Assisted Rehabilitation Care During Post-stroke mANaGement: fEasibiLity Assessment
NCT ID: NCT03787433
Last Updated: 2020-06-16
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
41 participants
OBSERVATIONAL
2018-11-23
2020-06-12
Brief Summary
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Detailed Description
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Despite evidence that participation in formal rehabilitative therapies lessens disability after stroke, less than a third receive inpatient or outpatient therapies. Of those who do access therapies, the frequency of use varies by geographic location and socioeconomic status. In this context, the development of new strategies able to expand the access to rehabilitation to an increased number of stroke patients, also enabling home-based conduction and monitoring, are increasingly necessary both for patients, their families and for the healthcare and social services sustainability. Since many barriers could limit access to continuous physical rehabilitation for these patients, devices that complement or assist in the rehabilitation process can be of great help.
Among different approaches proposed by the scientific community, technological systems based on accelerometers seem to be among the most promising. Accelerometers are small low cost electronic devices, able to measure body parts acceleration on three axes. Many researchers have already highlighted that accelerometers have the capability to provide reliable and objective information on quantity and intensity of patient limbs movements during recovery process.
Wearable devices such as accelerometers allow to monitor exercises and daily activities. Machine learning methodologies have already been applied for modelling and contextualizing accelerometric signals to identify activity types (walking, dressing, eating, washing up, etc.) or to recognize to which rehabilitative exercise these signals are linked to. These techniques allow to estimate the recorded movement quality, providing information useful to identify the context in which movements are performed. Results of these type of studies are promising and they demonstrate that machine learning is a preferred approach for accelerometric data analysis, since able to exceed actual limits that today are hampering commercial product development for real time analysis of movement.
Within this scenario, Camlin-ARC takes its place. ARC is a platform based on wearable inertial sensors and machine learning algorithms, designed to bring the rehabilitation at post-stroke patients' home, following hospital discharge.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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ARC - Assisted Rehabilitation Care
All study participants will be asked to use ARC during for their post-stroke home based rehabilitation for up to 6 months.
ARC - Assisted Rehabilitation Care
ARC is a platform based on wearable inertial sensors and machine learning algorithms, designed to bring the rehabilitation at post-stroke patients' home, following hospital discharge.
The product has been created with the purpose to improve physical skills and patient independence accordingly, in the six months following the acute event. ARC aims to optimize, ease and make more accessible the path of post-stroke rehabilitation during post-acute phase, in real life settings.
Interventions
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ARC - Assisted Rehabilitation Care
ARC is a platform based on wearable inertial sensors and machine learning algorithms, designed to bring the rehabilitation at post-stroke patients' home, following hospital discharge.
The product has been created with the purpose to improve physical skills and patient independence accordingly, in the six months following the acute event. ARC aims to optimize, ease and make more accessible the path of post-stroke rehabilitation during post-acute phase, in real life settings.
Eligibility Criteria
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Inclusion Criteria
* Age \> 18
* Modified Rankin score lower or equal to 4 or Barthel Index score greater than 10 at the time of enrollment
* Patients must be able to keep the standing position without or with minimum assistance
* Patient giving written consent and engage
Exclusion Criteria
* Poor communication or reading skills - judged by a Speech and Language Therapist
* Orthopedic limitation (fractures, amputations, advance osteoarthritis, active rheumatoid arthritis)
* Head trauma
* Epilepsy, not pharmacologically controlled
* Severe spatial neglect
* Neurodegenerative and neuromuscular diseases
* Severe spasticity
* Patient not giving written consent and not engage
18 Years
ALL
No
Sponsors
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Northern Health and Social Care Trust
OTHER_GOV
Azienda Sanitaria Locale 3, Torino
OTHER
Camlin Ltd
INDUSTRY
Responsible Party
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Principal Investigators
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Frances Johnston, MSc
Role: STUDY_DIRECTOR
Northern Health and Social Care Trust
Locations
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Azienda Sanitaria Locale 3, Torino
Pinerolo, , Italy
Northern Health and Social Care Trust
Antrim, Northern Ireland, United Kingdom
Countries
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References
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Krueger H, Koot J, Hall RE, O'Callaghan C, Bayley M, Corbett D. Prevalence of Individuals Experiencing the Effects of Stroke in Canada: Trends and Projections. Stroke. 2015 Aug;46(8):2226-31. doi: 10.1161/STROKEAHA.115.009616.
