Internet-based Interventions - Competencies

Overview
Rapid progress in technology has diversified the way behavior change methods such as psychotherapy or coaching are provided. The global movement to limit face-to-face contact caused by the COVID-19 pandemic has accelerated the provision of behavior change methods delivered via the internet. As an example, Cognitive Behavioral Therapy (CBT)  has been adapted in various ways. Therapy materials, video clips, and worksheets are provided online, and symptom questionnaires and self-assessments are used to monitor progress as usual but these may be collected and scored in different formats. Generally speaking, there are two types of remote CBT provision. First, clients practice CBT without the support of a therapist. Second, a therapist supports a client by using a remote method such as an online meeting, mail, chat, and telephone. VR has also been used to provide CBT (Maheu, Pulier, McMenamin, & Posen, 2012).

Terminology
Psychological services or interventions provided via the internet have been described in the literature with a variety of terms including "telepsychological services," "Digital mental health technology," "web-based," "Internet CBT," and "tele-CBT." Another complication is that internet-based interventions have sometimes been described as "telehealth" yet the term telehealth has been used in multiple ways, sometimes referring to the provision of psychological services specifically and sometimes referring more generally to both medical and behavioral health interventions. This proliferation of terms has been called “terminology chaos (Barak, 2013). Term reduction is required to evaluate the effectiveness and tasks of remote psychological services or interventions (Andersson, Titov, Dear, Rozental, & Carlbring, 2019). Here we use Internet CBT (ICBT) to represent all types of remote CBTs.

Benefits
Internet-based psychological interventions, including ICBT, have significant advantages over face-to-face psychological services or interventions in terms of time, effort, and costs. A client can choose a good therapist without considering distance, even in a different country (subject to country-specific laws and profession-specific regulations). Internet-based services can be delivered to people with mobility difficulties, such as severe physical disability. Remote delivery of services offers an advantage to clinical research as well since much more participants can be recruited beyond geographical barriers (Andersson, et al., 2019), which may lead to more diverse research samples.

Issues
Data security is crucial, especially when sensitive information is exchanged. Legal requirements for managing personal data are being developed in many countries, and ICBT guidelines are also being provided by the American Psychological Association (Joint Taskforce for the Development of Telepsychology Guidelines for Psychologists, 2013) and Canada (Johnson, 2014). Older persons who are not familiar with using a computer and smartphone and people living in unavailable areas or countries would not be able to benefit from ICBT. The gap in dissemination has widened. Finally, the acceptance of ICBT by insurance companies depends on their country (Andersson et al., 2019).

Current evidence base
Many studies have investigated the effectiveness of ICBT. Recent meta-analyses have shown that ICBT is more effective than no treatment, and therapist-supported ICBT is the same as face-to-face CBT (Cuijpers, Cristea, Karyotaki, Reijnders, & Huibers, 2016; Cuijpers et al., 2009; Spek et al., 2007). A study comparing ICBT with and without therapist support showed that ICBT with support was more effective than that without support for anxiety disorders and depressions (Andrews et al., 2018; Olthuis, Watt, Bailey, Hayden, & Stewart, 2016; Sijbrandij, Kunovski, & Cuijpers, 2016). However, it should be noted that the quality of the evidence was low to moderate (Olthuis et al., 2016).

Trindade et al. (2021) completed a systematic review and meta-analysis of online-based delivery of ACT specifically for chronic pain. Online ACT demonstrated more effectiveness (relative to control conditions) on outcome measures of pain interference, pain intensity, depression, anxiety, mindfulness, and psychological flexibility.

CBS therapies delivered via the internet
Among the therapies that fall under the rubric of contextual behavior science (CBS), there have been meta-analyses of the effectiveness of internet delivery (mostly focused on Acceptance and Commitment Therapy). there are some meta-analyses. Effectiveness or feasibility has been characterized for several clinical targets: subjective wellbeing (Stenhoff, Steadman, Nevitt, Benson, & White, 2020), anxiety (Brown, Glendenning, Hoon, & John, 2016; Kelson, Rollin, Ridout, & Campbell, 2019), and depression (French, Golijani-Moghaddam, & Schröder, 2017; Thompson, Destree, Albertella, & Fontenelle, 2021; Trindade et al., 2021). Lakeman et al. (2022) and van Leeuwen et al. (2021) conducted systematic reviews of DBT provided by the Internet. Lakeman et al. (2022, p.11) reported “current research evidence does not support a permanent shift towards online or blended DBT. It is pivotal and timely to increase efforts to investigate the efficacy of online DBT, compared to standard face-to-face DBT.”

