source_paper
string | source_section
string | premise
string | target_doi
string | target_paper
string |
|---|---|---|---|---|
PMC10007007
|
Results
|
Among the 4 RCT studies, the study by Carrasco-Hernandez et al [23] reported the highest compliance relatively (8/12, 67%), followed closely by the study by Piao et al [21] (7/12, 58%).
|
10.2196/15085
|
PMC7267999
|
PMC10007007
|
Results
|
The study by Perski et al [24] reported compliance on only 50% (6/12) of the factors, closely followed by the study by Bickmore et al [33] (5/12, 42%).
|
10.1177/2055207619880676
|
PMC6775545
|
PMC10007007
|
Results
|
The study by Perski et al [24] reported compliance on only 50% (6/12) of the factors, closely followed by the study by Bickmore et al [33] (5/12, 42%).
|
10.1016/j.pec.2013.05.011
|
PMC3727973
|
PMC10007007
|
Results
|
Among the non-RCT studies, the studies by Maher et al [22] and Brar Prayaga et al [30] reported the highest compliance (5/9, 56%), followed closely by the studies by Masaki et al [25], Prochaska et al [31], and To et al [32] (4/9, 44%).
|
10.2196/17558
|
PMC7382010
|
PMC10007007
|
Results
|
Among the non-RCT studies, the studies by Maher et al [22] and Brar Prayaga et al [30] reported the highest compliance (5/9, 56%), followed closely by the studies by Masaki et al [25], Prochaska et al [31], and To et al [32] (4/9, 44%).
|
10.2196/15771
|
PMC6887813
|
PMC10007007
|
Results
|
Among the non-RCT studies, the studies by Maher et al [22] and Brar Prayaga et al [30] reported the highest compliance (5/9, 56%), followed closely by the studies by Masaki et al [25], Prochaska et al [31], and To et al [32] (4/9, 44%).
|
10.2196/12694
|
PMC6399570
|
PMC10007007
|
Results
|
Among the non-RCT studies, the studies by Maher et al [22] and Brar Prayaga et al [30] reported the highest compliance (5/9, 56%), followed closely by the studies by Masaki et al [25], Prochaska et al [31], and To et al [32] (4/9, 44%).
|
10.2196/24850
|
PMC8074987
|
PMC10007007
|
Results
|
Among the non-RCT studies, the studies by Maher et al [22] and Brar Prayaga et al [30] reported the highest compliance (5/9, 56%), followed closely by the studies by Masaki et al [25], Prochaska et al [31], and To et al [32] (4/9, 44%).
|
10.2196/28577
|
PMC8665384
|
PMC10007007
|
Results
|
Chaix et al [26] reported compliance on only 33% (3/9) of the factors, followed by the studies by Stein and Brooks [28] and Crutzen et al [29] (2/9, 22%).
|
10.2196/12856
|
PMC6521209
|
PMC10007007
|
Results
|
Chaix et al [26] reported compliance on only 33% (3/9) of the factors, followed by the studies by Stein and Brooks [28] and Crutzen et al [29] (2/9, 22%).
|
10.2196/diabetes.8590
|
PMC6238835
|
PMC10007007
|
Results
|
The remaining studies, that is, the studies by Stephens et al [6], Calvaresi et al [27], and Galvão Gomes da Silva et al [5], were complaint on only 11% (1/9) of the factors.
|
10.2196/jmir.7737
|
PMC5958282
|
PMC10007007
|
Results
|
The AI component of the chatbots was evaluated to demonstrate AI’s impact on health outcomes (Multimedia Appendix 3 [5,6,21-33]).
|
10.2196/jmir.7737
|
PMC5958282
|
PMC10007007
|
Results
|
The AI component of the chatbots was evaluated to demonstrate AI’s impact on health outcomes (Multimedia Appendix 3 [5,6,21-33]).
|
10.2196/15085
|
PMC7267999
|
PMC10007007
|
Results
|
The AI component of the chatbots was evaluated to demonstrate AI’s impact on health outcomes (Multimedia Appendix 3 [5,6,21-33]).
