Intention of the utilization of rearview mirrors: integrating TPB and TTF models to explore factors among Chinese electric bike users

OBJECTIVE

Considering the value of rearview mirrors in providing essential rear visual information to electric bike (e-bike) users, this study aims to analyze the psychological traits influencing users' willingness and behavior toward rearview mirror usage and to understand their decision-making processes. Combined the Theory of Planned Behavior (TPB) and Task Technology Fit (TTF), and introduced the extended variable of perceived danger, this study explores the psychological factors that shape e-bike users' willingness and behavior regarding rearview mirror usage from a social psychology perspective.

METHODS

A questionnaire survey was conducted on e-bike users (N = 704) in China. The data collected included demographic characteristics, components integrated the TPB and TTF, and relevant extended variables. Structural equation analysis (SEM) was used to analyze the data, as well as demographic variable analysis.

RESULTS

The integration of the TPB and TTF models provides an effective framework for explaining and predicting the behavior and intentions of Chinese e-bike users regarding rearview mirror usage. The results of the model show that rearview mirrors fit e-bikes riding task, attitudes toward rearview mirror usage, and e-bike users characteristics associated with rearview mirror use increase e-bike users' willingness to use rearview mirrors. The extended TPB construct of perceived risk did not emerge as a formidable predictor in the e-bike riders' adoption of rearview mirrors. Additionally, results from multiple-group SEM analysis of four demographic variables (age, gender, e-bike riding experience, rearview mirror usage experience) suggest significant differences among e-bike users in using rearview mirrors while riding on the road.

CONCLUSIONS

This study establishes the validity of the integrated model of TPB and TTF model in predicting the use of rearview mirrors by e-bike users. Furthermore, the current findings may provide theoretical support for developing intervention strategies to promote rearview mirror usage, safety education initiatives, and the design improvements of rearview mirrors for e-bikes users.

Kinematic characterization of micro-mobility vehicles during evasive maneuvers

INTRODUCTION

Over the last decade, the increasing popularity of Micromobility Vehicles (MMVs) has led to profound changes in personal mobility, raising concerns about road safety and public health. Therefore, the effective characterization of their kinematic performances and safety boundaries is becoming crucial. Hence, this study aims to: (1) characterize the MMVs kinematic behaviors during emergency maneuvers; (2) examine how various power sources affect their performances; and (3) assess the suitability of a piecewise linear model for modeling their trajectories.

METHOD

We conducted a test track experiment involving 40 frequent riders performing emergency braking and swerving maneuvers on different electric MMVs, their traditional counterparts, and behaving as running pedestrians. A second experiment determined the swerving boundaries of different devices estimating their minimum radius of curvature.

RESULTS

Electric MMVs displayed superior braking capabilities compared to their traditional counterparts, while the opposite was observed in terms of swerving performances. Performances significantly varied across MMV-types, with handlebar-based devices (bicycles and scooters) consistently outperforming the handlebar-less MMVs (skateboards and onewheel). The piecewise linear models used for braking profiles well fitted most MMV trajectories, except for skateboards and pedestrians due their foot-ground interaction.

CONCLUSIONS

This research highlights the influence of MMVs-specific characteristics on their maneuverability, underscoring that steering or braking effectiveness in collisions may vary depending on device type and power source. Piecewise linear models effectively generated parameterized functions for modeling braking trajectories, despite further improvements are suggested given the inapplicability of the single brake-ramp assumption to all the MMVs.

PRACTICAL APPLICATIONS

The identified similarities and distinctions between MMVs could offer insights to traffic regulators and may assist MMV designers and manufacturers in enhancing the devices users' safety. The piecewise model results allow traffic events reconstructions and simulations, enabling intelligent driving system to predict MMV riders' evasive actions in critical situations.

Maxillofacial Injuries Sustained by Riders of Electric-Powered Bikes and Electric-Powered Scooters

OBJECTIVES

The purpose of our study is to retrospectively analyze and compare the patterns of maxillofacial-related injuries among rides of electric-powered bikes (E-bikes) and electric-powered scooters (E-scooters), the associated risk factors, and the required treatment.

