Use of the Chatbot “Vivibot” to Deliver Positive Psychology Skills and Promote Well-Being Among Young People After Cancer Treatment: Randomized Controlled Feasibility Trial
Open accessPublished version of a preprint
Abstract
Background: Positive psychology interventions show promise for reducing psychosocial distress associated with health adversity and have the potential to be widely disseminated to young adults through technology. Objective: This pilot randomized controlled trial examined the feasibility of delivering positive psychology skills via the Vivibot chatbot and its effects on key psychosocial well-being outcomes in young adults treated for cancer. Methods: Young adults (age 18-29 years) were recruited within 5 years of completing active cancer treatment by using the Vivibot chatbot on Facebook messenger. Participants were randomized to either immediate access to Vivibot content (experimental group) or access to only daily emotion ratings and access to full chatbot content after 4 weeks (control). Created using a human-centered design process with young adults treated for cancer, Vivibot content includes 4 weeks of positive psychology skills, daily emotion ratings, video, and other material produced by survivors, and periodic feedback check-ins. All participants were assessed for psychosocial well-being via online surveys at baseline and weeks 2, 4, and 8. Analyses examined chatbot engagement and open-ended feedback on likability and perceived helpfulness and compared experimental and control groups with regard to anxiety and depression symptoms and positive and negative emotion changes between baseline and 4 weeks. To verify the main effects, follow-up analyses compared changes in the main outcomes between 4 and 8 weeks in the control group once participants had access to all chatbot content. Results: Data from 45 young adults (36 women; mean age: 25 [SD 2.9]; experimental group: n=25; control group: n=20) were analyzed. Participants in the experimental group spent an average of 74 minutes across an average of 12 active sessions chatting with Vivibot and rated their experience as helpful (mean 2.0/3, SD 0.72) and would recommend it to a friend (mean 6.9/10; SD 2.6). Open-ended feedback noted its nonjudgmental nature as a particular benefit of the chatbot. After 4 weeks, participants in the experimental group reported an average reduction in anxiety of 2.58 standardized t-score units, while the control group reported an increase in anxiety of 0.7 units. A mixed-effects models revealed a trend-level (P=.09) interaction between group and time, with an effect size of 0.41. Those in the experimental group also experienced greater reductions in anxiety when they engaged in more sessions (z=–1.9, P=.06). There were no significant (or trend level) effects by group on changes in depression, positive emotion, or negative emotion. Conclusions: The chatbot format provides a useful and acceptable way of delivering positive psychology skills to young adults who have undergone cancer treatment and supports anxiety reduction. Further analysis with a larger sample size is required to confirm this pattern.
AI snapshot
Written from the paper’s full text.
TL;DR
In a 4-week pilot randomized controlled trial, the Vivibot chatbot was an engaging and acceptable way to deliver positive psychology skills to young adults after cancer treatment and showed a trend-level reduction in anxiety versus an emotion-rating control. No significant or trend-level group effects were found for depression, positive emotion, or negative emotion, and the authors call for a larger trial.
Key findings
- Among 45 analyzed participants (36 women; mean age 25, SD 2.9; experimental n=25, control n=20), the experimental group spent an average of 73.8 min (SD 52) across 12.1 engaged sessions (SD 7.1) with Vivibot, while the control group spent 27.13 min (SD 15.8) across 18.1 engaged sessions (SD 8.6) completing daily emotion ratings only. Experimental participants rated Vivibot as helpful (mean 2.03/3, SD 0.72) and would recommend it to a friend (mean 6.9/10, SD 2.6).
- Anxiety decreased by 2.58 t-score units in the experimental group versus an increase of 0.7 units in the control group, a trend-level group-by-time interaction (z=–1.70; Cohen d=–0.41; P=.09). Greater engagement in sessions was associated with greater anxiety reductions (z=–1.9, P=.06).
- No significant or trend-level group effects were found for depression (experimental reduction=1.83 vs control reduction=1.38; P=.77), negative emotion (experimental –0.31 vs control –0.23; P=.97), or positive emotion (experimental 0.04 vs control –0.08; P=.82). Daily positive emotion ratings showed a significant interaction in the opposite direction from the hypothesis, with greater increases in the control group (z=–2.07, P=.04).
- In a post hoc control-group analysis after delayed access to full content, anxiety dropped by 2.72 standardized points from weeks 4 to 8 (P=.13), with no significant depression reduction.
Methods
- This was a 4-week pilot randomized controlled trial of young adults aged 18-29 years recruited within 5 years of completing active cancer treatment. Recruitment came through Facebook advertising (67% of final sample), survivorship organizations (15%), and direct email (17%); enrollment was managed through the chatbot. Participants were randomized 1:1 to immediate full Vivibot content or to daily emotion ratings only with full access after 4 weeks. Of 51 randomized, 6 control participants were excluded after a technical error gave them full access at 2 weeks, leaving 45 analyzed.
- Vivibot delivered 28 days of positive psychology content via Facebook Messenger, covering eight skills in seven conversational teaching lessons and seven practice lessons repeated three times, plus daily emotion ratings, survivor-produced video/content, and feedback check-ins. Assessments occurred online at baseline and weeks 2, 4, and 8 using PROMIS 4-item anxiety and depression short forms, a modified Differential Emotions Scale for retrospective positive/negative emotion, and six daily chatbot emotion ratings.
- Main analyses used multilevel mixed-effects linear models for intention-to-treat, testing condition-by-time interactions for each outcome. Follow-up models examined changes within the control group from weeks 4 to 8 after delayed access, and engagement models tested number of engaged sessions as a predictor. Open-ended feedback was reviewed and quotes selected for prominent themes.
Limitations
- The paper states that this was a small pilot study not powered to detect significant effects in psychological outcomes, that recruitment of this population is difficult, and that results need replication in a larger sample able to reliably detect a moderate-sized between-group effect.
- The paper also states that the study was limited to participants willing and able to access Vivibot via Facebook Messenger, that Facebook privacy controversies during the study period may have affected engagement, and that neither cancer type nor stage was considered because analyses collapsed across diagnoses.
- Evident from the text: follow-up completion was 73% at 2 weeks, 73% at 4 weeks, and 58% at 8 weeks, with significantly lower week-8 completion in the experimental group (44% vs 75%; P=.04); the control condition was active because it included daily emotion ratings, which the authors note may itself have produced benefits and limits isolation of Vivibot-specific effects; and six control participants were excluded after a technical error.
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Claims the paper makes
0 of 45 cited sources checked · 0 supported · 0 flagged
- Findingown data
Participants particularly enjoyed the positive psychology content itself.
“Participants also particularly enjoyed the positive psychology content itself:”
Quote in the paperNot a substantial claim - Findingown data
Participants in the experimental group rated their experience of chatting with Vivibot as helpful, with an average rating of 2.03/3 (SD 0.72).
“On average, participants in the experimental group rated their experience of chatting with Vivibot as helpful, with an average rating of 2.03/3 (SD 0.72; range: 0-3).”
Quote in the paperNot a substantial claim - Findingown data
A mixed-effects model revealed a trend-level (P=.09) interaction between group and time, with an effect size of 0.41.
“A mixed-effects models revealed a trend-level (P=.09) interaction between group and time, with an effect size of 0.41.”
Quote in the paperSubstantial claim - Findingown data
The control group spent an average of 27.13 (SD 15.8) min across an average of 18.1 (SD 8.6) engaged sessions completing the 6-item emotion ratings only.
“The control group spent an average of 27.13 (SD 15.8) min across an average of 18.1 (SD 8.6) engaged sessions completing the 6-item emotion ratings only.”
Quote in the paperNot a substantial claim - Findingown data
Open-ended feedback noted the nonjudgmental nature of talking to an automated agent as a particular benefit of the chatbot.
“Open-ended feedback noted its nonjudgmental nature as a particular benefit of the chatbot.”
- Also stated in: § Perceived Helpfulness and Open-Ended Feedback. · plain
“When asked why they were likely or unlikely to recommend Vivibot to a friend, participants remarked on the utility and nonjudgmental nature of talking to an automated agent:”
Quote in the paperNot a substantial claim - Also stated in: § Perceived Helpfulness and Open-Ended Feedback. · plain
- Findingown data
Those in the experimental group experienced greater reductions in anxiety when they engaged in more sessions (z=-1.9, P=.06).
“Those in the experimental group also experienced greater reductions in anxiety when they engaged in more sessions (z=–1.9, P=.06).”
Quote in the paperNot a substantial claim - Findingown data
Participants in the experimental group were likely to recommend Vivibot to a friend, with an average rating of 6.9/10 (SD 2.6).
“They were also likely to recommend Vivibot to a friend, with an average rating of 6.9/10 (SD 2.6; range: 0-10).”
Quote in the paperNot a substantial claim - Findingown data
The experimental group spent an average of 73.8 (SD 52) min across an average of 12.1 (SD 7.1) engaged sessions chatting with Vivibot.
“During the 4 active weeks of the study, the experimental group spent an average of 73.8 (SD 52) min across an average of 12.1 (SD 7.1) engaged sessions chatting with Vivibot.”
Quote in the paperSubstantial claim - Findingown data
In total, 51 participants completed a baseline assessment and were randomized (experimental group: 25; control group: 26).
“In total, 51 participants completed a baseline assessment and were randomized to a study condition (experimental group: 25; control group: 26; Figure 1).”
Quote in the paperNot a substantial claim - Findingown data
Higher rates of survey completion were observed in the control group, reaching statistical significance at week 8 (75% control vs 44% experimental, χ2=4.28, P=.04).
“We noted differences by group, with higher rates of survey completion in the control group, reaching statistical significance in week 8 (2 weeks: 80% in the control group, 68% in the experimental group, χ2: 0.8, P=.37; 4 weeks: 85% in the control group, 64% in the experimental group, χ2: 2.5, P=.12; 8 weeks: 75% in the control group, 44% in the experimental group, χ2: 4.28, P=.04).”
Quote in the paperNot a substantial claim - Findingown data
After excluding 6 control participants, the final analytic sample comprised 45 participants.
“After excluding 6 control participants who were erroneously given access to chatbot content at 2 weeks instead of 4 weeks, the final analytic sample comprised 45 participants.”
