offered educational sessions on the benefits of physical activity (Ida [32]) and healthy diet (Paola [22]) and information on sex, drugs, and alcohol (Bzz [29]).	10.2196/17558	PMC7382010
PMC10007007	Results	The chatbots that targeted smoking cessation (4/8, 50%; DigiQuit [23]; SFA [24]; CureApp Smoking Cessation [CASC], [25]; and SMAG [27]) educated users on the benefits of being a nonsmoker, implications of abrupt cessation, and alternatives to smoking.	10.2196/17530	PMC7215523
PMC10007007	Results	The chatbots that targeted smoking cessation (4/8, 50%; DigiQuit [23]; SFA [24]; CureApp Smoking Cessation [CASC], [25]; and SMAG [27]) educated users on the benefits of being a nonsmoker, implications of abrupt cessation, and alternatives to smoking.	10.1177/2055207619880676	PMC6775545
PMC10007007	Results	The chatbots that targeted smoking cessation (4/8, 50%; DigiQuit [23]; SFA [24]; CureApp Smoking Cessation [CASC], [25]; and SMAG [27]) educated users on the benefits of being a nonsmoker, implications of abrupt cessation, and alternatives to smoking.	10.2196/12694	PMC6399570
PMC10007007	Results	The chatbot that targeted medication or treatment adherence (1/8, 12%; Vik [26]) offered information on the health issue (breast cancer) for which the users were taking medication.	10.2196/12856	PMC6521209
PMC10007007	Results	The chatbots that targeted healthy lifestyles (3/8, 38%) offered feedback on behaviors (HLCC and Ida [32]) and reinforced optimism to change behaviors through planning and imagining change (NAO [5] and Ida [32]).	10.2196/28577	PMC8665384
PMC10007007	Results	The chatbots that targeted healthy lifestyles (3/8, 38%) offered feedback on behaviors (HLCC and Ida [32]) and reinforced optimism to change behaviors through planning and imagining change (NAO [5] and Ida [32]).	10.2196/jmir.7737	PMC5958282
PMC10007007	Results	The chatbots that targeted smoking cessation (4/8, 50%) reinforced motivation through personalized messages based on TTM (DigiQuit [23]), scoreboards and trackers of milestones (SFA [24]), and motivational messages (CASC [25] and SMAG [27]).	10.2196/17530	PMC7215523
PMC10007007	Results	The chatbots that targeted smoking cessation (4/8, 50%) reinforced motivation through personalized messages based on TTM (DigiQuit [23]), scoreboards and trackers of milestones (SFA [24]), and motivational messages (CASC [25] and SMAG [27]).	10.1177/2055207619880676	PMC6775545
PMC10007007	Results	The chatbots that targeted smoking cessation (4/8, 50%) reinforced motivation through personalized messages based on TTM (DigiQuit [23]), scoreboards and trackers of milestones (SFA [24]), and motivational messages (CASC [25] and SMAG [27]).	10.2196/12694	PMC6399570
PMC10007007	Results	The chatbot that targeted reduction in substance misuse focused on motivation and engagement through individualized weekly reports to foster reflection (Woebot [31]).	10.2196/24850	PMC8074987
PMC10007007	Results	Among the interventions that targeted healthy lifestyles, Tess [6] offered empathetic health counseling or compassionate care through ML-driven emotional algorithms; NAO [5], the social robot, expressed empathy through humanized robot interaction, and HCAI [28] mimicked health professionals’ empathetic health counseling.	10.2196/jmir.7737	PMC5958282
PMC10007007	Results	Among the interventions that targeted healthy lifestyles, Tess [6] offered empathetic health counseling or compassionate care through ML-driven emotional algorithms; NAO [5], the social robot, expressed empathy through humanized robot interaction, and HCAI [28] mimicked health professionals’ empathetic health counseling.	10.2196/diabetes.8590	PMC6238835
PMC10007007	Results	The intervention that targeted reduction in substance misuse, Woebot [31], offered empathic responses by tailoring to users stated mood.	10.2196/24850	PMC8074987
PMC10007007	Results	Sixth, 7% (1/15) of studies (CASC [25]) delivered provider-recommendation system services.	10.2196/12694	PMC6399570
PMC10007007	Results	CASC [25] offered advice and counseling support to physicians.	10.2196/12694	PMC6399570
PMC10007007	Results	The chatbots that targeted healthy lifestyles (4/7, 57%; Paola [22], Tess [6], HCAI [28], and Bzz [29]) offered on-demand support, unlimited conversations, and answers to infinite number of questions.	10.2196/17558	PMC7382010
PMC10007007	Results	The chatbots that targeted healthy lifestyles (4/7, 57%; Paola [22], Tess [6], HCAI [28], and Bzz [29]) offered on-demand support, unlimited conversations, and answers to infinite number of questions.	10.2196/diabetes.8590	PMC6238835
PMC10007007	Results	The chatbots that targeted smoking cessation (3/7, 43%) offered on-demand emergency support via an AI nurse (CASC [25]), support during periods of high cravings (SMAG [27]), and unlimited availability for conversations (SFA [24]).	10.2196/12694	PMC6399570
PMC10007007	Results	The chatbots that targeted smoking cessation (3/7, 43%) offered on-demand emergency support via an AI nurse (CASC [25]), support during periods of high cravings (SMAG [27]), and unlimited availability for conversations (SFA [24]).	10.1177/2055207619880676	PMC6775545
PMC10007007	Results	Chat1 [33] offered homework assignments, whereas Woebot [31] required mindfulness exercises, gratitude journaling, or reflecting upon patterns and lessons already covered.	10.1016/j.pec.2013.05.011	PMC3727973
PMC10007007	Results	Chat1 [33] offered homework assignments, whereas Woebot [31] required mindfulness exercises, gratitude journaling, or reflecting upon patterns and lessons already covered.	10.2196/24850	PMC8074987
PMC10007007	Results	ML-driven emotional algorithms were used in Tess [6] and HCAI [28] to provide empathetic counseling or compassionate care (emotion-based response).	10.2196/diabetes.8590	PMC6238835
PMC10007007	Results	NLP and ML techniques were used in Paola [22], Vik [26], Ida [32], and Woebot [31] to identify and categorize user intents and entities by analyzing unstructured messages.	10.2196/17558	PMC7382010
PMC10007007	Results	NLP and ML techniques were used in Paola [22], Vik [26], Ida [32], and Woebot [31] to identify and categorize user intents and entities by analyzing unstructured messages.	10.2196/12856	PMC6521209
