COPDNCT05918003
Actimetry Protocol in COPD Patients
An observational study of COPD and Inactivity, Physical, run by Association pour la Complementarite des Connaissances et des Pratiques de la Pneumologie at 1 site in France.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- COPD, Inactivity, Physical
- How many people
- 104 participants
- Where
- 1 siteFrance
- Study dates
- 1 Jan 2023to 31 Dec 2023
- How long will I take part?
- 1 Year
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- Any patient with a diagnosis of COPD, all GOLD stages combined, who receives a properly informed Colibri-COPD digital consultation.
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
Lara Andres-Rieth contact
France1 site, 1 recruiting
- Recruiting
Private Practice
Grenoble
Anne Rigal, Pharm. S.
Bernard Aguilaniu, M.D. PhD
About this study
Official title: Validation by Three-dimensional Actimetry Measurements of a Predictive Algorithm for Excessive Inactivity in Chronic Obstructive Pulmonary Disease (COPD) Patients
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.
Read the detailed description
It has been shown that COPD patients have a significantly decreased daily physical activity (DPA) compared to matched subjects. Moreover, the severity of inactivity is correlated with several prognostic indices such as the frequency of exacerbations, quality of life and mortality. These findings lead to the recommendation, with a level of evidence A, of DPA in the context of medically supervised respiratory rehabilitation programs and/or by encouraging patients to participate in programs promoting physical activity.
However, despite the established benefits, it is estimated that this rehabilitative management actually involves only 10% of the patients who should benefit from it. Among the various causes of this situation, the underestimation of excessive inactivity (EI) by pulmonologists is one of the causes of this care deficit.
Currently, only actimetry can accurately assess the patient's level of physical activity.
To alert pulmonologists to this excessive situation justifying priority care, without resorting to actimetry, the aCCPP developed a Machine Learning Algorithm (MLA) based on clinical data from the Colibri-BPCO digital consultation that predicts excessive inactivity.
In this study, the GOLD standard EI was defined using clinical criteria summarized below. EI patients reported walking for an average of less than 10 minutes per day, and the pulmonologist judged on questioning that the DPA was indeed essentially "domestic." The objective of the MLA was to correctly classify EI subjects versus obviously active subjects hereafter referred to as Overtly Active (OA).
The MLA was validated on a test sample with an error rate of 13.7% (AUROC: 0.84, IC95% [0.75- 0.92]). In the total population studied (n=1409), 34% of patients were finally classified as EIs, in line with the results of studies using actimetry as the GOLD standard.
Following the publication of this work , the principal investigator would like to verify the validity of the algorithm on a new population using the recognized GOLD standard: three-dimensional actimetry measurements.
- Conditions
- COPD, Inactivity, Physical
- Keywords
- COPD, Physical Inactivity, Medical Decision, Machine Learning, Respiratory Medicine
- Medical subject headings
- Pulmonary Disease, Chronic Obstructive, Sedentary Behavior
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: December 2023
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Jun 2023
- Study start1 Jan 2023
- Primary completion30 Sep 2023 (estimated)
- Study completion31 Dec 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for COPDStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 104 participants (estimated)
- Target duration
- 1 Year
Sponsor and investigators
- Lead sponsor
- Association pour la Complementarite des Connaissances et des Pratiques de la Pneumologie
- Sponsor type
- University, hospital or other
- Collaborators
- Icadom
- Responsible party
- Sponsor
- Investigators
- Bernard Aguilaniu, M.D. PhD, Study director, Association pour la Complementarite des Connaissances et des Pratiques de la Pneumologie
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05918003
- Sponsor's protocol ID
- ACCPP-PROT2022-02
- Status last verified
- Jun 2023
- First submitted
- 13 Jun 2023
- Last updated
- 26 Jun 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
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.
PubMed 25359358Furlanetto 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.
PubMed 28270544Rabinovich 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.
PubMed 23397303Schneider 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.
PubMed 30822241van 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.
PubMed 23133612Weng 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.
