Self-Guided Web-Based Interventions: Scoping Review on User Needs and the Potential of Embodied Conversational Agents to Address Them
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Abstract
BACKGROUND: Web-based mental health interventions have evolved from innovative prototypes to evidence-based and clinically applied solutions for mental diseases such as depression and anxiety. Open-access, self-guided types of these solutions hold the promise of reaching and treating a large population at a reasonable cost. However, a considerable factor that currently hinders the effectiveness of these self-guided Web-based interventions is the high level of nonadherence. The absence of a human caregiver apparently has a negative effect on user adherence. It is unknown to what extent this human support can be handed over to the technology of the intervention to mitigate this negative effect. OBJECTIVE: The first objective of this paper was to explore what is known in literature about what support a user needs to stay motivated and engaged in an electronic health (eHealth) intervention that requires repeated use. The second objective was to explore the current potential of embodied conversational agents (ECAs) to provide this support. METHODS: This study reviews and interprets the available literature on (1) support within eHealth interventions that require repeated use and (2) the potential of ECAs by means of a scoping review. The rationale for choosing a scoping review is that the subject is broad, diverse, and largely unexplored. Themes for (1) and (2) were proposed based on grounded theory and mapped on each other to find relationships. RESULTS: The results of the first part of this study suggest the presence of user needs that largely remain implicit and unaddressed. These support needs can be categorized as task-related support and emotion-related support. The results of the second part of this study suggest that ECAs are capable of engaging and motivating users of information technology applications in the domains of learning and behavioral change. Longitudinal studies must be conducted to determine under what circumstances ECAs can create and maintain a productive user relationship. Mapping the user needs on the ECAs' capabilities suggests that different kinds of ECAs may provide different solutions for improving the adherence levels. CONCLUSIONS: Autonomous ECAs that do not respond to a user's expressed emotion in real time but take on empathic roles may be sufficient to motivate users to some extent. It is unclear whether those types of ECAs are competent enough and create sufficient believability among users to address the user's deeper needs for support and empathy. Responsive ECAs may offer a better solution. However, at present, most of these ECAs have difficulties to assess a user's emotional state in real time during an open dialogue. By conducting future research with relationship theory-based ECAs, the added value of ECAs toward user needs can be better understood.
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Of extant systematic reviews on AI chatbots, 6 targeted assessing efficacy in enhancing mental health outcomes, 2 examined feasibility in health care settings, and 1 described technical architectures and characteristics in chronic conditions.
“Of the extant systematic reviews on AI chatbots, 6 articles targeted at assessing efficacy of AI-chatbots in enhancing mental health outcomes [1,7,9-12], 2 examined the feasibility of AI-chatbots in health care settings [8,13], and 1 described the technical architectures and characteristics of the AI chatbots used in chronic conditions [14].”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [13]
The citation [1] identifies this paper as one of six reviews assessing AI chatbots’ efficacy for mental health outcomes. Its Introduction (page unavailable) instead states that its aim was to analyze users’ needs and the capabilities of embodied conversational agents to meet them, with a view to improving adherence—not to assess efficacy on mental health outcomes. (LLM, conf=0.84)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredBecause many literature studies in the contextual inquiry have a broad setup, it is often not possible or desirable to include a quality appraisal or only include experimental studies.
“Third, because many literature studies in the contextual inquiry have a broad setup, it is often not possible or desirable to include a quality appraisal or only include experimental studies.”
From Methods for Human-Centered eHealth Development: Narrative Scoping Review (2022) · cites this paper as [160]
Existing systematic and scoping reviews of conversational agent interventions in mental health have focused on a subset of conversational agents with a visual character (embodied) or are now outdated.
“Recent existing reviews have focused exclusively on a subset of embodied conversational agent interventions despite other modalities aiming to achieve the common goal of improved mental health.”
From Conversational agents in the treatment of mental health problems: mixed-method systematic review (2019) · cites this paper as [9]
Patients' adherence poses a major challenge for computer-based therapies.
“In this vein, the patients' adherence still poses a major challenge for computer-based therapies that have to be addressed in future work (53).”
From Computer-assisted avatar-based treatment for dysfunctional beliefs in depressive inpatients: A pilot study (2021) · cites this paper as [53]
Recent meta-analyses demonstrated that embodied CAs delivering help programs are successful treatments, particularly when human-supported.