MATERIALS AND METHODS

The medical files of all riders presenting to the emergency department at the Tel Aviv Sourasky Medical Center between 2019 and 2020 with oral- and maxillofacial-related injuries due to E-bike and E-scooter accidents were reviewed.

RESULTS

A total of 320 riders sustained oral- and maxillofacial-related injuries due to trauma involving E-bikes and E-scooters during the study period. E-scooter riders were involved in 238 accidents (74.5%) while E-bike riders accounted for the remaining 82 accidents (27.5%). Eighty-four out of 320 riders (26.25%) were hospitalized and required surgical interventions. Most of the 232 riders (72.5%) who reported not wearing a protective helmet during the index accident were E-scooter riders. In addition, 39 riders (18.66%) were riding either of these electric-powered vehicles under the influence of alcohol.

CONCLUSIONS

E-bike riders are more likely to sustain a maxillofacial fracture than E-scooter riders. Not wearing a protective helmet and riding under the influence of alcohol are major risk factors for maxillofacial injuries.

The suppression of spontaneous face touch and resulting consequences on memory performance of high and low self-touching individuals

Spontaneous touching of one's own face (sFST) is an everyday behavior that occurs primarily in cognitively and emotionally demanding situations, regardless of a persons' age or gender. Recently, sFST have sparked scientific interest since they are associated with self-inoculation and transmission of respiratory diseases. Several studies addressed the need to reduce sFST behaviors without discussing the underlying functions of this spontaneous behavior. In addition, the question of why this behavior occurs very frequently in some individuals (high self-touching individuals, HT) but less frequently in others (low self-touching individuals, LT) has not yet been addressed. For the first time, we distinguished between HT and LT and investigated the behavioral consequences of sFST suppression in these two groups. For this purpose, we examined performance outcomes of 49 participants depending on sFST behaviors during a haptic working memory task. In addition, we assessed personality traits of HT and LT using the Freiburg Personality Inventory (FPI-R). The results of our study reveal that suppressing sFST in HT is negatively related to memory performance outcomes. Moreover, HT show tendencies to differ from LT in certain personality traits. Our results highlight the relevance of distinguishing between HT and LT in future studies of sFST.

Increased injury severity and hospitalization rates following crashes with e-bikes versus conventional bicycles: an observational cohort study from a regional level II trauma center in Switzerland

Background

As electric bicycles (e-bikes) become increasingly popular, reports of injuries associated with e-bike usage are also rising. Patterns, characteristics, and severity of injuries following e-bike crashes need further investigation, particularly in contrast to injuries from conventional bicycle crashes.

Methods

This prospective observational study included 82 patients treated at a Level II trauma center for injuries resulting from an electric or conventional bicycle crash. Data were collected over one year (05.09.2017–19.09.2018) during in- and outpatient visits. A study-specific case report form was used to identify the bicycle type, cycling behavior (e.g., use of a helmet, safety gear, alcohol), and circumstances of the crash (e.g., road conditions, speed, cause of the incident, time of day, season). Additional information about patient demographics, treatment, and injury characteristics, such as the Injury Severity Score (ISS) and body region injured, were documented. Results were analyzed using chi-square, Fisher’s exact, or Wilcoxon tests. Simple logistic or linear regression models were used to estimate associations.

Results

Of the 82 patients, 56 (67%) were riding a conventional bike and 27 (33%) were using an e-bike. Most incidents were either single-bicycle crashes (66%) or automobile collisions (26%), with no notable difference in prevalence rates between groups. Although a higher proportion of conventional bikers were male (67% vs. 48%), the difference was not significant. E-bikers were older (median 60 years (IQR 44–70) vs. 45 years (IQR 32–62); p = 0.008), were hospitalized more often (48% vs. 24%, p = 0.025), and had worse ISS (median 3 (IQR 2–4) vs. 1 (IQR 1–3), p < 0.001), respectively. Body regions most affected were the extremities (78%) and external/skin (46%), and these were distributed similarly in both groups. Concomitant injury patterns of the thorax/chest with external/skin were higher among e-bikers (p < 0.001). When we controlled for the difference in the median age of the two groups, only the injury severity score of e-bikers remained significantly worse.

Conclusions

Hospitalization and chest trauma rates were higher among e-bikers. After controlling for the older age of this group, the severity of their injuries remained worse than in conventional cyclists. Initial clinical assessments at trauma units should include an evaluation of the thorax/chest, particularly among elderly e-bikers.