PMC10007007	Results	NLP and ML techniques were used in Paola [22], Vik [26], Ida [32], and Woebot [31] to identify and categorize user intents and entities by analyzing unstructured messages.	10.2196/28577	PMC8665384
PMC10007007	Results	NLP and ML techniques were used in Paola [22], Vik [26], Ida [32], and Woebot [31] to identify and categorize user intents and entities by analyzing unstructured messages.	10.2196/24850	PMC8074987
PMC10007007	Results	Bickmore et al’s [33] Chat1 used procedural and epistemological knowledge–based AI algorithms that facilitated therapeutic dialog actions (talk therapy).	10.1016/j.pec.2013.05.011	PMC3727973
PMC10007007	Results	A hybrid technique combining NLP and conversational AI or ML was adopted by mPulse [30] to ensure smooth, continuous, and uninterrupted conversations.	10.2196/15771	PMC6887813
PMC10007007	Results	Hybrid Health Recommender System was adopted by Carrasco-Hernandez et al’s [23] AI chatbot to personalize messages based on user demographics, content (interest of the user), and utility (ratings on each message by the user).	10.2196/17530	PMC7215523
PMC10007007	Results	Face-tracking technology was integrated into NAO [5] (the social robot) to track participants’ faces to humanize the interaction experience.	10.2196/jmir.7737	PMC5958282
PMC10007007	Results	All chatbots except NAO [5] (14/15, 93%) used text-based communication with the users, among which 2 (14%; Tess [6] and Vik [26]) chatbots also used voice-based communication.	10.2196/jmir.7737	PMC5958282
PMC10007007	Results	All chatbots except NAO [5] (14/15, 93%) used text-based communication with the users, among which 2 (14%; Tess [6] and Vik [26]) chatbots also used voice-based communication.	10.2196/12856	PMC6521209
PMC10007007	Results	NAO [5] used only voice-based communication, as it was deployed via a social robot.	10.2196/jmir.7737	PMC5958282
PMC10007007	Results	Out of the 13 chatbots that reported the frequency of engagement, all chatbots, except NAO [5], interacted with the users daily.	10.2196/jmir.7737	PMC5958282
PMC10007007	Results	NAO [5] interacted only once because it was delivered in person through a social robot.	10.2196/jmir.7737	PMC5958282
PMC10007007	Results	Vik [26], SMAG [27], Tess [6], and Ida [32] were integrated into Facebook (Meta Platforms, Inc) Messenger.	10.2196/12856	PMC6521209
PMC10007007	Results	Vik [26], SMAG [27], Tess [6], and Ida [32] were integrated into Facebook (Meta Platforms, Inc) Messenger.	10.2196/28577	PMC8665384
PMC10007007	Results	HLCC [21] was integrated with KakaoTalk (Kakao Corp), a popular messenger app in South Korea, and mPulse [30] was integrated with mobile SMS.	10.2196/15085	PMC7267999
PMC10007007	Results	HLCC [21] was integrated with KakaoTalk (Kakao Corp), a popular messenger app in South Korea, and mPulse [30] was integrated with mobile SMS.	10.2196/15771	PMC6887813
PMC10007007	Results	All chatbots except NAO [5], Ida [32], and Chat1 [33] (12/15, 80%) were deployed through smartphones, among which 3 (27%; Vik [26], SMAG [27], and Bzz [29]) chatbots were also deployed through computers.	10.2196/jmir.7737	PMC5958282
PMC10007007	Results	All chatbots except NAO [5], Ida [32], and Chat1 [33] (12/15, 80%) were deployed through smartphones, among which 3 (27%; Vik [26], SMAG [27], and Bzz [29]) chatbots were also deployed through computers.	10.2196/28577	PMC8665384
PMC10007007	Results	All chatbots except NAO [5], Ida [32], and Chat1 [33] (12/15, 80%) were deployed through smartphones, among which 3 (27%; Vik [26], SMAG [27], and Bzz [29]) chatbots were also deployed through computers.	10.1016/j.pec.2013.05.011	PMC3727973
PMC10007007	Results	All chatbots except NAO [5], Ida [32], and Chat1 [33] (12/15, 80%) were deployed through smartphones, among which 3 (27%; Vik [26], SMAG [27], and Bzz [29]) chatbots were also deployed through computers.	10.2196/12856	PMC6521209
PMC10007007	Results	Chat1 [33] was deployed only through computers.	10.1016/j.pec.2013.05.011	PMC3727973
PMC10007007	Results	NAO [5] was deployed through a social robot, and Ida [32] was deployed through Fitbit Flex 1 (Fitbit LLC).	10.2196/jmir.7737	PMC5958282
PMC10007007	Results	NAO [5] was deployed through a social robot, and Ida [32] was deployed through Fitbit Flex 1 (Fitbit LLC).	10.2196/28577	PMC8665384
PMC10007007	Results	Three chatbots (HLCC, Paola [22], and Ida [32]) integrated an existing AI-driven conversational platform, that is, the Watson conversation tool (HLCC and Paola [22]) and Dialogflow, an advanced Google ML algorithm (Ida [32]).	10.2196/17558	PMC7382010
PMC10007007	Results	Three chatbots (HLCC, Paola [22], and Ida [32]) integrated an existing AI-driven conversational platform, that is, the Watson conversation tool (HLCC and Paola [22]) and Dialogflow, an advanced Google ML algorithm (Ida [32]).	10.2196/28577	PMC8665384
PMC10007007	Results	Paola [22] measured the baseline level of physical activity and Mediterranean diet; SFA [24] measured time to first cigarette and cigarettes per day; CASC [25] measured demographics, motivation levels for smoking cessation, number of cigarettes smoked per day, and years of smoking; SMAG [27] measured demographics and type of smoking dependence; and Tess [6] used electronic health records.	10.2196/17558	PMC7382010
PMC10007007	Results	Paola [22] measured the baseline level of physical activity and Mediterranean diet; SFA [24] measured time to first cigarette and cigarettes per day; CASC [25] measured demographics, motivation levels for smoking cessation, number of cigarettes smoked per day, and years of smoking; SMAG [27] measured demographics and type of smoking dependence; and Tess [6] used electronic health records.	10.1177/2055207619880676	PMC6775545
PMC10007007	Results	Paola [22] measured the baseline level of physical activity and Mediterranean diet; SFA [24] measured time to first cigarette and cigarettes per day; CASC [25] measured demographics, motivation levels for smoking cessation, number of cigarettes smoked per day, and years of smoking; SMAG [27] measured demographics and type of smoking dependence; and Tess [6] used electronic health records.	10.2196/12694	PMC6399570
