Validation by Three-dimensional Actimetry Measurements of a Predictive Algorithm for Excessive Inactivity in Chronic Obstructive Pulmonary Disease (COPD) Patients
Conditions: COPD, Inactivity, Physical
Sponsor: Association pour la Complementarite des Connaissances et des Pratiques de la Pneumologie

 
1 
 
 
 
 
Protocol No: P2022-02 
IDRCB N°: 2022-A00488-35 
 
 
VERSION N°3 OF 30/09/2022 
 
 
Validation by three-dimensional actimetry measurements of a predictive 
algorithm for excessive inactivity in COPD patients  
Multicenter study 

 
2 
 
 
 
INFORMATION SHEET 
 
First Name: Bernard 
 
Last Name: AGUILANIU 
Date of birth: 05.01.1954 
Address: aCCPP, 19 Av. Marcelin Berthelot, 38100 Grenoble  
Telephone: +33680984146 
E-mail: b.aguilaniu@gmail.com  
Title: Associate Professor of Pulmonology 
Duties: Pulmonologist (Private Practice) 
Name of the Head of Department or Laboratory: NA 
Project title: Validation of a predictive algorithm for excessive inactivity in COPD patients using three-
dimensional actimetry measurements  
Project sites: Pulmonologists (University Hospitals, General Hospitals, Private Hospitals) in Auvergne 
Rhône-Alpes and Alpes Maritimes, France 
Schedule:   
• 
Start of inclusions: January 2023 
• 
End of inclusions: July 2023 
• 
End of the study: December 2023 
Name and address of the structure that carries the research project: 
aCCPP, Association pour la Complémentarité des Connaissances et des Pratiques en Pneumologie, 
represented by its President (Pr Christophe Pison) and Vice-President (Dr Eric Kelkel), 19 avenue 
Marcelin Berthelot, 38 100 Grenoble – FRANCE 
Email aCCPP: recherche@accpp.fr 
Email contact: anne.rigal@colibri-pneumo.fr  
 
Address: 19 avenue Marcelin Berthelot, 38 100 Grenoble – FRANCE 
Telephone: +33685317892 
 
Data Protection Officer: 
Email: dpo@colibri-pneumo.fr  
Address: 19 avenue Marcelin Berthelot, 38 100 Grenoble – FRANCE 
 
CLASSIFICATION OF THE STUDY OR RESEARCH PROJECT: COPD (other respiratory diseases / non-
transmissible chronic respiratory diseases) 

 
3 
 
 
 
 
PROTOCOL SIGNATURE PAGE 
 
Study title: Validation of a predictive algorithm for excessive inactivity in COPD patients using three-
dimensional actimetry measurements 
Protocol version and date: version n°3 of 30/09/2022 
Internal number: P2022-02 
This protocol has been read and approved on the date noted below. 
The parties agree to conduct the research in accordance with the protocol and the legislative and 
regulatory provisions in force. 
Sponsor's representative: aCCPP 
Mr. Christophe Pison 
President 
19 avenue Marcelin Berthelot 
38 100 GRENOBLE 
Date: 30/09/2022 
Signature 
 
Mrs Anne Rigal 
Project Manager 
19 avenue Marcelin Berthelot 
38 100 GRENOBLE 
Date: 30/09/2022 
Signature 
 
Principal Investigator/Coordinator 
Pr. Bernard Aguilaniu 
Pulmonologist (Private Practice) 
14 Jean Bocq Street  
38 000 GRENOBLE 
Date: 30/09/2022 
Signature 
 
 
 
 

 
4 
 
 
 