“Likewise, recent meta-analyses demonstrated that embodied CAs delivering help programs are successful treatments (31, 53), particularly when being human-supported, e.g., due to an increased adherence (54).”
From Computer-assisted avatar-based treatment for dysfunctional beliefs in depressive inpatients: A pilot study (2021) · cites this paper as [53]
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“Computers as social actors Systematic search: [22] Hand search: [52-54] (concept=multimedia) Embodied conversational agents (ECAs) have the ability to have an open verbal dialogue with users.”
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“Measures of the human-ECA relationship Systematic search: [23] Hand search: [63,74-81] (concept=relationship) Computers should have the ability to notice and respond to verbally and nonverbally expressed emotions from their user to create a more natural interaction.”
BackgroundJalil, S., Myers, T., & Atkinson, I. (2015) · Journal of Medical Systems · doi:10.1007/s10916-015-0191-9 · Cited by 37 · cited 1× · see citing works
“[6,27,32-34,36-39] Emotion-related supportb could be delivered in terms of praising the user or delivering rewards or by other types of encouraging behavior.”
BackgroundMcClay, C.-A., Waters, L., McHale, C., Schmidt, U. H., & Williams, C. J. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2083 · Cited by 37 · cited 3× · see citing works
“As formulated by McClay [31], “The findings showed that the desire for privacy and secrecy had a detrimental impact on participants’ help-seeking, their use of the intervention, and the support they could seek from family and friends” and, as expressed by Foster [41], “the difficulties of talking…”
BackgroundUsing mixed methods to develop and evaluate an online weight management intervention
Bradbury, K., Dennison, L., Little, P., & Yardley, L. (2015) · British Journal of Health Psychology · doi:10.1111/bjhp.12125 · Cited by 34 · cited 3× · see citing works
“Users coping with experiences of negative affect during their change process [29,30,33,38,39,44-47] Task-related support a can play a positive role by objective goal setting, measuring the goals set, reminding the user of their goal set, and indicating which of these goals have (not yet) been met.”
BackgroundRajan, S., Craig, S. D., Gholson, B., Person, N. K., & Graesser, A. C. (2001) · International Journal of Speech Technology · doi:10.1023/a:1017319110294 · Cited by 31 · cited 1× · see citing works
“Other critique is provided by Rajan et al [62] who demonstrated that it is first and foremost the voice (and not the visibility of the ECA) that is responsible for positive learning effects.”
Kraft, P., Drozd, F., & Olsen, E. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-68504-3_16 · Cited by 30 · cited 3× · see citing works
“Too much burden on the self-regulation process leads to ego depletion [43], a status of a low level of mental energy.”
BackgroundMethodInfluentialGorlick, A., Bantum, E. O., & Owen, J. E. (2014) · Psycho-Oncology · doi:10.1002/pon.3443 · Cited by 30 · cited 2× · see citing works
“Users coping with experiences of negative affect during their change process [29,30,33,38,39,44-47] Task-related support a can play a positive role by objective goal setting, measuring the goals set, reminding the user of their goal set, and indicating which of these goals have (not yet) been met.”
BackgroundFoster, C. L., Calman, L. A., Grimmett, C., Breckons, M., Cotterell, P., Yardley, L., Joseph, J. A., Hughes, S. F., Jones, R., Leonidou, C., et al. (2015) · Psycho-Oncology · doi:10.1002/pon.3747 · Cited by 26 · cited 2× · see citing works
“Foster [41] described this as “Users are encouraged to set weekly SMART goals on the basis of the content of these sessions.””
BackgroundRelational Agents for Chronic Disease Self-Management
Bickmore, T. (2010) · The MIT Press eBooks · doi:10.7551/mitpress/9780262014328.003.0007 · Cited by 23 · see citing works
What Do Learners and Pedagogical Agents Discuss When Given Opportunities for Open-Ended Dialogue?
Veletsianos, G., & Russell, G. S. (2013) · Journal of Educational Computing Research · doi:10.2190/ec.48.3.e · Cited by 17 · cited 1× · see citing works
“A third study [53] aimed to explore where open-dialogue options between users and ECAs would lead to.”
