Chatbot features for anxiety and depression: a scoping review
Open accessAbstract
Chatbots can provide valuable support to patients in assessing and guiding management of various health problems particularly when human resources are scarce. Chatbots can be affordable and efficient on-demand virtual assistants for mental health conditions, including anxiety and depression. We review features of chatbots available for anxiety or depression. Six bibliographic databases were searched including backward and forwards reference list checking. The initial search returned 1302 citations. Post-filtering, 42 studies remained forming the final dataset for this scoping review. Most of the studies were from conference proceedings (62%, 26/42), followed by journal articles (26%, 11/42), reports (7%, 3/42), or book chapters (5%, 2/42). About half of the reviewed chatbots had functionality targeting both anxiety and depression (60%, 25/42), whereas 38% (16/42) targeted only depression, 38% (16/42) anxiety and the remaining addressed other mental health issues along with anxiety and depression. Avatars or fictional characters were rarely used in these studies only 26% (11/42) despite their increasing popularity. Mental health chatbots could benefit in helping patients with anxiety and depression and provide valuable support to mental healthcare workers, particularly when resources are scarce. Real-time personal virtual assistance fills in this gap . Their role in mental health care is expected to increase.
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Digital technologies enable remote collection of large volumes of clinically relevant data that may be less burdensome than traditional in-clinic visits and more reflective of clinically relevant changes.
“These technologies enable the remote collection of large volumes of clinically relevant data, which may be less burdensome than traditional in-clinic visits and more reflective of clinically relevant changes (20, 21).”
From A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms (2023) · cites this paper as [21]
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- Authors
- Ahmed, A., Hassan, A., Aziz, S., Abd-alrazaq, A. A., Ali, N., Alzubaidi, M., Al-Thani, D., Elhusein, B., Siddig, M. A., Ahmed, M., & Househ, M.
- Affiliations
- Weill Cornell Medical College in Qatar (QA)
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
- Hamad bin Khalifa University (QA)
- Hamad Medical Corporation (QA)
- Corresponding author
- Househ, Mowafa
- ORCID
- Venue
- Health Informatics Journal
- Publisher
- SAGE Publications
- Published
- 2023-01
- Volume
- 29
- Issue
- 1
- Pages
- 14604582221146719-14604582221146719
- ISSN
- 1355-4743 (ISSN-L)
- 1460-4582
- 1741-2811
- OpenAlex
- W4317870342
- Cited by
- 82
- References
- 50
- Citations by year
- 2026: 18 · 2025: 30 · 2024: 27 · 2023: 7
- FWCI
- 2.75
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Health Informatics Journal (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by-nc)
- Funders
- Qatar National Research Fund (NPRP12S-0303-190204)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- chatbots, anxiety, depression, mental health, avatars, virtual assistants, mental health services, resource-scarce settings
- MeSH terms
- Anxiety, Mental Disorders, Depression, Humans, Mental Health, Software
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