Quote in the paperNot a substantial claim - Findingown data
Among those who used Vivibot, depression symptoms were not reduced compared to those who only completed daily emotion ratings.
“Among those who used Vivibot, depression symptoms were not reduced compared to those who only completed daily emotion ratings.”
Quote in the paperSubstantial claim - Findingown data
When using engaged sessions to predict anxiety outcomes, there was a stronger trend-level relationship for group by engaged sessions interaction (z=-1.9, P=.06).
“When using engaged sessions to predict anxiety outcomes (as opposed to timepoint alone), there was a stronger trend-level relationship for group by engaged sessions interaction (z=–1.9, P=.06).”
Quote in the paperNot a substantial claim - Findingown data
Positive psychology skills delivered by a chatbot were perceived as helpful and nonjudgmental by young adults who had undergone cancer treatment.
“Overall, positive psychology skills, delivered by a chatbot, were perceived as helpful and nonjudgmental by young adults who had undergone cancer treatment.”
Quote in the paperSubstantial claim - Findingown data
Daily emotion ratings reported directly in the chatbot showed a significant main effect of time for increased positive emotion across groups (z=3.56; P=.002).
“Interestingly, the daily emotion ratings reported directly in the chatbot showed a significant main effect of time for increased positive emotion across groups (z=3.56; P=.002).”
Quote in the paperNot a substantial claim - Findingnone given
The Vivibot chatbot was engaging to users.
“The Vivibot chatbot was engaging to users and resulted in anxiety reduction comparable to that found in the literature with digital health interventions targeting anxiety and depression.”
Quote in the paperSubstantial claim - Findingown data
In the post hoc control subsample, there was no significant reduction in depression.
“As in the experimental group, there was no significant reduction in depression in this subsample”
Quote in the paperNot a substantial claim - Findingcitation
The depression symptom reduction, converted to Patient Health Questionnaire-9 equivalent, in the experimental group (1.5 points over 4 weeks) was comparable to that reported in the trial of the X2AI tool (0.9 points over 4 weeks).
“Of note, the depression symptom reduction, when converted to the Patient Health Questionnaire - 9 items equivalent, in the experimental group in our trial (1.5 points over 4 weeks) was comparable to that reported in the trial of the X2AI tool (0.9 points over 4 weeks) [51].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [51] source
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- Findingown data
There was no trend for depression outcomes when using engaged sessions to predict them (z=-0.60, P=.55).
“There was no such trend for depression outcomes (z=–0.60, P=.55).”
Quote in the paperNot a substantial claim - Findingown data
Engagement with Vivibot was associated with a reduction in anxiety symptoms.
“Engagement with Vivibot was associated with a reduction in anxiety symptoms.”
Quote in the paperSubstantial claim - Findingown data
Both experimental and control groups showed almost no change in retrospective positive emotion ratings from baseline to week 4 (experimental mean difference 0.04, control -0.08).
“Both the experimental group and control group showed almost no change in retrospective positive emotion ratings from baseline to week 4 (mean difference: experimental group: 0.04, control group: –0.08).”
Quote in the paperNot a substantial claim - Premisecitation
Adolescents and young adults are most likely to experience depression, heightened anxiety, distress, and posttraumatic stress disorder in their first 12-24 months after completing treatment.
“Adolescents and young adults are most likely to experience depression, heightened anxiety, distress, and posttraumatic stress disorder in their first 12-24 months after completing treatment, which poses significant unmet mental health needs in the period after cancer treatment [2-4].”
Quote in the paperSubstantial claim0 of 3 sources checked - Premisecitation
Positive psychology interventions show promise for reducing psychosocial distress associated with health adversity.
“Positive psychology interventions show promise for reducing psychosocial distress associated with health adversity and have the potential to be widely disseminated to young adults through technology.”
- Also stated in: § Introduction · hedged
“Advances in the field of positive psychology have shown promise in addressing the psychological distress associated with health adversity [10].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [10] source
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- Also stated in: § Introduction · hedged
- Premisecitation
Young adults treated for cancer have significant and unique psychosocial needs.
“In addition to this disproportionate disease burden, there are significant and unique psychosocial needs for adolescents and young adults diagnosed with cancer [2].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [2] source
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- Premisecitation
Interventions that explicitly target positive emotion show promise for improving health outcomes in a number of chronic illnesses.
“Interventions that explicitly target positive emotion show promise for improving health outcomes in a number of chronic illnesses, including diabetes [16,17], heart disease [18,19], hypertension [20,21], depression [22,23], and HIV [24].”
Quote in the paperSubstantial claim0 of 9 sources checked- cites [16] source
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- Premisecitation
There is a lack of age-appropriate psychosocial support for adolescents and young adults after cancer treatment.
“Despite this disproportionate need, there remains a lack of age-appropriate psychosocial support for adolescents and young adults after cancer treatment, which is further complicated by the difficulty reaching this geographically dispersed population [9].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [9] source
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- Premisecitation
Interventions based on increasing positive emotion have been shown to relieve symptoms of depression in a meta-analysis.
“interventions based on increasing positive emotion have been shown to relieve symptoms of depression in a meta-analysis [23]”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [23] source
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- Premisecitation
The Woebot trial found a significant reduction in depression symptoms after 2 weeks of use in college students seeking mental health support.
“The notable exception was the trial evaluating the Woebot chatbot, which found a significant reduction in depression symptoms after 2 weeks of use in college students seeking mental health support [35].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [35] source
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- Premisecitation
Positive emotion is uniquely associated with a lower risk of morbidity and mortality in healthy and chronically ill samples, independent of the effects of negative emotion.
“This work is built upon the importance of positive emotion in the maintenance of psychological and physical well-being [11] and evidence that positive emotion is uniquely associated with a lower risk of morbidity and mortality in healthy and chronically ill samples, independent of the effects of negative emotion [12-15].”
Quote in the paperSubstantial claim0 of 4 sources checked - Study design and conductcitation
The PROMIS anxiety measure has demonstrated clinical validity in patients with chronic health conditions including cancer as well as pediatric patients.
“Anxiety symptoms were measured by the 4-item PROMIS Emotional Distress-Anxiety, Short Form [39]. This measure has demonstrated clinical validity in patients with chronic health conditions including cancer [41] as well as pediatric patients [42].”
Quote in the paperNot a substantial claim0 of 2 sources checked - Premisecitation
Depression is common among young people with cancer.
“Depression is common among young people with cancer [31]”
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- Premisecitation
Vivibot delivers a cognitive and behavioral intervention to increase positive emotion developed by Moskowitz et al.
“Vivibot delivers a cognitive and behavioral intervention to increase positive emotion developed by Moskowitz et al (eg, [24]).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [24] source
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- Premisecitation
Fully automated conversational agents (chatbots) have the distinct advantage of being perceived as accessible to youth.
“Within the vast array of digital intervention platforms, fully automated conversational agents (“chatbots”) have the distinct advantage of being perceived as accessible to youth [32] and can deliver a structured set of content that can simulate the content experienced by real-life conversation (eg, with a supportive friend).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [32] SkjuveMBBrandtzægPBChatbots as a new user interface for providing health information to young people2018Social Health Bots https://www.researchgate.net/publication/332859163_Chatbots_as_a_new_user_interface_for_providing_health_information_to_young_people
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- Study design and conductcitation
The PROMIS depression measure performs similar to legacy measures (Beck Depression Inventory, Center for Epidemiological Studies Depression) in adults diagnosed with cancer.
“Depression symptoms were measured by the 4-item PROMIS Emotional Distress-Depression, Short Form [39], which captures self-reported depression symptoms, and performs similar to legacy measures (Beck Depression Inventory, Center for Epidemiological Studies Depression) in adults diagnosed with cancer [43].”
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- Interpretationreasoning
This study provides early evidence that social media-mediated intervention can be harnessed to reduce mental health symptoms.
“this study provides early evidence that social media–mediated intervention can be harnessed to reduce mental health symptoms.”
Quote in the paperSubstantial claim - Premisecitation
Vivibot included many features found to be associated with positive outcomes in internet-based interventions for depression among adolescents, including surface credibility, esthetics, content appeal to the specific population, appropriate reduction of content, and use of self-monitoring.
“Vivibot included many features found to be associated with positive outcomes in internet-based interventions for depression among adolescents, including surface credibility, esthetics, and content appeal to the specific population; appropriate reduction of content compared to that in traditional interventions; and use of self-monitoring [54].”
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- Interpretationcitation
Acceptance of this positive psychology intervention is consistent with the broad acceptance found for online-based interventions for depression and anxiety among adolescents.
“Acceptance of this positive psychology intervention is therefore not surprisingly consistent with the broad acceptance found for online-based interventions for depression and anxiety among adolescents [55].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [55] source
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- Interpretationown data
Vivibot resulted in anxiety reduction comparable to that found in the literature with digital health interventions targeting anxiety and depression.
“The Vivibot chatbot was engaging to users and resulted in anxiety reduction comparable to that found in the literature with digital health interventions targeting anxiety and depression.”
Quote in the paperSubstantial claim - Interpretationreasoning
Vivibot is associated with, at a minimum, a comparable reduction in anxiety symptoms as other psychosocial interventions for people with cancer and similar digital health tools available on the market for general populations.
“Thus, Vivibot is associated with, at a minimum, a comparable reduction in anxiety symptoms as other psychosocial interventions for people with cancer and similar digital health tools available on the market for general populations.”
Quote in the paperSubstantial claim - Study design and conductreasoning
Neither the type nor stage of cancer was considered in the analyses.
“A third limitation was that neither the type nor stage of cancer was considered in the analyses.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Given their short duration, non-artificial intelligence-based content-delivery system results, and ease of delivery in Facebook Messenger or other chat platforms, chatbots are promising for supporting the mental health needs of young people with cancer at a vulnerable time in their lives.
“Given their short duration, non–artificial intelligence-based content-delivery system results, and ease with which it could be delivered in Facebook Messenger or other chat platforms, chatbots are promising for supporting the mental health needs of young people with cancer at a vulnerable time in their lives.”
Quote in the paperSubstantial claim - Study design and conductnone given
This study was limited to participants willing and able to access Vivibot via Facebook Messenger.