TABLE OF CONTENTS 
INFORMATION SHEET ............................................................................................................................. 2 
PROTOCOL SIGNATURE PAGE ................................................................................................................. 3 
TABLE OF CONTENTS .............................................................................................................................. 4 
SUMMARY .............................................................................................................................................. 6 
ABSTRACT IN ENGLISH FOR CLINICALTRIALS.GOV .................................................................................. 7 
PROJECT SYNOPSIS ................................................................................................................................. 9 
A. 
Title ............................................................................................................................................ 9 
B. 
Sponsor ...................................................................................................................................... 9 
C. 
Site(s).......................................................................................................................................... 9 
D. 
Objective .................................................................................................................................... 9 
E. 
Methodology .............................................................................................................................. 9 
F. 
Number of subjects .................................................................................................................... 9 
G. 
Inclusion criteria ......................................................................................................................... 9 
H. 
Exclusion Criteria ........................................................................................................................ 9 
I. 
Evaluation criteria & statistics .................................................................................................... 9 
J. 
Information for the Scientific Council ....................................................................................... 10 
STUDY OR RESEARCH PROJECT BUDGET .............................................................................................. 10 
A. 
Expenses ................................................................................................................................... 10 
A. 
Funding ..................................................................................................................................... 11 
LIST OF RESEARCHERS PARTICIPATING IN THE RESEARCH ................................................................... 11 
PUBLICATIONS OF THE TEAM IN THE RELATED FIELD........................................................................... 12 
PRESENTATION OF THE RESEARCH ....................................................................................................... 12 
A. 
Title .......................................................................................................................................... 12 
B. 
Objectives of the study or research .......................................................................................... 13 
C. 
Current status of the issue ....................................................................................................... 13 
D. 
Short bibliography on the theme ............................................................................................. 14 
E. 
Application perspective ............................................................................................................ 15 
PROTOCOL DESCRIPTION ..................................................................................................................... 15 
A. 
Type of cohort .......................................................................................................................... 15 
B. 
Materials and Populations ........................................................................................................ 15 
C. 
Methodological approaches and patient number calculation .................................................. 17 
D. 
Evaluation criteria .................................................................................................................... 17 

 
5 
 
 
 
E. 
Primary and associated factors whose influence is assessed ................................................... 18 
F. 
Main experimental steps .......................................................................................................... 18 
G. 
Risks, constraints and expected benefits .................................................................................. 19 
H. 
Follow-up of the French HURIET law guidelines ....................................................................... 20 
I. 
Key words ................................................................................................................................. 20 
J. 
Suggested experts .................................................................................................................... 20 
GLOSSARY ............................................................................................................................................. 21 
 

 
6 
 
 
 
SUMMARY 
Project title: Validation of a predictive algorithm for excessive inactivity in COPD patients using 
three-dimensional actimetry measurements. 
Name of the candidate: Pr. Bernard Aguilaniu for the aCCPP 
Summary: 
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, we 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). 
 
  
 

 
7 
 
 
 
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 work1 , we would like to verify the validity of the algorithm on 
a new population using the recognized GOLD standard: three-dimensional actimetry 
measurements. 
ABSTRACT IN ENGLISH FOR CLINICALTRIALS.GOV 
Title 
Validation of a predictive algorithm for excessive inactivity (EI) in COPD 
patients by three-dimensional actimetry measurements 
Collaborators (funding 
agency) 
• 
Association pour la Complémentarité des Connaissances et des 
Pratiques de la Pneumologie (aCCPP) 
• 
Fondation Banque Populaire Auvergne Rhône-Alpes 
Brief summary 
(10-12 lines) 
Recently, we 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 our 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.  
We 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. 
Observational Study 
Model  
☐Cohort      ☐ Case Control 
☐ Case Only    ☐ Case Crossover 
☐ Ecologic or community         ☐ Family Based 
☒ Other      
Time Perspective  
☐ Retrospective             ☒ Prospective 
☐ Cross sectional         ☐ Other 
 
1 B. Aguilaniu et al. A machine learning approach to predict extreme inactivity in COPD patients using non-
activity-related clinical data, PlosOne 2021 

 
8 
 
 
 
Number of arm(s) (write 
"1" if a single arm) 
1 
Number of groups and 
interventions 
1 
Follow-Up duration 
1 year 
 
Primary outcome 
measure 
A contingency table recording the algorithm's performance metrics will 
be constructed in parallel with the actimetry data. The AUC-ROC will be 
used as the primary outcome to assess the performance of the 
algorithm. 
Time point(s) at which 
primary outcome 
measure is assessed 
September 2023 
 