BackgroundSinclair, C. (2015) · Clinical Medicine Insights Therapeutics · doi:10.4137/cmt.s18553 · Cited by 15 · cited 1× · see citing works
“Users coping with experiences of negative affect during their change process [29,30,33,38,39,44-47] Task-related support a can play a positive role by objective goal setting, measuring the goals set, reminding the user of their goal set, and indicating which of these goals have (not yet) been met.”
BackgroundThe Influence of Real Human Personality on Social Presence with a Virtual Human in Augmented Reality
Kim, K., Bruder, G., Maloney, D., & Welch, G. (2016) · ICAT-EGVE · doi:10.2312/egve.20161443 · Cited by 15 · see citing works
Talbot, F. (2012) · Behaviour Change · doi:10.1017/bec.2012.4 · Cited by 15 · cited 1× · see citing works
“Interestingly enough, Talbot [10] concludes in her meta-study that the involvement of a professional support provider, a therapist, is not key.”
BackgroundTechnology and adherence in web-based interventions for weight control
Kelders, S. M., Kok, R. N., & van Gemert‐Pijnen, J. E. W. C. (2011) · doi:10.1145/2467803.2467806 · Cited by 12 · cited 3× · see citing works
“The ability for the user to refine the communication process [6,27,32-34 5.”
BackgroundSelf-citationModeling Behavioral Manifestations of Coordination and Rapport over Multiple Conversations
Schulman, D. J., & Bickmore, T. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15892-6_14 · Cited by 11 · cited 3× · see citing works
“Moreover, these studies addressed a wide range of topics, from physical attributes [18], architecture [19], route planning [20], nonverbal behavior [21], virtual museum guide [22], and empathy [23], to theoretical models [24] and articulation rates [25].”
BackgroundApostol, S., Manasia, L., Stefan, C., & Soica, O. (2013) · eLearning and Software for Education · doi:10.12753/2066-026x-13-194 · Cited by 2 · see citing works
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Sorted by citations
Chatbots and Conversational Agents in Mental Health: A Review of the Psychiatric Landscape
Vaidyam, A. N., Wisniewski, H., Halamka, J. D., Kashavan, M. S., & Torous, J. B. (2019) · The Canadian Journal of Psychiatry · doi:10.1177/0706743719828977 · Cited by 1,048 · see citing works
Fiske, A., Henningsen, P., & Buyx, A. (2019) · Journal of Medical Internet Research · doi:10.2196/13216 · Cited by 857 · see citing works
“One study found that embodied conversational agents had difficulty evaluating a user’s emotional state in a real-time dialog and that the absence of a human therapist in Web-based mental health interventions for treating depression and anxiety had a negative effect on user adherence to the programs [78].”
Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review.
Aggarwal, A., Tam, C. C., Wu, D., Li, X., & Qiao, S. (2023) · Journal of medical Internet research · doi:10.2196/40789 · Cited by 683 · see citing works
“…6 articles targeted at assessing efficacy of AI-chatbots in enhancing mental health outcomes [1,7,9-12], 2 examined the feasibility of AI-chatbots in health care settings [8,13], and 1 described the technical architectures and characteristics of the AI chatbots used in chronic conditions [14].”
InfluentialConversational agents in the treatment of mental health problems: mixed-method systematic review
Gaffney, H., Mansell, W., & Tai, S. (2019) · JMIR Mental Health · doi:10.2196/14166 · Cited by 275 · see citing works
Artificial intelligence technologies and compassion in healthcare: A systematic scoping review
Morrow, E. M., Zidaru, T., Ross, F., Mason, C. L., Patel, K. D., Ream, M., & Stockley, R. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.971044 · Cited by 255 · 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
“…that simulate key properties of human face-to-face conversation, both verbal and nonverbal (eg, speech, gestures, and facial expressions) [62], called virtual human [63] and digital human [64]; (3) CA in virtual reality (VR), which offers a safe, convenient, and accessible medium to the…”
Design Features of Embodied Conversational Agents in eHealth: a Literature Review
ter Stal, S., Kramer, L. L., Tabak, M., op den Akker, H., & Hermens, H. (2020) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2020.102409 · Cited by 188 · see citing works
“…not on ECAs specifically (Laranjo et al., 2018; Rist et al., 2004), are not up-to-date (Rist et al., 2004), focus on a broader context than health (Scholten et al., 2017) or focus on a subarea of health, such as clinical psychology (Kramer et al., 2019; Provoost et al., 2017; Rist et al., 2004).”