“Second, this study was limited to participants willing and able to access Vivibot via Facebook Messenger.”
Quote in the paperNot a substantial claim - Premisecitation
Emotion labeling itself can contribute to beneficial emotion regulation in response to negative events.
“This is consistent with the literature showing that emotion labeling itself can contribute to beneficial emotion regulation in response to negative events [52].”
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- Interpretationreasoning
The chatbot interface, which is less costly than a human-delivered intervention, is a promising format to deliver psychosocial interventions to young adults.
“Our work further suggests the chatbot interface, which is less costly than a human-delivered intervention, is a promising format to deliver psychosocial interventions to young adults.”
Quote in the paperSubstantial claim - Interpretationcitation
The effect size for the main anxiety analysis of .41 in this pilot trial was small to moderate by Cohen standards.
“Although not significant, the effect size for the main anxiety analysis of .41 in this pilot trial was small to moderate by Cohen [48] standards.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [48] Cohen, J, 2013, Statistical Power Analysis for the Behavioral Sciences, 2nd Ed source
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- Premisecitation
Internet-based interventions have been shown to work at least as effectively as face-to-face intervention in improving mental health across a range of conditions.
“Internet-based interventions have been shown to work at least as effectively as face-to-face intervention in improving mental health across a range of conditions.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [53] source
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- Interpretationown data
High engagement and positive ratings of Vivibot suggest that a chatbot provides a useful and acceptable format for young adults to connect with a positive psychology intervention.
“High engagement and positive ratings of Vivibot suggest that a chatbot provides a useful and acceptable format for young adults to connect with a positive psychology intervention.”
Quote in the paperSubstantial claim - Interpretationown data
The chatbot supports anxiety reduction.
“supports anxiety reduction”
Quote in the paperSubstantial claim- Rests on an assumption: The 4-week intervention period is sufficient to produce detectable changes in psychosocial well-being outcomes.
- Rests on an assumption: The control condition (daily emotion ratings only) is an adequate comparator for the experimental condition.
- Rests on an assumption: Participants' self-reported survey and daily emotion ratings accurately reflect their psychosocial well-being.
- Rests on an assumption: A trend-level (P=.09) interaction is sufficient to support a conclusion of anxiety reduction.
- Interpretationreasoning
This work contributes to the growing body of literature showing promise for interventions delivered via social media in supporting short-term symptom improvement and behavior change among young adults.
“Further, this work contributes to the growing body of literature showing promise for interventions delivered via social media in supporting short-term symptom improvement and behavior change among young adults.”
Quote in the paperSubstantial claim
- May combine two claims. 21
- Classified as probably not a substantial claim. 13
May combine two claims.
Findingown dataAfter 4 weeks, participants in the experimental group reported an average reduction in anxiety of 2.58 standardized t-score units, while the control group reported an increase in anxiety of 0.7 units.
“After 4 weeks, participants in the experimental group reported an average reduction in anxiety of 2.58 standardized t-score units, while the control group reported an increase in anxiety of 0.7 units.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThere were no significant (or trend level) effects by group on changes in depression, positive emotion, or negative emotion.
“There were no significant (or trend level) effects by group on changes in depression, positive emotion, or negative emotion.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataParticipants were on average 2.7 (SD 2.0) years postdiagnosis and 1.6 (SD 1.3) years postcompletion of active cancer treatment.
“Participants were on average 2.7 (SD 2.0) years postdiagnosis and 1.6 (SD 1.3) years postcompletion of active cancer treatment.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataParticipants were mostly female (36/45, 80%), with an average age of 25 (SD 2.9; range 19-29 years).
“Participants were mostly female (36/45, 80%), with an average age of 25 (SD 2.9; range: 19-29 years).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataAdditional themes in open-ended feedback related to having a shared experience with others who had undergone cancer treatment and being able to just vent.
“Additional themes related to having a shared experience with others who had undergone cancer treatment and being able to just “vent”:”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe rate of follow-up survey completion was 73% at 2 weeks, 73% at 4 weeks, and 58% at 8 weeks.
“The rate of follow-up survey completion was 73% (33/45) at 2 weeks, 73% (33/45) at 4 weeks, and 58% (26/45) at 8 weeks.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataAnecdotally, some users remarked that completing the emotion ratings was helpful to them when asked why they would be willing to refer Vivibot to a friend.
“Additionally, anecdotally, some users remarked on how completing the emotion ratings was helpful to them when asked why they would be willing to refer Vivibot to a friend”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThere was a significant interaction by group for daily positive emotion ratings (z=-2.07; P=.04), in the opposite direction from the hypothesis, with the control group showing greater increases than the experimental group.
“Further, there was a significant interaction by group (z=–2.07; P=.04); however, this effect was in the opposite direction from our hypothesis, as the control group showed greater increases in positive daily emotion ratings reported directly in the chatbot, compared to the experimental group.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataParticipants in the experimental group reported a greater reduction in anxiety than the control group at a trend level of statistical significance and with a small-to-moderate effect size (experimental reduction 2.58 t-score units vs control 0.7 units; z=-1.70; Cohen d=-0.41; P=.09).
“Participants in the experimental group reported a greater reduction in anxiety than the control group at a trend level of statistical significance and small-to-moderate effect size (experimental reduction of 2.58 t-score units vs control of 0.7 units; z=–1.70; Cohen d=–0.41; P=.09; Table 2).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataBoth experimental and control groups showed a similar magnitude decrease in negative emotion in retrospective ratings from baseline to week 4 (experimental mean difference -0.31, control -0.23), with no significant or trend-level interaction by group at 4 weeks (z=0.23; Cohen d=-0.01; P=.97).
“Both the experimental group and the control group showed a similar magnitude decrease in negative emotion in retrospective emotion ratings from baseline to week 4 (mean difference: experimental group: –0.31, control group: –0.23), with no significant or trend-level interaction by group for negative emotion at 4 weeks (z=0.23; Cohen d=–0.01; P=.97).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataBoth experimental and control groups showed slight decreases in depressive symptom ratings with no evidence of a condition by time interaction (experimental reduction 1.83; control reduction 1.38; z=0.30; Cohen d=0.09; P=.77).
“Both experimental and control groups showed slight decreases in depressive symptom ratings with no evidence of a condition by time interaction (experimental reduction=1.83; control reduction=1.38; z=0.30; Cohen d=0.09; P=.77).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataDaily emotion ratings recorded in the chatbot showed a significant decrease in negative emotion reporting (z=-2.44; P=.02), but no significant interaction between groups (z=-0.74; P=.46).
“This pattern was also reflected in the daily emotion ratings recorded in the chatbot, which showed a significant decrease in negative emotion reporting (z=–2.44; P=.02), but no significant interaction between groups (z=–0.74; P=.46).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataIn a post hoc analysis of the control group (N=17) after receiving access to the full 28-day chatbot content, anxiety symptoms dropped by a similar magnitude (2.72 standardized points) with a statistical trend (P=.13).
“To validate the results, a post hoc analysis of the change between 4 weeks and 8 weeks in the control group (N=17), after receiving access to the full 28-day chatbot content, revealed a similar magnitude drop in anxiety symptoms (2.72 standardized points) and a statistical trend (P=.13).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataAt baseline, both the experimental and control groups had moderate anxiety (experimental mean 64.5, SD 6.1; control mean 62.6, SD 7.9; threshold 60) and mild depression (experimental mean 60.1, SD 7.4; control mean 59.0, SD 9.2; threshold 60).
“At baseline, both experimental and control groups presented with moderate levels of anxiety (experimental group: mean 64.5 [SD 6.1]; control group: mean 62.6 [SD 7.9]; threshold=60) and mild levels of depression (experimental group: mean 60.1 [SD 7.4]; control group: mean 59.0 [SD 9.2]; threshold=60).”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationCompared to siblings, adolescents and young adults with cancer have poorer overall mental health and are twice as likely to report clinical levels of emotional distress.
“Compared to their siblings, adolescents and young adults with cancer have reported poorer overall mental health [5] and are twice as likely to report clinical levels of emotional distress [6-8].”
Quote in the paperSubstantial claim0 of 4 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation20%-30% of young people treated for cancer report moderate-to-severe psychological distress lasting into adulthood.
“Several studies have suggested that 20%-30% of young people treated for cancer report moderate-to-severe psychological distress lasting into adulthood [3].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [3] source
Not checked in the free run.
May combine two claims.
Interpretationown dataThe chatbot format provides a useful and acceptable way of delivering positive psychology skills to young adults who have undergone cancer treatment.
“The chatbot format provides a useful and acceptable way of delivering positive psychology skills to young adults who have undergone cancer treatment and supports anxiety reduction.”
Quote in the paperSubstantial claim- Rests on an assumption: The 4-week intervention period is sufficient to produce detectable changes in psychosocial well-being outcomes.
- Rests on an assumption: The control condition (daily emotion ratings only) is an adequate comparator for the experimental condition.
- Rests on an assumption: Participants' self-reported survey and daily emotion ratings accurately reflect their psychosocial well-being.
- Rests on an assumption: The chatbot delivered the intended positive psychology content to participants as designed.
Classified as probably not a substantial claim.
Study design and conductown dataThe chatbot randomized users 1:1 to one of two groups.
“The chatbot then randomized users 1:1 to one of two groups: (1) immediate access to the full Vivibot chatbot content (experimental group) or (2) access to daily emotion ratings through Facebook Messenger with delayed full access to the full Vivibot chatbot only after 4 weeks (control group).”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationThe PRISM clinical trial showed statistically significant improvements in patient-reported resilience and cancer-specific quality of life, as well as reduced psychological distress, among adolescents and young adults after cancer treatment aged 12-25 years (N=100).
“A clinical trial testing the efficacy of the PRISM skills intervention compared to usual care among adolescents and young adults after cancer treatment, aged 12-25 years (N=100), showed statistically significant improvements in patient‐reported resilience and cancer‐specific quality of life, as well as reduced psychological distress [29].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [29] source
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductown dataWell-being outcomes were assessed using multilevel mixed-effects linear models for intention to treat.
“Well-being outcomes were assessed using a series of multilevel mixed-effects linear models for intention to treat.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataA set of six participants in the control condition encountered a technical error after the 2-week survey, resulting in access to the full chatbot content at 2 weeks instead of 4 weeks.
