Benjamin Marais
email: benjamin.marais@orange.com

Tony Quertier
email: tony.quertier@orange.com

Stéphane Morucci
email: stephane.morucci@orange.com

Ai-Based

AI-based Malware and Ransomware Detection Models

Keywords: Malware, Ransomware, PE files, Antivirus, Cybersecurity, Artificial Intelligence

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

I. INTRODUCTION

The cybercrime economy has never been so lucrative. In 2021, the global cost of cybercrime campaigns damages reached about 6 trillion USD for individuals and companies [START_REF] Morgan | Cybercrime to cost the world $10.5 trillion annually by[END_REF]. The trend is not expected to change since the estimated cost by 2025 is about 10.5 trillion USD. Since the beginning of the COVID-19 pandemic, cyberattack campaigns have multiplied [START_REF] Singh Lallie | Cyber security in the age of COVID-19: A timeline and analysis of cyber-crime and cyber-attacks during the pandemic[END_REF] and according to AV-Test [START_REF]Av-test[END_REF], 150 million new malicious files have been discovered in 2021, an increase of 36% compared to 2020. Malware detection is therefore a major issue for individuals, companies and even governments. Recently, Republic of Costa Rica had to declare a state of national emergency due to a ransomware attack carried out by Conti hacker group [START_REF] Drew | Costa rica declares state of emergency after conti ransomware attack[END_REF]. Despite the progress made in malware and ransomware detection, the problem remains and intensifies over time, mainly because hackers techniques are constantly and rapidly evolving. To perform faster remediation activities, it is very important for security teams to quickly identify the family or the category of a detected malware. This classification problem can be successfully addressed using Machine Learning (ML) and Deep Learning (DL) techniques as soon as enough labeled data are available. In this work, we are training different algorithms to detect malware and ransomware. In particular, we build a bi-layered ransomware detection model based on two ML and DL optimized models. Static techniques are used to extract prevalent features from Windows Portable Executable (PE) files to train our models.

A. Background and Related Work

Malicious software analysis is a major research topic due to the damages malware cause [START_REF] Anderson | Measuring the changing cost of cybercrime[END_REF]. With the recent advances in Artificial Intelligence (AI), cybersecurity researchers are shifting their attention to Machine Learning (ML) and Deep Learning (DL) methods to improve malicious files detection and classification [START_REF] Ucci | Survey of machine learning techniques for malware analysis[END_REF]- [START_REF] Gibert | The rise of machine learning for detection and classification of malware: Research developments, trends and challenges[END_REF]. Even if results are decent, these methods still need improvements [START_REF] Michael | Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Malware Analysis[END_REF].

Another hot research area in malware analysis relates to detection of a particular malicious file type, like a ransomware for instance. Ransomware aim at disabling the functionality of a computer, either by encrypting the machine (cryptographic-ransomware) as done by the well-known Wannacry virus [START_REF] Mohurle | A brief study of Wannacry Threat: Ransomware Attack 2017[END_REF], [START_REF] Connolly | The rise of cryptoransomware in a changing cybercrime landscape: Taxonomising countermeasures[END_REF], or by blocking access to the machine (locker-ransomware) as performed by Reveton malware [START_REF] Kharraz | Cutting the gordian knot: A look under the hood of ransomware attacks[END_REF]. To regain the control of a computer, malware authors usually require to pay a ransom. Although this threat has been around for decades, it has intensified with the rise of cryptocurrencies which make it possible to receive a payment with a certain level of anonymity. Even though defensive methods exist to prevent such attack [START_REF] Richardson | Ransomware: Evolution, Mitigation and Prevention[END_REF]- [START_REF] Labs | Understanding Ransomware and Strategies to Defeat it[END_REF], 54% of them succeeded in 2021 at an estimated average unitary cost of 1.85 million USD. [START_REF]The State of Ransomware[END_REF].

B. Contributions

This study aims to provide different approaches for malware detection with a particular focus on ransomware detection. Our main contribution is an algorithm that chains two models optimized for malicious files detection and ransomware detection respectively. This solution, called "bi-layered detection model", takes as input a PE file, outputs the maliciousness of the file in a second step, and then determines if this PE file can be classified as a ransomware or not. To the best of our knowledge, we didn't find any similar approach for ransomware detection in the existing literature. Our final model is thus composed of two specialized models that can be independently trained which makes it very flexible during optimization phases and in a production environment.

C. Outline

In Section II, we present datasets and feature extraction methods used for our experiments. In Section III, we train the model for the first step: malicious file detection. In Section IV, we create a model for the classification of malware families and in particular of the ransomware category. In Section V, we train a bi-layered model that performs malware and ransomware detection and compare it to a reference model. Finally, Section VI summarizes this paper and discusses future works.

II. DATASETS, FEATURES AND MODELS

A. Datasets

For our experiments, we rely on three different datasets. They all contain malicious and benign Portable Executable (PE) files in two different formats. These datasets are Ember [START_REF] Hyrum | EMBER: An open dataset for training static pe malware machine learning models[END_REF], Bodmas [START_REF] Yang | BODMAS: An Open Dataset for Learning based Temporal Analysis of PE Malware[END_REF] and PEMachine-Learning [START_REF] Lester | Pratical Security Analytics -PE Malware Machine Learning Dataset[END_REF]. PE files distribution and format are summarized in Table I. Ember: Ember is a dataset provided by Anderson et al. [START_REF] Hyrum | EMBER: An open dataset for training static pe malware machine learning models[END_REF]. It contains a total of 1.1M features extracted from 400K malicious files, 400K benign files and 300K unlabeled files. Anderson et al. also provide the necessary tooling to generate a feature-based dataset.

Bodmas: Bodmas [START_REF] Yang | BODMAS: An Open Dataset for Learning based Temporal Analysis of PE Malware[END_REF] shared with our team a dataset that contains 134, 435 binary files in the same format as Ember with pre-extracted features together with the 57, 293 malicious files in raw PE format. These files have been collected during one year between August 2019 and September 2020 and labeled: authors indicate the category each file belongs to. Table II presents the distribution of malware families in the Bodmas dataset. The category "other" includes about ten other categories less represented in the Bodmas dataset such as "dropper", "downloader" or "informationstealer" for instance. PEMachineLearning Dataset: The third dataset used is PEMachineLearning, made available by M. Lester [START_REF] Lester | Pratical Security Analytics -PE Malware Machine Learning Dataset[END_REF]. It contains 201, 549 binary files including 114, 737 malicious files. These files have been gathered from different sources such as VirusShare1 , MalShare2 and TheZoo3 .

B. Features

Training models for malware detection is a multi-step process. The first step consists in extracting information (i.e. features) from the PE files. For this, we rely on two pre-processing algorithms. The first one, a.k.a the Ember method, is detailed in Anderson et al. [START_REF] Hyrum | EMBER: An open dataset for training static pe malware machine learning models[END_REF] and converts a PE file into a vector of 2, 381 features. Some of these features are listed in Table III. The second one, a.k.a Grayscale method, was initially submitted by Nataraj et al. [START_REF] Nataraj | Malware images: Visualization and automatic classification[END_REF] and converts a binary file into an image, as can be seen in Figure 1. To train our models, we choose to resize grayscale images 64 × 64 pixels.