PMC10007007	Results	HLCC [21] asked the users (office workers) to set realistic stair climbing goals, Paola [22] enabled the users to set dietary goals and daily step target every week based on the previous week’s outcomes, and SFA [24] asked the users to set the target quit date for smoking.	10.2196/15085	PMC7267999
PMC10007007	Results	HLCC [21] asked the users (office workers) to set realistic stair climbing goals, Paola [22] enabled the users to set dietary goals and daily step target every week based on the previous week’s outcomes, and SFA [24] asked the users to set the target quit date for smoking.	10.2196/17558	PMC7382010
PMC10007007	Results	HLCC [21] asked the users (office workers) to set realistic stair climbing goals, Paola [22] enabled the users to set dietary goals and daily step target every week based on the previous week’s outcomes, and SFA [24] asked the users to set the target quit date for smoking.	10.1177/2055207619880676	PMC6775545
PMC10007007	Results	DigiQuit [23] collected feedback on the message content and timing, Tess [6] collected data on the usefulness of the message, and Vik [26] collected data on the relevance of the reminders.	10.2196/17530	PMC7215523
PMC10007007	Results	DigiQuit [23] collected feedback on the message content and timing, Tess [6] collected data on the usefulness of the message, and Vik [26] collected data on the relevance of the reminders.	10.2196/12856	PMC6521209
PMC10007007	Results	HLCC [21] collected performance content and pictures; Paola [22] collected data on daily steps and dietary patterns; Vik [26] collected data on medication adherence levels; SMAG [27] monitored the users’ smoking levels along with information on location, alone or accompanied, ongoing activity, and mood to create smoking profiles for them; and HCAI [28] gathered data automatically through sensors on phones and integrated devices such as wearables and self-reported information such as on dietary consumption.	10.2196/15085	PMC7267999
PMC10007007	Results	HLCC [21] collected performance content and pictures; Paola [22] collected data on daily steps and dietary patterns; Vik [26] collected data on medication adherence levels; SMAG [27] monitored the users’ smoking levels along with information on location, alone or accompanied, ongoing activity, and mood to create smoking profiles for them; and HCAI [28] gathered data automatically through sensors on phones and integrated devices such as wearables and self-reported information such as on dietary consumption.	10.2196/17558	PMC7382010
PMC10007007	Results	HLCC [21] collected performance content and pictures; Paola [22] collected data on daily steps and dietary patterns; Vik [26] collected data on medication adherence levels; SMAG [27] monitored the users’ smoking levels along with information on location, alone or accompanied, ongoing activity, and mood to create smoking profiles for them; and HCAI [28] gathered data automatically through sensors on phones and integrated devices such as wearables and self-reported information such as on dietary consumption.	10.2196/12856	PMC6521209
PMC10007007	Results	HLCC [21] collected performance content and pictures; Paola [22] collected data on daily steps and dietary patterns; Vik [26] collected data on medication adherence levels; SMAG [27] monitored the users’ smoking levels along with information on location, alone or accompanied, ongoing activity, and mood to create smoking profiles for them; and HCAI [28] gathered data automatically through sensors on phones and integrated devices such as wearables and self-reported information such as on dietary consumption.	10.2196/diabetes.8590	PMC6238835
PMC10007007	Results	Tess [6] used clinical scripts targeted at behavior change, CASC [25] used national guidelines on counseling support, and HCAI [28] used content from the Diabetes Prevention Program’s curriculum.	10.2196/12694	PMC6399570
PMC10007007	Results	Tess [6] used clinical scripts targeted at behavior change, CASC [25] used national guidelines on counseling support, and HCAI [28] used content from the Diabetes Prevention Program’s curriculum.	10.2196/diabetes.8590	PMC6238835
PMC10007007	Discussion	These findings are consistent with previous systematic reviews that reported the use of AI chatbots for improvement in physical activity levels and improvement in medication adherence [2,5], treatment adherence [14], adherence to self-management practices [1], smoking cessation [12], and reduction in substance abuse [12].	10.1186/s12966-021-01224-6	PMC8665320
PMC10007007	Discussion	These findings are consistent with previous systematic reviews that reported the use of AI chatbots for improvement in physical activity levels and improvement in medication adherence [2,5], treatment adherence [14], adherence to self-management practices [1], smoking cessation [12], and reduction in substance abuse [12].	10.2196/jmir.7737	PMC5958282
PMC10007007	Discussion	These findings are consistent with previous systematic reviews that reported the use of AI chatbots for improvement in physical activity levels and improvement in medication adherence [2,5], treatment adherence [14], adherence to self-management practices [1], smoking cessation [12], and reduction in substance abuse [12].	10.2196/20701	PMC7522733
PMC10007007	Discussion	These findings are consistent with previous systematic reviews that reported the use of AI chatbots for improvement in physical activity levels and improvement in medication adherence [2,5], treatment adherence [14], adherence to self-management practices [1], smoking cessation [12], and reduction in substance abuse [12].	10.1093/jamia/ocy072	PMC6118869
PMC10007007	Discussion	These findings are consistent with previous systematic reviews that reported the use of AI chatbots for improvement in physical activity levels and improvement in medication adherence [2,5], treatment adherence [14], adherence to self-management practices [1], smoking cessation [12], and reduction in substance abuse [12].	10.2196/jmir.6553	PMC5442350