“A set of six participants in the control condition encountered a technical error after the 2-week survey. This resulted in access to the full chatbot content at 2 weeks instead of the full 4 weeks.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningThese six participants have been excluded from reported analyses to test outcomes at 4 weeks across a clean sample.
“These six participants have been excluded from reported analyses to test outcomes at 4 weeks across a clean sample.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationAnxiety is common among young people with cancer, with effective treatments either not available in the settings in which adolescents and young adults with cancer receive treatment or not offered by referring clinicians.
“Anxiety is also common among young people with cancer, with effective treatments either not available in the settings in which adolescents and young adults with cancer receive treatment or not offered by referring clinicians. [31].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [31] source
Not checked in the free run.
Classified as probably not a substantial claim.
InterpretationreasoningThe experience of a bot-based intervention as nonjudgmental shows promise for future study using this format with young people.
“The experience of a bot-based intervention as nonjudgmental shows promise for future study using this format with young people.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
InterpretationreasoningDue to the unique developmental and psychosocial needs of young people who have been treated for cancer, this investigation collapsed across cancer diagnoses to focus on a more targeted age range.
“Due to the unique developmental and psychosocial needs of young people who have been treated for cancer, this investigation collapsed across cancer diagnoses to focus on a more targeted age range.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningDue to the relatively small population size and heterogeneity of treatment facilities, it is difficult to identify and recruit adolescents and young adults who had completed treatment for cancer for research studies.
“Due to the relatively small population size and heterogeneity of treatment facilities, it is difficult to identify and recruit adolescents and young adults who had completed treatment for cancer for research studies.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationreasoningThis work extends findings showing promise for technology-enabled solutions to address mental health problems.
“This work extends findings showing promise for technology-enabled solutions to address mental health problems.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationThe anxiety effect size was comparable to that found in a meta-analysis of psychosocial support for cancer (effect size=0.42) and a positive psychology intervention for caregivers (effect size=0.33).
“Further, it was comparable to that found in a meta-analysis of psychosocial support for cancer (effect size=0.42) [49] and a positive psychology intervention for caregivers (effect size=0.33) [50].”
Quote in the paperSubstantial claim0 of 2 sources checkedMay combine two claims.
Interpretationown dataThe emotion rating control on its own appeared to carry some benefits for participants, with rating emotions associated with decreased negative emotion and increased positive emotion.
“The emotion rating control on its own appeared to carry some benefits for participants (rating emotions was associated with decreased negative emotion and increased positive emotion).”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationcitationThe randomized design of this trial extends the promising findings of other positive psychology interventions delivered online and gives confidence that the effect size seen with anxiety is worthy of follow-up.
“The randomized design of this trial extends the promising findings of other positive psychology interventions delivered online [59] and gives confidence that the effect size seen with anxiety is worthy of follow-up.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [59] source
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationPositive emotion, when stimulated through skills-based interventions, is thought to influence health outcomes, both directly and through mediating factors including changing health behaviors, improving physiological functioning, and increasing resources that influence health.
“Positive emotion, when stimulated through skills-based interventions, is thought to influence health outcomes, both directly and through the mediating influence of factors including changing health behaviors, improving physiological functioning, and increasing resources that influence health [15,46].”
Quote in the paperSubstantial claim0 of 2 sources checkedMay combine two claims.
Study design and conductreasoningThis was a small pilot study that was not powered to detect significant effects in the psychological outcomes measured.
“First, this was a small pilot study that was not powered to detect significant effects in the psychological outcomes measured.”
Quote in the paperSubstantial claim
The paper relies on this without support.
The 4-week intervention period is sufficient to produce detectable changes in psychosocial well-being outcomes.
“This was a 4-week pilot randomized controlled trial evaluating feasibility, usability, and initial efficacy of the Vivibot chatbot.”
“After 4 weeks, participants in the experimental group reported an average reduction in anxiety of 2.58 standardized t-score units, while the control group reported an increase in anxiety of 0.7 units.”
The paper relies on this without support.
The control condition (daily emotion ratings only) is an adequate comparator for the experimental condition.
“Participants were randomized to either immediate access to Vivibot content (experimental group) or access to only daily emotion ratings and access to full chatbot content after 4 weeks (control).”
The paper relies on this without support.
Participants' self-reported survey and daily emotion ratings accurately reflect their psychosocial well-being.
“All participants were assessed for psychosocial well-being via online surveys at baseline and weeks 2, 4, and 8.”
The paper relies on this without support.
The chatbot delivered the intended positive psychology content to participants as designed.
“Vivibot is a chatbot designed to deliver prewritten and automatically delivered material to users online via a decision tree structure.”
The paper relies on this without support.
A trend-level (P=.09) interaction is sufficient to support a conclusion of anxiety reduction.
“A mixed-effects models revealed a trend-level (P=.09) interaction between group and time, with an effect size of 0.41.”
“supports anxiety reduction”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Greer's Vivibot chatbot leveraged a positive psychology framework to support emotional regulation in young adult cancer survivors, resulting in reduced anxiety and high user satisfaction.
“Greer’s Vivibot chatbot leveraged a positive psychology framework to support emotional regulation in young adult cancer survivors, resulting in reduced anxiety and high user satisfaction [14].”
From Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice (2026) · cites this paper as [14]
The Methods (Intervention Conditions) describe Vivibot’s positive-psychology skills, and the Results report helpfulness ratings and an average reduction in anxiety in the intervention group. However, the between-group anxiety finding was only a nonsignificant trend (P=.09), so “resulting in reduced anxiety” implies a firmer causal finding than this feasibility trial establishes. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe unique emotional needs of teen and young adult patients with cancer were addressed by the delivery of positive psychology skills via the Vivibot chatbot.
“the unique emotional needs of teen and young adult patients with cancer were addressed by the delivery of positive psychology skills via the Vivibot chatbot [14].”
From Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice (2026) · cites this paper as [14]
The Methods (Intervention Conditions) say Vivibot’s content was tailored with input from adolescents and young adults treated for cancer, and the Abstract (Methods) says it delivered positive psychology skills. But the trial enrolled only adults aged 18–29 who had completed treatment; it does not show that the chatbot addressed the needs of teen patients or patients undergoing treatment. (LLM, conf=0.88)
Experimental — accuracy not yet measuredPatients are receptive to receiving education, treatment planning, and emotional support from AI systems.
“Moreover, the high levels of user satisfaction reported in interventions such as Vivibot [14], Vik [17], Rosa [18], and social media–based studies [193257] underscore that patients are receptive to receiving education, treatment planning, and emotional support from AI systems.”
From Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice (2026) · cites this paper as [14]
The Results (Perceived Helpfulness and Open-Ended Feedback; page not provided) report that young people after cancer treatment found Vivibot helpful and valued its nonjudgmental support. The Methods (Intervention Conditions; page not provided) describe lessons in positive psychology skills, but not education about cancer care or treatment planning. The findings therefore support receptivity to this form of emotional support, not the full range of AI services claimed. (LLM, conf=0.88)
Experimental — accuracy not yet measuredCAs were valuable, informative, and reliable.
“CAs were valuable, informative, and reliable (Bibault, Chaix, Guillemassé, et al. 2019;Chetlen et al. 2019;Greer et al. 2019;Siglen et al. 2023;Tawfik et al. 2023;Visvanathan et al. 2023).”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Greer et al. 2019
In Results > Perceived Helpfulness and Open-Ended Feedback (page not provided), participants rated Vivibot as helpful and described its lessons as useful, supporting the claims that it was valuable and informative. The paper does not establish that Vivibot was reliable. (LLM, conf=0.86)
Experimental — accuracy not yet measuredFive studies showed that most patients were satisfied with the information provided by CAs.
“Five studies showed that most patients were satisfied with the information provided by CAs CAs were valuable, informative, and reliable (Bibault, Chaix, Guillemassé, et al. 2019;Chetlen et al. 2019;Greer et al. 2019;Siglen et al. 2023;Tawfik et al. 2023;Visvanathan et al. 2023).”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Greer et al. 2019
In Results > Perceived Helpfulness and Open-Ended Feedback (page unavailable), participants rated Vivibot helpful on average and some praised its lessons. The paper does not report that most participants were satisfied with information it provided or that they considered it reliable; its participants were young people after cancer treatment, not patients generally. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Patients were willing to recommend CAs to their family and friends to help them understand disease-related knowledge.
“They were also willing to recommend CAs to their family and friends to help them understand disease-related knowledge (Chaix et al. 2019;Greer et al. 2019).”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Greer et al. 2019
The Results section (“Perceived Helpfulness and Open-Ended Feedback”; page not provided) reports that participants were likely to recommend Vivibot to a friend. It does not report recommendations to family or say the purpose was to help others understand disease-related knowledge; the reported feedback concerns emotional support and positive psychology skills. (LLM, conf=0.90)
Experimental — accuracy not yet measuredCompared with routine online interventions, CAs delivering positive psychological skills equally reduced patients' anxiety.
“Compared with routine online interventions, CAs delivering positive psychological skills equally reduced patients' anxiety (Greer et al. 2019).”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Greer et al. 2019
The Results (Well-Being Outcomes) report reduced anxiety in the Vivibot group, and the Discussion (Conclusions) describes that reduction as comparable to findings for other digital health interventions. But the trial did not compare Vivibot with a routine online intervention: the Methods (Intervention Conditions) say the control group received delayed access and no other chatbot content during the comparison period. The finding also concerns young adults after cancer treatment, not patients generally. (LLM, conf=0.88)
Experimental — accuracy not yet measuredBoth quantitative and qualitative studies indicated that CAs provided emotional support for cancer patients before talking to doctors face-to-face.
“Additionally, both quantitative (Chaix et al. 2019;Greer et al. 2019) and qualitative studies (Siglen et al. 2023) indicated that CAs provided emotional support for cancer patients before talking to doctors face-to-face.”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Greer et al. 2019
The Results and Discussion report that young adults who had undergone cancer treatment found Vivibot helpful and nonjudgmental, with a tentative reduction in anxiety. That supports the emotional-support aspect, but the paper does not say participants used the chatbot before speaking with doctors face-to-face. (LLM, conf=0.87)
Experimental — accuracy not yet measuredIndividual studies have investigated positive psychology support after cancer treatment.
