# Wearable airbag for powered two-wheelers: what is the profile of users involved in road traffic crashes and how does it affect their fatality risk?

Fégens Saint-Louis, Lydiane Agier, Thibaut Deville, Oscar Cherta-Ballester, Valentin Honoré, Catherine Masson, Olivier Monneuse, Céline Vernet

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## To cite this version:

Fégens Saint-Louis, Lydiane Agier, Thibaut Deville, Oscar Cherta-Ballester, Valentin Honoré, et al. Wearable airbag for powered two-wheelers: what is the profile of users involved in road traffic crashes and how does it affect their fatality risk?. Journal of Safety Research, 2025, 94, pp.265 - 274. 10.1016/j.jsr.2025.06.024. hal-05266505

HAL Id: hal-05266505

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Submitted on 18 Sep 2025

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Wearable airbag for powered two-wheelers: what is the profile of users involved in road traffic crashes and how does it affect their fatality risk?

Fégens Saint-Louis<sup>a, c, *</sup>, Lydiane Agier<sup>a, D</sup>, Thibaut Deville<sup>b, d</sup>, Oscar Cherta-Ballester<sup>c</sup>, Valentin Honoré<sup>c</sup>, Catherine Masson<sup>b</sup>, Olivier Monneuse<sup>a, e</sup>, Céline Vernet<sup>a, D</sup>

ARTICLE INFO

Keywords:

Road traffic crashes

Powered two-wheelers

Wearable airbag

Personal protective equipment

Fatality

A B S T R A C T

**Background:** Traffic fatalities within Powered Two-Wheelers (PTW) users are mostly due to head and thoracic injuries. This study assesses airbag use prevalence among PTW users involved in Road Traffic Crashes (RTCs) in France and aims to estimate airbag effectiveness in protecting against fatality risk. **Methods:** A cross-sectional study was conducted using French national police crash data from 1 January 2019 to 31 December 2022, including a total of 69,350 PTW users. Pearson Chi-squared and Mann-Whitney tests were used to compare airbag users and non-users. Multiple logistic regression models were fitted to estimate odds ratios (OR) and 95% confidence intervals (95%CI) assessing the relationship between airbag use, adjustment factors, and crash-related fatality. **Results:** Airbag use in our population was 0.83%, with an increasing rate over the study period. Airbag users were older than non-users, used higher engine displacement PTWs, and crashed more often in rural settings and on roads with higher speed limits. The proportion of fatalities was higher among airbag users (4.7%) than among non-users (3.8%). After adjustment, the fatality risk was not statistically different between airbag users and non-users, although a 20 % lower risk was observed for airbag users (OR[95%CI]) = 0.80[0.53-1.21]). **Conclusion:** Wearable airbag use was low, though equipped motorcyclists increased over the study period. Airbag users involved in RTCs had different profiles from non-users, which may partly explain their higher fatality rate. While we did not show a significant protective effect of airbag on fatality risk, the limited number of airbag users in our population is an important limitation to consider in the detection of the airbag effect. **Practical Application:** Our findings underscore the need for future evaluations to better estimate airbag effectiveness by focusing on torso injury risk and the related fatality risk in larger populations with precise data on injuries, crash circumstances and demographic characteristics.

1. Introduction

Road Traffic Crashes (RTCs) are among the leading causes of injury,
disability, and death worldwide, making them a major public health
issue. According to the World Health Organization's road safety statis-
tics, approximately 1.19 million people worldwide are killed by RTCs
each year, among which 30% are Powered Two-Wheelers (PTWs)
(motorcycles and mopeds) users (Global Status Report on Road Safety,
2023).

In France, in 2022, PTW crashes were responsible for 22% of all road-

related deaths, although PTWs account for less than 2% of road traffic
(Bilan, 2022). The fatality risk for PTW users has been estimated to be 20
to 32 times higher than for car occupants considering the traveled dis-
tance, the time spent on the road, and their trip frequency (Blaizot et al.,
2013; Bouaoun et al., 2015).

Epidemiological studies have analyzed factors associated with PTW
road fatalities and the effectiveness of Personal Protective Equipment
(PPE) for motorcyclists (Santos et al., 2021; Champahom et al., 2023;
Kureshi et al., 2024; de Lapparent, 2006; Talbot et al., 2020; Barzegar
et al., 2020). These safety devices encompass helmets, protective

<footnote><sup>a</sup> Unité Mixte de Recherche Epidémiologique et de Surveillance Transports, Travail, Environnement (UMRESTTE), Gustave Eiffel University, Claude Bernard Lyon 1 University, Bron, France</footnote>

<footnote><sup>b</sup> Laboratoire de Biomécanique Appliquée (LBA), Gustave Eiffel University, Aix Marseille University, Marseille, France</footnote>

<footnote><sup>c</sup> IN&MOTION S.A.S., 10 Rue de la Lyre, 74960 Annecy, France</footnote>

<footnote><sup>d</sup> UTAC S.A.S, Autodrome Linas, Montlhéry, France</footnote>

<footnote><sup>e</sup> Département de Chirurgie D'Urgence Et de Chirurgie Générale, Hospices Civils de Lyon, Hôpital Edouard Herriot, Lyon, France</footnote>

<footnote>* Corresponding author at: Gustave Eiffel University/UMRESTTE, 25, Avenue François-Mitterrand, Case 24, 69675 Bron Cedex, France.
E-mail address: fegens.saint-louis@univ-eiffel.fr (F. Saint-Louis).</footnote>
clothing (jackets, trousers, gloves, or boots), and impact protectors such as back protectors (Araujo et al., 2017; de Rome et al., 2012; de Rome et al., 2011; Wu et al., 2019; Wu et al., 2019). Among these, helmets have been identified as the most effective in reducing the risk of severe and fatal injuries by safeguarding the head (Wu et al., 2019; Liu et al., 2008; Lucci et al., 2021). However, in France, the reduction in the incidence of serious injuries and fatalities among PTW users has not been sufficient, despite the mandatory use of helmets for the past 50 years and their widespread adoption, with still 718 PTW users killed on roads in 2022 (Bilan, 2022).

Several studies have consistently reported that thoracic injuries are among the most frequent serious injuries in PTW crashes (i.e., with an Abbreviated Injury Scale score of three or higher (AIS3+)), sometimes ahead of head injuries (Bambach and Mitchell, 2014; Cherta Ballester et al., 2019; Gidion et al., 2021; Moskal et al., 2007; Fierro et al., 2019; Liasidis et al., 2023; Granieri et al., 2020; Loftis, Price, & Gillich, 2018). Protecting the thorax of PTW users is therefore crucial to reduce the risk of serious injuries and death.

Torso airbags could reduce the severity of these injuries according to numerical simulation and experimental tests (Girardi, Marconi, & Massaro, 2019; Serre et al., 2019; Thollon et al., 2010; Cherta Ballester et al., 2021). These systems consist of one or more inflatable bags integrated into the motorcyclist's garments (e.g., jackets, vests, suits), which are activated and inflated in the event of a crash to protect the torso from impacts. Currently, these safety systems are available for purchase, however, no epidemiological study has yet analyzed airbag use and the personal, demographic and crash characteristics of users, nor quantified the effectiveness of airbags in protecting PTW users.

Our study had two objectives: first, to provide a first estimate of airbag use prevalence among PTW users involved in RTCs; and second, to provide a first assessment of the characteristics of airbag users compared with non-users, and of factors associated with fatality risk based on the national French road crash database.

## 2. Materials and methods

### 2.1. Study design and setting

We conducted a retrospective observational study based on existing police traffic crash reports of PTW crashes that occurred in France between 1 January 2019 and 31 December 2022.

Our study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline (Elm et al., 2007) and respected the ethical rules on biomedical studies.

### 2.2. Police crash report

French police officers are mandated to draw up a report for every road crash occurring on a public road in France, involving at least one vehicle, and causing at least one human casualty, defined as any form (minor or serious) of human injury. This report contains detailed information about the environment, crash circumstances as well as the vehicles and people involved (Méthodologies Statistiques, 2025). Three fields are available to describe PPEs worn by PTW users or other road users involved in RTCs. These fields include three types of PPEs for PTW users: helmet, gloves, and airbag (added to the PPE list in 2019) or the combination of equipment "gloves + airbag" when users were wearing both. We extracted the use of airbag and each other PPE from these different fields. Details of all final variables used in our study and their completeness are provided in the Supplementary Material (Table A of the Supplementary Material).

The database does not include all crashes (estimated reporting rate: ~40%), as police officers are not always called or do not intervene in every incident (Amoros, Martin, & Laumon, 2006). Nevertheless, serious and fatal crashes are more comprehensively reported. To ensure data integrity, multiple verification processes are conducted using

supplementary sources, such as information from the prefectural network. This is followed by a validation step conducted by county-level road safety observatories.

Finally, the information is gathered in a publicly available database (BAAC: Bulletin d'Analyse des Accidents de la Circulation, Bulletin of Analysis of Road Traffic Accidents) published each year by the National Inter-ministerial Road Safety Observatory (ONISR) (Recueil de Données Annuelles, 2023). At the time of this study, data were available up to the year 2022.

### 2.3. Airbag use and population characteristics

All PTW users involved in RTCs over the 2019-2022 time period were eligible for this study. For our analysis, we disaggregated them into two groups: those equipped with an airbag and those without an airbag. The use of wearable airbags during the crash (yes, no) was derived from several variables describing the use of protective equipment, as detailed in the previous section. For the present study, we assumed all airbags were equivalent and properly activated during the crash. No further information on airbags is provided by the French police in these reports; the types of wearable airbag (e.g., mechanically or electronically activated), body areas covered, whether the system has been activated or whether the device was inflated at the moment of the crash are unknown.

In addition to airbag use, several casualty characteristics have been considered in our analysis: road user type (rider, i.e., the one controlling the PTW, or passenger, i.e. the one seated on the pillion seat), age, sex, socio-professional status, results from alcohol testing after the crash (positive/negative), crash location county ("département") in Metropolitan France (yes/no), helmet use, gloves use, and travel motive. In addition, several vehicle and environment-related crash characteristics were considered: PTW category determined by engine displacement, first impact location on the PTW (none/front/rear-end/broadside/multiple), weather conditions (dry/rain or snow/other), surface conditions (dry/wet or other), day of the week, season, time of day (day/night), luminosity (daylight/darkness or other), road speed limit, crash setting (urban/rural), intersection (yes/no), direction of traffic (one-way/two way/others), object hit during the crash (none/fixed object or other/vehicle), and collision type (none/two vehicles/three vehicles or more/other collision type).