Duncan PW, Zorowitz R, Bates B, Choi JY, Glasberg JJ, Graham GD, Katz RC, Lamberty K, Reker D. Management of Adult Stroke Rehabilitation Care: a clinical practice guideline. Stroke. 2005 Sep;36(9):e100-43. doi: 10.1161/01.STR.0000180861.54180.FF. No abstract available.
Hankey GJ, Jamrozik K, Broadhurst RJ, Forbes S, Anderson CS. Long-term disability after first-ever stroke and related prognostic factors in the Perth Community Stroke Study, 1989-1990. Stroke. 2002 Apr;33(4):1034-40. doi: 10.1161/01.str.0000012515.66889.24.
Hackett ML, Duncan JR, Anderson CS, Broad JB, Bonita R. Health-related quality of life among long-term survivors of stroke : results from the Auckland Stroke Study, 1991-1992. Stroke. 2000 Feb;31(2):440-7. doi: 10.1161/01.str.31.2.440.
Dobkin BH, Dorsch A. New evidence for therapies in stroke rehabilitation. Curr Atheroscler Rep. 2013 Jun;15(6):331. doi: 10.1007/s11883-013-0331-y.
Noorkoiv M, Rodgers H, Price CI. Accelerometer measurement of upper extremity movement after stroke: a systematic review of clinical studies. J Neuroeng Rehabil. 2014 Oct 9;11:144. doi: 10.1186/1743-0003-11-144.
Uswatte G, Foo WL, Olmstead H, Lopez K, Holand A, Simms LB. Ambulatory monitoring of arm movement using accelerometry: an objective measure of upper-extremity rehabilitation in persons with chronic stroke. Arch Phys Med Rehabil. 2005 Jul;86(7):1498-501. doi: 10.1016/j.apmr.2005.01.010.
Wong WY, Wong MS, Lo KH. Clinical applications of sensors for human posture and movement analysis: a review. Prosthet Orthot Int. 2007 Mar;31(1):62-75. doi: 10.1080/03093640600983949.
Zhou H, Hu H, Harris N. Application of wearable inertial sensors in stroke rehabilitation. Conf Proc IEEE Eng Med Biol Soc. 2005;2005:6825-8. doi: 10.1109/IEMBS.2005.1616072.
Lara González-Villanueva et al., A Tool for Linguistic Assessment of Rehabilitation Exercises. Applied Soft Computing, Special issue on hybrid intelligent methods for health technologies 14(Part A): 120-31, 2013. doi:10.1016/j.asoc.2013.07.010.
Mannini A, Sabatini AM. Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors (Basel). 2010;10(2):1154-75. doi: 10.3390/s100201154. Epub 2010 Feb 1.
Parkka J, Ermes M, Korpipaa P, Mantyjarvi J, Peltola J, Korhonen I. Activity classification using realistic data from wearable sensors. IEEE Trans Inf Technol Biomed. 2006 Jan;10(1):119-28. doi: 10.1109/titb.2005.856863.
Lara OD, Labrador MA. A Survey on Human Activity Recognition using Wearable Sensors. IEEE Communications Surveys & Tutorial 15(3), 2013.
Garcia-Ceja E, Brena RF, Carrasco-Jimenez JC, Garrido L. Long-term activity recognition from wristwatch accelerometer data. Sensors (Basel). 2014 Nov 27;14(12):22500-24. doi: 10.3390/s141222500.
Other Identifiers
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ARCANGEL
Identifier Type: -
Identifier Source: org_study_id
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