|
10.1016/j.pec.2013.05.011
|
PMC3727973
|
PMC10007007
|
Results
|
Out of the 15 studies, 7 (47%) studies [5,21,22,28,29,32,33] targeted healthy lifestyles, and 5 (33%) studies [21,22,28,32,33] assessed the efficacy of AI chatbots in promoting healthy lifestyles through (1) physical activity levels, (2) healthy diet, (3) blood pressure, and (4) BMI.
|
10.2196/jmir.7737
|
PMC5958282
|
PMC10007007
|
Results
|
Out of the 15 studies, 7 (47%) studies [5,21,22,28,29,32,33] targeted healthy lifestyles, and 5 (33%) studies [21,22,28,32,33] assessed the efficacy of AI chatbots in promoting healthy lifestyles through (1) physical activity levels, (2) healthy diet, (3) blood pressure, and (4) BMI.
|
10.2196/15085
|
PMC7267999
|
PMC10007007
|
Results
|
Out of the 15 studies, 7 (47%) studies [5,21,22,28,29,32,33] targeted healthy lifestyles, and 5 (33%) studies [21,22,28,32,33] assessed the efficacy of AI chatbots in promoting healthy lifestyles through (1) physical activity levels, (2) healthy diet, (3) blood pressure, and (4) BMI.
|
10.2196/17558
|
PMC7382010
|
PMC10007007
|
Results
|
Out of the 15 studies, 7 (47%) studies [5,21,22,28,29,32,33] targeted healthy lifestyles, and 5 (33%) studies [21,22,28,32,33] assessed the efficacy of AI chatbots in promoting healthy lifestyles through (1) physical activity levels, (2) healthy diet, (3) blood pressure, and (4) BMI.
|
10.2196/diabetes.8590
|
PMC6238835
|
PMC10007007
|
Results
|
Out of the 15 studies, 7 (47%) studies [5,21,22,28,29,32,33] targeted healthy lifestyles, and 5 (33%) studies [21,22,28,32,33] assessed the efficacy of AI chatbots in promoting healthy lifestyles through (1) physical activity levels, (2) healthy diet, (3) blood pressure, and (4) BMI.
|
10.2196/28577
|
PMC8665384
|
PMC10007007
|
Results
|
Out of the 15 studies, 7 (47%) studies [5,21,22,28,29,32,33] targeted healthy lifestyles, and 5 (33%) studies [21,22,28,32,33] assessed the efficacy of AI chatbots in promoting healthy lifestyles through (1) physical activity levels, (2) healthy diet, (3) blood pressure, and (4) BMI.
|
10.1016/j.pec.2013.05.011
|
PMC3727973
|
PMC10007007
|
Results
|
First, 80% (4/5) of studies [33] reported an increase in physical activity.
|
10.1016/j.pec.2013.05.011
|
PMC3727973
|
PMC10007007
|
Results
|
Stein and Brooks [28] reported that the increase in physical activity led to an average weight loss of 2.38% in 75.7% of the users (n=53).
|
10.2196/diabetes.8590
|
PMC6238835
|
PMC10007007
|
Results
|
Maher et al [22] reported an increase in physical activity by 109.8 minutes (P=.005) and a decrease in the average weight and waist circumference by 1.3 kg (P=.01) and 2.1 cm (P=.003), respectively.
|
10.2196/17558
|
PMC7382010
|
PMC10007007
|
Results
|
Piao et al [21] reported significant between-group differences in the Self-Report Habit Index when controlled for intrinsic reward via chatbot enables app (P=.008).
|
10.2196/15085
|
PMC7267999
|
PMC10007007
|
Results
|
To et al [32] reported that the participants recorded more steps (P<.01) and more total physical activity (3.58 times higher; P<.001).
|
10.2196/28577
|
PMC8665384
|
PMC10007007
|
Results
|
However, only Bickmore et al [33] reported no significant differences among the conditions in the International Physical Activity Questionnaire (P=.37).
