Intelligent real-time therapy: Harnessing the power of machine learning to optimise the delivery of momentary cognitive–behavioural interventions
Abstract
BACKGROUND: Experience sampling methodology (ESM) [Csikszentmihalyi, M. & Larson, R. (1987). Validity and reliability of the experience-sampling method. Journal of Nervous and Mental Disease, 175(9), 526-536] has been used to elucidate the cognitive-behavioural mechanisms underlying the development and maintenance of complex mental disorders as well as mechanisms involved in resilience from such states. We present an argument for the development of intelligent real-time therapy (iRTT). Machine learning and reinforcement learning specifically may be used to optimise the delivery of interventions by observing and altering the timing of real-time therapies based on ongoing ESM measures. AIMS: The aims of the present article are to outline the principles of iRTT and to consider how it would be applied to complex problems such as suicide prevention. METHODS: Relevant literature was identified through use of PychInfo. RESULTS: iRTT may provide an important and ecologically valid adjunct to traditional CBT, providing a means of balancing population-based data with individual data, thus addressing the "knowledge-practice gap" [Tarrier, N. (2010b). The cognitive and behavioral treatment of PTSD, what is known and what is known to be unknown: How not to fall into the practice gap. Clinical Psychology: Science and Practice, 17(2), 134-143] and facilitating the delivery of interventions in situ, thereby addressing the "therapy-real-world gap". CONCLUSIONS: iRTT may provide a platform for the development of individualised and multifaceted momentary intervention strategies that are ecologically valid and aimed at attenuating pathological pathways to complex mental health problems and amplifying pathways associated with resilience.
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It is less frequent to use AI for the real- or close to real-time improvement of psychological interventions.
“It is less frequent, however, to use AI for the real- or close to real-time improvement of psychological interventions [74,75].”
From Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review (2022) · cites this paper as [75]
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“There are published reports of intelligent Real Time Therapy (iRTT), using experiential sampling methods (ESM) [15], however, none of the available interventions in this area can be considered intelligent using the current criteria for artificial intelligence.”
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“…Just-in-Time Adaptive Interventions (Spruijt-Metz and Nilsen, 2014), dynamic tailoring (Kennedy et al., 2012), and intelligent real-time therapy (Kelly et al., 2012), are being used to intervene on physical activity (King et al., 2013), eating disorders (Bauer et al., 2012), alcohol use…”
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“…pre-liminary support for the use of AI in psychotherapy for emo - tional problems in real-time, demonstrating that ChatGPT can act with a level of empathy and emotional understanding fit for psychotherapeutic intervention [23 – 25 ]. decision-making, and patient education, reliably [1, 7 – 9 ].”
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“Digital mental health interventions such as just-in-time adaptive interventions (86), intelligent real-time therapy (87) and personalization strategies are often driven by analytical methods such as decision rules, data analytics and machine learning (88).”
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- Authors
- Kelly, J., Gooding, P., Pratt, D., Ainsworth, J., Welford, M., & Tarrier, N.
- Affiliations
- Greater Manchester Mental Health NHS Foundation Trust (GB)
- University of Manchester (GB)
- Corresponding author
- Kelly, James
- Venue
- Journal of Mental Health
- Publisher
- Informa UK Limited
- Published
- 2012-01-17
- Volume
- 21
- Issue
- 4
- Pages
- 404-414
- ISSN
- 0963-8237 (ISSN-L)
- 1360-0567
- OpenAlex
- W2161511153
- Cited by
- 73
- References
- 46
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 4 · 2022: 5 · 2021: 3 · 2020: 6 · 2019: 5 · 2018: 8 · 2017: 5 · 2016: 7 · 2015: 5 · 2014: 6 · 2013: 6 · 2012: 5
- FWCI
- 9.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Mental Health (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- Research Explorer (The University of Manchester) (submittedVersion)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Keywords
- ecological momentary assessment, cognitive-behavioural intervention, reinforcement learning, machine learning, suicide prevention, personalized treatment, knowledge-practice gap, resilience, complex mental disorders, ecological validity
- MeSH terms
- Suicide Prevention, Artificial Intelligence, Humans, Therapy, Computer-Assisted, User-Computer Interface, Cognitive Behavioral Therapy, Computers, Handheld, Cell Phone
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