Theodore Lytras1

Anna Beckmeyer-Borowko3

Manolis Kogevinas1

Hans Kromhout7

Anne-Elie Carsin1

Josep Maria Ant

Hayat Bentouhami8

Joost Weyler8

Joachim Heinrich9

Dennis Nowak9

Isabel Urrutia11

Jes ´us Mart´ınez-Moratalla12

Jos ´e

Antonio Gull

Antonio Pereira Vega15

Chantal Raherison Semjen16

Isabelle Pin17

Pascal Demoly20

B´en ´edicte Leynaert22

Simona Villani23

Thorarinn Gislason24

Øistein Svanes26

Mathias Holm27

Bertil Forsberg28

Dan Norb

Amar J Mehta30

Dirk Keidel3

David Vernez31

Geza Benke32

Rain Jõgi33

Kjell Tor

Torben Sigsgaard35

Vivi Schl

Mario Olivieri37

Paul D Blanc38

John Watkins40

Roberto Bono41

Giulia Squillacioti41

A Sonia Buist42

Roel Vermeulen7

Deborah Jarvis43

Nicole Probst-Hensch3

Jan-Paul Zock1

Cumulative Occupational Exposures and Lung-Function Decline in Two Large General-Population Cohorts

Keywords: spirometry, lung function, occupational exposure, occupational disease, longitudinal studies

Rationale: Few longitudinal studies have assessed the relationship between occupational exposures and lung-function decline in the general population with a sufficiently long follow-up.

Objectives: To examine the potential association in two large cohorts: the ECRHS (European Community Respiratory Health Survey) and the SAPALDIA (Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults). Methods: General-population samples of individuals aged 18 to 62 were randomly selected in 1991-1993 and followed up approximately 10 and 20 years later. Spirometry (without bronchodilation) was performed at each visit. Coded complete job histories during follow-up visits were linked to a job-exposure matrix, generating cumulative exposure estimates for 12 occupational exposures. Forced expiratory volume in 1 second (FEV1) and forced vital capacity (FVC) were jointly modeled in linear mixed-effects models, fitted in a Bayesian framework, taking into account age and smoking.

Results: A total of 40,024 lung-function measurements from 17,833 study participants were analyzed. We found accelerated declines in FEV1 and the FEV1/FVC ratio for exposure to biological dust, mineral dust, and metals (FEV1 = 215.1 ml, 214.4 ml, and 218.7 ml, respectively; and FEV1/FVC ratio = 20.52%, 20.43%, and 20.36%, respectively; per 25 intensity-years of exposure). These declines were comparable in magnitude with those associated with long-term smoking. No effect modification by sex or smoking status was identified. Findings were similar between the ECRHS and the SAPALDIA cohorts.

Conclusions: Our results greatly strengthen the evidence base implicating occupation, independent of smoking, as a risk factor for lung-function decline. This highlights the need to prevent or control these exposures in the workplace.

Chronic obstructive pulmonary disease

(COPD) is an important cause of population morbidity and mortality, characterized by a low degree of lung function and persistent airflow limitation [START_REF] Vogelmeier | Global Initiative for Chronic Obstructive Lung Disease. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease 2017 report. GOLD executive summary[END_REF]. The most well-recognized risk factor is tobacco smoking, which is associated with the majority of COPD cases. Other environmental risk factors are also implicated in the pathogenesis of COPD [START_REF] Eisner | Committee on Nonsmoking COPD, Environmental and Occupational Health Assembly. An official American Thoracic Society public policy statement: novel risk factors and the global burden of chronic obstructive pulmonary disease[END_REF], including occupational exposures [START_REF] Blanc | The occupational burden of non-malignant respiratory diseases: an official American Thoracic Society and European Respiratory Society Statement[END_REF]. The ECRHS (European Community Respiratory Health Survey) has recently shown a higher incidence of COPD in workers exposed to biological dusts, gases, fumes, and pesticides, with a combined population-attributable fraction of 21% [START_REF] Lytras | Occupational exposures and 20-year incidence of COPD: the European Community Respiratory Health Survey[END_REF].

Lung function declines naturally with age, but an accelerated decline is a primary, although not obligatory, cause of COPD [START_REF] Lange | Lung function trajectories leading to chronic obstructive pulmonary disease[END_REF] and a long-term feature of asthma [START_REF] Lange | A 15-year follow up study of ventilatory function in adults with asthma[END_REF]. Despite a large number of studies, including both population-and industry-based studies, demonstrating an association of asthma and COPD with occupational exposures [START_REF] Blanc | The occupational burden of non-malignant respiratory diseases: an official American Thoracic Society and European Respiratory Society Statement[END_REF][START_REF] Omland | Occupational chronic obstructive pulmonary disease: a systematic literature review[END_REF][START_REF] De Matteis | The occupations at increased risk of COPD: analysis of lifetime job-histories in the population-based UK Biobank Cohort[END_REF], relatively few population-based longitudinal studies have examined the relationship between lung-function decline and occupational exposures, as estimated by a job-exposure matrix (JEM) [START_REF] Sunyer | Occupational Group of the European Community Respiratory Health Survey. Lung function decline, chronic bronchitis, and occupational exposures in young adults[END_REF][START_REF] De Jong | Association of occupational pesticide exposure with accelerated longitudinal decline in lung function[END_REF][START_REF] Liao | Occupational exposures and longitudinal lung function decline[END_REF]. A previous analysis from the first 10year follow-up of the ECRHS did not show a steeper decline in lung-function in people exposed to vapors, gases, dusts, or fumes [START_REF] Sunyer | Occupational Group of the European Community Respiratory Health Survey. Lung function decline, chronic bronchitis, and occupational exposures in young adults[END_REF]; the cohort, however, was fairly young then (30-55 yr at the time), and the follow-up time may have been too short to detect an association. Therefore, our aim was to examine the association between occupational exposures and the rate of lung-function decline, given its potential relevance to COPD risk. To do so, we combined two large prospective cohorts participating in the Ageing Lungs in European Cohorts study (www.alecstudy.org): the ECRHS [START_REF] Burney | The European Community Respiratory Health Survey[END_REF] and the SAPALDIA (Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults) [START_REF] Martin | SAPALDIA: methods and participation in the cross sectional part of the Swiss Study on Air Pollution and Lung Diseases in Adults[END_REF]. Both cohorts have accumulated two decades of follow-up with more participants over 50 years of age, thus allowing better and more precise estimates of the determinants of lung-function decline. In addition, we examined whether the association between occupation and lung-function decline is different between men and women and between smokers and nonsmokers.

Methods

The ECRHS is a multicenter longitudinal study initiated in 1991-1993 that enrolled random generalpopulation samples of individuals aged 20-44 years in 55 centers from 23 countries [START_REF] Burney | The European Community Respiratory Health Survey[END_REF]. Participants at baseline (ECRHS I) completed a detailed questionnaire via a face-to-face interview and underwent a clinical examination, spirometry, and other measurements. They were followed up between 1998 and 2002 (ECRHS II) and a second time between 2010 and 2012 (ECRHS III). SAPALDIA is also a multicenter longitudinal study with objectives, methods, and protocols very similar to those of the ECRHS, which was initiated in 1991 (SAPALDIA 1) and enrolled a random general-population sample of individuals aged 18-62 years from eight regions in Switzerland [START_REF] Martin | SAPALDIA: methods and participation in the cross sectional part of the Swiss Study on Air Pollution and Lung Diseases in Adults[END_REF]. SAPALDIA participants were also followed up in 2001 and 2011 (SAPALDIA 2 and 3). A flowchart of study participants from both cohorts can be found in the online supplement (see Figure E1 in the online supplement).

During both follow-ups, participants in both cohorts were asked to provide a detailed list of their occupations and industries from all jobs held since the last study visit; these were recorded in free text and subsequently coded in the International Classification of Occupations-88 (ISCO-88) standard by trained local coders who were blind to participant status. Ethical approval for each center was obtained from their respective competent bodies, and written informed consent was obtained from all participants.

