Petsani Despoina, Konstantinidis Evdokimos, Katsouli Aikaterini-Marina, Zilidou Vasiliki, Dias Sofia B, Hadjileontiadis Leontios, Bamidis Panagiotis
Medical Physics and Digital Innovation Laboratory, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Centro Interdisciplinar de Estudo da Performance Humana, Faculdade de Motricidade Humana, Universidade de Lisboa, Lisbon, Portugal.
JMIR Serious Games. 2022 Sep 13;10(3):e34768. doi: 10.2196/34768.
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.
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.
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.
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.
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.
利用新技术对患者状态或幸福感进行生态有效评估是当代老年人口健康与幸福感研究的关键问题。用户与严肃游戏（SG）互动产生的游戏内指标有可能用于预测或描述用户的健康和幸福状态。目前，越来越多的研究在探讨将与游戏的互动测量作为健康和幸福的数字生物标志物。
本文旨在以标准临床评估测试值为基准，从与SG互动期间收集的数据中预测幸福感数字生物标志物。
数据集是在与帕金森病患者使用webFitForAll健身游戏平台互动期间收集的，该平台是一个旨在促进老年人、患者和弱势群体进行体育活动的SG引擎。收集到的数据，即游戏内指标，代表由3D传感器摄像头捕捉并转化为游戏分析的身体动作。在与健身游戏进行长期互动之前和之后收集的标准临床测试（干预前测试与干预后测试）用于提供用户基线。
我们的结果表明，游戏内指标可以有效地将参与者分类到不同认知和身体状态的组中。不同的游戏内指标对特定测试具有更高的描述价值，可用于预测这些测试的值范围。
我们的结果为游戏内指标作为数字生物标志物的价值提供了令人鼓舞的证据，并可推动对改进游戏内指标的分析以获得更详细的结果。