“Individual studies have investigated positive psychology support after cancer treatment”
Using an identical search strategy, the authors updated the review in January 2020 and identified a further 11 studies; most recent studies were aimed at mental health self-management (n=5).
“Using an identical search strategy, we updated this review in January 2020 and identified a further 11 studies. Similar to the previous review, most recent studies were aimed at mental health self-management (n=5) [10-12].”
From A Physical Activity and Diet Program Delivered by Artificially Intelligent Virtual Health Coach: Proof-of-Concept Study (2020) · cites this paper as [10]
Statistics recomputed
Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.
- Consistentz = -1.9reported p = .06 · recomputed p = .057
“(z=-1.9, p = .06”
- Consistentz = -0.6reported p = .55 · recomputed p = .549
“(z=-0.60, p = .55”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Averaged emotion ratings cannot have the stated 0-24 range
MinorEach of the three daily items is rated from 0 to 8, so their average ranges from 0 to 8, not 0 to 24. A 0-24 range would apply to their sum.
“Responses from the three positive and three negative emotion items were each averaged to make a single positive and negative emotion score (range: 0-24) for each time period of interest (daily for daily prompts and at each 2- and 4-week surveys).”
“Six discrete emotions (happy, excited, content, worried, irritable/angry, and sad) were rated on a scale from 0 (“not at all”) to 8 (“extremely”).”
Experimental group's baseline depression is classified below its stated cutoff
MinorThe paper calls the experimental group's mean baseline depression mild, although its mean of 60.1 exceeds the stated moderate cutoff of 60.
“At baseline, both experimental and control groups presented with moderate levels of anxiety (experimental group: mean 64.5 [SD 6.1]; control group: mean 62.6 [SD 7.9]; threshold=60) and mild levels of depression (experimental group: mean 60.1 [SD 7.4]; control group: mean 59.0 [SD 9.2]; threshold=60).”
“The cutoffs on both scales are as follows: mild, t=55; moderate, t=60; and severe, t=70 [44].”
Cites
Statistical Power Analysis for the Behavioral Sciences
Cohen, J. (2013) · doi:10.4324/9780203771587 · Cited by 25,373 · see citing works
Siegel, R. L., Miller, K. D., & Jemal, A. (2018) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.21442 · Cited by 16,632 · see citing works
Positive Psychology Progress: Empirical Validation of Interventions.
Seligman, M. E. P., Steen, T. A., Park, N., & Peterson, C. (2005) · American Psychologist · doi:10.1037/0003-066x.60.5.410 · Cited by 5,947 · see citing works
Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., Amtmann, D., Bode, R., Buysse, D., Choi, S., et al. (2010) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2010.04.011 · Cited by 5,128 · see citing works
Sin, N. L., & Lyubomirsky, S. (2009) · Journal of Clinical Psychology · doi:10.1002/jclp.20593 · Cited by 2,955 · cited 2× · see citing works
“Depression is common among young people with cancer [31], and interventions based on increasing positive emotion have been shown to relieve symptoms of depression in a meta-analysis [23].”
Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017) · JMIR Mental Health · doi:10.2196/mental.7785 · Cited by 2,730 · cited 2× · see citing works
“The notable exception was the trial evaluating the Woebot chatbot, which found a significant reduction in depression symptoms after 2 weeks of use in college students seeking mental health support [35].”
Positive affect and the other side of coping.
Folkman, S., & Moskowitz, J. T. (2000) · American Psychologist · doi:10.1037/0003-066x.55.6.647 · Cited by 2,471 · see citing works
Self-citationFredrickson, B. L., Cohn, M. A., Coffey, K. A., Pek, J., & Finkel, S. M. (2008) · Journal of Personality and Social Psychology · doi:10.1037/a0013262 · Cited by 2,365 · see citing works
Does positive affect influence health?
Pressman, S. D., & Cohen, S. (2005) · Psychological Bulletin · doi:10.1037/0033-2909.131.6.925 · Cited by 2,087 · see citing works
The New Life Stage of Emerging Adulthood at Ages 18–29 Years: Implications for Mental Health
Arnett, J. J., Žukauskienė, R., & Sugimura, K. (2014) · The Lancet Psychiatry · doi:10.1016/s2215-0366(14)00080-7 · Cited by 1,896 · see citing works
Chida, Y., & Steptoe, A. (2008) · Psychosomatic Medicine · doi:10.1097/psy.0b013e31818105ba · Cited by 1,592 · see citing works
Evaluating the psychometric properties of the mental health continuum-short form (MHC-SF)
Lamers, S. M. A., Westerhof, G. J., Bohlmeijer, E. T., ten Klooster, P. M., & Keyes, C. L. M. (2011) · Journal of Clinical Psychology · doi:10.1002/jclp.20741 · Cited by 1,402 · see citing works
Conversational agents in healthcare: a systematic review
Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi, F., Lau, A. Y. S., et al. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy072 · Cited by 1,388 · cited 1× · see citing works
“A review of 14 chatbots in health care found that their use is still rare relative to other areas [34].”
Kok, B. E., Coffey, K. A., Cohn, M. A., Catalino, L. I., Vacharkulksemsuk, T., Algoe, S. B., Brantley, M., & Fredrickson, B. L. (2013) · Psychological Science · doi:10.1177/0956797612470827 · Cited by 755 · see citing works
Fulmer, R., Joerin, A., Gentile, B., Lakerink, L., & Rauws, M. (2018) · doi:10.2196/preprints.9782 · Cited by 715 · see citing works
Zeltzer, L. K., Recklitis, C., Buchbinder, D., Zebrack, B., Casillas, J., Tsao, J. C. I., Lu, Q., & Krull, K. (2009) · Journal of Clinical Oncology · doi:10.1200/jco.2008.21.1433 · Cited by 660 · see citing works
Sheard, T., & Maguire, P. (1999) · British Journal of Cancer · doi:10.1038/sj.bjc.6690596 · Cited by 533 · see citing works
Clinical validity of PROMIS depression, anxiety, and anger across diverse clinical samples
Schalet, B. D., Pilkonis, P. A., Yu, L., Dodds, N., Johnston, K. L., Yount, S., Riley, W., & Cella, D. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2015.08.036 · Cited by 507 · see citing works
Establishing the computer-patient working alliance in automated health behavior change interventions
Bickmore, T., Gruber, A., & Picard, R. (2005) · Patient Education and Counseling · doi:10.1016/j.pec.2004.09.008 · Cited by 494 · see citing works
Cohen, S., & Pressman, S. D. (2006) · Current Directions in Psychological Science · doi:10.1111/j.0963-7214.2006.00420.x · Cited by 436 · see citing works
Putting feelings into words: Affect labeling as implicit emotion regulation
Torre, J. B., & Lieberman, M. D. (2018) · Emotion Review · doi:10.1177/1754073917742706 · Cited by 402 · see citing works
Positive Affect Predicts Lower Risk of AIDS Mortality
Moskowitz, J. T. (2003) · Psychosomatic Medicine · doi:10.1097/01.psy.0000073873.74829.23 · Cited by 291 · see citing works
Self-citationKwak, M., Zebrack, B. J., Meeske, K. A., Embry, L., Aguilar, C., Block, R., Hayes-Lattin, B., Li, Y., Butler, M., & Cole, S. (2013) · Journal of Clinical Oncology · doi:10.1200/jco.2012.45.9222 · Cited by 234 · see citing works
Ogedegbe, G. O. (2012) · Archives of Internal Medicine · doi:10.1001/archinternmed.2011.1307 · Cited by 230 · see citing works
Mendoza, J. A., Baker, K. S., Moreno, M. A., Whitlock, K. B., Abbey‐Lambertz, M., Waite, A., Colburn, T. A., & Chow, E. J. (2017) · Pediatric Blood & Cancer · doi:10.1002/pbc.26660 · Cited by 222 · see citing works
Randomized controlled trial of a positive affect intervention for people newly diagnosed with HIV.
Moskowitz, J. T., Carrico, A. W., Duncan, L. G., Cohn, M. A., Cheung, E. O., Batchelder, A., Martinez, L., Segawa, E., Acree, M., & Folkman, S. (2017) · Journal of Consulting and Clinical Psychology · doi:10.1037/ccp0000188 · Cited by 220 · see citing works
Self-citationRosenberg, A. R., Bradford, M. C., McCauley, E., Curtis, J. R., Wolfe, J., Baker, K. S., & Yi‐Frazier, J. P. (2018) · Cancer · doi:10.1002/cncr.31666 · Cited by 215 · see citing works
Positive affect uniquely predicts lower risk of mortality in people with diabetes.
