TY - JOUR

T1 - Intelligent real-time therapy

T2 - Harnessing the power of machine learning to optimise the delivery of momentary cognitive–behavioural interventions

AU - Kelly, James

AU - Gooding, Patricia A.

AU - Pratt, Daniel

AU - Ainsworth, John

AU - Welford, Mary

AU - Tarrier, Nicholas

PY - 2012

Y1 - 2012

N2 - 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.AimsThe 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.

AB - 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.AimsThe 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.

U2 - 10.3109/09638237.2011.638001

DO - 10.3109/09638237.2011.638001

M3 - Journal article

VL - 21

SP - 404

EP - 414

JO - Journal of Mental Health

JF - Journal of Mental Health

SN - 0963-8237

IS - 4

ER -