Self-guided and AI-informed approaches
The use of online Cognitive Behavioral Therapy (CBT) is growing rapidly. It is helpful to make a distinction about the different kind of delivery formats. Telehealth therapy is similar to in-person therapy in which therapists and clients directly interact--the principal difference is that in telehealth therapy, technology allows the interaction to occur without clients and therapists sharing the same physical space. Self-guided/self-help interventions allow clients to access some or all of the treatment intervention on their own. AI-informed interventions tailor intervention in some way based on the ongoing interaction. We are now seeing the introduction of self-help programs, chatbots, and even AI-driven therapy (Omarov, Zhumanov, Gumar, & Kuntunova, 2023). AI is also being used in research to analyze therapy skills (Flemotomos et al., 2021), to check if therapy is being delivered correctly, known as "fidelity" (Creed et al., 2022; Imel, Steyvers, & Atkins, 2015; Gibson et al., 2019; Chen et al., 2022; Atkins, Steyvers, Imel, & Smyth, 2014), and to assist in therapist supervision (Cioffi et al., 2025; Creed et al., 2022).

There is interest in incorporating AI approaches into Contextual Behavioral Science (CBS) approaches. However, CBS therapies, such as Acceptance and Commitment Therapy (ACT), present a unique challenge. CBS is grounded in a functional analysis of the live interaction between the client and therapist. Most current AI analysis of CBT has been topographically focused—meaning that the analysis looks at the form or structure of the conversation (e.g., "Did the therapist ask a question?").

Very little research has used AI to analyze the function of the conversation (e.g., "Did the therapist's question help the client connect with their values?"). This is a significant gap. To use AI for training and supervising CBS therapists, we must first understand the current landscape and its challenges.

How effective is online ACT (iACT)?
Two recent major reviews looked at the effectiveness of self-guided online ACT (iACT).

• Han & Kim (2022): This review found that iACT is better than doing nothing (e.g., being on a waitlist). It showed small-to-medium effects on depression, anxiety, and stress¹¹. However, iACT was not clearly better than other active online treatments (like online CBT).
• Klimczak et al. (2023): This large review of 53 trials found that iACT was only slightly better than other active treatments in one area: "psychological flexibility," and only immediately after the treatment ended. It showed no significant advantage for anxiety or depression.

The takeaway: iACT seems helpful, but it has not been shown to be superior to other established online therapies. Furthermore, many of these "self-guided" programs still required some human support (like coaching or feedback), which makes them less practical for large-scale use.

A major problem with self-guided/self-help interventions: People don't finish the programs
A serious challenge for all online self-help programs is that many users drop out.

• The Klimczak et al. (2023) review found that, on average, only about 58% of participants finished all the therapy modules.
• A 2022 pilot study by Lavelle et al. (2022) on a chatbot had an extremely high dropout rate of 72%. This was blamed on the bot's poor usability—it was not AI-powered and often got stuck in conversation loops or gave inflexible responses.
• A 2021 study (Barrett & Stewart, 2021) on healthcare workers saw a 38% dropout rate.
• A Japanese study (Fujita et al., 2025) attempting to use an AI chatbot for youth on a psychiatric waitlist had to be canceled because they could not recruit any participants. This highlights the immense difficulty of implementing these digital tools in the real world.

Online ACT vs. Online CBT
One study (Barrett & Stewart, 2021) directly compared short online ACT and CBT programs for stress in healthcare workers. Both programs successfully reduced stress and burnout. There was no significant difference in effectiveness between the two groups.

AI-Powered Chatbots Show More Promise

• Kai.ai: An AI-powered platform based on ACT (Kai.ai) has shown positive results in several studies. One retrospective study of 2,909 users found their well-being scores significantly improved (Naor, Frenkel, & Winsberg, 2022). A separate study on 10,387 adolescents also found their well-being scores increased significantly over time.
• Chatbot Design Matters: The failed chatbot study (Lavelle et al., 2022) highlights why AI is so important. That bot, which was not AI-powered, could not respond flexibly or build rapport, which likely led to the high dropout rate and poor results.

