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The Fast and the Fragile: Neurosurgical Trauma in the Age of Micromobility. BACKGROUND AND OBJECTIVES: The rapid rise of electric and mechanical bikes and scooters has transformed urban transportation, but their neurosurgical consequences remain underexplored. This study aimed to evaluate micromobility-related injuries over time, examining mechanisms of injury, patient risk factors, injury patterns, and associated clinical outcomes at a Level-1 trauma center over a 5-year period. METHODS: We performed a retrospective review of patients who sustained micromobility-related injuries and presented to the Bellevue Hospital Center between 2018 and 2023. The cohort included riders of electric or mechanical bikes and scooters, as well as pedestrians struck by these devices. Key clinical variables and outcomes were compared across device types, both before and after propensity score matching. Unlike national database studies, this hospital-based analysis provides detailed clinical and neurosurgical outcome data. RESULTS: A total of 914 patients presented with micromobility-related injuries, accounting for 6.9% of all trauma admissions. Annual case volume and electric device involvement increased over time. The most common mechanism was collision with a motor vehicle (49.9%). Most patients (68.7%) required admission; 30.2% required intensive care. The median length of hospital stay was 3 days [IQR 1-5]. Half underwent a surgical intervention or procedure, and the overall mortality was 1.2%. Helmet use was low (31.7%). Pedestrians experienced the most severe outcomes, particularly when struck by electric devices. Injuries clustered during evening hours, suggesting modifiable environmental and behavioral risk factors. CONCLUSION: Micromobility-related trauma imposes a substantial neurosurgical burden, with frequent traumatic brain injury, intensive care unit utilization, and operative intervention. Unlike previous database studies, this hospital-based analysis provides detailed neurosurgical outcome data and identifies prevention targets-including helmet use, intoxication, and urban infrastructure-to reduce morbidity and resource utilization.

We performed a retrospective cohort study at the Bellevue Hospital Center, a Level-1 trauma center in New York City, using the Bellevue Trauma Registry and the trauma surgery consultation database. The protocol was approved by the New York University Institutional Review Board with waiver of consent. All patients presenting between January 2018 and August 2023 with trauma activation or trauma surgery consultation for micromobility-related injuries were included. This encompassed riders of electric and mechanical bicycles and scooters, as well as pedestrians struck by these devices. Patients were excluded if they sustained injuries while operating motorcycles or motor vehicles or if they were managed exclusively by emergency medicine without trauma team involvement. Minor traumatic injuries managed exclusively by emergency medicine without trauma activation are not included in the trauma registry and therefore were not captured in this study. Variables included age, sex, race/ethnicity, helmet use, device type, mechanism, arrival Glasgow Coma Scale (GCS), and blood alcohol concentration. Injuries captured were TBI, craniofacial, spine, and other noncranial trauma; comorbidities and Charlson Comorbidity Index were recorded.14 Initial noncontrast head computed tomography (CT) findings were reviewed. TBI was defined as a traumatic intracranial finding on initial CT; neurological severity used arrival GCS (mild 13-15, moderate 9-12, severe ≤8).

l, spine, and other noncranial trauma; comorbidities and Charlson Comorbidity Index were recorded.14 Initial noncontrast head computed tomography (CT) findings were reviewed. TBI was defined as a traumatic intracranial finding on initial CT; neurological severity used arrival GCS (mild 13-15, moderate 9-12, severe ≤8). Primary outcomes included injury severity (Injury Severity Score [ISS]), neurological injury severity (arrival GCS), hospital admission, intensive care unit (ICU) admission, surgical intervention or procedure (defined as any invasive therapeutic procedure performed in the emergency department, operating room, or intensive care unit, including but not limited to chest tube thoracostomy, central/arterial line placement when performed for definitive trauma management, operative fracture fixation, laparotomy, craniotomy/decompressive hemicraniectomy, external ventricular drain/intracranial pressure monitor placement, and spinal decompression/instrumentation), length of hospital stay (LOS), discharge disposition, and in-hospital mortality. “Neurosurgical intervention” was predefined as the subset of procedures involving the brain, craniofacial skeleton, or spine.

y, external ventricular drain/intracranial pressure monitor placement, and spinal decompression/instrumentation), length of hospital stay (LOS), discharge disposition, and in-hospital mortality. “Neurosurgical intervention” was predefined as the subset of procedures involving the brain, craniofacial skeleton, or spine. Analyses were conducted using IBM SPSS Statistics v29.0.2.0 (IBM Corp.). Descriptive statistics were reported as proportions for categorical variables and medians with IQRs for continuous variables. Pearson's χ2 and Fisher's exact tests were used for categorical comparisons. The Mann–Whitney U-test was applied for continuous variables that were not normally distributed. Statistical significance was set at a two-sided P-value of <.05. To adjust for confounding variables when comparing outcomes between electric and mechanical micromobility users, propensity score matching was performed. The treatment variable was electric device use, with covariates including age, sex, race, helmet use, and comorbidities. Matching was performed using nearest-neighbor methodology (1:1 ratio; caliper = 0.2) with the MatchIt package in R version 4.4.1 (R Foundation for Statistical Computing). Covariate balance was assessed using standardized mean differences and visualized using love plots.

