Utilization of a Virtual Patient Model to Enable Tailored Therapy for Depressed Patients

Abstract

Abstract. Major depression is a prominent mental disorder that has significant impact upon the patient suffering from the depression as well as on the society as a whole. Currently, therapies are offered via the Internet in the form of selfhelp modules, and they have shown to be as effective as face-to-face counseling. In order to take automated therapies a step further, models which describe the development of the internal states associated with depression can be of great help to give dedicated advice and feedback to the patient e.g. by means of making predictions using the model. In this paper, an existing computational model for states related to depression (e.g. mood) is taken as a basis in combination with models that express the influence of various therapies upon these states. These models are utilized to give dedicated feedback to the patient, tailor the parameters towards the observed patient behavior, and give an appropriate advice regarding the therapy to be followed.

A Mobile System for Treatment of Depression

“…As a result, the component derives whether a therapeutic change should be advised. A more detailed description of the evaluation process can be found in [14].…”

Users' mental models forcomputer‐mediatedcommunication: Theorizing emerging technology and behavior ineHealthapplications

“…This technology and its applications are considered more efficient and affordable forms of healthcare practice (Mikulic, 2020) and have grown significantly over the past 20 years and are projected to grow even more globally (World Health Organization, 2016). These applications have been used in the following ways: in the context of peer support communities, as an alternative to in‐person psychotherapies, using avatars or virtual agents to augment in‐person treatment, packaged as serious games, and in the form of communication with an autonomous virtual agent (Both & Hoogendoorn, 2011; Mohr, Cuijpers, & Lehman, 2011; Rehm et al, 2016). For example, when comparing the use of virtual agents versus in‐person clinicians during mental health interviews, participants perceived more rapport and disclosed more mental symptoms with in‐person clinicians, but disclosed more sex‐related symptoms to the virtual agents (Yokotani, Takagi, & Wakashima, 2018).…”

Predictive modeling in e-mental health: A common language framework

“… 1 Asselbergs et al (2016) Smartphone measures Estimation of mood Regression model Failed replication of Likamwa et al (2013) 2 Daugherty et al (2009) Mood measures Estimation of mood swing cycles and treatment influence Dynamic model Simulation of treatment and coupling behavior for bipolar individuals. 2 Both et al (2010) , Both and Hoogendoorn (2011) , Both, Hoogendoorn and Klein (2012) EMA Simulates client's symptom trajectory Dynamic model Allows predictions about the client's recovery curve and simulates the influence of various therapy forms. 2 Touboul et al (2010) EMA Estimation of recovery curve and relapse risk Dynamic model The identification of underlying model parameters allow client specific predictions.…”

“…With the model, the development of internal states can be simulated to predict mood during treatment. The model incorporates hypothesized working mechanisms of different therapeutic interventions, such as activity scheduling or cognitive behavior therapy (CBT), to enable simulations of the potential effects of these interventions, which in turn could be used to identify the intervention that could benefit the patient the most ( Both and Hoogendoorn, 2011 ). To do this effectively, additional model states are necessary to account for external variables that are not controllable by the client ( Both, Hoogendoorn and Klein, 2012 ).…”