|
10.1016/j.pec.2013.05.011
|
PMC3727973
|
PMC10007007
|
Results
|
Second, 20% (3/15) of studies [22,28,33] reported an improvement in diet.
|
10.2196/17558
|
PMC7382010
|
PMC10007007
|
Results
|
Second, 20% (3/15) of studies [22,28,33] reported an improvement in diet.
|
10.2196/diabetes.8590
|
PMC6238835
|
PMC10007007
|
Results
|
Second, 20% (3/15) of studies [22,28,33] reported an improvement in diet.
|
10.1016/j.pec.2013.05.011
|
PMC3727973
|
PMC10007007
|
Results
|
Stein and Brooks [28] reported that the percentage of healthy meals increased by 31% and the percentage of unhealthy meals decreased by 54%.
|
10.2196/diabetes.8590
|
PMC6238835
|
PMC10007007
|
Results
|
Maher et al [22] reported an increase in the mean of Mediterranean diet (healthy meal) scores by 5.7 points (P<.001).
|
10.2196/17558
|
PMC7382010
|
PMC10007007
|
Results
|
Bickmore et al [33] reported that the group with only diet-related intervention consumed significantly more fruits and vegetables than the groups which received only physical activity intervention or both physical activity and diet intervention (P=.005); however, there were no significant differences among different groups for weight (P=.37).
|
10.1016/j.pec.2013.05.011
|
PMC3727973
|
PMC10007007
|
Results
|
Third, Maher et al [22] assessed blood pressure level after intervention as a secondary outcome; however, the mean improvement in systolic blood pressure (-0.2 mmHg; P=.90) and diastolic blood pressure (−1.0 mmHg; P=.54) were not significant.
|
10.2196/17558
|
PMC7382010
|
PMC10007007
|
Results
|
Fourth, only To et al [32] reported that the decrease in BMI was not significant (95% CI −0.37 to 0.11).
|
10.2196/28577
|
PMC8665384
|
PMC10007007
|
Results
|
Out of the 15 studies, 4 (27%) studies [23-25,27] assessed the efficacy of AI chatbots in smoking cessation.
|
10.2196/17530
|
PMC7215523
|
PMC10007007
|
Results
|
Out of the 15 studies, 4 (27%) studies [23-25,27] assessed the efficacy of AI chatbots in smoking cessation.
|
10.2196/12694
|
PMC6399570
|
PMC10007007
|
Results
|
Perski et al [24] reported that the intervention group had 2.44 times greater odds of abstinence at the 1-month follow-up than the control group (P<.001).
|
10.1177/2055207619880676
|
PMC6775545
|
PMC10007007
|
Results
|
Masaki et al [25] reported that the overall continuous abstinence rate results (76%, 12 weeks; 64%, 24 weeks; and 58%, 52 weeks) were better than the results of the outpatient clinic (calculated through the national survey) and the varenicline (medication for smoking cessation) phase 3 trial in the United States and Japan.
|
10.2196/12694
|
PMC6399570
|
PMC10007007
|
Results
|
Masaki et al [25] reported a decrease in social nicotine dependence (mean −6.7, SD 5.2), tobacco craving (mean −0.6, SD 1.5), and withdrawal symptoms (mean −6.4, SD 5.8), their secondary outcomes.
|
10.2196/12694
|
PMC6399570
|
PMC10007007
|
Results
|
Carrasco-Hernandez et al [23] reported that smoking abstinence (exhaled carbon monoxide and urine cotinine test) was 2.15 times (P=.02) higher in the intervention group than in the control group.
|
10.2196/17530
|
PMC7215523
|
PMC10007007
|
Results
|
Out of the 15 studies, only 1 (7%) study [31] aimed at reducing problematic substance use.
