Prevalence of fatigue while driving among two-wheeled vehicle drivers and associated factors: Exploratory approach from secondary analysis based on hospital data, Benin

Abstract

Fatigue while driving is one of the risk factors of road crashes. It's still poorly considered in interventions because of insufficient literature. In addition, the literature on this issue doesn't focus on two-wheelers, the most frequent users in the Benin context. The study examined the prevalence of fatigue while driving among two-wheeled vehicle drivers and the related factors. It's a secondary baseline data analysis from a cohort of road crash victims recruited from five hospitals in Benin. Data were collected from July 2019 to January 2020. Patients who identified themselves as drivers during the accident were included. Data on individual characteristics, including fatigue status in the moments preceding the collision, and other risk factors and environmental settings, were extracted. We used multivariate logistic regression. Among the respondents, 12.20% (95% CI=10.20-14.53) reported fatigue in the moments preceding the collision. The odds of fatigue while driving were significantly higher in male drivers (aOR=3.60; 95% CI=1.08-11.98), during professional trips (aOR=2.09; 95% CI=1.30-3.37), in non-helmet wearers (aOR=1.85; 95% CI=1.09-3.13), in users of stimulants (aOR=3.13; 95% CI=1.50-6.54), in those with a history of chronic diseases (aOR=1.95; 95% CI=1.16-3.27), at dusk (aOR=4.22; 95% CI=2.22-8.02), at night (aOR=6.90; 95% CI=3.95-12.05), and on Inter-State National Roads (aOR=2.01; 95% CI=1.18-3.43). Fatigue is a risk factor for road crashes in Benin, associated with other risk factors that highlight particularly vulnerable profiles and groups. Integrating prevention policies based on these cumulative risk factors will result in efficiency improvements.

Introduction

In 2019, 103.2 million people (1,299 per 100,000) were involved in road crashes worldwide vs. 63.2 million (1,193 per 100,000) in 1990¹. Concurrently, each year from 2000 to 2016, it is estimated that 1.15-1.35 million (about 18 deaths per 100,000) individuals died as a result of road crashes, with regional disparities that disproportionately affect Sub-Saharan Africa (2). The latest estimates suggest that road crashes constitute the seventh leading cause of DALYs (Disability-Adjusted Life Years) for all age groups, ahead of malaria, HIV/AIDS, and tuberculosis, and the leading cause for young people aged 10 to 29³. Benin is a West African country characterized by a road transport system dominated by two-wheeled vehicles and a high involvement (68%) of these vehicles' users among the victims of road crashes (4). In 2018, 5,358 road crashes were recorded in Benin (5), with a road mortality rate of 27.5 per 100,000 in 2016, slightly higher than the African average (2).

In recent years, there has been an increase in literature on predictors of the occurrence and severity of road crashes, which has generated evidence needed to design and implement appropriate road safety strategies. Overall, these studies have highlighted the influence of factors related to the individual, the vehicle and the environment (6-9). However, it is considered that human factors are responsible for 95% of road crashes (10). Fatigue while driving is cited in the literature as one of the human factors contributing to the occurrence and severity of road crashes (7,9). It is a state of exhaustion in the driver on the road, and its principal sign may be drowsiness. It harms the driver's performance on the road by reducing their attention and reactivity and can be associated with falling asleep while driving (11-13). In developed countries, fatigue while driving is responsible for 5-50% of road crashes, with median values of 15-25% (14,15). In a 2021 study in Benin, fatigue while driving among motorbike riders increased their risk of experiencing a traumatic brain injury by 1.90 times after an accident (16).

A range of studies has sought to understand the predictors of fatigue while driving. It is worth noting that a large part of the work on this issue has focused on professional drivers, such as truck drivers, taxi drivers, bus drivers, etc. (17-22). Indeed, this group of road users is considered vulnerable to fatigue because they travel long distances, both day and night, and are often responsible for several passengers (17,21,23,24). Among this group of users, an older age, excessive consumption of stimulants (caffeine), poor quality of sleep, trip-based payment type, long hours of driving, long distance trips, lack of rest time, additional employment (part-time), bad health status as well as social aspects such as loneliness are associated with fatigue on the road (18-20,22,25). Considering that the risk of fatigue applies to all drivers, including two-wheeled vehicle drivers, it is necessary to understand the extent of this phenomenon and the factors that promote its occurrence as the first step in implementing interventions to contribute to the reduction of road crashes and their consequences. In Benin, a literature review did not reveal any research on the determinants of fatigue during driving, among drivers of vehicles in general, or even among professionals. To address this gap, the present study aims to identify factors associated with fatigue during driving among two-wheeled vehicle drivers.