C. Models

We selected four machine learning (ML) and deep learning (DL) models:

• Three models are trained with features extracted from PE files using the Ember extractor.

-LightGBM, -XGBoost, -Dense Neural Network (DNN),

• a Convolution Neural Network (CNN) trained with PE files images created by the Grayscale extractor. For LightGBM and XGBoost models, we use the default parameters provided by the two python packages. For DNN and CNN models, we use Tensorflow package with the Adam optimization function [START_REF] Diederik | Adam: A method for stochastic optimization[END_REF] and a learning rate of 0.01.

III. DETECTION OF MALICIOUS FILES

In this section, we are discussing the detection of malicious software using different models of machine learning. For this, we use PEMachineLearning and then Bodmas dataset. PEMachineLearning has been divided into three subsets for training (70%), validation (15%) and testing (15%) of our models.

The four models are first trained and tested on PEMa-chineLearning. We compare the models results using the F1 score and the accuracy score. Table IV presents the results on the test subset. The algorithm XGboost has the best results although the performance of LightGBM and DNN are relatively close. We observe that the CNN is less efficient than the three other models even if scores are close to 0.95. Subsequently, we test our model on malicious files from Bodmas dataset: the purpose is to validate the robustness of our trained models and to determine if they do not produce too many false negatives predictions i.e. true malware detected as benign. We focus on the false negative rate (FNR) as we consider it as a prevalent metric. For each model, FNR is showed in Table V. We also add the number of undetected malware out of 57,293 files from Bodmas. The CNN model has a high FNR compared to the other models: it does not detect enough malware from Bodmas, meaning that this model does not have a good generalization capacity. On the contrary, the XGBoost and LightGBM models have very close and low FNRs. They achieve good performances during the testing phase and have good generalization capacities. Finally, the DNN has a perfect score of zero undetected malware.

We compare our results on Bodmas with a current state of the art model. We test the LightGBM model provided by Ember [START_REF] Hyrum | EMBER: An open dataset for training static pe malware machine learning models[END_REF] on the Bodmas dataset and it achieves a FNR of 1.42 • 10 -2 . With the exception of the CNN, the models we propose are slightly better than Ember's model, even though they are trained with less but more recent data. Given results from Table IV and Table V, the XG-Boost model seems to be the most effective model for detecting malicious files during training and testing phases. Moreover, it shows a good capacity of generalization with a low FNR on Bodmas dataset. LightGBM model provides slightly lower but very close results. Its main advantage over XGBoost is its faster computing time [START_REF] Ke | LightGBM: A highly efficient gradient boosting decision tree[END_REF]. Even though results for the CNN model are quite good, its poor FNR performance indicates a low generalization capacity. Finally, the DNN seems to perform better than others with results close to XGBoost on the testing subset and the lowest FNR, equal to zero, on the Bodmas dataset. On the other hand, it requires more computing power for a rather small performance increase. Thus, XGBoost model could be considered as a good trade-off between computing time and performance.

IV. MALWARE CATEGORIES CLASSIFICATION AND RANSOMWARE DETECTION

The purpose of this section is to present some experiments and results in the context of malware classification. We pursue two objectives: the first one consists in identifying four of the most popular malware categories (cf Section IV-A). The second objective aims at detecting ransomware only and results are presented in Section IV-B. In both cases, we use the same two extractors and the same models as presented in Section II.

Regarding the datasets at our disposal, we mainly use Bodmas because it provides labeled malicious files. However, due to a lack of ransomware, we had to manually labeled several malware from PEMachineLearning to reach a total of 2,000 labeled files.

A. Malware classification

In this part, we are classifying malware into four popular and frequently encountered malware's categories i.e. Trojan, Worm, Backdoor and Ransomware. All other types of malicious files fall into the "Other" category. To limit overfitting, we decided to use a balanced dataset instead of using all available data at our disposal. Thus, for each category of malware, we selected 2,000 malicious files from Bodmas and PEMachileLearning dataset (for ransomware). This balanced dataset has been split into a training subset (70%), a validation subset (15%) and a test subset (15%).

To compare and determine which model is the most effective for malware classification, we rely on the accuracy and F1 scores, summarized in TableVI. The best results are obtained using LightGBM, even if XGBoost and DNN performances are really close. With an average accuracy score of 0.9413 and an average F1 score of 0.9412, these three models appear to be good candidates for malware classification even if these classification results could be improved.

On the contrary, CNN doesn't perform as good as other models and its results cannot be considered as reliable. According to us, this is not only due to a low number of samples during training phase, but also to a sub-optimized pre-processing step.

B. Ransomware Detection

In this section, we are focusing on detecting ransomware among other malicious files. For this purpose, we train models on the same dataset as in Section IV-A. It is composed of 2,000 ransomware files and 8,000 malicious files. To compare the four models trained for ransomware detection, we use F1 and accuracy scores as metrics, summarized in Table VII. LightGBM, XGBoost and DNN achieve almost the same performance with scores close to 1 on the test subset. For this classification task, CNN also achieves good results. Once again, LGBM performs better even if DNN and XGBoost are also good candidates. Our results demonstrate that it seems easier to separate the ransomware category from others than to classify malware into five categories as done in Section IV-A.

V. BI-LAYERED MODEL FOR MALWARE AND RANSOMWARE DETECTION

In this section, we are considering an algorithm referred to as "bi-layered detection model". Its purpose is to detect PE files that are malicious, and also if they belong to the ransomware category. This algorithm consists in two detection layers. The first one relies on a model from Section III and its purpose is to detect malicious files. The second layer uses a model trained for ransomware detection given a malicious file (cf Section IV-B). We are combining them as presented in Figure 2a to predict if a file is benign and if it can be classified as a ransomware or not. To evaluate the performances of this combined model, we are training several reference models, referred to as "benchmark models". They are based on LightGBM, XGBoost and DNN using dataset from Section IV-B and 8, 000 benign files from PEMachineLearning. VIII summarizes the accuracy and F1 scores. Firstly, we see that our bi-layered detection models have slightly better scores than benchmark models. Overall, bi-layered detection models appear to perform better than benchmark models. We also present confusion matrices of the bi-layered and benchmark XGboost models in Figure 3. We can notice that bilayered XGBoost returns less misclassified PE files than benchmark, which confirms that our bi-layered detection models seem to be more accurate. Finally, the XGBoost bi-layered model has a better efficiency than DNN and LightGBM, even if results are close.

Moreover, a significant advantage of our bi-layer detection models is that they are composed of two submodels trained for particular sub-tasks i.e. malware or ransomware detection. In consequence, each sub-model can be retrained independently if we have new data, and possibly be replaced if a better model becomes available. We are convinced that such flexibility makes our models good candidates for production environment.