|
10.2196/24850
|
PMC8074987
|
PMC10007007
|
Results
|
Prochaska et al [31] reported a significant increase in the confidence to resist urges to use substances (mean score change +16.9, SD 21.4; P<.001) and a significant decrease in the following: substance use occasions (mean change −9.3, SD 14.1; P<.001) and the scores of Alcohol Use Disorders Identification Test-Concise (mean change −1.3, SD 2.6; P<.001), 10-item Drug Abuse Screening Test (mean change −1.2, SD 2.0; P<.001), Patient Health Questionnaire-8 item (mean change 2.1, SD 5.2; P=.005), Generalized Anxiety Disorder-7 (mean change 2.3, SD 4.7; P=.001), and cravings scale (68.6% vs 47.1% moderate to extreme; P=.01).
|
10.2196/24850
|
PMC8074987
|
PMC10007007
|
Results
|
Out of the 15 studies, 3 (20%) studies [6,26,30] targeted medication or treatment adherence, but only 2 (67%) of these studies [26,30] reported the efficacy of AI chatbots in increasing treatment or medication adherence through timely and personalized reminders.
|
10.2196/12856
|
PMC6521209
|
PMC10007007
|
Results
|
Out of the 15 studies, 3 (20%) studies [6,26,30] targeted medication or treatment adherence, but only 2 (67%) of these studies [26,30] reported the efficacy of AI chatbots in increasing treatment or medication adherence through timely and personalized reminders.
|
10.2196/15771
|
PMC6887813
|
PMC10007007
|
Results
|
Brar Prayaga et al [30] reported that out of the total refill reminders (n=273,356), 17.4% (n=47,552) resulted in actual refill requests.
|
10.2196/15771
|
PMC6887813
|
PMC10007007
|
Results
|
Chaix et al [26] reported that the average medication adherence rate improved by more than 20% in 4 weeks (P=.40) through the prescription reminder feature.
|
10.2196/12856
|
PMC6521209
|
PMC10007007
|
Results
|
Only one study conducted a qualitative analysis, that is, the study by Galvão Gomes da Silva et al [5].
|
10.2196/jmir.7737
|
PMC5958282
|
PMC10007007
|
Results
|
The outcomes of the selected studies are reported in Multimedia Appendix 4 [5,6,21-33].
|
10.2196/jmir.7737
|
PMC5958282
|
PMC10007007
|
Results
|
The outcomes of the selected studies are reported in Multimedia Appendix 4 [5,6,21-33].
|
10.2196/15085
|
PMC7267999
|
PMC10007007
|
Results
|
The outcomes of the selected studies are reported in Multimedia Appendix 4 [5,6,21-33].
|
10.1016/j.pec.2013.05.011
|
PMC3727973
|
PMC10007007
|
Results
|
Out of the 15 studies, 11 (73%) reported the feasibility of AI chatbots in terms of (1) safety [22] (ie, no adverse events were reported), (2) messages exchanged with the chatbot [6,26,29,31,32], (3) retention rate [22,26], and (4) duration of engagement.
|
10.2196/17558
|
PMC7382010
|
PMC10007007
|
Results
|
Out of the 15 studies, 11 (73%) reported the feasibility of AI chatbots in terms of (1) safety [22] (ie, no adverse events were reported), (2) messages exchanged with the chatbot [6,26,29,31,32], (3) retention rate [22,26], and (4) duration of engagement.
|
10.2196/12856
|
PMC6521209
|
PMC10007007
|
Results
|
Out of the 15 studies, 11 (73%) reported the feasibility of AI chatbots in terms of (1) safety [22] (ie, no adverse events were reported), (2) messages exchanged with the chatbot [6,26,29,31,32], (3) retention rate [22,26], and (4) duration of engagement.
|
10.2196/24850
|
PMC8074987
|
PMC10007007
|
Results
|
Out of the 15 studies, 11 (73%) reported the feasibility of AI chatbots in terms of (1) safety [22] (ie, no adverse events were reported), (2) messages exchanged with the chatbot [6,26,29,31,32], (3) retention rate [22,26], and (4) duration of engagement.
|
10.2196/28577
|
PMC8665384
|
PMC10007007
|
Results
|
Only 7% (1/15) of studies [22] reported the chatbot’s safety in terms of the absence of adverse events.