Moskowitz, J. T., Epel, E. S., & Acree, M. (2008) · Health Psychology · doi:10.1037/0278-6133.27.1.s73 · Cited by 191 · see citing works
Self-citationAn online positive affect skills intervention reduces depression in adults with type 2 diabetes
Cohn, M. A., Pietrucha, M. E., Saslow, L. R., Hult, J. R., & Moskowitz, J. T. (2014) · The Journal of Positive Psychology · doi:10.1080/17439760.2014.920410 · Cited by 188 · see citing works
Self-citationResilience Among Patients Across the Cancer Continuum
Molina, Y., Yi, J. C., Martínez-Gutiérrez, J., Reding, K. W., Yi‐Frazier, J. P., & Rosenberg, A. R. (2014) · Clinical journal of oncology nursing · doi:10.1188/14.cjon.93-101 · Cited by 185 · see citing works
Rosenberg, A. R., Yi-Frazier, J. P., Eaton, L., Wharton, C., Cochrane, K., Pihoker, C., Baker, K. S., & McCauley, E. (2015) · Journal of Pediatric Psychology · doi:10.1093/jpepsy/jsv004 · Cited by 179 · see citing works
Peterson, J. C. (2012) · Archives of Internal Medicine · doi:10.1001/archinternmed.2011.1311 · Cited by 178 · see citing works
Michel, G., Rebholz, C. E., von der Weid, N. X., Bergstraesser, E., & Kuehni, C. E. (2010) · Journal of Clinical Oncology · doi:10.1200/jco.2009.23.4534 · Cited by 164 · see citing works
Adaptation and validation of an estonian version of the general self-efficacy scale (ESES)
Rimm, H., & Jerusalem, M. (1999) · Anxiety Stress & Coping · doi:10.1080/10615809908250481 · Cited by 163 · see citing works
Realizing the Potential of Behavioral Intervention Technologies
Schueller, S. M., Muñoz, R. F., & Mohr, D. C. (2013) · Current Directions in Psychological Science · doi:10.1177/0963721413495872 · Cited by 152 · see citing works
Development of a positive psychology intervention for patients with acute cardiovascular disease
Huffman, J. C., Mastromauro, C. A., Boehm, J. K., Seabrook, R., Fricchione, G. L., Denninger, J. W., & Lyubomirsky, S. (2011) · Heart International · doi:10.4081/hi.2011.e14 · Cited by 148 · see citing works
Claremont Purpose Scale: A Measure that Assesses the Three Dimensions of Purpose among Adolescents
Bronk, K. C., Riches, B. R., & Mangan, S. A. (2018) · Research in Human Development · doi:10.1080/15427609.2018.1441577 · Cited by 142 · see citing works
Adolescent and young adult patients with cancer: a milieu of unique features
Sender, L., & Zabokrtsky, K. B. (2015) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2015.92 · Cited by 130 · see citing works
Huffman, J. C., DuBois, C. M., Millstein, R. A., Celano, C. M., & Wexler, D. (2015) · Journal of Diabetes Research · doi:10.1155/2015/428349 · Cited by 127 · see citing works
Moskowitz, J. T., Hult, J. R., Duncan, L. G., Cohn, M. A., Maurer, S., Bussolari, C., & Acree, M. (2012) · Journal of Health Psychology · doi:10.1177/1359105311425275 · Cited by 125 · cited 1× · see citing works
“Rationale for inclusion of each of the positive emotion skills is provided elsewhere [38].”
Self-citationAge-Dependent Changes in Health Status in the Childhood Cancer Survivor Cohort
Hudson, M. M., Oeffinger, K. C., Jones, K., Brinkman, T. M., Krull, K. R., Mulrooney, D. A., Mertens, A., Castellino, S. M., Casillas, J., Gurney, J. G., et al. (2015) · Journal of Clinical Oncology · doi:10.1200/jco.2014.57.4863 · Cited by 113 · see citing works
Hope and benefit finding: Results from the PRISM randomized controlled trial
Rosenberg, A. R., Bradford, M. C., Barton, K. S., Etsekson, N., McCauley, E., Curtis, J. R., Wolfe, J., Baker, K. S., & Yi‐Frazier, J. P. (2018) · Pediatric Blood & Cancer · doi:10.1002/pbc.27485 · Cited by 110 · see citing works
Reulen, R. C., Winter, D. L., Lancashire, E. R., Zeegers, M. P., Jenney, M. E., Walters, S. J., Jenkinson, C., & Hawkins, M. M. (2007) · International Journal of Cancer · doi:10.1002/ijc.22658 · Cited by 106 · see citing works
Well-being interventions for individuals with diabetes: A systematic review
Massey, C. N., Feig, E. H., Duque-Serrano, L., Wexler, D., Moskowitz, J. T., & Huffman, J. C. (2019) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2018.11.014 · Cited by 96 · see citing works
Self-citationWozney, L., Huguet, A., Bennett, K., Radomski, A. D., Hartling, L., Dyson, M., McGrath, P. J., & Newton, A. S. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7573 · Cited by 93 · cited 1× · see citing works
“Vivibot included many features found to be associated with positive outcomes in internet-based interventions for depression among adolescents, including surface credibility, esthetics, and content appeal to the specific population; appropriate reduction of content compared to that in traditional interventions; and use…”
Cheung, E. O., Cohn, M. A., Dunn, L. B., Melisko, M. E., Morgan, S., Penedo, F. J., Salsman, J. M., Shumay, D. M., & Moskowitz, J. T. (2017) · Psycho-Oncology · doi:10.1002/pon.4312 · Cited by 90 · see citing works
Self-citationRamo, D. E., Thrul, J., Delucchi, K., Hall, S. M., Ling, P. M., Belohlavek, A., & Prochaska, J. J. (2018) · Addiction · doi:10.1111/add.14245 · Cited by 79 · see citing works
Self-citationContributors and Inhibitors of Resilience Among Adolescents and Young Adults with Cancer
Rosenberg, A. R., Yi-Frazier, J. P., Wharton, C., Gordon, K., & Jones, B. (2014) · Journal of Adolescent and Young Adult Oncology · doi:10.1089/jayao.2014.0033 · Cited by 78 · cited 1× · see citing works
“In the area of adolescent and young adult cancer survivorship, there is evidence that several personal resources have been shown to mitigate negative and promote positive psychosocial outcomes among young people treated for cancer [25-27].”
Phillips‐Salimi, C. R., Lommel, K., & Andrykowski, M. A. (2012) · Pediatric Blood & Cancer · doi:10.1002/pbc.23359 · Cited by 68 · see citing works
Self-Efficacy and Quality of Life in People with Spinal Cord Injuries in China
Hampton, N. Z. (2000) · Rehabilitation Counseling Bulletin · doi:10.1177/003435520004300202 · Cited by 67 · see citing works
Miller, K. A., Wojcik, K. Y., Ramirez, C. N., Ritt-Olson, A., Freyer, D. R., Hamilton, A. S., & Milam, J. E. (2016) · Pediatric Blood & Cancer · doi:10.1002/pbc.26209 · Cited by 67 · see citing works
Clover, K., Lambert, S. D., Oldmeadow, C., Britton, B., King, M. T., Mitchell, A. J., & Carter, G. (2018) · Quality of Life Research · doi:10.1007/s11136-018-1803-x · Cited by 65 · see citing works
Self-efficacy for coping with cancer: Revision of the Cancer Behavior Inventory (Version 3.0).
Merluzzi, T. V., Philip, E. J., Heitzmann, C. A., Liu, H., Yang, M., & Conley, C. C. (2017) · Psychological Assessment · doi:10.1037/pas0000483 · Cited by 60 · see citing works
Hernandez, R., Burrows, B., Wilund, K., Cohn, M., Xu, S., & Moskowitz, J. T. (2018) · Social Work in Health Care · doi:10.1080/00981389.2018.1523268 · Cited by 54 · see citing works
Self-citationWozney, L., McGrath, P. J., Gehring, N. D., Bennett, K., Huguet, A., Hartling, L., Dyson, M. P., Soleimani, A., & Newton, A. S. (2018) · JMIR Mental Health · doi:10.2196/mental.9655 · Cited by 50 · see citing works
Treatment of Anxiety and Depression in Adolescents and Young Adults With Cancer
Lauer, A. L. (2015) · Journal of Pediatric Oncology Nursing · doi:10.1177/1043454214563406 · Cited by 45 · see citing works
Lazor, T., Tigelaar, L., Pole, J. D., De Souza, C., Tomlinson, D., & Sung, L. (2017) · Supportive Care in Cancer · doi:10.1007/s00520-017-3743-3 · Cited by 42 · see citing works
Boutin-Foster, C., Offidani, E., Kanna, B., Ogedegbe, G., Ravenell, J., Scott, E., Rodriguez, A., Ramos, R., Michelen, W., Gerber, L. M., et al. (2016) · Ethnicity & Disease · doi:10.18865/ed.26.1.51 · Cited by 41 · see citing works
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- SkjuveMBBrandtzægPBChatbots as a new user interface for providing health information to young people2018Social Health Bots https://www.researchgate.net/publication/332859163_Chatbots_as_a_new_user_interface_for_providing_health_information_to_young_people
- Ethical and Independent Review Services20102019-05-03Independence, MOEthical and Independent Review Serviceshttp://www.eandireview.com/
- 2013, Diagnostic and Statistical Manual of Mental Disorders (DSM-5®), 978
Cited by
Sorted by citations
Rapp, A., Curti, L., & Boldi, A. (2021) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2021.102630 · Cited by 679 · see citing works
Xu, L., Sanders, L., Li, K., & Chow, J. C. L. (2021) · JMIR Cancer · doi:10.2196/27850 · Cited by 571 · see citing works
A systematic review and meta-analysis of psychological interventions to improve mental wellbeing
van Agteren, J., Iasiello, M., Lo, L., Bartholomaeus, J., Kopsaftis, Z., Carey, M., & Kyrios, M. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01093-w · Cited by 510 · see citing works
Artificially intelligent chatbots in digital mental health interventions: a review
Boucher, E. M., Harake, N. R., Ward, H. E., Stoeckl, S. E., Vargas, J., Minkel, J., Parks, A. C., & Zilca, R. (2021) · Expert Review of Medical Devices · doi:10.1080/17434440.2021.2013200 · Cited by 440 · see citing works
Haque, M. D. R., & Rubya, S. (2023) · JMIR mHealth and uHealth · doi:10.2196/44838 · Cited by 404 · see citing works
Liu, H., Peng, H., Song, X., Xu, C., & Zhang, M. (2022) · Internet Interventions · doi:10.1016/j.invent.2022.100495 · Cited by 296 · see citing works
“Researchers have so far endeavored to deploy mobile-based therapy chatbots for college students (Dekker et al., 2020), teenagers (Huang et al., 2015), the elderly (Ryu et al., 2020), patients (Greer et al., 2019), and so on.”
