Digital Biomarkers for Well-being Through Exergame Interactions: Exploratory Study

Despoina Petsani; Evdokimos Konstantinidis; Aikaterini-Marina Katsouli; Vasiliki Zilidou; Sofia B Dias; Leontios Hadjileontiadis; Panagiotis Bamidis

doi:10.2196/34768

Digital Biomarkers for Well-being Through Exergame Interactions: Exploratory Study

JMIR Serious Games. 2022 Sep 13;10(3):e34768. doi: 10.2196/34768.

Authors

Despoina Petsani¹, Evdokimos Konstantinidis¹, Aikaterini-Marina Katsouli¹, Vasiliki Zilidou¹, Sofia B Dias², Leontios Hadjileontiadis^{3

4}, Panagiotis Bamidis¹

Affiliations

¹ Medical Physics and Digital Innovation Laboratory, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
² Centro Interdisciplinar de Estudo da Performance Humana, Faculdade de Motricidade Humana, Universidade de Lisboa, Lisbon, Portugal.
³ Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
⁴ Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.

PMID: 36099000
PMCID: PMC9516369
DOI: 10.2196/34768

Abstract

Background: Ecologically valid evaluations of patient states or well-being by means of new technologies is a key issue in contemporary research in health and well-being of the aging population. The in-game metrics generated from the interaction of users with serious games (SG) can potentially be used to predict or characterize a user's state of health and well-being. There is currently an increasing body of research that investigates the use of measures of interaction with games as digital biomarkers for health and well-being.

Objective: The aim of this paper is to predict well-being digital biomarkers from data collected during interactions with SG, using the values of standard clinical assessment tests as ground truth.

Methods: The data set was gathered during the interaction with patients with Parkinson disease with the webFitForAll exergame platform, an SG engine designed to promote physical activity among older adults, patients, and vulnerable populations. The collected data, referred to as in-game metrics, represent the body movements captured by a 3D sensor camera and translated into game analytics. Standard clinical tests gathered before and after the long-term interaction with exergames (preintervention test vs postintervention test) were used to provide user baselines.

Results: Our results showed that in-game metrics can effectively categorize participants into groups of different cognitive and physical states. Different in-game metrics have higher descriptive values for specific tests and can be used to predict the value range for these tests.

Conclusions: Our results provide encouraging evidence for the value of in-game metrics as digital biomarkers and can boost the analysis of improving in-game metrics to obtain more detailed results.

Keywords: cognitive well-being; machine learning; physical well-being; serious games.

©Despoina Petsani, Evdokimos Konstantinidis, Aikaterini-Marina Katsouli, Vasiliki Zilidou, Sofia B Dias, Leontios Hadjileontiadis, Panagiotis Bamidis. Originally published in JMIR Serious Games (https://games.jmir.org), 13.09.2022.