ng age, sex, race, helmet use, and comorbidities. Matching was performed using nearest-neighbor methodology (1:1 ratio; caliper = 0.2) with the MatchIt package in R version 4.4.1 (R Foundation for Statistical Computing). Covariate balance was assessed using standardized mean differences and visualized using love plots. Additional subgroup analyses (comparing pedestrians vs riders and electric vs mechanical devices) were conducted to identify factors associated with adverse outcomes, including comparisons by helmet use, alcohol intoxication (defined as positive blood alcohol concentration), timing of presentation (overnight defined as 7 pm-7 am), weekday vs weekend (weekend defined as Friday 8 pm-Monday 6 am), and discharge disposition. Prolonged hospitalization was defined as LOS >3 days, based on the cohort's median. This study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines.

We performed a retrospective cohort study at the Bellevue Hospital Center, a Level-1 trauma center in New York City, using the Bellevue Trauma Registry and the trauma surgery consultation database. The protocol was approved by the New York University Institutional Review Board with waiver of consent.

All patients presenting between January 2018 and August 2023 with trauma activation or trauma surgery consultation for micromobility-related injuries were included. This encompassed riders of electric and mechanical bicycles and scooters, as well as pedestrians struck by these devices. Patients were excluded if they sustained injuries while operating motorcycles or motor vehicles or if they were managed exclusively by emergency medicine without trauma team involvement. Minor traumatic injuries managed exclusively by emergency medicine without trauma activation are not included in the trauma registry and therefore were not captured in this study.

Variables included age, sex, race/ethnicity, helmet use, device type, mechanism, arrival Glasgow Coma Scale (GCS), and blood alcohol concentration. Injuries captured were TBI, craniofacial, spine, and other noncranial trauma; comorbidities and Charlson Comorbidity Index were recorded.14 Initial noncontrast head computed tomography (CT) findings were reviewed. TBI was defined as a traumatic intracranial finding on initial CT; neurological severity used arrival GCS (mild 13-15, moderate 9-12, severe ≤8).

Primary outcomes included injury severity (Injury Severity Score [ISS]), neurological injury severity (arrival GCS), hospital admission, intensive care unit (ICU) admission, surgical intervention or procedure (defined as any invasive therapeutic procedure performed in the emergency department, operating room, or intensive care unit, including but not limited to chest tube thoracostomy, central/arterial line placement when performed for definitive trauma management, operative fracture fixation, laparotomy, craniotomy/decompressive hemicraniectomy, external ventricular drain/intracranial pressure monitor placement, and spinal decompression/instrumentation), length of hospital stay (LOS), discharge disposition, and in-hospital mortality. “Neurosurgical intervention” was predefined as the subset of procedures involving the brain, craniofacial skeleton, or spine.

Analyses were conducted using IBM SPSS Statistics v29.0.2.0 (IBM Corp.). Descriptive statistics were reported as proportions for categorical variables and medians with IQRs for continuous variables. Pearson's χ2 and Fisher's exact tests were used for categorical comparisons. The Mann–Whitney U-test was applied for continuous variables that were not normally distributed. Statistical significance was set at a two-sided P-value of <.05.

To adjust for confounding variables when comparing outcomes between electric and mechanical micromobility users, propensity score matching was performed. The treatment variable was electric device use, with covariates including age, sex, race, helmet use, and comorbidities. Matching was performed using nearest-neighbor methodology (1:1 ratio; caliper = 0.2) with the MatchIt package in R version 4.4.1 (R Foundation for Statistical Computing). Covariate balance was assessed using standardized mean differences and visualized using love plots.

Additional subgroup analyses (comparing pedestrians vs riders and electric vs mechanical devices) were conducted to identify factors associated with adverse outcomes, including comparisons by helmet use, alcohol intoxication (defined as positive blood alcohol concentration), timing of presentation (overnight defined as 7 pm-7 am), weekday vs weekend (weekend defined as Friday 8 pm-Monday 6 am), and discharge disposition. Prolonged hospitalization was defined as LOS >3 days, based on the cohort's median. This study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines.

A total of 914 patients sustained injuries related to micromobility device accidents during the study period (Table 1). The cohort was predominantly male (82.3%), with a median age of 40.1 years [IQR 28-36]. Racial and ethnic distribution included 30.6% of White, 13.2% of Black, 9.3% of Asian, and 36.9% of Hispanic patients. Over the study period, there were 9036 total trauma service admissions and 1846 trauma consults from the emergency department. Admitted patients in this study made up 6.9% of all admitted patients with trauma during the study period. Baseline Demographic and Clinical Characteristics of Patients Presenting With Micromobility-Related Injuries (N = 914) GCS, Glasgow Coma Scale; TBI, traumatic brain injury. Patients were riding the specified micromobility device. Of patients with documented helmet status. Of patients with documented blood alcohol tests. Of patients who were admitted. Data reflect demographic and clinical variables for all patients presenting to a Level-1 trauma center with micromobility-related injuries from 2018 to 2023. Values are presented as number (%) unless otherwise noted.