BackgroundRoles, Users, Benefits, and Limitations of Chatbots in Health Care: Rapid Review (Preprint)
Laymouna, M., Ma, Y., Lessard, D., Schuster, T., Engler, K., & Lebouché, B. (2024) · doi:10.2196/preprints.56930 · Cited by 302 · see citing works
Roles, Users, Benefits, and Limitations of Chatbots in Health Care: Rapid Review
Laymouna, M., Ma, Y., Lessard, D., Schuster, T., Engler, K., & Lebouché, B. (2024) · Journal of Medical Internet Research · doi:10.2196/56930 · Cited by 305 · see citing works
A critical review of state‐of‐the‐art chatbot designs and applications
Luo, B., Lau, R. Y. K., Li, C., & Si, Y. (2021) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1434 · Cited by 244 · see citing works
He, Y., Yang, L., Qian, C., Li, T., Su, Z., Zhang, Q., & Hou, X. (2023) · Journal of Medical Internet Research · doi:10.2196/43862 · Cited by 213 · see citing works
Machine Learning in Healthcare Communication
Siddique, S., & Chow, J. C. L. (2021) · Encyclopedia · doi:10.3390/encyclopedia1010021 · Cited by 195 · see citing works
Kurniawan, M. H., Handiyani, H., Nuraini, T., Hariyati, R. T. S., & Sutrisno, S. (2024) · Annals of Medicine · doi:10.1080/07853890.2024.2302980 · Cited by 196 · see citing works
He, Y., Yang, L., Zhu, X., Wu, B., Zhang, S., Qian, C., & Tian, T. (2022) · Journal of Medical Internet Research · doi:10.2196/40719 · Cited by 178 · see citing works
Zhong, W., Luo, J., & Zhang, H. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.04.057 · Cited by 204 · see citing works
AI Chatbots for Mental Health: A Scoping Review of Effectiveness, Feasibility, and Applications
Casu, M., Triscari, S., Battiato, S., Guarnera, L., & Caponnetto, P. (2024) · Applied Sciences · doi:10.3390/app14135889 · Cited by 205 · see citing works
Donaldson, S. I., van Zyl, L. V., & Donaldson, S. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.817244 · Cited by 145 · see citing works
Chin, H., Song, H., Baek, G., Shin, M., Jung, C., Cha, M., Choi, J., & Cha, C. (2023) · Journal of Medical Internet Research · doi:10.2196/51712 · Cited by 170 · see citing works
Trauma-Informed Computing: Towards Safer Technology Experiences for All
Chen, J. X., McDonald, A., Zou, Y., Tseng, E., Roundy, K., Tamersoy, A., Schaub, F., Ristenpart, T., & Dell, N. L. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517475 · Cited by 169 · see citing works
Maher, C. A., Davis, C. R., Curtis, R. G., Short, C. E., & Murphy, K. J. (2020) · JMIR mHealth and uHealth · doi:10.2196/17558 · Cited by 165 · see citing works
Comparing artificial intelligence and human coaching goal attainment efficacy
Terblanche, N. H. D., Molyn, J., de Haan, E., & Nilsson, V. O. (2022) · PLoS ONE · doi:10.1371/journal.pone.0270255 · Cited by 154 · see citing works
The Development and Use of Chatbots in Public Health: Scoping Review
Wilson, L., & Mărăşoiu, M. (2022) · JMIR Human Factors · doi:10.2196/35882 · Cited by 135 · see citing works
Artificial intelligence and COVID-19: A multidisciplinary approach
Ahuja, A. S., Reddy, V. P., & Marques, O. (2020) · Integrative Medicine Research · doi:10.1016/j.imr.2020.100434 · Cited by 130 · see citing works
Chatbots to Support Young Adults’ Mental Health: An Exploratory Study of Acceptability
Koulouri, T., Macredie, R. D., & Olakitan, D. (2022) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3485874 · Cited by 127 · see citing works
Physician and artificial intelligence chatbot responses to cancer questions from social media
Chen, D., Parsa, R., Hope, A., Hannon, B., Mak, E., Eng, L., Liu, F.-F., Fallah-Rad, N., Heesters, A. M., & Raman, S. (2024) · JAMA Oncology · doi:10.1001/jamaoncol.2024.0836 · Cited by 120 · see citing works
Artificial Intelligence Chatbot for Depression: Descriptive Study of Usage.
Dosovitsky, G., Pineda, B. S., Jacobson, N. C., Chang, C., Escoredo, M., & Bunge, E. L. (2020) · JMIR Formative Research · doi:10.2196/17065 · Cited by 118 · see citing works
Bin Sawad, A., Narayan, B., Alnefaie, A., Maqbool, A., Mckie, I., Smith, J., Yuksel, B., Puthal, D., Prasad, M., & Kocaballi, A. B. (2022) · Sensors · doi:10.3390/s22072625 · Cited by 113 · see citing works
Wutz, M., Hermes, M., Winter, V., & Köberlein–Neu, J. (2023) · Journal of Medical Internet Research · doi:10.2196/46548 · Cited by 111 · see citing works
Security, privacy, and healthcare-related conversational agents: a scoping review
May, R., & Denecke, K. (2021) · Informatics for Health and Social Care · doi:10.1080/17538157.2021.1983578 · Cited by 91 · see citing works
Loveys, K., Sagar, M. A., Pickering, I., & Broadbent, E. (2021) · JMIR Mental Health · doi:10.2196/31586 · Cited by 88 · see citing works
Co-design of digital health interventions with young people: A scoping review
Malloy, J. A., Partridge, S. R., Kemper, J. A., Braakhuis, A. J., & Roy, R. (2023) · Digital Health · doi:10.1177/20552076231219117 · Cited by 90 · see citing works
Tawfik, E., Ghallab, E., & Moustafa, A. (2023) · BMC Nursing · doi:10.1186/s12912-023-01243-7 · Cited by 82 · see citing works
Pharmacogenomic Clinical Decision Support: A Review, How‐to Guide, and Future Vision
Wake, D. T., Smith, D. M., Kazi, S., & Dunnenberger, H. M. (2021) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2387 · Cited by 83 · see citing works
Opportunities and risks of large language models in psychiatry
Obradovich, N., Khalsa, S., Khan, W., Suh, J., Perlis, R. H., Ajilore, O., & Paulus, M. P. (2024) · NPP—Digital Psychiatry and Neuroscience · doi:10.1038/s44277-024-00010-z · Cited by 107 · see citing works
Lee, K., Kim, S., Kim, S. H., Yoo, S., Sung, J. H., Oh, E. G., Kim, N. W., & Lee, J. (2023) · Journal of Medical Internet Research · doi:10.2196/38333 · Cited by 79 · see citing works
Social media-based interventions for adolescent and young adult mental health: A scoping review
Kruzan, K. P., Williams, K. D. A., Meyerhoff, J., Yoo, D. W., O’Dwyer, L., Choudhury, M. D., & Mohr, D. C. (2022) · Internet Interventions · doi:10.1016/j.invent.2022.100578 · Cited by 90 · see citing works
Pandey, S., & Sharma, S. (2023) · Healthcare Analytics · doi:10.1016/j.health.2023.100198 · Cited by 89 · see citing works
Li, Y., Liang, S., Zhu, B., Liu, X., Li, J., Chen, D., Qin, J., & Bressington, D. T. (2023) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2023.104494 · Cited by 74 · see citing works
Yuan, A., Colato, E. G., Pescosolido, B. A., Song, H., & Samtani, S. (2024) · ACM Transactions on Management Information Systems · doi:10.1145/3701041 · Cited by 74 · see citing works
Jabir, A. I., Lin, X., Martinengo, L., Sharp, G., Theng, Y. L., & Car, L. T. (2023) · Journal of Medical Internet Research · doi:10.2196/48168 · Cited by 70 · see citing works
van Bussel, M. J. P., Odekerken–Schröder, G. J., Ou, C., Swart, R. R., & Jacobs, M. J. G. (2022) · BMC Health Services Research · doi:10.1186/s12913-022-08189-7 · Cited by 60 · see citing works
“For young adults who have undergone cancer treatment, a VA delivering positive psychology skills was perceived as helpful and reduced anxiety [46].”
BackgroundPotts, C., Lindström, F., Bond, R., Mulvenna, M. D., Booth, F. G., Ennis, E., Parding, K., Kostenius, C., Broderick, T., Boyd, K., et al. (2023) · Journal of Medical Internet Research · doi:10.2196/43051 · Cited by 68 · see citing works
Jabir, A. I., Martinengo, L., Lin, X., Torous, J., Subramaniam, M., & Tudor Car, L. (2023) · Journal of Medical Internet Research · doi:10.2196/44548 · Cited by 63 · see citing works
“[24,33,36,42,46,48], or monitoring of well-being indicators, such as daily emotions (11/17, 65”
Limpanopparat, S., Gibson, E., & Harris, D. A. (2024) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2024.100081 · Cited by 71 · see citing works
Automated conversational agents for post-intervention follow-up: a systematic review
Geoghegan, L., Scarborough, A., Wormald, J. C. R., Harrison, C. J., Collins, D. P., Gardiner, M. D., Bruce, J., & Rodrigues, J. N. (2021) · BJS Open · doi:10.1093/bjsopen/zrab070 · Cited by 64 · see citing works
Validity of Chatbot Use for Mental Health Assessment: Experimental Study
Schick, A., Feine, J., Morana, S., Maedche, A., & Reininghaus, U. (2022) · JMIR mhealth and uhealth · doi:10.2196/28082 · Cited by 71 · see citing works
Use of automated conversational agents in improving young population mental health: a scoping review
Balan, R., Dobrean, A., & Poetar, C. R. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01072-1 · Cited by 56 · see citing works
“However, an improvement in positive affect was found in 3 studies 34,41,43 , while the other 3 remaining studies reported no difference betweengroupsonthisoutcome 36,42,44 .”