How to Train an AI Therapist (It's Not Just About Copying)
A critical 2025 study by Tahir (2025) explored the best way to train a large language model (LLM) to deliver ACT. The study compared two methods:

1. SFT (Supervised Fine-Tuning): This method trains the AI to imitate examples of good therapy conversations. It learns what to say.
2. ORPO (Odds Ratio Policy Optimization): This is a more advanced method (a type of reinforcement learning) that teaches the AI to prefer good responses over bad ones. It learns how to respond.

The Result: The ORPO-trained model performed significantly better on both ACT fidelity (doing ACT correctly) and therapeutic empathy.

Why this matters: ACT is a therapy focused on the process (how you relate to your thoughts), not just the content (what you think). SFT learns the content (the "what"), but ORPO seems to learn the therapeutic process (the "how"), such as listening, pacing, and therapeutic stance. This suggests that to build an effective AI therapist, simply copying scripts is not enough; the AI must learn the underlying style and process of the therapy.

Major ethical concerns with AI approaches
Using AI in therapy raises serious ethical, social, and cultural questions. The American Psychological Association (APA, 2025) released comprehensive guidelines in 2025 that highlight these key areas:

1. Transparency & Informed Consent: Psychologists must clearly tell clients they are using AI, explain the risks and benefits , and inform them of their right to opt-out.
2. Mitigating Bias: AI systems must be checked for biases that could worsen health disparities. Psychologists should review the AI's training data to ensure it represents diverse experiences.
3. Data Privacy & Security: AI tools must comply with privacy laws like HIPAA (Health Insurance Portability and Accountability. Act). Clients must be informed how their data is used, stored, and shared.
4. Accuracy & Misinformation: AI tools should be rigorously validated⁴⁸. Psychologists must critically evaluate any content the AI generates before using it in a clinical setting.
5. Human Oversight & Professional Judgment: AI should augment (support) human decision-making, not replace it. The human therapist remains responsible for all final clinical decisions.
6. Liability & Responsibility: Relying on unverified AI creates legal risks. Psychologists must participate in continuing education to stay informed about AI developments.

Conclusion
Clinicians have an ethical duty to prioritize client safety, confidentiality, and fairness when using AI. AI should only function as a tool to support professional judgment, not replace it.

Clinicians are strongly encouraged to actively participate in discussions with AI developers and policymakers. If the profession fails to engage, critical decisions about these tools will be left to those without psychological expertise. Balancing innovation with ethical responsibility is essential to maintain public trust.

Competency measures
To date, there are limited measures of competency for internet-based interventions. There are several best practice guidelines for the use of telepsychology, which can be used for self-assessment of one's fidelity to the guidelines. For example, see Figure 1 from Maheu et al. (2021) describing telebehavioral health competencies:

These telepsychology guidelines typically describe ways of effectively delivering services via remote electronic means, and there are meant to apply to a range of psychological interventions spanning a variety of theoretical approaches (e.g., cognitive-behavioral, psychodynamic, humanistic). In a scoping review, McCord et al. (2020) distilled a set of guidelines.

Specific interventions, such as ACT, may have a set of competency measures. Generally, these competencies are not altered when the intervention is delivered remotely. Essentially, practitioners are expected to meet the competencies of telepsychology and the intervention-specific competencies at the same time. As McCord et al (2020) noted: in their discussion of telepsychology competence: 

"Most basically, a clinician must first be competent in his or her ability to effectively enact the content of the chosen treatment, regardless of the mode of communication (i.e., telepsychology vs. in‐person). No matter the means of transmitting the services, a provider must be extensively trained and prepared in treatments that they are offering to clients. This includes receiving proper supervision and feedback throughout the training process. For example, a psychologist conducting cognitive behavioral therapy over videoconferencing would not only need to be properly trained in this treatment but also be able to make proper adaptations to treatment based on any available best practices and clinical judgment. An example of an adaptation may be mailing worksheets or sending them over a secure file transfer electronically so that they client is able to follow along and participate in homework. (p. 1076)."

Recently, Weisenmuller & Luzier (2022) called for technology to be considered a core competency for psychologists to develop.

How is culture addressed In the competencies?

