Evaluating Conversational Agents for Mental Health: Scoping Review of Outcomes and Outcome Measurement Instruments
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Abstract
Background: Rapid proliferation of mental health interventions delivered through conversational agents (CAs) calls for high-quality evidence to support their implementation and adoption. Selecting appropriate outcomes, instruments for measuring outcomes, and assessment methods are crucial for ensuring that interventions are evaluated effectively and with a high level of quality. Objective: We aimed to identify the types of outcomes, outcome measurement instruments, and assessment methods used to assess the clinical, user experience, and technical outcomes in studies that evaluated the effectiveness of CA interventions for mental health. Methods: We undertook a scoping review of the relevant literature to review the types of outcomes, outcome measurement instruments, and assessment methods in studies that evaluated the effectiveness of CA interventions for mental health. We performed a comprehensive search of electronic databases, including PubMed, Cochrane Central Register of Controlled Trials, Embase (Ovid), PsychINFO, and Web of Science, as well as Google Scholar and Google. We included experimental studies evaluating CA mental health interventions. The screening and data extraction were performed independently by 2 review authors in parallel. Descriptive and thematic analyses of the findings were performed. Results: We included 32 studies that targeted the promotion of mental well-being (17/32, 53%) and the treatment and monitoring of mental health symptoms (21/32, 66%). The studies reported 203 outcome measurement instruments used to measure clinical outcomes (123/203, 60.6%), user experience outcomes (75/203, 36.9%), technical outcomes (2/203, 1.0%), and other outcomes (3/203, 1.5%). Most of the outcome measurement instruments were used in only 1 study (150/203, 73.9%) and were self-reported questionnaires (170/203, 83.7%), and most were delivered electronically via survey platforms (61/203, 30.0%). No validity evidence was cited for more than half of the outcome measurement instruments (107/203, 52.7%), which were largely created or adapted for the study in which they were used (95/107, 88.8%). Conclusions: The diversity of outcomes and the choice of outcome measurement instruments employed in studies on CAs for mental health point to the need for an established minimum core outcome set and greater use of validated instruments. Future studies should also capitalize on the affordances made available by CAs and smartphones to streamline the evaluation and reduce participants’ input burden inherent to self-reporting.
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Relational agents have been shown to foster therapeutic rapport with users, support long-term engagement, and have been deployed across various clinical and nonclinical populations, including cognitively healthy older adults.
“Relational agents have been shown to foster therapeutic rapport with users [32-34], support long-term engagement [35], and have been deployed across various clinical [35-37] and nonclinical populations [38-40], including (cognitively healthy) older adults [41-43].”
From Toward a Smartphone-Based and Conversational Agent–Delivered Just-in-Time Adaptive Holistic Lifestyle Intervention for Older Adults Affected by Cognitive Decline: Two-Week Proof-of-Concept Study (2025) · cites this paper as [34]
The Discussion (Comparison With Prior Work; page unavailable) says early evidence suggests users may develop a therapeutic alliance with conversational agents, but describes that evidence as limited. The Results (Characteristics of the Included Studies; page unavailable) include healthy adults and inpatients or outpatients, and the references identify a study of older adults. The review measures engagement and reports some studies lasting at least eight weeks, but does not establish that relational agents foster long-term engagement or specify that the older adults were cognitively healthy. (LLM, conf=0.84)
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“For instance, a study may report subjective feedback from users but not include objective measurements, such as average duration of use and the number of modules completed, to provide a better understanding of the context of use [11,13].”
InfluentialBlissful Agents: Adjuncts to Group Medical Visits for Chronic Pain and Depression
Shamekhi, A., Bickmore, T., Lessios, A. S., Negash, N. L., & Gardiner, P. M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-47665-0_49 · Cited by 13 · cited 1× · see citing works
“%) were from the United States of America [3,4,24,30,32-34 ,42-49 42-49].”
