Response to a relational agent by hospital patients with depressive symptoms

Bickmore, Timothy W.; Mitchell, Suzanne E.; Jack, Brian W.; Paasche-Orlow, Michael K.; Pfeifer, Laura M.; O’Donnell, Julie

doi:10.1016/j.intcom.2009.12.001

Cited by 219Social Robot Interaction and HRIDigital Mental Health InterventionsPatient-Provider Communication in Healthcare

Response to a relational agent by hospital patients with depressive symptoms

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Bickmore, Timothy W., Mitchell, Suzanne E., Jack, Brian W., Paasche-Orlow, Michael K., Pfeifer, Laura M., O’Donnell, Julie·2010·Interacting with Computers

DOI 10.1016/j.intcom.2009.12.001PMC PMC2901553

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Abstract

Depression affects approximately 15% of the US population, and is recognized as an important risk factor for poor outcomes among patients with various illnesses. Automated health education and behavior change programs have the potential to help address many of the shortcomings in health care. However, the role of these systems in the care of patients with depression has been insufficiently examined. In the current study, we sought to evaluate how hospitalized medical patients would respond to a computer animated conversational agent that has been developed to provide information in an empathic fashion about a patient's hospital discharge plan. In particular, we sought to examine how patients who have a high level of depressive symptoms respond to this system. Therapeutic alliance-the trust and belief that a patient and provider have in working together to achieve a desired therapeutic outcome- was used as the primary outcome measure, since it has been shown to be important in predicting outcomes across a wide range of health problems, including depression. In an evaluation of 139 hospital patients who interacted with the agent at the time of discharge, all patients, regardless of depressive symptoms, rated the agent very high on measures of satisfaction and ease of use, and most preferred receiving their discharge information from the agent compared to their doctors or nurses in the hospital. In addition, we found that patients with symptoms indicative of major depression rated the agent significantly higher on therapeutic alliance compared to patients who did not have major depressive symptoms. We conclude that empathic agents represent a promising technology for patient assessment, education and counseling for those most in need of comfort and caring in the inpatient setting.

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Previous studies have demonstrated that chatbot interventions improve psychological difficulties including major depressive disorder, panic disorder, posttraumatic stress disorder, antipsychotic medication adherence, and perceived stress.
“Previous studies have demonstrated that chatbot interventions improve psychological difficulties including major depressive disorder [15], panic disorder [16], posttraumatic stress disorder [17], antipsychotic medication adherence [18], and perceived stress [19].”
From A Cognitive Behavioral Therapy Chatbot (Otis) for Health Anxiety Management: Mixed Methods Pilot Study (2022) · cites this paper as [15]
Citation [15] is attached to major depressive disorder, not the other conditions in the sentence. The Related Work section says internet-based cognitive behavioral therapy can relieve depressive symptoms, but does not identify those interventions as chatbots. This paper’s Discussion reports that patients with major depressive symptoms accepted and formed a stronger alliance with the relational agent; it does not report that the agent improved their depression. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Designing for certain character traits, such as empathy, in a chatbot can increase the likelihood of users establishing relationships with it.
“Designing for certain character traits, such as empathy, in a chatbot can increase the likelihood of users establishing relationships with it (Bickmore et al., 2010).”
From My Chatbot Companion - a Study of Human-Chatbot Relationships (2021) · cites this paper as Bickmore et al., 2010
Patients with depression and/or low health literacy reported stronger therapeutic alliance with the conversational agent.
“In one example, patients reported high satisfaction with a conversational agent that delivered hospital discharge instructions, and patients with depression and/or low health literacy reported stronger therapeutic alliance with the agent.”
From Behavioral Intervention Technologies: Evidence review and recommendations for future research in mental health (2013) · cites this paper as [57]
The conversational agent elicited more questions from those with lower health literacy.
“The agent also elicited more questions from those with lower health literacy, suggesting such agents may have the potential to provide health information in a supportive, non-threatening manner (57).”
From Behavioral Intervention Technologies: Evidence review and recommendations for future research in mental health (2013) · cites this paper as [57]
Conversational agents may have the potential to provide health information in a supportive, non-threatening manner.
