# paper.pdf ## Metadata - PDFFormatVersion=1.4 - Language=en-US - IsLinearized=false - IsAcroFormPresent=false - IsXFAPresent=false - IsCollectionPresent=false - IsSignaturesPresent=false - Creator=Arbortext Advanced Print Publisher 9.1.510/W Unicode - ModDate=D:20260324121059+05'30' - Producer=Acrobat Distiller 7.0 (Windows) - CreationDate=D:20260324120903+05'30' - xap:createdate=1900-01-00T00:00:224485000+05:30 - xap:creatortool=PTC Arbortext Layout Developer 12.3.6865/W-x64 - xap:metadatadate=1900-01-00T00:00:224485000+05:30 - xap:modifydate=1900-01-00T00:00:224485000+05:30 - pdfuaid:part=1 - dc:format=application/pdf - dc:title=NEU-D-25-01173_The Fast and the Fragile: Neurosurgical Trauma in the Age of Micromobility - pdfaextension:schemas=PDF/UA Universal Accessibility Schemahttp://www.aiim.org/pdfua/ns/id/pdfuaidpartIntegerinternalIndicates, which part of ISO 14289 standard is followed ## Contents ### Page 1 ![Figure]() ![Figure]() # The Fast and the Fragile: Neurosurgical Trauma in the Age ofMicromobility Hannah Weiss, MDPaul P. Huang, MD1,5 - 1, Roee Ber, MD2, Mason Blacker, MD3, Nora Kim, MD1, Cordelia Orillac, MD1, Clotilde Balucani, MD4, - 1Department of Neurosurgery, NYU Langone Health, New York, New York, USA;2Department of Neurosurgery, Maimonides Medical Center, Brooklyn, New York, USA;3BarrowNeurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, Arizona, USA;4Department of Neurology, Northwell Health, New York, New York, USA;5Department ofNeurosurgery, Bellevue Hospital Center, New York, New York, USA Correspondence: Hannah Weiss, MD Department of Neurosurgery NYU Langone Health 530 First Ave, Suite 8R New York, NY 10016, USA. Email: X: @hannahkweiss Received, June 22, 2025; Accepted, February 09, 2026; Published Online, April 15, 2026. Neurosurgery 98:974–983, 2026 Copyright © 2026 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Congress of Neurological Surgeons. This is an open access article distributed under theterms of the, where it is permissible to download and share the work provided it isproperly cited. The work cannot be changed in any way or used commercially without permission from the journal. BACKGROUND AND OBJECTIVES: The rapid rise of electric and mechanical bikes and scooters has transformed urbantransportation, but their neurosurgical consequences remain underexplored. This study aimed to evaluate micromobilityrelated injuries over time, examining mechanisms of injury, patient risk factors, injury patterns, and associated clinicaloutcomes 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 bikesand scooters, as well as pedestrians struck by these devices. Key clinical variables and outcomes were compared acrossdevice types, both before and after propensity score matching. Unlike national database studies, this hospital-basedanalysis 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 wascollision with a motor vehicle (49.9%). Most patients (68.7%) required admission; 30.2% required intensive care. The medianlength of hospital stay was 3 days [IQR 1-5]. Half underwent a surgical intervention or procedure, and the overall mortalitywas 1.2%. Helmet use was low (31.7%). Pedestrians experienced the most severe outcomes, particularly when struck byelectric 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 braininjury, intensive care unit utilization, and operative intervention. Unlike previous database studies, this hospital-basedanalysis provides detailed neurosurgical outcome data and identifies prevention targets—including helmet use, intoxication, and urban infrastructure—to reduce morbidity and resource utilization. KEY WORDS: Micromobility, Electric scooters, Electric bikes, Traumatic brain injury, Urban trauma, Helmet use Micromobility devices—most commonly bicycles andscooters—have long been part of the urban transportation landscape.1 In recent years, however, the expansion of electric micromobility options has rapid ABBREVIATIONS: ISS, Injury Severity Score; LOS, length of hospital stay;TBI, traumatic brain injury. Supplemental digital content is available for this article at. transformed travel in dense metropolitan areas. These devices,now widely available through rental platforms, are efficient alternatives to cars, with their usage in the United States increasing35-fold since 2010.2-4 Electric bikes and scooters facilitate longer commutes with lessphysical exertion, contributing to their rapid adoption for bothrecreational and commercial use. In