|
10.2196/17558
|
PMC7382010
|
PMC10007007
|
Results
|
Many studies reported the total number of messages exchanged with the chatbot (5/15, 33%) [6,26,29,31,32]; however, only 7% (1/15) of studies reported the exact proportion of user-initiated conversations (approximately 30%) [6], which depicted the participants’ level of interest in having health-related conversations with the chatbot.
|
10.2196/12856
|
PMC6521209
|
PMC10007007
|
Results
|
Many studies reported the total number of messages exchanged with the chatbot (5/15, 33%) [6,26,29,31,32]; however, only 7% (1/15) of studies reported the exact proportion of user-initiated conversations (approximately 30%) [6], which depicted the participants’ level of interest in having health-related conversations with the chatbot.
|
10.2196/24850
|
PMC8074987
|
PMC10007007
|
Results
|
Many studies reported the total number of messages exchanged with the chatbot (5/15, 33%) [6,26,29,31,32]; however, only 7% (1/15) of studies reported the exact proportion of user-initiated conversations (approximately 30%) [6], which depicted the participants’ level of interest in having health-related conversations with the chatbot.
|
10.2196/28577
|
PMC8665384
|
PMC10007007
|
Results
|
Few studies [22,23,26] reported variability in engagement and retention rate across study durations.
|
10.2196/17558
|
PMC7382010
|
PMC10007007
|
Results
|
Few studies [22,23,26] reported variability in engagement and retention rate across study durations.
|
10.2196/17530
|
PMC7215523
|
PMC10007007
|
Results
|
Few studies [22,23,26] reported variability in engagement and retention rate across study durations.
|
10.2196/12856
|
PMC6521209
|
PMC10007007
|
Results
|
Overall, 7% (1/15) of studies reported a gradual decrease in the retention rate (users who sent at least 1 message per month for over 8 months)—from 72% (second month) to 31% (eighth month) [26].
|
10.2196/12856
|
PMC6521209
|
PMC10007007
|
Results
|
Similarly, another study reported that engagement was highest at the first month and reduced gradually, becoming lowest at the 12th month [23].
|
10.2196/17530
|
PMC7215523
|
PMC10007007
|
Results
|
Similarly, another study [22] reported a decrease in check-ins by 20% midprogram, followed by an increase to 70% in the final week.
|
10.2196/17558
|
PMC7382010
|
PMC10007007
|
Results
|
It was also interesting to note that in one of the studies (7%), the engagement rate decreased over time but increased at the end [22].
|
10.2196/17558
|
PMC7382010
|
PMC10007007
|
Results
|
In the case of satisfaction, 7% (1/15) of studies reported that approximately one-quarter of the participants liked the messages [32], and another (7%) reported that the satisfaction of the participants with the web-based agent was above average [33].
|
10.2196/28577
|
PMC8665384
|
PMC10007007
|
Results
|
In the case of satisfaction, 7% (1/15) of studies reported that approximately one-quarter of the participants liked the messages [32], and another (7%) reported that the satisfaction of the participants with the web-based agent was above average [33].
|
10.1016/j.pec.2013.05.011
|
PMC3727973
|
PMC10007007
|
Results
|
In 7% (1/15) of studies, only one-third of the participants reported the desire to use the chatbot in the future [32], and in another study (7%), on average, the participants reported a below-average desire to continue with the agent in the future [33].
|
10.2196/28577
|
PMC8665384
|
PMC10007007
|
Results
|
In 7% (1/15) of studies, only one-third of the participants reported the desire to use the chatbot in the future [32], and in another study (7%), on average, the participants reported a below-average desire to continue with the agent in the future [33].
|
10.1016/j.pec.2013.05.011
|
PMC3727973
|
PMC10007007
|
Results
|
Similarly, another study (7%) reported that the participants liked the chatbot’s advice one-third of the times [25].
|
10.2196/12694
|
PMC6399570
|
PMC10007007
|
Results
|
Only 7% (1/15) of studies [26] reported high user satisfaction (93.95%).
|
10.2196/12856
|
PMC6521209
|
PMC10007007
|
Results
|
Overall, 20% (3/15) of studies reported that the AI platforms offered a nonjudgmental safe space for users to share detailed and sensitive information [5,26,29].