The population for this analysis includes all participants who underwent spirometry at baseline (ECRHS I/SAPALDIA 1) and were followed up at least once (at ECRHS II/SAPALDIA 2 and/or ECRHS III/SAPALDIA 3). All spirometric examinations were performed without bronchodilation and according to the American Thoracic Society/European Respiratory Society standards for reproducibility, using the maximum forced vital capacity (FVC) and forced expiratory volume in 1 second (FEV1) for each participant. Occupational exposures were determined by linking the participants' ISCO-88-coded occupations to the semiquantitative ALOHA(1) JEM [START_REF] De Jong | Association of occupational pesticide exposure with accelerated longitudinal decline in lung function[END_REF]. This JEM assigns, for every ISCO-88 job code, three grades of exposure (none, low, high) to 12 agents (biological dusts, mineral dusts, gases/fumes, herbicides, insecticides, fungicides, aromatic solvents, chlorinated solvents, other solvents, and metals) including two composites of the above (all pesticides and vapors/gases/dusts/fumes [VGDF]). For each participant, a cumulative exposure to each agent in intensity-years was calculated by multiplying the duration of each job with the intensity of exposure (0 for none, 1 for low, and 4 for high); these intensities reflect the lognormal distribution of occupational exposure concentrations when calculating cumulative exposures.

Covariates used for adjustments at each visit included age, sex, height (including its square, to allow for nonlinear associations), current asthma, current smoking, lifetime smoking pack-years, socioeconomic status (SES), and early-life disadvantage score; the latter is a composite variable that includes maternal smoking, maternal asthma, paternal asthma, childhood asthma (before age 10), and having a serious respiratory infection before age 5 [START_REF] Svanes | Early life origins of chronic obstructive pulmonary disease[END_REF]. Current asthma was defined as a positive response to any of the following three questions: "Have you had an attack of asthma in the last 12 months?"; "Are you currently taking any medicines for asthma?"; and "Have you been woken by an attack of shortness of breath at any time in the last 12 months?" SES was defined according to the participants' age of completion of formal education and classified into three categories: high (under 19 yr), middle (16-19 yr), low (over 16 yr).

Associations between cumulative occupational exposures and lung function (FEV1, FVC, and the FEV1/FVC ratio) were assessed using linear mixed-effects models, providing the mean change in lung function per intensity-year of exposure. FEV1 and FVC were jointly modeled, and all models included participant level, random intercepts, and slopes, taking account of the correlations between both random intercepts and slopes as well as FEV1 and FVC. For each ALOHA(1) exposure agent, we fitted two joint models, one using absolute FEV1 and FVC ("linear model") and one using their logarithms as the outcome ("log-linear model"); from the latter, we calculated the effects of exposures on the FEV1/FVC ratio as the difference between model parameters for log(FEV1)and log(FVC). In all instances, the comparison was between participants who were exposed and participants who were unexposed to the particular ALOHA(1) agent rather than between exposed participants and participants without any occupational exposure.

The models were fitted in a Bayesian framework with the JAGS software, setting noninformative priors for all parameters, and using four chains and 20,000 iterations per chain, discarding the first 2,000 as burn-in, and with a thinning interval of 5. Convergence was checked by visual inspection of the Markov chain Monte Carlo trace plots and by the Gelman-Rubin statistic. Furthermore, all models included a fully Bayesian imputation submodel for handling item (covariate) missingness, with hyperparameters set to noninformative priors (see online supplement for details). Uncertainty for the fixed effects was expressed with 95% credible intervals (95% CrIs).

In addition to the unstratified models, we fitted another set of models with added interaction terms for sex and smoking status (ever-/never-smoker), thereby calculating stratified estimates for the effect of occupational exposures in men and women and in ever-smokers and never-smokers. As a criterion to determine the presence of interaction (effect modification), we used the posterior probability distribution of the interaction term, at least 95% of which should lie above or below zero. As a sensitivity analysis, we also fitted all models to the data from each cohort (ECRHS and SAPALDIA) separately.

All analyses were performed with the R statistical environment, version 3.6.0 [START_REF] Core | R: a language and environment for statistical computing[END_REF].

Results

Table 1 highlights the characteristics of the study population. We analyzed a total of 40,024 lungfunction measurements from 17,833 study participants across 38 ECRHS and SAPALDIA centers, each of whom completed at least one follow-up visit after baseline, with a mean follow-up duration of 16.3 years (range, 4.3-22.6 yr). Of these participants, 10,803 (60.6%) completed both visits over a mean duration of 19.6 years (range, 15.7-22.6 yr); 5,793 (32.5%) participants completed the initial and the first follow-up visits only, whereas 1,237 (6.9%) completed the initial and the second followup visits only. Slightly less than half of our sample had never smoked, and about a third were current smokers at baseline, dropping almost by half to 18.3% at the second follow-up. A little less than half of all participants had been occupationally exposed to VGDF at some point during follow-up (39.8%), whereas fewer had been exposed to solvents (24.2%), metals (9.3%), or pesticides (3.3%). Men were overall more likely than women to be occupationally exposed to most agents, with the exception of biological dust (Table 2). In addition, many exposures showed substantial overlap with each other (Figure 1). A list of the most common jobs by exposure category can be found in the online supplement (Table E1); notably, "occupational cleaner" was the most frequent job title among those exposed to dusts, gases, and fumes (426 study participants).

Lifetime smoking pack-years were missing in 7.8% of all observations and had to be imputed in our models as described in the METHODS section; in addition, current smoking status was missing in 2.9%, current asthma status was missing in 0.5%, and SES was missing in 1.1% of all observations. Lung function in the study population naturally declined with advancing age across both follow-ups, and our Bayesian mixed-effects model was reliable in describing both mean lung function by age and the variability around the mean (Figure 2). Table 3 summarizes the main results from our analysis, which included the effect of 25 intensityyears of occupational exposure to each ALOHA(1) agent on the three lung-function parameters (FEV1, FVC, and the FEV1/FVC ratio), both overall and stratified by sex and smoking status. A negative sign indicates reduced lung function as compared with no occupational exposure to the respective agent, given the same age, sex, and other covariates (i.e., an accelerated lung-function decline), whereas a positive number indicates a slower lung-function decline. As the numbers per year are very small, the effect per 25 intensity-years is presented rather than the effect per 1 intensity-year of exposure.

A decreased FEV1/FVC ratio was observed for biological-dust and mineral-dust exposure (20.52% and 20.43%, respectively) (Table 3), which was purely attributed to a FEV1 decline (215.08 ml and 214.42 ml, respectively), with no change in the FVC. A significant decline in FEV1 only was also observed for metal exposure (218.73 ml; 95% CrI, 234.41 to 22.60 ml), and a lower FEV1/FVC ratio was observed for the composite VGDF exposure (20.34%; 95% CrI, 20.56 to 20.12%). On the other hand, smaller lungfunction declines were seen for gases and fumes (27.35 ml in the FEV1 and 20.24% in the FEV1/FVC ratio) as well as for pesticides, especially in terms of the FEV1, although the results were very imprecise, with a wider 95% CrI than other exposures (Table 3). Among solvents, only exposure to chlorinated solvents was weakly associated with both lower FEV1 and lower FVC (216.98 ml and 214.59 ml). For comparison, as estimated in the model, 25 pack-years of smoking reduced FEV1 by an additional 211.07 ml (95% CrI, 222.27 to 2.49 ml), reduced FVC by 214.83 ml (95%CrI, 232.29 to 20.55 ml), and reduced the FEV1/FVC ratio by 20.21% (95% CrI, 20.44% to 0.03%). In addition, current asthma was associated with an FEV1 that was 280.28 ml lower (95% CrI, 292.72 to 267.71 ml), an FVC that was lower by 213.60 ml (95% CrI, 227.44 to 20.05 ml), and an FEV1/FVC ratio that was 22.16%lower (95% CrI, 22.52% to 21.81%).

