Benefits of a Wearable Cyborg HAL (Hybrid Assistive Limb) in Patients with Childhood-Onset Motor Disabilities: A 1-Year Follow-Up Study.
Mayumi Matsuda KurodaNobuaki IwasakiHirotaka MutsuzakiKenichi YoshikawaKazushi TakahashiTomohiro NakayamaJunko NakayamaRyoko TakeuchiYuki MatakiHaruka OhguroKazuhide TomitaPublished in: Pediatric reports (2023)
Rehabilitation robots have shown promise in improving the gait of children with childhood-onset motor disabilities. This study aimed to investigate the long-term benefits of training using a wearable Hybrid Assistive Limb (HAL) in these patients. Training using a HAL was performed for 20 min a day, two to four times a week, over four weeks (12 sessions in total). The Gross Motor Function Measure (GMFM) was the primary outcome measure, and the secondary outcome measures were gait speed, step length, cadence, 6-min walking distance (6MD), Pediatric Evaluation of Disability Inventory, and Canadian Occupational Performance Measure (COPM). Patients underwent assessments before the intervention, immediately after the intervention, and at 1-, 2-, 3-month and 1-year follow-ups. Nine participants (five males, four females; mean age: 18.9 years) with cerebral palsy ( n = 7), critical illness polyneuropathy ( n = 1), and encephalitis ( n = 1) were enrolled. After training using HAL, GMFM, gait speed, cadence, 6MD, and COPM significantly improved (all p < 0.05). Improvements in GMFM were maintained one year after the intervention ( p < 0.001) and in self-selected gait speed and 6MD three months after the intervention ( p < 0.05). Training using HAL may be safe and feasible for childhood-onset motor disabilities and may maintain long-term improvements in motor function and walking ability.
Keyphrases
- cerebral palsy
- randomized controlled trial
- end stage renal disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- multiple sclerosis
- young adults
- blood pressure
- clinical trial
- prognostic factors
- patient reported outcomes
- virtual reality
- early life
- childhood cancer
- psychometric properties
- big data
- deep learning
- lower limb
- gestational age