Secondary outcome 
measures* 
n/a 
Time point(s) at which 
Secondary outcome 
measure is assessed* 
n/a 
 
Inclusion criteria* 
All patients with a diagnosis of COPD, all GOLD stages combined, who 
receive a properly informed Colibri-BPCO digital consultation. 
Exclusion criteria* 
• 
Patients receiving treatment for another unstable pathology,  
• 
Unstable patients:  
o Patients who had an exacerbation in the previous 2 
months,  
o Patients who have had surgery, an infarction, a fall, or 
an accident limiting usual movements in the previous 
3 months. 
o Protected patients within the meaning of the French 
Public Health Code (Code de la Santé Publique (CSP)) 
Number of subjects (or in 
each group)  
104 
Date of start and end of 
inclusions 
January 2023 to July 2023 
* Record only the top 2-3 secondary endpoints and the top 5-10 inclusion/non-inclusion criteria 
 
 

 
9 
 
 
 
PROJECT SYNOPSIS 
A. 
Title 
Validation by three-dimensional actimetry measurements of a predictive algorithm for excessive 
inactivity in COPD patients 
B. 
Sponsor 
The aCCPP, association pour la Complémentarité des Connaissances et des Pratiques de la 
Pneumologie, represented by its President (Pr Christophe Pison) and Vice-President (Dr Eric Kelkel) 
C. 
Site(s) 
Pulmonologists (University Hospitals, General Hospitals, Private Hospitals) in Auvergne Rhône-Alpes 
and Alpes Maritimes, France  
D. 
Objective 
To validate a predictive algorithm for excessive inactivity in COPD patients using three-dimensional 
actimetry measurements. 
E. 
Methodology 
Recording of daily physical activities by three-dimensional actimetry for 7 consecutive days (WE 
included) in all COPD patients whose clinical data are collected on the digital consultation Colibri-COPD. 
F. 
Number of subjects 
104 
G. 
Inclusion criteria 
Any patient with a diagnosis of COPD, all GOLD stages combined, who receives a properly informed 
Colibri-BPCO digital consultation.  
H. 
Exclusion Criteria 
- 
Patients with other unstable conditions with treatment,  
- 
Unstable patients:  
o who had an exacerbation in the previous 2 months,  
o 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 
I. 
Evaluation criteria & statistics 
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. 
The statistical analyses performed will focus on measuring the performance of the prediction algorithm. 
The metrics used will be the following: Overall error, Accuracy, Positive Predictive Value, Negative 

 
10 
 
 
 
Predictive Value, Sensitivity, Specificity, AUPRC (Area Under the Precision and Recall Curve) AUROC 
(Area Under the Receiving Operating characteristic Curve). Precision and AUROC values will be 
presented with their 95% confidence intervals. 
Clinical and functional characteristics of patients will be analyzed and compared by Kruskal–Wallis test 
and ANOVA for factors with ordered levels (ordAOV). 
J. 
Information for the Scientific Council 
• 
Study under the jurisdiction of the French Huriet law: NO  
• 
Research not involving the human person (RIPH) (1, 2 or 3): 3 
 
STUDY OR RESEARCH PROJECT BUDGET 
A. Expenses 
  
  
Estimated budget 
ACCPP: Project Sponsor 
1. Project structuring 
Project coordination, budget management 
                   4.000,00 €  
Drafting and submission of the protocol, 
information note, informed consent, drafting 
and submission of the annual report and the 
end of the study report 
                   4.000,00 €  
Regulatory submission 
                   1.000,00 €  
Production and archiving of the study’s 
documents 
                   1.200,00 €  
IT development 
                   3.600,00 €  
Writing and publishing the article 
                   2.500,00 €  
2. Implementation, 
monitoring and closure 
Recruitment and follow-up of investigators 
                   2.000,00 €  
Monitoring of Colibri data 
                   3.000,00 €  
Opening and closing of the centers 
                   3.800,00 €  
4. Data Management and 
Statistical Analysis 
Analysis of the actimetry data and the algorithm                    5.500,00 €  
TOTAL ACCPP 
  