Level of evidence

Level III.

A retrospective study of spine injuries in electric bicycles related collisions

BACKGROUND

Electric bicycles related collisions could lead to severe consequences in spine injuries, while no study had comprehensively investigated the epidemiology and demography of spine injuries in electric bicycles related collisions.

QUESTIONS/PURPOSES

The aim of this study is to (1) describe the epidemiological characteristics of spine injuries in electric bicycles related collisions, (2) develop clinical guideline of spine injuries in electric bicycles related collisions and (3) support the new road safety policy for electric bicycle riders.

METHODS

A retrospective review of spine injuries in electric bicycle related collisions was performed from 86 patients in an urban trauma center between 2018 and 2020. The variables including gender, age, radiographic findings, associated injuries, neurologic injuries, treatment, average length of stay were fully collected. Chi-square test and paired sample mean t-test were used to test for statistically significant differences. All statistical analyses were performed using Statistical Product and Service Solutions 20.0.0 software. A P<0.001 was considered as significant.

RESULTS

A total of 86 cases were involved in electric bicycles related collisions lead to spine injuries. The spine injuries victims were predominantly male (79.07%) and middle-aged (41-60years, 44.19%). The most common spine injuries were L1 fractures (10.48%). The most common fracture type was AO Fracture Classification type A (71.40%). Age and multivertebral fractures had significant difference between patients who suffered from an ASOI and from those who did not (P-value: 0.005, 0.005). There was significant difference between patients who suffered from neurologic injuries and from those who did not in AO Classification, multivertebral fractures and ASOI (P-value: 0.0001, 0.001, 0.032). Age, AO Classification, multivertebral fractures, ASOI and neurologic injuries had significant difference to influence patients' ISS and ALOS.

CONCLUSIONS

Spine injuries in electric bicycles related collisions may make patients suffer from severe consequences including their potential permanent disability, economic cost, or even life safety. Patients with spine injuries in electric bicycles related collisions should be paid close attention to avoid missed diagnosis and misdiagnosis. Helmet, protective clothing or other safety gear should be required to use to provide greater protection for electric bicycle riders.

Influence factors on injury severity of bicycle-motor vehicle crashes: A two-stage comparative analysis of urban and suburban areas in Beijing

OBJECTIVE

More attention should be given to bicycle-motor vehicle (BMV) crashes, as cyclists are at a higher risk of suffering injuries than motor vehicle users in a crash. This study aims to explore the factors influencing the injury severity of bicycle-motor vehicle (BMV) crashes in Beijing (China) and discusses the commonalities and differences between the urban and suburban areas.

METHODS

Information regarding 1,136 crashes between bicycles and motor vehicles were collected using police reported data from 2014 to 2015. A two-stage approach integrating random parameters logit (RP-logit) model and two-step clustering (TSC) algorithm was proposed to investigate the significant influence factors and their combination characteristics. Specifically, the RP-logit model was first used to identify the significant influence factors of urban and suburban areas, and then the TSC algorithm was applied to reveal the combination characteristics of significant influence factors for the fatal crashes.

RESULTS

Five factors were found to be statistically significant and had random effects on the injury severity in urban areas, i.e., type of motor vehicle, motor vehicle license ownership, type of bicycle, signal control mode and lighting condition; and seven factors were found to be statistically significant on the injury severity in suburban areas, i.e., type of motor vehicle, motor vehicle license ownership, physical isolation facility, signal control mode, weather, visibility and lighting condition. Based on TSC, the combination of significant factors showed different characteristics for fatal crashes in urban and suburban areas, in which two types of the scene including five factors should be concerned in urban areas while one type of scene containing four factors in suburban areas.

CONCLUSIONS

The results suggest that different influence factors and individual heterogeneity exist in the RP-logit model for injury severity analysis of BMV crashes in urban and suburban areas. It shows that in urban areas, heavy truck, light truck and bus significantly increase the likelihood of fatal injury than that of suburban areas. These findings can provide valuable reference information for BMV crashes response, such as heavy truck restriction, to facilitate regional safety measures for urban and suburban areas.

