Measuring Anhedonia Through a Gamified Approach

Abstract

To date, the most widely used evaluation of mental health treatment is the subjective self-report. While this measure is important, more objective tools are needed as a complement to reliably track symptom development. The current study contributes to the investigation of how features of common mental disorders can be measured using a gamified approach. In a bigger scope, the aim is to develop a reliable, objective, and gamified tool for efficient treatment monitoring and prediction of treatment outcome.

The current study focuses on measuring anhedonia, the absence of pleasurable experience, as a key feature of depression and anxiety within the positive effect circuit in Williams' [1] taxonomy of putative brain types of dysfunction. Subjective measures of anhedonia are correlated with objective measures within the gamified tool, as well as with physiological data obtained during the game. 

An experiment is set up in which the Snaith-Hamilton Pleasure Scale (SHAPS) [2] will be administered to a non-clinical sample. Participants are connected to an ECG to measure heart rate and a wearable device to measure skin conductance and temperature. They play a computer game with the goal of steering a boat to a shore to acquire a treasure, while avoiding a shark. After each trial, participants are asked how much pleasure they experienced from winning a treasure.

The analysis investigates a possible correlation of the patients' subjective reports on the SHAPS with the pleasure reports during the game and the ECG and wearable data. The aim is to bridge subjective and objective measures and understand the relationships between them. Results may be used to aid the development of a computational model of the characteristics of common mental disorders that can be used to assess treatment progress and effectivity.

The study has important implications for mental health care. The effectiveness of both psychological therapy and pharmacotherapy has been stagnant for decades [3], with a substantial number of patients not benefiting from treatment even after personalization [3].  Efficacy could be boosted by having robust computational models to predict treatment outcomes. In addition, these models can be used to make a clearer diagnosis when a patient's symptoms are first assessed.

References

[2] R. P. Snaith et al., “A scale for the assessment of hedonic tone the snaith–hamilton pleasure scale,” British Journal of Psychiatry, vol. 167, no. 1, pp. 99–103, Jul. 1995. doi:10.1192/bjp.167.1.99 

[1] L. M. Williams, “Precision psychiatry: A neural circuit taxonomy for depression and anxiety,” The Lancet Psychiatry, vol. 3, no. 5, pp. 472–480, May 2016. doi:10.1016/s2215-0366(15)00579-9

[3] M. Barkham and M. J. Lambert, “The efficacy and effectiveness of psychological therapies.” Bergin and Garfield’s handbook of psychotherapy and behavior change, pp. 135-189, Aug. 2021