RESUMO
Physical exercise and cognitive training were previously demonstrated to improve the physical functioning and decrease the incidence of falls for older adults. This study aimed to utilize an interactive exergame mat system to develop a novel cognitive-physical training program and explore the training effects on physical performance, cognitive function, dual-task walking (DTW), and fall risk compared to the control condition. In this quasi-experimental non-randomized controlled intervention study, 110 community-dwelling older adults participated. The exercise group (n = 56; mean age, 70.7 ± 4.6 years) performed ladder-type, three-by-three grid-type, and circle-type mat exergames with simultaneous cognitive-physical training (EMAT), while the control group (n = 54; mean age, 72.0 ± 5.7 years) underwent a multicomponent exercise intervention focused on physical and cognitive training. A 2 h training session was completed weekly for 3 months. Functional fitness (including upper- and lower-extremity strength and flexibility, grasp strength, aerobic endurance, static balance, dynamic balance and agility), a foot tapping test (FTT), the Montreal Cognitive Assessment (MoCA), DTW, and a fall risk questionnaire (FRQ) were assessed before and after the interventions. The EMAT program enhanced upper-extremity strength, lower-extremity strength and flexibility, aerobic endurance, and dynamic balance and agility; improved DTW and FTT performances; and decreased FRQ score. EMAT also showed a significant advantage over control in terms of lower-extremity strength and flexibility, aerobic endurance, dynamic balance and agility, and FRQ score (all P < 0.05). The current study provides evidence of the effects of a novel mat exergaming program on physical and cognitive performance. EMAT effectively reduced the fall risk and increased the dual-task ability of walking, factors that are important in fall prevention for community-dwelling older adults.
RESUMO
Recent studies in rehabilitation of Parkinson's disease (PD) have shown that cycling on a tandem bike at a high pedaling rate can reduce the symptoms of the disease. In this research, a smart motorized bicycle has been designed and built for assisting Parkinson's patients with exercise to improve motor function. The exercise bike can accurately control the rider's experience at an accelerated pedaling rate while capturing real-time test data. Here, the design and development of the electronics and hardware as well as the software and control algorithms are presented. Two control algorithms have been developed for the bike; one that implements an inertia load (static mode) and one that implements a speed reference (dynamic mode). In static mode the bike operates as a regular exercise bike with programmable resistance (load) that captures and records the required signals such as heart rate, cadence and power. In dynamic mode the bike operates at a user-selected speed (cadence) with programmable variability in speed that has been shown to be essential to achieving the desired motor performance benefits for PD patients. In addition, the flexible and extensible design of the bike permits readily changing the control algorithm and incorporating additional I/O as needed to provide a wide range of riding experiences. Furthermore, the network-enabled controller provides remote access to bike data during a riding session.