Road safety situation of electric bike riders: A cross-sectional study in courier and take-out food delivery population

OBJECTIVE

Almost all courier and take-out food delivery riders in China use electric bikes as the main transport mode. This study aims to understand their riding behaviors and road traffic injury information of this population.

METHODS

A cross-sectional field study focused on this population was conducted, including roadside observation and face-to-face retrospective street intercept interviews.

RESULTS

Six hundred target delivery riders were observed and 480 were interviewed. The rate of overspeeding was 91.3%, and windshield use during winter was 91.2%. Traffic violation behaviors observed included riding in the motor vehicle lane (32.8%), not waiting behind the white line at a red light (23.3%), and using cell phone when riding (21.2%). Helmet use was significantly more common during the day (73.0%) than at night (64.7%; P = .028). About 46.5% of respondents rode an e-bike more than 8 h per day. In addition, 76.5% of interviewees had been involved in a traffic crash at least once. About 13.9% of crashes happened in motor vehicle lanes and 8.2% on sidewalks. Logistic regression analysis indicated that compared with uninjured riders, injured riders showed significantly greater odds ratios of unsafe behaviors for running red lights (odds ratio [OR] = 1.75) and protective factors for wearing a helmet (OR = 0.56).

CONCLUSIONS

The courier and take-out food delivery population is a vulnerable occupational group and road traffic injuries related to e-bike use require more attention.

Comparison of Injury Patterns between Electric Bicycle, Bicycle and Motorcycle Accidents

Background: Electric bicycles (E-bikes) are an increasingly popular means of transport, and have been designed for a higher speed comparable to that of small motorcycles. Accident statistics show that E-bikes are increasingly involved in traffic accidents. To test the hypothesis of whether accidents involving E-bikes bear more resemblance to motorcycle accidents than conventional bicyclists, this study evaluates the injury pattern and severity of E-bike injuries in direct comparison to injuries involving motorcycle and bicycle accidents. Methods: In this retrospective cohort study, the data of 1796 patients who were treated at a Level I Trauma Center between 2009 and 2018 due to traffic accident, involving bicycles, E-bikes or motorcycles, were evaluated and compared with regard to injury patterns and injury severity. Accident victims treated as inpatients at least 16 years of age or older were included in this study. Pillion passengers and outpatients were excluded. Results: The following distribution was found in the individual groups: 67 E-bike, 1141 bicycle and 588 motorcycle accidents. The injury pattern of E-bikers resembled that of bicyclists much more than that of motorcyclists. The patients with E-bike accidents were almost 14 years older and had a higher incidence of moderate traumatic brain injuries than patients with bicycle accidents, in spite of the fact that E-bike riders were nearly twice as likely to wear a helmet as compared to bicycle riders. The rate of pelvic injuries in E-bike accidents was twice as high compared with bicycle accidents, whereas the rate of upper extremity injuries was higher following bicycle accidents. Conclusion: The overall E-bike injury pattern is similar to that of cyclists. The differences in the injury pattern to motorcycle accidents could be due to the higher speeds at the time of the accident, the different protection and vehicle architecture. What is striking, however, is the higher age and the increased craniocerebral trauma of the E-bikers involved in accidents compared to the cyclists. We speculate that older and untrained people who have a slower reaction time and less control over the E-bike could benefit from head protection or practical courses similar to motorcyclists.

Dental and maxillofacial injuries associated with electric-powered bikes and scooters in Israel: A report for 2014-2019

BACKGROUND/AIMS

Electric-Powered Bikes and powered scooters present a new method of transportation and are becoming commonly used worldwide. However, the reports on traumatic dental injuries related to their use are scarce. The aim of this study was to report the frequency and severity of dental and maxillofacial injuries associated with electric-powered bikes and scooters in Israel between the years 2014 and 2019.

METHODS

This was a retrospective cohort study based on data from the Israeli National Trauma Registry (INTR). The INTR provides comprehensive data on hospitalized patients from all six Level I trauma centers (TC) and 15 of the 20 Level II TCs in Israel. All injured patients who were hospitalized due to a traffic collision between 2014 and 2019 were identified. The data for those hospitalized due to an e-bike or motorized scooter accident were extracted as well as for pedestrians who were injured as a result of a crash with these vehicles.