“The agent also elicited more questions from those with lower health literacy, suggesting such agents may have the potential to provide health information in a supportive, non-threatening manner (57).”
From Behavioral Intervention Technologies: Evidence review and recommendations for future research in mental health (2013) · cites this paper as [57]

In one example, patients reported high satisfaction with a conversational agent that delivered hospital discharge instructions.
“In one example, patients reported high satisfaction with a conversational agent that delivered hospital discharge instructions, and patients with depression and/or low health literacy reported stronger therapeutic alliance with the agent.”
From Behavioral Intervention Technologies: Evidence review and recommendations for future research in mental health (2013) · cites this paper as [57]
A written chatbot for symptoms of depression showed an accuracy of 99.2% and a specificity of 99.7%.
“The study for symptoms of depression noted that the written chatbot showed an accuracy of 99.2% and a specificity of 99.7% [52].”
From A systematic review on healthcare artificial intelligent conversational agents for chronic conditions (2022) · cites this paper as [52]
Other uses of conversational agents include replacing nurses to provide hospital discharge planning, collecting relevant health information (such as public health surveys), training health-care providers by simulating patients, and providing public health information and education.
“Other use of conversational agents include replacing nurses to provide hospital discharge planning, for collecting relevant health information (such as public health surveys), for training health-care providers by simulating patients, and for providing public health information and education.3,5–9”
From Ethical implications of conversational agents in global public health (2020) · cites this paper as [7]
Users may experience less anxiety when discussing private concerns, such as disease exposure risk behaviours, with a conversational agent than they would with another person.
“Thus, users may experience less anxiety when discussing private concerns, such as disease exposure risk behaviours, with a conversational agent than they would with another person.3,4,7”
From Ethical implications of conversational agents in global public health (2020) · cites this paper as [7]
Conversational agents have shown promising results with respect to patient satisfaction.
“conversational agents (ie, computer programs that imitate interaction with human beings) have shown promising results with respect to patient satisfaction [15]”
From Conversational Agents as Mediating Social Actors in Chronic Disease Management Involving Health Care Professionals, Patients, and Family Members: Multisite Single-Arm Feasibility Study (2021) · cites this paper as [15]

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Abd-Alrazaq, A. A., Alajlani, M., Ali, N., Denecke, K., Bewick, B. M., & Househ, M. (2021) · Journal of Medical Internet Research · doi:10.2196/17828 · Cited by 301 · see citing works
A Therapeutic Relational Agent for Reducing Problematic Substance Use (Woebot): Development and Usability Study
Prochaska, J. J., Vogel, E. A., Chieng, A., Kendra, M., Baiocchi, M., Pajarito, S., & Robinson, A. (2021) · Journal of Medical Internet Research · doi:10.2196/24850 · Cited by 257 · see citing works
“Finally, although conversational agents can present in different modalities, including text, verbal [18,19], and animation [20-25], preliminary research on modality for psychoeducation delivery specifically found that text-based presentation resulted in higher program adherence than verbal presentation [26].”
