IAPT care pathways and treatment outcomes for people with long-term condition

Lead Supervisor
Professor Rona Moss-Morris
Professor of Psychology as Applied to Medicine
Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King’s College London

Dr Sam Norton, Dr Joanna Hudson
Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King’s College London

Project Details

Primary aim(s):

The overarching aim is to understand the reasons for poorer treatment outcomes in people with long-term health conditions (LTCs) accessing Improving Access to Psychological Therapy (IAPT) services. The research programme will:
● Compare IAPT care pathways and treatment outcomes for people with and without LTCs receiving treatment from IAPT LTC pathfinder services
● Use novel network-based methods to model treatment outcomes at the symptom level and examine how the presence of an LTC impacts on symptom level outcomes
● Understand whether the presence of LTCs alongside socioeconomic factors (e.g. ethnicity, deprivation) interact to influence treatment response using the IAPT version 2.0 dataset


It is estimated that 15.4 million people in England (30% of the population) are living with one or more medical long-term conditions LTCs. Of those with at least one LTC, 30% also have a comorbid mental health condition (Naylor et al., 2012). This is mostly accounted for by anxiety and depression, which are 2-3 times more likely to occur in people with LTCs than those who do not have co-morbid LTCs (National Institute for Health and Clinical Excellence, 2010). People experiencing co-morbid mental and physical health conditions experience the highest rates of disability (Moussavi et al., 2007) and have higher health care costs (Naylor et al., 2012). Those with LTCs accessing IAPT have worse outcomes and increased likelihood of requiring high-intensity services (Delgadillo, Dawson, Gilbody, & Böhnke, 2017).

In response to these findings, the NHS Five Year Forward View (Mental Health Task Force, 2016) prioritised the implementation of integrated mental and physical health care for adults with physical LTCs. A target was set to provide 1.5 million adults in England with access to psychological therapies by 2020 of which two thirds of this expansion should focus on providing psychological interventions to people with physical LTCs or persistent physical symptoms (PPS) (National Collaborating Centre for Mental Health, 2018). However, to date, no robust evaluation of this expansion has been conducted on a national scale with research findings published in peer reviewed journals.

Reasons for poorer outcomes in those with LTCs are multifaceted and complicated to understand. People with LTCs may be less likely or able to access and engage with IAPT services. This may directly relate to their physical health condition but social factors are also likely to play a role since LTCs are more common in socially disadvantaged and BAME groups (Mercer & Watt, 2007). Even for those able to engage with treatment, low-intensity therapies not tailored to account for concomitant physical health problems may be less effective (Delgadillo et al., 2017). Furthermore, outcome assessments may be biased by the presence of physical health problem, since LTC symptomatology often overlaps with symptoms of depression and anxiety (e.g. fatigue, sleep, cognitive function). This has been shown to impact on the functioning of key outcomes used in IAPT services such as the PHQ9 (Kroenke, Spitzer, & Williams, 2001) and GAD7 (Spitzer, Kroenke, Williams, & Löwe, 2006) (National Institute for Health and Clinical Excellence, 2010).

In recent years, there has been a move towards transdiagnostic treatment approaches and the understanding of psychological phenomena as complex systems of interacting symptoms (Borsboom & Cramer, 2013). Traditionally, symptoms have been understood as being caused by an unobserved ‘disease’ variable, which can be captured using a total scores of symptom severity scales such as the PHQ9 And GAD7. However, the use of total scores to monitor treatment outcomes is a relatively blunt tool essentially representing the average change across a set of symptoms, masking heterogeneity in changes at the symptom level. A complex systems approach, using novel network-based methods (Epskamp, 2020), provides an opportunity to monitor treatment outcomes at the symptom level and to understand how treatment impacts on the interactions between symptoms. Specifically, this approach will provide novel insights to help to understand why those with LTCs experience poorer treatment outcomes, the role of social factors in influencing outcomes in LTCs, and identify specific symptom targets for intervention. This knowledge will help IAPT services provide effective and efficient treatments.

The proposed supervisory team at King’s College London have experience of conducting research in IAPT settings and likewise managing large real-world datasets. Specifically, Prof Rona Moss-Morris was the former Long-Term Condition National Advisor to IAPT and is now principal investigator on two digital health products that are being/soon to be implemented in IAPT services to support people in living with their LTCs. Dr Joanna Hudson is Programme Co-ordinator for London High Intensity LTC training programme and is a collaborator on a digital health program currently being implemented in our local SLAM site using IAPTus PRISM software. Dr Sam Norton is Senior Lecturer with expertise in psychometrics and longitudinal data analysis. He holds an jointly funded MQ/Versus Arthritis fellowship focusing on mental health in musculoskeletal conditions and has considerable experience working with large complex datasets including electronic health records.

Project outputs:

Publication 1: Uptake, engagement, and treatment outcomes for people with LTCs receiving treatment from an IAPT LTC pathfinder service
Publication 2: Modelling symptom level treatment outcomes for the PHQ9 and GAD7 using dynamical network analysis
Publication 3: Combined impact of LTCs and socioeconomic factors on treatment outcomes in IAPT services

Data sources:

Publications 1 and 2: Access to LTC pathfinder site data collected from sites that were using IAPTus software patient management software.
Publication 3: Access to NHS Digital Version 2 Dataset once sufficient data on LTCs has accumulated (e.g. in Year 2/3 of the proposed PhD).


Training will be provided in advanced statistical methods as part of their PhD programme through opportunities available via King’s. Further specific training will be provided in methods related to longitudinal data analysis and application dynamic symptom network modelling


Borsboom, D., & Cramer, A. O. J. (2013). Network Analysis: An Integrative Approach to the Structure of Psychopathology. Annual review of clinical psychology, 9(1), 91-121. doi:10.1146/annurev-clinpsy-050212-185608

Delgadillo, J., Dawson, A., Gilbody, S., & Böhnke, J. R. (2017). Impact of long-term medical conditions on the outcomes of psychological therapy for depression and anxiety. British Journal of Psychiatry, 210(1), 47-53. doi:10.1192/bjp.bp.116.189027

Epskamp, S. (2020). Psychometric network models from time-series and panel data. Psychometrika, 85(1), 206-231. doi:10.1007/s11336-020-09697-3

Kroenke, K., Spitzer, R. L., & Williams, J. B. (2001). The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med, 16(9), 606-613. doi:10.1046/j.1525-1497.2001.016009606.x

Mental Health Task Force. (2016). The Five Year Forward View for Mental Health London: NHS England.

Mercer, S. W., & Watt, G. C. (2007). The inverse care law: clinical primary care encounters in deprived and affluent areas of Scotland. The Annals of Family Medicine, 5(6), 503-510.

Moussavi, S., Chatterji, S., Verdes, E., Tandon, A., Patel, V., & Ustun, B. (2007). Depression, chronic diseases, and decrements in health: results from the World Health Surveys. The Lancet, 370(9590), 851-858. doi: https://doi.org/10.1016/S0140-6736(07)61415-9

National Collaborating Centre for Mental Health. (2018). The Improving Access to Psychological Therapies (IAPT) Pathway for People with Long-term Physical Health Conditions and Medically Unexplained Symptoms: Full implementation guidance London: NICE.

National Institute for Health and Clinical Excellence. (2010). Depression: The treatment and management of depression in adults (update). Retrieved from http://www.nice.org.uk/nicemedia/pdf/Depression_Update_FULL_GUIDELINE.pdf

Naylor, C., Parsonage, M., McDaid, D., Knapp, M., Fossey, M., & Galea, A. (2012). Long-term conditions and mental health: the cost of co-morbidities. Retrieved from https://www.kingsfund.org.uk/sites/default/files/field/field_publication_file/long-term-conditions-mental-health-cost-comorbidities-naylor-feb12.pdf

Spitzer, R. L., Kroenke, K., Williams, J. B., & Löwe, B. (2006). A brief measure for assessing generalized anxiety disorder: the GAD-7. Archives of Internal Medicine, 166(10), 1092-1097