Some studies have attempted to adapt or modify ICBT programs developed in Western countries to culturally different countries. Patel et al. (2016) and Abuwalla (2017) adapted the CATCH-IT (Competent Adulthood Transition with Cognitive-Behavioral, Humanistic and Interpersonal Training program), which is an internet-based intervention targeting teens at risk for developing depression, to China and Arabian countries. Zemestani, Hosseini, Petersen, & Twohig (2022) reported internet-based ACT (iACT) in Iran was equally effective as ACT delivered in Western countries. Ramaiya et al.,（2017）provided DBT to persons in Nepal. They conducted the study with three phases. One was qualitative interviews with major Nepali mental health stakeholders, the second was an adaptation workshop with 15 Nepali counselors, and the third was a small-scale treatment pilot with eligible clients in one rural district. They concluded that while DBT concepts were the least comprehensible to clients, the high program completion rate suggested the utility of structured, skills-based treatment of DBT. A systematic review of DBT (Haft, O'Grady, Shaller, & Liu, 2022) revealed that most adaptations involved modifications to language, metaphors, methods, and context, and there was insufficient evidence to determine the effectiveness of culturally adapted DBT. Competency has usually been assessed in these studies by measuring fidelity to the specified intervention. Fidelity measures may include items that address the aim of successfully adapting the intervention to a different cultural context, but competency in doing so is rarely measured.

Willis et al. (2022) made recommendations for increasing the cultural responsivity of telepsychology (and mHealth) interventions, but they did not call for new directions in measures of competence.

How have competencies been operationalized in diverse practice settings and delivery modalities? 
Aside from resources on best-practices in delivering therapy through telehealth, no specific guidance was found. McCord et al. (2020) identified key variations in practice settings and delivery modalities for telepsychology, but called for an overall focus on competence and multicultural competence that did not vary across settings.

Materials / Assessments / Work Products
To date, no scale has been developed to measure therapist competency in ICBT. Several guidelines for the implementation of Internet-based telepsychology have been reported, and McCord et al. (2020) summarized them and proposed a practice model. One of the cube models summarizes considerations related to Internet-based psychotherapy (IBT). These are briefly summarized below.

Client appropriateness
Therapists should consider the client's history (e.g., repetitive crises and comfort-receiving telepsychology services). In particular, clients receiving telepsychology services, as opposed to in-person services, are in a situation where self-harm and other behaviors are difficult to control. It is also necessary
to consider the appropriateness of a client's literacy regarding technology.

Informed consents
Therapists should obtain informed consent about the risks, benefits, and alternatives to telepsychology services.

Professional boundaries
Therapists should maintain professional language through email and texting communications. They should clarify that these messages are for the client only and are not shared with others. Therapists should be mindful of the ubiquitous nature of social media and should maintain boundaries by not contacting clients through social media. Therapists should explain to clients their social media policy (e.g., not becoming friends on social media) at the start of the service.

Privacy and confidentiality
Therapists should develop policies regarding security issues (including the use of encryption, transmission, storage, and disposal). Therapists should clearly explain to clients that digital information such as telephone records, videos, and emails is protected and will not be shared outside the organization.

Managing outages and downtime,
Therapists should ensure that the system reliably provides services. At the same time, they should consider that it is inevitable that the system will fail and make plans for this.

Competent
Therapists should be competent in the interventions they provide, regardless of the mode in which the service is provided, for example, telepsychology vs. in-person. This includes receiving ongoing SV. Therapists delivering CBT through videoconferencing should also adapt their in-person interventions, for example, by sending worksheets to clients via secure file transfers.

Termination of services
Therapists should ask clients to assess their satisfaction and the quality of the services provided if the termination of services is deemed appropriate.

Dissemination and maintenance of competencies
Implementation of internet-based interventions has been studied (see e.g., Benavides-Vaello, Strode, & Sheeran, 2013; Jang-Jaccard, Nepal, Alem, & Li, 2014), and the adoption of best-practices has also been described, particularly in the context of the Covid-19 pandemic (Alqahtani et al., 2021; Dopp et al., 2020; Penney et al., 2021) Little is known about the maintenance of competencies specific to internet-based interventions.

References

Abuwalla, Z., Kadhem, Z., Gladstone, T., Mikhael, E., Bishay, A., & Van Voorhees, B. W. (2017). Proposed model for the cultural adaptation of an Internet-based depression prevention intervention (CATCH-IT) for Arab adolescents. International Journal of Adolescent Medicine and Health, 31(1), 20160147.

Alqahtani, M. M. J., Alkhamees, H. A., Alkhalaf, A. M., Alarjan, S. S., Alzahrani, H. S., AlSaad, G. F., ... & Alqahtani, K. M. M. (2021). Toward establishing telepsychology guideline. Turning the challenges of COVID-19 into opportunity. Ethics, Medicine and Public Health, 16, 100612.