PMC10007007	Discussion	In the case of feasibility, evidence on the safety of chatbots was quite less because only 7% (1/15) of studies reported safety [22].	10.2196/17558	PMC7382010
PMC10007007	Discussion	Some of the previous systematic reviews reported feasibility in the form of engagement with AI chatbots; however, the feasibility metrics differed across studies, and there was strong evidence regarding decrease in engagement rates over time [11,13,14].	10.2196/jmir.7023	PMC5595406
PMC10007007	Discussion	Some of the previous systematic reviews reported feasibility in the form of engagement with AI chatbots; however, the feasibility metrics differed across studies, and there was strong evidence regarding decrease in engagement rates over time [11,13,14].	10.2196/jmir.7351	PMC5709656
PMC10007007	Discussion	Some of the previous systematic reviews reported feasibility in the form of engagement with AI chatbots; however, the feasibility metrics differed across studies, and there was strong evidence regarding decrease in engagement rates over time [11,13,14].	10.2196/20701	PMC7522733
PMC10007007	Discussion	These findings are partially aligned with previous systematic reviews that reported acceptability [11,13,14] and on-demand availability, accessibility, and satisfaction [7].	10.2196/jmir.7023	PMC5595406
PMC10007007	Discussion	These findings are partially aligned with previous systematic reviews that reported acceptability [11,13,14] and on-demand availability, accessibility, and satisfaction [7].	10.2196/jmir.7351	PMC5709656
PMC10007007	Discussion	These findings are partially aligned with previous systematic reviews that reported acceptability [11,13,14] and on-demand availability, accessibility, and satisfaction [7].	10.2196/20701	PMC7522733
PMC10007007	Discussion	These findings are partially aligned with previous systematic reviews that reported acceptability [11,13,14] and on-demand availability, accessibility, and satisfaction [7].	10.2196/20346	PMC7644372
PMC10007007	Discussion	These findings are partially aligned with previous systematic reviews on AI chatbots that reported that chatbots provided helpful information and were easy to use [7].	10.2196/20346	PMC7644372
PMC10007007	Discussion	Overall, our mixed results regarding feasibility, acceptability, and usability are partially aligned with the findings of the existing systematic reviews that reported heterogeneity in these secondary outcome measures and results across studies [1,2,7,9-11].	10.1093/jamia/ocy072	PMC6118869
PMC10007007	Discussion	Overall, our mixed results regarding feasibility, acceptability, and usability are partially aligned with the findings of the existing systematic reviews that reported heterogeneity in these secondary outcome measures and results across studies [1,2,7,9-11].	10.1186/s12966-021-01224-6	PMC8665320
PMC10007007	Discussion	Overall, our mixed results regarding feasibility, acceptability, and usability are partially aligned with the findings of the existing systematic reviews that reported heterogeneity in these secondary outcome measures and results across studies [1,2,7,9-11].	10.2196/20346	PMC7644372
PMC10007007	Discussion	Overall, our mixed results regarding feasibility, acceptability, and usability are partially aligned with the findings of the existing systematic reviews that reported heterogeneity in these secondary outcome measures and results across studies [1,2,7,9-11].	10.2196/16021	PMC7385637
PMC10007007	Discussion	Overall, our mixed results regarding feasibility, acceptability, and usability are partially aligned with the findings of the existing systematic reviews that reported heterogeneity in these secondary outcome measures and results across studies [1,2,7,9-11].	10.2196/jmir.7023	PMC5595406
PMC10007007	Discussion	Previous systematic reviews also reported that the use of CBT [2,11], habit formation model, emotionally focused therapy, and motivational interviewing [2] for designing behavior change strategies for AI chatbots contributed to better engagement, user motivation, and health behavior outcomes.	10.1186/s12966-021-01224-6	PMC8665320
PMC10007007	Discussion	Previous systematic reviews also reported that the use of CBT [2,11], habit formation model, emotionally focused therapy, and motivational interviewing [2] for designing behavior change strategies for AI chatbots contributed to better engagement, user motivation, and health behavior outcomes.	10.2196/jmir.7023	PMC5595406
PMC10007007	Discussion	This finding is consistent with previous systematic reviews that reported the need for greater personalization in AI chatbots through feedback on user performance, accountability, encouragement, and deep interest in the user’s situation [13].	10.2196/jmir.7351	PMC5709656
PMC10007007	Discussion	Similarly, Milne-Ives et al [8] reported a need for greater interactivity or relational skills, empathetic conversations, and a sense of personal connection with the user through compassionate responses.	10.2196/14166	PMC6914342
PMC10007007	Discussion	The need for greater interactivity can also be associated with the fluctuations in user engagement found in 13% (2/15) of studies [22,23].	10.2196/17558	PMC7382010
PMC10007007	Discussion	The need for greater interactivity can also be associated with the fluctuations in user engagement found in 13% (2/15) of studies [22,23].	10.2196/17530	PMC7215523
PMC10007007	Discussion	In one of these studies, the engagement rates decreased gradually as the intervention progressed [23], and in the other study, the engagement rates decreased significantly by midprogram but increased to 70% in the final week, that is, the 12th week [22].	10.2196/17530	PMC7215523
PMC10007007	Discussion	In one of these studies, the engagement rates decreased gradually as the intervention progressed [23], and in the other study, the engagement rates decreased significantly by midprogram but increased to 70% in the final week, that is, the 12th week [22].	10.2196/17558	PMC7382010
PMC10007007	Discussion	As specified in the AI chatbot design literature, user engagement is dependent on the chatbot’s ability to understand the user’s background, build a relation, be persuasive, and offer quick feedback [34].	10.2196/22845	PMC7557439