Background
Evaluating the Therapeutic Alliance With a Free-Text CBT Conversational Agent (Wysa): A Mixed-Methods Study
Beatty, C., Malik, T., Meheli, S., & Sinha, C. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.847991 · Cited by 224 · see citing works
“…digital mental health space is gaining traction and they have recently been implemented to help track medication and physical activity adherence (26, 27), provide cognitive behavioral therapy (CBT) (28), and deliver healthy lifestyle recommendations (29) across clinical and non-clinical…”
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Recommendations for the ethical use and design of artificial intelligent care providers
Luxton, D. D. (2014) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2014.06.004 · Cited by 243 · see citing works
A smartphone-based health care chatbot to promote self-management of chronic pain (SELMA): pilot randomized controlled trial
Hauser-Ulrich, S., Künzli, H., Meier-Peterhans, D., & Kowatsch, T. (2020) · JMIR mHealth and uHealth · doi:10.2196/15806 · Cited by 210 · see citing works
The Next Generation: Chatbots in Clinical Psychology and Psychotherapy to Foster Mental Health – A Scoping Review
Bendig, E., Erb, B., Schulze-Thuesing, L., & Baumeister, H. (2022) · Verhaltenstherapie · doi:10.1159/000501812 · Cited by 214 · see citing works
An Introduction to Artificial Intelligence in Behavioral and Mental Health Care
Luxton, D. D. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-420248-1.00001-5 · Cited by 208 · see citing works
Engagement with online mental health interventions: an exploratory clinical study of a treatment for depression
Doherty, G., Coyle, D., & Sharry, J. (2012) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2208602 · Cited by 198 · see citing works
Technical metrics used to evaluate health care chatbots: scoping review
Abd-Alrazaq, A., Safi, Z., Alajlani, M., Warren, J., Househ, M., & Denecke, K. (2020) · Journal of Medical Internet Research · doi:10.2196/18301 · Cited by 150 · see citing works
What Makes a Good Conversation? Challenges in Designing Truly Conversational Agents
Clark, L., Pantidi, N., Cooney, O., Doyle, P. R., Garaialde, D., Edwards, J., Spillane, B., Murad, C., Munteanu, C., Wade, V., et al. (2019) · Cork Open Research Archive (University College Cork, Ireland) · doi:10.48550/arxiv.1901.06525 · Cited by 143 · see citing works
Chatbot usage intention analysis: Veterinary consultation
Huang, D.-H., & Chueh, H.-E. (2020) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2020.09.002 · Cited by 138 · see citing works
Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review
Safi, Z., Abd-Alrazaq, A. A., Khalifa, M., & Househ, M. S. (2020) · Journal of Medical Internet Research · doi:10.2196/19127 · Cited by 129 · see citing works
What Role Can Avatars Play in e-Mental Health Interventions? Exploring New Models of Client–Therapist Interaction
Rehm, I. C., Foenander, E., Wallace, K., Abbott, J.-A., Kyrios, M., & Thomas, N. (2016) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2016.00186 · Cited by 125 · see citing works
The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors
Fonseka, T. M., Bhat, V., & Kennedy, S. H. (2019) · Australian & New Zealand Journal of Psychiatry · doi:10.1177/0004867419864428 · Cited by 126 · see citing works
First Impressions in Human--Agent Virtual Encounters
Cafaro, A., Vilhjálmsson, H. H., & Bickmore, T. (2016) · ACM Transactions on Computer-Human Interaction · doi:10.1145/2940325 · Cited by 120 · see citing works
Self-citation
A systematic review on healthcare artificial intelligent conversational agents for chronic conditions
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
Ethical implications of conversational agents in global public health
Luxton, D. D. (2020) · Bulletin of the World Health Organization · doi:10.2471/blt.19.237636 · Cited by 108 · see citing works
Breeze
Shih, C.-H. I., Tomita, N., Lukic, Y. X., Reguera, Á. H., Fleisch, E., & Kowatsch, T. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3369835 · Cited by 104 · see citing works
UX Research on Conversational Human-AI Interaction: A Literature Review of the ACM Digital Library
Zheng, Q., Tang, Y., Liu, Y., Liu, W., & Huang, Y. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501855 · Cited by 114 · see citing works
