The predictive value of patient, therapist, and in‐session ratings of motivational factors early in remote cognitive behavioural therapy for severe health anxiety

Abstract

Objectives. Remote psychotherapy and the prevalence of Severe Health Anxiety (SHA) are both growing as a result of the COVID-19 pandemic. Remotely delivered Cognitive Behavioural Therapy (rCBT) for SHA is evidenced as effective, but many who seek help do not benefit. Motivational processes can influence outcomes, but it is unclear what assessment methods offer the best clinical utility in rCBT for SHA.Design. This study compared the predictive validity of patient, therapist and in-session ratings of motivational factors taken at session two of rCBT for SHA among high healthcare users experiencing multimorbidity.Methods. Motivational factors were assessed for 56 participants who attended at least two sessions of CBT for SHA delivered via video-conferencing or telephone. Following session two, therapists and patients completed online assessments of patient motivation. Two trained observers also rated motivational factors and therapeutic alliance from insession interactions using session two recordings and transcripts. Multilevel modelling was used to predict health anxiety and a range of secondary health outcomes from motivation assessments.Results. Where patients were more actively engaged in discussion of positive changes during session two, greater outcome improvements ensued in health anxiety and all secondary outcomes. Conversely, larger proportions of session two spent describing problems predicted poorer outcomes. Therapist and patient assessments of motivation did not predict health anxiety, but therapist assessments of client confidence and motivation predicted all secondary outcomes.Conclusions. Motivation remains an important process in CBT when delivered remotely, and motivational factors may predict outcomes more consistently from insession interactions, compared to self-reports.The COVID-19 pandemic has led to a dramatic increase in the provision of psychotherapy via video-conferencing (Wind, Rijkeboer, Andersson, & Riper, 2020). An increase in demand for psychotherapy is also expected for Severe Health Anxiety (SHA; Asmundson

Developing an Automated Assessment of In-session Patient Activation for Psychological Therapy: Codevelopment Approach

“…The most reliable finding from predictive modeling with the CICS so far is that the greater the proportion of sessions taken up with problem or context description interactions, the poorer the outcome. In this way, problem or context description interactions were predictive of poorer generalized anxiety, health anxiety, depression, quality of life, and general health across a 12-month follow-up [ 41 ]; they also negatively predicted well-being rated across therapy sessions and significantly reduced in frequency directly before sudden sustained outcome improvements [ 40 ]. Despite being associated with poorer outcomes, problem or context description interactions are conceptualized as neutral, not negative, interactions—describing problems is a necessary and normal part of psychological therapy; however, excessive focus on problem description alone may crowd out space for other types of interactions, particularly those where higher patient activation is indicated and greater active engagement may be stimulated.…”

“…Higher scores denote greater patient activation. Ratings on the CICS have been shown to be associated with working alliance, therapist competence, multiple physical and mental health outcomes, and important clinical changes within therapy among patients with multimorbidity receiving psychological therapy over videoconferencing [ 32 , 40 , 41 ]. The CICS could address some of the key gaps in AI use for psychological therapy, particularly among patients with multimorbidity and in applications of remote psychological therapy.…”