No effect modification by sex was detected for any occupational exposure or any lung-function parameter, with one exception: women exposed to aromatic solvents had a slower FVC decline than unexposed women (186.87 ml; 95% CrI, 13.80 to 161.25 ml), whereas no significant difference was found among men (27.84 ml; 95% CrI, 234.96 to 20.18 ml). In addition, no effect modification by smoking status was Table 1. Characteristics of study participants by study wave detected, although in most cases, the effects of occupational exposures in ever-smokers tended to be slightly lower than in never-smokers (Table 3). In the sensitivity analysis, results were similar between the two cohorts and showed wide overlap; pesticide exposure resulted in greater FEV1 decline among ECRHS participants, whereas solvents resulted in slightly greater declines among SAPALDIA participants (Table E2).

Discussion

In this pooled analysis from two large longitudinal cohorts, we showed that certain groups of occupational exposures, namely biological dust, mineral dust, and metal exposure, were prospectively associated with accelerated lung-function decline, specifically with respect to FEV1 and the FEV1/FVC ratio. This loss of lung function is comparable with that associated with smoking, highlighting the importance of these occupational exposures in respiratory health. Our study provides significant new evidence on the topic, as few such studies have examined longitudinal lungfunction decline in a general-population setting [START_REF] De Jong | Association of occupational pesticide exposure with accelerated longitudinal decline in lung function[END_REF][START_REF] Liao | Occupational exposures and longitudinal lung function decline[END_REF][START_REF] Kauffmann | Occupational exposure and 12-year spirometric changes among Paris area workers[END_REF][START_REF] Krzyzanowski | Factors associated with the change in ventilatory function and the development of chronic obstructive pulmonary disease in a 13-year follow-up of the Cracow Study. Risk of chronic obstructive pulmonary disease[END_REF][START_REF] Harber | Effect of occupational exposures on decline of lung function in early chronic obstructive pulmonary disease[END_REF]. In comparison with industry-based studies, studies of general-population cohorts can provide more generalizable information by including all types of exposures across all industries and by adjusting for SES and other covariates. Previous analyses from both ECRHS and SAPALDIA have shown an increased asthma risk for certain occupational exposures [START_REF] Kogevinas | Exposure to substances in the workplace and new-onset asthma: an international prospective population-based study (ECRHS-II)[END_REF], an increased incidence of COPD in participants occupationally exposed to biological dust and mineral dust [START_REF] Lytras | Occupational exposures and 20-year incidence of COPD: the European Community Respiratory Health Survey[END_REF][START_REF] Mehta | SAPALDIA Team. Occupational exposure to dusts, gases, and fumes and incidence of chronic obstructive pulmonary disease in the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults[END_REF], and an increased incidence of chronic cough and chronic phlegm symptoms in those exposed to mineral dust and metals [START_REF] Lytras | Occupational exposures and incidence of chronic bronchitis and related symptoms over two decades: the European Community Respiratory Health Survey[END_REF]. Our current analysis corroborates previous findings for biological dust in a much larger study population with additional follow-up. It also indicates that mineral dust and metal exposure are associated not only with chronic bronchitis symptoms but also with lung-function decline. The latter result is in agreement with recent findings from the smaller Tasmanian longitudinal health study cohort [START_REF] Alif | Occupational exposure to solvents and lung function decline: a population based study[END_REF] and with a systematic review of exposure to welding fumes and lung-function decline [START_REF] Szram | Welding, longitudinal lung function decline and chronic respiratory symptoms: a systematic review of cohort studies[END_REF]. We also found cleaner to be a frequent jobtitle among exposed occupations, one that was recently associated with an accelerated decline in lung function [START_REF] Svanes | Cleaning at home and at work in relation to lung function decline and airway obstruction[END_REF].

A striking finding is the very small absolute effect sizes observed, only a few milliliters (or a fraction of a percentage point) of lung-function decline after 25 intensity-years of exposure, which, for many workers, may represent their entire lifetime exposure. At first glance, such reductions could not plausibly account for an increased COPD risk, as has been seen in previous analyses [START_REF] Lytras | Occupational exposures and 20-year incidence of COPD: the European Community Respiratory Health Survey[END_REF][START_REF] De Matteis | The occupations at increased risk of COPD: analysis of lifetime job-histories in the population-based UK Biobank Cohort[END_REF][START_REF] Mehta | SAPALDIA Team. Occupational exposure to dusts, gases, and fumes and incidence of chronic obstructive pulmonary disease in the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults[END_REF]; however, these are average subject-specific effects over an exposure group, and small average reductions can mask a larger effect for certain individuals inside the group [START_REF] Petersen | Rapid lung function decline in smokers is a risk factor for COPD and is attenuated by angiotensin-converting enzyme inhibitor use[END_REF], especially if one also considers the sizable individual variation in lung function (Figure 2). In addition, the comparator is not a fully occupationally unexposed group but is rather composed of participants unexposed to the agent under consideration (who may have other occupational exposures). Furthermore, nondifferential misclassification is the main limitation in using JEMs in population-based occupational epidemiology studies, mostly stemming from the variability in job tasks and actual exposures between workers belonging in the same JEM exposure category [START_REF] Kauppinen | Assessment of exposure in occupational epidemiology[END_REF]. Grouping exposures in a JEM does not in itself bias regression coefficients toward the null but rather results in imprecision because of Berkson-type error [START_REF] Tielemans | Individual based and group-based occupational exposure assessment: some equations to evaluate different strategies[END_REF]; nevertheless, regressiondilution bias can be introduced if the estimated group mean is different from the true group mean [START_REF] Armstrong | The effects of measurement errors on relative risk regressions[END_REF]. Intraindividual variability in exposure over time is another consideration, which is particularly relevant when calculating a cumulative exposure estimate on the basis of job duration and a JEM classification, which was, in this case, calculated over a long period of 20 years [START_REF] Preller | Modeling long-term average exposure in occupational exposure-response analysis[END_REF]. All of these factors mean that the small average declines observed in our analysis should not be used for prediction, as they are unlikely to represent the actual magnitude of the effect of these exposures on lung function in any particular individual. For the same reasons, our results cannot be used to definitively rule out an effect of occupational solvent exposure on lung function, as recently observed in the Tasmanian longitudinal health study cohort [START_REF] Alif | Occupational exposure to solvents and lung function decline: a population based study[END_REF], nor can they be used to rule out an effect of pesticide exposure [START_REF] Lytras | Occupational exposures and 20-year incidence of COPD: the European Community Respiratory Health Survey[END_REF][START_REF] De Jong | Association of occupational pesticide exposure with accelerated longitudinal decline in lung function[END_REF]. Especially regarding pesticides, the small percentage of exposed participants (less than 5%) (Table 2) has substantially reduced precision, as reflected in the wider CrIs of the lung function-decline estimates.

Smoking is known to induce inflammation and impair the host-defense mechanisms of the lung [START_REF] Herr | R Bals for the CAPNETZ Study Group. Suppression of pulmonary innate host defence in smokers[END_REF][START_REF] Lugade | Cigarette smoke exposure exacerbates lung inflammation and compromises immunity to bacterial infection[END_REF] and has been reported to increase the adverse effect of occupational exposures on lung-function decline and COPD risk [START_REF] Boggia | Burden of smoking and occupational exposure on etiology of chronic obstructive pulmonary disease in workers of Southern Italy[END_REF][START_REF] Blanc | Occupational exposures and the risk of COPD: dusty trades revisited[END_REF][START_REF] Hu | Increased risk of chronic obstructive pulmonary diseases in coke oven workers: interaction between occupational exposure and smoking[END_REF]. However, in our analyses, we found little evidence of effect modification by smoking after tightly adjusting both for current smoking status and cumulative packyears. This indicates that all workers may be at risk of airway obstruction because of these occupational exposures, even though this may be more clinically significant for smokers, who represent the majority of patients with COPD in most countries, who already have lower levels of lung function, such that any additional loss or acceleration of decline may push them over the diagnostic cut off. Similarly, we found little difference between men and women on the effect of occupational exposures, even though the distribution of jobs for each exposure category was different between men and women. This may partially be explained by the lower proportion of occupationally exposed women in our cohorts, particularly for exposures like metals, pesticides, and solvents. Future occupational epidemiology studies should try to recruit more women to investigate effect modification by sex.

