Assisted Rehabilitation Care During Post-stroke mANaGement: fEasibiLity Assessment

NCT03787433 | Completed

• 1 other identifier: ARCANGEL
• Study Type: observational
• Target: 41
• Locations: 2 countries
• Sites: 2

Brief Summary

The ARCANGEL study evaluates the feasibility of introducing ARC (Assisted Rehabilitation Care), a new device for home-based post-stroke rehabilitation in the current clinical practise. All the stroke survivors included in the study will received their own equipment to be used at home for 6 months.

Conditions

Stroke

Keywords

Rehabilitation; Accelerometers; Wearable sensors; Artificial Intelligence; Stroke; Home-based Rehabilitation; Activities of Daily Living; Post-stroke rehabilitation

Outcome Measures

Primary Outcomes (9)

• Number of patients successfully completing the study

  Number of patients completing the 6-month observation period

  Through study completion, an average of 15 months

• Ratio between the total number of subjects refusing to participate before training and the total number of subjects screened

  Ratio between the total number of patients refusing to participate BEFORE starting trainings and the number of patients screened, as calculated by means of the Screening and Enrollment Log to be completed by each Site, the baseline baseline assessment (reporting a number of training sessions performed, which should be EQUAL TO 0), and the end of study visit.

  Through study completion, an average of 15 months

• Ratio between the total number of subjects refusing to participate after training and the total number of subjects screened

  Ratio between the total number of patients refusing to participate AFTER training and the total number of patients screened, as calculated by means of the Screening and Enrollment Log to be completed by each Site, the baseline baseline assessment (reporting a number of training sessions performed, which should be at least EQUAL TO 1), and the end of study visit.

  Through study completion, an average of 15 months

• Number of training sessions

  Average number of training sessions needed for a patient to be able to use ARC at home

  Through study completion, an average of 15 months

• Overall training period duration

  Average time (days) needed to complete training sessions

  Through study completion, an average of 15 months

• Assisted Rehabilitation Care (ARC) questionnaire score

  Average score from the ARC questionnaire, specifically designed to assess the following sub-scales: Use of Technology, ARC Usability, Wearability and Global Satisfaction For each dimension, a subscore is calculated as the sum of the value associated to each possible answer (one single answer is allowed for each question), from 1 (Strongly disagree) to 5 (Strongly agree). Finally, the total score is calculated as sum of sub-scores.

  6-month assessment

• Assisted Rehabilitation Care (ARC) questionnaire change

  Change at 6 months of ARC questionnaire score. The change is calculated as difference between the average total score calculated at 6 months and the average total score calculated at 3 months. (Score calculation method ref. Outcome 6)

  Evaluations at 3 and 6 months

• ARC global satisfaction score

  Global score on the ARC user satisfaction ranging from 1 (very low) to 5 (very high).

  6-month assessment

• Modified version of Adult Carer Quality of Life Questionnaire (AC-QoL) total score

  In order to score the AC-QoL use the following scoring framework. Some of the questionnaire items are negatively worded (Value from 0 to 3, Never = 0 - Always = 3) and some are positively worded (Value from 0 to 3, Never = 3 - Always = 0). To calculate the total score, a calculation algorithm adds up each row for the score for each sub-scale, and add all the scores for the sub-scales to calculate the overall quality of life score.

  6-month evaluation

Secondary Outcomes (8)

• Device-related adverse effects

  Through study completion, an average of 15 months

• Modified Rankin Score change (N.Ireland)

  Change at 6-month from baseline

• Barthel Index change (Italy)

  Baseline assessment and 6-month visit

• Euro Quality of Life - 5 Dimension (EQ-5D) Health Questionnaire summary index

  6-month evaluation

• Euro Quality of Life - 5 Dimension (EQ-5D) Health Questionnaire summary index change

  Baseline assessment and 6-month visit

Study Arms (1)

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.

Device: ARC - Assisted Rehabilitation Care

Interventions

ARC - Assisted Rehabilitation Care DEVICE

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.

ARC - Assisted Rehabilitation Care

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All patients diagnosed with stroke among those admitted to the acute and community hospitals among the Northern Health and Social Care Trust in Northern Ireland, and to Azienda Sanitaria Locale 3, Turin (Italy) will be considered eligible for this study. Patients will be recruited during their hospital stay, or after hospital discharge, proven they have had a stroke in the previous 6 months. Among these, only patients who have given their informed consent to participate in the study and who meet all the inclusion and exclusion criteria will be considered eligible.

You may qualify if:

• Stroke Diagnosis, with a stable clinical condition
• 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

You may not qualify if:

• Significant cognitive impairment and behavioral disorders - judged by a responsible clinician
• 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

Sponsors & Collaborators

• Camlin Ltd: lead
• Northern Health and Social Care Trust: collaborator
• Azienda Sanitaria Locale 3, Torino: collaborator

Study Sites (2)

Azienda Sanitaria Locale 3, Torino

Pinerolo, 10064, Italy

Location

Northern Health and Social Care Trust

Antrim, Northern Ireland, BT412RL, United Kingdom

Location

Related Publications (14)

• 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.

  PMID: 26205371 BACKGROUND

• 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.

  PMID: 16120836 BACKGROUND

• 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.

  PMID: 11935057 BACKGROUND

• 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.

  PMID: 10657420 BACKGROUND

• 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.

  PMID: 23591673 BACKGROUND

• 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.

  PMID: 25297823 BACKGROUND

• 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.

  PMID: 16003690 BACKGROUND

• 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.

  PMID: 17365886 BACKGROUND

• 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.

  PMID: 17281841 BACKGROUND

• 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.

  BACKGROUND

• 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.

  PMID: 22205862 BACKGROUND

• 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.

  PMID: 16445257 BACKGROUND

• Lara OD, Labrador MA. A Survey on Human Activity Recognition using Wearable Sensors. IEEE Communications Surveys & Tutorial 15(3), 2013.

  BACKGROUND

• 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.

  PMID: 25436652 BACKGROUND

MeSH Terms

Conditions

Stroke

Condition Hierarchy (Ancestors)

Cerebrovascular Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Vascular Diseases; Cardiovascular Diseases

Study Officials

• Frances Johnston, MSc

  Northern Health and Social Care Trust

  STUDY DIRECTOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: December 13, 2018
• First Posted: December 26, 2018
• Study Start: November 23, 2018
• Primary Completion: June 12, 2020
• Study Completion: June 12, 2020
• Last Updated: June 16, 2020

Record last verified: 2020-06

Locations

IT (1); GB (1)