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Gamified Digital Balance Assessment
Sponsor: Shanghai Jiao Tong University School of Medicine
Summary
A randomized controlled trial involving 30 older adults will compare the digitalized Brief-BESTest and the GDBA. Quantitative outcomes included perceived exertion, enjoyment, competence, pressure, and intention to continue use. Qualitative interviews explore user experience.
Official title: Evaluating the Effectiveness of a Gamification in Balance Assessment Tools
Key Details
Gender
All
Age Range
60 Years - Any
Study Type
INTERVENTIONAL
Enrollment
30
Start Date
2025-04-20
Completion Date
2025-12-20
Last Updated
2025-07-28
Healthy Volunteers
Yes
Conditions
Interventions
Gamified Digital Balance Assessment
The GDBA further enhances the digitalized Brief-BESTest experience by incorporating gamification elements tailored to older adults, including points, avatars, real-time performance graphs, and leaderboards. The system provides automated feedback and maintains engagement through periodic avatar demonstrations when user inactivity is detected. Upon meeting task initiation criteria, a countdown triggers data capture. The interface is designed for accessibility, featuring a high-contrast color scheme (black background with orange/green highlights), voice prompts, and intuitive controls. Upon completion, users receive a comprehensive report including total score, task-level feedback and training recommendations. A leaderboard feature promotes continued engagement, with gamified training modules under development. At the end of the assessment, the system displays a summary including total balance score, task-specific feedback, a fall risk rating, and personalized training suggestions. Users
Digitalized Brief-BESTest design
The digitalized Brief-BESTest was designed to digitize and automate the Brief-BESTest. While the traditional clinician-administered Brief-BESTest relies on subjective scoring, the digitalized Brief-BESTest enables self-guided assessments with automated, objective scoring-improving accessibility in community and home settings. The system employs OpenPose to capture skeletal data via a standard 2D camera, tracking 17 anatomical landmarks (e.g., nose, neck, shoulders, hips, knees). Ten joint angles relevant to static and dynamic postural tasks (e.g., standing, sitting, single-leg stance, and simulated falls) are computed. The torso is defined as a vector from the neck to the midpoint between the hips, serving as a reference for postural alignment. To convert pixel-based coordinates into metric units, the system uses the user's self-reported height with adjustments based on ISO anthropometric standards (correction factors: 10.77 cm for males, 10.06 cm for females) to approximate true body
Locations (1)
Hongqiao Community
Shanghai, Shanghai Municipality, China