PMC10007007

Results

The chatbots that targeted healthy lifestyles (5/11, 45%; HLCC, Paola [22], HCAI [28], Ida [32], and Chat1 [33]) enabled behavioral monitoring by consistently providing feedback through performance content and pictures, weekly check-ins, and data-based inputs on performance.

10.2196/diabetes.8590

PMC6238835

PMC10007007

Results

The chatbots that targeted healthy lifestyles (5/11, 45%; HLCC, Paola [22], HCAI [28], Ida [32], and Chat1 [33]) enabled behavioral monitoring by consistently providing feedback through performance content and pictures, weekly check-ins, and data-based inputs on performance.

10.2196/28577

PMC8665384

PMC10007007

Results

The chatbots that targeted healthy lifestyles (5/11, 45%; HLCC, Paola [22], HCAI [28], Ida [32], and Chat1 [33]) enabled behavioral monitoring by consistently providing feedback through performance content and pictures, weekly check-ins, and data-based inputs on performance.

10.1016/j.pec.2013.05.011

PMC3727973

PMC10007007

Results

The chatbots that targeted smoking cessation (3/11, 27%; DigiQuit [23], SFA [24], and SMAG [27]) offered data-driven feedback on health indicators through web-based diaries and graphs.

10.2196/17530

PMC7215523

PMC10007007

Results

The chatbots that targeted smoking cessation (3/11, 27%; DigiQuit [23], SFA [24], and SMAG [27]) offered data-driven feedback on health indicators through web-based diaries and graphs.

10.1177/2055207619880676

PMC6775545

PMC10007007

Results

The chatbots that targeted medication or treatment adherence (2/11, 18%; Vik [26] and mPulse [30]) offered timely reminders to take medications or refill medicines.

10.2196/12856

PMC6521209

PMC10007007

Results

The chatbots that targeted medication or treatment adherence (2/11, 18%; Vik [26] and mPulse [30]) offered timely reminders to take medications or refill medicines.

10.2196/15771

PMC6887813

PMC10007007

Results

The chatbot that targeted the reduction in substance misuse performed mood tracking and regular check-ins to maintain accountability (1/11, 9%; Woebot [31]).

10.2196/24850

PMC8074987

PMC10007007

Results

The chatbots that targeted healthy lifestyles (3/8, 38%) offered educational sessions on the benefits of physical activity (Ida [32]) and healthy diet (Paola [22]) and information on sex, drugs, and alcohol (Bzz [29]).

10.2196/28577

PMC8665384

PMC10007007

Results

The chatbots that targeted healthy lifestyles (3/8, 38%) offered educational sessions on the benefits of physical activity (Ida [32]) and healthy diet (Paola [22]) and information on sex, drugs, and alcohol (Bzz [29]).

10.2196/17558

PMC7382010

PMC10007007

Results

The chatbots that targeted smoking cessation (4/8, 50%; DigiQuit [23]; SFA [24]; CureApp Smoking Cessation [CASC], [25]; and SMAG [27]) educated users on the benefits of being a nonsmoker, implications of abrupt cessation, and alternatives to smoking.

10.2196/17530

PMC7215523

PMC10007007

Results

The chatbots that targeted smoking cessation (4/8, 50%; DigiQuit [23]; SFA [24]; CureApp Smoking Cessation [CASC], [25]; and SMAG [27]) educated users on the benefits of being a nonsmoker, implications of abrupt cessation, and alternatives to smoking.

10.1177/2055207619880676

PMC6775545

PMC10007007

Results

The chatbots that targeted smoking cessation (4/8, 50%; DigiQuit [23]; SFA [24]; CureApp Smoking Cessation [CASC], [25]; and SMAG [27]) educated users on the benefits of being a nonsmoker, implications of abrupt cessation, and alternatives to smoking.

10.2196/12694

PMC6399570

PMC10007007

Results

The chatbot that targeted medication or treatment adherence (1/8, 12%; Vik [26]) offered information on the health issue (breast cancer) for which the users were taking medication.

10.2196/12856

PMC6521209

PMC10007007

Results

The chatbots that targeted healthy lifestyles (3/8, 38%) offered feedback on behaviors (HLCC and Ida [32]) and reinforced optimism to change behaviors through planning and imagining change (NAO [5] and Ida [32]).

10.2196/28577

PMC8665384

PMC10007007

Results

The chatbots that targeted healthy lifestyles (3/8, 38%) offered feedback on behaviors (HLCC and Ida [32]) and reinforced optimism to change behaviors through planning and imagining change (NAO [5] and Ida [32]).

10.2196/jmir.7737

PMC5958282

PMC10007007

Results

The chatbots that targeted smoking cessation (4/8, 50%) reinforced motivation through personalized messages based on TTM (DigiQuit [23]), scoreboards and trackers of milestones (SFA [24]), and motivational messages (CASC [25] and SMAG [27]).

10.2196/17530

PMC7215523

PMC10007007

Results

The chatbots that targeted smoking cessation (4/8, 50%) reinforced motivation through personalized messages based on TTM (DigiQuit [23]), scoreboards and trackers of milestones (SFA [24]), and motivational messages (CASC [25] and SMAG [27]).

10.1177/2055207619880676

PMC6775545

PMC10007007

Results

The chatbots that targeted smoking cessation (4/8, 50%) reinforced motivation through personalized messages based on TTM (DigiQuit [23]), scoreboards and trackers of milestones (SFA [24]), and motivational messages (CASC [25] and SMAG [27]).