Automated Chat Application Surveys Using Whatsapp: Evidence from Panel Surveys and a Mode ExperimentPreprint
Fei, J., Wolff, J. S., Hotard, M., Ingham, H., Khanna, S., Lawrence, D., Tesfaye, B., Weinstein, J. M., Yasenov, V. I., & Hainmueller, J. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4114839 · Cited by 12 · cited 1× · see citing works
“Traditional methods via pen-and-paper and phone-based surveys can be costly and burdensome to participants and researchers alike [17].”
An affect-aware dialogue system for counseling
Ring, L. (2017) · doi:10.17760/d20258254 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [55]Hodrien A Fernando T A Review of Post-Study and Post- Task Subjective Questionnaires to Guide Assessment of System Usability Journal of Usability Studies 2021 16 3 203 232
Cited by
Sorted by citations
Li, H., Zhang, R., Lee, Y.-C., Kraut, R. E., & Mohr, D. C. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00979-5 · Cited by 598 · see citing works
Chatbots in Health Care: Connecting Patients to Information
Clark, M., & Bailey, S. M. (2024) · Canadian Journal of Health Technologies · doi:10.51731/cjht.2024.818 · Cited by 103 · 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
Self-citationVowels, L. M., Francois-Walcott, R. R. R., & Darwiche, J. (2024) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2024.100078 · Cited by 59 · see citing works
The use of AI in mental health services to support decision-making: scoping review
Auf, H., Svedberg, P., Nygren, J., Nair, M., & Lundgren, L. E. (2025) · Journal of Medical Internet Research · doi:10.2196/63548 · Cited by 56 · see citing works
“Jabir et al [89] examined how conversational agents are typically evaluated and found that effectiveness assessment usually includes at least 1 clinical outcome and 1 user experience outcome.”
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
“Finally, as this is a scoping review without synthesis of the evidence, we present the outcomes as reported by the authors of primary studies; therefore, no determinations or recommendations are made regarding the appropriateness or utility of the outcomes.”
Kiuchi, K., Otsu, K., & Hayashi, Y. (2023) · Behaviour and Information Technology · doi:10.1080/0144929x.2023.2286528 · Cited by 50 · see citing works
“…medical conditions (Bérubé et al. 2021), mental health (AbdAlrazaq et al. 2020; Bendig et al. 2019; Ga ff ney, Mansell, and Tai 2019; He et al. 2023; Jabir et al. 2023), neurodevelopmental disorders and dementia/ cognitive impairment (Guemghar et al. 2022), and neurodevelopmental disorders and…”
Al Shboul, M. K. I., Alwreikat, A., & Alotaibi, F. A. (2024) · Science & Technology Libraries · doi:10.1080/0194262x.2023.2250835 · Cited by 38 · see citing works
“…that Chatbots could improve participants’ self-efficacy, disease knowledge, and medication adherence (Aggarwal et al. 2023) Similarly, one study found that using a Chatbot for mental health screening was effective in identifying individuals at risk of depression and anxiety (Jabir et al. 2023).”
Are chatbots the new relationship experts? Insights from three studies
Vowels, L. M. (2024) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2024.100077 · Cited by 42 · see citing works
Martinengo, L., Lin, X., Jabir, A. I., Kowatsch, T., Atun, R. A., Car, J., & Car, L. T. (2023) · Journal of Medical Internet Research · doi:10.2196/50767 · Cited by 37 · see citing works
“The definition and classification of CAs were supplemented by the authors’ previous and ongoing work, consisting of a series of scoping reviews on several aspects of health care CAs [1,7,31] and an analysis of definitions presented in several reviews [3,4,32-40] and a book [5].”
MethodSelf-citationVillarreal‐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
“A previous scoping review highlighted the heterogeneity of the tools used to assess the effects of dialogue interventions on mental health [53].”
MethodChatbots and mental health: a scoping review of reviews
Mayor, E. (2025) · Current Psychology · doi:10.1007/s12144-025-08094-2 · Cited by 35 · see citing works
“Whereas the reviews indicate validated instruments are used in most of the studies, the review of Jabir et al. (2023) suggests about half of the used instruments are not validated.”
