[article]
Titre : |
Effectiveness of upper-limb robotic-assisted therapy in the early rehabilitation phase after stroke: A single-blind, randomised, controlled trial |
Type de document : |
texte imprimé |
Auteurs : |
Stéphanie Dehem ; Maxime Gilliaux ; Gaëtan G. Stoquart ; Christine Detrembleur ; Géraldine Jacquemin ; Sara Palumbo ; Anne Frederick ; Thierry Lejeune |
Année de publication : |
2019 |
Article en page(s) : |
p. 313-320 |
Note générale : |
doi.org/10.1016/j.rehab.2019.04.002 |
Langues : |
Anglais (eng) |
Mots-clés : |
Stroke Upper extremity Robotics Rehabilitation Recovery of function |
Résumé : |
Background
Upper-limb robotic-assisted therapy (RAT) is promising for stroke rehabilitation, particularly in the early phase. When RAT is provided as partial substitution of conventional therapy, it is expected to be at least as effective or might be more effective than conventional therapy. Assessments have usually been restricted to the first 2 domains of the International classification of functioning, disability and health (ICF).
Objective
This was a pragmatic, multicentric, single-blind, randomized controlled trial to evaluate the effectiveness of upper-limb RAT used as partial substitution to conventional therapy in the early phase of stroke rehabilitation, following the 3 ICF domains.
Methods
We randomized 45 patients with acute stroke into 2 groups (conventional therapy, n = 22, and RAT, n = 23). Both interventions were dose-matched regarding treatment duration and lasted 9 weeks. The conventional therapy group followed a standard rehabilitation. In the RAT group, 4 sessions of conventional therapy (25%) were substituted by RAT each week. RAT consisted of moving the paretic upper limb along a reference trajectory while the robot provided assistance as needed. A blinded assessor evaluated participants before, just after the intervention and 6 months post-stroke, according to the ICF domains UL motor impairments, activity limitations, and social participation restriction.
Results
In total, 28 individuals were assessed after the intervention. The following were more improved in the RAT than conventional therapy group at 6 months post-stroke: gross manual dexterity (Box and Block test +7.7 blocks; P = 0.02), upper-limb ability during functional tasks (Wolf Motor Function test +12%; P = 0.02) and patient social participation (Stroke Impact Scale +18%; P = 0.01). Participants’ abilities to perform manual activities and activities of daily living improved similarly in both groups.
Conclusion
For the same duration of daily rehabilitation, RAT combined with conventional therapy during the early rehabilitation phase after stroke is more effective than conventional therapy alone to improve gross manual dexterity, upper-limb ability during functional tasks and patient social participation. |
Permalink : |
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in Annals of physical and rehabilitation medicine > Vol. 62, n°5 (Septembre 2019) . - p. 313-320
[article] Effectiveness of upper-limb robotic-assisted therapy in the early rehabilitation phase after stroke: A single-blind, randomised, controlled trial [texte imprimé] / Stéphanie Dehem ; Maxime Gilliaux ; Gaëtan G. Stoquart ; Christine Detrembleur ; Géraldine Jacquemin ; Sara Palumbo ; Anne Frederick ; Thierry Lejeune . - 2019 . - p. 313-320. doi.org/10.1016/j.rehab.2019.04.002 Langues : Anglais ( eng) in Annals of physical and rehabilitation medicine > Vol. 62, n°5 (Septembre 2019) . - p. 313-320
Mots-clés : |
Stroke Upper extremity Robotics Rehabilitation Recovery of function |
Résumé : |
Background
Upper-limb robotic-assisted therapy (RAT) is promising for stroke rehabilitation, particularly in the early phase. When RAT is provided as partial substitution of conventional therapy, it is expected to be at least as effective or might be more effective than conventional therapy. Assessments have usually been restricted to the first 2 domains of the International classification of functioning, disability and health (ICF).
Objective
This was a pragmatic, multicentric, single-blind, randomized controlled trial to evaluate the effectiveness of upper-limb RAT used as partial substitution to conventional therapy in the early phase of stroke rehabilitation, following the 3 ICF domains.
Methods
We randomized 45 patients with acute stroke into 2 groups (conventional therapy, n = 22, and RAT, n = 23). Both interventions were dose-matched regarding treatment duration and lasted 9 weeks. The conventional therapy group followed a standard rehabilitation. In the RAT group, 4 sessions of conventional therapy (25%) were substituted by RAT each week. RAT consisted of moving the paretic upper limb along a reference trajectory while the robot provided assistance as needed. A blinded assessor evaluated participants before, just after the intervention and 6 months post-stroke, according to the ICF domains UL motor impairments, activity limitations, and social participation restriction.
Results
In total, 28 individuals were assessed after the intervention. The following were more improved in the RAT than conventional therapy group at 6 months post-stroke: gross manual dexterity (Box and Block test +7.7 blocks; P = 0.02), upper-limb ability during functional tasks (Wolf Motor Function test +12%; P = 0.02) and patient social participation (Stroke Impact Scale +18%; P = 0.01). Participants’ abilities to perform manual activities and activities of daily living improved similarly in both groups.
Conclusion
For the same duration of daily rehabilitation, RAT combined with conventional therapy during the early rehabilitation phase after stroke is more effective than conventional therapy alone to improve gross manual dexterity, upper-limb ability during functional tasks and patient social participation. |
Permalink : |
./index.php?lvl=notice_display&id=84140 |
| ![Effectiveness of upper-limb robotic-assisted therapy in the early rehabilitation phase after stroke: A single-blind, randomised, controlled trial 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) 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