Of patients with documented helmet status. Of patients with documented blood alcohol tests. Of patients who were admitted. Data reflect demographic and clinical variables for all patients presenting to a Level-1 trauma center with micromobility-related injuries from 2018 to 2023. Values are presented as number (%) unless otherwise noted. In the overall cohort, hospital admission occurred in 67.9% (n = 621) of patients; among those admitted, 30.2% (n = 187) required ICU care. Overall, 50.4% (n = 461) underwent at least 1 surgical intervention or procedure. The median LOS was 3 days [IQR 1-5]. Most patients were discharged home (89.5%, n = 556), and the in-hospital mortality was 1.2% (n = 11). Traumatic injuries were identified in 85.9% (n = 785) of patients, including TBI in 32.7% (n = 299) and craniofacial trauma in 26.4% (n = 241). Among patients with TBI, 65.4% had concussion (CT-negative), whereas radiographic findings included traumatic subarachnoid or intraventricular hemorrhage in 42.9%, basal cistern effacement in 14.2%, epidural mass effect in 13.8%, and midline shift in 5.9%. The majority presented with mild neurological injury, with GCS >12 in 94.5% (n = 864). Spinal injuries occurred in 6.8% (n = 62), and other noncranial trauma occurred in 64% (n = 585). The median ISS was 9 [IQR 5-14] overall, 10 [9-14] among pedestrians, and 9 [5-14] among riders (Table 1). Neurosurgical interventions were performed in 34 patients (3.7%), including craniotomy or decompressive hemicraniectomy in 10 (1.1%), external ventricular drain or intracranial pressure monitor placement in 7 (0.8%), and spinal decompression or instrumentation in 6 (0.7%).

estrians, and 9 [5-14] among riders (Table 1). Neurosurgical interventions were performed in 34 patients (3.7%), including craniotomy or decompressive hemicraniectomy in 10 (1.1%), external ventricular drain or intracranial pressure monitor placement in 7 (0.8%), and spinal decompression or instrumentation in 6 (0.7%). Electric micromobility devices were involved in 33.2% (n = 303) of cases. The most frequent mechanism of injury was collision with a motor vehicle (n = 456, 49.9%), followed by falls from the device (n = 309, 33.8%), pedestrian struck by a micromobility device (n = 69, 7.5%), and collisions between micromobility users (n = 64, 7.0%) (Figure 1). Most patients presented directly to the trauma center, whereas 10.9% was transferred from outside hospitals. Among patients with documented helmet status (n = 728), 31.7% was wearing a helmet at the time of injury. Mechanisms of injury among micromobility users. This pie chart visualizes the distribution of injury mechanisms among micromobility device users, including collisions and falls. The largest proportion of injuries resulted from bicycle vs car collisions, followed by falls from bicycles and electric scooters. Other mechanisms included collisions between bikes, electric bikes, scooters, pedestrians, and motor vehicles. The size of each section represents the relative frequency of each mechanism.

The largest proportion of injuries resulted from bicycle vs car collisions, followed by falls from bicycles and electric scooters. Other mechanisms included collisions between bikes, electric bikes, scooters, pedestrians, and motor vehicles. The size of each section represents the relative frequency of each mechanism. Among patients with known helmet status, 67.8% (n = 486) was not wearing a helmet at the time of injury. Helmeted riders were older (42.4 vs 37.7 years, P < .001) and more likely to have used electric devices (39.4% vs 29.4%, P = .008). The absence of helmet was associated with higher rates of TBI (36.8% vs 22.9%, P < .001) and craniofacial trauma (31.1% vs 13.9%, P < .001). Conversely, helmeted riders sustained more noncranial injuries (69.7% vs 60.7%, P = .019). Of the 814 patients tested for alcohol, 20.4% (n = 166) was intoxicated. Alcohol use was associated with significantly lower helmet use (15.1% vs 27.0%, P < .001), lower GCS on arrival (13.9 vs 14.5, P < .001), and higher rates of TBI (47.0% vs 31.0%, P < .001), craniofacial trauma (41.5% vs 23.1%, P < .001), and other injuries (64.8% vs 54.8%, P = .017). Intoxicated patients were more likely to present during night-time hours (63.3% vs 35.5%, P < .001). Over half of injuries occurred overnight (55.1%, n = 504), with peak presentations at 6 pm (7.2%) and 8 pm (7.1%), and the lowest at 6 am (1.1%). Electric micromobility users were more frequently injured during commuting hours (7 am, 8 am, 3 pm; Figure 2).