New York City, the number ofCiti Bike docking stations more than quadrupled since 2015,following the rollout of electric pedal-assist bikes in 2018, whileelectric scooter sharing programs—nonexistent in the United States ### Page 2 as recently as 2017—expanded to 300 active systems by 2021.5,6This growth has paralleled the rise of app-based urban deliveryservices.7-10 Despite their increasing presence, micromobility-relatedtrauma remains undercharacterized in the neurosurgical literature. Previous national database studies describe injury trends butlack granular data on helmet use, mechanisms of injury, andneurosurgical outcomes, including traumatic brain injury (TBI)and operative intervention.,- To address this gap, we performed a 5-year analysis ofmicromobility-related injuries presenting to a Level-1 traumacenter, comparing electric and mechanical devices with a focus onneurosurgical injury patterns and outcomes. ## METHODS ### Study Design and Setting We performed a retrospective cohort study at the Bellevue HospitalCenter, a Level-1 trauma center in New York City, using the BellevueTrauma Registry and the trauma surgery consultation database. Theprotocol was approved by the New York University Institutional ReviewBoard with waiver of consent. ### Patient Identification All patients presenting between January 2018 and August 2023 withtrauma activation or trauma surgery consultation for micromobilityrelated injuries were included. This encompassed riders of electric andmechanical bicycles and scooters, as well as pedestrians struck by thesedevices. Patients were excluded if they sustained injuries while operatingmotorcycles or motor vehicles or if they were managed exclusively byemergency medicine without trauma team involvement. Minor traumaticinjuries managed exclusively by emergency medicine without traumaactivation are not included in the trauma registry and therefore were notcaptured in this study. ### Data Collection Variables included age, sex, race/ethnicity, helmet use, device type,mechanism, arrival Glasgow Coma Scale (GCS), and blood alcoholconcentration. Injuries captured were TBI, craniofacial, spine, and othernoncranial trauma; comorbidities and Charlson Comorbidity Index wererecorded.14 Initial noncontrast head computed tomography (CT)findings were reviewed. TBI was defined as a traumatic intracranialfinding on initial CT; neurological severity used arrival GCS (mild 13-15,moderate 9-12, severe ≤8). ### Primary Outcomes 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 butnot limited to chest tube thoracostomy, central/arterial line placementwhen performed for definitive trauma management, operative fracturefixation, 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. ### Statistical Analysis Analyses were conducted using IBM SPSS Statistics v29.0.2.0 (IBMCorp.). Descriptive statistics were reported as proportions for categoricalvariables and medians with IQRs for continuous variables. Pearson’s χ2and Fisher’s exact tests were used for categorical comparisons. TheMann–Whitney U-test was applied for continuous variables that were notnormally distributed. Statistical significance was set at a two-sided P-valueof <.05. ### Propensity Score Matching To adjust for confounding variables when comparing outcomes between electric and mechanical micromobility users, propensity scorematching 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 Foundationfor Statistical Computing). Covariate balance was assessed using standardized mean differences and visualized using love plots. ### Subgroup Analyses Additional subgroup analyses (comparing pedestrians vs riders andelectric 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 vsweekend (weekend defined as Friday 8 PM-Monday 6 AM), and dischargedisposition. Prolonged hospitalization was defined as LOS >3 days, basedon the cohort’s median. This study adheres to the Strengthening theReporting of Observational Studies in Epidemiology guidelines. ## RESULTS A total of 914 patients sustained injuries related to micromobilitydevice accidents during the study period (Table). The cohort waspredominantly male (82.3%), with a median age of 40.1 years[IQR 28-36]. Racial and ethnic distribution included 30.6% ofWhite, 13.2% of Black, 9.3% of Asian, and 36.9% of Hispanicpatients. Over the study period, there were 9036 total traumaservice admissions and 1846 trauma consults from the emergencydepartment. Admitted patients in this study made up 6.9% of