BackgroundLim, S. M., Shiau, C. W. C., Cheng, L. J., & Lau, Y. (2022) · Behavior Therapy · doi:10.1016/j.beth.2021.09.007 · Cited by 163 · see citing works
Conversational Agents: Goals, Technologies, Vision and Challenges
Allouch, M., Azaria, A., & Azoulay, R. (2021) · Sensors · doi:10.3390/s21248448 · Cited by 168 · see citing works
“Scholten et al. [179] surveyed several CAs in the field of healthcare.”
BackgroundPotential Applications of Social Robots in Robot-Assisted Interventions for Social Anxiety
Rasouli, S., Gupta, G., Nilsen, E. S., & Dautenhahn, K. (2022) · International Journal of Social Robotics · doi:10.1007/s12369-021-00851-0 · Cited by 151 · see citing works
Jang, S., Kim, J., Kim, S., Hong, J., Kim, S., & Kim, E. J. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104440 · Cited by 146 · see citing works
Applying Self-Regulation Principles in the Delivery of Parenting Interventions
Sanders, M. R., Turner, K., & Metzler, C. W. (2019) · Clinical Child and Family Psychology Review · doi:10.1007/s10567-019-00287-z · Cited by 141 · 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
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
“Some studies even showed that the empathic abilities of CAs can be compared with those of a real person [149].”
Wu, C., Xu, H., Bai, D., Chen, X., Gao, J., & Jiang, X. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-066322 · Cited by 81 · see citing works
Methods for Human-Centered eHealth Development: Narrative Scoping Review
Kip, H., Keizer, J., da Silva, M. C., Beerlage-de Jong, N., Köhle, N., & Kelders, S. M. (2022) · Journal of Medical Internet Research · doi:10.2196/31858 · Cited by 67 · see citing works
“In any case, it is important to reflect on the choices that were made in the reviewing process when interpreting the results [158-160].”
Self-citationHardy, A., Wojdecka, A. B., West, J., Matthews, E., Golby, C., Ward, T., Lopez, N. D., Freeman, D. H., Waller, H., Kuipers, E., et al. (2018) · JMIR Mental Health · doi:10.2196/11222 · Cited by 72 · see citing works
“However, dependent on the trial outcomes, there are several target areas for further innovation, which may involve additional behavior change methods and technologies (eg, embodied conversational agents, online support groups, instant messaging, wearable biofeedback, and gamification) [68-71].”
BackgroundJabir, 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
Methods for Human-Centered eHealth Development: Narrative Scoping Review (Preprint)
Kip, H., Keizer, J., da Silva, M. C., Beerlage-de Jong, N., Köhle, N., & Kelders, S. M. (2021) · doi:10.2196/preprints.31858 · Cited by 67 · see citing works
Self-citationLoveys, K., Hiko, C., Sagar, M. A., Zhang, X., & Broadbent, E. (2022) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2021.102771 · Cited by 67 · 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
“For example, it has been shown that users may develop a strong social relationship with a chatbot when it expresses empathetic support [21,51,85,87-90].”
BackgroundInvestigating Students’ Use of a Mental Health Chatbot to Alleviate Academic Stress
Nieva, J. O. D., Joaquin, J. A., Tan, C. B., Te, R. K. M., & Ong, E. (2020) · International HCI and UX Conference in Indonesia (CHIuXiD) · doi:10.1145/3431656.3431657 · Cited by 56 · see citing works
Kiuchi, K., Otsu, K., & Hayashi, Y. (2023) · Behaviour and Information Technology · doi:10.1080/0144929x.2023.2286528 · Cited by 50 · see citing works
“The relevant challenges include the real-time assessment of emotional states in psychiatry (Scholten, Kelders, and Van Gemert-Pijnen 2017) and the assessment of learners ’ emotional states and motivational responses in educational treatment and education (Martha and Santoso 2019).”
Mobile health interventions: exploring the use of common relationship factors
Grekin, E. R., Beatty, J. R., & Ondersma, S. J. (2019) · JMIR mhealth and uhealth · doi:10.2196/11245 · Cited by 42 · see citing works
“A growing body of literature suggests that computerized relational agents are satisfying to work with, can provide support, and can help with a variety of diverse tasks [22,23].”