PubMed 28376093
Study at a glance
From the registry record, last updated by the sponsor on 26 Jun 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Validation by Three-dimensional Actimetry Measurements of a Predictive Algorithm for Excessive Inactivity in Chronic Obstructive Pulmonary Disease (COPD) Patients
- ClinicalTrials.gov ID
- NCT05918003
- Acronym / sponsor ID
- ACCPP-PROT2022-02
- Conditions
- COPD, Inactivity, Physical
- Keywords
- COPD, Physical Inactivity, Medical Decision, Machine Learning, Respiratory Medicine
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 104 participants (estimated)
- Lead sponsor
- Association pour la Complementarite des Connaissances et des Pratiques de la PneumologieUniversity, hospital or other
- Collaborators
- Icadom
- Study start
- 1 Jan 2023 (actual)
- Primary completion
- 30 Sep 2023 (estimated)
- Study completion
- 31 Dec 2023 (estimated)
- First posted
- 26 Jun 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
It has been shown that COPD patients have a significantly decreased daily physical activity (DPA) compared to matched subjects. Moreover, the severity of inactivity is correlated with several prognostic indices such as the frequency of exacerbations, quality of life and mortality. These findings lead to the recommendation, with a level of evidence A, of DPA in the context of medically supervised respiratory rehabilitation programs and/or by encouraging patients to participate in programs promoting physical activity.
However, despite the established benefits, it is estimated that this rehabilitative management actually involves only 10% of the patients who should benefit from it. Among the various causes of this situation, the underestimation of excessive inactivity (EI) by pulmonologists is one of the causes of this care deficit.
Currently, only actimetry can accurately assess the patient's level of physical activity.
To alert pulmonologists to this excessive situation justifying priority care, without resorting to actimetry, the aCCPP developed a Machine Learning Algorithm (MLA) based on clinical data from the Colibri-BPCO digital consultation that predicts excessive inactivity.
In this study, the GOLD standard EI was defined using clinical criteria summarized below. EI patients reported walking for an average of less than 10 minutes per day, and the pulmonologist judged on questioning that the DPA was indeed essentially "domestic." The objective of the MLA was to correctly classify EI subjects versus obviously active subjects hereafter referred to as Overtly Active (OA).
The MLA was validated on a test sample with an error rate of 13.7% (AUROC: 0.84, IC95% [0.75- 0.92]). In the total population studied (n=1409), 34% of patients were finally classified as EIs, in line with the results of studies using actimetry as the GOLD standard.
Following the publication of this work , the principal investigator would like to verify the validity of the algorithm on a new population using the recognized GOLD standard: three-dimensional actimetry measurements.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Year
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (1 listed)
- Any patient with a diagnosis of COPD, all GOLD stages combined, who receives a properly informed Colibri-COPD digital consultation.
Key exclusion criteria (5 listed)
- Patients with other unstable conditions with treatment,
- Unstable patients:
- Protected patients within the meaning of the French Public Health Code
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
AUC-ROC as the primary endpoint to judge the performance of the algorithm
Time frame: December 2023
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.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Jun 2023.
- Background: PubMed 25359358Watz 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.
- Background: PubMed 28270544Furlanetto 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.
- Background: PubMed 23397303Rabinovich 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.
- Background: PubMed 30822241Schneider 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.
- Background: PubMed 23133612van 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.
- Background: PubMed 28376093Weng 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.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Lara Andres-Rieth contact
Bernard Aguilaniu, M.D. PhD study director
Association pour la Complementarite des Connaissances et des Pratiques de la Pneumologie
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of COPD and Inactivity, Physical, run by Association pour la Complementarite des Connaissances et des Pratiques de la Pneumologie at 1 site in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 26 Jun 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Anne Rigal, Pharma. S. (+33 685 317 892, anne.rigal@colibri-pneumo.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AUC-ROC as the primary endpoint to judge the performance of the algorithm (time frame: December 2023). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05918003, which also lists 6 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Jun 2023.