Materials and methods

Study setting.

Benin is a country of 114,763 km² that administratively comprises 12 departments divided into 77 communes (26). The National Institute of Statistics and Demography (INSTaD) projected 12,535,929 inhabitants in 2021²⁷. In a context marked by the high use of motorized two-wheeled vehicles, professional drivers using this type of vehicle have emerged, called motorcycle taxi drivers. Motorcycle taxis are an accessible, fast, and affordable public transportation service for people or goods using a motorcycle. Due to the low implementation of public transport services and their ease of movement in congested traffic and poorly maintained roads, this service has become the most widely used public transport mode (28).

Study type.

We performed secondary analyses of a database established as part of the TraumAR prospective cohort of road crash victims in Benin in 2019⁴. The aim of establishing this cohort was to make reliable data available that could serve as a basis for conducting studies on the issue of road crashes in Benin. Individuals who were victims of road crashes between July 2019 and January 2020, residing in Benin and admitted alive to one of the five selected reference hospitals were eligible to be included in the cohort after obtaining their written, free and informed consent. The sites of enrolment were: the Hubert Koutoukou Maga National University Hospital Center, the Oueme-Plateau University Hospital Center, the Ménontin Zone Hospital, the Borgou-Alibori University Hospital Center, and the Boko Zone Hospital. For each patient enrolled in the cohort, initial data were collected at enrolment through the direct administration of a detailed questionnaire and the exploitation of their medical records. Investigators were asked to judge the opportune moment to approach the victims and their caregivers. The initial data were joined by follow-up data at six and twelve months after the crash. The present study used the initial data. Additional details on the TraumAR cohort are available elsewhere (4,16,29).

Population.

The study population drawn from the TraumAR cohort consisted of patients who were victims of road crashes as drivers of motorized two-wheeled vehicles, living at admission and having complete data for all study variables.

Variables.

The dependent variable was fatigue (yes vs. no), reported by the respondents. Participants were asked if they had signs of fatigue in the moments preceding the collision.

The independent variables were related to the driver and the accident environment. These included age in years (<30, 30-49, ≥50), sex (male, female), reason for the trip (professional, private), helmet use (yes, no), alcohol consumption (yes, no), tobacco use (yes, no), use of sleeping pills (yes, no), use of stimulants (yes, no), phone use in the moments preceding the collision (yes, no), history of chronic illness (yes, no), history of road crashes (yes, no), time of the collision (dawn, daytime, dusk, night-time), visibility at the collision site (poor, acceptable, good), and type of road at the collision site (Inter-State National Road, other).

Data analysis.

Data analysis was conducted using Stata 15 in two steps. First, we described study variables based on their respective frequencies (n) and percentages (%). The prevalence of fatigue while driving was calculated with a 95% Confidence Interval (95% CI). The second step involved identifying factors associated to fatigue while driving. In simple logistic regression, each independent variable was cross-tabulated with the dependent variable. Variables with P<0.20 were entered into multivariate logistic regression. Following a stepwise downward strategy, we progressively eliminated the less significant variables to have only variables with P<0.05 in the final model. Results were presented as Odds Ratios (OR) with 95% CI. The Hosmer-Lemeshow test was used to check the goodness of fit of the final model.

Ethical considerations.

The protocol of TraumAR study gets approbation by the University of Parakou (Benin) Ethics Committee under the number 0182/CLERB-UP/P/SP/R/SA. Before the interviews, eligible respondents provided free, written, and informed consent. In addition, the dataset used for analysis was anonymized so that survey respondents could not be identified in any way.

Results

Basic characteristics of the participants.

The study included 885 motorized two-wheeler drivers involved in traffic crashes. Nearly half (49.04%) were 30 to 49 (Table I). About nine out of ten participants (89.94%) were male. For 30% of the respondents, the accident occurred during a professional trip. About 82% of the drivers were wearing a helmet. The prevalence of alcohol and tobacco consumption was 66.55 and 11.53%, respectively. Besides, 2.71 and 5.54% of the respondents reported using sleeping pills and stimulants, respectively. Fourteen participants (1.58%) used their phones in the moments leading up to the collision. History of chronic diseases and road crashes were noted in 16.38 and 35.48% of the participants, respectively. The accident occurred during the day for a little over half (50.62%) of the drivers, in good lighting conditions for 71.77%, and in poor lighting conditions for 15.59%. The Inter-State National Roads were the location of the accident for 14.96% of respondents.

Table I.

Basic characteristics of the participants, Benin.

Variables	n	%
Age in years
<30	311	35.14
30-49	434	49.04
≥50	140	15.82
Sex
Male	796	89.94
Female	89	10.06
Reason for the trip
Professional	269	30.40
Private	616	69.60
Helmet use
Yes	729	82.37
No	156	17.63
Alcohol consumption
Yes	589	66.55
No	296	33.45
Tobacco use
Yes	102	11.53
No	783	88.47
Use of sleeping pills
Yes	24	2.71
No	861	97.29
Use of stimulants
Yes	49	5.54
No	836	94.46
Phone use
Yes	14	1.58
No	871	98.42
History of chronic illness
Yes	145	16.38
No	740	83.62
History of road crashes
Yes	314	35.48
No	571	64.52
Time of the collision
Dawn	59	6.67
Daytime	447	50.51
Dusk	143	16.16
Night-time	236	26.67
Visibility at the collision site
Poor	138	15.59
Acceptable	112	12.66
Good	635	71.75
Type of road at the collision site
Inter-State National Road	132	14.92
Other	753	85.08

Prevalence of fatigue while driving.

Among the participants, 12.20% (95% CI=10.20-14.53) were fatigued while driving. Table II shows the prevalence of fatigue while driving according to the characteristics of the study population with its 95% confidence interval. The prevalence of fatigue while driving was high among drivers aged 30 years and over, with a significant difference in the age group of 30-49 years (13.82% vs. 9.00%, P<0.05). The prevalence was also higher among men than women (13.19% vs. 3.37%, P=0.014). About 17% of those who travelled in a professional setting reported being fatigued, compared to 10.06% travelling in a private capacity (P=0.004). The frequency of fatigue while driving was higher among non-helmet wearers (17.95% vs. 10.97%, P=0.017), and alcohol (13.92% vs. 8.78%, P=0.029) and stimulant consumers (34.69% vs. 10.89%, P<0.001). On the other hand, the prevalence of fatigue while driving was lower among drivers without chronic diseases (10.54% vs. 20.69%, P=0.001) as well as those who travelled during the day (4.92% vs. 16.78% vs. 24.15%, P<0.001), in a setting with good visibility (7.40% vs. 24.11 vs. 24.64%, P<0.001), and on Inter-State National Roads (10.23% vs. 23.48%, P<0.001).

Factors associated with fatigue while driving.

Table III shows the univariate and multivariate analysis of factors associated with fatigue while driving among the participants. During the univariate analysis, the following variables were significant at P<0.20 and were kept for the multivariate regression: age in years, sex, reason for the trip, helmet use, alcohol consumption, tobacco use, use of stimulants, history of chronic illness, time of the collision, visibility at the collision site, and type of road at the collision site. At the end of the multivariate analysis, sex, reason for the trip, helmet use, use of stimulants, history of chronic illness, time of the collision, visibility of the collision site, and the type of road were the factors associated with fatigue while driving. Males were 3.60 times (95% CI=1.08-11.98) more likely to report fatigue while driving than females. Drivers travelling for work (aOR=2.09; 95% CI=1.30-3.37) had higher odds of driving fatigue than those on private trips. Drivers who did not wear helmets (aOR=1.85; 95% CI=1.09-3.13) and those who reported using stimulants (aOR=3.13; 95% CI=1.50-6.54) were more at risk of fatigue while driving. Individuals with a history of chronic diseases were 1.95 times (95% CI=1.16-3.27) more likely to be fatigued while driving. The odds of driving fatigue were higher for those involved in crashes at dusk (aOR=4.22; 95% CI=2.22-8.02) and at night (aOR=6.90; 95% CI=3.95-12.05) than for those in daytime crashes. When respondents travelled on Inter-State National Road, they had twice the odds (aOR=2.01; 95% CI=1.18-3.43) of reporting fatigue while driving as those who travelled on other roads.

Table II.

Prevalence of driver fatigue by basic characteristics of two-wheeled drivers, Benin.

Variables	n	%	95% CI
Age in years
<30	28	9.00	6.28-12.75
30-49	60	13.82	10.88-17.42
50 and over	20	14.29	9.36-21.21
Sex
Male	105	13.19	11.01-15.73
Female	3	3.37	1.07-10.14
Reason for the trip
Professional	46	17.10	13.03-22.11
Private	62	10.06	7.92-12.71
Helmet use
Yes	80	10.97	8.90-13.46
No	28	17.95	12.64-24.85
Alcohol consumption
Yes	82	13.92	11.35-16.97
No	26	8.78	6.04-12.61
Tobacco use
Yes	17	16.67	10.54-25.35
No	91	11.62	9.55-14.07
Use of sleeping pills
Yes	3	12.50	3.73-34.49
No	105	12.20	10.17-14.56
Use of stimulants
Yes	17	34.69	22.41-49.43
No	91	10.89	8.94-13.19
Phone use
Yes	3	21.43	5.96-54.02
No	105	12.06	10.05-14.40
History of chronic illness
Yes	30	20.69	14.80-28.15
No	78	10.54	8.52-12.97
History of road crashes
Yes	34	10.83	7.83-14.80
No	74	12.96	10.44-15.98
Time of the collision
Dawn	5	8.47	3.48-19.22
Daytime	22	4.92	3.26-7.37
Dusk	24	16.78	11.46-23.92
Night-time	57	24.15	19.08-30.07
Visibility at the collision site
Poor	34	24.64	18.09-32.61
Acceptable	27	24.11	16.99-33.02
Good	47	7.40	5.60-9.72
Type of road at the collision site
Inter-State National Road	31	23.48	16.95-31.57
Other	77	10.23	8.25-12.61

Table III.

Factors associated with driver fatigue, Benin.

	Univariate analysis			Multivariate analysis
Variables	cOR	95% CI	P	aOR	95% CI	P
Age in years
<30	1.00
30-49	1.62	1.01-2.61	0.046
50 and over	1.68	0.91-3.11	0.095
Sex
Male	4.36	1.35-14.03	0.014	3.60	1.08-11.98	0.037
Female	1.00					1.00
Reason for the trip
Professional	1.84	1.22-2.78	0.004	2.09	1.30-3.37	0.002
Private	1.00					1.00
Helmet use
Yes	1.00					1.00
No	1.77	1.11-2.84	0.017	1.85	1.09-3.13	0.022
Alcohol consumption
Yes	1.68	1.05-2.67	0.029
No	1.00
Tobacco use
Yes	1.52	0.86-2.68	0.146
No	1.00
Use of sleeping pills
Yes	1.03	0.30-3.51	0.964
No	1.00
Use of stimulants
Yes	4.35	2.32-8.14	<0.001	3.13	1.50-6.54	0.002
No	1.00					1.00
Phone use
Yes	1.99	0.55-7.25	0.297
No	1.00
History of chronic
illness
Yes	2.21	1.39-3.53	0.001	1.95	1.16-3.27	0.011
No	1.00					1.00
History of road
crashes
Yes	0.82	0.53-1.26	0.355
No	1.00
Time of the collision
Dawn	1.79	0.65-4.92	0.260	1.62	0.57-4.62	0.369
Daytime	1.00					1.00
Dusk	3.90	2.11-7.19	<0.001	4.22	2.22-8.02	<0.001
Night-time	6.15	3.65-10.37	<0.001	6.90	3.95-12.05	<0.001
Visibility at the
collision site
Poor	4.09	2.51-6.66	<0.001
Acceptable	3.97	2.35-6.72	<0.001
Good	1.00
Type of road at the
collision site
Inter-State National	2.69	1.69-4.30	<0.001	2.01	1.18-3.43	0.011
Road
Other	1.00					1.00

Goodness of fit test Pearson chi2(94)=80.44 Prob>chi2=0.839.

Discussion

This exploratory study aimed to determine the prevalence of fatigue while driving among motorcyclists and identify associated factors. Based on the participants' self-reported statements, 12.20% were fatigued.

In the literature, studies investigating the extent of fatigue while driving among drivers have focused more on professionals and shown prevalence ranging from 27 to 62% among truck, taxi, and bus drivers (18-22). These percentages are all higher than the one we found. These findings agrees with studies suggesting an excess risk of fatigue on the road among professionals compared to other drivers (17,21,23,24). Furthermore, this study focused on two-wheeled vehicle drivers who seem less likely to travel long distances than car drivers, reducing the risk of fatigue while driving. In addition, there is heterogeneity in the approaches used to measure fatigue while driving. According to the study, drivers were asked to indicate how often they experienced fatigue while driving (22), the frequency with which they were fatigued on the road in the 12 months before the survey (20), whether they were fatigued during their last trip (18), and to complete the Epworth Sleepiness Scale to assess daytime sleepiness (25). In our study, we asked participants if they were in a state of fatigue immediately preceding the collision. The common thread among these approaches, including our paper, is that data on fatigue while driving was self-reported by participants, which is a source of several biases. Respondents may have difficulty remembering their past behaviors or experiences accurately, especially in this study, which focused on road crash victims and required information on their fatigue state immediately before the collision. Some participants may be reluctant to answer honestly or deliberately underestimate the frequency they were tired while driving. There is also some subjectivity related to variable manifestations of fatigue symptoms from one individual to another. It highlights the need for standardized data collection methods to facilitate comparisons.

Male drivers were more likely to fatigue while driving than their female counterparts. Previous studies have shown that men are more prone to adopting risky behaviours on the road (30-33).

We found that people travelling for work were twice as likely to be fatigued while driving as those making private trips. In Benin, professional drivers using two-wheeled vehicles are mostly accounted for by motorcycle taxi drivers. With professionalization, motorcycle taxi drivers are one of the categories of users who record the highest number of hours spent on the roads (to have maximum customers) without being able to take sufficient breaks, which can lead to chronic fatigue. It is up to motorcycle taxi drivers not to ignore the fatigue that may result from the exercise of their activity by planning their work schedule, observing regular breaks, sleeping sufficiently, and limiting the consumption of psychoactive substances.

We observed that drivers with a history of chronic diseases were about two times more likely to fatigue while driving than individuals who are not carriers of these conditions. Some chronic diseases or risk factors for chronic diseases are likely to disrupt the quality and quantity of sleep, which can lead to increased fatigue. According to a 2021 study, the likelihood of severe fatigue is 1.6 to 5.5 times higher in carriers of chronic diseases (34). In a 2018 study, obesity, hypertension, hypercholesterolemia, diabetes, and depression were associated with shorter sleep duration (35). We can also mention sleep apnea syndrome, a respiratory disorder occurring in sleep, where breathing is repeatedly interrupted (36). Drivers who suffer from obstructive sleep apnoea experience a significantly higher number of instances of sleepiness while driving and in road traffic crashes (37). The link between a history of chronic diseases and fatigue while driving can also be explained by the side effects of certain medications prescribed to treat these conditions (38,39) Strengthening medical follow-up and adjusting the driving time of people with chronic diseases are ways to reduce the risk of driver fatigue for this group.

Individuals who did not wear a helmet were more at risk of fatigue while driving. Not wearing a helmet is risky behaviour that appears to be associated with other risky behaviours, including driving while fatigued. The consumption of stimulants by drivers has also been identified as an associated factor of fatigue while driving. Some drivers, especially professionals, may use stimulants to reduce the feeling of fatigue during their activity (40). A study in 2015 showed 45% addiction to Tramadol among motorcycle taxi drivers in Parakou (Benin) (40). Over time, this substance consumption can lead to insomnia and disrupt sleep quality, resulting in daytime fatigue or drowsiness (41). Some people may rely more on stimulants to counteract this fatigue, worsening insomnia and perpetuating the vicious cycle. In the long term, frequent use of stimulants can also lead to addiction, which can lead to sleep disturbances and fatigue. It is essential to break this vicious cycle by adopting healthy sleep management strategies and limiting or eliminating the use of stimulants.

Dusk and night were associated with a higher risk of fatigue while driving among participants. The production of melatonin, a hormone associated with sleep, naturally increases when darkness sets in, increasing the risk of drowsiness in the driver (42).

When respondents travelled on roads other than Inter-State National Road (roads that connect the country to neighbouring states), they were less likely to be fatigued while driving. Inter-State National Roads are generally larger and better maintained than other roads and facilitate travel and trade. Driving on roads other than Inter-State National Road, which are in a poorer condition in terms of infrastructure, may require more vigilance of the driver.

The study has some limitations. Data came from five hospitals that may not necessarily be representatives of the country, even though the selection was based on the use of services related to the care of traffic accident victims. The retrospective nature of some questions, which rely on memory, could be a source of recall bias. Questions about risky driving behaviours could be subject to social desirability bias, resulting in an underestimation of the prevalence of these behaviours. Moreover, since this study used an existing database, it does not include all variables that could be considered as predictors of driver fatigue. These include variables related to professional activities, type of work, work schedules, and the number of work hours, as well as variables related to sleep, such as daily sleep duration or the presence of sleep disorders such as sleep apnea. Hence, it is essential to consider these different aspects in addition to the factors identified here in future studies on the topic.

Conclusions

A relatively high proportion of road crash victims had shown signs of fatigue in the moments leading up to the crashes. Depending on their characteristics and those of the road environment, fatigue while driving did not affect participants equally. The risk of fatigue while driving was significantly higher among male drivers, during professional travel, non-helmet users, users of stimulants, those with a history of chronic diseases, at dusk and night, and on National Inter-State Roads. Driving fatigue is a risk factor for road crashes in Benin, associated with other known risk factors that highlight priority targets for road safety. The integration of prevention policies based on the identification of these cumulative risk factors will contribute to the efficiency of improving road safety.

Funding Statement

Funding: None.