Modelling therapy adherence: Tuning patient feedback to motivational state
|has title::Modelling therapy adherence: Tuning patient feedback to motivational state|
|Master:||project within::Cognitive Science|
|Student name:||student name::V.M. Kattenberg|
|Second reader:||has second reader::Michel Klein|
Major depression is currently one of the disorders imposing the highest disease burden in high-income countries. Current treatment methods can reduce the burden (both economically as well as for the well-being of the directly involved) of this disorder significantly. Recent computational models that formalize the dynamics of mood and depression (Both, Hoogendoorn, Klein, & Treur, 2008) coupled with extensive trials and experiments with internet based treatment of depression (Spek et al., 2007) pave the way for a more elaborate ICT based support system where the patient is monitored closely and treatment is tailored to the individual patient and his or her treatment progress.
While the dynamics of mood and depression have been studied and conclusions on the emotional state of the patient can be derived - given personality traits and negative events - the role of feedback in the treatment progress and the need, quantity and quality of this feedback have not yet been examined as such. With respect to an ICT based support system for treatment of depression, patient feedback can be seen as having a role in motivating a patient to progress with treatment, as a tool to avoid relapse and for informing the patient of therapy and therapy progress.
In addition, it is widely recognized that internet based interventions suffer a high rate of attrition, in some cases fewer than 1% of patients return after a first visit (Christensen, Griffiths, & L.Farrer, 2009; Farvolden, Denisoff, Selby, Bagby, & Rudy, 2005). As such maintaining a model of a patients therapy adherence and motivation might be used as a tool to tailor patient feedback both quantitavely (how often will a patient receive feedback) and qualitatively (what tone of feedback is necessary or desired). Furthermore, modelling the dynamics of therapy adherence might give human therapists a measure to gauge when their support and intervention is required.
This study aims to review the theoretical frameworks on the psychology of motivation and their applicability for an ICT based support system for depression. An important question which will be studied in this review is if, and in what way motivational state is modulated by depression. Then, based on the review findings a formalized model of patient motivation and therapy adherence will be explored: can both quality and quantity of feedback be determined from reports on therapy progress and patient state? Does modelled therapy adherence coupled closely to motivational state provide a usefull formalization for internet based treatements?
Both, F., Hoogendoorn, M., Klein, M., & Treur, J. (2008). Formalizing dynamics of mood and depression. In Proceedings of the 18th european conference on artificial intelligence.
Christensen, H., Griffiths, K., & L.Farrer. (2009). Adherence in internet internet interventions for anxiety and depression. Journal of Medical Internet Research, 11.
Farvolden, P., Denisoff, E., Selby, P., Bagby, R., & Rudy, L. (2005). Adherence in internet interventions for anxiety and depression. Journal of Medical Internet Research, 7.
Spek, V., Cuijpers, P., Nyklicek, I., Riper, H., Keyzer, J., & Pop, V. (2007). Internet-based cognitive behavior therapy for mood and anxiety disorders: a meta-analysis. Psychological Medicine, 37.