BackgroundTailoring coaching strategies to users’ motivation in a multi-agent health coaching application
Beinema, T. C., den Akker, H. O., van Velsen, L., & Hermens, H. J. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106787 · Cited by 38 · see citing works
“Second, their potential to engage the user can help improve users ’ adherence and participation (Scholten et al., 2017; Bickmore et al., 2010) and can potentially increase an application ’ s effect in changing the user ’ s behaviour (Ma et al., 2019).”
BackgroundWeimann, T. G., Schlieter, H., & Brendel, B. (2022) · Business & Information Systems Engineering · doi:10.1007/s12599-022-00757-9 · Cited by 37 · see citing works
“The latter one refers to a narrow understanding of VCs as software agents, i.e., autonomous systems, that provide coaching functionality (Kamphorst 2017; Scholten et al. 2017).”
BackgroundInfluentialPetsolari, M., Ibrahim, S. B., & Slovak, P. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642619 · Cited by 35 · see citing works
“…programs delivered online (instead of in-person) might increase scalability and reach (e.g., [25, 58]) there remains a need to design for situated support and learning (i.e., efectively scafold the implementation of skills to their everyday life) to support long-term outcomes [103, 108, 152, 305].”
Griffin, A. C., Xing, Z., Mikles, S. P., Bailey, S., Khairat, S., Arguello, J., Wang, Y., & Chung, A. E. (2021) · JAMIA Open · doi:10.1093/jamiaopen/ooab021 · Cited by 31 · see citing works
Mehrotra, S., Sudhir, P. M., Rao, G. N., Thirthalli, J., & Srikanth, T. K. (2018) · Behavioral Sciences · doi:10.3390/bs8040036 · Cited by 33 · see citing works
Potential of Mobile Technology to Relieve the Urgent Mental Health Needs in China: Web-Based Survey
Tan, Y., Teng, Z., Qiu, Y., Tang, H., Xiang, H., & Chen, J. (2020) · JMIR mhealth and uhealth · doi:10.2196/16215 · Cited by 32 · see citing works
“First, nonindividualized health education information is not as helpful as what personal physicians or counselors can provide for patients to solve their mental health problems [24].”
BackgroundMapping the Landscape of Digital Health Intervention Strategies: 25-Year Synthesis
Liu, S., Ma, J., Sun, M., Zhang, C., Gao, Y., & Xu, J. (2025) · Journal of Medical Internet Research · doi:10.2196/59027 · Cited by 34 · see citing works
“This change aligned with emerging evidence suggesting that interactive and personalized digital health interventions can lead to better user engagement and health outcomes [46,47].”
Gaffney, H., Mansell, W., & Tai, S. (2020) · Digital Health · doi:10.1177/2055207620911580 · Cited by 26 · see citing works
Adaptive Systems for Internet-Delivered Psychological Treatments
Mukhiya, S. K., Wake, J. D., İnal, Y., & Lamo, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3002793 · Cited by 28 · see citing works
“User preferences [41] may influence other aspects of design, the modality of content, choice of videos, images, texts, or a combination of these that impact user engagement with online content [42].”
BackgroundHurmuz, M. Z. M., Jansen-Kosterink, S. M., Beinema, T. C., Fischer, K., den Akker, H. O., & Hermens, H. J. (2022) · Internet Interventions · doi:10.1016/j.invent.2022.100501 · Cited by 27 · see citing works
Teo, C. H., Ng, C. J., Lo, S. K., Lim, C. D., & White, A. (2019) · JMIR mhealth and uhealth · doi:10.2196/10216 · Cited by 23 · see citing works
“In addition, the use of [26,46-49] Dr ScreenMen avatar might be useful too, as there are preliminary studies that showed promising results of using avatars or embodied conversational agents (ECA) in health interventions, where an ECA is capable in engaging and motivating users in terms of learning and behavioral chang…”
Wong, J., Foussat, A. C., Ting, S., Acerbi, E., van Elburg, R. M., & Chua, M. C. (2021) · JMIR Pediatrics and Parenting · doi:10.2196/30169 · Cited by 21 · see citing works
“Web-based interventions for mental health have shown some success in conditions such as depression and anxiety [10].”