Conversational Agents as Mediating Social Actors in Chronic Disease Management Involving Health Care Professionals, Patients, and Family Members: Multisite Single-Arm Feasibility Study
Kowatsch, T., Schachner, T., Harperink, S., Barata, F., Dittler, U., Xiao, G., Stanger, C., v Wangenheim, F., Fleisch, E., Oswald, H., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/25060 · Cited by 103 · see citing works
Development of a Positive Body Image Chatbot (KIT) With Young People and Parents/Carers: Qualitative Focus Group Study
Beilharz, F., Sukunesan, S., Rossell, S. L., Kulkarni, J., & Sharp, G. (2021) · Journal of Medical Internet Research · doi:10.2196/27807 · Cited by 98 · see citing works
A Review of Artificial Intelligence and Robotics in Transformed Health Ecosystems
Denecke, K., & Baudoin, C. R. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.795957 · Cited by 102 · see citing works
Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine
Iqbal, J., Jaimes, D. C. C., Makineni, P., Subramani, S., Hemaida, S., Thugu, T. R., Butt, A. N., Sikto, J. T., Kaur, P., Lak, M. A., et al. (2023) · Cureus · doi:10.7759/cureus.44658 · Cited by 109 · 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
A theory of multiformat communication: mechanisms, dynamics, and strategies
Moffett, J. W., Folse, J. A. G., & Palmatier, R. W. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-020-00750-2 · Cited by 84 · see citing works
Changes to the Psychiatric Chatbot Landscape: A Systematic Review of Conversational Agents in Serious Mental Illness: Changements du paysage psychiatrique des chatbots: une revue systématique des agents conversationnels dans la maladie mentale sérieuse
Vaidyam, A. N., Linggonegoro, D. W., & Torous, J. B. (2020) · The Canadian Journal of Psychiatry · doi:10.1177/0706743720966429 · Cited by 83 · see citing works
A nurse versus a chatbot ‒ the effect of an empowerment program on chemotherapy-related side effects and the self-care behaviors of women living with breast Cancer: a randomized controlled trial
Tawfik, E., Ghallab, E., & Moustafa, A. (2023) · BMC Nursing · doi:10.1186/s12912-023-01243-7 · Cited by 82 · see citing works
Evaluation of the Use of Digital Mental Health Platforms and Interventions: Scoping Review
Balcombe, L., & Leo, D. D. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20010362 · Cited by 83 · see citing works
Investigating intervention components and exploring states of receptivity for a smartphone app to promote physical activity: Protocol of a microrandomized trial
Kramer, J.-N., Künzler, F., Mishra, V., Presset, B., Kotz, D., Smith, S., Scholz, U., & Kowatsch, T. (2019) · JMIR Research Protocols · doi:10.2196/11540 · Cited by 74 · see citing works
Using technology to engage hospitalised patients in their care: a realist review
Roberts, S., Chaboyer, W. P., Crespo, R. A. G., & Marshall, A. P. (2017) · BMC Health Services Research · doi:10.1186/s12913-017-2314-0 · Cited by 76 · see citing works
Text-based Healthcare Chatbots Supporting Patient and Health Professional Teams: Preliminary Results of a Randomized Controlled Trial on Childhood Obesity
Kowatsch, T., Nißen, M. K., Shih, C.-H. I., Rüegger, D., Volland, D., Filler, A., Künzler, F., Barata, F., Hung, S., Büchter, D., et al. (2017) · ETH Zurich · doi:10.3929/ethz-b-000218776 · Cited by 74 · see citing works
Combining e‐mental health intervention development with human computer interaction ( <scp>HCI</scp> ) design to enhance technology‐facilitated recovery for people with depression and/or anxiety conditions: An integrative literature review
Søgaard Neilsen, A., & Wilson, R. L. (2019) · International Journal of Mental Health Nursing · doi:10.1111/inm.12527 · Cited by 71 · see citing works
Understanding the conflicting demands of family caregivers caring for depressed family members
Yamashita, N., Kuzuoka, H., Hirata, K., & Kudo, T. (2013) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2470654.2481365 · Cited by 65 · see citing works
Changing Moods
Yamashita, N., Kuzuoka, H., Hirata, K., Kudo, T., Aramaki, E., & Hattori, K. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025843 · Cited by 65 · see citing works
Analyzing the determinants to accept a virtual assistant and use cases among cancer patients: a mixed methods study
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
“Patients rated the therapeutic alliance between a VA significantly higher than with a clinician [43].”
