Sensors for HEalth Recording and Physical Activity Monitoring

NCT03673189 | Completed

• 1 other identifier: 35RC17_8836
• Study Type: interventional
• Target: 30
• Locations: 1 country
• Sites: 1

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.

Conditions

Physical Activity

Keywords

Sensors; Health Recording

Outcome Measures

Primary Outcomes (1)

• asses the continuous acquisition of data by sensors

  transmission and reception of physiological parameters of a patient

  every day (during 3 weeks)

Secondary Outcomes (4)

• Test acceptability of SHERPAM Device

  after 7 days of use

• Test acceptability of Sherpam Device

  after 21 days of use

• Test usability of Sherpam Device

  after 7 days of use

• Test usability of Sherpam Device

  after 21 days of use

Study Arms (2)

Healthy volunteers

EXPERIMENTAL

Sensors assigned for 3 weeks

Device: sensors

Patients with arythmic disease or peripheral vascular disease

EXPERIMENTAL

Sensors assigned for 3 weeks

Device: sensors

Interventions

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.

Healthy volunteers; Patients with arythmic disease or peripheral vascular disease

Eligibility Criteria

• Age: 18 Years - 75 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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

You may not qualify if:

• 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.

Sponsors & Collaborators

• Rennes University Hospital: lead
• LTSI-INSERM U1099: collaborator
• CIC-IT 14-14: collaborator

Study Sites (1)

Unité de Biologie et médecine du Sport

Rennes, Brittany Region, 35000, France

Location

Related Publications (3)

• 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.

  PMID: 30818285 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.

  PMID: 28612121 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.

  PMID: 31217092 RESULT

MeSH Terms

Conditions

Motor Activity

Condition Hierarchy (Ancestors)

Behavior

Study Officials

• Carre François, MD

  Rennes University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: DEVICE FEASIBILITY
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: Prospective, monocentric, with healthy volunteers and patients

Study Record Dates

• First Submitted: June 12, 2018
• First Posted: September 17, 2018
• Study Start: June 28, 2018
• Primary Completion: September 12, 2019
• Study Completion: September 12, 2019
• Last Updated: May 24, 2023

Record last verified: 2023-05

Data Sharing

• IPD Sharing: Will not share

Locations

FR (1)