10.2196/12694

PMC6399570

PMC10007007

Results

The chatbot that targeted reduction in substance misuse focused on motivation and engagement through individualized weekly reports to foster reflection (Woebot [31]).

10.2196/24850

PMC8074987

PMC10007007

Results

Among the interventions that targeted healthy lifestyles, Tess [6] offered empathetic health counseling or compassionate care through ML-driven emotional algorithms; NAO [5], the social robot, expressed empathy through humanized robot interaction, and HCAI [28] mimicked health professionals’ empathetic health counseling.

10.2196/jmir.7737

PMC5958282

PMC10007007

Results

Among the interventions that targeted healthy lifestyles, Tess [6] offered empathetic health counseling or compassionate care through ML-driven emotional algorithms; NAO [5], the social robot, expressed empathy through humanized robot interaction, and HCAI [28] mimicked health professionals’ empathetic health counseling.

10.2196/diabetes.8590

PMC6238835

PMC10007007

Results

The intervention that targeted reduction in substance misuse, Woebot [31], offered empathic responses by tailoring to users stated mood.

10.2196/24850

PMC8074987

PMC10007007

Results

Sixth, 7% (1/15) of studies (CASC [25]) delivered provider-recommendation system services.

10.2196/12694

PMC6399570

PMC10007007

Results

CASC [25] offered advice and counseling support to physicians.

10.2196/12694

PMC6399570

PMC10007007

Results

The chatbots that targeted healthy lifestyles (4/7, 57%; Paola [22], Tess [6], HCAI [28], and Bzz [29]) offered on-demand support, unlimited conversations, and answers to infinite number of questions.

10.2196/17558

PMC7382010

PMC10007007

Results

The chatbots that targeted healthy lifestyles (4/7, 57%; Paola [22], Tess [6], HCAI [28], and Bzz [29]) offered on-demand support, unlimited conversations, and answers to infinite number of questions.

10.2196/diabetes.8590

PMC6238835

PMC10007007

Results

The chatbots that targeted smoking cessation (3/7, 43%) offered on-demand emergency support via an AI nurse (CASC [25]), support during periods of high cravings (SMAG [27]), and unlimited availability for conversations (SFA [24]).

10.2196/12694

PMC6399570

PMC10007007

Results

The chatbots that targeted smoking cessation (3/7, 43%) offered on-demand emergency support via an AI nurse (CASC [25]), support during periods of high cravings (SMAG [27]), and unlimited availability for conversations (SFA [24]).

10.1177/2055207619880676

PMC6775545

PMC10007007

Results

Chat1 [33] offered homework assignments, whereas Woebot [31] required mindfulness exercises, gratitude journaling, or reflecting upon patterns and lessons already covered.

10.1016/j.pec.2013.05.011

PMC3727973

PMC10007007

Results

Chat1 [33] offered homework assignments, whereas Woebot [31] required mindfulness exercises, gratitude journaling, or reflecting upon patterns and lessons already covered.

10.2196/24850

PMC8074987

PMC10007007

Results

ML-driven emotional algorithms were used in Tess [6] and HCAI [28] to provide empathetic counseling or compassionate care (emotion-based response).

10.2196/diabetes.8590

PMC6238835

PMC10007007

Results

NLP and ML techniques were used in Paola [22], Vik [26], Ida [32], and Woebot [31] to identify and categorize user intents and entities by analyzing unstructured messages.

10.2196/17558

PMC7382010

PMC10007007

Results

NLP and ML techniques were used in Paola [22], Vik [26], Ida [32], and Woebot [31] to identify and categorize user intents and entities by analyzing unstructured messages.

10.2196/12856

PMC6521209

PMC10007007

Results

NLP and ML techniques were used in Paola [22], Vik [26], Ida [32], and Woebot [31] to identify and categorize user intents and entities by analyzing unstructured messages.

10.2196/28577

PMC8665384

PMC10007007

Results

NLP and ML techniques were used in Paola [22], Vik [26], Ida [32], and Woebot [31] to identify and categorize user intents and entities by analyzing unstructured messages.

10.2196/24850

PMC8074987

PMC10007007

Results

Bickmore et al’s [33] Chat1 used procedural and epistemological knowledge–based AI algorithms that facilitated therapeutic dialog actions (talk therapy).

10.1016/j.pec.2013.05.011

PMC3727973

PMC10007007

Results

A hybrid technique combining NLP and conversational AI or ML was adopted by mPulse [30] to ensure smooth, continuous, and uninterrupted conversations.

10.2196/15771

PMC6887813

PMC10007007

Results

Hybrid Health Recommender System was adopted by Carrasco-Hernandez et al’s [23] AI chatbot to personalize messages based on user demographics, content (interest of the user), and utility (ratings on each message by the user).

10.2196/17530

PMC7215523

PMC10007007

Results

Face-tracking technology was integrated into NAO [5] (the social robot) to track participants’ faces to humanize the interaction experience.

10.2196/jmir.7737

PMC5958282

PMC10007007

Results

All chatbots except NAO [5] (14/15, 93%) used text-based communication with the users, among which 2 (14%; Tess [6] and Vik [26]) chatbots also used voice-based communication.

10.2196/jmir.7737

PMC5958282

PMC10007007

Results

All chatbots except NAO [5] (14/15, 93%) used text-based communication with the users, among which 2 (14%; Tess [6] and Vik [26]) chatbots also used voice-based communication.

10.2196/12856

PMC6521209

PMC10007007

Results

NAO [5] used only voice-based communication, as it was deployed via a social robot.

10.2196/jmir.7737

PMC5958282

PMC10007007

Results

Out of the 13 chatbots that reported the frequency of engagement, all chatbots, except NAO [5], interacted with the users daily.

10.2196/jmir.7737

PMC5958282

PMC10007007

Results

NAO [5] interacted only once because it was delivered in person through a social robot.