BackgroundInfluentialGÜLTEKİN, M., & ŞAHİN, M. (2024) · Cyberpsychology Journal of Psychosocial Research on Cyberspace · doi:10.5817/cp2024-1-6 · Cited by 23 · see citing works
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
“One way to achieve this may be to design Ragts that respond emphatically in real time, based on the participant’s expressed emotions [46].”
BackgroundZhao, Y., Qian, W., Chen, Y.-R., Wu, D., Luo, Y., Gao, C. F., Wu, K., & Liu, Z. (2025) · Applied Psychology Health and Well-Being · doi:10.1111/aphw.70067 · Cited by 17 · see citing works
PROs in the wild: Assessing the validity of patient reported outcomes in an electronic registry
Cabitza, F., Dui, L. G., & Banfi, G. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.01.009 · Cited by 17 · see citing works
Kwakkenbos, L., Carrier, M., Welling, J., Levis, B., Levis, A. W., Sauvé, M., Turner, K. A., Tao, L., Aguila, K., Carboni-Jiménez, A., et al. (2022) · Trials · doi:10.1186/s13063-022-06923-4 · Cited by 16 · see citing works
Tan, Y., Lattie, E. G., Qiu, Y., Teng, Z., Wu, C., Tang, H., & Chen, J. (2021) · Internet Interventions · doi:10.1016/j.invent.2021.100475 · Cited by 16 · see citing works
Hocking, J., Oster, C., Maeder, A. J., & Lange, B. S. (2022) · JBI Evidence Synthesis · doi:10.11124/jbies-22-00025 · Cited by 16 · see citing works
Scholten, M. R., Kelders, S. M., & van Gemert‐Pijnen, J. E. W. C. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.01063 · Cited by 18 · see citing works
“To answer this question, in an earlier study, we have analyzed (Scholten et al., 2017) studies on support needs as expressed by eHealth users.”
Self-citationKocur, M., Dechant, M., Wolff, C., Nothdurfter, C., Wetter, T. C., Rupprecht, R., & Shiban, Y. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.608997 · Cited by 15 · see citing works
“In this vein, the patients’ adherence still poses a major challenge for computer-based therapies that have to be addressed in future work (53).”
BackgroundRicci, F., Giallanella, D., Gaggiano, C., Torales, J. C., Castaldelli-Maia, J. M., Liebrenz, M., Bener, A., & Ventriglio, A. (2024) · International Review of Psychiatry · doi:10.1080/09540261.2024.2384727 · Cited by 15 · see citing works
“In published research, chatbots have been based on the Persuasive Systems Design (PSD) model (Scholten et al., 2017) that includes three elements: the intent, the event, and the strategy.”
Conversational Agents for Chronic Disease Self-Management: A Systematic Review (Preprint)Preprint
Griffin, A. C., Xing, Z., Khairat, S., Wang, Y., Bailey, S. C., Arguello, J., & Chung, A. E. (2019) · doi:10.2196/preprints.14989 · Cited by 13 · see citing works
Informing the design of question-asking conversational agents for reflection
Karaturhan, P., Orhan, İ., Kuşçu, K., & Yantaç, A. E. (2024) · Personal and Ubiquitous Computing · doi:10.1007/s00779-024-01831-7 · Cited by 12 · see citing works
“Extensive research shows that interacting with CAs can build rapport [12], enhance user engagement [13], and lower burden [14].”
Look who's talking: appearance of embodied conversational agents in eHealth
ter Stal, S. (2021) · doi:10.3990/1.9789036551267 · Cited by 9 · see citing works
Peltola, J., Kaipainen, K., Keinonen, K., Kiuru, N. H., & Turunen, M. (2023) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3571884.3597142 · Cited by 10 · see citing works
Alattas, A., Teepe, G. W., Leidenberger, K., Fleisch, E., Car, L. T., Salamanca-Sanabria, A., & Kowatsch, T. (2021) · International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) · doi:10.5220/0010413308010808 · Cited by 9 · see citing works
“These include design characteristics such as whether or not the CA is embodied, duration of interaction with the CA, its speech and/or textual output, personality and aesthetic features like resemblance to human figures versus other non-human characters (Stal et al., 2020; Scholten et al., 2017).”