|
10.2196/jmir.7737
|
PMC5958282
|
PMC10007007
|
Results
|
Overall, 20% (3/15) of studies reported that the AI platforms offered a nonjudgmental safe space for users to share detailed and sensitive information [5,26,29].
|
10.2196/12856
|
PMC6521209
|
PMC10007007
|
Results
|
The participants reported that the chatbots provided a personal space and time to think and respond uninterruptedly [5]; to share personal and intimate information such as sexuality, which they could not share with their physician directly [26]; and to ask questions regarding sex, drugs, and alcohol as they considered chatbots to be more anonymous and faster than information lines and search engines [29].
|
10.2196/jmir.7737
|
PMC5958282
|
PMC10007007
|
Results
|
The participants reported that the chatbots provided a personal space and time to think and respond uninterruptedly [5]; to share personal and intimate information such as sexuality, which they could not share with their physician directly [26]; and to ask questions regarding sex, drugs, and alcohol as they considered chatbots to be more anonymous and faster than information lines and search engines [29].
|
10.2196/12856
|
PMC6521209
|
PMC10007007
|
Results
|
Masaki et al [25] reported the number of calls made to the AI nurse to seek assistance for smoking impulses or side effects (mean 1.7 times, SD 2.4), demonstrating the need for AI chatbot at a critical time.
|
10.2196/12694
|
PMC6399570
|
PMC10007007
|
Results
|
In 7% (1/15) of studies [32], most participants reported technical issues in using the chatbot (82.3%), one of the reasons being that they stopped receiving the chatbot messages during the study period (84.1%).
|
10.2196/28577
|
PMC8665384
|
PMC10007007
|
Results
|
The chatbot intervention characteristics are summarized in Multimedia Appendix 5 [5,6,21-33].
|
10.2196/jmir.7737
|
PMC5958282
|
PMC10007007
|
Results
|
The chatbot intervention characteristics are summarized in Multimedia Appendix 5 [5,6,21-33].
|
10.2196/15085
|
PMC7267999
|
PMC10007007
|
Results
|
The chatbot intervention characteristics are summarized in Multimedia Appendix 5 [5,6,21-33].
|
10.1016/j.pec.2013.05.011
|
PMC3727973
|
PMC10007007
|
Results
|
The cognitive behavioral therapy (CBT) was used in Tess [6], Lark Health Coach (HCAI) [28], and Woebot [31] to devise strategies that enhance self-efficacy and sustain behavior change.
|
10.2196/diabetes.8590
|
PMC6238835
|
PMC10007007
|
Results
|
The cognitive behavioral therapy (CBT) was used in Tess [6], Lark Health Coach (HCAI) [28], and Woebot [31] to devise strategies that enhance self-efficacy and sustain behavior change.
|
10.2196/24850
|
PMC8074987
|
PMC10007007
|
Results
|
Similarly, in Woebot [31], CBT was clubbed with motivational interviewing and dialectical behavior therapy to provide emotional support and personalized psychoeducation to resist substance misuse.
|
10.2196/24850
|
PMC8074987
|
PMC10007007
|
Results
|
The theory of motivational interviewing was also used to devise interview questions addressed by NAO [5] (the social robot) and the motivation reinforcement messages provided by Bickmore et al’s [33] Chat1.
|
10.2196/jmir.7737
|
PMC5958282
|
PMC10007007
|
Results
|
The theory of motivational interviewing was also used to devise interview questions addressed by NAO [5] (the social robot) and the motivation reinforcement messages provided by Bickmore et al’s [33] Chat1.
|
10.1016/j.pec.2013.05.011
|
PMC3727973
|
PMC10007007
|
Results
|
In HCAI [28], CBT was clubbed with the Diabetes Prevention Program’s curriculum to develop content for conversations on weight loss.
|
10.2196/diabetes.8590
|
PMC6238835
|
PMC10007007
|
Results
|
The Mohr’s Model of Supportive Accountability, which states that the inclusion of human support in digital interventions increases engagement, was used to mimic human support in Smoke Free app (SFA) [24] to increase accountability and belongingness.