InfluentialKhosravi, M., & Azar, G. (2024) · Digital Health · doi:10.1177/20552076241247983 · Cited by 24 · see citing works
Park, J., Singh, V. K., & Wiśniewski, P. (2024) · JMIR Medical Informatics · doi:10.2196/62758 · Cited by 24 · see citing works
“This concerning trend was consistent with the mental health CAs for general adults [72].”
Towards Multimodal Emotional Support Conversation Systems
Chu, Y., Liao, L., Zhou, Z., Ngo, C., & Hong, R. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3604951 · Cited by 24 · see citing works
“1109/TMM.2025.3604951 enhancing therapist-client interactions [1], [2].”
InfluentialUnleashing the potential of chatbots in mental health: bibliometric analysis
Qing, H., & Zhao, C. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1494355 · Cited by 23 · see citing works
Li, H. Z., Zhang, R., Lee, Y., Kraut, R. E., & Mohr, D. C. (2023) · doi:10.31234/osf.io/m3vjt · Cited by 19 · see citing works
Sobowale, K., & Humphrey, D. K. (2025) · JMIR Formative Research · doi:10.2196/65605 · Cited by 15 · see citing works
“However, a recent meta-analysis found limited objective measures of their performance [12].”
Sharp, J., Kelson, J. N., South, D., Saliba, A. J., & Kabir, M. A. (2025) · Acta Astronautica · doi:10.1016/j.actaastro.2025.04.002 · Cited by 15 · see citing works
“The first 10 pages of Google Scholar were also searched [32, 33].”
Уэтова, Е., Hederman, L., Ross, R., & O’Sullivan, D. M. (2024) · Digital Health · doi:10.1177/20552076241277693 · Cited by 13 · see citing works
“Existing reviews often restricted their focus to speci fi c health areas, such as mental health, 15 – 17 smoking cessation, 18 physical activity 19 or body weight management 20,21 ; population, e.g., young (25 years and younger), 17 or adults (18 years and older) 22 ; or agent types, e.g., embodied,…”
BackgroundDisrupted self, therapy, and the limits of conversational AI
Babushkina, D., & de Boer, B. (2024) · Philosophical Psychology · doi:10.1080/09515089.2024.2397004 · Cited by 13 · see citing works
“Whereas technology-driven research that highlights the benefits of CAs (e.g., Ahmad et al., 2022) or suggest ways to overcome user resistance (e.g., Jabir et al., 2023; Prakash & Das, 2020) is plentiful, critical evaluation of the effectiveness of such claims and aspirations seems to be lagging…”
A conversational agent framework for mental health screening: design, implementation, and usability
Boian, R., Bucur, A.-M., Todea, D., Luca, A., Rebedea, T. E., & Podina, I. R. (2024) · Behaviour and Information Technology · doi:10.1080/0144929x.2024.2332934 · Cited by 11 · see citing works
“…to mental health screening, it should be capable of screening for a range of mental health conditions using validated surveys (Giunti et al. 2021; Jabir et al. 2023) and interpreting users ’ responses accurately, promptly, and in real-time, while providing a fi nal summed score that categorises…”
An Automated Conversational Agent Self-Help Program: Randomized Controlled Trial
Foran, H. M., Kubb, C., Mueller, J., Poff, S., Ung, M., Li, M., Smith, E. M., Akinyemi, A., Kambadur, M., Waller, F., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/53829 · Cited by 9 · see citing works
“Additionally, a significant number of studies examining conversational agents have used various psychological interventions without a solid theoretical foundation [17] or lack standardized measurement outcomes [19].”
Olisaeloka, L., Richardson, C. G., Wang, A. Y., Munthali, R. J., & Vigo, D. (2026) · Healthcare · doi:10.3390/healthcare14101395 · Cited by 13 · see citing works
Krath, J., Schmidt-Kraepelin, M., Schmähl, K., Schütz, C., Morschheuser, B., & Sunyaev, A. (2025) · Journal of Medical Internet Research · doi:10.2196/65214 · Cited by 7 · see citing works
“Although intelligent conversational agents can serve as a valuable alternative for individual coaching in other health care areas [95-97], their potential in research on BCSSs for procrastination remains unexplored [17,98].”