BackgroundEffects of Contributor Experience on the Quality of Health-Related Wikipedia Articles
Holtz, P., Fetahu, B., & Kimmerle, J. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9683 · Cited by 9 · see citing works
“Discussions regarding the quality of health-related information on the internet go back as far as the late 1990s [1,2] and have continued recently [3-5].”
BackgroundLohaus, T., Rogalla, S., & Thoma, P. (2022) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2022.0037 · Cited by 8 · see citing works
Arguedas, M., Daradoumis, Τ., & Caballé, S. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1495342 · Cited by 7 · see citing works
What patients want from healthcare chatbots: insights from a mixed-methods study
Dellavalle, N. S., Ellis, J. R., Moore, A., Akerson, M., Andazola, M., Campbell, E. G., & DeCamp, M. W. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf164 · Cited by 9 · see citing works
Tailoring coaching conversations with virtual health coaches
Beinema, T. C. (2021) · doi:10.3990/1.9789036552608 · Cited by 6 · see citing works
Çolakoğlu, S., Durmus, M., Polat, Z. P., Yildiz, A., & Sezgın, E. (2025) · Journal of Medical Systems · doi:10.1007/s10916-025-02202-2 · Cited by 6 · see citing works
“2 Trend in daily usage within first 50 days in number of chat sessions [ 32 ].”
Chung, J., Winship, J., Parsons, P. L., Falls, K., & Bleich, M. R. (2022) · Journal of Gerontological Nursing · doi:10.3928/00989134-20220908-05 · Cited by 6 · see citing works
Time to Go ONLINE! A Modular Framework for Building Internet-based Socially Interactive Agents
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“Other health-related agents have been emerging [5, 10, 19, 21], but their lack of availability on the web diminishes their potential impact.”
BackgroundPsychiatrists’ Views on Robot-Assisted Diagnostics of Peripartum Depression
Zhong, M., Bilal, A.-M., Papadopoulos, F. C., & Castellano, G. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-90525-5_40 · Cited by 6 · see citing works
Sánchez-Ortuño, M. M., Pécune, F., Coelho, J., Micoulaud‐Franchi, J., Salles, N., Auriacombe, M., Serre, F., Levavasseur, Y., de Sevin, É., Sagaspe, P., et al. (2024) · JMIR Mental Health · doi:10.2196/51022 · Cited by 6 · see citing works
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“The quality of this user-ECA relationship is usually measured by the construct of rapport (Scholten et al., 2017).”
MethodSelf-citationReusable virtual coach for smoking cessation and physical activity coaching
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“Since appearance functions as an important heuristic cue of source credibility in communication (Joseph 1982; Scholten, Kelders, and Van Gemert-Pijnen 2017), it is important to understand how researchers have evaluated the appearance and anthropomorphism of conversational AI.”
InfluentialPeperkorn, N., Ohse, J., Fox, J., Kuhlencord, M., Grevenhaus, C. J., Krutzki, J., Witthöft, M., & Shiban, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-96228-8 · Cited by 3 · see citing works
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“In a scoping review, Scholten et al [33] investigated how embodied conversational agents can support users of self-guided WBIs and contribute to adherence.”
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Digital begleitet – Tele-Coaching
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- Authors
- Scholten, M. R., Kelders, S. M., & Van Gemert-Pijnen, J. E.
- Affiliations
- University of Twente (NL)
- North-West University (ZA)
- Corresponding author
- Scholten, Mark R
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2017-11-16
- Volume
- 19
- Issue
- 11
- Pages
- e383
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC5709656
- OpenAlex
- W2768275546
- Cited by
- 97
- References
- 76
- Citations by year
- 2026: 9 · 2025: 13 · 2024: 13 · 2023: 13 · 2022: 11 · 2021: 17 · 2020: 6 · 2019: 11 · 2018: 4
- FWCI
- 0.67
- 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)
- University of Twente Research Information (publishedVersion, cc-by)
- University of Twente Research Information (submittedVersion, cc-by)
- 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
- embodied conversational agents, e-health, user adherence, user needs, motivation, engagement, depression, anxiety, relationship theory, user-technology relationship, emotion measurement, behavior change, user needs mapping, grounded theory, technology acceptance
- MeSH terms
- Humans, Health Behavior, Telemedicine, Internet
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.
Statistics and arithmetic are recomputed, not judged.
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