Background
Dialogue System for Early Mental Illness Detection: Toward a Digital Twin Solution
Abilkaiyrkyzy, A., Laamarti, F., Hamdi, M., & Saddik, A. E. (2024) · IEEE Access · doi:10.1109/access.2023.3348783 · Cited by 71 · see citing works
Charlie: A chatbot to improve the elderly quality of life and to make them more active to fight their sense of loneliness
Valtolina, S., & Hu, L. (2021) · Biannual Conference of the Italian SIGCHI Chapter (CHItaly) · doi:10.1145/3464385.3464726 · Cited by 63 · see citing works
Acceptability, Feasibility, and Preliminary Efficacy of a Theory-Based Relational Embodied Conversational Agent Mobile Phone Intervention to Promote HIV Medication Adherence in Young HIV-Positive African American MSM
Dworkin, M. S., Lee, S., Chakraborty, A., Monahan, C., Hightow‐Weidman, L., Garofalo, R., Qato, D. M., Liu, L., & Jimenez, A. D. (2019) · AIDS Education and Prevention · doi:10.1521/aeap.2019.31.1.17 · Cited by 58 · see citing works
Die nächste Generation: Chatbots in der klinischen Psychologie und Psychotherapie zur Förderung mentaler Gesundheit – Ein Scoping-Review
Bendig, E., Erb, B., Schulze-Thuesing, L., & Baumeister, H. (2019) · Verhaltenstherapie · doi:10.1159/000499492 · Cited by 62 · see citing works
The potential of conversational agents to provide a rapid HIV counseling and testing services
van Heerden, A., Ntinga, X., & Vilakazi, K. (2017) · 2017 International Conference on the Frontiers and Advances in Data Science (FADS) · doi:10.1109/fads.2017.8253198 · Cited by 57 · see citing works
Meeting Users Where They Are: User-centered Design of an Automated Text Messaging Tool to Support the Mental Health of Young Adults
Kornfield, R., Meyerhoff, J., Studd, H., Bhattacharjee, A., Williams, J. J., Reddy, M. C., & Mohr, D. C. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502046 · Cited by 55 · see citing works
Examining the potential of virtual reality to deliver remote rehabilitation
Pedram, S., Palmisano, S. A., Perez, P., Mursic, R. A., & Farrelly, M. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.106223 · Cited by 53 · see citing works
Embodied Conversational Agents for Chronic Diseases: Scoping Review
Jiang, Z., Huang, X., Wang, Z., Liu, Y., Huang, L., & Luo, X. (2023) · Journal of Medical Internet Research · doi:10.2196/47134 · Cited by 51 · see citing works
Support for Carers of Young People with Mental Illness: Design and Trial of a Technology-Mediated Therapy
Lederman, R., Gleeson, J., Wadley, G., D’alfonso, S., Rice, S., Santesteban-Echarri, O., & Alvarez-Jimenez, M. (2019) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3301421 · Cited by 46 · see citing works
Racial mirroring effects on human-agent interaction in psychotherapeutic conversations
Liao, Y., & He, J. (2020) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3377325.3377488 · Cited by 48 · see citing works
Conversational Bots for Psychotherapy: A Study of Generative Transformer Models Using Domain-specific Dialogues
Das, A., Selek, S., Warner, A. R., Zuo, X., Hu, Y., Kuttichi Keloth, V., Li, J., Zheng, W. J., & Xu, H. (2022) · Proceedings of the 21st Workshop on Biomedical Language Processing · doi:10.18653/v1/2022.bionlp-1.27 · Cited by 47 · see citing works
Replika in the Metaverse: the moral problem with empathy in ‘It from Bit’
McStay, A. (2022) · AI and Ethics · doi:10.1007/s43681-022-00252-7 · Cited by 46 · see citing works
Digital Behavior Change Interventions for the Prevention and Management of Type 2 Diabetes: Systematic Market Analysis
Keller, R., Hartmann, S., Teepe, G. W., Lohse, K.-M., Alattas, A., Car, L. T., Müller‐Riemenschneider, F., von Wangenheim, F., Mair, J. L., & Kowatsch, T. (2021) · Journal of Medical Internet Research · doi:10.2196/33348 · Cited by 43 · see citing works
Designing for Health ChatbotsPreprint
Fadhil, A., & Schiavo, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.09022 · Cited by 61 · see citing works
A Cognitive Behavioral Therapy Chatbot (Otis) for Health Anxiety Management: Mixed Methods Pilot Study
Goonesekera, Y., & Donkin, L. (2022) · JMIR Formative Research · doi:10.2196/37877 · Cited by 43 · see citing works
“Previous studies have demonstrated that chatbot interventions improve psychological difficulties including major depressive disorder [15], panic disorder [16], posttraumatic stress disorder [17], antipsychotic medication adherence [18], and perceived stress [19].”