                 30.600,00 €  
ICADOM: ACCPP's subcontractor for the Pre-Inactiv project 
3. Performing actimetry 
Flat rate of 195 Euros per patient, realized over 
7 days 
                 19.500,00 €  
TOTAL ICADOM 
  
                 19.500,00 €  
TOTAL BUDGET 
  
               50.100,00 €  
 
 
 

 
11 
 
 
 
A. 
Funding 
FUNDING €HT 
Category 
Amount 
CAPITAL RESOURCES 
20.100 € 
PATRONAGE 
30.000 € 
Fondation BP AURA 
10.000 € 
Other patron(s) 
20.000 € 
TOTAL FINANCING excluding 
valorizations 
50.100 € 
 
LIST OF RESEARCHERS PARTICIPATING IN THE RESEARCH 
Last Name 
First name 
Hospital/research unit 
Mailing address 
Phone 
1 
Claustre 
Johanna 
Métropole Savoie General 
Hospital – Aix-les-Bains 
49 avenue du Grand Port  
73 105 Aix-les-Bains – France 
+33479886161 
2 
Destors 
Marie 
Grenoble-Alpes University 
Hospital 
Boulevard de la Chantourne  
38 700 La Tronche – France 
+33476765085 
3 
Falque 
Loïc 
Grenoble-Alpes University 
Hospital 
Boulevard de la Chantourne  
38 700 La Tronche – France 
+33476765085 
4 
Leroy 
Sylvie 
Nice University Hospital 
30 avenue de la Voie Romaine 
06 001 Nice – France 
+33492038580 
5 
Marquette 
Charles-Hugo 
Nice University Hospital 
30 avenue de la Voie Romaine 
06 001 Nice – France 
+33492038580 
6 
Degoutte 
Aurélien 
Nice University Hospital 
30 avenue de la Voie Romaine 
06 001 Nice – France 
+33492038580 
7 
Louerat 
Cécile 
Bonnefon Private Hospital 
45 avenue Carnot  
30 104 Ales – France 
+33466072657 
8 
Bognie 
Michèle 
Bonnefon Private Hospital 
45 avenue Carnot  
30 104 Ales – France 
+33466072657 
9 
Aklouche 
Soualah 
Safia 
Bonnefon Private Hospital 
45 avenue Carnot  
30 104 Ales – France 
+33466072657 
10 Kelkel 
Eric 
Métropole Savoie General 
Hospital 
Place Lucien Biset 
73 000 Chambéry – France 
+33479965086 
11 Gheerbrant 
Hubert 
Métropole Savoie General 
Hospital 
Place Lucien Biset 
73 000 Chambéry – France 
+33479965086 
12 Bosc 
Cécile 
Private practice 
3 All. Paul Feval 
38 130 Échirolles – France 
+33476333476 
13 Arbib 
François 
Private practice 
3 All. Paul Feval 
38 130 Échirolles – France 
+33476333476 
14 Jenmard 
Michel 
Private practice 
3 All. Paul Feval 
38 130 Échirolles – France 
+33476333476 
15 Aguilaniu 
Bernard 
Private practice 
14 rue Jean Bocq 
38 000 Grenoble – France 
+33680984146 
16 Ferrer 
Léonie 
Private practice 
5 rue Felix Poulat 
38 000 Grenoble – France 
+33476547004 

 
12 
 
 
 
17 Badrignans 
Pauline 
Private practice 
3 All. Paul Feval 
38 130 Échirolles – France 
+33476333476 
18 Bertrand 
Dominique 
Private practice 
35b All. de Champrond 
38 330 Saint-Ismier – France 
+33476771243 
19 Pernot 
Julien 
Respiratory Rehabilitation 
Center Aix les Bains 
49 avenue du Grand Port 
73 100 Aix les Bains – France 
+33479965050 
20 Fedi 
Arnaud 
GHM of Grenoble 
8 rue Docteur Calmette 
38 000 Grenoble – France 
+33476707272 
 