Zhang, Z., Sun, S., Moradbakhti, L., Hall, A., Mougenot, C., Chen, J., & Calvo, R. A. (2025) · JMIR Mental Health · doi:10.2196/67190 · Cited by 6 · see citing works
“Nonetheless, more studies are needed to address the ethical and methodological shortfalls [76,77] and tailor the algorithms for unique cohorts like HCPs.”
Frischholz, K., Tanaka, H., Shidara, K., Onishi, K., & Nakamura, S. (2024) · JMIR Formative Research · doi:10.2196/55234 · Cited by 5 · see citing works
“For example, by implementing CBT techniques in mental health apps (see [9] for a review) or delivering CBT via conversational agents in the form of robots [10] or virtual agents (see [11,12] for reviews), clients can receive mental health interventions with little to no help from a human provider.”
BackgroundMethodResultInfluentialBrill, E., Vinay, R., Nißen, M., Joshi, P., Klöppel, S., & Kowatsch, T. (2025) · JMIR Formative Research · doi:10.2196/66885 · Cited by 5 · see citing works
“Relational agents have been shown to foster therapeutic rapport with users [32-34], support long-term engagement [35], and have been deployed across various clinical [35-37] and nonclinical populations [38-40], including (cognitively healthy) older adults [41-43].”
College Tales: Pilot Study on Large Language Models Generated Narratives for Mental Health Literacy
Sousa, J. P. V., Campos, P., & Bala, P. (2024) · International Academic Mindtrek Conference (Mindtrek) · doi:10.1145/3681716.3689447 · Cited by 4 · see citing works
“While existing work has focused on supporting existing MH practitioners [5, 11], recent studies explore alternative interventions using games [5, 9, 11] or Artificial Intelligence (AI) [4, 15, 17].”
Lim, S., Yang, Q., Meng, J., & Rains, S. A. (2025) · Behaviour and Information Technology · doi:10.1080/0144929x.2025.2567989 · Cited by 3 · see citing works
“These 18 studies did not specific therapy or counselling strategies: Abd-Alrazaq, Safi et al. 2020; Gabarron et al. 2020; Hocking et al. 2023; Jabir et al. 2023; Luo et al. 2021; Milne-Ives et al. 2020; Montenegro, da Costa, and da Rosa Righi 2019; Pereira and Díaz 2019; Phiri and Munoriyarwa 2023;…”
User experience and safety of generative AI-based mental health chatbots: Scoping review protocol
Olisaeloka, L., Richardson, C., & Vigo, D. (2026) · PLoS ONE · doi:10.1371/journal.pone.0341631 · Cited by 3 · see citing works
“In the context of DMHIs, this encompasses measures of acceptability, usability, perceived impact, and engagement [9,30].”
Towards a Design Framework for Conversational Agents for Diabetes Prevention
Sawad, A. B., Alakhtar, R. A., Alturki, B., Narayan, B., Lin, S., Prasad, M., & Kocaballi, A. B. (2023) · Australian Conference on Computer-Human Interaction (OzCHI) · doi:10.1145/3638380.3638421 · Cited by 3 · see citing works
“Absence of comparison groups: Several studies on conversational agents did not include control or comparison groups, which makes it difficult to evaluate the exact impact of conversational agent treatments in contrast to other variables or interventions [18, 19, 28].”
BackgroundMULTIWD: Multiple Wellness Dimensions in Social Media PostsPreprint
Sathvik, M., & Garg, M. (2023) · doi:10.36227/techrxiv.22816586 · Cited by 3 · see citing works
Harris, D. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2375687 · Cited by 2 · see citing works
“While prior knowledge syntheses, like Al Jabir et al. (2023), Williams and Pykett (2022), and Zidaru et al. (2021), do not specifically focus on affordances in OMHRs, these reviews do investigate the quantitative, qualitative, and mixed methods used across digital health literature.”