alladmitted patients with trauma during the study period. In the overall cohort, hospital admission occurred in 67.9% (n =621) of patients; among those admitted, 30.2% (n = 187) requiredICU care. Overall, 50.4% (n = 461) underwent at least 1 surgicalintervention or procedure. The median LOS was 3 days [IQR 1-5].Most patients were discharged home (89.5%, n = 556), and the inhospital mortality was 1.2% (n = 11). Traumatic injuries wereidentified in 85.9% (n = 785) of patients, including TBI in 32.7%(n = 299) and craniofacial trauma in 26.4% (n = 241). Amongpatients with TBI, 65.4% had concussion (CT-negative), whereas ### Page 3 | Characteristics | (n = 914) | | - | - | | Age, y (mean [SD]) | 40.1 [16.6] | | Sex, male (%) | 743 (82.3) | | Race and ethnicity (%) | | White | 280 (30.6) | | Asian | 85 (9.3) | | Black | 121 (13.2) | | Hispanic | 337 (36.9) | | Native Hawaiian/Pacific Islander | 2 (0.2) | | Native American | 3 (0.3) | | Other | 20 (2.2) | | Unknown | 66 (7.2) | | Electric micromobility device (%) | 303 (33.2) | | Mechanism (%)a | | Biker | 816 (89.3) | | Scooter rider | 159 (17.4) | | Pedestrian | 69 (7.5) | | Helmet use (%)b | 231 (31.7) | | Transfer from outside hospital (%) | 100 (10.9) | | GCS on arrival (%) | | GCS >12 | 864 (94.5) | | GCS <9 | 30 (3.3) | | Injury severity score | 9 [5-14] | | Alcohol intoxication (%)c | 168 (18.4) | | TBI (%) | 281 (30.7) | | Craniofacial injury (%) | 241 (26.4) | | Spine injury (%) | 62 (6.8) | | Any injury (%) | 785 (85.9) | | Surgical intervention (%) | 461 (50.4) | | Admission (%) | 620 (67.8) | | Length of stay, days (median [IQR])d | 3 [1-5] | | Death (%) | 11 (1.2) | TABLE 1. Baseline Demographic and Clinical Characteristics ofPatients Presenting With Micromobility-Related Injuries (N = 914) GCS, Glasgow Coma Scale; TBI, traumatic brain injury. aPatients were riding the specified micromobility device. bOf patients with documented helmet status. cOf patients with documented blood alcohol tests. dOf patients who were admitted. Data reflect demographic and clinical variables for all patients presenting to a Level-1 traumacenter with micromobility-related injuries from 2018 to 2023. Values are presented as number(%) unless otherwise noted. radiographic findings included traumatic subarachnoid or intraventricular hemorrhage in 42.9%, basal cistern effacement in14.2%, epidural mass effect in 13.8%, and midline shift in 5.9%.The majority presented with mild neurological injury, withGCS >12 in 94.5% (n = 864). Spinal injuries occurred in 6.8% (n =62), and other noncranial trauma occurred in 64% (n = 585). Themedian ISS was 9 [IQR 5-14] overall, 10 [9-14] among pedestrians,and 9 [5-14] among riders (Table). Neurosurgical interventionswere performed in 34 patients (3.7%), including craniotomy ordecompressive hemicraniectomy in 10 (1.1%), external ventriculardrain or intracranial pressure monitor placement in 7 (0.8%), andspinal 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 witha 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). Most patients presented directly to the traumacenter, 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. ### Safety Risk Factors: Helmet and Alcohol Use Among patients with known helmet status, 67.8% (n = 486)was not wearing a helmet at the time of injury. Helmeted riderswere older (42.4 vs 37.7 years, P < .001) and more likely to haveused electric devices (39.4% vs 29.4%, P = .008). The absence ofhelmet 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) wasintoxicated. Alcohol use was associated with significantly lowerhelmet 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% vs31.0%, P < .001), craniofacial trauma (41.5% vs 23.1%, P <.001), and other injuries (64.8% vs 54.8%, P = .017). Intoxicatedpatients were more likely to present during night-time hours(63.3% vs 35.5%, P < .001). ### Time of Presentation Over half of injuries occurred overnight (55.1%, n = 504), withpeak presentations at 6 PM (7.2%) and 8 PM (7.1%), and the lowestat 6 AM (1.1%). Electric micromobility users were more frequentlyinjured during commuting hours (7 AM, 8 AM, 3 PM; Figure). 