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| Name | Class |
|---|---|
| LTSI-INSERM U1099 | UNKNOWN |
| CIC-IT 14-14 | UNKNOWN |
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The SHERPAM project is part of a scientific and technological context which aim is to record, transmit, analyse the physiological parameters of a patient, as well as to record the feedback to the patient and health professional to suggest the best individualised attitude. The questions of SHERPAM are generic. However, two specific applications will be addressed, in which the partners have already acquired some expertise: the recognition and quantification of physical activity with energy expenditure estimation, and the assessment of walking ability in patients with obliterative vascular disease in the lower limbs. Another application concerns the monitoring of the practice of physical activity and some biological signs in subjects with cardiovascular risk and in cardiac patients (arrhythmogenic diseases). Despite various clinical contexts and health goals, a common approach will be developed.
To assess the SHERPAM Device (DS) by the continuous acquisition and transmission of data (accelerations, rotations, alterations in thoracic volume, heart rate, electrocardiogram) in a real life physical activity practice (subjects in their living environment and during their usual physical practices) using the DS's real-time communication tools, automatic data processing, and the DS's ability to produce information.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Healthy volunteers | Experimental | Sensors assigned for 3 weeks |
|
| Patients with arythmic disease or peripheral vascular disease | Experimental | Sensors assigned for 3 weeks |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| sensors | Device | Acquisition and transmission of exploitable recordings in the public targeted by the DS, that is to say which allow to draw clinical information in relation to the objectives of the DS (detection of the cardiac rhythm): data without artefact (saturation), in a good signal to noise ratio. |
| Measure | Description | Time Frame |
|---|---|---|
| asses the continuous acquisition of data by sensors | transmission and reception of physiological parameters of a patient | every day (during 3 weeks) |
| Measure | Description | Time Frame |
|---|---|---|
| Test acceptability of SHERPAM Device | questionnary | after 7 days of use |
| Test acceptability of Sherpam Device | questionnary | after 21 days of use |
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Inclusion Criteria:
Healthy volunteers Healthy active subjects aged 50 or over (ie without diagnosed disease, without chronic treatment) and recruited in the sports associations of Ille et Vilaine (cycle tourism clubs).
Common to all subjects
Exclusion Criteria:
Common to all subjects
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| Name | Affiliation | Role |
|---|---|---|
| Carre François, MD | University of Rennes | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Unité de Biologie et médecine du Sport | Rennes | Brittany Region | 35000 | France |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 30818285 | Result | Houssein A, Ge D, Gastinger S, Dumond R, Prioux J. Estimation of respiratory variables from thoracoabdominal breathing distance: a review of different techniques and calibration methods. Physiol Meas. 2019 Apr 3;40(3):03TR01. doi: 10.1088/1361-6579/ab0b63. | |
| 28612121 | Result | Dumond R, Gastinger S, Rahman HA, Le Faucheur A, Quinton P, Kang H, Prioux J. Estimation of respiratory volume from thoracoabdominal breathing distances: comparison of two models of machine learning. Eur J Appl Physiol. 2017 Aug;117(8):1533-1555. doi: 10.1007/s00421-017-3630-0. Epub 2017 Jun 13. |
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| ID | Term |
|---|---|
| D009043 | Motor Activity |
| ID | Term |
|---|---|
| D001519 | Behavior |
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Prospective, monocentric, with healthy volunteers and patients
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|
| Test usability of Sherpam Device | questionnary | after 7 days of use |
| Test usability of Sherpam Device | questionnary | after 21 days of use |
| 31217092 | Result | Khreis S, Ge D, Rahman HA, Carrault G. Breathing Rate Estimation Using Kalman Smoother With Electrocardiogram and Photoplethysmogram. IEEE Trans Biomed Eng. 2020 Mar;67(3):893-904. doi: 10.1109/TBME.2019.2923448. Epub 2019 Jun 17. |