10.2196/jmir.7737

PMC5958282

PMC10007007

Results

Vik [26], SMAG [27], Tess [6], and Ida [32] were integrated into Facebook (Meta Platforms, Inc) Messenger.

10.2196/12856

PMC6521209

PMC10007007

Results

Vik [26], SMAG [27], Tess [6], and Ida [32] were integrated into Facebook (Meta Platforms, Inc) Messenger.

10.2196/28577

PMC8665384

PMC10007007

Results

HLCC [21] was integrated with KakaoTalk (Kakao Corp), a popular messenger app in South Korea, and mPulse [30] was integrated with mobile SMS.

10.2196/15085

PMC7267999

PMC10007007

Results

HLCC [21] was integrated with KakaoTalk (Kakao Corp), a popular messenger app in South Korea, and mPulse [30] was integrated with mobile SMS.

10.2196/15771

PMC6887813

PMC10007007

Results

All chatbots except NAO [5], Ida [32], and Chat1 [33] (12/15, 80%) were deployed through smartphones, among which 3 (27%; Vik [26], SMAG [27], and Bzz [29]) chatbots were also deployed through computers.

10.2196/jmir.7737

PMC5958282

PMC10007007

Results

All chatbots except NAO [5], Ida [32], and Chat1 [33] (12/15, 80%) were deployed through smartphones, among which 3 (27%; Vik [26], SMAG [27], and Bzz [29]) chatbots were also deployed through computers.

10.2196/28577

PMC8665384

PMC10007007

Results

All chatbots except NAO [5], Ida [32], and Chat1 [33] (12/15, 80%) were deployed through smartphones, among which 3 (27%; Vik [26], SMAG [27], and Bzz [29]) chatbots were also deployed through computers.

10.1016/j.pec.2013.05.011

PMC3727973

PMC10007007

Results

All chatbots except NAO [5], Ida [32], and Chat1 [33] (12/15, 80%) were deployed through smartphones, among which 3 (27%; Vik [26], SMAG [27], and Bzz [29]) chatbots were also deployed through computers.

10.2196/12856

PMC6521209

PMC10007007

Results

Chat1 [33] was deployed only through computers.

10.1016/j.pec.2013.05.011

PMC3727973

PMC10007007

Results

NAO [5] was deployed through a social robot, and Ida [32] was deployed through Fitbit Flex 1 (Fitbit LLC).

10.2196/jmir.7737

PMC5958282

PMC10007007

Results

NAO [5] was deployed through a social robot, and Ida [32] was deployed through Fitbit Flex 1 (Fitbit LLC).

10.2196/28577

PMC8665384

PMC10007007

Results

Three chatbots (HLCC, Paola [22], and Ida [32]) integrated an existing AI-driven conversational platform, that is, the Watson conversation tool (HLCC and Paola [22]) and Dialogflow, an advanced Google ML algorithm (Ida [32]).

10.2196/17558

PMC7382010

PMC10007007

Results

Three chatbots (HLCC, Paola [22], and Ida [32]) integrated an existing AI-driven conversational platform, that is, the Watson conversation tool (HLCC and Paola [22]) and Dialogflow, an advanced Google ML algorithm (Ida [32]).

10.2196/28577

PMC8665384

PMC10007007

Results

Paola [22] measured the baseline level of physical activity and Mediterranean diet; SFA [24] measured time to first cigarette and cigarettes per day; CASC [25] measured demographics, motivation levels for smoking cessation, number of cigarettes smoked per day, and years of smoking; SMAG [27] measured demographics and type of smoking dependence; and Tess [6] used electronic health records.

10.2196/17558

PMC7382010

PMC10007007

Results

Paola [22] measured the baseline level of physical activity and Mediterranean diet; SFA [24] measured time to first cigarette and cigarettes per day; CASC [25] measured demographics, motivation levels for smoking cessation, number of cigarettes smoked per day, and years of smoking; SMAG [27] measured demographics and type of smoking dependence; and Tess [6] used electronic health records.

10.1177/2055207619880676

PMC6775545

PMC10007007

Results

Paola [22] measured the baseline level of physical activity and Mediterranean diet; SFA [24] measured time to first cigarette and cigarettes per day; CASC [25] measured demographics, motivation levels for smoking cessation, number of cigarettes smoked per day, and years of smoking; SMAG [27] measured demographics and type of smoking dependence; and Tess [6] used electronic health records.

10.2196/12694

PMC6399570

PMC10007007

Results

HLCC [21] asked the users (office workers) to set realistic stair climbing goals, Paola [22] enabled the users to set dietary goals and daily step target every week based on the previous week’s outcomes, and SFA [24] asked the users to set the target quit date for smoking.

10.2196/15085

PMC7267999

PMC10007007

Results

HLCC [21] asked the users (office workers) to set realistic stair climbing goals, Paola [22] enabled the users to set dietary goals and daily step target every week based on the previous week’s outcomes, and SFA [24] asked the users to set the target quit date for smoking.

10.2196/17558

PMC7382010

PMC10007007

Results

HLCC [21] asked the users (office workers) to set realistic stair climbing goals, Paola [22] enabled the users to set dietary goals and daily step target every week based on the previous week’s outcomes, and SFA [24] asked the users to set the target quit date for smoking.

10.1177/2055207619880676

PMC6775545

PMC10007007

Results

DigiQuit [23] collected feedback on the message content and timing, Tess [6] collected data on the usefulness of the message, and Vik [26] collected data on the relevance of the reminders.

10.2196/17530

PMC7215523

PMC10007007

Results

DigiQuit [23] collected feedback on the message content and timing, Tess [6] collected data on the usefulness of the message, and Vik [26] collected data on the relevance of the reminders.

