A Conversational Agent for an Online Mental Health Intervention
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The largest proportion of included conversational agents (12/31, 39%) addressed mental health issues.
“The largest proportion of them (12/31, 39%) addressed mental health issues [13,32-42]”
From The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review (2020) · cites this paper as [33]
The most frequently raised issue with conversational agents (9 studies) was poor understanding because of limited vocabulary, voice recognition accuracy, or error management of word inputs.
“The most frequently raised issue with conversational agents (9 studies) was poor understanding because of limited vocabulary, voice recognition accuracy, or error management of word inputs [13,32-37,41,52].”
From The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review (2020) · cites this paper as [33]
Several studies reported feedback concerned with customization or availability of feature options: 2 studies commented positively, and 3 studies desired more features and more control.
“However, several studies reported feedback concerned with customization or availability of feature options, with 2 studies commenting on it positively (eg, having both voice and touch modes to allow hands-free work and rapid data input on a triage system for nurses) [8,35], and 3 studies desiring more features and more control [33,37,48].”
From The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review (2020) · cites this paper as [33]
Cites
Bush, K. (1998) · Archives of Internal Medicine · doi:10.1001/archinte.158.16.1789 · Cited by 6,196 · see citing works
Assessment of client/patient satisfaction: development of a general scale
Larsen, D. L., Attkisson, C. C., Hargreaves, W. A., & Nguyen, T. D. (1979) · Evaluation and Program Planning · doi:10.1016/0149-7189(79)90094-6 · Cited by 2,887 · see citing works
When and how do young people seek professional help for mental health problems?
Rickwood, D. J., Deane, F. P., & Wilson, C. J. (2007) · Medical Journal of Australia · doi:10.5694/j.1326-5377.2007.tb01334.x · Cited by 1,195 · see citing works
Mohr, D. C., Burns, M. N., Schueller, S. M., Clarke, G., & Klinkman, M. (2013) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2013.03.008 · Cited by 693 · see citing works
Wallace, R. S. (2007) · Springer eBooks · doi:10.1007/978-1-4020-6710-5_13 · Cited by 335 · see citing works
Web-based interventions for substance use disorders
Copeland, J., & Martin, G. (2004) · Journal of Substance Abuse Treatment · doi:10.1016/s0740-5472(03)00165-x · Cited by 168 · see citing works
Different measurements metrics to evaluate a chatbot system
Shawar, B. A., & Atwell, E. (2007) · doi:10.3115/1556328.1556341 · Cited by 151 · see citing works
Internet-based interventions for young people with problematic substance use: a systematic review
Tait, R. J., & Christensen, H. (2010) · Medical Journal of Australia · doi:10.5694/j.1326-5377.2010.tb03687.x · Cited by 115 · see citing works
Evaluating mass knowledge acquisition using the ALICE chatterbot: The AZ-ALICE dialog system
Schumaker, R. P., Liu, Y., Ginsburg, M., & Chen, H. (2006) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2006.06.008 · Cited by 26 · see citing works
Comorbidity of mental disorders and substance use: A brief guide for the primary care clinician
Holmwood, C. B. (2008) · Flinders Academic Commons (Flinders University) · Cited by 17 · see citing works
Telehealth Interventions in Rehabilitation Psychology: Postcards From the Edge.
Wade, S. L., & Wolfe, C. R. (2005) · Rehabilitation Psychology · doi:10.1037/0090-5550.50.4.323 · Cited by 16 · see citing works
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Nadarzynski, T., Miles, O., Cowie, A., & Ridge, D. (2019) · DIGITAL HEALTH · doi:10.1177/2055207619871808 · Cited by 810 · see citing works
The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review
Milne-Ives, M., de Cock, C., Lim, E., Shehadeh, M. H., de Pennington, N., Mole, G., Normando, E., & Meinert, E. (2020) · Journal of Medical Internet Research · doi:10.2196/20346 · Cited by 626 · see citing works
Haque, M. D. R., & Rubya, S. (2023) · JMIR mHealth and uHealth · doi:10.2196/44838 · Cited by 404 · see citing works
Perceptions and opinions of patients about mental health chatbots: scoping review
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
Oh, K.-J., Lee, D., Ko, B., & Choi, H.-J. (2017) · 2017 18th IEEE International Conference on Mobile Data Management (MDM) · doi:10.1109/mdm.2017.64 · Cited by 285 · see citing works
Using Health Chatbots for Behavior Change: A Mapping Study
Pereira, J., & Díaz, Ó. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1237-1 · Cited by 199 · 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
Ruggiano, N., Brown, E. L., Roberts, L., Framil Suarez, C. V., Luo, Y., Hao, Z., & Hristidis, V. (2021) · Journal of Medical Internet Research · doi:10.2196/25006 · Cited by 100 · see citing works
“Over time, chatbots have been developed and tested to address a number of health conditions and topics, including HIV and sexual health [21], substance abuse, assessment, mental health assessment [22], and weight management [23].”
Chatbot features for anxiety and depression: a scoping review
Ahmed, A., Hassan, A., Aziz, S., Abd-alrazaq, A. A., Ali, N., Alzubaidi, M., Al-Thani, D., Elhusein, B., Siddig, M. A., Ahmed, M., et al. (2023) · Health Informatics Journal · doi:10.1177/14604582221146719 · Cited by 82 · see citing works
TensioBot: a chatbot assistant for self-managed in-house blood pressure checking
Echeazarra, L., Pereira, J., & Saracho, R. (2021) · Journal of Medical Systems · doi:10.1007/s10916-021-01730-x · Cited by 66 · see citing works
Pereira, J., Fernández-Raga, M., Osuna-Acedo, S., Roura-Redondo, M., Almazán-López, O., & Buldón-Olalla, A. (2019) · Technology Knowledge and Learning · doi:10.1007/s10758-019-09414-9 · Cited by 59 · see citing works
An Evaluation of Generative Pre-Training Model-based Therapy Chatbot for Caregivers
Wang, L., Mujib, M. I., Williams, J., Demiris, G., & Huh-Yoo, J. (2021) · arXiv · doi:10.48550/arxiv.2107.13115 · Cited by 21 · see citing works
- Authors
- Elmasri, D., & Maeder, A.
- Affiliations
- Western Sydney University (AU)
- Flinders University (AU)
- Corresponding author
- Maeder, Anthony
- ORCID
- Maeder, Anthony
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 243-251
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2523328169
- Cited by
- 68
- References
- 11
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 8 · 2023: 11 · 2022: 7 · 2021: 6 · 2020: 13 · 2019: 10 · 2018: 2 · 2017: 3
- FWCI
- 11.71
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- chatbots, digital mental health interventions, alcohol drinking habits, AUDIT-C, young adults, health risk assessment, usability, user satisfaction
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