Internet of Things (IoT) Enables Robot-Assisted Therapy as a Home Program for Training Upper Limb Functions in Chronic Stroke: A Randomized Control Crossover Study |
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| Institution: | 1. Department of Occupational Therapy, College of Medicine, National Cheng Kung University, Tainan, Taiwan;2. Medical Device Innovation Center, National Cheng Kung University, Tainan, Taiwan;3. Department of Physical Medicine and Rehabilitation, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan;4. Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan, Taiwan;6. Department of Occupational Therapy, Da-Yeh University, Changhua, Taiwan;1. Massachusetts Veterans Epidemiology and Research Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA;2. Department of Gerontology, University of Massachusetts Boston, Boston, MA;3. Medical Practice Evaluation Center and Center for Aging and Serious Illness, Mongan Institute, Department of Medicine, Massachusetts General Hospital, Boston, MA;4. Department of Clinical Research, Copenhagen University Hospital, Hvidovre, Denmark;5. Department of Clinical Medicine, University of Copenhagen, Denmark;6. New England Geriatric Research Education and Clinical Center, VA Boston Healthcare System, Boston, MA;7. Harvard Medical School, Boston, MA;8. Boston University, Boston, MA;9. Brigham and Women''s Hospital, Boston, MA;10. Geriatrics and Extended Care, VA Boston Healthcare System, Boston, MA;11. Spaulding Rehabilitation Hospital, Boston, MA;1. Department of Physical Therapy, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil;2. Department of Physical Therapy, Faculdade Ciências Médicas de Minas Gerais (FCM-MG), Belo Horizonte, Minas Gerais, Brazil;1. Department of Epidemiology and Biostatistics, Michigan State University – College of Human Medicine, Grand Rapids, MI;2. John F. Butzer Center for Research and Innovation, Mary Free Bed Rehabilitation Hospital, Grand Rapids, MI;3. Division of Rehabilitation, Michigan State University – College of Human Medicine, Grand Rapids, MI;4. Department of Biostatistics, Grand Valley State University, Grand Rapids, MI;1. IRCCS Istituto Ortopedico Galeazzi, Unit of Clinical Epidemiology, Milan, Italy;2. Unità Operativa di Neuropsichiatria Infanzia e Adolescenza (UONPIA), ASST Pavia, Italy;3. Vita-Salute San Raffaele University, Milan, Italy;4. Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael''s Hospital, Unity Health Toronto, Cochrane Hypertension Review Group, the Therapeutics Initiative, University of British Columbia, Canada;1. Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL;2. University of Chicago Medical Center, Department of Solid Organ Transplant, Chicago, IL;1. Exercise Biology, Department of Public Health, Aarhus University, Denmark;2. University of Lyon, UJM-Saint-Etienne, Inter-University Laboratory of Human Movement Biology, Saint-Etienne, France;3. The Danish MS Hospitals, Ry and Haslev, Denmark |
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| Abstract: | ObjectiveTo compare the effects of using an Internet of things (IoT)-assisted tenodesis-induced-grip exoskeleton robot (TIGER) and task-specific motor training (TSMT) as home programs for the upper-limb (UL) functions of patients with chronic stroke to overturn conventional treatment modes for stroke rehabilitation.DesignA randomized 2-period crossover study.SettingA university hospital.ParticipantsEighteen chronic stroke patients were recruited and randomized to receive either the IoT-assisted TIGER first or TSMT first at the beginning of the experiment (N=18).InterventionIn addition to the standard hospital-based therapy, participants were allocated to receive a 30-minute home-based, self-administered program of either IoT-assisted TIGER first or TSMT first twice daily for 4 weeks, with the order of both treatments reversed after a 12-week washout period. The exercise mode of the TIGER training included continuous passive motion and the functional mode of gripping pegs. The TSMT involved various movement components of the wrist and hand.Main Outcome MeasuresThe outcome measures included the box and block test (BBT), the Fugl-Meyer assessment for upper extremity (FMA-UE), the motor activity log, the Semmes-Weinstein Monofilament test, the range of motion (ROM) of the wrist joint, and the modified Ashworth scale.ResultsSignificant treatment-by-time interaction effects emerged in the results for the BBT (F(1.31)=5.212 and P=.022), the FMA-UE (F(1.31)=6.807 and P=.042), and the ROM of the wrist extension (F(1.31)=8.618 and P=.009). The participants who trained at home with the IoT-assisted TIGER showed more improvement of their UL functions.ConclusionsThe IoT-assisted TIGER training has the potential for restoring the UL functions of stroke patients. |
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| Keywords: | Telerehabilitation |
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