Andersson, G., Titov, N., Dear, B. F., Rozental, A., & Carlbring, P. (2019). Internet‐delivered psychological treatments: from innovation to implementation. World Psychiatry, 18(1), 20-28.

Andrews, G., Basu, A., Cuijpers, P., Craske, M., McEvoy, P., English, C., & Newby, J. (2018). Computer therapy for the anxiety and depression disorders is effective, acceptable and practical health care: an updated meta-analysis. Journal of anxiety disorders, 55, 70-78.

Association, A. P. (2025). Ethical guidance for AI in the professional practice of health service psychology. Retrieved from. https://www.apa.org/topics/artificial-intelligence-machine-learning/ethical-guidance-ai-professional-practice

Atkins, D. C., Steyvers, M., Imel, Z. E., & Smyth, P. (2014). Scaling up the evaluation of psychotherapy: evaluating motivational interviewing fidelity via statistical text classification. Implementation Science, 9(1), 49. https://doi.org/10.1186/1748-5908-9-49

Barak, A. (2013). Concepts, definitions, and applications: the terminology chaos of Internet supported psychotherapeutic interventions. Paper presented at the 6th Meeting of the International Society for Research on Internet Interventions, Chicago.

Barrett, K., & Stewart, I. (2021). A preliminary comparison of the efficacy of online Acceptance and Commitment Therapy (ACT) and Cognitive Behavioural Therapy (CBT) stress management interventions for social and healthcare workers. Health & Social Care in the Community, 29(1), 113–126. https://doi.org/10.1111/hsc.13074

Benavides-Vaello, S., Strode, A., & Sheeran, B. C. (2013). Using technology in the delivery of mental health and substance abuse treatment in rural communities: a review. The journal of behavioral health services & research, 40(1), 111-120.

Brown, M., Glendenning, A., Hoon, A. E., & John, A. (2016). Effectiveness of web-delivered acceptance and commitment therapy in relation to mental health and well-being: a systematic review and meta-analysis. Journal of medical Internet research, 18(8), e221.

Chen, Z., Flemotomos, N., Singla, K., Creed, T. A., Atkins, D. C., & Narayanan, S. (2022). An automated quality evaluation framework of psychotherapy conversations with local quality estimates. Computer speech & language, 75, 101380. https://doi.org/10.1016/j.csl.2022.101380

Cioffi, V., Ragozzino, O., Mosca, L. L., Moretto, E., Tortora, E., Acocella, A., . . . Gigante, E. (2025). Can AI Technologies Support Clinical Supervision? Assessing the Potential of ChatGPT. Paper presented at the Informatics. https://doi.org/10.3390/informatics12010029

Creed, T. A., Kuo, P. B., Oziel, R., Reich, D., Thomas, M., O’Connor, S., . . . Atkins, D. C. (2022). Knowledge and attitudes toward an artificial intelligence-based fidelity measurement in community cognitive behavioral therapy supervision. Administration and Policy in Mental Health and Mental Health Services Research, 49(3), 343–356. https://doi.org/10.1007/s10488-021-01167-x

Cuijpers, P., Cristea, I. A., Karyotaki, E., Reijnders, M., & Huibers, M. J. (2016). How effective are cognitive behavior therapies for major depression and anxiety disorders? A meta‐analytic update of the evidence. World Psychiatry, 15(3), 245-258.

Cuijpers, P., Marks, I. M., van Straten, A., Cavanagh, K., Gega, L., & Andersson, G. (2009). Computer‐aided psychotherapy for anxiety disorders: A meta‐analytic review. Cognitive Behaviour Therapy, 38(2), 66-82.

Dopp, A. R., Mapes, A. R., Wolkowicz, N. R., McCord, C. E., & Feldner, M. T. (2021). Incorporating telehealth into health service psychology training: A mixed-method study of student perspectives. Digital health, 7, 2055207620980222.

Joint Taskforce for the Development of Telepsychology Guidelines for Psychologists (2013). Guidelines for the practice of Telepsychology. The American psychologist, 68(9), 791-800.

Flemotomos, N., Martinez, V. R., Chen, Z., Creed, T. A., Atkins, D. C., & Narayanan, S. (2021). Automated quality assessment of cognitive behavioral therapy sessions through highly contextualized language representations. PLOS ONE, 16(10), e0258639. doi:10.1371/journal.pone.0258639 

French, K., Golijani-Moghaddam, N., & Schr?der, T. (2017). What is the evidence for the efficacy of self-help acceptance and commitment therapy? A systematic review and meta-analysis. Journal of Contextual Behavioral Science, 6(4), 360-374.

