The Effects of POWER Training in Young and Older Adults after Stroke.
Jennifer L HunnicuttStacey E AaronAaron E EmbryBrian CencePatrick MorganMark G BowdenChris M GregoryPublished in: Stroke research and treatment (2016)
Background. Approximately 35,000 strokes occur annually in adults below the age of 40, and there is disappointingly little data describing their responses to rehabilitation. The purpose of this analysis was to determine the effects of Poststroke Optimization of Walking using Explosive Resistance (POWER) training in young (<40 years) and older (>60 years) adults and to describe relationships between training-induced improvements in muscular and locomotor function. Methods. Data was analyzed from 16 individuals with chronic stroke who participated in 24 sessions of POWER training. Outcomes included muscle power generation, self-selected walking speed (SSWS), 6-minute walk test, Fugl-Meyer motor assessment, Berg Balance Scale, and Dynamic Gait Index. Results. There were no significant differences between groups at baseline. Within-group comparisons revealed significant improvements in paretic and nonparetic knee extensor muscle power generation in both groups. Additionally, young participants significantly improved SSWS. Improvements in SSWS were more strongly associated with improvements in power generation on both sides in young versus older participants. Conclusions. Younger adults after stroke seem to preferentially benefit from POWER training, particularly when increasing gait speed is a rehabilitation goal. Future research should aim to further understand age-related differences in response to training to provide optimal treatments for all individuals following stroke.
Keyphrases
- middle aged
- virtual reality
- atrial fibrillation
- physical activity
- skeletal muscle
- electronic health record
- spinal cord injury
- total knee arthroplasty
- machine learning
- knee osteoarthritis
- insulin resistance
- cerebral ischemia
- subarachnoid hemorrhage
- single cell
- brain injury
- blood brain barrier
- data analysis
- deep learning
- body composition
- lower limb
- stress induced