RESULTS

A total of 3,686 hospital admissions were related to electric-powered bikes and scooters. Of those, 378 (10.3%) were oral and maxillofacial injuries. Most of the oral and maxillofacial injuries were attributed to powered bikes (321 out of 378; 84.92%) and the rest to powered scooters. There was a constant increase in general as well as the oral and maxillofacial injuries during the study years. Almost 20% of the cases involved injuries to the teeth. Overall, 291 pedestrians were reported to be injured due to electric-powered bikes and scooters; 29 (9.97%) of them, suffered from oral and maxillofacial injuries. Most of those were children aged 0-15 years (41.38%) and elders older than 60 years (37.39%).

CONCLUSIONS

Trauma related to electric-powered bikes and scooters is an increasing concern. Dental professionals should be actively involved in educational and legislative efforts focusing on the prevention of e-bike and scooter-related injuries, in general, and specifically maxillofacial injuries.

Riding behavior and electric bike traffic crashes: A Chinese case-control study

Objective: The present case-control study sought to explore at-risk riding behaviors associated with e-bike related traffic crashes among e-bike riders in China.Methods: Cases were recruited from residents aged 16 years and over in communities which stated "selected e-bikes as travel tools and experienced traffic crashes in the last year". Two controls for each case were randomly selected from a population of e-bike riders who had not experienced a traffic crash in the past year. The cases and controls were matched by gender, age (within 5 years) and school education level. Data were collected using questionnaires and face-to-face interviews from July 2015 to September 2015 in China. After conducting univariate logistic analysis on study variables, a conditional logistic regression model based on the 1:2 matched case-control study design was developed.Results: Multiple-factor conditional logistic regression analysis of e-bike related traffic crashes showed that running red lights (always vs. never, AOR = 3.094, 95% CI, 1.077-8.891, P < .05), riding after drinking (yes vs. no, AOR = 1.578, 95% CI, 1.102-2.259, P < .05), carrying adults while riding (always vs. never, AOR = 2.140, 95% CI, 1.273-3.595, P < .05), turning without signaling (sometimes vs. never, AOR = 1.446, 95% CI, 1.805-1.928, P < .05), riding in the motor vehicle lane (always vs. never, AOR = 2.413, 95% CI, 1.576-3.695, P < .01), prior crash history (yes vs. no, AOR = 1.670, 95% CI, 1.257-2.220, P < .05), and type of e-bikes (scooter-style e-bikes vs. bicycle-style e-bikes, AOR = 1.471, 95% CI, 1.068-2.026, P < .05) were identified as possible risk factors for e-bike traffic crashes.Conclusion: The findings of this research provide evidence about specific risky behaviors related to road traffic crashes involving e-bikes and indicated that behavioral intervention and education need to be strengthened to reduce dangerous riding behaviors. These results will be helpful for design of e-bike road risk prevention programs.

Injuries associated with electric-powered bikes and scooters: analysis of US consumer product data

BACKGROUND

Powered, two-wheeled transportation devices like electric bicycles (E-bikes) and scooters are increasingly popular, but little is known about their relative injury risk compared to pedal operated bicycles.

METHODS

Descriptive and comparative analysis of injury patterns and trends associated with E-bikes, powered scooters and pedal bicycles from 2000 to 2017 using the US National Electronic Injury Surveillance System.

RESULTS

While persons injured using E-bikes were more likely to suffer internal injuries (17.1%; 95% CI 5.6 to 28.6) and require hospital admission (OR=2.8, 95% CI 1.3 to 6.1), powered scooter injuries were nearly three times more likely to result in a diagnosis of concussion (3% of scooter injuries vs 0.5% of E-bike injuries). E-bike-related injuries were also more than three times more likely to involve a collision with a pedestrian than either pedal bicycles (OR=3.3, 95% CI 0.5 to 23.6) or powered scooters (OR=3.3, 95% CI 0.3 to 32.9), but there was no evidence that powered scooters were more likely than bicycles to be involved in a collision with a pedestrian (OR=1.0, 95% CI 0.3 to 3.1). While population-based rates of pedal bicycle-related injuries have been decreasing, particularly among children, reported E-bike injuries have been increasing dramatically particularly among older persons.

CONCLUSIONS

E-bike and powered scooter use and injury patterns differ from more traditional pedal operated bicycles. Efforts to address injury prevention and control are warranted, and further studies examining demographics and hospital resource utilisation are necessary.

Comparison of the Incidence and Severity of Traumatic Brain Injury Caused by Electrical Bicycle and Bicycle Accidents-A Retrospective Cohort Study From a Swiss Level I Trauma Center

BACKGROUND

Electrical bicycles (E-bikes) allow people of all ages to ride at high speeds but have an inherent risk of traumatic brain injury (TBI). Their sales have increased tremendously in recent years.

METHODS

We performed a retrospective cohort study to compare the incidence and severity of TBI in E-bikers and conventional bicyclists. We included patients at a Swiss level 1 trauma center admitted from 2010 to 2015. The primary outcome was the association between TBI and the bicycle type. The secondary outcome was the association between helmet use and TBI severity.

RESULTS

Of 557 patients injured riding an E-bike (n = 73) or a bicycle (n = 484), 60% sustained a TBI, most of which were mild (Glasgow coma scale [GCS] score, 13-15; E-bike, 78%; bicycle, 88%). TBI was more often moderate (GCS score, 9-12) or severe (GCS score, 3-8) in E-bikers than in bicyclists (P = 0.04). Intracranial hemorrhage, traumatic subarachnoid hemorrhage, and subdural hematoma occurred significantly more often in E-bikers than in bicyclists (P < 0.05). Neurosurgical intervention was necessary for 5 E-bikers (7%) and 25 (5%) bicyclists (P = 0.15). Wearing a helmet correlated with a lower risk of neurosurgical intervention in bicyclists (odds ratio [OR], 0.21; 95% confidence interval [CI], 0.06-0.73; P = 0.01) and a lower risk of calvarial fractures in both bicyclists (OR, 0.16; 95% CI, 0.06-0.42; P < 0.01) and E-bikers (OR, 0.21; 95% CI, 0.05-0.84; P = 0.03).

CONCLUSIONS

E-bikers had a significantly greater risk of moderate to severe TBI compared with bicyclists. Helmet use was associated with decreased odds of severe TBI in bicyclists and a tendency toward a more favorable outcome for E-bikers.

Awareness, riding behaviors, and legislative attitudes toward electric bikes among two types of road users: An investigation in Tianjin, a municipality in China

OBJECTIVE

This study aimed to understand the awareness, riding behaviors, and legislative attitudes of electric bike (e-bike) users and non-e-bike road users toward e-bikes in Tianjin, a municipality in China.

METHODS

This cross-sectional investigation included 2 components. The field survey was conducted in 9 districts (4 urban and 5 rural) from 2015 to 2017. Adult participants were interviewed using a street intercept method. The online survey was conducted using the WeChat social platform. A series of questions related to awareness and practice of e-bike safety-related issues was asked.

RESULTS

A total of 16,859 interviews were completed, including 1,653 (794 e-bike users and 859 other road users) in-person interviews by field survey and 15,206 (4,426 e-bike users and 10,780 other road users) by online survey. Except for ignoring a red light when making a left turn (P = .000) and waiting across the white line at a red light (P = .000), which e-bike users were lower than other road users, there were no significant differences between e-bike users and other road users in regards to the correct awareness rate of riding in the opposite direction, riding in a motor vehicle lane, running red lights, jaywalking, speeding, carrying adult passengers, and riding on the expressway (P > .05). In addition, 74.2% of e-bike users considered helmet use necessary (P = .000) and 54.7% thought that installing a windshield is wrong (P = .000), which was higher than other road users (49.1 and 48.4%, respectively). Other road users had greater awareness than e-bike users regarding whether turning left at a red light is wrong (91.7 vs. 85.1%), waiting across the white line when the light is red is wrong (86.6 vs. 75.9%), lighs should be turned on at night (94.8 vs. 90.3%). Correct practices were far behind awareness in all kinds of violation behaviors (P = .000) among e-bike users in the field survey. The rate of public support for legislation including those who very much support and support legislation was 93.34% in the field survey and 87.68% in the online survey.

CONCLUSIONS

E-bike users had lower correct awareness rate than other road users regarding e-bike violation behaviors but were more concerned about safety protection than other road users. E-bike users' correct practices were far behind their awareness for all kinds of violation behaviors. Public education on the traffic law needs to be strengthened among e-bike users. The public strongly supported special regulations for e-bike use, such as helmet use.

Increasing Active Transportation Through E-Bike Use: Pilot Study Comparing the Health Benefits, Attitudes, and Beliefs Surrounding E-Bikes and Conventional Bikes

BACKGROUND

The emergence of electric pedal-assist bicycles (e-bikes) presents an opportunity to increase active transportation by minimizing personal barriers of engaging in physical activity.

OBJECTIVES

The aim of this study was to assess the beliefs of individuals using e-bikes for active transport and report preliminary biometric measurements while using e-bikes for physical activity compared with conventional bikes.

METHODS

Participants used both conventional bicycles and e-bikes to compare energy expenditure while riding on the study route. Apple smart watches were used to track each participant's heart rate, distance, speed, and time while riding both bicycles. A total of 3 survey instruments were used to estimate beliefs: one administered before riding the bicycles, a second administered after riding a conventional bike, and the final survey completed after riding an e-bike. Survey instruments were constructed using constructs from the theory of planned behavior.

RESULTS

The study sample (N=33) included adults aged between 19 and 28 years. Paired t test analysis revealed that participants believed a conventional bike was more likely than an e-bike to benefit their physical health (P=.002) and save them money (P=.005), while an e-bike was perceived to be more likely than a conventional bike to save them time (P<.001). Paired t test analysis revealed participants significantly agreed more with the statement that they could ride an e-bike most days (P=.006) compared with a conventional bike. After participants traveled approximately 10 miles on each type of bicycle, participants' mean average heart rate while riding the e-bike was 6.21 beats per minute lower than when riding the conventional bike (P=.04), but both were significantly higher than resting heart rate (P<.001).

CONCLUSIONS

This pilot study suggests that e-bikes are an active form of transportation capable of providing much of the cardiovascular health benefits obtained during conventional bike use. E-bikes may help reduce some of the obstacles to conventional bike use, such as increased transportation time, decreased convenience, and physical fatigue.

Road traffic injuries among riders of electric bike/electric moped in southern China

OBJECTIVE

Electric bike/moped-related road traffic injuries have become a burgeoning public health problem in China. The objective of this study was to identify the prevalence and potential risk factors of electric bike/moped-related road traffic injuries among electric bike/moped riders in southern China.

METHODS

A cross-sectional study was used to interview 3,151 electric bike/moped riders in southern China. Electric bike/moped-related road traffic injuries that occurred from July 2014 to June 2015 were investigated. Data were collected by face-to-face interviews and analyzed between July 2015 and June 2017.

RESULTS

The prevalence of electric bike/moped-related road traffic injuries among the investigated riders was 15.99%. Electric bike/moped-related road traffic injuries were significantly associated with category of electric bike (adjusted odds ratio [AOR] = 1.36, 95% confidence interval [CI], 1.01-1.82), self-reported confusion (AOR = 1.77, 95% CI, 1.13-2.78), history of crashes (AOR = 6.14, 95% CI, 4.68-8.07), running red lights (AOR = 3.57, 95% CI, 2.42-5.25), carrying children while riding (AOR = 1.96, 95% CI, 1.37-2.85), carrying adults while riding (AOR = 1.68, 95% CI, 1.23-2.28), riding in the motor lane (AOR = 2.42, 95% CI, 1.05-3.93), and riding in the wrong traffic direction (AOR = 1.63, 95% CI, 1.13-2.35). In over 77.58% of electric bike/moped-related road traffic crashes, riders were determined by the police to be responsible for the crash. Major crash-causing factors included violating traffic signals or signs, careless riding, speeding, and riding in the wrong lane.

CONCLUSION

Traffic safety related to electric bikes/moped is becoming more problematic with growing popularity compared with other 2-wheeled vehicles. Programs need to be developed to prevent electric bike/moped-related road traffic injuries in this emerging country.

Orthopaedic injuries among electric bicycle users

INTRODUCTION

The use of electric bicycles (E-bike) has dramatically increased. E-bikes offer convenient, environmental-friendly, and less expensive alternative to other forms of transport. However, E-bikes provide a new public health challenge in terms of safety and injury prevention. This study is the first to specifically investigate the E-bike related orthopaedic injuries, based on a national trauma registry.

METHODS

Data from a National Trauma Registry were reviewed for patients hospitalized following E-bike related injuries. Between Jan 2014 to Dec 2015, a total of 549 patients were reviewed. Data were analyzed according to demography, type of orthopaedic injury, associated injuries and severity, injury mechanism and treatment in the operating room.

RESULTS

A total of 360 (65%) patients sustained orthopaedic injuries, out of them 230 (63.8%) sustained limb/pelvis/spine fractures. Lower extremity fractures were more prevalent than upper extremity fractures (p<0.001). The tibia was the most fractured bone (19.2%). Patients over the age of 50 years were at the highest risk for spine (20. 5%, p=0.0001), pelvis (15.9%, p=0.0001) and femoral neck (15.9%, p=0.0172) fractures relative to other age groups. Approximately 42% of patients sustained associated injuries, with head/neck/face injuries being the most prevalent (30.3%). followed by chest (11.9%) and abdominal injury (13.3%). A collision between E-bike and a motorized vehicle was the mechanism of injury in 35% of cases. In this mechanism of injury, patients had 1.7 times the risk for associated injuries (p<0.0001) and the risk for major trauma (ISS score ≥16) was more than the double (p=0.03). One third of patients with orthopaedic injuries required treatment in the operating room. Treatment varied depending on the type of fracture.

CONCLUSIONS

This study provides unique information on epidemiological characteristics of orthpaedic injuries caused be E-bikes, pertinent both to medical care providers, as well as to health policy-makers allocating resources and formulating prevention strategies.

Demographic Factors Associated With Leading Causes of Injury Mortality in Ningbo, China: 2004-2013

Injuries have emerged as a crucial public health concern in China. Data were obtained from the death registry system in Ningbo during 2004-2013. Mortality rates resulting from all injuries and the 6 most common types of injuries were analyzed to identify time trends using linear regression models on both the absolute scale and log scale. A decreased male to female ratio and increasing age were observed among the total injury deaths. The annual all-injury mortality rates declined considerably during the observation period. Injury mortality rates for motor vehicle traffic crashes, drowning, and suicide all showed a deceasing trend; however, only mortality from falls showed an increasing trend. There was a sharp increasing trend among females, with a disproportionate number of falls. The injury patterns in Ningbo are mostly comparable to those in high-income countries. Appropriate preventive strategies should be urgently tailored to control this aggressive evolution in older individuals and females.

Healthcare costs and productivity costs of hand and wrist injuries by external cause: A population-based study in working-age adults in the period 2008-2012

BACKGROUND

Hand and wrist injuries are very common at the Emergency Departments (ED), and among the most costly injury types in the working population. The purpose of this study was to explore the causes of non-trivial hand and wrist injuries (i.e., hand fractures, wrist fractures and complex soft-tissue injuries) in working-age adults in order to identify target areas for prevention.

METHODS

Data were extracted from the Dutch Injury Surveillance System, from the National Hospital Discharge Registry and from a patient follow-up survey in working-age adults (aged 20-64 years) in the period 2008-2012. An incidence-based cost model was used to estimate healthcare costs, and an absenteeism model for estimating the productivity costs. Total costs were calculated by external cause, subdivided in their main categories (home, sports, work, traffic and violence) and their most important subclasses.

RESULTS

Total costs of these injuries in The Netherlands were US $410 million per year, of which 75% (US $307 million) productivity costs. Males represented 66% (US $271 million) of the total costs. Within the male group, the group 35-49 years had the highest contribution to total costs (US $112 million), as well as the highest costs per case (US $10,675). Work-related injuries showed the highest costs per case (US $11,797), however, only 25% of the total costs were work-related. The top five causes in terms of total costs were: accidents at home (falls 23%, contact with an object 17%), traffic (cycling 9%) and work (industrial work 4%, and construction work 4%).

CONCLUSION

Hand and wrist injuries are a major cause of healthcare and productivity costs in working-age adults. To reduce the costs to society, prevention initiatives should be targeted at major contributing causes, that are mainly related to activities at home (falls, contact with an object) and accidents at the road (cycling).