10.2196/12856

PMC6521209

PMC10007007

Results

HLCC [21] collected performance content and pictures; Paola [22] collected data on daily steps and dietary patterns; Vik [26] collected data on medication adherence levels; SMAG [27] monitored the users’ smoking levels along with information on location, alone or accompanied, ongoing activity, and mood to create smoking profiles for them; and HCAI [28] gathered data automatically through sensors on phones and integrated devices such as wearables and self-reported information such as on dietary consumption.

10.2196/15085

PMC7267999

PMC10007007

Results

HLCC [21] collected performance content and pictures; Paola [22] collected data on daily steps and dietary patterns; Vik [26] collected data on medication adherence levels; SMAG [27] monitored the users’ smoking levels along with information on location, alone or accompanied, ongoing activity, and mood to create smoking profiles for them; and HCAI [28] gathered data automatically through sensors on phones and integrated devices such as wearables and self-reported information such as on dietary consumption.

10.2196/17558

PMC7382010

PMC10007007

Results

HLCC [21] collected performance content and pictures; Paola [22] collected data on daily steps and dietary patterns; Vik [26] collected data on medication adherence levels; SMAG [27] monitored the users’ smoking levels along with information on location, alone or accompanied, ongoing activity, and mood to create smoking profiles for them; and HCAI [28] gathered data automatically through sensors on phones and integrated devices such as wearables and self-reported information such as on dietary consumption.

10.2196/12856

PMC6521209

PMC10007007

Results

HLCC [21] collected performance content and pictures; Paola [22] collected data on daily steps and dietary patterns; Vik [26] collected data on medication adherence levels; SMAG [27] monitored the users’ smoking levels along with information on location, alone or accompanied, ongoing activity, and mood to create smoking profiles for them; and HCAI [28] gathered data automatically through sensors on phones and integrated devices such as wearables and self-reported information such as on dietary consumption.

10.2196/diabetes.8590

PMC6238835

PMC10007007

Results

Tess [6] used clinical scripts targeted at behavior change, CASC [25] used national guidelines on counseling support, and HCAI [28] used content from the Diabetes Prevention Program’s curriculum.

10.2196/12694

PMC6399570

PMC10007007

Results

Tess [6] used clinical scripts targeted at behavior change, CASC [25] used national guidelines on counseling support, and HCAI [28] used content from the Diabetes Prevention Program’s curriculum.

10.2196/diabetes.8590

PMC6238835

PMC10007007

Discussion

These findings are consistent with previous systematic reviews that reported the use of AI chatbots for improvement in physical activity levels and improvement in medication adherence [2,5], treatment adherence [14], adherence to self-management practices [1], smoking cessation [12], and reduction in substance abuse [12].

10.1186/s12966-021-01224-6

PMC8665320

PMC10007007

Discussion

These findings are consistent with previous systematic reviews that reported the use of AI chatbots for improvement in physical activity levels and improvement in medication adherence [2,5], treatment adherence [14], adherence to self-management practices [1], smoking cessation [12], and reduction in substance abuse [12].

10.2196/jmir.7737

PMC5958282

PMC10007007

Discussion

These findings are consistent with previous systematic reviews that reported the use of AI chatbots for improvement in physical activity levels and improvement in medication adherence [2,5], treatment adherence [14], adherence to self-management practices [1], smoking cessation [12], and reduction in substance abuse [12].

10.2196/20701

PMC7522733

PMC10007007

Discussion

These findings are consistent with previous systematic reviews that reported the use of AI chatbots for improvement in physical activity levels and improvement in medication adherence [2,5], treatment adherence [14], adherence to self-management practices [1], smoking cessation [12], and reduction in substance abuse [12].

10.1093/jamia/ocy072

PMC6118869

PMC10007007

Discussion

These findings are consistent with previous systematic reviews that reported the use of AI chatbots for improvement in physical activity levels and improvement in medication adherence [2,5], treatment adherence [14], adherence to self-management practices [1], smoking cessation [12], and reduction in substance abuse [12].

10.2196/jmir.6553

PMC5442350

PMC10007007

Discussion

In the case of feasibility, evidence on the safety of chatbots was quite less because only 7% (1/15) of studies reported safety [22].

10.2196/17558

PMC7382010

PMC10007007

Discussion

Some of the previous systematic reviews reported feasibility in the form of engagement with AI chatbots; however, the feasibility metrics differed across studies, and there was strong evidence regarding decrease in engagement rates over time [11,13,14].

10.2196/jmir.7023

PMC5595406

PMC10007007

Discussion

Some of the previous systematic reviews reported feasibility in the form of engagement with AI chatbots; however, the feasibility metrics differed across studies, and there was strong evidence regarding decrease in engagement rates over time [11,13,14].

10.2196/jmir.7351

PMC5709656

PMC10007007

Discussion

Some of the previous systematic reviews reported feasibility in the form of engagement with AI chatbots; however, the feasibility metrics differed across studies, and there was strong evidence regarding decrease in engagement rates over time [11,13,14].

10.2196/20701

PMC7522733

PMC10007007

Discussion

These findings are partially aligned with previous systematic reviews that reported acceptability [11,13,14] and on-demand availability, accessibility, and satisfaction [7].

10.2196/jmir.7023

PMC5595406

PMC10007007

Discussion

These findings are partially aligned with previous systematic reviews that reported acceptability [11,13,14] and on-demand availability, accessibility, and satisfaction [7].

10.2196/jmir.7351

PMC5709656

PMC10007007

Discussion

These findings are partially aligned with previous systematic reviews that reported acceptability [11,13,14] and on-demand availability, accessibility, and satisfaction [7].

10.2196/20701

PMC7522733

PMC10007007

Discussion

These findings are partially aligned with previous systematic reviews that reported acceptability [11,13,14] and on-demand availability, accessibility, and satisfaction [7].

10.2196/20346

PMC7644372

PMC10007007

Discussion

These findings are partially aligned with previous systematic reviews on AI chatbots that reported that chatbots provided helpful information and were easy to use [7].

10.2196/20346

PMC7644372

PMC10007007

Discussion