Background
Effectiveness and Acceptability of Conversational Agents for Smoking Cessation: A Systematic Review and Meta-analysis
He, L., Balaji, D., Wiers, R. W. H. J., Antheunis, M. L., & Krahmer, E. (2022) · Nicotine & Tobacco Research · doi:10.1093/ntr/ntac281 · Cited by 49 · see citing works
A Realistic Talking Human Embodied Agent Mobile Phone Intervention to Promote HIV Medication Adherence and Retention in Care in Young HIV-Positive African American Men Who Have Sex With Men: Qualitative Study
Dworkin, M., Chakraborty, A., Lee, S., Monahan, C., Hightow-Weidman, L., Garofalo, R., Qato, D., & Jimenez, A. (2018) · JMIR mHealth and uHealth · doi:10.2196/10211 · Cited by 43 · see citing works
Artificial intelligence and robotics in rehabilitation.
Luxton, D. D., & Riek, L. D. (2019) · American Psychological Association eBooks · doi:10.1037/0000129-031 · Cited by 40 · see citing works
Hybrid Ubiquitous Coaching With a Novel Combination of Mobile and Holographic Conversational Agents Targeting Adherence to Home Exercises: Four Design and Evaluation Studies
Kowatsch, T., Lohse, K.-M., Erb, V., Schittenhelm, L., Galliker, H., Lehner, R., & Huang, E. M. (2021) · Journal of Medical Internet Research · doi:10.2196/23612 · Cited by 39 · see citing works
A Chatbot-Delivered Stress Management Coaching for Students (MISHA App): Pilot Randomized Controlled Trial
Hauser-Ulrich, S., Lienhard, N., Künzli, H., & Kowatsch, T. (2024) · JMIR mhealth and uhealth · doi:10.2196/54945 · Cited by 49 · see citing works
User Perceptions of Extraversion in Chatbots after Repeated Use
Völkel, S. T., Schoedel, R., Kaya, L., & Mayer, S. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502058 · Cited by 37 · see citing works
Development of a Complex Intervention to Improve Adherence to Antidiabetic Medication in Older People Using an Anthropomorphic Virtual Assistant Software
Félix, I., Guerreiro, M., Cavaco, A., Cláudio, A., Mendes, A., Balsa, J., Carmo, M. B., Pimenta, N., & Henriques, A. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.00680 · Cited by 40 · see citing works
Emotional Reactions and Likelihood of Response to Questions Designed for a Mental Health Chatbot Among Adolescents: Experimental Study
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
University Students’ Opinions on Using Intelligent Agents to Cope with Stress and Anxiety in Social Situations
Rasouli, S., Ghafurian, M., Nilsen, E. S., & Dautenhahn, K. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.108072 · Cited by 41 · see citing works
Mobile Conversational Agents for Context-Aware Care Applications
Griol, D., & Callejas, Z. (2016) · Cognitive Computation · doi:10.1007/s12559-015-9352-x · Cited by 33 · see citing works
AR-Therapist: Design and Simulation of an AR-Game Environment as a CBT for Patients with ADHD
Alqithami, S., Alzahrani, M., Alzahrani, A., & Mostafa, A. M. (2019) · Healthcare · doi:10.3390/healthcare7040146 · Cited by 33 · see citing works
Consumers' responses to personalized service from medical artificial intelligence and human doctors
Zhang, Y., Tan, W., & Lee, E. (2023) · Psychology and Marketing · doi:10.1002/mar.21911 · Cited by 33 · see citing works
Using mimicry of body movements by a virtual agent to increase synchronization behavior and rapport in individuals with schizophrenia
Raffard, S., Salesse, R. N., Bortolon, C., Bardy, B. G., Henriques, J., Marín, L., Stricker, D., & Capdevielle, D. (2018) · Scientific Reports · doi:10.1038/s41598-018-35813-6 · Cited by 30 · see citing works
Experiences of a Speech-enabled Conversational Agent for the Self-report of Well-being among People Living with Affective Disorders: An In-the-Wild Study
Maharjan, R., Doherty, K., Rohani, D. A., Bækgaard, P., & Bardram, J. E. (2022) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3484508 · Cited by 29 · see citing works
