Actimetry Protocol in COPD Patients

NCT05918003 | Unknown

• 1 other identifier: ACCPP-PROT2022-02
• Study Type: observational
• Target: 104
• Locations: 1 country
• Sites: 1

Brief Summary

Recently, the principal investigator published an EI predictive Machine Learning algorithm based solely on clinical data, without any physical activity measures, collected from 1 409 patients. The GOLD standard of EI was defined on the basis of interrogation criteria. Patients considered as EI reported walking less than 10 minutes per day on average, and the pulmonologist judged that the patient had mainly "domestic activities". Despite the subjective nature of the GOLD standard, the algorithm validated on a test sample had an error rate of only 13.7% (AUROC: 0.84, CI95% \[0.75-0.92\]). In the total study population (n=1409), 34% of patients were ultimately classified as EIs by the algorithm, in agreement with the results of studies using actimetry as the GOLD standard. The principal investigator now wish to verify and improve the validity of the MLA on a new smaller population of 104 patients, using a physiological GOLD standard such as three-dimensional actimetry.

Conditions

COPD; Inactivity, Physical

Keywords

COPD; Physical Inactivity; Medical Decision; Machine Learning; Respiratory Medicine

Outcome Measures

Primary Outcomes (1)

• AUC-ROC as the primary endpoint to judge the performance of the algorithm

  A contingency table recording the algorithm's performance metrics will be constructed in parallel from the actimetry data. The AUC-ROC will be used as the primary endpoint to judge the performance of the algorithm.

  December 2023

Eligibility Criteria

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

Study Population

Any patient with a diagnosis of COPD, all GOLD stages combined, who receives a properly informed Colibri-COPD digital consultation and who gives his or her informed consent to participate in this study.

You may qualify if:

• Any patient with a diagnosis of COPD, all GOLD stages combined, who receives a properly informed Colibri-COPD digital consultation.

You may not qualify if:

• Patients with other unstable conditions with treatment,
• Unstable patients:
• Who had an exacerbation in the previous 2 months,
• Patients who have had surgery, a heart attack, a fall, or an accident limiting usual movements in the previous 3 months.
• Protected patients within the meaning of the French Public Health Code

Sponsors & Collaborators

• Association pour la Complementarite des Connaissances et des Pratiques de la Pneumologie: lead
• Icadom: collaborator

Study Sites (1)

Private Practice

Grenoble, France

RECRUITING

Related Publications (6)

• Watz H, Pitta F, Rochester CL, Garcia-Aymerich J, ZuWallack R, Troosters T, Vaes AW, Puhan MA, Jehn M, Polkey MI, Vogiatzis I, Clini EM, Toth M, Gimeno-Santos E, Waschki B, Esteban C, Hayot M, Casaburi R, Porszasz J, McAuley E, Singh SJ, Langer D, Wouters EF, Magnussen H, Spruit MA. An official European Respiratory Society statement on physical activity in COPD. Eur Respir J. 2014 Dec;44(6):1521-37. doi: 10.1183/09031936.00046814. Epub 2014 Oct 30.

  PMID: 25359358 BACKGROUND

• Furlanetto KC, Donaria L, Schneider LP, Lopes JR, Ribeiro M, Fernandes KB, Hernandes NA, Pitta F. Sedentary Behavior Is an Independent Predictor of Mortality in Subjects With COPD. Respir Care. 2017 May;62(5):579-587. doi: 10.4187/respcare.05306. Epub 2017 Mar 7.

  PMID: 28270544 BACKGROUND

• Rabinovich RA, Louvaris Z, Raste Y, Langer D, Van Remoortel H, Giavedoni S, Burtin C, Regueiro EM, Vogiatzis I, Hopkinson NS, Polkey MI, Wilson FJ, Macnee W, Westerterp KR, Troosters T; PROactive Consortium. Validity of physical activity monitors during daily life in patients with COPD. Eur Respir J. 2013 Nov;42(5):1205-15. doi: 10.1183/09031936.00134312. Epub 2013 Feb 8.

  PMID: 23397303 BACKGROUND

• Schneider LP, Furlanetto KC, Rodrigues A, Lopes JR, Hernandes NA, Pitta F. Sedentary Behaviour and Physical Inactivity in Patients with Chronic Obstructive Pulmonary Disease: Two Sides of the Same Coin? COPD. 2018 Oct;15(5):432-438. doi: 10.1080/15412555.2018.1548587.

  PMID: 30822241 BACKGROUND

• van Gestel AJ, Clarenbach CF, Stowhas AC, Rossi VA, Sievi NA, Camen G, Russi EW, Kohler M. Predicting daily physical activity in patients with chronic obstructive pulmonary disease. PLoS One. 2012;7(11):e48081. doi: 10.1371/journal.pone.0048081. Epub 2012 Nov 2.

  PMID: 23133612 BACKGROUND

• Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One. 2017 Apr 4;12(4):e0174944. doi: 10.1371/journal.pone.0174944. eCollection 2017.

  PMID: 28376093 BACKGROUND

MeSH Terms

Conditions

Pulmonary Disease, Chronic Obstructive; Sedentary Behavior

Condition Hierarchy (Ancestors)

Lung Diseases, Obstructive; Lung Diseases; Respiratory Tract Diseases; Chronic Disease; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Behavior

Study Officials

• Bernard Aguilaniu, M.D. PhD

  Association pour la Complementarite des Connaissances et des Pratiques de la Pneumologie

  STUDY DIRECTOR

Central Study Contacts

Anne Rigal, Pharma. S.

CONTACT

+33 685 317 892; anne.rigal@colibri-pneumo.fr

Lara Andres-Rieth

CONTACT

recherche@accpp.fr

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Year
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 13, 2023
• First Posted: June 26, 2023
• Study Start: January 1, 2023
• Primary Completion: September 30, 2023
• Study Completion: December 31, 2023
• Last Updated: June 26, 2023

Record last verified: 2023-06

Locations

FR (1)