Zhang, Y., Ma, Y., Fu, D., Portales, S. Z., Fjeld, M., & Kragić, D. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2675404 · Cited by 2 · see citing works
Villarreal‐Zegarra, D., Paredes-Gonzales, Y., Dámaso-Román, A., Quiñones-Inga, J., Centeno-Terrazas, G., & Lozada, Y. P. A.-M. (2026) · PLoS ONE · doi:10.1371/journal.pone.0344939 · Cited by 2 · see citing works
“This is compounded by marked heterogeneity in measurement instruments, since an analysis of 203 tools identified that 73.9% were used solely in individual studies, and 52.7% lacked cited evidence of validity, being mostly created or adapted exclusively for the study in question [5].”
InfluentialVowels, L., Vohra, P., Li, D.-Y., Zeinoddin, P., Elswick, A., Marcantonio, T., Wood, N. D., & Vowels, M. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001352 · Cited by 2 · see citing works
“Systematic reviews and meta-analyses consistently show that chatbots can support users in managing stress, anxiety, and depression, and may even foster therapeutic alliance in ways that encourage treat-ment adherence [8–13].”
Sharp, G., Marini, S., Tam, E., & Hu, H. (2026) · JMIR Human Factors · doi:10.2196/90783 · Cited by 1 · see citing works
“Real-world evaluations of mental health–specific chatbots are seemingly quite rare [30].”
Hardiek, M. (2025) · Beratung aktuell · doi:10.30820/1437-3181-2025-2-49 · Cited by 1 · see citing works
“…bereits er-zielt haben, wie bspw. die Reduktion depressiver Symptome, sowie der all-gemeinen Akzeptanz solch digitaler Angebote (Abd-Alrazaq et al., 2020; Boucher et al., 2021; Jabir, 2023), stellt sich aktuell die Frage, wie sie auch in der Paartherapie erfolgreich eingesetzt werden könnten.”
MULTIWD: Multiple Wellness Dimensions in Social Media PostsPreprint
Sathvik, M., & Garg, M. (2023) · doi:10.36227/techrxiv.22816586.v1 · Cited by 1 · see citing works
Chatbots and mental health: A scoping review of reviewsPreprint
Mayor, E. (2024) · doi:10.31234/osf.io/j6825 · Cited by 1 · see citing works
Villarreal‐Zegarra, D., Paredes-Gonzales, Y., Dámaso-Román, A., Quiñones-Inga, J., Centeno-Terrazas, G., & Lozada, Y. P. A.-M. (2025) · medRxiv · doi:10.1101/2025.09.22.25336411 · Cited by 1 · see citing works
Villarreal‐Zegarra, D., García-Serna, J., Quispe-Callo, G., Lázaro-Cruz, G., Centeno-Terrazas, G., Gálvez-Arévalo, R. A., Escobar-Agreda, S., Rodríguez, A. D., Reátegui-Rivera, C. M., & Finkelstein, J. (2024) · doi:10.2196/preprints.59560
Sousa, J. P. V., Campos, P., & Bala, P. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-12405-0_12
AI Companion: Revolutionising Mental Well-Being Through Conversational UX
Soundarya, N. P., Bhoomika, S., Ashok, V., & Gupta, H. K. (2025) · Lecture notes in mechanical engineering · doi:10.1007/978-981-96-5495-6_49
AI-powered conversational framework for mental health diagnosis
Diwakar, D., Raj, D., Prasad, A., Ali, G., & ELAffendi, M. A. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3602
Chung, K.-M., Kung, L.-H., & Yan, Y. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1792791
“Evidence from mental health–focused reviews further suggests that conversational agents may alleviate psychological distress and improve wellbeing through sustained engagement and perceived support (8, 9).”
Kim, D., Ju, S., Lee, H. M., Kang, M., Kim, M., Park, Y., Song, H., Sundar, S. S., & Han, J. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3799023
Insuasti, J. P. B., Vargas, D. P. C., & Yánez, V. A. V. (2026) · Esprint Investigación · doi:10.61347/ei.v5i1.283
Participative Interpretation Using Interactive Digital Storytelling
Barbara, J. (2026) · Springer eBooks · doi:10.1007/978-3-032-22974-8_4
Chu, Y., Guo, Y., & Hong, R. (2026) · Multimedia Systems · doi:10.1007/s00530-026-02385-9
“Conversational Artificial Intelligence (AI) is increasingly crucial in mental health care [1, 2 ].”