Strengths of the current study include its prospective population-based design and long follow-up of 20 years. Full job histories were collected for the study period, and cumulative occupational exposures were calculated using a JEM instead of self-report; the latter could be vulnerable to recall bias, especially given the long follow-up involved. Lung function was modeled in great detail using a mixed-effects model with random intercepts and slopes and accounting for the correlation between FEV1 and FVC; joint modeling of both spirometric parameters is important, as we identified positive correlations not only between the participant-level random intercepts and slopes for FEV1 and FVC but also between the intercepts for FEV1 and FVC and the slopes for FEV1 and FVC (data not shown). In addition, we controlled for multiple confounders, including SES, current asthma, and, especially, lifetime smoking pack-years, to minimize confounding by intensity of smoking. The sample size was very large, thanks to the pooling of two large cohorts, facilitating the detection of associations of very small magnitude.

On the other hand, there are certain limitations to our study. Spirometric examinations were performed without bronchodilation, and the results should be interpreted accordingly. Accelerated lung-function decline in and of itself does not equate with COPD risk; in particular, occupation can also be a risk factor for asthma, and it is thus not possible to distinguish lung-function decline due to new-onset occupational asthma from that contributing to increased COPD incidence. The application of a JEM may ensure more objective exposure estimates, which are free from exposure-specific recall but can introduce both imprecision because of Berkson-type error and some bias toward the null; therefore, despite its large size, the sample may not have always been sufficient to detect an association (e.g., the effects of pesticides or identification of an effect modification). For the same reason, we cannot disentangle the effect of multiple overlapping exposures, which would require a large number of small subgroup analyses. We also could not assess the heterogeneity of the results across study centers or countries because the necessary inclusion of participant-level random effects left almost no variance to be explained by study center. Finally, residual confounding cannot be completely ruled out, despite the detailed model adjustments, and may have affected the observed associations, given their small magnitude. As this is an observational study, some degree of selection and response bias also cannot be definitely ruled out.

In conclusion, long-term occupational exposure to biological dust, mineral dust, and metals over two decades of follow-up was associated with an accelerated decline in FEV1 and the FEV1/FVC ratio, which would potentially translate to an increased risk of airway obstruction and COPD. This decline was comparable with that associated with smoking and was similar between men and women as well as between smokers and nonsmokers. These results strengthen the case for occupation as a modifiable risk factor for asthma and COPD, in agreement with previous studies. And they make the workplace, or controlling them with appropriate protective measures, to protect the respiratory health of workers.


Pascal Andujar

Dominique Courbon

Emilie Bizard

Elisabeth Marcos

Serge Adnot

Laurent Boyer

Pascal Demoly

Debbie Jarvis

Catherine Neukirch

Isabelle Pin

Gabriel Thabut

Jorge Boczkowski

Bénédicte Leynaert
email: benedicte.leynaert@inserm.fr

Smoking, telomere length and lung function decline: a longitudinal population-based study

Telomere shortening is associated with COPD and impaired lung function in cross-sectional studies, but there is no longitudinal study. We used data from 448 participants recruited as part of the French follow-up of the ECRHS. We found no relationship between telomere length at baseline and FEV1 decline after 11 years of follow-up. However, heavy smoking was associated with an accelerated FEV1 decline in individuals with short telomeres, but not in subjects with longer telomeres (p for interaction p=0.08). Our findings suggest that short telomere length in peripheral leukocytes might be a marker for increased susceptibility to the effect of smoking.

Introduction

Not all smokers will develop COPD but the reasons why some individuals are more susceptible to accelerated loss of lung function when exposed to tobacco smoke are still largely unexplained. Telomeres are structures at the chromosomal ends involved in chromosomal stability. COPD patients have been shown to have shorter telomeres compared to healthy smokers, and correlations between telomere shortening, premature senescence and lung inflammation have been reported [START_REF] Mercado | Accelerated ageing of the lung in COPD: new concepts[END_REF][START_REF] Rutten | Various Mechanistic Pathways Representing the Aging Process Are Altered in COPD[END_REF][START_REF] Savale | Shortened telomeres in circulating leukocytes of patients with chronic obstructive pulmonary disease[END_REF][START_REF] Amsellem | Telomere dysfunction causes sustained inflammation in chronic obstructive pulmonary disease[END_REF][START_REF] Alder | Telomere length is a determinant of emphysema susceptibility[END_REF][START_REF] Birch | DNA damage response at telomeres contributes to lung aging and chronic obstructive pulmonary disease[END_REF]. Experimental studies show that telomere dysfunction may result in replicative senescence and release of pro-inflammatory mediators [START_REF] Amsellem | Telomere dysfunction causes sustained inflammation in chronic obstructive pulmonary disease[END_REF][START_REF] Zhang | Ageing and the telomere connection: An intimate relationship with inflammation[END_REF], leading to hypotheses that telomere shortening might contribute and maintain the inflammatory process causing airway remodelling and COPD. Consistent with this, mice with short telomeres secondary to knocking down telomerase hTR -/-were more likely to develop emphysema after cigarette smoke exposure than wild-type controls [START_REF] Alder | Telomere length is a determinant of emphysema susceptibility[END_REF]. Altogether, these data suggest that individuals with short telomere might be at increased risk of accelerated lung function decline when exposed to smoking. Telomere shortening has been associated with impaired lung function in cross-sectional studies of COPD patients and in cross-sectional analyses of large population-based samples [START_REF] Rutten | Various Mechanistic Pathways Representing the Aging Process Are Altered in COPD[END_REF][START_REF] Rode | Short telomere length, lung function and chronic obstructive pulmonary disease in 46,396 individuals[END_REF][START_REF] Albrecht | Telomere length in circulating leukocytes is associated with lung function and disease[END_REF]. We are unaware of any longitudinal study that has examined this.

Using information from 448 middle-aged adults randomly selected from the general population and followed-up as part of the European Community Respiratory Health Survey (ECRHS) in 3 French centres [START_REF] Guenegou | Interaction between a heme oxygenase-1 gene promoter polymorphism and serum beta-carotene levels on 8-year[END_REF], we assessed whether lung function decline after 11 years of follow-up was associated with telomere length at baseline (2000)(2001)(2002), and whether the effect of smoking was modified by telomere length. Relative telomere length in peripheral leukocytes was measured by quantitative PCR (details in the Online Repository), and categorised into tertiles. The methods for ECRHS, statistical analysis, and participants'characteristics are described in the online supplement.

Results

Cross-sectional analysis at baseline.

At baseline, relative telomere length was negatively associated with age, but was not associated with sex, pack-years smoked, body-mass index (BMI), physical activity, atopic sensitisation, asthma or other respiratory symptoms or comorbidities (Table E1). There was no relationship between telomere length and FEV% or FVC%predicted. The percentage of subjects with pre-bronchodilator(BD) FEV1/FVC<lower limit of normal (LLN) at baseline was quite low (18/444) and did not vary with telomere length (Table E2).

Telomere length at baseline and lung function at follow-up

Participants with short and long telomeres showed similar patterns of smoking and BMI change over the follow-up (Table E1). At follow-up, there was a non-significant increased risk of pre-BD FEV1/FVC<LLN in individuals with telomere in the first tertile (short telomeres) as compared to those in the third tertile (Table E2), (odds-ratio adjusted for age, sex, centre, BMI, physical activity and smoking status: 2.18; 95%CI 0.85to5.57; p=0.10).

When considering post-bronchodilator spirometry (only performed at follow-up), the percentage of subjects with fixed airflow obstruction was low (n=17/404) and did not vary with telomere length.

Lung function decline by smoking and tertiles of telomere length

There was no association of FEV1 decline with telomere length at baseline (Table E2). FEV1 decline was strongly associated with smoking. However, there was some evidence that this association was modified by telomere length. As shown in figure 1, the effect of smoking on lung function decline was highly significant in subjects with short telomeres (1 st tertile), and largely reduced and non-significant in subjects with long telomeres (3 rd tertile). In subjects with short telomeres, heavy-smokers had an accelerated decline of FEV1, with an additional loss of -20.1mL/yr (95%CI -32.7 to -7.5) as compared to never smokers (Table 1). In contrast, no effect of smoking was observed in subjects with long telomeres. Test for interaction showed significant interactions of short telomere length with both moderate (p=0.03) and heavy smoking (p=0.02) (Table E3). Similar patterns were seen in the sensitivity analyses (Table 1). Consistently, heavy-smoking was associated with a reduction in FEV1%predicted at follow-up of -14.9 (95%CI -22.3 to -7.4) in subjects with short telomere length, but no reduction (+2.3%) in those with long telomeres (Table E4).

Discussion

We believe this is the first population-based longitudinal study investigating the relationships between telomere length and lung function decline. We found that smoking was associated with an accelerated loss in lung function in individuals with short telomeres and with little/no effect in those with longer telomeres.

In our cross-sectional analyses, we found no association of telomere length with FEV1%predicted or FVC%predicted. In a very large sample of the Danish population (comprising +20,000 participants aged over 60 years), there were significant associations of telomere length with FEV1 and FVC but these were largely attenuated after adjustment for age and other covariates [START_REF] Rode | Short telomere length, lung function and chronic obstructive pulmonary disease in 46,396 individuals[END_REF]. A meta-analysis of seven European studies unselected for lung disease showed cross-sectional associations of telomere length with FEV1, FVC and FEV1/FVC ratio, but that were significant only in women [START_REF] Albrecht | Telomere length in circulating leukocytes is associated with lung function and disease[END_REF].

Epidemiological studies suggest that variability in telomere length is partly determined by genetic factors, but also by modifiable lifestyle factors such as smoking, obesity, physical activity or diet. However, to date, few prospective studies have investigated these relationships. In our sample of mostly healthy individuals, we did not find association between telomere length and lifestyle habits. Telomere length was lower in older participants, which supports the validity of measurements. Our study has some limitations including the relatively small sample size and the limited number of heavy-smokers. Because of the relatively young age of the sample, we used a low cut-off (>15 pack-years) to define heavysmokers. Lung function was measured following international guidelines, but spirometers were changed between baseline and follow-up. However, the sensitivity analyses considering smokers with >20 pack-years as heavy-smokers, or using lung function corrected for change in spirometers yielded similar relationships.

While tobacco smoke may influence telomere dysfunction, our findings are consistent with the hypothesis that short telomere length might contribute to the differences in susceptibility to the deleterious effect of cigarette smoke in the lung [START_REF] Alder | Telomere length is a determinant of emphysema susceptibility[END_REF]. This is consistent with the large cross-sectional Danish study in which participants with COPD had shorter telomere length as compared to subjects with the same history of smoking but who had not developed COPD [START_REF] Rode | Short telomere length, lung function and chronic obstructive pulmonary disease in 46,396 individuals[END_REF]. To summarize, this population-based longitudinal study suggests that people with shorter telomere length are at increased risk of spirometrically defined COPD. Furthermore, smoking was associated with an accelerated loss of lung function only in individuals with shorter telomeres, suggesting that telomere shortening might be a marker for increased susceptibility to the effect of smoking on lung function decline. Further prospective studies, with repeated measures of both telomere length and lung function, and including sufficient smokers, are warranted to further investigate the predictive value of this biomarker. lung function decline in a general population: the European Community Respiratory Health Survey (France). Am J Epidemiol. 2008;167(2):139-44. Smoking status is defined according to smoking status and cumulative pack-years smoked at ECRHS III. Details on correction used in sensitivity analysis are provided in the online supplement. ** change in pack-years smoking between baseline and follow-up, in current smokers at the end of follow-up. Smokers who quitted smoking during the follow-up (n=45) were excluded from this analysis † p<0.05 ‡ p<0.01 § p<0.10, as compared to never -smokers

Figure legend

  It is also supported by Savale et al. showing shorter telomere length in COPD patients but no difference in telomere length between control smokers without COPD and control nonsmokers (3).

Figure 1 :

 1 Figure 1: Adjusted mean* (95%CI) annual FEV1 decline according to smoking status and

Table 1 . Difference in annual decline in smokers as compared to never-smokers, stratified by tertiles of telomere length Tertile of telomere length at baseline

 1

			Tertile1 (shortest)	Tertile2	Tertile3 (longest)
	Annual FEV1 decline (ml/year)
	Never smokers	n=164	(ref)	(ref)	(ref)
	Ex-smokers	n=151	-8.1 (-17.8 to 1.5) §	-2.6 (-15.3 to 10.2)	-5.2 (-14.3 to 3.8)
	Smokers ≤ 15 Pack-yrs	n=49	-16.6 (-29.8 to -3.4) †	-4.6 (-24.2 to 15.0)	6.0 (-5.7 to 17.7)
	Smokers > 15 Pack-yrs	n=76	-20.1 (-32.7 to -7.5) ‡	-14.4 (-28.1 to -0.61) †	3.0 (-8.5 to 14.4)
	p for trend		0.001	0.06	0.28
	Sensitivity Analysis
	Annual FEV1 decline (ml/year) corrected as Gerbase
	Never smokers	n=164	(ref)	(ref)	(ref)
	Ex-smokers	n=151	-8.1 (-17.8 to 1.5) §	-2.6 (-15.3 to 10.1)	-5.2 (-14.3 to 3.8)
	Smokers ≤ 15 Pack-yrs	n=49	-16.5 (-29.7 to -3.3) †	-4.7 (-24.3 to 14.9)	6.0 (-5.8 to 17.6)
	Smokers > 15 Pack-yrs	n=76	-20.1 (-32.6 to -7.5) ‡	-14.3 (-28.1 to -0.58) †	3.0 (-8.4 to 14.4)
	p for trend		0.001	0.06	0.28
	Annual FEV1 decline (ml/year) corrected as Bridevaux
	Never smokers	n=164	(ref)	(ref)	(ref)
	Ex-smokers	n=151	-8.1 (-17.8 to 1.5) §	-2.6 (-15.3 to 10.1)	-5.2 (-14.2 to 3.9)
	Smokers ≤ 15 Pack-yrs	n=49	-16.3 (-29.5 to -3.1) †	-4.7 (-24.3 to 14.8)	5.9 (-5.8 to 17.6)
	Smokers >15 Pack-yrs	n=76	-20.0 (-32.5 to -7.4) ‡	-14.3 (-28.0 to -0.54) †	3.0 (-8.4 to 14.4)
	p for trend		0.001	0.06	0.28
	Annual FEV1

decline -Using a cumulative history of >20 pack-years as a cut-off to define heavy smoking

	Never smokers	n=164	(ref)	(ref)	(ref)
	Ex-smokers	n=151	-8.1 (-17.8 to 1.5) §	-2.6 (-15.3 to 10.2)	-5.2 (-14.3 to 3.8)
	Smokers ≤ 20 Pack-yrs	n=59	-16.4 (-29.2 to -3.6) †	-7.8 (-25.3 to 9.8)	8.2 (-2.7 to 19.1)
	Smokers > 20 Pack-yrs	n=66	-20.6 (-33.5 to -7.7) ‡	-14.0 (-28.6 to 0.50) †	-1.0 (-13.4 to 11.5)
	p for trend		0.001	0.05	0.59
	Annual

FEV1 decline -considering tobacco consumption over the follow-up instead of cumulative history

  Data are regression coefficient (95%CI). Telomere length at baseline. All models adjusted for age, gender, centre, BMI and physical activity. Regression models for FEV1 decline additionally adjusted for baseline FEV1.