The police report describes the health condition of road users involved in the crash as "uninjured," "slightly injured," "hospitalized injured," or "killed." "Slightly injured" casualties are out-patients and those hospitalized for less than 24 h; "hospitalized injured" refer to those admitted for over 24 h; and "killed" are those who died within 30 days post-crash.

### 2.4. Statistical analysis

Frequencies and percentages were used to describe categorical variables, while medians and interquartile ranges (IQR) were used for the description of continuous variables. Characteristics of PTW users, including riders and passengers, involved in RTCs were compared between wearable airbag users and non-users using the Pearson Chi-squared test for categorical variables and the Wilcoxon-Mann-Whitney test for continuous variables. A simple negative binomial regression was also conducted to assess the trend in the number of airbag users over the years.

We assessed the relationship between wearable airbag use and crash-related fatality, defined as a binary variable indicating whether the person died or not within 30 days post-crash. We did not assess the relationship between airbag use and the risk of crash-related injury, as the categorization of health condition of nonfatal PTW users injured in our database (i.e. "slightly injured" vs. "hospitalized injured") is not based on a standardized injury severity scale, such as AIS score (Amoros, Martin, Chiron, & Laumon, 2007). We used multiple logistic regression
models to estimate the adjusted odds ratio (aOR) and corresponding 95% confidence intervals (CI). Several exclusion criteria were consid-
ered prior to analysis to limit the risk of selection bias (see Fig. e1 in
*Supplementary Material*. First, related to age, PTW users younger than
18 years old (y.o.) (only five were equipped with a wearable airbag),
those older than 75 y.o. (being few in number, none wearing an airbag,
and more likely to have comorbidities, hence increasing the risk of
serious injury and fatality), and those with unknown birth information
were excluded. Second, non-helmeted PTW users were excluded, as
none was equipped with a wearable airbag. Third, RTCs occurring in
overseas departments and territories (i.e., not in Metropolitan France)
were also excluded to ensure consistency in terms of road traffic system,
climate, environment, and seasonal characteristics. Fourth, this analysis
was restricted to riders, that is, excluding passengers who account for
less than 10% of all injured or killed to guarantee greater uniformity in
profiles as crash kinematics and injury mechanisms may vary between
riders and passengers (Evans et al., 2018; Carmai et al., 2019; Chiang
et al., 2014).

Adjustment variables for the association between airbag use and
crash-related fatality were selected as follows. First, each variable was
tested for association with the outcome using univariate logistic
regression model. We excluded the following variables from the multiple
analysis: those not significantly associated at the 20% significance level,
those not identified in the literature as risk factors, and those with over
25% of missing data (socio-professional status and travel motive). Sec-
ond, to avoid any multicollinearity between the retained variables, we
computed the pairwise Cramer's V correlation between all pairs of
variables. When two variables were correlated at a level greater than
0.3, one was excluded (variable selection was based on expected mea-
surement error, correlation with other variables and epidemiological
interpretability). For instance, the "crash setting" and "road speed
limits" variables had a Cramer's V correlation coefficient of 0.7. Due to
inconsistencies in "crash setting" definition linked to road type and
signage, and because it correlated with other variables (V correlation =
0.3 with "luminosity" and "PTW category"), it was excluded and the
"road speed limits" variable was retained. We performed five multiple
imputations using the Mice package in R (Masson, 2023) to impute the
missing values in all the remaining adjustment factors (van Buuren et al.,
2023). The final set of variables considered as adjustment factors
included sex, age, PTW category, surface condition, time of the crash,
road speed limits, direction of traffic, object hit, and initial impact
location.

Furthermore, interaction terms between each variable in the final set of adjustment factors and airbag use were tested to determine whether the effect of the airbag could vary across factor categories. The calcu- lated likelihood ratio test was non-significant for all interaction terms tested (p-value > 0.05), discarding the addition of any interaction term in the final model (results not shown). All statistical analyses were performed using R version 4.3.0 (2023-04-21 ucrt) and RStudio version "2023.06.0 + 421."

2.5. *Sensitivity analyses*

Several sensitivity analyses were conducted to assess the robustness
of the obtained results. First, regression models were fitted on complete
cases, that is, excluding the missing data, to assess the impact of missing
data on our results. Second, regression models were fitted on data
limited to individuals under 65 y.o. to ensure that results were not
affected by an age-related vulnerability effect. Third, regression models
were fitted on data including passengers to benefit from a larger sta-
tistical power and to assess the stability of the regression coefficients on
a larger database. Fourth, models were fitted excluding low displace-
ment engine PTWs (≤50 cm³) to ensure that the differences in profiles
and riding behaviors of their users compared to high displacement en-
gine PTW users involved in crashes did not affect our results. Indeed, we
expect users of low displacement engine PTWs to be younger, with a

reduced likelihood to be equipped with airbags (given the price) and to
ride at lower speeds (given the small power of their engine). Finally,
models were fitted excluding PTW riders with no recorded information
on alcohol use. Alcohol testing is typically conducted by police officers
in crashes involving bodily injury or presumed offences, but not neces-
sarily in other types of crashes. Moreover, alcohol testing may have been
infeasible when the rider was killed, severely injured, or had fled the
scene. Therefore, alcohol-related information may be subject to selec-
tion bias, and missing data could not be imputed as it was likely missing
not at random (van Buuren et al., 2023).

3. Results

3.1. *Description of the study population*

Over the study period, 69,350 PTW users involved in RTCs were identified. Their characteristics are described in Table 1. Most of them were men (87.90%), resulting in a 7.3 man-to-woman ratio, and the majority were riders (90.43%). The median age was 33 y.o. (IQR: 24-47 y.o.). Regarding PPEs, the majority of the PTW users were wearing a helmet (96.03%), and over half (55.49%) were wearing gloves during the crash. The prevalence of airbag use among PTW users involved in RTCs was 0.83% (n = 578), with a significant upward trend over the years (from 139 in 2019 to 178 in 2022, corresponding to a 28% increase; p = 0.01 in the negative binomial test), despite a plateau in 2020-2021, which is likely due to the influence of the global SARS-CoV-2 pandemic lockdowns in France. Regarding health outcomes, 7,274 (10.49%) PTW users were “uninjured,” 39,433 (56.88%) “slightly injured,” 19,978 (28.82%) “hospitalized,” and 2,644 (3.81%) “killed.” For both airbag users and non-users, there is an upward trend in the proportion of fatalities between 2019 and 2022. The proportion did not statistically differ from year to year (p = 0.07 in Pearson Chi-squared test) among PTW users wearing airbags, but it did among non-users (p = 0.007 in Pearson Chi-squared test) (Fig. 1).

3.2. *Comparison of PTW users involved in RTCs with and without an airbag system*

Several significant differences between airbag users and non-users were found among PTW users involved in RTCs (Table 1). The fatality rate was higher among airbag users compared to non-users (4.67% vs. 3.81%, p < 0.001). Regarding personal and crash characteristics, airbag users were older than non-users (median age 39 vs. 33 year-old, p < 0.0001), were more often managers/higher intellectual professions (10.40% vs. 6.95%, p < 0.0001) or employees/intermediate occupations (55.45% vs. 33.40%, p < 0.0001), were more likely to be riders (95.33% vs. 90.40%, p < 0.0001), were using PTW with higher displacement engine (87.20% vs. 56.24% of 125 cm³ or more, p < 0.0001), were more prone to travel for professional purposes (31.57% vs. 6.95%, p < 0.0001), and were more likely to wear PPE, including helmets (100% vs. 96%, p < 0.0001) and gloves (78.72% vs. 55.30%, p < 0.0001). Crashes involving airbag users occurred significantly less frequently at night (27.68% vs. 35.96%, p < 0.0001), in darkness/other (20.42% vs. 29.01%, p < 0.0001), in urban area (42.56% vs. 68.78%, p < 0.0001), at intersection (23.36% vs. 30.35%, p < 0.0001), in collisions with a vehicle (57.79% vs. 65.79%, p < 0.0001), or in one-way traffic direction (12.43% vs. 19.75%, p < 0.0001) compared to non-users; and took place on roads with higher speed limitations (median of 70 vs. 50 km.h⁻¹, p < 0.0001). PTW users involved in RTCs and using airbags were less likely to be tested positive for alcohol consumption (2.50% vs. 7.14%, p < 0.001). Sex, weather conditions, surface condition, day of the week, season, and initial impact location on the PTW were not significantly different between the two groups of PTW users.
<caption>**Table 1**</caption>

Characteristics of the global population, of airbag users and of non-users among PTW users involved in RTCs and included in the study, 2019–2022, France.