Cerniglia, L., & Cimino, S. (2026) · JMIR Pediatrics and Parenting · doi:10.2196/91292
“Analyses of influencer culture suggest that high-visibility maternal personas often embody intensive parenting ideals, combining constant child-centeredness with aestheticized domesticity and entrepreneurial self-branding [36].”
McAlister, K. L., Jewell, C. C., & Huberty, J. (2026) · JMIR Formative Research · doi:10.2196/95811
“Few studies report when agents are used, how often users return between sessions, or how session-level satisfaction unfolds in naturalistic contexts [13-15].”
Semantic context improvisational retrieval-augmented generation for empathic conversational AI
Tahir, S. A., Johnson, J., Abu-Khalaf, J., & Shah, S. A. A. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-12167-z
“This becomes a bottleneck for delivering empathetic and trustworthy experiences, particularly in therapeutic conversations [ 12, 13].”
Can AI Heal? A Conceptual Exploration of the Potential of AI in Mental Healthcare
Nair, H. B., & Vowels, L. M. (2026) · Springer eBooks · doi:10.1007/978-3-032-23626-5_2
Structure Matters: Evaluating Multi-Agents Orchestration in Generative Therapeutic Chatbots
Elahimanesh, S., Mohammadkhani, M., Movahed, S., Abootorabi, M. M., Salehi, S., & Edalat, A. (2026) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3816046.3816273
“Similarly, other therapeutic chatbots, including CBT systems [18, 21], Evebot [44], GPT-2–based therapy bots [43], and student distress-detection frameworks [30], combine emotion recognition with neural generation but generally lack explicit therapeutic state modeling, memory, and robustness.”
Aguiar, Y., Morais, S., Medeiros, M., Andrade, L., Pereira, J. K. D. C., Souza, D. Y., Rêgo, A. S. D. C., & Mendes, R. (2026) · doi:10.5753/sbcas_estendido.2026.26522
“These systems can provide initial support, triage, continuous monitoring, and support self-care strategies, operating as an accessible and non-stigmatizing communication channel [Jabir et al. 2023].”
Addressing Low Self Esteem in Pakistani University Students via an Embodied Conversational Agent
Tahir, H. M., Richards, D., & Kangas, M. (2026) · ACM International Conference on Intelligent Virtual Agents (IVA) · doi:10.1145/3806774.3827981
Menard, C., Thual, L., Philip, P., & Pécune, F. (2026) · ACM International Conference on Intelligent Virtual Agents (IVA) · doi:10.1145/3806774.3832792
Yan, R., Wang, S. W., Shi, D., & Ren, X. (2026) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2026.103946
Dritsas, E., & Τρίγκα, Μ. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.135207
Wei, X., Chen, X., Jin, W., Cai, J., 駱旭琛, & Deng, J. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2026.1963839
“Relational safety was assessed across three prede fi ned dimensions: dependency reinforcement, agency preservation, and human-support preservation (50).”
- Authors
- Jabir, A. I., Martinengo, L., Lin, X., Torous, J., Subramaniam, M., & Tudor Car, L.
- Affiliations
- Nanyang Technological University (SG)
- Beth Israel Deaconess Medical Center (US)
- National University of Singapore (SG)
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2023-04-19
- Volume
- 25
- Pages
- e44548
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC10157460
- OpenAlex
- W4361994630
- Cited by
- 63
- References
- 68
- Citations by year
- 2026: 24 · 2025: 13 · 2024: 16 · 2023: 10
- FWCI
- 2.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DR-NTU (Nanyang Technological University) (submittedVersion, cc-by)
- Funders
- National Research Foundation
- National Research Foundation Singapore
- Eidgenössische Technische Hochschule Zürich
- Lee Kong Chian School of Medicine, Nanyang Technological University
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- chatbots, mental health interventions, outcome measurement instruments, outcome validity, clinical outcomes, self-report questionnaire, smartphone-based assessment
- MeSH terms
- Communication, Humans, Mental Health, Outcome Assessment, Health Care
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