	Never smokers	n=164	(ref)	(ref)	(ref)
	Ex-smokers	n=151	-7.6(-16.6 to 1.4)	-1.4 (-14.4 to 11.6)	-5.2 (-14.4 to 4.1)
	ΔPack-yrs ≤ 5 Pack-yrs** n=31	-6.9 (-21.0 to -7.2)	-9.5 (-37.9 to 18.9)	5.0 (-9.3 to 19.4)
	ΔPack-yrs> 5 Pack-yrs** n=49	-19.0 (-32.6 to -5.4) †	-21.5 (-37.7 to -5.2) †	2.7 (-10.5 to 15.8)
	p for trend		0.01	0.01	0.40
	Decline in FEV1%predicted; 10yrs
	Never smokers	n=164	(ref)	(ref)	(ref)
	Ex-smokers	n=151	-2.8 (-6.1 to 0.6)	-0.3 (-4.2 to 3.5)	-1.9 (-4.9 to 1.02)
	Smokers ≤ 15 Pack-yrs	n=49	-5.3 (-9.8 to -0.8) †	-0.7 (-6.6 to 5.2)	2.2 (-1.7 to 6.0)
	Smokers > 15 Pack-yrs	n=76	-5.5 (-9.7 to -1.2) †	-3.2 (-7.3 to 0.8)	1.3 (-2.4 to 5.0)
	p for trend		0.007	0.15	0.18

*


Murad Shoman
email: murad.shoman@ifsttar.fr

Hocine Imine

Modelling and Simulation of Bicycle Dynamics

Keywords: Bicycle Modeling, Bicycle Simulator, Simulation, Dynamics, Road characteristics

In this paper the authors present an experimental validation of the bicycle simulator developed by two IFSTTAR laboratories: LEPSIS and LPC. In the first part, we focus on improving the dynamics of the bicycle model and simulate the effects of road geometry and surface characteristics such as radius of curvature, road adhesion and unevenness of road profile. In order to verify the accuracy of the developed model, experimental results are shown. For future work, the authors will study the effect of these characteristics on user behaviour to improve the safety and stability of bicycles, particularly in bad weather conditions.

Introduction

The use of simulators of conduct seems to be an interesting alternative for responding to several challenges such as learning to drive, awareness of risks, road safety, etc. (for example, Babu et al. (2011)), [START_REF] Pieroni | Light vehicle model for dynamic car simulator[END_REF] and [START_REF] Ghasemi | Longitudinal Motion Cueing Effects on Driver Behaviour: A Driving Simulator Study[END_REF]). Analysing the cyclist as the centre of this modelling will provide important data on how to improve the quality of modelling as well as understanding the cyclist's behaviour.

Two categories of simulators can be distinguished: motionless simulators and mobile-based simulators. The first is built around a screen providing visual feedback, while the latter provides, in addition to visual information, indices of movements consistent with those of a real vehicle [START_REF] Nehaoua | Conception et réalisation d'une plateforme mécatronique dédiée à la simulation de conduite des véhicules deux-roues motorisés[END_REF]). It has been recognized that often a mobile platform, if well controlled, can significantly improve the realism of the simulator of conduct; a good example for this type of simulators is the KAIST bicycle simulator [START_REF] Kwon | KAIST interactive bicycle racing simulator: the 2nd version with advanced features[END_REF] and ( 2002)) which is consisting of a motion generation system of 6 linear actuators, upper and lower platforms and equipped with an electric Stewart platform on which a bike frame is fixed to provide 6-dof to the handlebar and rear wheel.

There are two main approaches to construct a vehicle mathematical model: The methods of theoretical physics such as Lagrange or Euler are used in order to produce an exact model. The other approach attempts to model the vehicle as simply as possible with little computing-time (Kiencken and Nielsen (2000)). [START_REF] Owczarkowski | Dynamic modeling and simulation of a bicycle stabilized by LQR control[END_REF] used the detailed nonlinear Whipple scientific description to analyse the bicycle mathematical model in which the control law is solved by LQR algorithm. Another bicycle mathematical model was presented by (Qichang He et al. (2005)). The model inhering an interactive bicycle simulator resembling the Korea KAIST bicycle simulator in hardware and two slightly coupled sub-models, namely, the stability sub-model and vibration sub-model. The motion equations were developed based on the Lagrange's equation, and also the Runge-Kutta method was applied to make numerical simulation. Simplifying calculation of the bicycle kinetic energy, the bicycle system is divided into two separate parts; rear and front.

Accordingly, the authors used the classical single-track model, which gives good results for non-critical cycling situations. The developed model will be applied to IFSTTAR bicycle simulator, as well as on an instrumented bicycle in the subsequent work.

Based on the anticipated use of the bicycle simulator, LEPSIS researchers have established a list of features to ensure and constraints to meet these elements (Caro et al. (2015) and Caro & Bernardi (2015)) together with a state of the art on the bicycle simulators which allow to make the technical choices prior to building the simulator. The authors consider the bicycle simulator as a control system in order to obtain the necessary actions to ensure the vehicle's path, such as handlebar angles, pressure on the pedal, etc. The bicycle simulator characteristics make it possible to carry out studies of certain number of situations but does not make it possible to answer all the needs. After the development of the simulator its usability with populations of confirmed cyclists and non-cyclists will be studied. This tool can now be used in studies that require observing the behaviour of cyclists in safe and controllable conditions and measuring the observable variables.

The paper is organised as following, the second part is devoted to bicycle modelling. In the third part, validation results of the model are shown. The fourth part is to present the first experimental results of a developed model of the bicycle simulator. Finally, a conclusion part with some perspectives are presented.

Bycile modeling

The bicycle simulator of IFSTTAR shown in the figure 1, has one degree of freedom (steering angle) and is equipped with two force feedback devices: one providing haptic feedback in the handlebars and the other dedicated to the rear wheel. The speed of the rear wheel, the angular position of the handlebar, and the gear are measured and logged. Force feedback is applied to the rear wheel using a cylinder in contact with the wheel.

The new model added the following features to the old one developed by Car and al ( 2015):

• The bicycle model has been developed more and is detailed.

• The vertical model was developed considering the road profile, the vertical displacement of the wheels' centre of gravity, the static normal force and the normal force according to the vertical stiffness of the bicycle. • The road adhesion was considered when building the longitudinal model of the bicycle wheels. • The lateral model considers the effect of the side slip angle and the lateral stiffness of the bicycle wheels.

A scheme for different interactive parts of the model is presented in Fig. 2. Each part will be detailed in the next sections.

The mechanical torque of the force feedback is related to the speed as indicated by the following equation:

𝐹 = 10 + 𝑘. 𝑉
(1) Where F is the resisting force applied to the pneumatic in N. V is the tangential speed of the pneumatic in m/s (i.e., the speed at which the bicycle should move). The constant k can be manipulated.

Vertical modeling

The tyres of the front and rear wheels are modelled by a spring with coefficient kF and kR. The wheel mass is given by 𝑚 𝐹 𝑎𝑛𝑑 𝑚 𝑅 . At the tire contact, the road profile, the skid resistance, and the radius of curvature are considered inputs of the system. The road profile is represented by the variable u. The pitch angle of the bicycle is neglected. Fig. 3 shows the vertical model of the bicycle. The vertical acceleration values of the wheels are obtained using the following equations:

𝑧̈𝐹 = -k 𝐹 𝑧 𝐹 + k 𝐹 u F 𝑚 𝐹 (2) 𝑧̈𝑅 = -k 𝑅 𝑧 𝑅 + k 𝑅 𝑢 𝑅 𝑚 𝑅 (3)
Where 𝑚 𝐹 𝑎𝑛𝑑 𝑚 𝑅 are the masses of the front and rear wheel, 𝑘 𝐹 and 𝑘 𝑅 are the front and rear tyres vertical stiffness, 𝑧 𝐹 𝑎𝑛𝑑 𝑧 𝑅 are the vertical displacement of the COG of the front and rear wheel respectively, 𝑢 𝐹 and 𝑢 𝑅 are respectively the front and rear value of road profile. To obtain the vertical displacement 𝑧 𝑅 we integrate twice this acceleration.