Fujita, J., Yano, Y., Shinoda, S., Sho, N., Otsuki, M., Suda, A., . . . Ishii, M. (2025). Challenges in implementing a mobile AI chatbot intervention for depression among youth on psychiatric waiting lists: randomized controlled study termination report. JMIRx Med, 6(1), e70960. doi:10.2196/70960

Gibson, J., Atkins, D. C., Creed, T. A., Imel, Z., Georgiou, P., & Narayanan, S. (2019). Multi-label multi-task deep learning for behavioral coding. IEEE Transactions on Affective Computing, 13(1), 508–518. https://doi.org/10.48550/arXiv.1810.12349

Haft, S. L., O'Grady, S. M., Shaller, E. A., & Liu, N. H. (2022). Cultural adaptations of dialectical behavior therapy: A systematic review. Journal of consulting and clinical psychology.

Han, A., & Kim, T. H. (2022). Efficacy of internet-based acceptance and commitment therapy for depressive symptoms, anxiety, stress, psychological distress, and quality of life: systematic review and meta-analysis. Journal of medical Internet research, 24(12), e39727. doi:10.2196/39727

Imel, Z. E., Steyvers, M., & Atkins, D. C. (2015). Computational psychotherapy research: Scaling up the evaluation of patient–provider interactions. Psychotherapy, 52(1), 19. doi: 10.1037/a0036841

Jang-Jaccard, J., Nepal, S., Alem, L., & Li, J. (2014). Barriers for delivering telehealth in rural Australia: a review based on Australian trials and studies. Telemedicine and e-Health, 20(5), 496-504.

Johnson, G. R. (2014). Toward uniform competency standards in telepsychology: A proposed framework for Canadian psychologists. Canadian Psychology/Psychologie canadienne, 55(4), 291.

Kelson, J., Rollin, A., Ridout, B., & Campbell, A. (2019). Internet-delivered acceptance and commitment therapy for anxiety treatment: systematic review. Journal of medical Internet research, 21(1), e12530.

Klimczak, K. S., San Miguel, G. G., Mukasa, M. N., Twohig, M. P., & Levin, M. E. (2023). A systematic review and meta-analysis of self-guided online acceptance and commitment therapy as a transdiagnostic self-help intervention. Cognitive Behaviour Therapy, 52(3), 269–294. https://doi.org/10.1080/16506073.2023.2178498

Lakeman, R., King, P., Hurley, J., Tranter, R., Leggett, A., Campbell, K., & Herrera, C. (2022). Towards online delivery of Dialectical Behaviour Therapy: A scoping review. International Journal of Mental Health Nursing, 31(4), 843-856.

Lavelle, J., Dunne, N., Mulcahy, H. E., & McHugh, L. (2022). Chatbot-delivered cognitive defusion versus cognitive restructuring for negative self-referential thoughts: a pilot study. The psychological record, 72(2), 247–261. https://doi.org/10.1007/s40732-021-00478-7

Maheu, M. M., Pulier, M. L., McMenamin, J. P., & Posen, L. (2012). Future of telepsychology, telehealth, and various technologies in psychological research and practice. Professional psychology: Research and practice, 43(6), 613.

Maheu, M. M., Wright, S. D., Neufeld, J., Drude, K. P., Hilty, D. M., Baker, D. C., & Callan, J. E. (2021). Interprofessional telebehavioral health competencies framework: Implications for telepsychology. Professional Psychology: Research and Practice, 52(5), 439.

McCord, C., Bernhard, P., Walsh, M., Rosner, C., & Console, K. (2020). A consolidated model for telepsychology practice. Journal of Clinical Psychology, 76(6), 1060-1082.

Naor, N., Frenkel, A., & Winsberg, M. (2022). Improving Well-being with a Mobile Artificial Intelligence–Powered Acceptance Commitment Therapy Tool: Pragmatic Retrospective Study. JMIR Form Res, 6(7), e36018. doi:10.2196/36018 

Olthuis, J. V., Watt, M. C., Bailey, K., Hayden, J. A., & Stewart, S. H. (2016). Therapist‐supported Internet cognitive behavioural therapy for anxiety disorders in adults. Cochrane Database of Systematic Reviews(3).