InfluentialSocial Chatbot: My Friend in My Distress
Ali, F., Zhang, Q., Tauni, M. Z., & Shahzad, K. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2150745 · Cited by 58 · see citing works
Kim, H. K. (2024) · Healthcare · doi:10.3390/healthcare12050534 · Cited by 53 · see citing works
Feng, X., Tian, L., Ho, G. W. K., Yorke, J., & Hui, V. (2025) · Journal of Medical Internet Research · doi:10.2196/79850 · Cited by 51 · see citing works
Martinengo, L., Jabir, A. I., Goh, W. W. T., Lo, N. Y. W., Ho, M. R., Kowatsch, T., Atun, R. A., Michie, S., & Car, L. T. (2022) · Journal of Medical Internet Research · doi:10.2196/39243 · Cited by 48 · see citing works
Dwyer, T., Hoit, G., Burns, D., Higgins, J. P., Chang, J. S., Whelan, D. B., Kiroplis, I., & Chahal, J. (2023) · Arthroscopy Sports Medicine and Rehabilitation · doi:10.1016/j.asmr.2023.01.020 · Cited by 48 · see citing works
Liu, I., Liu, F., Xiao, Y., Huang, Y., Wu, S., & Ni, S. (2025) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2023.2300015 · Cited by 44 · see citing works
Conley, C. S., Raposa, E. B., Bartolotta, K., Broner, S. E., Hareli, M., Forbes, N., Christensen, K. M., & Assink, M. (2022) · JMIR Mental Health · doi:10.2196/34254 · Cited by 49 · see citing works
Debate and Dilemmas Regarding Generative AI in Mental Health Care: Scoping Review
Xian, X., Chang, A., Xiang, Y., & Liu, M. T. (2024) · Interactive Journal of Medical Research · doi:10.2196/53672 · Cited by 45 · see citing works
te Pas, M. E., Rutten, W. G. M. M., Bouwman, R. A., & Buise, M. P. (2020) · JMIR Medical Informatics · doi:10.2196/21982 · Cited by 43 · see citing works
Wrightson‐Hester, A., Anderson, G., Dunstan, J., McEvoy, P. M., Sutton, C., MYERS, B. J., Egan, S. J., Tai, S., Johnston-Hollitt, M., Chen, W., et al. (2023) · JMIR Human Factors · doi:10.2196/46849 · Cited by 45 · see citing works
Digital technologies in cancer care: a review from the clinician's perspective
Briggs, L. G., Labban, M., Alkhatib, K. Y., Nguyen, D., Cole, A. P., & Trinh, Q. (2022) · Journal of Comparative Effectiveness Research · doi:10.2217/cer-2021-0263 · Cited by 41 · see citing works
Chan, J. M., Blarigan, E. L. V., Langlais, C. S., Zhao, S., Ramsdill, J. W., Daniel, K., Macaire, G., Wang, E. Y., Paich, K. S., Kessler, E. R., et al. (2020) · Journal of Medical Internet Research · doi:10.2196/19238 · Cited by 42 · see citing works
21-Day Stress Detox: Open Trial of a Universal Well-Being Chatbot for Young Adults
Williams, R. M., Hopkins, S. A., Frampton, C., Holt‐Quick, C., Merry, S., & Stasiak, K. (2021) · Social Sciences · doi:10.3390/socsci10110416 · Cited by 43 · see citing works
Saboor, S., Medina, A., & Marciano, L. (2024) · JMIR Mental Health · doi:10.2196/56045 · Cited by 40 · see citing works
The Use of AI-Supported Chatbot in Psychology
Uludağ, K. (2025) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-3112-5.ch001 · Cited by 41 · see citing works
Joshi, S., Bisht, B., Kumar, V., Singh, N., Begum, J. P. S., Singh, N., & Kumar, S. (2023) · Systems Microbiology and Biomanufacturing · doi:10.1007/s43393-023-00200-4 · Cited by 44 · see citing works
Bots as Active News Promoters: A Digital Analysis of COVID-19 Tweets
Al‐Rawi, A., & Shukla, V. (2020) · Information · doi:10.3390/info11100461 · Cited by 39 · see citing works
Roca, S., del Hoyo, M. L. L., García, J., & Alesanco, Á. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182212056 · Cited by 40 · see citing works
Knowledge Enhanced Reflection Generation for Counseling Dialogues
Shen, S., Pérez‐Rosas, V., WELCH, C. A., Poria, S., & Mihalcea, R. F. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.221 · Cited by 37 · see citing works
Dhinagaran, D. A., Martinengo, L., Ho, M. R., Joty, S., Kowatsch, T., Atun, R. A., & Car, L. T. (2022) · JMIR mhealth and uhealth · doi:10.2196/38740 · Cited by 36 · see citing works
Mauriello, M. L., Tantivasadakarn, N., Mora-Mendoza, M. A., Lincoln, E. T., Hon, G., Nowruzi, P., Simon, D., Hansen, L., Goenawan, N. H., Kim, J., et al. (2021) · JMIR Formative Research · doi:10.2196/25294 · Cited by 35 · see citing works
Zhang, T., Ren, Z., Wakefield, C. E., Hui, B. P. H., Akechi, T., Shi, C., Du, X., Chen, W., Lai, L., Zhao, C., et al. (2024) · Clinical Psychology Review · doi:10.1016/j.cpr.2024.102520 · Cited by 41 · see citing works
Mariamo, A., Temcheff, C. E., Léger, P., Sénécal, S., & Lau, M. A. (2021) · JMIR Human Factors · doi:10.2196/24343 · Cited by 35 · see citing works
“I’m Here for You”: Can Social Chatbots Truly Support Their Users? A Literature Review
van Wezel, M. M. C., Croes, E. A. J., & Antheunis, M. L. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68288-0_7 · Cited by 36 · see citing works
Psihogios, A. M., King‐Dowling, S., O’Hagan, B., Darabos, K., Maurer, L. A., Young, J., Fleisher, L. G., Barakat, L. P., Szalda, D. E., Hill‐Kayser, C. E., et al. (2021) · Annals of Behavioral Medicine · doi:10.1093/abm/kaab008 · Cited by 34 · see citing works
Cevasco, K. E., Morrison Brown, R. E., Woldeselassie, R., & Kaplan, S. (2024) · Journal of Medical Systems · doi:10.1007/s10916-024-02059-x · Cited by 35 · see citing works
Lazarou, I., Krooupa, A.-M., Nikolopoulos, S., Apostolidis, L., Sarris, N., Papadopoulos, S., & Kompatsiaris, I. Y. (2024) · Cancers · doi:10.3390/cancers16132293 · Cited by 35 · see citing works
Villarreal‐Zegarra, D., Reátegui-Rivera, C. M., García-Serna, J., Quispe-Callo, G., Lázaro-Cruz, G., Centeno-Terrazas, G., Gálvez-Arévalo, R. A., Escobar-Agreda, S., Rodríguez, A. D., & Finkelstein, J. (2024) · JMIR Mental Health · doi:10.2196/59560 · Cited by 36 · see citing works
Luca, S., Clausen, M., Shaw, A., Lee, W., Krishnapillai, S., Adi-Wauran, E., Faghfoury, H., Costain, G., Jobling, R. K., Aronson, M. D., et al. (2023) · Human Genetics · doi:10.1007/s00439-022-02512-2 · Cited by 36 · see citing works
Chua, J. Y. X., Choolani, M. A., Chee, C. Y. I., Chan, Y. H., Lalor, J., Chong, Y. S., & Shorey, S. (2023) · Journal of Midwifery & Women s Health · doi:10.1111/jmwh.13472 · Cited by 33 · see citing works
Cho, Y., Rai, S., Ungar, L., Sedoc, J., & Guntuku, S. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.698 · Cited by 33 · see citing works
Kleinau, E., Lamba, T., Jaskiewicz, W., Gorentz, K., Hungerbuehler, I., Rahimi, D., Kokota, D., Maliwichi, L., Jamu, E. S., Zumazuma, A., et al. (2024) · PLoS ONE · doi:10.1371/journal.pone.0303370 · Cited by 43 · see citing works
Damij, N., & Bhattacharya, S. D. (2022) · IEEE Technology and Engineering Management Conference (TEMSCON) · doi:10.1109/temsconeurope54743.2022.9801962 · Cited by 34 · see citing works
Chandeying, N., & Thongseiratch, T. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.784615 · Cited by 32 · see citing works
Mitsea, E., Drigas, A. S., & Skianis, C. A. (2024) · Psych · doi:10.3390/psych6010019 · Cited by 39 · see citing works
Developing a natural language-based AI-chatbot for social work training: an illustrative case study
Chan, C., & Li, F. (2023) · China Journal of Social Work · doi:10.1080/17525098.2023.2176901 · Cited by 27 · see citing works
Salsman, J. M., McLouth, L. E. S., Tooze, J. A., Little‐Greene, D., Cohn, M., Kehoe, M. S., & Moskowitz, J. T. (2023) · International Journal of Behavioral Medicine · doi:10.1007/s12529-023-10162-5 · Cited by 29 · see citing works
Self-citationGomaa, S., Posey, J. A., Bashir, B., Mallick, A. B., Vanderklok, E., Schnoll, M., Zhan, T., & Wen, K. (2023) · JMIR Formative Research · doi:10.2196/46128 · Cited by 28 · see citing works
Hoffman, B. D., Oppert, M. L., & Owen, M. S. (2024) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2024.100086 · Cited by 34 · see citing works
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A Formal Understanding of Computational Empathy in Interactive Agents
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Moskowitz, J. T., Addington, E. L., Shiu, E. W., Bassett, S. M., Schuette, S., Kwok, I., Freedman, M. E., Leykin, Y., Saslow, L. R., Cohn, M., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/25922 · Cited by 21 · see citing works
Self-citationQiu, L., Kanski, B., Doerksen, S., Winkels, R., Schmitz, K. H., & Abdullah, S. (2021) · Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411763.3451827 · Cited by 23 · see citing works
- Authors
- Greer, S., Ramo, D., Chang, Y.-J., Fu, M., Moskowitz, J., & Haritatos, J.
- Affiliations
- HopeLab (US)
- University of California, San Francisco (US)
- Northwestern University (US)
- Corresponding author
- Ramo, Danielle
- Venue
- JMIR mHealth and uHealth
- Publisher
- JMIR Publications Inc.
- Published
- 2019-10-31
- Volume
- 7
- Issue
- 10
- Pages
- e15018
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC6913733
- OpenAlex
- W2975920545
- Semantic Scholar
- 30f0daf859185860c42a66c0a171b94bba92ca6a
- Cited by
- 265
- References
- 58
- Citations by year
- 2026: 37 · 2025: 52 · 2024: 54 · 2023: 45 · 2022: 32 · 2021: 34 · 2020: 10 · 2019: 1
- FWCI
- 21.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Oncology › Cancer survivorship and care
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- chatbots, positive psychology interventions, young adults, cancer survivors, psychosocial well-being, anxiety, depression, emotion regulation, Facebook Messenger, feasibility, digital mental health, survivorship
- MeSH terms
- Psychology, Positive, Adolescent, Adult, Anxiety, Child Welfare, Depression, Feasibility Studies, Female, Humans, Male, Neoplasms, Pilot Projects, Psychometrics, Surveys and Questionnaires, Survivors
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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