The normal forces 𝐹 𝑛𝐹 𝑎𝑛𝑑 𝐹 𝑁𝑅 acting on the wheels are calculated as following:

𝐹 𝑛𝐹 = 𝐹 𝑐𝐹 + 𝑘 𝐹 (𝑢 𝐹 -𝑧 𝐹 ) (4
)

𝐹 𝑅 = 𝐹 𝑐𝑅 + 𝑘 𝑅 (𝑢 𝑅 -𝑧 𝑅 ) (5)
Where 𝐹 𝑐𝐹 𝑎𝑛𝑑 𝐹 𝑐𝑅 are the static force due to the static mass of the vehicle.

In this paper, the force generated by damping effects is neglected, in comparison with spring forces 𝑘 𝑖 (𝑢 𝑖 -𝑧 𝑖 ).

Lateral modeling

In order to calculate the wheel forces, it is necessary to know wheel slip, tire side slip angle and friction coefficients, as these are inputs for the force equations. For the tire side slip angle calculation, the wheel caster has to be taken into consideration and the curve radii of the individual wheels, see equations (6 to 11).

𝛼 𝐹 = -𝛽 + 𝛿 𝑊 -𝑙 𝐹 ⋅ 𝜓 v𝐶𝑂𝐺 (6)

𝛼 𝑅 = -𝛽 + 𝑙 𝑅 ⋅ 𝜓 v𝐶𝑂𝐺 (7)
Where 𝛼 𝐹 𝑎𝑛𝑑 𝛼 𝑅 are the side slip angles for the front and rear wheel respectively, 𝛽 is vehicle body side slip angle, 𝛿 𝑊 is the steering angle of the handlebar, 𝑙 𝐹 𝑎𝑛𝑑 𝑙 𝑅 are distances from COG to front and rear axles,𝑣 𝐶𝑂𝐺 is the Centre of Gravity velocity, 𝜓 ̇ is the yaw rate calculated using equation ( 7)

𝜓 ̇= 𝑉 𝑅 ( 8
)
Where V is the bicycle speed and R is the radius of curvature calculated using following equation ( 9).

𝑅 = 𝑙 𝑓 + 𝑙 𝑟 sin 𝛿 𝑠 (9)
Where 𝛿 𝑠 is the steering angle.

After calculation of the different parameters, it is possible to calculate the lateral force for both the front and rear wheel using the following equations.

𝐹 𝑦𝐹 = 𝛼 𝐹 × 𝐶 𝑦 (10)
Where 𝐹 𝑦𝐹 𝑎𝑛𝑑 𝐹 𝑦𝑅 are the lateral forces of the front and rear wheel respectively and 𝐶 𝑌 is the tyre lateral stiffness.

Longitudinal modeling

The Longitudinal frictional forces of the front and rear wheels can be calculated from the adhesion co-efficient using Equation ( 12) and ( 13). This provides the frictional forces in the direction of the wheel ground contact velocity.

𝐹 𝑥𝐹 = 𝜇 × 𝐹 𝑧𝐹 × cos 𝛼 𝐹 (11)

𝐹 𝑥𝑅 = 𝜇 × 𝐹 𝑧𝑅 × 𝑐𝑜𝑠 𝛼 𝑅 (12)
Where 𝐹 𝑥𝐹 𝑎𝑛𝑑 𝐹 𝑥𝑅 are the longitudinal forces for the front and rear wheel respectively, 𝜇 is the adhesion coefficient of the surface and 𝐹 𝑧𝐹 𝑎𝑛𝑑 𝐹 𝑧𝑅 are the vertical forces applied on the front and rear wheels. In the next section, some validation results will be presented in order to show the accuracy of the presented model.

Validation results

Several tests and scenarios have been realized at various speeds with the bicycle simulator. Selected results of the vertical displacement and forces, side slip angle, lateral forces and longitudinal forces are presented in the following. The dynamic parameters and the static vertical forces are stated from the literature (Moore et al. ( 2009) and ( 2010) [START_REF] Bulsink | The effect of tyre and rider properties on the stability of a bicycle[END_REF]). The values of the static front and rear vertical forces (which were calculated according to the combined mass of the bicycle and rider by applying the equilibrium equations with known COG) are 230 and 630 N, respectively.

Vertical displacement and force

The longitudinal road profile, which has been measured in a previous experiment conducted by IFSTTAR) is used as an input signal for the Simulink model (see Fig.

Lateral trajectory and force

Side slip angle and lateral force

The steering angle of the bicycle handlebar during the test (shown in Fig. 7) was measured using an incremental encoder connected to the handlebar of the bicycle simulator. The side slip angle shown in Fig. 8 noticeably reflects the angular movement of the handlebar. The lateral trajectory of the bicycle as shown in Fig. 9 is perceived to be affected by the steering angle. With a closer look at the span between [10,15] s, the effect of high steering angle on the trajectory can be observed.

Zoom

Fig. 10 shows the lateral force of the front and rear wheels. The graphs show that the increase of the sides slip angle causes an increase in the lateral force, this becomes obvious at 40 th and 45 th second.

Longitudinal force

In the simulation several adhesion coefficient representing different surfaces and different weather conditions came to use. The longitudinal force shown in Fig. 12, which was calculated based on the adhesion coefficient (Fig. 11), shows the impact of different adhesion coefficient. While the road surface is dry (𝜇 = 0.8) the longitudinal friction force in high whereas a wet surface (𝜇 = 0.2) results in a low longitudinal friction force.

Experimentation description

Experimental setup

The bicycle simulator has been built by placing a real bicycle on a platform (see Fig. 13). The computing environment is constituted by a PC and the software: Matlab, Simulink and Real Time Workshop. In order to provide more realistic circumstances, the simulator consists of several components:

• 5 visual displays installed in front of the bicycle to provide simulated conditions similar to real-life environment.

The visual displays provide a visual angle of 225 degrees in the horizontal direction and 55 degrees in the vertical direction. • A flywheel attached to the rear wheel simulates inertia equal to 60 kg mass in actual cycling. The simulator is also equipped with an incremental encoder able to increase the inertia up to 85 kg as in actual cycling. • A fan is placed in front of the bicycle reproducing the airflow felt by cyclists in a real situation. The airflow speed is proportional to the wheel's speed. • A passive mechanical system allows participants to slightly tilt the bike.

Ten participants (6 male; mean age=28.17, SD=3.76 and 4 females; mean age=25.25, SD=2.06) took part in this experiment. All had normal or corrected-to-normal vision. The mean cycling experience of the participants was 12.9 years. The average number of cycling kilometers per month was 62. None of them rode a bicycle simulator before.

The experiment took place in a simulated urban environment. The road consists of two sections: the first is a bicycle-bus shared lane, the latter with a separated bicycle lane. The participants took a pre-ride for 2 minutes in order to familiarize themselves with the simulator. After the end of the familiarization, the participants rode the bicycle for 10 minutes. The traffic was generated at the same and opposite directions of the cyclist and buses were passing beside the cyclist from time to time. The participants were asked to maneuver with the simulator using the different features such as: handlebar, pedals, gear and brakes.