<table><caption>**Table 1 (continued)**</caption><thead><tr><th rowspan="2">Characteristic</th><th rowspan="2">Total<br>N = 69<br>350<sup>a</sup></th><th colspan="2">Population with airbag (yes/no)</th><th rowspan="2">p-value<sup>b</sup></th></tr><tr><th>No, N = 68<br>772<sup>a</sup></th><th>Yes, N =<br>578<sup>a</sup></th></tr></thead><tbody><tr><td><strong>PTW user</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Age (years)</td><td>33 [24-47]</td><td>33 [24-47]</td><td>39 [29-50]</td><td>&lt;0.0001</td></tr><tr><td>Missing</td><td>1 001</td><td>1 001</td><td>0</td><td></td></tr><tr><td>Age category</td><td></td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td>&lt;17 y.o.</td><td>4 109<br>(6.01%)</td><td>4 101<br>(6.05%)</td><td>8 (1.38%)</td><td></td></tr><tr><td>18-24 y.o.</td><td>13 942<br>(20.40%)</td><td>13 868<br>(20.46%)</td><td>74<br>(12.80%)</td><td></td></tr><tr><td>25-49 y.o.</td><td>36 707<br>(53.71%)</td><td>36 354<br>(53.64%)</td><td>353<br>(61.07%)</td><td></td></tr><tr><td>50-75 y.o.</td><td>13 343<br>(19.52%)</td><td>13 200<br>(19.48%)</td><td>143<br>(24.74%)</td><td></td></tr><tr><td>&gt;75 y.o.</td><td>248<br>(0.36%)</td><td>248<br>(0.37%)</td><td>0 (0.00%)</td><td></td></tr><tr><td>Missing</td><td>1 001</td><td>1 001</td><td>0</td><td></td></tr><tr><td><strong>Sex</strong></td><td></td><td></td><td></td><td>0.6</td></tr><tr><td>Woman</td><td>8 390<br>(12.10%)</td><td>8 324<br>(12.10%)</td><td>66<br>(11.42%)</td><td></td></tr><tr><td>Man</td><td>60 960<br>(87.90%)</td><td>60 448<br>(87.90%)</td><td>512<br>(88.58%)</td><td></td></tr><tr><td><strong>Road user type</strong></td><td></td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td>Rider</td><td>62 744<br>(90.47%)</td><td>62 193<br>(90.43%)</td><td>551<br>(95.33%)</td><td></td></tr><tr><td>Passenger</td><td>6 606<br>(9.53%)</td><td>6 579<br>(9.57%)</td><td>27 (4.67%)</td><td></td></tr><tr><td><strong>Socio-professional status</strong></td><td></td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td>Professional Rider</td><td>3 443<br>(8.92%)</td><td>3 409<br>(8.92%)</td><td>34 (8.42%)</td><td></td></tr><tr><td>Managers and higher<br>intellectual professions</td><td>2 695<br>(6.98%)</td><td>2 653<br>(6.95%)</td><td>42<br>(10.40%)</td><td></td></tr><tr><td>Farmers/Craftsmen/<br>Traders</td><td>2 849<br>(7.38%)</td><td>2 836<br>(7.42%)</td><td>13 (3.22%)</td><td></td></tr><tr><td>Intermediate<br>occupations/<br>Employees</td><td>12 981<br>(33.63%)</td><td>12 757<br>(33.40%)</td><td>224<br>(55.45%)</td><td></td></tr><tr><td>Workers</td><td>6 032<br>(15.63%)</td><td>5 998<br>(15.70%)</td><td>34 (8.42%)</td><td></td></tr><tr><td>Other</td><td>10 601<br>(27.46%)</td><td>10 544<br>(27.60%)</td><td>57<br>(14.11%)</td><td></td></tr><tr><td>Missing</td><td>30 749</td><td>30 575</td><td>174</td><td></td></tr><tr><td><strong>Alcohol test</strong></td><td></td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>Negative</td><td>46 904<br>(92.90%)</td><td>46 437<br>(92.86%)</td><td>467<br>(97.50%)</td><td></td></tr><tr><td>Positive</td><td>3 582<br>(7.10%)</td><td>3 570<br>(7.14%)</td><td>12 (2.50%)</td><td></td></tr><tr><td>Missing</td><td>18 864</td><td>18 765</td><td>99</td><td></td></tr><tr><td><strong>Crash location county in Metropolitan France</strong></td><td></td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td>Yes</td><td>65 015<br>(93.75%)</td><td>64 453<br>(93.72%)</td><td>562<br>(97.23%)</td><td></td></tr><tr><td>No</td><td>4 335<br>(6.25%)</td><td>4 319<br>(6.28%)</td><td>16 (2.77%)</td><td></td></tr><tr><td><strong>Personal Protective Equipment</strong></td><td></td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td><strong>Helmet use</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>2 677<br>(3.97%)</td><td>2 677<br>(4.00%)</td><td>0 (0.00%)</td><td></td></tr><tr><td>Yes</td><td>64 799<br>(96.03%)</td><td>64 228<br>(96.00%)</td><td>571<br>(100.00%)</td><td></td></tr><tr><td>Missing</td><td>1 874</td><td>1 867</td><td>7</td><td></td></tr><tr><td><strong>Gloves use</strong></td><td></td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td>No</td><td>30 866<br>(44.51%)</td><td>30 743<br>(44.70%)</td><td>123<br>(21.28%)</td><td></td></tr><tr><td>Yes</td><td>38 484<br>(55.49%)</td><td>38 029<br>(55.30%)</td><td>455<br>(78.72%)</td><td></td></tr><tr><td><strong>Travel motive</strong></td><td></td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td>Leisure/other</td><td>31 006<br>(62.24%)</td><td>30 777<br>(62.40%)</td><td>229<br>(46.64%)</td><td></td></tr></tbody></table>

<table><thead><tr><th rowspan="2">Characteristic</th><th rowspan="2">Total<br>N = 69<br>350<sup>a</sup></th><th colspan="3">Population with airbag (yes/no)</th><th rowspan="2">p-value<sup>b</sup></th></tr><tr><th>No, N = 68<br>772<sup>a</sup></th><th>Yes, N =<br>578<sup>a</sup></th><th>p-value<sup>b</sup></th></tr></thead><tbody><tr><td>Personal</td><td>15 226<br>(30.57%)</td><td>15 119<br>(30.65%)</td><td>107<br>(21.79%)</td><td></td><td></td></tr><tr><td>Professional</td><td>3 583<br>(7.19%)</td><td>3 428<br>(6.95%)</td><td>155<br>(31.57%)</td><td></td><td></td></tr><tr><td>Missing</td><td>19 535</td><td>19 448</td><td>87</td><td></td><td></td></tr><tr><td><strong>Vehicle</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PTW category</td><td></td><td></td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td>&lt;50 cm<sup>3</sup></td><td>13 261<br>(19.12%)</td><td>13 248<br>(19.26%)</td><td>13 (2.25%)</td><td></td><td></td></tr><tr><td>&gt;50 and ≤125 cm<sup>3</sup></td><td>16 910<br>(24.38%)</td><td>16 849<br>(24.50%)</td><td>61<br>(10.55%)</td><td></td><td></td></tr><tr><td>&gt;125 cm<sup>3</sup></td><td>39 179<br>(56.49%)</td><td>38 675<br>(56.24%)</td><td>504<br>(87.20%)</td><td></td><td></td></tr><tr><td>First PTW impact location</td><td></td><td></td><td></td><td></td><td>0.49</td></tr><tr><td>None</td><td>6 430<br>(9.28%)</td><td>6 374<br>(9.27%)</td><td>56 (9.69%)</td><td></td><td></td></tr><tr><td>Front</td><td>44 204<br>(63.77%)</td><td>43 853<br>(63.79%)</td><td>351<br>(60.73%)</td><td></td><td></td></tr><tr><td>Rear-end</td><td>4 614<br>(6.66%)</td><td>4 568<br>(6.65%)</td><td>46 (7.96%)</td><td></td><td></td></tr><tr><td>Broadside (left or right)</td><td>13 073<br>(18.86%)</td><td>12 955<br>(18.85%)</td><td>118<br>(20.42%)</td><td></td><td></td></tr><tr><td>Multiple impacts</td><td>1 000<br>(1.44%)</td><td>993<br>(1.44%)</td><td>7 (1.21%)</td><td></td><td></td></tr><tr><td>Missing</td><td>29</td><td>29</td><td>0</td><td></td><td></td></tr><tr><td><strong>Environment</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Weather conditions</td><td></td><td></td><td></td><td></td><td>0.73</td></tr><tr><td>Dry</td><td>59 297<br>(85.51%)</td><td>58 809<br>(85.52%)</td><td>488<br>(84.43%)</td><td></td><td></td></tr><tr><td>Rain/snow</td><td>6 153<br>(8.87%)</td><td>6 099<br>(8.87%)</td><td>54 (9.34%)</td><td></td><td></td></tr><tr><td>Other</td><td>3 895<br>(5.62%)</td><td>3 859<br>(5.61%)</td><td>36 (6.23%)</td><td></td><td></td></tr><tr><td>Missing</td><td>5</td><td>5</td><td>0</td><td></td><td></td></tr><tr><td>Road surface condition</td><td></td><td></td><td></td><td></td><td>0.085</td></tr><tr><td>Dry</td><td>59 601<br>(85.96%)</td><td>59 119<br>(85.98%)</td><td>482<br>(83.39%)</td><td></td><td></td></tr><tr><td>Wet/Other</td><td>9 738<br>(14.04%)</td><td>9 642<br>(14.02%)</td><td>96<br>(16.61%)</td><td></td><td></td></tr><tr><td>Missing</td><td>11</td><td>11</td><td>0</td><td></td><td></td></tr><tr><td>Day of the week</td><td></td><td></td><td></td><td></td><td>0.29</td></tr><tr><td>Weekday</td><td>50 467<br>(72.77%)</td><td>50 058<br>(72.79%)</td><td>409<br>(70.76%)</td><td></td><td></td></tr><tr><td>Weekend</td><td>18 883<br>(27.23%)</td><td>18 714<br>(27.21%)</td><td>169<br>(29.24%)</td><td></td><td></td></tr><tr><td>Season</td><td></td><td></td><td></td><td></td><td>0.91</td></tr><tr><td>Autumn</td><td>16 020<br>(23.10%)</td><td>15 885<br>(23.10%)</td><td>135<br>(23.36%)</td><td></td><td></td></tr><tr><td>Summer</td><td>22 035<br>(31.77%)</td><td>21 859<br>(31.78%)</td><td>176<br>(30.45%)</td><td></td><td></td></tr><tr><td>Spring</td><td>18 331<br>(26.43%)</td><td>18 176<br>(26.43%)</td><td>155<br>(26.82%)</td><td></td><td></td></tr><tr><td>Winter</td><td>12 964<br>(18.69%)</td><td>12 852<br>(18.69%)</td><td>112<br>(19.38%)</td><td></td><td></td></tr><tr><td>Time of crash</td><td></td><td></td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td>Day</td><td>44 458<br>(64.11%)</td><td>44 040<br>(64.04%)</td><td>418<br>(72.32%)</td><td></td><td></td></tr><tr><td>Night</td><td>24 892<br>(35.89%)</td><td>24 732<br>(35.96%)</td><td>160<br>(27.68%)</td><td></td><td></td></tr><tr><td>Luminosity</td><td></td><td></td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td>Daylight</td><td>49 281<br>(71.06%)</td><td>48 821<br>(70.99%)</td><td>460<br>(79.58%)</td><td></td><td></td></tr><tr><td>Darkness/other</td><td>20 069<br>(28.94%)</td><td>19 951<br>(29.01%)</td><td>118<br>(20.42%)</td><td></td><td></td></tr><tr><td>Road speed limits (km. h<sup>-1</sup>)</td><td>50 [50-70]</td><td>50 [50-70]</td><td>70 [50-80]</td><td></td><td>&lt;0.0001</td></tr><tr><td>Missing</td><td>1 138</td><td>1 125</td><td>13</td><td></td><td></td></tr><tr><td>Crash setting</td><td></td><td></td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td>Urban</td><td>47 545<br>(68.56%)</td><td>47 299<br>(68.78%)</td><td>246<br>(42.56%)</td><td></td><td></td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th rowspan="2">Characteristic</th><th rowspan="2">Total<br>N = 69<br>350<sup>a</sup></th><th colspan="3">Population with airbag (yes/no)</th></tr><tr><th>No, N = 68<br>772<sup>a</sup></th><th>Yes, N =<br>578<sup>a</sup></th><th>p-value<sup>b</sup></th></tr></thead><tbody><tr><td>Rural</td><td>21 805<br>(31.44%)</td><td>21 473<br>(31.22%)</td><td>332<br>(57.44%)</td><td></td></tr><tr><td>Intersection</td><td></td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td>No</td><td>41 718<br>(60.16%)</td><td>41 318<br>(60.08%)</td><td>400<br>(69.20%)</td><td></td></tr><tr><td>Yes</td><td>21 005<br>(30.29%)</td><td>20 870<br>(30.35%)</td><td>135<br>(23.36%)</td><td></td></tr><tr><td>Other</td><td>6 622<br>(9.55%)</td><td>6 579<br>(9.57%)</td><td>43 (7.44%)</td><td></td></tr><tr><td>Missing</td><td>5</td><td>5</td><td>0</td><td></td></tr><tr><td>Direction of traffic</td><td></td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td>One-way</td><td>12 948<br>(19.69%)</td><td>12 879<br>(19.75%)</td><td>69<br>(12.43%)</td><td></td></tr><tr><td>Two-way</td><td>43 656<br>(66.37%)</td><td>43 266<br>(66.34%)</td><td>390<br>(70.27%)</td><td></td></tr><tr><td>Other</td><td>9 170<br>(13.94%)</td><td>9 074<br>(13.91%)</td><td>96<br>(17.30%)</td><td></td></tr><tr><td>Missing</td><td>3 576</td><td>3 553</td><td>23</td><td></td></tr><tr><td>Object hit</td><td></td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td>None</td><td>8 071<br>(11.64%)</td><td>7 968<br>(11.59%)</td><td>103<br>(17.82%)</td><td></td></tr><tr><td>Fixed object or other</td><td>12 489<br>(18.02%)</td><td>12 370<br>(17.99%)</td><td>119<br>(20.59%)</td><td></td></tr><tr><td>Vehicle</td><td>48 763<br>(70.34%)</td><td>48 407<br>(70.42%)</td><td>356<br>(61.59%)</td><td></td></tr><tr><td>Missing</td><td>27</td><td>27</td><td>0</td><td></td></tr><tr><td>Collision type</td><td></td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td>None</td><td>9 214<br>(13.40%)</td><td>9 089<br>(13.33%)</td><td>125<br>(21.89%)</td><td></td></tr><tr><td>Two vehicles</td><td>45 175<br>(65.72%)</td><td>44 845<br>(65.79%)</td><td>330<br>(57.79%)</td><td></td></tr><tr><td>Three vehicles or more</td><td>3 702<br>(5.39%)</td><td>3 659<br>(5.37%)</td><td>43 (7.53%)</td><td></td></tr><tr><td>Other collision type</td><td>10 646<br>(15.49%)</td><td>10 573<br>(15.51%)</td><td>73<br>(12.78%)</td><td></td></tr><tr><td>Missing</td><td>613</td><td>606</td><td>7</td><td></td></tr><tr><td>Health status</td><td></td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>Uninjured</td><td>7 274<br>(10.49%)</td><td>7 243<br>(10.54%)</td><td>31 (5.36%)</td><td></td></tr><tr><td>Slightly injured</td><td>39 433<br>(56.88%)</td><td>39 121<br>(56.90%)</td><td>312<br>(53.98%)</td><td></td></tr><tr><td>Hospitalised</td><td>19 978<br>(28.82%)</td><td>19 770<br>(28.76%)</td><td>208<br>(35.99%)</td><td></td></tr><tr><td>Killed</td><td>2 644<br>(3.81%)</td><td>2 617<br>(3.81%)</td><td>27 (4.67%)</td><td></td></tr><tr><td>Missing</td><td>21</td><td>21</td><td>0</td><td></td></tr></tbody></table>