|
10.1177/2055207619880676
|
PMC6775545
|
PMC10007007
|
Results
|
Furthermore, SFA’s [24] behavior change techniques were coded against a 44-item taxonomy of behavior change techniques in individual behavioral support for smoking cessation.
|
10.1177/2055207619880676
|
PMC6775545
|
PMC10007007
|
Results
|
The transtheoretical model (TTM) of behavior change was used by Carrasco-Hernandez et al [23] to determine message frequency for the AI chatbot.
|
10.2196/17530
|
PMC7215523
|
PMC10007007
|
Results
|
Similarly, TTM was used in Bickmore et al’s [33] Chat1 to design the behavioral monitoring process, which included reviewing progress, identifying barriers, and solving problems.
|
10.1016/j.pec.2013.05.011
|
PMC3727973
|
PMC10007007
|
Results
|
The Capability, Opportunity, Motivation, Behavior model, the core of the Behavior Change Wheel, a behavioral system focusing on 3 components—capability, opportunity, and motivation—was used in To et al’s [32] Ida to set goals, monitor behavior, reinforce behavior change through motivational messages.
|
10.2196/28577
|
PMC8665384
|
PMC10007007
|
Results
|
Social cognitive theory was also used in Ida [32] to facilitate therapeutic dialog actions (ie, talk therapy) and homework sessions outside the agent counseling sessions.
|
10.2196/28577
|
PMC8665384
|
PMC10007007
|
Results
|
These chatbots targeted healthy lifestyles (7/8, 88%; HLCC, Paola [22], SFA [24], NAO [5], HCAI [28], Ida [32], and Chat1 [33]) and the reduction of substance misuse (1/8, 12%; Woebot [31]).
|
10.2196/17558
|
PMC7382010
|
PMC10007007
|
Results
|
These chatbots targeted healthy lifestyles (7/8, 88%; HLCC, Paola [22], SFA [24], NAO [5], HCAI [28], Ida [32], and Chat1 [33]) and the reduction of substance misuse (1/8, 12%; Woebot [31]).
|
10.1177/2055207619880676
|
PMC6775545
|
PMC10007007
|
Results
|
These chatbots targeted healthy lifestyles (7/8, 88%; HLCC, Paola [22], SFA [24], NAO [5], HCAI [28], Ida [32], and Chat1 [33]) and the reduction of substance misuse (1/8, 12%; Woebot [31]).
|
10.2196/jmir.7737
|
PMC5958282
|
PMC10007007
|
Results
|
These chatbots targeted healthy lifestyles (7/8, 88%; HLCC, Paola [22], SFA [24], NAO [5], HCAI [28], Ida [32], and Chat1 [33]) and the reduction of substance misuse (1/8, 12%; Woebot [31]).
|
10.2196/diabetes.8590
|
PMC6238835
|
PMC10007007
|
Results
|
These chatbots targeted healthy lifestyles (7/8, 88%; HLCC, Paola [22], SFA [24], NAO [5], HCAI [28], Ida [32], and Chat1 [33]) and the reduction of substance misuse (1/8, 12%; Woebot [31]).
|
10.2196/28577
|
PMC8665384
|
PMC10007007
|
Results
|
These chatbots targeted healthy lifestyles (7/8, 88%; HLCC, Paola [22], SFA [24], NAO [5], HCAI [28], Ida [32], and Chat1 [33]) and the reduction of substance misuse (1/8, 12%; Woebot [31]).
|
10.1016/j.pec.2013.05.011
|
PMC3727973
|
PMC10007007
|
Results
|
These chatbots targeted healthy lifestyles (7/8, 88%; HLCC, Paola [22], SFA [24], NAO [5], HCAI [28], Ida [32], and Chat1 [33]) and the reduction of substance misuse (1/8, 12%; Woebot [31]).
|
10.2196/24850
|
PMC8074987
|
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.
|
10.2196/17558
|
PMC7382010
|
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