INTRODUCTION: Finger stiffness after treatment for metacarpal fractures often occurs due to poor compliance to the conventional rehabilitation programs. Gamification has shown success in improving adherence to and effectiveness of various therapies.
PURPOSE OF THE STUDY: The purpose of this study was to evaluate whether gamification, using cost-effective devices was comparable with conventional physiotherapy in improving hand functions and adherence to rehabilitation in metacarpal fractures.
METHODS: A 2-group randomized controlled trial involving 19 patients was conducted. Participants were randomized to a control (conventional physiotherapy, n = 10) or interventional group (gamification, n = 9). The grips strength and composite finger range of motion were measured at the baseline and each follow-up together with Patient-Rated Wrist and Hand Evaluation scores and compliance.
RESULTS: There were no significant differences on improvements of grip strength (means difference 24.38 vs 20.44, P = .289) and composite finger range of motion (means difference 50.50 vs 51.11, P = .886). However, the gamification group showed better results in Patient-Rated Wrist and Hand Evaluation (mean 0.44 vs 8.45, P = .038) and compliance (P
METHODS: The system components and hand prototypes involve the anthropometry, CAD design and prototyping, biomechatronics engineering together with the prosthetics. The modeler construction of the system develop allows the ultrasonic sensors that are placed on the shoulder to generate the wrist movement of the prosthesis. The kinematics of wrist movement, which are the pronation/supination and flexion/extension were tested using the motion analysis and general motion of human hand were compared. The study also evaluated the require degree of detection for the input of the ultrasonic sensor to generate the wrist movements.
RESULTS: The values collected by the vicon motion analysis for biomechatronics prosthesis system were reliable to do the common tasks in daily life. The degree of the head needed to bend to give the full input wave was about 45°-55° of rotation or about 14 cm-16 cm. The biomechatronics wrist prosthesis gave higher degree of rotation to do the daily tasks but did not achieve the maximum degree of rotation.
CONCLUSION: The new development of using sensor and actuator in generating the wrist movements will be interesting for used list in medicine, robotics technology, rehabilitations, prosthetics and orthotics.