PUBLICATIONS OF THE TEAM IN THE RELATED FIELD  
 
1. Aguilaniu B, Gonzalez-Bermejo J, Regnault A, Barbosa CD, Arnould B, Mueser M. Disability 
related to COPD tool (DIRECT): towards an assessment of COPD-related disability in routine 
practice. Int J COPD. :12. 
2. Aguilaniu B, Roche N. Erratum: The difficulties of measuring and improving physical activity in 
COPD. Npj Prim Care Respir Med [Internet]. 2014 Nov [cited 2018 Dec 22];24(1).  
3. COLIBRI COPD Research Group, Roche N, Antoniadis A, Hess D, Li PZ, Kelkel E, et al. Are there 
specific clinical characteristics associated with physician's treatment choices in COPD? Respir 
Res [Internet]. 2019 Dec [cited 2019 Aug 28];20(1).  
4. Guzun R, Aguilaniu B, Wuyam B, Mezin P, Koechlin-Ramonatxo C, Auffray C, et al. Effects of 
training at mild exercise intensities on quadriceps muscle energy metabolism in patients with 
chronic obstructive pulmonary disease: Mild-intensity exercise on muscle bioenergetics in 
COPD. Acta Physiol. 2012 Jun;205(2):236-46. 
5. Kelkel E, Herengt F, Ben Saidane H, Veale D, Jeanjean C, Pison C, et al. COLIBRI: optimizing 
clinical practice and generating relevant scientific data. Rev Mal Respir. 2016 Jan;33(1):5-16. 
6. Aguilaniu B et al. Digital consultation, machine learning & medical decisions: example of 
Colibri-pneumo. Breathing Info (2019) 
7. Roche N. et al. Are there specific clinical characteristics associated with respiratory 
consultant's treatment choices in COPD? Respiratory Research (2019) 
8. Roche N. et al. Trends over time in COPD treatment choices by respiratory consultants: An 
analysis from the COLIBRI-COPD French cohort. Respiratory Medicine (2019) 
9. Aguilaniu B et al. A machine learning approach to predict extreme inactivity in COPD patients 
using non-activity-related clinical data, PlosOne 2021 
 
PRESENTATION OF THE RESEARCH 
A. 
Title 
Validation by three-dimensional actimetry measurements of a predictive algorithm for excessive 
inactivity in COPD patients. 

 
13 
 
 
 
B. 
Objectives of the study or research  
Main objective: To validate the Machine Learning algorithm predictive of EI in COPD patients using 
three-dimensional actimetry as the GOLD standard. 
The main hypothesis: 
is that the Machine Learning Algorithm developed from Colibri-Pneumo's real-life data is effective in 
classifying individuals according to their level of physical activity and in particular in detecting 
extremely inactive behavior. 
Secondary objectives: 
- 
To validate the GOLD standard "cross-judgment” 
- 
To assess the influence of sedentary versus inactive time defined by three-dimensional 
actimetry on the prediction of EI behavior. 
The second hypothesis: 
is that the "standard GOLD" of Excessive Inactivity (EI) used to develop the predictive MLA does indeed 
correspond to an EI behavior, i.e., combining a high sedentary time and a very low level of daily physical 
activity. 
C. 
Current status of the issue 
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 care actually only 
involves 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, we developed an AR predictive Machine Learning Algorithm based on clinical data from 
the Colibri-BPCO digital consultation. 
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). 

 
14 
 
 
 
 
The ML algorithm was validated on a test sample with an error rate of 13.7% (AUROC: 0.84, CI95% 
[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 work2 , we would like to verify the validity of the algorithm on 
a new population using the recognized GOLD standard: three-dimensional actimetry 
measurements. 
D. 
Short bibliography on the theme 
1. Watz H, Pitta F, Rochester CL, Garcia-Aymerich J, ZuWallack R, Troosters T, et al. An official 
European Respiratory Society statement on physical activity in COPD. Eur Respir J. 2014; 
44(6):1521-1537. 
2. Furlanetto KC, Donária L, Schneider LP, Lopes JR, Ribeiro M, Fernandes KB, et al. Sedentary 
Behavior Is an Independent Predictor of Mortality in Subjects With COPD. Respir Care. 
2017;62(5):579-587. 
3. Rabinovich RA, Louvaris Z, Raste Y, Langer D, Van Remoortel H, Giavedoni S, et al. Validity of 
physical activity monitors during daily life in patients with COPD. Eur Respir J. 2013;42(5):1205-
1215. 
4. 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 J Chronic Obstr Pulm Dis. 2018;15(5):432-438. 
5. Van Gestel AJR, Clarenbach CF, Stöwhas AC, Rossi VA, Sievi NA, Camen G, et al. Predicting Daily 
Physical Activity in Patients with Chronic Obstructive Pulmonary Disease. Morty RE, editor. 
PLoS ONE. 2012;7(11):e48081. 
6. Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular 
risk prediction using routine clinical data? Liu B, editor. PLOS ONE. 2017 4;12(4)e0174944 
 
