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| Name | Class |
|---|---|
| Northern Health and Social Care Trust | OTHER_GOV |
| Azienda Sanitaria Locale 3, Torino | OTHER |
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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.
Some relevant studies have indicated that approximately 36% of these survivors (i.e. more than 9 million in 2013 only) are left with significant disabilities 5 years after their stroke, and >40% (i.e. more than 10 million) require assistance with activities of daily living.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| Device-related adverse effects | Number of device-related adverse effects on the total number of adverse events reported. | Through study completion, an average of 15 months |
| Modified Rankin Score change (N.Ireland) |
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Inclusion Criteria:
Exclusion Criteria:
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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.
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| Name | Affiliation | Role |
|---|---|---|
| Frances Johnston, MSc | Northern Health and Social Care Trust | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Azienda Sanitaria Locale 3, Torino | Pinerolo | 10064 | Italy | |||
| Northern Health and Social Care Trust |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 26205371 | Background | 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. | |
| 16120836 | 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. |
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| ID | Term |
|---|---|
| D020521 | Stroke |
| ID | Term |
|---|---|
| D002561 | Cerebrovascular Disorders |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
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|
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 |
The modified Rankin Score measures the degree of disability or dependence in the daily activities of people who have suffered a stroke or other causes of neurological disability.
The scale has the following items and associated values:
No symptoms at all = 0 No significant disability despite symptoms; able to carry out all usual duties and activities = +1 Slight disability; unable to carry out all previous activities, but able to look after own affairs without assistance = +2 Moderate disability; requiring some help, but able to walk without assistance = +3 Moderately severe disability; unable to walk and attend to bodily needs without assistance = +4 Severe disability; bedridden, incontinent and requiring constant nursing care and attention = +5 Dead = +6 This clinical outcome is used in Northern Ireland (UK) as part of the clinical practice.
| Change at 6-month from baseline |
| Barthel Index change (Italy) | The Barthel Index for Activities of Daily Living (ADL) assesses functional independence in stroke patients. To each area, a score 0 (=impaired), 5 (needs help) or 10 (=independent) is to be associated. Areas included are: feeding, bathing, grooming, dressing, bowel control, bladder control, toilet use, transfers, mobility on level surfaces, stairs. Score 5 is not allowed for some of the areas enlisted. The score is calculated as sum of the value achieved in each area. This clinical outcome is used in Italy as part of the clinical practice. | Baseline assessment and 6-month visit |
| Euro Quality of Life - 5 Dimension (EQ-5D) Health Questionnaire summary index | Euro Quality of Life - 5 Dimension (EQ-5D) is a standardised instrument that measures the health-related quality of life. It consists of a descriptive system and a Visual Analogue Scale (VAS). The descriptive system comprises 5 dimensions: mobility, self-care, usual activities, pain/discomfort and anxiety/depression. Each dimension is divided into 3 levels (1 = no problem, 2 = some problems, 3 = to extreme problems). A unique health state is defined by combining 1 level from each of the 5 dimensions. Each state is represented by a 5 digit code (eg. 11111 = no problems on any of the 5 dimensions; 11223 = no problems with mobility and self care, some problems with usual activities, moderate pain or discomfort and extreme anxiety or depression). The EQ VAS records the patient's self-rated health (from 100 = best, to 0 = worst imaginable state). The EQ VAS is used to convert the EQ-5D states into a single index value, based on reference values available on EuroQoL Group website. | 6-month evaluation |
| Euro Quality of Life - 5 Dimension (EQ-5D) Health Questionnaire summary index change | The Change from baseline measured after 6-month is calculated as the difference between the two average (i.e. 6-month and V0) summary indices. The descriptive system comprises 5 dimensions: mobility, self-care, usual activities, pain/discomfort and anxiety/depression. Each dimension is divided into 3 levels (1 = no problem, 2 = some problems, 3 = to extreme problems). A unique health state is defined by combining 1 level from each of the 5 dimensions. Each state is represented by a 5 digit code (eg. 11111 = no problems on any of the 5 dimensions; 11223 = no problems with mobility and self care, some problems with usual activities, moderate pain or discomfort and extreme anxiety or depression). The EQ VAS records the patient's self-rated health (from 100 = best, to 0 = worst imaginable state). The EQ VAS is used to convert the EQ-5D states into a single index value, based on reference values available on EuroQoL Group website. | Baseline assessment and 6-month visit |
| Signs of Depression Scale (SODS, N.Ireland) | Number of patients with a depressive mood, as assessed with the Signs of Depression Scale (SODS, English version used in N.Ireland). The scale consists of 6 questions for which allowed answers are yes (value = 1) or no (Value = 0). The total score is the sum of the values of each answer provided. | Baseline assessment and 6-month visit |
| Zung Self-Rating Depression Scale (SDS, Italy) | Number of patients with a depressive mood, as assessed with the Zung Self-Rating Depression Scale (SDS) (Italian validated questionnaire). The Zung Self-Rating Depression Scale is a short self-administered survey to quantify the depressed status of a patient. There are 20 items on the scale that rate the four common characteristics of depression: the pervasive effect, the physiological equivalents, other disturbances, and psychomotor activities. There are ten positively worded and ten negatively worded questions. Each question is scored on a scale of 1-4 (a little of the time, some of the time, good part of the time, most of the time). The scores range from 25-100. | Baseline assessment and 6-month visit |
| Resource consumption | Average number of unscheduled face-to-face visits required | Through study completion, an average of 15 months |
| Antrim |
| Northern Ireland |
| BT412RL |
| United Kingdom |
| 11935057 | 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. |
| 10657420 | 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. |
| 23591673 | 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. |
| 25297823 | 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. |
| 16003690 | 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. |
| 17365886 | 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. |
| 17281841 | 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. |
| 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. |
| 22205862 | 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. |
| 16445257 | 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. |
| Background | Lara OD, Labrador MA. A Survey on Human Activity Recognition using Wearable Sensors. IEEE Communications Surveys & Tutorial 15(3), 2013. |
| 25436652 | 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. |
| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |