Clinical trial NCT03673189: Sensors for HEalth Recording and Physical Activity Monitoring
Official title: Sensors for HEalth Recording and Physical Activity Monitoring
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Physical Activity
Keywords: Sensors
Health Recording
Lead sponsor: Rennes University Hospital
Sponsor class: OTHER
Collaborators: name: LTSI-INSERM U1099
name: CIC-IT 14-14
First posted: 2018-09-17
Enrollment: 30
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NON_RANDOMIZED
Intervention model: PARALLEL
Intervention model description: Prospective, monocentric, with healthy volunteers and patients
Primary purpose: DEVICE_FEASIBILITY

Brief summary: 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.

Detailed description: 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.

Eligibility criteria: 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).

  * having benefited from an oral or written prescription of physical activity carried out by a health professional and applying this prescription daily in autonomy or in a sports club.

Common to all subjects

* physically active (adherent to a club or sports association or practicing independently according to the recommendations of their physician);
* practicing at least once a week;
* residence located less than 100 km return from Rennes University Hospital
* affiliate or beneficiary of a social protection scheme;
* having given his written consent

Exclusion Criteria:

Common to all subjects

* wearing a pacemaker or implanted cardiac defibrillator (precaution because using telemetry);
* participation in another research protocol;
* persons aver 18 yrs-old subject to legal protection (legal safeguards, guardianship, tutorship), persons deprived of their liberty;
* pregnant or nursing woman.

Primary outcomes:
measure: asses the continuous acquisition of data by sensors
timeFrame: every day (during 3 weeks)
Secondary outcomes:
measure: Test acceptability of SHERPAM Device
timeFrame: after 7 days of use
measure: Test acceptability of Sherpam Device
timeFrame: after 21 days of use
measure: Test usability of Sherpam Device
timeFrame: after 7 days of use
measure: Test usability of Sherpam Device
timeFrame: after 21 days of use
Other outcomes:
Locations:
city: Rennes
country: France
facility: Unité de Biologie et médecine du Sport
state: Bretagne
Study officials:
affiliation: Rennes University
name: Carre François, MD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
citation: 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.