Of the 814 patients tested for alcohol, 20.4% (n = 166) was intoxicated. Alcohol use was associated with significantly lower helmet use (15.1% vs 27.0%, P < .001), lower GCS on arrival (13.9 vs 14.5, P < .001), and higher rates of TBI (47.0% vs 31.0%, P < .001), craniofacial trauma (41.5% vs 23.1%, P < .001), and other injuries (64.8% vs 54.8%, P = .017). Intoxicated patients were more likely to present during night-time hours (63.3% vs 35.5%, P < .001). Over half of injuries occurred overnight (55.1%, n = 504), with peak presentations at 6 pm (7.2%) and 8 pm (7.1%), and the lowest at 6 am (1.1%). Electric micromobility users were more frequently injured during commuting hours (7 am, 8 am, 3 pm; Figure 2). Time of arrival for micromobility-related injuries. This bar graph displays the distribution of micromobility-related injury presentations by time of day, stratified by involvement of electric (orange) vs mechanical (yellow) devices. Injury presentations peaked in the evening, particularly between 6 and 9 pm, with a smaller peak around 1 am. Mechanical devices accounted for most of the injuries at all time points. Patients presenting overnight were younger (38.0 vs 43.2 years, P < .001) with lower rates of helmet use (22.5% vs 38.5%, P = .005). Night-time injuries were more common on weekends (43.4% vs 23.9%, P < .001) and among non-White individuals (69.0% vs 60.5%, P = .027).

Time of arrival for micromobility-related injuries. This bar graph displays the distribution of micromobility-related injury presentations by time of day, stratified by involvement of electric (orange) vs mechanical (yellow) devices. Injury presentations peaked in the evening, particularly between 6 and 9 pm, with a smaller peak around 1 am. Mechanical devices accounted for most of the injuries at all time points. Patients presenting overnight were younger (38.0 vs 43.2 years, P < .001) with lower rates of helmet use (22.5% vs 38.5%, P = .005). Night-time injuries were more common on weekends (43.4% vs 23.9%, P < .001) and among non-White individuals (69.0% vs 60.5%, P = .027). Pedestrians struck by micromobility devices were more likely to present on weekdays (9.2% vs 4.4%, P = .008). Weekend injuries were associated with lower GCS (14.4 vs 14.5, P = .049), ICU admission (27.8% vs 13.0%, P < .001), and alcohol intoxication (27.8% vs 13.0%, P < .001). Mechanical micromobility users had higher admission rates than electric users (38.6% vs 30.6%, P = .02); pedestrians were admitted more often than riders (9.2% vs 4.1%, P = .007). Helmeted riders had lower admission rates than nonhelmeted riders (24.8% vs 26.3%, P = .002). Overnight presentations had higher admission rates than daytime (49.9% vs 23.3%, P = .035), and non-White patients were admitted more frequently than White patients (63.0% vs 58.7%, P < .001). Patients requiring ICU were more likely to be pedestrians (15.5% vs 6.5%, P < .001) and, among riders, less likely to have worn a helmet (19.3% vs 27.2%, P = .009).

Mechanical micromobility users had higher admission rates than electric users (38.6% vs 30.6%, P = .02); pedestrians were admitted more often than riders (9.2% vs 4.1%, P = .007). Helmeted riders had lower admission rates than nonhelmeted riders (24.8% vs 26.3%, P = .002). Overnight presentations had higher admission rates than daytime (49.9% vs 23.3%, P = .035), and non-White patients were admitted more frequently than White patients (63.0% vs 58.7%, P < .001). Patients requiring ICU were more likely to be pedestrians (15.5% vs 6.5%, P < .001) and, among riders, less likely to have worn a helmet (19.3% vs 27.2%, P = .009). Among those not discharged home, 5.7% (n = 36) required home services, and 4.8% (n = 30) was discharged to inpatient rehabilitation. These patients presented with lower GCS (10.7 vs 14.7, P < .001) and had higher rates of TBI (61.5% vs 30.5%, P < .001), craniofacial trauma (43.1% vs 24.9%, P = .005), and spinal injury (26.2% vs 5.3%, P < .001). Surgical intervention or procedure was associated with lower likelihood of home discharge (46.8% vs 69.2%, P < .001), longer LOS (12.4 vs 3.6 days, P < .001), and non-White race (64.6% vs 61.4%, P = .048). Patients with prolonged hospitalization (>3 days) had lower GCS on arrival (13.9 vs 14.5, P < .001) and sustained more TBIs (50.0% vs 35.1%, P < .001), craniofacial trauma (40.7% vs 28.0%, P = .003), and spinal injuries (14.9% vs 5.1%, P < .001). Longer LOS was also associated with higher comorbidity burden (Charlson Comorbidity Index: 16.1% vs 8.8%, P = .003) and mental health diagnoses (16.1% vs 8.8%, P = .007).

1) and sustained more TBIs (50.0% vs 35.1%, P < .001), craniofacial trauma (40.7% vs 28.0%, P = .003), and spinal injuries (14.9% vs 5.1%, P < .001). Longer LOS was also associated with higher comorbidity burden (Charlson Comorbidity Index: 16.1% vs 8.8%, P = .003) and mental health diagnoses (16.1% vs 8.8%, P = .007). Micromobility-related trauma increased across the study period, with electric devices comprising a growing share—rising from 7.9% of cases in 2018 to 55.1% by 2023 (Figure 3). Pedestrian injuries involving electric devices also rose; while no pedestrians were struck by electric devices until Q3 2020, they accounted for 37.9% of pedestrian incidents from Q4 2020 to Q3 2023 (Figure 4). Trends in bike and scooter injuries over time. This stacked bar and line graph illustrate the number of micromobility-related injuries over time, categorized by mechanical (light blue) and electric (dark blue) bikes and scooters. The yellow line represents the proportion of injuries involving electric devices relative to mechanical devices. The total number of injuries increased over the study period, with a notable rise in electric micromobility involvement, reaching over 60% by the study's final quarter.

ectric (dark blue) bikes and scooters. The yellow line represents the proportion of injuries involving electric devices relative to mechanical devices. The total number of injuries increased over the study period, with a notable rise in electric micromobility involvement, reaching over 60% by the study's final quarter. Trends in pedestrians struck by micromobility devices over time. This bar graph illustrates the quarterly trends in pedestrian injuries caused by micromobility devices, categorized by mechanical (light bars) and electric (dark bars) devices. The yellow line represents the proportion of injuries involving electric devices relative to mechanical devices. The number of pedestrian-related incidents increased over time, with a notable rise in electric micromobility involvement in later years.

egorized by mechanical (light bars) and electric (dark bars) devices. The yellow line represents the proportion of injuries involving electric devices relative to mechanical devices. The number of pedestrian-related incidents increased over time, with a notable rise in electric micromobility involvement in later years. Among the 69 pedestrians struck, individuals were older (53 vs 39 years, P < .001) and more likely to be female (85.3% vs 36.2%, P < .001), with more comorbidities (52.2% vs 36.7%, P < .001). They sustained more severe injuries, including higher rates of TBI (56.5% vs 30.8%, P < .001) and craniofacial trauma (46.6% vs 24.5%, P < .001). Hospital admission (82.6% vs 66.8%, P = .007) and ICU care (42.0% vs 18.7%, P < .001) were more common. Neurological injury severity by GCS did not differ between pedestrians struck by electric vs mechanical devices (median 15 [IQR 15-15] vs 15 [15-15], P = .373), whereas overall injury severity by ISS was higher with electric devices (median 13.5 [IQR 10-17] vs 9 [9-11], P = .014). Most were struck by mechanical bikes (69.6%), followed by electric scooters (21.7%) and electric bikes (8.7%). Among riders, 81.3% used mechanical bikes and 18.7% used electric bikes. Electric bike riders were more likely to wear helmets (41.2% vs 28.5%, P = .032). There were no significant differences in TBI rates (28.7% vs 34.7%, P = .073), length of stay (3 [2-5] vs 3 [1-5] days, P = .253), or need for surgical intervention or procedure (13.5% vs 29.0%, P = .101) between electric and mechanical riders.

ic bike riders were more likely to wear helmets (41.2% vs 28.5%, P = .032). There were no significant differences in TBI rates (28.7% vs 34.7%, P = .073), length of stay (3 [2-5] vs 3 [1-5] days, P = .253), or need for surgical intervention or procedure (13.5% vs 29.0%, P = .101) between electric and mechanical riders. Electric scooter riders were older than mechanical scooter riders (38 vs 17 years, P < .001) and more frequently intoxicated (18.4% vs 8.3%, P = .002). Mechanical scooter riders had higher admission rates (91.7% vs 59.9%, P = .031), whereas admitted electric scooter riders had longer hospitalizations (3.9 vs 1.6 days, P = .016). After propensity score matching, electric and mechanical users were balanced by age, sex, race, helmet use, and comorbidities (Tables 2 and 3, Supplemental Digital Content 1, Figure 1, http://links.lww.com/NEU/F333). No significant differences were observed between electric and mechanical users in overall injury rate (65.8% vs 67.7%, P = .642), surgical intervention or procedure (51.5% vs 53.1%, P = .725), ICU admission (17.3% vs 19.6%, P = .498), LOS (3.0 [3.0] vs 3.0 [4.0], P = .932), or nonhome discharge (4.2% vs 7.3%, P = .187). Covariate Balance Before and After Propensity Score Matching Between Electric and Mechanical Micromobility Device Users SMD, standardized mean difference.

After propensity score matching, electric and mechanical users were balanced by age, sex, race, helmet use, and comorbidities (Tables 2 and 3, Supplemental Digital Content 1, Figure 1, http://links.lww.com/NEU/F333). No significant differences were observed between electric and mechanical users in overall injury rate (65.8% vs 67.7%, P = .642), surgical intervention or procedure (51.5% vs 53.1%, P = .725), ICU admission (17.3% vs 19.6%, P = .498), LOS (3.0 [3.0] vs 3.0 [4.0], P = .932), or nonhome discharge (4.2% vs 7.3%, P = .187). Covariate Balance Before and After Propensity Score Matching Between Electric and Mechanical Micromobility Device Users SMD, standardized mean difference. This table presents the SMDs for key demographic and clinical covariates before and after propensity score matching, comparing patients who used electric vs mechanical micromobility devices. Variables include age, sex, race, helmet use, and comorbidities. An SMD of <0.1 is generally considered indicative of good covariate balance. Comparison of Clinical Characteristics and Outcomes Between Mechanical and Electric Micromobility Device Users GCS, Glasgow Coma Scale; ICU, intensive care unit; LOS, length of hospital stay; TBI, traumatic brain injury. Values represent absolute counts and percentages unless otherwise specified. Continuous variables are shown as mean [SD] or median [IQR], as appropriate. P-values reflect comparisons between groups using χ2 or Mann–Whitney U tests, with Z-values and asymptotic significance reported for nonparametric tests.

Among patients with known helmet status, 67.8% (n = 486) was not wearing a helmet at the time of injury. Helmeted riders were older (42.4 vs 37.7 years, P < .001) and more likely to have used electric devices (39.4% vs 29.4%, P = .008). The absence of helmet was associated with higher rates of TBI (36.8% vs 22.9%, P < .001) and craniofacial trauma (31.1% vs 13.9%, P < .001). Conversely, helmeted riders sustained more noncranial injuries (69.7% vs 60.7%, P = .019). Of the 814 patients tested for alcohol, 20.4% (n = 166) was intoxicated. Alcohol use was associated with significantly lower helmet use (15.1% vs 27.0%, P < .001), lower GCS on arrival (13.9 vs 14.5, P < .001), and higher rates of TBI (47.0% vs 31.0%, P < .001), craniofacial trauma (41.5% vs 23.1%, P < .001), and other injuries (64.8% vs 54.8%, P = .017). Intoxicated patients were more likely to present during night-time hours (63.3% vs 35.5%, P < .001).

Over half of injuries occurred overnight (55.1%, n = 504), with peak presentations at 6 pm (7.2%) and 8 pm (7.1%), and the lowest at 6 am (1.1%). Electric micromobility users were more frequently injured during commuting hours (7 am, 8 am, 3 pm; Figure 2). Time of arrival for micromobility-related injuries. This bar graph displays the distribution of micromobility-related injury presentations by time of day, stratified by involvement of electric (orange) vs mechanical (yellow) devices. Injury presentations peaked in the evening, particularly between 6 and 9 pm, with a smaller peak around 1 am. Mechanical devices accounted for most of the injuries at all time points. Patients presenting overnight were younger (38.0 vs 43.2 years, P < .001) with lower rates of helmet use (22.5% vs 38.5%, P = .005). Night-time injuries were more common on weekends (43.4% vs 23.9%, P < .001) and among non-White individuals (69.0% vs 60.5%, P = .027). Pedestrians struck by micromobility devices were more likely to present on weekdays (9.2% vs 4.4%, P = .008). Weekend injuries were associated with lower GCS (14.4 vs 14.5, P = .049), ICU admission (27.8% vs 13.0%, P < .001), and alcohol intoxication (27.8% vs 13.0%, P < .001).

Micromobility-related trauma increased across the study period, with electric devices comprising a growing share—rising from 7.9% of cases in 2018 to 55.1% by 2023 (Figure 3). Pedestrian injuries involving electric devices also rose; while no pedestrians were struck by electric devices until Q3 2020, they accounted for 37.9% of pedestrian incidents from Q4 2020 to Q3 2023 (Figure 4). Trends in bike and scooter injuries over time. This stacked bar and line graph illustrate the number of micromobility-related injuries over time, categorized by mechanical (light blue) and electric (dark blue) bikes and scooters. The yellow line represents the proportion of injuries involving electric devices relative to mechanical devices. The total number of injuries increased over the study period, with a notable rise in electric micromobility involvement, reaching over 60% by the study's final quarter. Trends in pedestrians struck by micromobility devices over time. This bar graph illustrates the quarterly trends in pedestrian injuries caused by micromobility devices, categorized by mechanical (light bars) and electric (dark bars) devices. The yellow line represents the proportion of injuries involving electric devices relative to mechanical devices. The number of pedestrian-related incidents increased over time, with a notable rise in electric micromobility involvement in later years.

Among the 69 pedestrians struck, individuals were older (53 vs 39 years, P < .001) and more likely to be female (85.3% vs 36.2%, P < .001), with more comorbidities (52.2% vs 36.7%, P < .001). They sustained more severe injuries, including higher rates of TBI (56.5% vs 30.8%, P < .001) and craniofacial trauma (46.6% vs 24.5%, P < .001). Hospital admission (82.6% vs 66.8%, P = .007) and ICU care (42.0% vs 18.7%, P < .001) were more common. Neurological injury severity by GCS did not differ between pedestrians struck by electric vs mechanical devices (median 15 [IQR 15-15] vs 15 [15-15], P = .373), whereas overall injury severity by ISS was higher with electric devices (median 13.5 [IQR 10-17] vs 9 [9-11], P = .014). Most were struck by mechanical bikes (69.6%), followed by electric scooters (21.7%) and electric bikes (8.7%).

Among riders, 81.3% used mechanical bikes and 18.7% used electric bikes. Electric bike riders were more likely to wear helmets (41.2% vs 28.5%, P = .032). There were no significant differences in TBI rates (28.7% vs 34.7%, P = .073), length of stay (3 [2-5] vs 3 [1-5] days, P = .253), or need for surgical intervention or procedure (13.5% vs 29.0%, P = .101) between electric and mechanical riders. Electric scooter riders were older than mechanical scooter riders (38 vs 17 years, P < .001) and more frequently intoxicated (18.4% vs 8.3%, P = .002). Mechanical scooter riders had higher admission rates (91.7% vs 59.9%, P = .031), whereas admitted electric scooter riders had longer hospitalizations (3.9 vs 1.6 days, P = .016). After propensity score matching, electric and mechanical users were balanced by age, sex, race, helmet use, and comorbidities (Tables 2 and 3, Supplemental Digital Content 1, Figure 1, http://links.lww.com/NEU/F333). No significant differences were observed between electric and mechanical users in overall injury rate (65.8% vs 67.7%, P = .642), surgical intervention or procedure (51.5% vs 53.1%, P = .725), ICU admission (17.3% vs 19.6%, P = .498), LOS (3.0 [3.0] vs 3.0 [4.0], P = .932), or nonhome discharge (4.2% vs 7.3%, P = .187). Covariate Balance Before and After Propensity Score Matching Between Electric and Mechanical Micromobility Device Users SMD, standardized mean difference.

Micromobility-related trauma is an increasingly significant contributor to urban neurosurgical and trauma system burden. In our cohort, micromobility injuries accounted for 6.9% of trauma admissions, with nearly one third of patients sustaining TBI and over 50% requiring surgical or procedural intervention. As electric micromobility adoptions expand, neurosurgical consultation for head, spine, and craniofacial injuries has become a routine component of acute trauma care in dense metropolitan environments. The rise in micromobility-related trauma parallels global trends in electric device adoption.4,11,13,15 Our data show a dramatic shift in injury epidemiology: electric micromobility involvement increased from under 10% at the start of the study period to over 50% by 2023. Comparable increases have been reported in both car-centric and densely populated regions. Previous studies from both dense metropolitan and car-centric regions report increasing rates of head injury, ICU utilization, and operative intervention associated with electric scooters and bicycles.11,16-18 International data similarly demonstrate high rates of head injury and low helmet compliance, suggesting that the Manhattan experience reflects a broader urban injury pattern rather than a region-specific phenomenon.19-21

CU utilization, and operative intervention associated with electric scooters and bicycles.11,16-18 International data similarly demonstrate high rates of head injury and low helmet compliance, suggesting that the Manhattan experience reflects a broader urban injury pattern rather than a region-specific phenomenon.19-21 Pedestrians represented the highest-risk subgroup in our cohort. Compared with riders, they were older and more often female, and had greater comorbidity burden, with higher rates of TBI, ICU admission, and nonhome discharge. Injury severity was greater when pedestrians were struck by electric devices, despite similar neurological severity by GCS. These events frequently occurred during evening hours, highlighting a distinct high-risk subgroup within urban micromobility trauma. Speed is a well-established determinant of injury severity in trauma. While electric devices can reach higher speeds, pedestrians struck by these devices demonstrated the highest rates of TBI, craniofacial trauma, ICU admission, and nonhome discharge, reflecting their vulnerability in the absence of protective equipment or mass.22 These findings underscore the need for targeted infrastructure and enforcement strategies to mitigate pedestrian risk.

truck by these devices demonstrated the highest rates of TBI, craniofacial trauma, ICU admission, and nonhome discharge, reflecting their vulnerability in the absence of protective equipment or mass.22 These findings underscore the need for targeted infrastructure and enforcement strategies to mitigate pedestrian risk. Helmet use remains alarmingly low, with fewer than one third of riders wearing a helmet at the time of injury. Consistent with the previous literature, helmet use was associated with significantly lower rates of TBI and craniofacial trauma and lower admission rates and less severe neurological injury.23-25 These findings reinforce helmet use as a critical, modifiable factor in reducing neurosurgical morbidity. Electric micromobility users wore helmets more frequently, possibly because of higher speeds or New York City regulations mandating helmet use for commercial cyclists (eg, food delivery workers) since 2007.26 A few jurisdictions (eg, Vancouver's bike share) have experimented with providing helmets alongside shared micromobility devices, which may suggest a promising model for improving helmet compliance.27 Alcohol intoxication was common and associated with higher rates of TBI, lower GCS, increased ICU utilization, and lower helmet use. These injury patterns parallel alcohol-related vehicular trauma and highlight intoxication as an important, modifiable risk factor amenable to targeted prevention strategies.23

Helmet use remains alarmingly low, with fewer than one third of riders wearing a helmet at the time of injury. Consistent with the previous literature, helmet use was associated with significantly lower rates of TBI and craniofacial trauma and lower admission rates and less severe neurological injury.23-25 These findings reinforce helmet use as a critical, modifiable factor in reducing neurosurgical morbidity. Electric micromobility users wore helmets more frequently, possibly because of higher speeds or New York City regulations mandating helmet use for commercial cyclists (eg, food delivery workers) since 2007.26 A few jurisdictions (eg, Vancouver's bike share) have experimented with providing helmets alongside shared micromobility devices, which may suggest a promising model for improving helmet compliance.27 Alcohol intoxication was common and associated with higher rates of TBI, lower GCS, increased ICU utilization, and lower helmet use. These injury patterns parallel alcohol-related vehicular trauma and highlight intoxication as an important, modifiable risk factor amenable to targeted prevention strategies.23 Injuries clustered during evening hours, with peaks at 6 and 8 pm, suggesting risk factors beyond routine commuting (if this trend were associated with purely commuter activity, a bimodal distribution with an additional peak during typical morning commuting hours would be expected). Reduced visibility, traffic density, fatigue, and increased commercial cycling activity may contribute to this pattern.7

actors beyond routine commuting (if this trend were associated with purely commuter activity, a bimodal distribution with an additional peak during typical morning commuting hours would be expected). Reduced visibility, traffic density, fatigue, and increased commercial cycling activity may contribute to this pattern.7 Sociodemographic disparities were evident. Non-White riders and pedestrians had higher rates of TBI, ICU admission, and nonhome discharge. These disparities may reflect differences in infrastructure, access to safety equipment, and exposure to high-risk environments.28 From a neurosurgical standpoint, the burden of severe head and spinal injuries in these populations highlights the need for equitable investment in road safety and public health intervention. Although our data did not demonstrate worse outcomes in electric vs mechanical device users, the dramatic increase in electric micromobility use may contribute to the rising absolute number of severe injuries. Nearly half of injuries in our cohort resulted from motor vehicle collisions, underscoring critical infrastructure deficiencies. Only 3% of Manhattan's bike lanes are protected, and in 2023, 94% of cycling fatalities occurred on roads without protected bike lanes.29 Infrastructure redesign—including separated lanes and intersection protections—may offer the most immediate opportunity to reduce neurosurgical trauma burden.

structure deficiencies. Only 3% of Manhattan's bike lanes are protected, and in 2023, 94% of cycling fatalities occurred on roads without protected bike lanes.29 Infrastructure redesign—including separated lanes and intersection protections—may offer the most immediate opportunity to reduce neurosurgical trauma burden. This study has limitations. As a single-center, retrospective analysis, findings may not be generalizable nationwide although they align with national and international trends.11-13,15,30 Reliance on registry and electronic medical record data might have resulted in missing information, particularly regarding crash circumstances, rider behavior, and environmental factors. Fatalities at the scene were not captured in this data set. According to the New York City Department of Transportation, 2023 was the deadliest year for bike accidents and 23 of the 30 bike riders who died were on electric bikes.30 Alcohol intoxication may confound neurological assessment by lowering GCS independent of intracranial injury. Detailed long-term neurologic outcomes, exposure-adjusted risk, and cost data were unavailable.

on, 2023 was the deadliest year for bike accidents and 23 of the 30 bike riders who died were on electric bikes.30 Alcohol intoxication may confound neurological assessment by lowering GCS independent of intracranial injury. Detailed long-term neurologic outcomes, exposure-adjusted risk, and cost data were unavailable. This hospital-based registry provides a comprehensive characterization of micromobility-related neurosurgical trauma, highlighting the growing burden within urban trauma systems. The rise in electric bikes and scooters has introduced new dynamics into urban trauma epidemiology. Neurosurgeons will increasingly encounter these injuries—from mild TBI to operative cranial and spinal trauma—within the emergency setting. Mitigation will require coordinated strategies spanning infrastructure design, helmet policy, intoxication enforcement, and equitable resource allocation. As micromobility adoption continues to expand globally, especially in densely populated urban centers, understanding the neurosurgical implications will be essential. Future prospective, multicenter studies should assess mechanisms of injury in greater detail, evaluate helmet compliance and enforcement efforts, and determine the impact of targeted interventions on clinical outcomes including operative and neurocritical care needs.

Micromobility-related trauma represents a growing neurosurgical burden, accounting for nearly 1 in 15 trauma admissions at our Level-1 center. Injury severity varied widely, with over two thirds of patients requiring hospital admission, nearly one third requiring ICU care, and half undergoing a surgical intervention or procedure. The median hospital stay was short, yet nonhome discharge and mortality—though infrequent—were concentrated among patients with TBI and multisystem injury. While riders of electric and mechanical devices had comparable injury severity and outcomes, pedestrians—particularly those struck by electric devices—sustained the most severe injuries, with higher rates of TBI, ICU admission, and nonhome discharge. These findings underscore the expanding neurosurgical role in managing micromobility-related head and spine trauma and highlight actionable targets for prevention, including helmet compliance, intoxication reduction, and infrastructure redesign.