Experimentation results

At the end of the experiment, the participants completed a questionnaire regarding their cycling experience and the impact of the bicycle simulator on their personal status. The questionnaire consists of the NASA Task Load Index (TLX) form [START_REF] Hart | NASA Task load Index (TLX)[END_REF]) which is used to assess the work load and the Simulator Sickness Questionnaire by [START_REF] Kennedy | Simulator Sickness Questionnaire: An enhanced method for quantifying simulator sickness[END_REF] to evaluate the different symptoms of the bicycle simulator.

The analysis of TLX for the 10 participants shows that the simulator requires a medium physical demand. This is explained by the effort the participants required to do when riding any bicycle since it is an active transport mode. Fig. 14 shows more results about the different aspects of the work load.

The analysis of the simulation sickness questionnaire shows that one participant had the symptoms of simulator sickness. The participant suffered from severe symptoms of eye strain, difficulty focusing, sweating, nausea and dizziness with eyes open. The participant was immediately asked to stop the experiment. 30% of the participants felt a slight general discomfort and 30% experienced a slight sweating increase. For the complete results of the simulator sickness questionnaire see Appendix A.1.

Conclusion

This work leads to develop and validate the existing bicycle simulator. By conducting comparative studies between the theoretical and practical aspects, it is possible to verify the reliability of the simulator. The results obtained from experimental studies show the accuracy of the developed model even if some additional works should be done to enhance the bicycle model. The results show that dynamic states of the bicycle model are well estimated.

(Estimation of the vertical displacement and side slip angle are presented.). the effect of these inputs on calculation of vertical, lateral and longitudinal forces are observed. The result shows the direct impact of steering angle on the side slip angle estimation which accordingly affect the calculation of the lateral and longitudinal forces, the impact of the vertical displacement is also observed on the vertical forces.

In perspective work a real time estimation of the road adhesion coefficient and wheel slip will be included in the model. In subsequent studies it would be interesting to reproduce these experiments in different scenarios of locations and countries in order to test the developed algorithms on the simulator and on the road. The bicycle simulator enables us to put cyclists in a riding situation and accurately measure their effective behavior, while controlling the variables at play and avoiding the risks associated with a real environment.

Figure 2 .

 2 Figure 2. Operating flow of the bicycle simulator

Figure 3 .

 3 Figure 3. Side view of the bicycle model.

Figure 4 .

 4 Figure 4. Road profile input.

Figure 6 .

 6 Figure 6. Vertical force.

Figure 7 .ZoomFigure 5 .

 75 Figure 7. Steering angle and velocity.Figure8. Side slip angle.

Figure 12 .

 12 Figure 12. Longitudinal force.

Figure 10 .

 10 Figure 10. Lateral force. Figure 9. Lateral position.

Figure 11 .

 11 Figure 11. Adhesion coefficient.

Figure 13 .

 13 Figure 13. IFSTTAR bicycle simulator.

Figure 14 .

 14 Figure 14. Work load of the bicycle simulator.

Acknowledgements

This works is funded by Marie Skłodowska-Curie actions (H2020 MGA MSCA-ITN) within the SAFERUP project (grant agreement number 765057). The authors gratefully acknowledge their contributions.

A.1 Results of the simulator sickness questionnaire

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(PICS-L),Components and systems department (cosys), Gustave Eiffel University, Champs sur Marne, 77420 France (e-mail: murad.shoman@univ-eiffel.fr); 0000-0002-6096-531X 2 same affiliation (e-mail: hocine.imine@univ-eiffel.fr), 0000-0002-5377-6510 *corresponding author.

Abstract:

Experimentation in real environment is the most appropriate means to understand cyclist's behavior and evaluate cycling safety (Shoman et al., 2023), but it not always the most appropriate method, due to its costs, uncontrolled variables and risks facing cyclists.

In contrast, simulators allow to detect the behavior of cyclists and other road users in various riding situations, while controlling variables at play and avoiding the risks associated with a real environment (Shoman et al., 2020), however, simulator studies are valid as far as they provide results generalizable to real-world situations. This study aims to compare between riding a bicycle simulator and cycling on real road, to validate the simulator from physical, subjective, and behavioral aspects.

We instrumented a city bicycle with a tri-accelerometers, IMU, GPS, potentiometer to measure the steering angle, a laser scanner, pedaling power meter, speed, and cadence sensors as in Figure 1. In addition to a mobile eye tracker wore by the cyclists. After the instrumentation and calibration of the sensors, an experiment conducted in the city of the Stockholm using the instrumented bicycle.

22 cyclists participated in this experiment riding the bicycle on pre-determined route of 3.6 km, the route was divided into 3 zones: zone 1 consists of a mixed traffic street with 30 km/h speed limit without separate cycling lane; zone 2 consists of an on-street separate cycling lane without physical barrier for most of it and a shared bicycle-bus lane for the rest; and zone 3 consists of shared pedestrian-cyclist way between tress and parking lots, the surface condition was a mix between snow and ice and 6 participants repeated the experiment on dry to wet surface. After the cycling task finished, the participants completed a questionnaire in regard to their evaluation of cycling safety and comfort, besides, the signals from the sensors were logged for analysis.

The same experiment was reproduced in PICS-L bicycle simulator in Paris [START_REF] Shoman | Bicycle Simulator Improvement and Validation[END_REF] by building a virtual environment of the experiment route in Stockholm. 31 cyclists rode the simulator for around 1.8 km; the same questionnaire was answered, and the same signals were collected. The results from both experiments were then analyzed and compared.

The data analysis of the sensors output for both the instrumented bicycle and the bicycle simulator show that the average moving velocity for all participants on icy/snowy surface is 12.45 km/h, 15.73 km/h on dry/wet surface, and 14.34 km/h for the bicycle simulator. While the average maximum speed recorded is 20.2 km/h on snowy/icy surface, 24.4 on dry/ wet surface and 22.9 km/h on the simulator. The average normalized power is 121 W on icy/snowy surface, 187 W on dry/wet surface and 63 W on the bicycle simulator. The analysis of the questionnaire of the real-life experiment shows that 13 cyclists (59%) felt safer on zone 3 of the experiment route due the physical barrier separating between cycling path and other vehicles, 17 cyclists (55%) choose the same zone on the simulator according to the same reason. 17 cyclists (77%) felt they consumed more power on zone 1 due to the steep slopes of the street, while on the simulator, 13 cyclists (42%) felt they consumed the same power during the experiment, while 7 of them (23%) felt they consumed more power in zone 3, as it was the end of the experimental route.

The experimental data of both the instrumented bicycle and the bicycle simulator was studied and analyzed, the comparison between the results shows that the virtual velocity of the bicycle simulator is 10% lower than cycling in real conditions when the surface is dry/wet; this difference increases to 15% faster than cycling on snowy/icy surface. The results also show that the average power consumed on the simulator is lower by 52% comparing to snowy/icy and by 52% when comparing to dry/wet surface conditions.

The subjective evaluation of safety for different traffic zones shows quite similar results: in reality, 59% of cyclists chose zone 3, while in simulation 55% of them chose the same zone. One of the perspectives of this research could be creating different routes and scenarios to test them on the simulator to evaluate cycling safety and establish recommendations to improve it.

A

  Comparison Study on the Validity of Bicycle Simulator Using an Instrumented City Bicycle Shoman M. 1, *, and Imine H. 2 de la route et de la rue Laboratoire sur la Perception, les Interactions, les Comportements et la Simulation des usagers 1

Figure 1 .

 1 Figure 1. The instrumented bicycle used to conduct the on-road experiment in Stockholm (to the left) and the bicycle simulator used in the in-lab simulation (to the right).

  Shoman, M. & Imine, H. (2023). A Comparison Study on the Validity of Bicycle Simulator Using an Instrumented City Bicycle. The Evolving Scholar -BMD 2023, 5th Edition. This work is licensed under a Creative Commons Attribution License (CC-BY).

	Name of Editor: Jason Moore
	Submitted: 21/02/2023
	Accepted: 20/04/2023
	Published: 25/02/2023
	Citation: