The feasibility of the adapted H-GRASP program for perceived and actual daily-life upper limb activity in the chronic phase post-stroke.
Bea EssersJanne M VeerbeekAndreas R LuftGeert VerheydenPublished in: Disability and rehabilitation (2024)
( Purpose : Assessing feasibility and initial impact of the Home-Graded Repetitive Arm Supplementary Program combined with in-home accelerometer-based feedback (AH-GRASP) on perceived and actual daily-life upper limb (UL) activity in stroke survivors during the chronic phase with good UL motor function but low perceived daily-life activity. Material and methods : A 4-week intervention program (4 contact hours, 48 h self-practice) encompassing task-oriented training, behavioral techniques, phone-based support, monitoring, and weekly feedback sessions using wrist-worn accelerometery was implemented using a pre-post double baseline repeated measures design. Feasibility, clinical assessments, patient-reported outcomes, and accelerometer data were investigated. Results : Of the 34 individuals approached, nineteen were included (recruitment rate 56%). Two dropped out, one due to increased UL pain (retention rate 89%). Seven (41%) achieved the prescribed exercise target (120 min/day, six days/week). Positive patient experiences and improvements in UL capacity, self-efficacy, and contribution of the affected UL to overall activity ( p < 0.05, small to large effect sizes) were observed. Additionally, seven participants (41%) surpassed the minimal clinically important difference in perceived UL activity. Conclusions : A home-based UL exercise program with accelerometer-based feedback holds promise for enhancing perceived and actual daily-life UL activity for our subgroup of chronic stroke survivors.
Keyphrases
- physical activity
- herpes simplex virus
- upper limb
- social support
- depressive symptoms
- mental health
- quality improvement
- patient reported outcomes
- healthcare
- randomized controlled trial
- atrial fibrillation
- high intensity
- chronic pain
- young adults
- machine learning
- electronic health record
- high resolution
- deep learning
- big data
- resistance training
- body composition
- single molecule
- subarachnoid hemorrhage