VI. CONCLUSION & FUTURE WORK A. Conclusion

In this paper, we have implemented and compared different methods, based on machine learning and deep learning algorithms, for malware detection and classification, and in particular ransomware detection. During each experimentation, CNN models do not perform as well as other models. We need to investigate further to identify the origin of the problem, even if we feel that the lack of data or prepossessing steps could be an explanation. In Section III, all other models achieve good results, in particular XGBoost which is the most effective on the test subset and DNN which seems to have better generalization capacity. Thus, XGBoost may appear as a good candidate for malware detection considering their respective training times. In Section IV-A, we show that about 5% of PE files are misclassified which leaves room for models optimizations. In Section IV-B, by focusing on ransomware detection only, we demonstrate that XGBoost, LightGBM and DNN have good performances with an average accuracy score of 0.9947. It coud be interesting to analyze if these findings are similar for other category of malicious files like trojan, worm or backdoor. Finally, in Section V, we propose an original approach that performs two tasks: malware detection and ransomware detection. Our "bi-layered detection model" combines two optimized models for this and achieves slightly better results than benchmark models. This combined model provides an interesting flexibility capability to envision easier optimization and evolution steps in a production environment. We hope this paper could help improve cybersecurity solutions for malicious files analysis by providing new insights.

B. Future Works

To accelerate and improve malware analysis activities, we plan to enrich results provided by ML and DL algorithms. In particular, it would be interesting to extend the method to other type of malware, and even to combine this method with the one we already proposed for the detection of packed and obfuscated files [START_REF] Marais | Malware analysis with artificial intelligence and a particular attention on results interpretability[END_REF]. We also plan to leverage Explainable Artificial Intelligence (XAI) and Interpretable Machine Learning (IML) concepts to provide decision support. In addition, we want to integrate clustering techniques and PE file behavioral analysis to improve malware classification results. Finally, we will have to assess the robustness of our different algorithms against adversarial attacks using for instance methods described in [START_REF] Quertier | Merlin-malware evasion with reinforcement learning[END_REF].

Fig. 1 :

 1 Fig. 1: Example of PE files transformation into grayscale images

  (a) Bi-Layered Model (b) Benchmark Model

Fig. 2 :

 2 Fig. 2: Ransomware models diagrams For each model, TableVIIIsummarizes the accuracy and F1 scores. Firstly, we see that our bi-layered detection models have slightly better scores than benchmark models. Overall, bi-layered detection models appear to perform better than benchmark models. We also present confusion matrices of the bi-layered and benchmark XGboost models in Figure3. We can notice that bilayered XGBoost returns less misclassified PE files than benchmark, which confirms that our bi-layered detection models seem to be more accurate. Finally, the XGBoost bi-layered model has a better efficiency than DNN and

Fig. 3 :

 3 Fig. 3: Confusion matrix of bi-layered and benchmark XGBoost on the test subset

TABLE I : Distribution and format of each dataset

 I

		Malicious	Benign files	Files format
		files
	Ember	400,000	400,000	Features
	Bodmas	57,293 57,293	77,142 0	Features PE files
	PEMachine	114,737	86,812	PE files
	Learning

TABLE II :

 II Distribution of malware families in the Bodmas dataset

	Category	Files count
	Trojan	29,972
	Worm	16,697
	Backdoor	7,331
	Ransomware	821
	Other	2,471
	Total	57,293

TABLE III :

 III Some features computed using Ember extractor

	Feature names	Index
	Byte histogram	1-256
	Byte entropy	257-512
	Strings	513-616
	General information	617-626
	Header information	627-688
	Section	689-943
	Imports	944-2223
	Exports	2224-2350
	Data directory information	2252 -2381

TABLE IV :

 IV Malware detection results on the PEMa-chineLearning test subset

		LGBM	XGBoost	DNN	CNN
	Accuracy	0.990	0.993	0.9902	0.9458
	F1 Score	0.991	0.994	0.991	0.95

TABLE V :

 V FNR and number of undetected malware from Bodmas dataset

		LGBM	XGBoost DNN	CNN	Ember
						LGBM
	FNR	1.56 •	0.96 •	0	0.13	1.42 •
		10 -3	10 -3			10 -2
	Undetected	84	55	0	7448	816
	malware

TABLE VI :

 VI Malware classification results ont the test subset

		LGBM	XGBoost	DNN	CNN
	Accuracy	0.9442	0.9427	0.9369	0.7270
	F1 Score	0.9440	0.9425	0.9370	0.7197

TABLE VII :

 VII Ransomware detection results on the test subset

		LGBM	XGBoost	DNN	CNN
	Accuracy	0.9954	0.9948	0.9938	0.9830
	F1 Score	0.9971	0.9969	0.9967	0.9823

TABLE VIII :

 VIII Results for Benchmark and Bi-Layered models on the test subset

		Accuracy	F1 Score
		Benchmark	Bi-	Benchmark	Bi-
			Layered		Layered
	LGBM	0.9896	0.9932	0.9895	0.9934
	XGBoost	0.988	0.9960	0.988	0.9966
	DNN	0.9867	0.9913	0.9868	0.9915
		(a) Bi-layered XGBoost
		(b) Benchmark XGBoost

https://virusshare.com/

https://malshare.com/

https://github.com/ytisf/theZoo


Leduc

Mathieu Lacome
email: mlacome@psg.fr

Concurrent validity of a continuous glucose monitoring system at rest, during and following a high-intensity interval training session

Purpose: To assess the concurrent validity of a continuous blood glucose monitoring system (CGM) Post-Breakfast, Pre-exercise, Exercise and Post-exercise, while assessing the impact of two different breakfasts on the observed level of validity. Methods: Eight non-diabetic recreational athletes (age: 30.8±9.5 years; height: 173.6±6.6 cm; body mass: 70.3±8.1 kg) took part in the study. Blood glucose concentration was monitored every 10 min using both a CGM (FreeStyle Libre, Abbott, France) and finger-prick blood glucose measurements (FreeStyle Optimum, Abbott, France) over 4 different periods (Post-Breakfast, Pre-Exercise, Exercise and Post-Exercise). Two different breakfasts (carbohydrates-[CHO] and protein-

[PROT] oriented) over two days (2x2 days in total) were used. Statistical analyses included the Bland-Altman method, standardized mean bias (expressed in standardized unit), median absolute relative difference (MARD) and the Clarke Error Grid (EGA). Results: Overall, mean bias was trivial-to-small at Post-Breakfast (effect size ± 90% confidence limits: -0.12±0.08), Pre-Exercise (-0.08±0.08) and Post-Exercise (0.25±0.14), while moderate during Exercise (0.66±0.09). Higher MARD was observed during Exercise (13.6% vs 7 to 9.5% for the other conditions). While there was no effect of the breakfast type on the MARD results, EGA revealed higher value in Zone D (i.e. clinically unsafe zone) during Exercise for CHO (10.5%) compared with PROT (1.6%). Conclusion: The CGM device examined in this study can only be validly used at rest, after both a CHO and PROT-rich breakfast. Using CGM to monitor blood glucose concentration during exercise is not recommended. Moreover, the accuracy decreased when carbohydrates are consumed before exercise.