 
2 B. Aguilaniu et al. A machine learning approach to predict extreme inactivity in COPD patients using non-
activity-related clinical data, PlosOne 2021 

 
15 
 
 
 
E. 
Application perspective 
➢ If the validation of the algorithm is obtained by the actimetry measurements: 
- We will submit a second article; 
- We will test on a new cohort whether the addition of additional easily accessible variables 
(simple podometry; 3-MINUTE CRT  Assesment of work functioning) improves the 
predictive power and especially the sensitivity of the MLA. 
- We will integrate this validated algorithm into the Colibri-BPCO consultation in order to 
alert the pulmonologist to the risk of extreme inactivity. It will allow COPD patients, whose 
quality of life is degraded due to this undetected extreme inactivity, to be directed towards 
an adapted care. 
➢ In the event that the algorithm is not validated, we plan to mine the data to reconstruct a new 
algorithm. A new validation study will then be necessary. 
 
PROTOCOL DESCRIPTION 
A. 
Type of cohort 
This is a prospective, diagnostic cohort study for COPD patients (all GOLD severity stages) with the aim 
of promoting the care management of excessive inactivity, which is a common goal in most chronic 
respiratory diseases. 
B. 
Materials and Populations 
Equipment: actimetry sensors 
 
The study requires the use of accelerometers, and of a software to analyze the accelerometers’ data. 
They are the property of Icadom, the aCCPP’s subcontractor for the realization of the actimetry. 
 
Accelerometer data:  
 
- Model Actigraph, wGT3x-BT 
- CE marking 
- Complies with the Council Directive 93/42/EEC concerning medical devices 
Data for the data analysis software:  
- Name : Actilife 6.  
- The software collects and provides access to the following data:  
Epoch; Weight; Age; Gender; Date; Hour; Day of week, Day of week num, kcals; METs ;Bouts ;Total time in Bouts; Avg time 
per bout; Total counts in bouts; bout start; bout end; time in bout; sedentary bouts; total time in sedentary bouts; avg time 
per sedentary bout; sedentary bout start; sedentary bout end; time in sedentary bout; time since last sedentary bout; 
Sedentary; light, moderate, vigorous, very vigorous; total MVPA; %MVPA; Average MVPA; In bed Date; In bed Time; Out bed 
Date; Out bed Time; Onset Date; Onset Time; Latency; Efficiency; Total Minutes in Bed; Total Sleep Time (TST); Wake After 
Sleep Onset (WASO); Number of Awakenings; Average of awakening length; Axis 1 counts, Axis 2 counts; Axis 3 counts; Axis 
1 average counts; axis 2 average counts; axis 3 average counts; Axis 1 max counts; axis 2 max counts; Axis 3 max counts; Axis 
1 CPM; Axis 2 CPM Axis 3 CPM; Vector Magnitude counts; Vector Magnitude Average Counts; Vector Magnitude Max Counts; 
Vector Magnitude CPM; Step Counts; Steps Average Counts; Steps Max Counts; Steps per minute; Lux average Counts; Lux 
Max counts; Number of Epochs; Time; Calendar Days; Wear Time Start; Wear Time End. 
 
Data collected by the actimetry sensor and available on the analysis software are de-identified using a 
research-specific code number.  

 
16 
 
 
 
Only the Icadom project manager has access to the correlation table between the patient identity and 
the code. The patient's identifying data are stored on a health data host certified for the 6 HDS levels.  
Hardware: e-CRF 
The e-CRF used corresponds to the content of the Colibri-BPCO digital consultation (available on 
www.colibripneumo.fr for pulmonologists) slightly amended to meet the needs of the study:  
- 
The data required for the study, to be entered in the fields of the digital consultation intended 
for this purpose, are the following:  
o 
Daily walking time (out-of-home, patient-rated): < 10 min. / 10-30 min. / 30-60 min. / > 1h 
o 
Activity classification (assessed by physician): Solely domestic / Recreational (significant activity 
out-of- home) / Fitness upkeep 
o 
Smoking yes/no  
▪ 
If yes: active / completely weaned 
o 
Number of pack-year 
o 
History / Comorbidities   
o 
Current medications  
o 
Exacerbations in the past year: number and severity (mild, moderate, severe, uncertain)  
o 
Clinical > Dyspnea (MRC SCALE) 
o 
Chronic cough: Never / Intermittent / Frequent / Permanent 
o 
Expectoration (frequency): Never / Intermittent / Frequent / Permanent 
o 
Weight 
o 
Size 
o 
RFE measurements: FEV1, FVC 
o Questionnaires > CAT, DIRECT, HADS 
 
- 
After completing the digital consultation during the patient interview, investigators will have 
access to a study-specific modal window. It will allow them to collect the patient's contact information, 
present the non-inclusion criteria, print information notes and collect the patient's informed consent. 
Only investigators have access to the patient's identifying data. Each patient has an identification 
number in Colibri, indicating the order of creation of the patients. It is this "ID" number that will appear 
during data exports and that will be used for research. 
All data entered are stored on a secured server certified for the storage of health data. The database 
has been authorized by the French Commission on Information Technology and Liberties (CNIL, 
authorization number #2013-526) after a favorable opinion from the Advisory Committee on 
Information Processing in Health Research (CCTIRS).  
Population 
Any patient with a diagnosis of COPD, all GOLD stages combined, who receives a properly informed 
Colibri-BPCO digital consultation.  
Main criteria for non-inclusion: 
• 
Patients with other unstable conditions with treatment,  
• 
Unstable patients: with 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, 

 
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• 
Protected patients within the meaning of the French Public Health Code. 
C. 
Methodological approaches and patient number calculation 
Definition of Prevalence in the study sub-population 
The sample size and the prevalence of EI behavior influence the sensitivity and specificity of the 
algorithm. The prevalence of EI behavior in COPD patients, studied by actimetry sensors, is 
estimated to be around 30%. The published ML predictive algorithm suggests an EI prevalence of 
34%.  
Sensitivity  
The selected sensitivity is 79.4%, which corresponds to the sensitivity of the current algorithm. 
Target width  
The target width chosen for the CI95% is +/-15 
Calculation of the number of patients to be recruited 
With these parameters, the required sample size is 94 patients. Assuming a dropout rate of 10%, 
we obtain a sample size of 104 patients. 
D. 
Evaluation criteria 
Reminder of the main objective: To validate the Machine Learning Algorithm predictive of EI in COPD 
patients using three-dimensional actimetry as the GOLD standard. 
Primary endpoint (related to the primary objective) 
A contingency table recording the algorithm's performance metrics will be constructed from the GOLD 
Standard actimetry. The AUC-ROC will be used as the primary endpoint to judge the performance of 
the algorithm. The algorithm will be validated if the AUC-ROC is greater than or equal to 0.80, CI95% 
[0.65- 0.95]. 
Reminder of secondary objectives: 
- 
To validate the GOLD standard "cross-judgment” 
- 
To assess the influence of sedentary versus inactive time defined by three-dimensional 
actimetry on the prediction of EI behavior. 
Evaluation criteria for secondary objectives  
- 
A contingency table recording the metrics of GOLD Standard performance "cross-
judgment" will be constructed from the GOLD Standard actimetry. The AUC-ROC will be used 
as the primary endpoint to judge the performance of the GOLD standard. The GOLD standard 
will be validated if the AUC-ROC is greater than or equal to 0.80, CI95% [0.65- 0.95]. 
- 
Discriminant analysis of explanatory variables in the predictive MLA. 
 

 
18 
 
 
 
E. 
Primary and associated factors whose influence is assessed 
The study will evaluate as secondary objectives the influence of sedentary time and inactive time, as 
defined by three-dimensional actimetry on the prediction of EI behavior. 
F. 
Main experimental steps 
Study Flowchart: 
 
 
 
 

 
19 
 
 
 
Steps: 
Only one consultation is necessary to collect all the patient's data. Three-dimensional actimetry 
measurements are taken over 7 consecutive days, including weekends.  
Step 1: the investigator completes a Colibri consultation, informs the patient and collects his or her 
informed consent. A minimum delay of one hour must be respected between the time of the 
information and the informed consent. However, it is possible that the patient does not stay one hour 
at the pulmonologist's office.  To guarantee this right, the Icadom operational team ensures that the 
patient gives his or her informed consent at the time of the call. 
The name of the investigating physician, the date and time of the collection of the informed consent 
to participate in the study and to the processing of personal health data for research purposes are 
saved in the patient's digital medical record. The date of the call from Icadom and the confirmation of 
the informed consent will also be saved 
Step 2: The informed coordinator checks the completeness of the digital consultation.  
Step 3: The patient completes the HADS, MRC SCALE, DIRECT, CAT questionnaires. 
Step 4: The Icadom team contacts the patient to set up the actimetry sensor. The team answers the 
patient's questions and makes sure that he gives his informed consent. 
Step 5: Sending of the actimetry sensor. Wearing by the patient, return on D+7. 
Step 6: Entry of the actimetry results on a dedicated application 
Step 7: Data Analysis 
Total duration of the study: 1 year 
 
The various stakeholders of the project have developed complementary expertise, ensuring the 
smooth running of the project: 
• 
Project coordination: A. Rigal and D. Hess from the aCCPP 
• 
Realization of the actimetry : J.C. Borel's team, from Icadom 
• 
Machine Learning and predictive statistics expertise: Pr. Anestis Antoniadis, Sophie 
Lambert-Lacroix 
• 
Exercise and COPD expertise: Pr. B. Aguilaniu for the aCCPP 
 
G. 
Risks, constraints and expected benefits 
The actimetry sensor has the appearance of a watch (weight: 19 grams). It is worn without connection 
to the skin, on the belt, continuously during the day. The device is designed to be disturbance-free and 
does not present any risk.  
The only constraint is that it must be worn during the day for 7 consecutive days.  
The expected benefits are multiple:  

 
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➢ It allows the physician to obtain objective data on the level of physical activity of his patient, 
allowing him to direct inactive patients to an adapted care. The quality of life of the patient is 
thus improved.   
➢ More broadly, this study aims to validate an algorithm for predicting extreme inactivity. Used 
in consultation, this simple and reliable measure could eventually replace actimetry without 
requiring the involvement of the patient.   
H. 
Follow-up of the French HURIET law guidelines 
N/A 
I. 
Key words 
COPD, Physical inactivity, Medical decision, Machine Learning 
 
J. 
Suggested experts 
Pr. Ruddy RICHARD 
Clermont Ferrand 
University Hospital 
rrichard@chu-clermontferrand.fr  
Dr David DEBEAUMONT 
Rouen University 
Hospital 
David.Debeaumont@chu-rouen.fr  
 
 
 

 
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GLOSSARY 
aCCPP: Association pour la Complémentarité des Connaissances et des Pratiques de la Pneumologie 
AUPRC: Area Under the Precision-Recall Curve 
AUC ROC or AUROC: Area Under the Curve” of the “Receiver Operating Characteristic” curve 
COPD: Chronic Obstructive Pulmonary Disease 
CAT: COPD Assessment Test 
COPD: Chronic Obstructive Pulmonary Disease 
DIRECT: DIsability RElated to COPD Tool 
DPO: Data Protection Officer 
EI: Extremely Inactive 
GOLD : Global initiative for chronic Obstructive Lung Disease 
HADS: Hospital Anxiety and Depression Scale 
MRC Scale: Medical Research Council Scale 
OA: Overtly Active 
3-minute CRT: 3-minute chair rise test