Omarov, B., Zhumanov, Z., Gumar, A., & Kuntunova, L. (2023). Artificial intelligence enabled mobile chatbot psychologist using AIML and cognitive behavioral therapy. International Journal of Advanced Computer Science and Applications, 14(6). DOI: 10.14569/IJACSA.2023.0140616

Patel, U., Sobowale, K., Fan, J., Liu, N., Kuwabara, S., Lei, Z., . . . Van Voorhees, B. (2016). Cultural considerations for the adaptation of an Internet-based intervention for depression prevention in Mainland China. International Journal of Adolescent Medicine and Health, 29(5), 20150099.

Penney, E., Reynolds, J., Knott, V., & Green, H. (2022). Lessons from 2020: practical and clinical aspects of rapid telepsychology adoption in clinical psychology postgraduate programs. Australian Psychologist, 57(3), 161-166.

Ramaiya, M. K., Fiorillo, D., Regmi, U., Robins, C. J., & Kohrt, B. A. (2017). A cultural adaptation of dialectical behavior therapy in Nepal. Cognitive and Behavioral Practice, 24(4), 428-444.

Sijbrandij, M., Kunovski, I., & Cuijpers, P. (2016). Effectiveness of internet‐delivered cognitive behavioral therapy for posttraumatic stress disorder: A systematic review and meta‐analysis. Depression and anxiety, 33(9), 783-791.

Spek, V., Cuijpers, P., Nykl??ek, I., Riper, H., Keyzer, J., & Pop, V. (2007). Internet-based cognitive behaviour therapy for symptoms of depression and anxiety: a meta-analysis. Psychologicalmedicine, 37(3), 319-328.

Stenhoff, A., Steadman, L., Nevitt, S., Benson, L., & White, R. G. (2020). Acceptance and commitment therapy and subjective wellbeing: A systematic review and meta-analyses of randomised controlled trials in adults. Journal of Contextual Behavioral Science, 18, 256-272.

Tahir, T. (2025). The Thinking Therapist: Training Large Language Models to Deliver Acceptance and Commitment Therapy using Supervised Fine-Tuning and Odds Ratio Policy Optimization. arXiv preprint arXiv:2509.09712. https://doi.org/10.48550/arXiv.2509.09712

Thompson, E. M., Destree, L., Albertella, L., & Fontenelle, L. F. (2021). Internet-based acceptance and commitment therapy: a transdiagnostic systematic review and meta-analysis for mental health outcomes. Behavior therapy, 52(2), 492-507.

Trindade, I. A., Guiomar, R., Carvalho, S. A., Duarte, J., Lapa, T., Menezes, P., . . . Castilho, P. (2021). Efficacy of online-based acceptance and commitment therapy for chronic pain: A systematic review and meta-analysis. The Journal of Pain, 22(11), 1328-1342.

van Leeuwen, H., Sinnaeve, R., Witteveen, U., Van Daele, T., Ossewaarde, L., Egger, J. I., & van den Bosch, L. (2021). Reviewing the availability, efficacy and clinical utility of Telepsychology in dialectical behavior therapy (Tele-DBT). Borderline personality disorder and emotion dysregulation, 8(1), 1-15.

Vertsberger, D., Naor, N., & Winsberg, M. (2022). Adolescents’ well-being while using a mobile artificial intelligence–Powered Acceptance Commitment therapy tool: Evidence from a longitudinal study. JMIR AI, 1(1), e38171. doi:10.2196/38171 

Weisenmuller, C. M., & Luzier, J. L. (2022). Technology is a core competency in professional psychology. Training and Education in Professional Psychology.

Willis, H. A., Gonzalez, J. C., Call, C. C., Quezada, D., Scholars for Elevating Equity and Diversity (SEED), & Galán, C. A. (2022). Culturally Responsive Telepsychology & mHealth Interventions for Racial-Ethnic Minoritized Youth: Research Gaps and Future Directions. Journal of Clinical Child & Adolescent Psychology, 51(6), 1053-1069.

Zemestani, M., Hosseini, M., Petersen, J. M., & Twohig, M. P. (2022). A pilot randomized controlled trial of culturally-adapted, telehealth group acceptance and commitment therapy for Iranian adolescent females reporting symptoms of anxiety. Journal of Contextual Behavioral Science, 25, 145-152.

Learn more about the CBS Competency and Dissemination Pillar that is working on this topic. 

Provide feedback to the pillar.