PTW = Powered two-wheeler; RTC = Road Traffic crash; y.o. = years old.

### 3.3. Association with crash-related fatality

For the analysis of the risk of fatality among PTW users involved in RTCs, 52,940 individuals (76.33% of the overall population) met all inclusion criteria. Table 2 shows the unadjusted and adjusted odds ratios for crash-related fatalities among airbag users compared to non-users. Unadjusted odds ratio showed a non-significant higher risk of fatality for airbag users (OR [95% CI] = 1.21 [0.79-1.77]). After adjustment for confounders, PTW riders wearing an airbag had a 20% lower risk of fatality compared with non-users, but this difference was not statistically significant (aOR [95% CI] = 0.80 [0.53-1.21]). Regarding other covariates, the adjusted risk of PTW crash-related fatality was significantly lower for women than for men (aOR [95% CI] = 0.57 [0.45-0.72]). In comparison to individuals aged between 25 and 50 y.o., those aged 18-24 y.o. exhibited a lower risk of fatality (aOR [95% CI] = 0.85 [0.75-0.97]), whereas riders of more than 50 y.o. had a higher risk of fatality (aOR [95% CI] = 1.34 [1.21-1.48]). Compared to small displacement engine PTWs (≤50 cm³), medium (>50 and ≤ 125 cm³) and high displacement engine PTWs (>125 cm³) were associated with a

higher risk of fatality (respectively, aOR [95% CI] = 1.59 [1.25-2.02] and 2.45 [1.96-3.06]). The risk of fatality was higher for crashes occurring at night compared to daytime (aOR [95% CI] = 1.45 [1.32-1.59]), on roads with speed limits higher than 50 km.h⁻¹ (aOR [95% CI] = 3.55 [3.20-3.94] and aOR [95% CI] = 3.00 [2.53-3.56], respectively, for roads with 60 to 80 km.h⁻¹ and ≥90 km.h⁻¹ speed limits) compared to roads with ≤ 50 km.h⁻¹ speed limits. Compared to riders who hit a vehicle, those who crashed into a fixed object had a higher risk of fatality (aOR [95% CI] = 2.13 [1.92-2.35]), while those that just fell had a significant lower risk (aOR [95% CI] = 0.66 [0.54-0.79]). The adjusted risk of fatality was lower when the initial PTW impact was on the rear or broadside compared to the front part (aOR [95% CI] = 0.48 [0.42-0.56]), when the crash occurred on wet or other surfaces compared to dry ones (aOR [95% CI] = 0.85 [0.73-0.99]), and in one-way and other-way traffic compared to two-way roads (aOR [95% CI] = 0.34 [0.29-0.40] and 0.56 [0.47-0.66], respectively).

### 3.4. Sensitivity analyses

Sensitivity analyses of crash-related fatalities were consistent with the main analysis (Table B of the Supplementary Material). Airbag users had lower risk of fatality among PTW riders involved in RTCs, ranging from 15% (aOR [95% CI] = 0.85 [0.56-1.28]) to 24% (aOR [95% CI] = 0.76 [0.50 - 1.15]) in the different sensitivity analyses, without being statistically significant (p-values range between 0.2 and 0.4).

## 4. Discussion

### 4.1. Main findings

To the best of our knowledge, this is the first epidemiological study assessing the prevalence of airbag use among PTW users involved in RTCs and estimating the effectiveness of the device in preventing crash-related fatalities. We found that wearable airbag use rate was very low (<1%) in our population of 69,350 French PTW users involved in RTCs between 2019 and 2022, but the increasing trend suggests that airbag use may become more common in the future. We also observed a higher fatality rate in airbag users compared to non-users, but this finding may be misleading given that airbag users, compared to non-users, were older, rode higher displacement engine PTWs, and crashed on higher speed limits roads, which are all factors associated with an increased fatality risk (Dischinger et al., 2006; Savolainen & Mannering, 2007; Pai, 2009). When accounting for these characteristics and for other adjustment factors, we found that airbag users were 20% less likely to die from their crash, though this difference was not statistically significant. Other personal and crash-related factors were significantly associated with lower risk of fatality among PTW riders involved in RTCs: being a woman, being younger than 25 y.o., having a crash on a wet surface, without collision, or having a first impact on the rear or broadside of the PTW. Riding by night or in the dark, a high displacement engine PTW, on roads with moderate-high speed limits, and having a collision with a fixed object were associated with a higher risk of fatality among PTW riders involved in RTCs.

### 4.2. Airbag use among PTW users involved in a crash

The low prevalence of airbag use among our population of PTW users involved in RTCs may be due to the lack of knowledge of the general population and PTW users regarding the existence of such safety devices, as well as their characteristics and effectiveness. These aspects could discourage PTW users from purchasing this non-mandatory equipment, as studies have shown that the perceived ineffectiveness of PPEs at protecting against injury, along with the absence of law enforcement, deterred PTW users from wearing PPEs (Germeni et al., 2009; Norris & Myers, 2013; Mohammadi et al., 2022). Other reasons may include the

<footnote>a Median [interquartile range]; or Frequency (%).</footnote>

<footnote>b Mann-Whitney Wilcoxon tests; Pearson's Chi-squared test.</footnote>
<figure><img src="image_4.png" /><figcaption>Fig. 1. Distribution of Powered Two-Wheeler (PTW) users involved in road traffic crashes health outcome per year in the sub-population without airbag (column A, n = 68 772) and in the airbag-equipped sub-population (column B, n = 578).</figcaption></figure>

recent development of airbag technology, introduced in France in the early 2000s; and the high entry price of this device compared to other PPEs such as helmets, although a notable reduction in price has been observed in the last five years (Tissot et al.). Additionally, the low prevalence may be a consequence of other factors such as riders' beliefs and needs including comfort, and aesthetics (Deliverable-D4.1.pdf [Internet]. [cited, 2024). Nevertheless, the rate of wearable airbag use among PTW users involved in a crash has increased during the four-year period of the study, especially in the last year. This is consistent with surveys conducted by a French insurance company, which found a 4 % rise in the use of airbag systems between 2016 (4% out of 2,195 riders) and 2019 (8% out of 1,868 riders) (Lab' 2-Roues; L'airbag à 23-roues). This increasing trend will likely continue in the coming decades (L'airbag à 23-roues), reinforcing the need for a robust evaluation of the effectiveness of the device to guide user's choice, and policy-makers' decisions.

### 4.3. The characteristics of airbag users among PTW users involved in RTCs

As observed in previous studies for other types of PPE, such as helmets (Liasidis et al., 2023; Brockhus et al., 2024; Khor et al., 2017; Liu et al., 2023; Mahdavi Sharif et al., 2023; Skalkidou et al., 1999; Díez-Navarro et al., 2024), airbag users had a specific socio-demographic profile, different from non-users.

In our study, we observed a risk aversion among airbag users, who were better equipped with protective gear such as helmets and gloves, and who were also more likely to test negative for alcohol consumption. This is supported by several previous studies showing that risk-averse riding behaviors are common among motorcyclists who have engaged in at least one other form of safe riding, that is, wearing protective gear and adhering to traffic laws (Liu et al., 2023; Heydari et al., 2016; Jou et al., 2012; dos Santos et al., 2019; Rossheim et al., 2014; Sarmiento et al., 2020). For instance, those riding with helmets were found to be less likely to ride while drunk and/or without a license (dos Santos et al., 2019). This form of risk awareness may lead to an overestimation of the effectiveness of wearable airbags. However, on the contrary, the use of airbags might increase the sensation of being well-protected, which could lead to a riskier behavior such as higher riding speed (airbag users in our population rode on higher-speed roads and with higher displacement engine PTWs, compared to non-airbag users), thus increasing the risk of fatal crashes and artificially diminishing the estimated effectiveness of the airbag device. In any event, it is essential for

further studies to explore how airbag use is related to specific behavior characteristics, including risky behavior and safety attitude (Hedlund, 2000; Bjørnskau et al., 2012).

### 4.4. Airbag use and risk factors for PTW rider crash-related fatalities

Ultimately, despite their greater use of PPEs and often negative results during alcohol tests, the rate of fatalities was higher among airbag users involved in RTCs compared to non-airbag users. This difference could be partly explained by the specific characteristics of airbag users such as older age, implying greater physical vulnerability, or potentially high-speed crashes as airbag users involved in RTCs were riding on roads with higher speed limits and higher displacement engine PTWs. Indeed, these factors were also associated with a greater risk of PTW fatal crashes in our study, and are well-recognized risk factors of severe injury and fatality (Dischinger et al., 2006; Savolainen & Mannering, 2007; Pai, 2009; Pai & Saleh, 2007; Kyrychenko & McCartt, 2006; Moon et al., 2023; Talving et al., 2010; Shaheed & Gkritza, 2014). In any case, caution is required in interpreting this fatality rate considering the very small number of airbag users and even smaller number of fatalities in this population, making the proportion very unstable as seen over the years of the study.

Adjusting for riders' profiles and crash circumstances influenced the comparison of the risk of fatality among PTW riders involved in RTCs, and adjusted results were non-significant, although they tended towards a fatality risk reduction for airbag users. Several numerical studies evaluating motorcycle airbag devices have demonstrated their efficacy in protecting against severe thoracic injuries (Thollon et al., 2010; Cherta Ballester et al., 2021; Wood et al.; Marconi, Gatto, & Massaro, 2018). Cherta-Ballester O. et al. (Cherta Ballester et al., 2021) concluded that airbag devices could reduce the likelihood of sustaining severe internal organ injuries by up to 22% and ribcage injuries severity by one AIS level. However, these results are based on numerical simulations of specific thoracic impact conditions. Population-based estimations are needed to assess wearable airbag effectiveness in real-life and on a large set of crash circumstances. Unfortunately, the French police database lacks detailed injury information and AIS scores, hindering the assessment of the effect of airbag devices on injury severity in the present work. Additionally, as the body location of the injuries that caused death is not specified in BAAC data, it prevents the selection of fatalities mainly due to torso injuries to study the effect of airbag devices on the body areas that are expected to be protected. We ensured a comparable risk of head-related fatality among both airbag users and non-users by
<caption>**Table 2**</caption>

Unadjusted and adjusted odds ratio for the association with crash-related fatality were estimated using simple and multiple logistic regression models (N = 52 940).

<table><thead><tr><th rowspan="2">Characteristic</th><th colspan="3">Univariate analysis</th><th colspan="3">Adjusted analysis</th></tr><tr><th>cOR</th><th>95% CI</th><th>p-value</th><th>aOR</th><th>95% CI</th><th>p-value</th></tr></thead><tbody><tr><td>Airbag use</td><td>1.21</td><td>0.79 to 1.77</td><td>0.4</td><td>0.80</td><td>0.53 to 1.21</td><td>0.3</td></tr><tr><td>Age category</td><td></td><td></td><td>&lt;0.0001</td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td>18-24 y.o.</td><td>0.80</td><td>0.70 to 0.91</td><td></td><td>0.85</td><td>0.75 to 0.97</td><td></td></tr><tr><td>25-50 y.o.</td><td>Ref</td><td></td><td></td><td>Ref</td><td></td><td></td></tr><tr><td>&gt;50 y.o.</td><td>1.57</td><td>1.42 to 1.73</td><td></td><td>1.34</td><td>1.21 to 1.48</td><td></td></tr><tr><td>Sex</td><td></td><td></td><td>&lt;0.0001</td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td>Man</td><td>Ref</td><td></td><td></td><td>Ref</td><td></td><td></td></tr><tr><td>Woman</td><td>0.45</td><td>0.36 to 0.58</td><td></td><td>0.57</td><td>0.45 to 0.72</td><td></td></tr><tr><td>PTW category</td><td></td><td></td><td>&lt;0.0001</td><td></td><td></td><td></td></tr><tr><td>≤50 cm³</td><td>Ref</td><td></td><td></td><td>Ref</td><td></td><td></td></tr><tr><td>&gt;50 and ≤125 cm³</td><td>2.12</td><td>1.68 to 2.70</td><td></td><td>1.59</td><td>1.25 to 2.02</td><td></td></tr><tr><td>&gt;125 cm³</td><td>4.81</td><td>3.91 to 5.99</td><td></td><td>2.45</td><td>1.96 to 3.06</td><td></td></tr><tr><td>First PTW impact location</td><td></td><td></td><td>&lt;0.0001</td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td>Front (left or right)</td><td>Ref</td><td></td><td></td><td>Ref</td><td></td><td></td></tr><tr><td>Rear-end/ Broadside (left or right)</td><td>0.40</td><td>0.35 to 0.45</td><td></td><td>0.48</td><td>0.42 to 0.56</td><td></td></tr><tr><td>None</td><td>1.13</td><td>0.98 to 1.30</td><td></td><td>1.16</td><td>0.99 to 1.36</td><td></td></tr><tr><td>Multiple impacts</td><td>1.62</td><td>1.22 to 2.12</td><td></td><td>1.17</td><td>0.88 to 1.57</td><td></td></tr><tr><td>Road surface condition</td><td></td><td></td><td>&lt;0.0001</td><td></td><td></td><td>0.033</td></tr><tr><td>Dry</td><td>Ref</td><td></td><td></td><td>Ref</td><td></td><td></td></tr><tr><td>Wet/Other<sup>a</sup></td><td>0.66</td><td>0.56 to 0.76</td><td></td><td>0.85</td><td>0.73 to 0.99</td><td></td></tr><tr><td>Time of crash</td><td></td><td></td><td>&lt;0.0001</td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td>Day</td><td>Ref</td><td></td><td></td><td>Ref</td><td></td><td></td></tr><tr><td>Night</td><td>1.25</td><td>1.14 to 1.36</td><td></td><td>1.45</td><td>1.32 to 1.59</td><td></td></tr><tr><td>Road speed limits category<sup>b</sup></td><td></td><td></td><td>&lt;0.0001</td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td>≤50 km.h<sup>-1</sup></td><td>Ref</td><td></td><td></td><td>Ref</td><td></td><td></td></tr><tr><td>60 to 80 km.h<sup>-1</sup></td><td>4.68</td><td>4.24 to 5.15</td><td></td><td>3.55</td><td>3.20 to 3.94</td><td></td></tr><tr><td>≥90 km.h<sup>-1</sup></td><td>2.36</td><td>2.04 to 2.72</td><td></td><td>3.00</td><td>2.53 to 3.56</td><td></td></tr><tr><td>Direction of traffic</td><td></td><td></td><td>&lt;0.0001</td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td>Two-way</td><td>Ref</td><td></td><td></td><td>Ref</td><td></td><td></td></tr><tr><td>Other<sup>c</sup></td><td>0.66</td><td>0.57 to 0.75</td><td></td><td>0.56</td><td>0.47 to 0.66</td><td></td></tr><tr><td>One-way</td><td>0.30</td><td>0.25 to 0.35</td><td></td><td>0.34</td><td>0.29 to 0.40</td><td></td></tr><tr><td>Object hit</td><td></td><td></td><td>&lt;0.0001</td><td></td><td></td><td>&lt;0.0001</td></tr><tr><td>Vehicle</td><td>Ref</td><td></td><td></td><td>Ref</td><td></td><td></td></tr><tr><td>Fixed object or other<sup>d</sup></td><td>2.77</td><td>2.52 to 3.04</td><td></td><td>2.13</td><td>1.92 to 2.35</td><td></td></tr><tr><td>None</td><td>0.85</td><td>0.72 to 1.00</td><td></td><td>0.66</td><td>0.54 to 0.79</td><td></td></tr></tbody></table>

cOR = Crude Odds Ratio, aOR = Adjusted Odds ratio, CI = Confidence Interval, Ref = Reference category, PTW = Powered two-wheeler, y.o. = years old.

All variables with missing values of less than 25 % were imputed using multiple imputations.

Age and road speed limits (km.h⁻¹) were categorized to estimate crude and adjusted odds ratios.

a Other in surface conditions includes puddles, flooded, snow, mud, glacial, grease-oil and other unspecified.

b Road speed limits variable was categorised for analysis purposes.

c Separate carriageways or variable assignment routes.

d Other includes pedestrians, animals and other unspecified.

restricting the study population to helmeted riders. However, based on the nature of the data it was not possible to exclude head trauma-related deaths. This could lead to an underestimation of the true effect of airbag devices, and may potentially explain the absence of a significant association. Furthermore, even with the reductions in thoracic injury computed by Cherta-Ballester O. et al. (Cherta Ballester et al., 2021), AIS 4+ injuries would still be associated with a very poor prognosis due to potential massive hemorrhage or acute respiratory failure (Battle & Evans, 2015). Hence, wearable airbags might not provide sufficient protection for the most severe torso injuries, limiting their potential to reduce the risk of crash-related fatalities.

### 4.5. Limits and strengths

A major limitation is that data recorded by police officers are subject to under-reporting, varying according to specific RTC characteristics. In particular, lower reporting rates have been noticed for single-vehicle and minor crashes (Amoros, Martin, & Laumon, 2006). Assuming that the airbag actually reduces the severity of torso injuries, this differential reporting would affect our results by disproportionately underestimating the number of crashes involving airbag users compared to that of non-airbag users. This bias would then tend to underestimate the protective effectiveness of wearable airbags. One way to partly solve this limitation would be to use under-reporting correction coefficients based on user profiles to adjust these results (Elliott et al., 2010); but this is beyond the scope of this study.

Although we believe that we have included the most important potential confounding factors in our study, covering the profiles of PTW users involved in RTCs and the characteristics of crashes, residual confounding may remain due to unmeasured factors such as impact velocity and behavior. Additionally, airbag performance could be influenced by factors related to crash kinematics such as body impact location, impacted obstacle, impact velocity or factors related to airbag design, for example, detection and inflation times, pressure, volume, thickness, shape, covered body areas. These data are not currently collected in the police reports. New generation of smart wearable airbags including GPS sensors could be an interesting way to monitor the velocity of the rider before the crash and at the moment of impact, which would allow to better control for crash kinematics and human body impact conditions in future research.

Data from non-crashed PTW users could help to identify behavioral differences between crashed and non-crashed PTW users with and without airbags, thereby mitigating potential selection biases. Unfortunately, to the best of our knowledge, no such database currently exists for the study population. Yet, evaluating safety equipment solely based on crash data is a well-established approach in road safety research, as demonstrated in studies on helmet effectiveness (Liu et al., 2008).

The risk for severe injury could also be the topic for future studies. We did not analyze this health outcome in this study because we lacked adequate data. Indeed, the only available data related to severity was hospital admission, which is a poor characterization of injury severity since it is also influenced by user's characteristics such as age or other medical conditions (Bergeron et al., 2005; Roger et al., 2019), or by the healthcare protocols of the hospital where the treatment is administered. Future studies could push further this analysis by incorporating trauma scoring systems, such as the AIS (Rapsang & Shyam, 2015), to provide details on injuries affecting the body regions expected to be primarily protected by airbags (thorax, abdomen, and spine).

Our study also has several strengths. First, to our knowledge, this is the first epidemiological study conducted at a national level on wearable airbag devices designed to protect the torso of PTW users in the event of a crash. Hence, this study provides valuable insights into the use of this device among PTW users involved in RTCs and its potential level of protection. Second, our study is based on four years of French national road crash data, providing a large, and up-to-date sample of PTW users injured or killed in RTCs in France. Finally, French police reports are
nearly exhaustive regarding the RTC casualties who died within 30 days of a crash, ensuring a reliable database for studying PTW-user-related fatalities.

## 5. Conclusions

In France, the reduction of serious injuries and fatalities among PTW users has not been satisfactory, despite the mandatory use of helmets and their widespread adoption. Wearable airbags are a recent and promising solution to attenuate motorcyclists' mortality and morbidity. Based on a French national crash database of almost 70,000 PTW users, we provided valuable insights into the prevalence of wearable airbag use and the characteristics of users involved in both fatal and nonfatal RTCs. The use of wearable airbags in our population was very low, even if an increasing rate was observed over the study period. Airbag users were older than non-users, used higher engine displacement PTWs, and crashed more often in rural settings and on roads with higher speed limits. Compared to non-users, we found a higher fatality rate among airbag users involved in RTCs. After adjustment, the risk of fatality among PTW riders involved in RTCs was not statistically different between airbag users and non-users. Nevertheless, the small number of airbag users in this study and the lack of detailed data are important limitations to consider in the detection of the airbag effect. As the number of airbag users is likely to continue to increase in the coming years, this study highlights the need for ad-hoc evaluations of wearable airbag effectiveness in order to draw applicable insights for public safety and injury prevention measures. Future investigations should involve larger airbag user samples, and more precise data on injury severity and location, airbag design and deployment, and vehicle speed at the time of the crash.

## Data availability

The data underlying this article are mainly available in the ONISR website at https://www.onisr.securite-routiere.gouv.fr/node/219, and can be accessed freely. For the privacy of individuals, data on some variables (e.g., professional status and alcohol use) are not available in the public ONISR website. These are available on a secure database managed by Gustave Eiffel University through a specific convention with the ONISR, and could be shared on reasonable request to the corresponding author and after the ONISR's agreement.

## CRediT authorship contribution statement

**Fégens Saint-Louis:** Writing – original draft, Validation, Software, Methodology, Formal analysis, Data curation, Conceptualization.
**Lydiane Agier:** Writing – review & editing, Validation, Supervision, Methodology, Formal analysis, Conceptualization.
**Thibaut Deville:** Writing – original draft, Validation, Formal analysis, Data curation, Conceptualization.
**Oscar Cherta-Ballester:** Writing – review & editing, Validation, Supervision, Methodology, Investigation, Conceptualization.
**Valentin Honoré:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Funding acquisition, Conceptualization.
**Catherine Masson:** Writing – review & editing, Validation, Supervision, Methodology, Investigation, Conceptualization.
**Olivier Monneuse:** Writing – review & editing, Validation, Supervision, Methodology, Formal analysis, Conceptualization.
**Céline Vernet:** Writing – review & editing, Validation, Supervision, Project administration, Methodology, Formal analysis, Conceptualization.

## Ethics approval

No ethical advice was needed because fully anonymized public data were used.

## Funding

This work was supported by In&motion and carried out in cooperation with Gustave Eiffel University and Aix-Marseille University as part of the EDAIR project (Grant Number 2022/1772).

## Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: SAINT-LOUIS reports financial support was provided by In&motion. HONORE reports financial support was provided by In&motion. CHERTA-BALLESTER reports financial support was provided by In&motion. SAINT-LOUIS reports a relationship with In&motion that includes: funding grants. HONORE reports a relationship with In&motion that includes: board membership and employment. CHERTA-BALLESTER reports a relationship with In&motion that includes: employment. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

We wish to thank the following: Professor Bruno FALISSARD for reviewing this work and providing invaluable guidance, Hélène TARDY for data extraction, Cébrail AKSOY for contributions to data management, Emmanuelle AMOROS for her expertise in understanding biases in BAAC data and her valuable contribution to data management, and David SWAN for his English language review.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jsr.2025.06.024.

## References

* Amoros, E., Martin, J.-L., & Laumon, B. (2006). Under-reporting of road crash casualties in France. *Accident Analysis & Prevention* [Internet], 38(4), 627–635. https://www.sciencedirect.com/science/article/pii/S0001457505001910.

* Amoros, E., Martin, J.-L., Chiron, M., & Laumon, B. (2007). Road crash casualties: Characteristics of police injury severity misclassification. Feb [cited 2025 Jan 9] *Journal of Trauma and Acute Care Surgery* [Internet], 62(2), 482 https://journals.lww.com/jtrauma/abstract/2007/02000/road_crash_casualties_characteristics_of_polic e.28.aspx.

* Araujo, M., Illanes, E., Chapman, E., & Rodrigues, E. (2017). Effectiveness of interventions to prevent motorcycle injuries: Systematic review of the literature. *International Journal of Injury Control and Safety Promotion*, 24(3), 406–422.

* Bambach, M. R., & Mitchell, R. J. (2014). The rising burden of serious thoracic trauma sustained by motorcyclists in road traffic crashes. *Accident Analysis & Prevention* [Internet], 62, 248–258. https://www.sciencedirect.com/science/article/pii/S0001457513004065.

* Barzegar, A., Ghadipasha, M., Forouzesh, M., Valiyari, S., & Khademi, A. (2020). Epidemiologic study of traffic crash mortality among motorcycle users in Iran (2011–2017). *Chinese Journal of Traumatology* [Internet], 23(4), 219–223. http s://www.ncbi.nlm.nih.gov/pmc/articles/PMC7451678/.

* Battle, C.E., Evans, P.A. 2015. Predictors of mortality in patients with flail chest: a systematic review. *Emergency Medicine Journal* [Internet]. BMJ Publishing Group Ltd and the British Association for Accident & Emergency Medicine; 2015 Dec 1 [cited 2024 Nov 27];32(12):961–965. https://emj.bmj.com/content/32/12/961.

* Bergeron, E., Lavoie, A., Moore, L., Clas, D., & Rossignol, M. (2005). Comorbidity and age are both independent predictors of length of hospitalization in trauma patients. *Canadian Journal of Surgery*, 48(5), 361–366. https://www.canjnsurg.ca/content/48/5/361.

* Bilan 2022 de la sécurité routière | Observatoire national interministériel de la sécurité routière [Internet]. [cited 2023 Jul 12]. https://www.onisr.securite-routiere.gouv.fr/etat-de-linsecurite-routiere/bilans-annuels-de-la-securite-routiere/bilan-2022-de-la-securite-routiere.

* Bjørnskau, T., Nævestad, T.-O., & Akhtar, J. (2012). Traffic safety among motorcyclists in Norway: A study of subgroups and risk factors. *Accident Analysis & Prevention* [Internet], 49, 50–57. https://www.sciencedirect.com/science/article/pii/S0001457511003149.

* Blaizot, S., Papon, F., Haddak, M. M., & Amoros, E. (2013). Injury incidence rates of cyclists compared to pedestrians, car occupants and powered two-wheeler riders,
using a medical registry and mobility data, Rhône County, France. Accident; Analysis and Prevention, 58, 35-45.

Bouaoun, L., Haddak, M. M., & Amoros, E. (2015). Road crash fatality rates in France: A comparison of road user types, taking account of travel practices. Accident; Analysis and Prevention, 75, 217-225.

Brockhus, L. A., Liasidis, P., Lewis, M., Jakob, D. A., & Demetriades, D. (2024). Injury patterns and outcomes in motorcycle driver crashes in the United States: The effect of helmet use. Mar 1 [cited 2024 Jun 4] *Injury [Internet]*, 55(3), Article 111196 https://www.sciencedirect.com/science/pii/S0020138323009130.

Carmal, J., Koetniyom, S., & Hossain, W. (2019). Analysis of rider and child pilot passenger kinematics along with injury mechanisms during motorcycle crash. Traffic Injury Prevention [Internet], 20(sup1), S13-S20. https://doi.org/10.1080/15389588.2019.1616180

Champahom, T., Se, C., Jomnonkwoa, S., Boonyoo, T., Leelamanothum, A., & Ratanavaraha, V. (2023). Temporal instability of motorcycle crash fatalities on local roadways: A random parameters approach with heterogeneity in means and variances. *International Journal of Environmental Research and Public Health* [Internet], 20(5), 3845. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10001501/.

Cherta Ballester, O., Llari, M., Afquir, S., et al. (2019). Analysis of trunk impact conditions in motorcycle road accidents based on epidemiological, accidentological data and multibody simulations. Accident; Analysis and Prevention, 127, 223-230.

Cherta Ballester, O., Llari, M., Honoré, V., Masson, C., & Arnoux, P.-J. (2021). An evaluation methodology for motorcyclists' wearable airbag protectors based on finite element simulations. *International Journal of Crashworthiness* [Internet], 26(1), 99-108. https://doi.org/10.1080/13588265.2019.1687218

Chiang, V. X. Y., Cheng, J. Y. X., Zhang, Z. C., & Teo, L.-T. (2014). Comparison of severity and pattern of injuries between motorcycle riders and their pillions: A matched study. *Injury [Internet]*, 45(1), 333-337.

de Lapparent, M. (2006). Empirical Bayesian Analysis of accident severity for motorcyclists in large French urban areas. *Accident Analysis & Prevention* [Internet], 38(2), 260-268. https://www.sciencedirect.com/science/article/pii/S0001457505001466.

Deliverable-D4.1.pdf [Internet]. [cited 2024 Oct 9]. https://pioneers-project.eu/wp-content/uploads/2020/12/Deliverable-D4.1.pdf.

de Rome, L., Ivers, R., Fitzharris, M., et al. (2011). Motorcycle protective clothing: Protection from injury or just the weather? *Accident Analysis & Prevention* [Internet], 43(6), 1893-1900. https://www.sciencedirect.com/science/article/pii/S000145711001163.

de Rome, L., Ivers, R., Fitzharris, M., Haworth, N., Heritier, S., & Richardson, D. (2012). Effectiveness of motorcycle protective clothing: Riders' health outcomes in the six months following a crash. *Injury [Internet]*, 43(12), 2035-2045. https://www.sciencedirect.com/science/article/pii/S00020138311005183.

Díez-Navarro, J. M., Leal-Costa, C., Planes-Muñoz, D., et al. (2024). High-power motorcycle accidents in Spain: a descriptive study. *European Journal of Trauma and Emergency Surgery [Internet]*, 50(2), 455-466. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11035440/.

Ditchinger, P. C., Ryb, G. E., Ho, S. M., & Braver, E. R. (2006). Injury patterns and severity among hospitalized motorcyclists: A comparison of younger and older riders. *Annual Proceedings of the Association for the Advancement of Automotive Medicine* [Internet], 50, 237-249. https://www.ncbi.nlm.nih.gov/pmc/articles/PM C3217482/.

dos Santos, W. J., da Coelho, V. M. da S., Bonfim, C. V. do, & Ceballos, A. G. da C. de (2019). Alcohol and risky behavior in traffic among motorcyclists involved in accidents in a city in northeastern Brazil. *Traffic Injury Prevention* [Internet], 20(3), 233-237. https://doi.org/10.1080/15389588.2019.1579318

Elliott, M. R., Resler, A., Flannagan, C. A., & Rupp, J. D. (2010). Appropriate analysis of CIREN data: Using NASS-CD5 to reduce bias in estimation of injury risk factors in passenger vehicle crashes. *Accident Analysis & Prevention* [Internet]., 42(2), 530-539. https://www.sciencedirect.com/science/article/pii/S0001457509002474.

Elm E. von, Altmann, D.G., Egger, M., Pocock, S.J., Göttsche, P.C., Vandenbroucke, J.P. 2007. Strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. *BMJ* [Internet]. British Medical Journal Publishing Group; [cited 2024 Jan 8];335(7624):806-808. https://www.bmj.com/content/335/7624/806.

Evans, T. A., Sasor, S., Duquette, S., et al. (2018). Comparison of neurologic trauma and motorcycle helmet use in drivers vs passengers. Feb [cited 2018 Sep 9] *JAMA Surgery* [Internet], 153(2), 183-184 https://www.ncbi.nlm.nih.gov/pmc/articles/PM C5838866/.

Fierro, N., Inaba, K., Aiolfi, A., et al. (2019). Motocross versus motorcycle injury patterns: A retrospective National Trauma Databank analysis. Aug [cited 2023 Sep 19] *Journal of Trauma and Acute Care Surgery* [Internet], 87(2), 402 https://journals.lww.com/jtrauma/abstract/2019/08000/motocross versus motorcycle injury patterns_a.19.aspx.

Germeni, E., Lionis, C., Davou, B., & Petridou, E. T. (2009). Understanding reasons for non-compliance in motorcycle helmet use among adolescents in Greece. *Injury Prevention* [Internet], 15(1), 19-23. https://injuryprevention.bmj.com/content/t15/1/19.

Gidion, F., Carroll, J., & Lubbe, N. (2021). Motorcyclist injuries: Analysis of German in-depth crash data to identify priorities for injury assessment and prevention. *Accident Analysis & Prevention* [Internet], 163. https://www.sciencedirect.com/science/article/pii/S0001457521004942.

Girardi, D., Marconi, E., Massaro, M. 2019. Assessment of Shoulder and Chest Protection of Wearable Motorcycle Airbags.

Global status report on road safety 2023 [Internet]. [cited 2024 Jan 19]. https://www.who.int/teams/social-determinants-of-health/safety-and-mobility/global-status-report-on-road-safety-2023.

Granieri, S. S., Reitano, E. E., Bindi, F. F., et al. (2020). Motorcycle-related trauma: Effects of age and site of injuries on mortality. A single-center, retrospective study. *World Journal of Emergency Surgery* [Internet], 15(1), 18. https://doi.org/10.1186/s13017-020-00297-1. Mar 10 [cited 2024 Jan 8].

Hedlund, J. (2000). Risky business: safety regulations, risk compensation, and individual behavior. *Injury Prevention* [Internet], 6(2), 82-90. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1730605/.

Heydari, S. T., Vossoughi, M., Akbarzadeh, A., et al. (2016). Prevalence and risk factors of alcohol and substance abuse among motorcycle drivers in Fars province, Iran. *Chinese Journal of Traumatology* [Internet], 19(2), 79-84. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4897829/.

Jou, R.-C., Yeh, T.-H., & Chen, R.-S. (2012). Risk factors in motorcyclist fatalities in Taiwan. *Traffic Injury Prevention* [Internet], 13(2), 155-162. https://doi.org/10.1080/15389588.2011.641166

Khor, D., Inaba, K., Aiolfi, A., et al. (2017). The impact of helmet use on outcomes after a motorcycle crash. May 1 [cited 2024 Jun 4] *Injury [Internet]*, 48(5), 1093-1097 https://www.sciencedirect.com/science/article/pii/S0020138317300773.

Kureshi, N., Walling, S., Erdogan, M., Opra, I., Green, R. S., & Clarke, D. B. (2024). Alcohol is a risk factor for helmet non-use and fatalities in off-road vehicle and motorcycle crashes. *European Journal of Trauma and Emergency Surgery* [Internet]. https://doi.org/10.1007/s00068-024-02572-1

Krychenko, M. S., & McCartt, A. T. (2006). Florida's weakened motorcycle helmet law: Effects on death rates in motorcycle crashes. Taylor & Francis *Traffic Injury Prevention* [Internet], 7(1), 55-60. https://doi.org/10.1080/15389580500377833.

Lab' 2-Routes. Les équipements de protection individuelle à moto | 2-Routes Lab' [Internet]. [cited 2024 Oct 9]. https://2roueslab.mutuelledesmotards.fr/etude/les-equipements-de-protection-individuelle-a-moto.

Liasidis, P., Benjamin, E., Jakob, D., Lewis, M., & Demetriades, D. (2023). Injury patterns and outcomes in motorcycle passengers. *Eur J Trauma Emerg Surg* [Internet], 49(6), 2447-2457. https://doi.org/10.1007/s00068-023-02296-8

Liu, B. C., Ivers, R., Norton, R., Boufous, S., Blows, S., & Lo, S. K. (2008). Helmets for preventing injury in motorcycle riders. *Cochrane Database of Systematic Reviews* [Internet], 1. https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.CD004333.pub3/full.

Liu, Y.-H., Wiratama, B. S., Chao, C.-J., et al. (2023). Unhelmeted riding, drunk riding, and unlicensed riding among motorcyclists: A population study in Taiwan during 2011-2016. *International Journal of Environmental Research and Public Health* [Internet], 20(2), 1412. https://www.ncbi.nlm.nih.gov/pmc/articles/PM C9864229/.

Loftis, K.L., Price, J., Gillich, P.J. 2018. Evolution of the Abbreviated Injury Scale: 1990-2015. *Traffic Injury Prevention* [Internet]. Taylor & Francis; [cited 2024 Jan 27];19(sup2);S109-S113. Doi: 10.1080/15389588.2018.1517247.

Lucci, C., Piantini, S., Savino, G., & Pierini, M. (2021). Motorcycle helmet selection and usage for improved safety: A systematic review on the protective effects of helmet type and fastening. *Traffic Injury Prevention* [Internet]. Taylor & Francis, 22(4), 301-306. https://doi.org/10.1080/15389588.2021.1894640

L'airbag à 2&3-routes | 2-Routes Lab' [Internet]. [cited 2024 Oct 9]. https://2roueslab.mutuelledesmotards.fr/etude/airbag-a-23-routes.

Mahdavi Sharif, P., Najafi Pazooki, S., Ghodsi, Z., et al. (2021). Effective factors of improved helmet use in motorcyclists: A systematic review. *BMC Public Health* [Internet], 23(1), 26. https://doi.org/10.1186/s12889-022-14893-0

Marconi, E., Gatto, F., Massaro, M. 2018. Numerical and Experimental Assessment of the Performance of Wearable Airbags for Motorcycle Riders.

Masson, E. 2023. L'imputation multiple des données manquantes aléatoirement : concepts généraux et présentation d'une méthode Monte-Carlo [Internet]. EM-Consulte [Internet Jun 7]. https://www.em-consulte.com/article/228906/limputation-multiple-des-donnees-manquantes-aleatoire.

Méthodologies statistiques | Observatoire national interministériel de la sécurité routière [Internet]. [cited 2025 Jan 14]. https://www.onisr.securite-routiere.gov.fr/outils-statistiques/methodologies-statistiques.

