Video game-based application for fall risk assessment: a proof-of-concept cohort study

Background Fall(s) are a significant cause of morbidity and mortality especially amongst elderly with polyneuropathy and cognitive decline. Conventional fall risk assessment tools are prone to low predictive values and do not address specific vulnerabilities. This study seeks to advance the development of an innovative, engaging fall prediction tool for a high-risk cohort diagnosed with diabetes. Methods In this proof-of-concept cohort study, between July 01, 2020, and May 31, 2022, 152 participants with diabetes performed clinical examinations to estimate individual risks of fall (timed " up and go" (TUG) test, dynamic gait index (DGI), Berg-Balance-Scale (BBS)) and participated in a video game-based fall risk assessment with sensor-equipped insoles as steering units. The participants engaged in four distinct video games, each designed to address capabilities pertinent to prevent fall(s): skillfulness, reaction time, sensation, endurance, balance, and muscle strength. Data were collected during both, seated and standing gaming sessions. By data analyses using binary machine learning models a classification of participants was achieved and compared with actual fall events reported for the past 24 months. Findings Overall 22 out of 152 participants (14.5%) underwent at least one episode of fall during the past 24 months. Adjusted risk classification accuracies of TUG, DGI, and BBS reached 58.7%, 58.3%, and 47.5%, respectively. Data analyses from gaming sessions in seated and standing positions yielded two models with six predictors from the four games with accuracies of 82.8% and 88.6% (area under the receiver-operating-characteristic curve 0.84 (95% confidence interval (CI): 0.77-0.91) and 0.91 (95% CI: 0.85-0.97), respectively). Key capabilities that were distinctly different between the groups related to endurance (0.6 +/- 0.1 vs. 0.5 +/- 0.2; p = 0.03) and balance (0.7 +/- 0.2 vs. 0.6 +/- 0.2; p = 0.05). The AI-driven analysis allowed to extract a list of game features that showed highly significant predictive values, e.g., reaction times in specific task, deviation from ideal steering routes in parcours and pressure-related parameters. Interpretation Thus, video game-based assessment of fall risk surpasses traditional clinical assessment tools and scores (e.g., TUG, DGI, and BBS) and may open a novel resource for patient evaluation in the future. Further research with larger, heterogeneous cohorts is needed to validate these fi ndings and especially predict future fall risk probabilities in clinical as well as outpatient settings.