Differential Impact and Use of a Telehealth Intervention by Persons with MS or SCI
Mercier, H. W., Ni, P., Houlihan, B. V., & Jette, A. M. (2015) · American Journal of Physical Medicine & Rehabilitation · doi:10.1097/phm.0000000000000291 · Cited by 29 · see citing works
Understanding young adults’ attitudes towards using AI chatbots for psychotherapy: The role of self-stigma
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
Effectiveness of Topic-Based Chatbots on Mental Health Self-Care and Mental Well-Being: Randomized Controlled Trial
Tong, A. C. Y., Wong, K. T. Y., Chung, W. W. T., & Mak, W. W. S. (2025) · Journal of Medical Internet Research · doi:10.2196/70436 · Cited by 28 · see citing works
Development, Feasibility, Acceptability, and Utility of an Expressive Speech-Enabled Digital Health Agent to Deliver Online, Brief Motivational Interviewing for Alcohol Misuse: Descriptive Study
Boustani, M. M., Lunn, S. J., Visser, U., & Lisetti, C. (2021) · Journal of Medical Internet Research · doi:10.2196/25837 · Cited by 28 · see citing works
Developing a Technical-Oriented Taxonomy to Define Archetypes of Conversational Agents in Health Care: Literature Review and Cluster Analysis
Denecke, K., & May, R. (2022) · Journal of Medical Internet Research · doi:10.2196/41583 · Cited by 27 · see citing works
Emotion assessment and application in human–computer interaction interface based on backpropagation neural network and artificial bee colony algorithm
Liu, J., Ang, M. C., Chaw, J. K., Kor, A.-L., & Ng, K. W. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120857 · Cited by 27 · see citing works
Exploring the acceptability of a digital mental health platform incorporating a virtual coach: The good, the bad, and the opportunities
Venning, A., Herd, M. C. E., Oswald, T., Razmi, S., Glover, F., Hawke, T., Quartermain, V., & Redpath, P. (2021) · Health Informatics Journal · doi:10.1177/1460458221994873 · Cited by 21 · see citing works
A Digital Health Intervention (SweetGoals) for Young Adults With Type 1 Diabetes: Protocol for a Factorial Randomized Trial
Stanger, C., Kowatsch, T., Xie, H., Nahum-Shani, I. B., Lim-Liberty, F., Anderson, M. A. B., Santhanam, P., Kaden, S. J., & Rosenberg, B. (2021) · JMIR Research Protocols · doi:10.2196/27109 · Cited by 22 · see citing works
Affective Conversational Agents
Callejas, Z., López-Cózar, R., Ábalos, N., & Griol, D. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-617-6.ch009 · Cited by 24 · see citing works
My Virtual Colleague: A State-of-the-Art Analysis of Conversational Agents for the Workplace
Feng, S., & Buxmann, P. (2020) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2020.020 · Cited by 24 · see citing works
Personalization of Conversational Agent-Patient Interaction Styles for Chronic Disease Management: Two Consecutive Cross-sectional Questionnaire Studies
Gross, C., Schachner, T., Hasl, A., Kohlbrenner, D., Clarenbach, C. F., von Wangenheim, F., & Kowatsch, T. (2021) · Journal of Medical Internet Research · doi:10.2196/26643 · Cited by 23 · see citing works
Virtual Assistant to Improve Self-care of Older People with Type 2 Diabetes: First Prototype
Buinhas, S., Cláudio, A. P., Carmo, M. B., Balsa, J., Cavaco, A. M. D. N., Mendes, A., Félix, I. B., Pimenta, N. M., & Guerreiro, M. P. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-16028-9_21 · Cited by 23 · see citing works
Using Worker Avatars to Improve Microtask Crowdsourcing
Qiu, S., Bozzon, A., Birk, M. V., & Gadiraju, U. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3476063 · Cited by 23 · see citing works
Conversational Agents for Mental Health and Wellbeing
Callejas, Z., & Griol, D. (2021) · Logic, argumentation & reasoning · doi:10.1007/978-3-030-61438-6_11 · Cited by 22 · see citing works