Mohammadi, E., Azadnajafabad, S., Keykhaei, M., et al. (2022). Barriers and factors associated with the use of helmets by motorcyclists: A scoping review. *Accident Analysis & Prevention* [Internet], 171. https://www.sciencedirect.com/science/article/pii/s0001457522001038.

Moon, S., Ryoo, H. W., Cho, J. W., Jung, H., Seo, K. S., & Lim, K. H. (2023). Comparison of incidence and outcome between occupational and non-occupational motorcycle injuries in Korea: A 7-years observational study. Mar 29 [cited 2023 Nov 24] *PloS One* [Internet], 18(3), Article e0283512 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10057783/.

Moskal, A., Martin, J.-L., Lenguerrand, E., Laumon, B. 2007. Injuries Among Motorised Two-Wheelers in Relation to Vehicle and Crash Characteristics in Rhone, France. [cited 2023 Jul 6]. https://wwwsemanticscholar.org/paper/Injuries-Among-Motorised-Two-Wheelers-in-Relation-Moskal-Martin/84a880300fb4ea14da3f2cb48af00ba89fc168.

Norris, E., & Myers, L. (2013). Determinants of personal protective Equipment (PPE) use in UK motorcyclists: Exploratory research applying an extended theory of planned behaviour. *Accident Analysis & Prevention* [Internet], 60, 219-230. https://www.sciencedirect.com/science/article/pii/S0001457513003539.

Pai, C.-W. (2009). Motorcyclist injury severity in angle crashes at T-junctions: Identifying significant factors and analysing what made motorists fail to yield to motorcycles. *Safety Science* [Internet], 47(8), 1097-1106. https://www.sciencedirect.com/science/article/pii/S0925753508002233.

Pai, C.-W., & Saleh, W. (2007). An analysis of motorcyclist injury severity under various traffic control measures at three-legged junctions in the UK. *Safety Science* [Internet], 45(8), 832-847. https://www.sciencedirect.com/science/article/pii/S0925753050001135.
Rapsang, A. G., & Shyam, D. C. (2015). Scoring systems of severity in patients with multiple trauma. *Cirugía Española [Internet], 93*(4), 213-221. https://www.elsevier.es/en-revista-cirugia-espanola-english-edition-436-articulo-scoring-systems-severit-y-in-patients-S2173507715000745.

Recueil de données annuelles | Observatoire national interministériel de la sécurité routière [Internet]. [cited 2023 Dec 25]. https://www.onisr.securite-routiere.gouv.fr/node/219.

Roger, C., Debuyzer, E., Dehl, M., et al. (2019). Facteurs influençant la durée de séjour, le mode de sortie et la réadmission à 30 jours après arthroplastie primaire de hanche et de genou. *Revue de Chirurgie Orthopédique et Traumatologique [Internet]*, 105(5), 606-612. https://www.sciencedirect.com/science/article/pii/S1877051719302023.

Rossheim, M. E., Wilson, F., Suzuki, S., Rodriguez, M., Walters, S., & Thombs, D. L. (2014). Associations between drug use and motorcycle helmet use in fatal crashes. *Traffic Injury Prevention* [Internet], 15(7), 678-684. https://doi.org/10.1080/15389588.2013.866235

Santos, K., Dias, J. P., Amado, C., Sousa, J., & Francisco, P. (2021). Risk factors associated with the increase of injury severity of powered two wheelers road accidents victims in Portugal. *Traffic Injury Prevention* [Internet]. Taylor & Francis, 22 (8), 646-650. https://doi.org/10.1080/15389588.2021.1987421

Sarmiento, J. M., Gogineni, A., Bernstein, J. N., et al. (2020). Alcohol/Illicit Substance Use in Fatal Motorcycle Crashes. *Journal of Surgical Research* [Internet], 256, 243-250. https://www.sciencedirect.com/science/article/pii/S0022480420304297.

Savolainen, P., & Mannering, F. (2007). Probabilistic models of motorcyclists' injury severities in single- and multi-vehicle crashes. *Accident Analysis & Prevention* [Internet], 39(5), 955-963. https://www.sciencedirect.com/science/article/pii/S0001457507000103.

Serre, T., Masson, C., Llari, M., Canu, B., Py, M., Perrin, C. 2019. Airbag jacket for motorcyclists: Evaluation of real effectiveness. p. 533-547.

Shaheed, M. S., & Gkritza, K. (2014). A latent class analysis of single-vehicle motorcycle crash severity outcomes. *Analytic Methods in Accident Research* [Internet], 2, 30-38. https://www.sciencedirect.com/science/article/pii/S2213665714000190.

Skalkidou, A., Petridou, E., Papadopoulos, F. C., Dessypris, N., & Trichopoulos, D. (1999). Factors affecting motorcycle helmet use in the population of Greater Athens, Greece. *Injury Prevention* [Internet], 5(4), 264-267. https://injuryprevention.bmj.com/content/5/4/264.

Talbot, R., Brown, L., & Morris, A. (2020). Why are powered two wheeler riders still fatally injured in road junction crashes? – A causation analysis. *Journal of Safety Research* [Internet], 75, 196-204. https://www.sciencedirect.com/science/article/pii/S0022437520301158.

Talving, P., Teixeira, P. G. R., Barmparas, G., et al. (2010). Motorcycle-related injuries: Effect of age on type and severity of injuries and mortality. *Journal of Trauma and Acute Care Surgery* [Internet], 68(2), 441. https://journals.lww.com/jtrauma/aabstract/2010/02000/motorcycle_related_injuries_effect_of_age_on_type.30.aspx.

Thollon, L., Godio, Y., Bidal, S., & Brunet, C. (2010). Evaluation of a new security system to reduce thoracic injuries in case of motorcycle accidents. *International Journal of Crashworthiness* [Internet], 15(2), 191-199. https://doi.org/10.1080/13588260903102062

Tissot, P.-F., Ballester, O.C., Honoré, V. Smart wearable airbags: Benefits for users and remaining challenges after 60 million kilometres and 3000 accidents.

Buuren, S. van, Groothuis-Oudshoorn, K., Vink, G., et al. 2023. mice: Multivariate Imputation by Chained Equations [Internet]. 2023 [cited 2023 Jun 7]. https://cran.r-project.org/web/packages/mice/index.html.

Wood, B., Kühn, M., Lang, A., Bauer, K., Peldschus, S., Halldin, P. SESSION 1: RIDER EQUIPMENT.

Wu, D., Hours, M., Ndiaye, A., Coquillat, A., Martin, J.-L. 2019. Effectiveness of protective clothing for motorized 2-wheeler riders. *Traffic Injury Prevention* [Internet]. Taylor & Francis; [cited 2023 Jul 12];20(2):196-203. Doi: 10.1080/15389588.2018.1545090.

Wu, D., Dufournet, M., & Martin, J.-L. (2019). Does a full-face helmet effectively protect against facial injuries? *Injury Epidemiology* [Internet], 6, 19. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6544930/.

Fégens Saint-Louis is Physician and currently PhD student at Claude Bernard Lyon 1 University and Gustave Eiffel University. He is working at In&motion which is a French company that develops smart wearable airbag systems for road users and sports: motor cycle, urban mobility, ski and horse-riding. His current research project is focused on the effectiveness of wearable airbags in protecting powered two wheelers in road traffic crashes.

Lydiane Agier is an accomplished researcher in statistics and epidemiology. She obtained her Ph.D. from Lancaster University, specializing in spatial epidemiology. Her post-doctoral work focused on environmental epidemiology, investigating associations between the early-life exposome (environmental exposure factors experienced by humans since intrauterine life) and health. Since 2021, she's been working at UMRESTTE (Unité Mixte de Recherche Épidémiologique et de Surveillance Transport Travail Environnement) in the field of road epidemiology. Her current research addresses mobility and safety issues related to the use of personal mobility devices and to the effectiveness of airbag vests for motorized two-wheeler users.

Thibaut Deville is a PhD student at Aix Marseille University and Gustave Eiffel University. He is working at UTAC which is an international group specialized in automotive testing, vehicle engineering and regulatory expertise. He holds a master's degree in sports engineering and ergonomics. He wrote his master's thesis on passive safety in French ski resorts and he is currently working on a new assessment method for wearable airbag for motorcyclists.

Oscar Cherta-Ballester is Lead Researcher at In&motion which is a French company that develops smart wearable airbag systems for road users and sports: motorcycle, urban mobility, ski and horse-riding. He has a PhD in Injury Biomechanics, an industrial engineering master's degree, a mechanical engineer degree and an electrical engineer degree. He is an active member of the working groups focused on motorcycle rider protective clothing (CEN/TC162/WG9), body protection for sports (CEN/TC162/WG11) and helmets for cyclists (CEN/TC158/WG4) of the European Committee for Standardization.

Valentin Honoré is Co-founder and Chief Technical Officer (CTO) at In&motion which is a French company that develops smart wearable airbag systems for road users and sports: motorcycle, urban mobility, ski and horse-riding. He has an engineer degree. He is an active member of the working groups focused on motorcycle rider protective clothing (CEN/TC162/WG9) and body protection for sports (CEN/TC162/WG11). He is the leader of the AdHoc Group dedicated to develop a new impact test method for motorcycle wearable airbags.

Catherine Masson is female senior researcher at the Laboratory of Biomechanics and Applications, a joint Gustave Eiffel University/Aix-Marseille University research unit. She graduated from Aix-Marseille University with a PhD in Mechanics and Acoustics, and an accreditation to direct research (HDR) in Biomechanics. Her activity ranges from mechanical properties identification of biological tissues to human body characterization. Experimental accident reconstruction in order to derive tolerance limits relative to specific injury mechanisms is a part of her research activities. A large part of her recent work addressed motorcyclist wearable airbag device.

Olivier Monneuse is professor of surgery, head of the acute care surgical department of the Edouard Herriot which is a Teaching Hospital. He is in charge of the surgical care of the trauma center of this hospital. He has a PhD in general surgery. He is interest in research in both prevention and trauma care especially for abdominal and thoracic trauma.

Céline Vernet is researcher in epidemiology. Initially trained as a midwife, she specialized in statistics and epidemiology with a PhD in environmental epidemiology and statistics. Her research focuses on environmental health and transport-related health, with a particular focus on the protection of motorized two-wheelers, the impact of autonomous vehicles on accident rates, the introduction of new modes of transport (personal mobility devices including electric scooters) in urban areas, and on the injuries sustained by road accident victims and their care. Since 2020, she has been an expert in the OECD's International Traffic Safety Data and Analysis Group (IRTAD).