Introduction

Regulation of blood glucose has first been widely studied from a health perspective.

Hyperglycemia for example, is believed to be an independent risk factor for the development of several diseases such as type II diabetes mellitus 1 and cardiovascular disease. [START_REF] Gallwitz | Implications of postprandial glucose and weight control in people with type 2 diabetes: understanding and implementing the International Diabetes Federation guidelines[END_REF] More recently, the monitoring of blood glucose concentration has also elicited great interest in sport, as hypoglycaemia influences both physical and cognitive performances. [START_REF] Harper | Physiological and performance effects of carbohydrate gels consumed prior to the extra-time period of prolonged simulated soccer match-play[END_REF] In particular, it is known that at the beginning of exercise or after half-time in team sports, athletes experience transient hypoglycemia, which may affect physical and cognitive performance. [START_REF] Kingsley | Effects of carbohydrate-hydration strategies on glucose metabolism, sprint performance and hydration during a soccer match simulation in recreational players[END_REF] Moreover, it has then been shown that a large glycemic variability exists among individuals in the general population. [START_REF] Hall | Glucotypes reveal new patterns of glucose dysregulation[END_REF] Additionally, similar results have been shown in sub-elite athletes, [START_REF] Thomas | Blood Glucose Levels of Subelite Athletes during 6 Days of Free Living[END_REF] suggesting that providing more individualized guidelines to regulate blood glucose would be beneficial for both health and performance goals.

The emergence of new technologies such as continuous glucose monitoring (CGM) devices has allowed blood glucose concentration dynamics to be captured more frequently and less invasively than traditional measures such as finger pricks. Indeed, as CGM devices only need to be placed once (usually on the back of the arm), it can be used for several days without disturbing sport practices. So far, these devices have been mainly used by diabetic populations but as the technology becomes more accurate, less invasive, and less expensive, their use has increased in other populations and especially in healthy individuals. Therefore, the inclusion of CGM in sport nutritionists' monitoring tool box could help to optimize nutritional strategies before and during exercise, and in turn, improve athletes' performance by preventing hypoglycemia. However, to date, the validity of these new systems at rest or during exercise has been only assessed in diabetics patients and showed promising results. [START_REF] Greene | State-of-the-art methods for skeletal muscle glycogen analysis in athletes-the need for novel non-invasive techniques[END_REF] Evidence regarding its relevance with an athletic population is still lacking. Moreover, the ability of such devices to detect potential glucose fluctuations due to different nutritional intakes need to be confirmed. Therefore, the aim of this study was to assess the concurrent validity of a new CGM device during different periods, i.e. pre, during and after exercise, while assessing the potential impact of different nutritional intakes in the observed level of validity.

Methodology

Study Population

Eight non-diabetic recreational athletes (5 females, 3 males) (age: 30.8 ± 9.5 years; height: 173.6 ± 6.6 cm; body mass: 70.3 ± 8.1 kg) who regularly participate in running and resistance-based training (8±2 hours per week) were included in the study. An a priori power analysis was conducted using the package pwr from R software (Version 4.0.0) for t-tests for non-parametric data with a significance level alpha of 0.05 a power of 0.8 and add a nonparametric correction of 15%. Result showed a minimal sample of 310 paired observations for 8 participants were necessary. Alcohol intake was prohibited during the study period.

Regarding female participants, we ensured they were all within the same menstrual phase during the study period.

Participants provided informed consent prior to starting the study. Ethics approval was granted before any data collection wwas undertaken and the recommendations of the Declaration of Helsinki were respected.

Design

A concurrent validity design was employed to assess the validity of a CGM system against finger prick measures which was considered as the reference method. Over 2 consecutive weeks, participants took part in 4 nonconsecutive standardized days. Each standardized day was broken-down into 4 distinct periods: 1) Post-Breakfast which corresponded to the first hour after the end of the Breakfast 2) Pre-Exercise which corresponded to the first hour following the Post-Breakfast, 3) Exercise, which started 2 hours after the end of the breakfast and lasted from the beginning of the warm up to the end of the workout and 4) Post-exercise, which started immediately at the end of the workout, and up to 30 min later. A detailed outline of the standardized day structure is provided in Figure 1. Nutritional intake during breakfast was manipulated in order to provide either a high carbohydrate (CHO) or protein (PROT) breakfast, to induce different levels of resting ad pre-exercise glycemia. Each typical breakfast was repeated twice. Over those standardized days, blood glucose was measured continuously with a CGM, while finger prick measures were taken every 10 minutes and. Day 1 was used for each participant to familiarize with the CGM and ensure calibration (as per manufacturer recommendations) before the experimentation could start. Between day 2 and Finger prick blood glucose. Finger prick (FreeStyle Optium, Abbott, France) measures were collected following the procedure described by Gomez. [START_REF] Gómez | Continuous Glucose Monitoring Versus Capillary Point-of-Care Testing for Inpatient Glycemic Control in Type 2 Diabetes Patients Hospitalized in the General Ward and Treated with a Basal Bolus Insulin Regimen[END_REF] Each sample was immediately analysed using the FreeStyle Libre reader (FreeStyle Libre Reader, Abbott, France) (The validity and reliability of this device has been previously confirmed. [START_REF] Rodrigo | Accuracy and reliability between glucose meters: A study under normal clinical practice conditions[END_REF] Breakfast. Two typical breakfasts were employed. The CHO breakfast contained a high proportion of carbohydrates (CHO) with 1 g . Kg -1 of body mass with a ceiling set at 70g of carbohydrates per breakfast (e.g. breakfast contained a mix of orange juice, bread and jam).The macronutrients and energy were as follow: 65±7g of carbohydrates, 9±1g of proteins and 1±0g of fat for a total of 311±31 Kcal. The protein (PROT) breakfast was isoenergetic compared with CHO (e.g. breakfast contained a mix of eggs, ham and cheese).

The macronutrients and energy were as follow: 1±0g of carbohydrates, 30±0g of proteins and 23±0g of fat for a total of 311±31 Kcal.

Standardized exercise. Participants completed the 30-15 Intermittent Fitness Test (30-15 IFT ) as described by Buchheit et al. [START_REF] Buchheit | The 30-15 Intermittent Fitness Test: Accuracy for Individualizing Interval Training of Young Intermittent Sport Players[END_REF] prior the beginning of the study. The speed (km•hr -1 ) achieved by each participant during the last successfully completed stage of the test was recorded (V IFT ) in order to prescribe exercise intensity. The standardized exercise started with a 10-min low-intensity run (30 to 40% of V IFT ) and was followed by a high-intensity intermittent training exercise performed outdoor. The trials consisted of six reps of 3-min running intervals interspersed with 2 min of passive recovery. Reps 1 and 2 were performed at 75% V IFT , reps 3 and 4 at 80% V IFT and reps 5 and 6 at 85% V IFT . The session was ended with a 10-min walk.

Data processing. Each time point within a specific period was averaged as described above to perform the concurrent validity analysis for each method (CGM and finger prick) and per specific period (Figure 1). Each standardized day was analyzed first without (overall) and then as a function of breakfast type (CHO and PROT).

Statistical Analysis

Bland-Altmann method for repeated measures and standardized mean bias were first applied to assess the agreement between CGM and finger prick measures at each specific period. [START_REF] Altman | sensitivity.pdf[END_REF] The following thresholds were applied to rate the magnitude of the bias as follow: >0.2 (small), >0.6 (moderate), >1.2 (large) and >2 (very large). [START_REF] Hopkins | Measures of Reliability in Sports Medicine and Science[END_REF] Additionally, analysis of the median average relative difference (MARD) [START_REF] Reiterer | Significance and Reliability of MARD for the Accuracy of CGM Systems[END_REF] and the Clarke Error Grid Analysis (EGA) [START_REF] Clarke | Evaluating clinical accuracy of systems for self-monitoring of blood glucose[END_REF] were conducted. Regarding MARD, further comparisons between the different periods were performed using Wilcoxon test and/or Kruskal-Wallis tests. Level of statistical significance was set at P<0.05. Results were further analyzed while calculating standardized differences, i.e. Wilcoxon effect sizes. The thresholds to rate the magnitude of the effects were the same than those used for mean bias. Regarding EGA, results were divided into 5 zones (A, B, C, D, E). Each zone denotes a degree of clinical implications of blood glucose concentration measures. Zones A and B were considered clinically acceptable while zone C, D and E (erroneous treatment) were deemed possibly unsafe. [START_REF] Clarke | Evaluating clinical accuracy of systems for self-monitoring of blood glucose[END_REF]

Results

The Bland-Altman analysis for the 4 periods is presented in Figure 2 and reported as mean bias (standard error). Irrespectively of the breakfast content, mean biases were trivial-to-small for Post-Breakfast (-2.99 [17.75] mg/dL), Pre-Exercise (-1.67 [10.95]

Discussion

The aim of this study was 1) to investigate the concurrent validity of a new CGM device in recreational athletes at Post-Breakfast, Pre-exercise, Exercise and Post-exercise, and 2) to assess the potential impact of either a CHO-rich or protein-rich breakfast on the observed level of validity. The main results highlighted that, while the validity of CGM was acceptable at rest (i.e. Post-Breakfast, Pre-Exercise and Post-Exercise), it was lower during Exercise and especially after the CHO breakfast.

The first results demonstrated trivial-to-small mean bias during all the non-exercise periods, irrespectively of nutritional intake. Moreover, all results from EGA fell into the "clinically safe zone" (A and B), albeit during Exercise. These results are similar to those shown previously in non-athletic diabetic populations. [START_REF] Freckmann | Performance evaluation of three continuous glucose monitoring systems: Comparison of six sensors per subject in parallel[END_REF] Indeed, the present results suggest that assessing glucose dynamics at rest is feasible with this CGM device. This could open the door to a better individualization of nutritional strategies. [START_REF] Hall | Glucotypes reveal new patterns of glucose dysregulation[END_REF] Yet, we observed a higher bias during Exercise compared with the other periods, confirming previous studies in a non-athletic diabetic population. [START_REF] Biagi | Accuracy of continuous glucose monitoring before, during, and after aerobic and anaerobic exercise in patients with type 1 diabetes mellitus[END_REF] Reasons that may contribute to the reduced validity of the CGM device in this context include microcirculation perturbations as a as a result of movements around or within the insertion area, increases in body temperature and rapid fluxes in glucose levels during exercise. [START_REF] Kumareswaran | Accuracy of continuous glucose monitoring during exercise in type 1 diabetes pregnancy[END_REF] Regarding the likely physiological time lag of glucose transport between blood and interstitial fluid compartments (see Figure 3, finger pricks measures changed faster Post-Breakfast than that of the CGM device), it should be noted that it might not have accounted for the observed difference in accuracy as the pattern is not only delayed but it varies with time and conditions. Indeed, while a clear hypoglycemia was observed with finger prick measures immediately at the start of exercise (which was the expected physiological response), the CGM showed an increased blood glucose response (Figure 2). Nonetheless, this discrepancy indicates that the CGM device was unable to detect a potential hypoglycemia observed at the onset of exercise, and could therefore not be used to assess strategies aiming at preventing this phenomenon in practice. It is worth mentioning that a trend for a better agreement was observed toward the end of the exercise periods (Figure 2). If the duration of the exercise also affects the accuracy of CGM, it means that while the device may not be suitable for sport including short and intermittent exercise durations, its use could perhaps be considered during longer event such as cycling, trail or triathlon. This potential better accuracy toward longer exercise duration highlights the need to conduct further research involving 1) longer exercise duration, 2) nutritional intake during long endurance race 3) various exercise modalities and 4) different intensities.

To examine the potential effect of the absolute levels of glycemia on the validity of the CGM device, different breakfasts were proposed (CHO and PRO). Similar MARD and EGA results were observed, suggesting that the CGM validity was not affected by the breakfast content during non-exercise periods (i.e. Post-Breakfast, Pre-Exercise, Post-Exercise). Specific precompetition nutritional strategies can have a positive influence on both the acute running performance among rugby league players [START_REF] Bradley | Muscle glycogen utilisation during Rugby match play: Effects of pre-game carbohydrate[END_REF] or endurance athletes, [START_REF] Rothschild | What should i eat before exercise? Pre-exercise nutrition and the response to endurance exercise: Current prospective and future directions[END_REF] and the chronic training adaptations to training. [START_REF] Jeukendrup | Periodized Nutrition for Athletes[END_REF] Consequently, the use of this CGM device could be considered by practitioners willing to monitor glycemic responses before and after competition or training, to ensure the efficacy of the nutritional strategies employed. However, during the Exercise period, the CGM accuracy was modulated by breakfast content. Indeed, a 10 times higher value in Zone D of the EGA (i.e. clinically unsafe) was observed post CHO (10.5%) compared with post PROT (1.6%) breakfast. In our study, zone D corresponds to the situation where finger prick measures indicate an hypoglycemic state whereas CGM measures are within the normal range [START_REF] Clarke | Evaluating clinical accuracy of systems for self-monitoring of blood glucose[END_REF] suggesting that CGM failed to detect the hypoglycemia occurring during exercise after the CHO-rich breakfast. It is well known there is a rapid drop of blood glucose concentration at the onset of exercise, due to an increased glucose uptake by exercising muscles. [START_REF] Richter | Exercise, GLUT4, and skeletal muscle glucose uptake[END_REF] This physiological mechanism could explain why the sensor lacks sensitivity to rapid changes in glucose concentration, as observed in the present study. As it stands, if practitioners want to monitor blood glucose during high-intensity intermittent exercise, they need to consider other devices than CGM (e.g. finger prick).

Practical applications

-The present CGM system provided valid measures at rest. Therefore, the use of such a system may allow for a better individualization of nutritional strategies before or after competition.

-The level of validity was lower during high-intensity intermittent training and was in addition influenced by the type of breakfast consumed (i.e. high carbohydrates or high protein). Consequently, practitioners should avoid using this device during intermittent exercise.

Conclusion

Daily monitoring of blood glucose is of importance in athletes given the likely impact of glycemia on performance and the individualized nutritional recommendations that can be made with CGM. Our results highlighted that the CGM device examined in the present study presented only trivial-to-small bias when compared with a traditional fingerpick device at rest, suggesting that it could be used confidently during this specific period. The CGM device is not valid enough to monitor glucose during intermittent exercise. Further analyses should however evaluate the validity of this device over longer exercise duration.

Table and figure caption

13 ,

 13 participants undertook at their convenience the 4 standardized days. They were also instructed to have at least one full day of recovery between each experimental day. **Insert Figure 1** Methodology Continuous glucose monitoring. Each participant was provided with a CGM system (FreeStyle Libre, Abbott, France) over the full duration of the study. Each participant inserted a sensor (FreeStyle Libre, Abbott, France) in their non-dominant upper arm (i.e. back the triceps brachialis) one day before the beginning of the study. Glucose concentration was recorded in the interstitial fluid every minute.

Figure 1 .

 1 Figure 1. Schematic representation of the study design.

Figure 2 .

 2 Figure 2. Bland-Altman analysis between the continuous glucose monitoring device (CGM)and finger prick measures (FPBG). Dash lines represent the limits of agreements.

Figure 3 .

 3 Figure 3. Continuous glucose monitoring (CGM) and finger prick measures during each standardized condition, when ingesting a carbohydrate-(upper) and protein-(lower) oriented breakfasts, with the 2 days of each breakfast condition pooled for each participant (n = 2 x 8 for each curve). Data are presented as mean (SE).

Figures Figure 1

 1 Figures

Figure

  Figure 2

2

 2 Figure 2

  Figure 3

Table 3 .

 3 mg/dL), Post-Exercise (4.18[17.88] mg/dL) and moderate during Exercise(12.25 [13.86] mg/dL). Regarding CHO breakfast, mean biases were trivial-to-small for Post-Breakfast (-1.43 [25.98] mg/dL), Pre-Exercise (-4.29[11.66] mg/dL), Post-Exercise (3.32[18.18] mg/dL) and moderate during Exercise (14.06[13.81] mg/dL). For PROT Breakfast, trivial mean bias was observed for Pre-Exercise (0.91[8.98] mg/dL), Post-Breakfast (-4.51[8.31] mg/dL) and Post-Exercise (5.13[15.98] mg/dL), while moderate mean biases were observed for Exercise(10.47 [13.19] Irrespectively of the breakfast content, Post-Breakfast, Pre-Exercise, and Post-Exercise periods fell into Zone A (accurate) and B (benign errors) (100%). However, during Exercise, 94% of the values fell into A (70.4%) and B (23.6%), and 6% in Zone D (failure to treat errors). For CHO breakfast, 10.5% of data fell into Zone D for Exercise, while the other periods fell into Zone A and B. Similarly, for PROT breakfast, 1.6% fell into Zone D during the Exercise period.

	mg/dL).
	**Insert Figure 2**
	**Insert Figure 3**
	The results of the MARD analysis between the different periods are presented in Table 1 and
	2.
	**Insert Table 1 and 2**
	Results regarding EGA are presented in

**Insert

Table 3**

Table 1 .

 1 Median Absolute Relative Difference between the continuous glucose monitoring device (CGM) and finger prick measures. Data are median (interquartile range) and expressed in percentage. * : significantly different from Post-Breakfast. # : significantly different from Pre-Exercise. † : significantly different from Exercise. Comparisons between period are presented as effect size with 90% confidence interval.

Table 2 .

 2 Comparisons between period are presented as effect size for Wilcoxon test with 90% confidence interval.

Table 3 .

 3 Clark Error Grid Analysis between the continuous glucose monitoring device (CGM) and finger prick measures. Zone A represents a clinically accurate measure. Zone B stands for benign errors. Zone C represents overcorrection errors. Zone D and E represent failure to treat errors and erroneous treatment errors respectively. For more details see Clarke et al. (1987).

Table 2

 2

		Post-Breakfast	Post-Breakfast	Pre-Exercise	Pre-Exercise	Exercise
		vs.	vs.	vs.	vs.	vs.
		Exercise	Post-Exercise	Exercise	Post-Exercise	Post Exercise
	Overall	0.24 (0.17 to 0.31)	0.07 (0.01 to 0.16)	0.31 (0.24 to 0.38)	0.16 (0.07 to 0.24)	0.15 (0.06 to 0.23)
	CHO	0.24 (0.13 to 0.34)	0.06 (0.01 to 0.18)	0.37 (0.27 to 0.46)	0.19 (0.07 to 0.31)	0.18 (0.07 to 0.28)
	PROT	0.24 (0.14 to 0.34)	0.08 (0.01 to 0.2)	0.26 (0.16 to 0.36)	0.18 (0.01 to 0.24)	0.12 (0.02 to 0.24)

Table 3 Overall A

 3A

		(Accurate)	189 (88.3%)	213 (93.4%)	176 (70.4%)	100 (76.3%)
		B (Benign errors)	25 (11.7%)	14 (6.1%)	59 (23.6%)	31 (23.7%)
		D (Failure to treat errors)	/	1 (0.5%)	15 (6.0%)	/
		A (Accurate)	85 (80.2%)	104 (92.0%)	81 (65.3%)	52 (75.4%)
	CHO	B (Benign errors)	21 (19.8%)	9 (8.0%)	30 (24.2%)	17 (24.7%)
		D (Failure to treat errors)	/	/	13 (10.5%)	/
		A (Accurate)	104 (96.3%)	109 (94.8%)	95 (75.4%)	48 (77.4%)
	PROT	B (Benign errors)	4 (3.7%)	5 (4.3%)	29 (23.0%)	14 (22.6%)
		D (Failure to treat errors)	/	1 (0.9%)	2 (1.6%)	/


Leduc

J-B Morin

Mathieu Lacome
email: mlacome@psg.fr

Concurrent validity and reliability of sprinting force-velocity profile assessed with GPS devices in Elite athletes

Keywords: Team sport, Force, Power, Running, Sports performance

Purpose: the aims of this study were to 1) assess the concurrent validity of global positioning systems (GPS) against a radar device to measure sprinting force-velocity (F-v) profiles and 2) evaluate the inter-unit reliability of GPS devices. Methods: 16 male elite U18 rugby union players (178.3 ± 7.6 cm; 78.3 ± 13.2 kg) participated. Two 50-m sprints interspersed with at least 5 min of recovery were used to obtain input (maximal sprint speed [MSS] and acceleration time constant τ) and output (theoretical maximal horizontal force [F0], sprinting velocity [V0], and horizontal power [Pmax]) F-v profile variables. Sprint running speed was concurrently measured with a radar and 2 GPS units placed on the upper back of the players.

Results: Moderate to nearly perfect correlations were observed between radar and GPSderived F-v variables, with small-to-large typical errors. Trivial-to-small coefficients of variation were found regarding the GPS inter-unit reliability. Conclusion: The GPS devices tested in this study represent a valid and reliable alternative to a radar device when assessing sprint acceleration F-v profiles in team sports players.

Introduction

To assess the force velocity (F-v) profile, practitioners need to set standardised sprinting protocols and record special temporal or running velocity-time data using specific devices such as a radar. [START_REF] Haugen | Power-Force-Velocity Profiling of Sprinting Athletes: Methodological and Practical Considerations When Using Timing Gates[END_REF] While the radar is considered as a reference to measure speed, [START_REF] Haugen | Power-Force-Velocity Profiling of Sprinting Athletes: Methodological and Practical Considerations When Using Timing Gates[END_REF] not all highlevel clubs have access to such a technology. Moreover, in elite sport environment, time is scarce and it may be difficult for practitioners to dedicate a full testing session to assess F-v profiles. [START_REF] Lacome | Can we use GPS for assessing sprinting performance in rugby sevens? A concurrent validity and between-device reliability study[END_REF] However, most elite teams are now equipped with global positing system (GPS) devices, [START_REF] Akenhead | Training load and player monitoring in high-level football: Current practice and perceptions[END_REF] which could represent a viable alternative to measure players' position-speed-time data and compute F-v profiles without additional equipment and associated time demand.

Recent investigations have already highlighted the possibility to use GPS devices to extrapolate sprint mechanical properties by analysing the validity against radar and laser devices [START_REF] Nagahara | Concurrent validity of GPS for deriving mechanical properties of sprint acceleration[END_REF] or timing gates, [START_REF] Hoppe | Validity and reliability of GPS and LPS for measuring distances covered and sprint mechanical properties in team sports[END_REF] with mixed results likely due to the limited accuracy of the GPS units used. Moreover, Lacome et al. [START_REF] Lacome | Force velocity profiling with GPS: is it reliable ?[END_REF] observed trivial-to-small typical error and good-to-very good between-device intraclass correlation coefficients, suggesting that practitioners could reliably examine F-v profile with GPS. While only considerations have been made on the output variables (i.e. F-v profile variables), the validity and reliability of the model's input variables (speed-time curve characteristics) remains unknown. Finally, it is yet to be known if the integration of double constellation system improves the accuracy of GPS devices to produce F-v profile variables.

Therefore, the aims of this study were 1) to assess the concurrent validity of input (maximal sprint speed [MSS] and acceleration constant τ) and output (theoretical maximal horizontal force [F0], sprinting velocity [V0], and horizontal power [Pmax]) variables obtained with GPS and compared with radar device and 2) assess the inter-unit reliability of F-v profile variables assessed using GPS-data.

Methods

Subjects

16 male elite U18 rugby union players (height: 178.3 ± 7.6 cm; body mass; 78.3 ± 13.2 kg) were included as part a training camp for the French national squad. Participants provided informed consent prior to starting the study. Ethics approval was granted by the Leeds Beckett University ethics board and the recommendations of the Declaration of Helsinki were respected.

Design

F-v profile was assessed at the beginning of a rugby training session and was performed on a natural open-field grass pitch. A 20-min warm up was performed including running drills and 2 progressive 30-m sprints. Following the warm up, 2 sprints of 50 m with at least 5 min recovery between each trial were performed. No specific signal to initiate the sprint was given to players. However, they were asked to stand still in order to avoid any backward movement prior to starting their sprint. Additionally, immediate maximal acceleration at the beginning was asked. Each sprint was concurrently measured using a radar device sampling at 46.875 Hz (Stalker Pro II Sports Radar Gun, Plano, TX) and two GPS units (Optimeye S7, Catapult Innovations, Melbourne, Australia). The radar was placed on a tripod 5 m behind the player and 1 m above the ground. Two GPS units were carried in a specific, tightly fit vest allowing the two units to be positioned side-by-side, 5 cm apart around C7-T1 were used. Each unit was sampled at 10 Hz and included a double constellation system. The average horizontal dilution of precision was 0.74 ± 0.10 and the number of satellites was 15.5 ± 1.5.

Raw data gathered via radar and GPS devices were uploaded into a custom-made script to calculate F-v profiles automatically with R statistical software (R v4.0.2. R Foundation for Statistical Computing) based on the computation method presented and validated by Samozino et al. [START_REF] Samozino | A simple method for computing sprint acceleration kinetics from running velocity data: Replication study with improved design[END_REF] and Morin et al. 8 The whole data processing and script is further explained in Figure 1 and supplemental material. *Insert Figure 1*

Statistical Analyses

All data were first log transformed to reduce bias arising from non-uniformity error. However, values presented in the text and figures are the back-transformed data. The concurrent validity was assessed with Bland-Altman method mean bias (90% confidence limits. CL), the typical error of the estimate (TEE, 90% CL) both in percentage and standardized units and Pearson correlation coefficients. The magnitude of the standardised mean bias, TEE and correlations were interpreted as proposed by Hopkins. [START_REF] Hopkins | Measures of Reliability in Sports Medicine and Science[END_REF] The inter-unit reliability of F-v profile measured with GPS was assessed with the typical error of measurement expressed as a coefficient of variation (CV, 90% CI) as well as in standardized units and intraclass correlation (ICC). Moreover, the smallest worthwhile change (0.2 x between-athletes SD) (SWC) was calculated. The sensitivity (signal to noise ratio) was classified as follows; good (CV < SWC), OK (CV = SWC) or poor (CV > SWC). [START_REF] Hopkins | How to interpret changes in an athletic performance test[END_REF]

Results

Data related to the concurrent validity analysis and inter-unit reliability are displayed in Table 1. Limits of agreements from the Bland et Altman analysis are reported in Figure 2

Discussion

The main findings showed moderate-to-nearly perfect correlation between radar and GPS devices for output variables and small GPS inter-unit typical error highlighting the good level of reliability of these devices to assess F-v profile variables.

The present results showed moderate-to-nearly perfect correlation and small to moderate error between GPS and radar devices regarding F-v profile-related variables (F0, V0, Pmax), which was similar to Naghara et al. [START_REF] Nagahara | Concurrent validity of GPS for deriving mechanical properties of sprint acceleration[END_REF] However, only a 10 Hz device including a double constellation system has been used in our study compared with Naghara et al. [START_REF] Nagahara | Concurrent validity of GPS for deriving mechanical properties of sprint acceleration[END_REF] who used 20 Hz GPS units (from a different brand) and a single constellation. A previous study showed significant improvements both in positioning accuracy and integrity monitoring as a result of the use of double constellations system. [START_REF] Feng | New methods for dual constellation single receiver positioning and integrity monitoring[END_REF] Moreover, our study was performed in an open-field (i.e. without surrounding metallic structure), which suggests that current GPS technology when combined with optimal environmental conditions is accurate enough to monitor F-v profiles.

However, lower levels of concurrent validity (large to very large correlation and moderate typical errors) were observed for Pmax and F0 compared with V0 (nearly perfect correlation and small typical error). The origin of this difference is unclear and could be attributed to the ~5 times lower sample frequency of the GPS compared with the radar. In their study, Naghara et al. [START_REF] Nagahara | Concurrent validity of GPS for deriving mechanical properties of sprint acceleration[END_REF] showed that the accuracy of GPS to measure F-v profiles was lowered when 5 Hz GPS was used compared to 20 Hz. Hence, with the inclusion of a double constellation system, the bias observed was similar despite lower sample frequency. Therefore, the integration of GPS systems with higher sampling rate is likely required to improve the validity of F-v profile measured with GPS devices. Moreover, while V0 is calculated based on MSS (corresponding to a steady state), F0 is mainly related to τ which is associated to a rate of state change and could therefore be more affected by the measurement system and/or data processing, explaining the higher typical error compared to V0. Nevertheless, the use of GPS devices to assess F-v profiles are now within reach and could be considered by practitioners in their daily practice since double constellations system are common.

The results of the present study highlighted that the inter-unit GPS reliability was very high when analysing F-v profile-related data. Similar results were observed by Lacome et al. [START_REF] Lacome | Force velocity profiling with GPS: is it reliable ?[END_REF] who reported small typical errors, supporting that GPS is a reliable method to monitor F-v profiles.

While lower sensitivity was observed for F0, this could be improved using more testing repetitions (as the error decrease by a factor of √ n repetitions [START_REF] Taylor | Sources of variability in Iso-Inertial jump assessments[END_REF] ), which would be more feasible by using GPS devices in practice (e.g. 4 to 6 sprints within warm-up). As only the inter-unit reliability has been measured in the present study, further research is necessary to understand the week-to-week variability and the sensitivity to changes (e.g. pre-post preseason) of the F-v profile obtained with GPS.

Practical applications

 F-v profile variables assessed through Catapult Optimeye S7 GPS devices (sampled at 10 Hz and including a double constellation system) presented small-to-moderate error compared with a radar device. Practitioner could consider these GPS devices as an alternative for more frequent assessment.

 F-v profile variables obtained with GPS showed a high inter-unit reliability, confirming previous studies findings that GPS units can be used interchangeably to measure F-v profiles in team sports athletes.

Conclusion

The present study confirms that the Catapult Optimeye S7 GPS devices could be a potential cost-effective, valid and reliable alternative to a radar device when assessing sprint acceleration F-v profiles in team sports players. Future studies need to compare F-v profile related kinetic variables with the gold standard (i.e. tracks equipped with force plates) or consider other reference systems with a higher sample frequency than radar devices (i.e. laser, robotic sprint resistance device).

Legend

Figure 1 .

 1 Figure 1. Schematic representation of the automatic data processing. The left panel represents the validity analysis where GPS was compared with radar. The right panel represents the inter-units reliability analysis. Both share the same data processing via the script. Upper panel represents the identification of the beginning and end of the sprint from the raw velocity signal. The script identifies the beginning (i.e. first speed value > 0.2 m.s -1 from 0) and the end of the sprint (i.e. negative acceleration after player reach maximal speed). The middle panel represents the raw velocity data fitting into a mono exponential equation using a least square regression method from the NLS optimization function of the nlstools package (version 3.6.2). A time delay (d) was integrated into the initial equation to improve the goodness of fit, if the actual start of the sprint did not occur at t=0 s (0.09 ± 0.04 s on average in the present study). The lower panel aimed to calculate the speed-time data, theoretical maximal horizontal force (F0 [N.kg -1 ]), maximal horizontal sprinting power (Pmax [W.kg -1 ]) and theoretical maximal sprinting velocity (V0 [m.s -1 ]). All data analysis were performed with R statistical software (R v4.0.2. R Foundation for Statistical Computing). MSS stands for maximal sprint speed, τ is the acceleration time constant and d the time delay.

Figure 2 .

 2 Figure 2. Bland et Altman analyses. Black line represents the bias. Das lines represents 90% limits of agreements.

Figures Figure 1

 1 FiguresFigure1

Figure

  Figure 2

2

 2 Figure 2

  284

Table 1 .

 1 Concurrent validity and inter-unit reliability analysis. Raw data for criterion(Radar) and practical (GPS) devices are presented as mean ± SD. Other concurrent validity statistics are presented with 90% confidence intervals. TEE stands for Typical error of Estimate. Raw data for GPS 1 and 2 are presented as mean ± SD. Reliability statistics are presented with 90% confidence intervals. TE stands for Typical Error. SWC stands for Smallest Worthwhile Change.

Table 1 MSS

 1

		(m . s -1 )	τ (s)	Pmax (W . kg -1 )	V0 (m . s -1 )	F0 (N . kg -1 )
	Validity
	Criterion
	(Radar)	8.84 ± 1.06	1.24 ± 1.14	16.7 ± 1.12	9.26 ± 1.06	6.98 ± 1.11
	Mean ± SD
	Practical
	(GPS)	8.81 ± 1.05	1.25 ± 1.08	16.08 ± 1.10	9.23 ± 1.06	6.96 ± 1.07
	Mean ± SD
	Standardised	-0.28	0.51	-0.56	-0.27	-0.19
	mean bias	(-0.63 to 0.07)	(-1.85 to 2.93)	(-2.25 to 1.17)	(-0.73 to 0.20)	(-2.33 to 1.99)
	Mean bias	1.7	11.30	7.99	2.16	10.12
	(%)	(1.4 to 2.0)	(9.77 to 13.45)	(6.92 to 9.48)	(1.88 to 2.55)	(8.76 to 12.04)
	Standardised	0.29	1.48	0.92	0.36	1.85
	TEE	(0.24 to 0.37)	(1.05 to 2.32)	(0.70 to 1.26)	(0.29 to 0.46)	(1.25 to 3.25)
	TEE as coefficient of variation (%)	0.29 (0.24 to 0.37)	1.48 (1.05 to 2.32)	0.92 (0.70 to 1.26)	0.36 (0.29 to 0.46)	1.85 (1.25 to 3.25)
	Reliability
	GPS 1 Mean ± SD	8.81 ± 1.06	1.25 ± 1.08	16.02 ± 1.10	9.24 ± 1.06	6.93 ± 1.07
	GPS 2 Mean ± SD	8.81 ± 1.05	1.25 ± 1.07	16.14 ± 1.11	9.23 ± 1.05	6.99 ± 1.07
	TE as coefficient of variation (%)	0.5 (0.4 to 0.7)	2.0 (1.7 to 2.6)	1.4 (1.2 to 1.8)	0.6 (0.5 to 0.8)	1.8 (1.5 to 2.4)
	Standardised	0.10	0.28	0.15	0.12	0.28
	TE	(0.08 to 0.12)	(0.23 to 0.36)	(0.12 to 0.18)	(0.10 to 0.15)	(0.23 to 0.35)
	ICC	0.99	0.93	0.98	0.99	0.93
	(90% CI)	(0.98 to 1.00)	(0.88 to 0.96)	(0.96 to 0.99)	(0.98 to 1.00)	(0.88 to 0.96)
	SWC (%)	1.0	1.5	2.0	1.1	1.4
	Sensitivity	Good	Poor	Good	Good	Poor