Modeling the user state for context-aware spoken interaction in ambient assisted living
Griol, D., Molina, J. M., & Callejas, Z. (2014) · Applied Intelligence · doi:10.1007/s10489-013-0503-z · Cited by 22 · see citing works
Robots to motivate elderly people: Present and future challenges
Pérez, J. G., Lohse, M., & Evers, V. (2013) · IEEE RO-MAN · doi:10.1109/roman.2013.6628392 · Cited by 21 · see citing works
The role of a clinician amid the rise of mobile health technology
Yang, W. E., Shah, L. M., Spaulding, E. M., Wang, J., Xun, H., Weng, D., Shan, R., Wongvibulsin, S., Marvel, F. A., & Martin, S. S. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz131 · Cited by 21 · see citing works
Involving Crowdworkers with Lived Experience in Content-Development for Push-Based Digital Mental Health Tools: Lessons Learned from Crowdsourcing Mental Health Messages
Kornfield, R., Mohr, D. C., Ranney, R. M., Lattie, E. G., Meyerhoff, J., Williams, J. J., & Reddy, M. C. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512946 · Cited by 19 · see citing works
Exploring Users’ Experiences With a Quick-Response Chatbot Within a Popular Smoking Cessation Smartphone App: Semistructured Interview Study
Alphonse, A., Stewart, K., Brown, J., & Perski, O. (2022) · JMIR Formative Research · doi:10.2196/36869 · Cited by 21 · see citing works
Using Relational Agents to Promote Family Communication Around Type 1 Diabetes Self-Management in the Diabetes Family Teamwork Online Intervention: Longitudinal Pilot Study
Thompson, D. I., Callender, C. S., Gonynor, C., Cullen, K. W., REDONDO, M. J., Butler, A. M., & Anderson, B. J. (2019) · Journal of Medical Internet Research · doi:10.2196/15318 · Cited by 20 · see citing works
A review on polyadic chatbots: trends, challenges, and future research directions
Kuhail, M. A., Taj, I., Alimamy, S., & Shawar, B. A. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02287-0 · Cited by 21 · see citing works
Hospital staffs’ perceptions of an electronic program to engage patients in nutrition care at the bedside: a qualitative study
Roberts, S., Marshall, A. P., & Chaboyer, W. P. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-017-0495-4 · Cited by 19 · see citing works
Improving adherence to an online intervention for low mood with a virtual coach: study protocol of a pilot randomized controlled trial
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Authors: Bickmore, T. W., Mitchell, S. E., Jack, B. W., Paasche-Orlow, M. K., Pfeifer, L. M., & O’Donnell, J.
Affiliations: Northeastern University (US)
Boston University (US)
Corresponding author: Bickmore, Timothy W.
ORCID: Bickmore, Timothy W.
Mitchell, Suzanne E.
Jack, Brian W.
O’Donnell, Julie
Venue: Interacting with Computers
Publisher: Oxford University Press (OUP)
Published: 2010-07
Volume: 22
Issue: 4
Pages: 289-298
ISSN: 0953-5438 (ISSN-L)
DOI: 10.1016/j.intcom.2009.12.001
PMC: PMC2901553
OpenAlex: W1989452694
Cited by: 218
References: 62
Citations by year: 2026: 17 · 2025: 22 · 2024: 23 · 2023: 23 · 2022: 27 · 2021: 28 · 2020: 20 · 2019: 24 · 2018: 5 · 2017: 9 · 2016: 3 · 2015: 4 · 2014: 4 · 2013: 6 · 2012: 2
FWCI: 1.31
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Interacting with Computers (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: National Institutes of Health
National Heart, Lung, and Blood Institute (R01HL081307)
Topics: Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
Health Sciences › Health Professions › General Health Professions › Patient-Provider Communication in Healthcare
Keywords: chatbots, depression, depressive symptoms, therapeutic relationship, discharge planning, patient satisfaction, ease of use, health education, inpatient care, patient-provider relationship

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