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 topresent on weekdays (9.2% vs 4.4%, P = .008). Weekend injurieswere associated with lower GCS (14.4 vs 14.5, P = .049), ICU ### Page 4 ![Figure]() admission (27.8% vs 13.0%, P < .001), and alcohol intoxication(27.8% vs 13.0%, P < .001). ### Associations With Worse Clinical Outcomes Mechanical micromobility users had higher admission ratesthan 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 nonhelmetedriders (24.8% vs 26.3%, P = .002). Overnight presentations hadhigher admission rates than daytime (49.9% vs 23.3%, P = .035),and non-White patients were admitted more frequently thanWhite patients (63.0% vs 58.7%, P < .001). ![Figure]() ### Page 5 Patients requiring ICU were more likely to be pedestrians(15.5% vs 6.5%, P < .001) and, among riders, less likely to haveworn a helmet (19.3% vs 27.2%, P = .009). Among those not discharged home, 5.7% (n = 36) requiredhome services, and 4.8% (n = 30) was discharged to inpatientrehabilitation. These patients presented with lower GCS (10.7 vs14.7, 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 spinalinjury (26.2% vs 5.3%, P < .001). Surgical intervention orprocedure 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 lowerGCS on arrival (13.9 vs 14.5, P < .001) and sustained more TBIs(50.0% vs 35.1%, P < .001), craniofacial trauma (40.7% vs28.0%, 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) andmental health diagnoses (16.1% vs 8.8%, P = .007). ### Trends Over Time Micromobility-related trauma increased across the study period, with electric devices comprising a growing share—risingfrom 7.9% of cases in 2018 to 55.1% by 2023 (Figure). Pedestrian injuries involving electric devices also rose; while nopedestrians were struck by electric devices until Q3 2020, theyaccounted for 37.9% of pedestrian incidents from Q4 2020 to Q32023 (Figure). ### Pedestrian vs Micromobility Device Riders Among the 69 pedestrians struck, individuals were older (53 vs39 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% vs24.5%, P < .001). Hospital admission (82.6% vs 66.8%, P =.007) and ICU care (42.0% vs 18.7%, P < .001) were morecommon. Neurological injury severity by GCS did not differbetween pedestrians struck by electric vs mechanical devices(median 15 [IQR 15-15] vs 15 [15-15], P = .373), whereas overallinjury severity by ISS was higher with electric devices (median13.5 [IQR 10-17] vs 9 [9-11], P = .014). Most were struck bymechanical bikes (69.6%), followed by electric scooters (21.7%)and electric bikes (8.7%). ### Electric vs Mechanical Micromobility Device Riders Among riders, 81.3% used mechanical bikes and 18.7% usedelectric bikes. Electric bike riders were more likely to wear helmets(41.2% vs 28.5%, P = .032). There were no significant differencesin TBI rates (28.7% vs 34.7%, P = .073), length of stay (3 [2-5] vs3 [1-5] days, P = .253), or need for surgical intervention orprocedure (13.5% vs 29.0%, P = .101) between electric andmechanical 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 admittedelectric scooter riders had longer hospitalizations (3.9 vs 1.6 days,P = .016). After propensity score matching, electric and mechanical userswere balanced by age, sex, race, helmet use, and comorbidities(Tables 2 and 3, Supplemental Digital Content 1, Figure 1,). No significant differenceswere observed between electric and mechanical users in overallinjury rate (65.8% vs 67.7%, P = .642), surgical intervention orprocedure (51.5% vs 53.1%, P = .725), ICU admission (17.3% vs19.6%, P = .498), LOS (3.0 [3.0] vs 3.0 [4.0], P = .932), ornonhome discharge (4.2% vs 7.3%, P = .187). ## DISCUSSION Micromobility-related trauma is an increasingly significantcontributor to urban neurosurgical and trauma system burden. Inour cohort, micromobility injuries accounted for 6.9% of traumaadmissions, with nearly one third of patients sustaining TBI andover 50% requiring surgical or procedural intervention. As electricmicromobility adoptions expand, neurosurgical consultation forhead, 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 trendsin electric device adoption.,,, Our data show a dramaticshift in injury epidemiology: electric micromobility involvementincreased from under 10% at the start of the study period to over50% by 2023. Comparable increases have been reported in bothcar-centric and densely populated regions. Previous studies fromboth dense metropolitan and car-centric regions report increasingrates of head injury, ICU utilization, and oerative interventionassociated with electric scooters and bicycles.11,- Internationaldata similarly demonstrate high rates of head injury and lowhelmet compliance, suggesting that the Manhattan experiencereflects a broader urban injury pattern rather than a region-specificphenomenon.19-21 Pedestrians represented the highest-risk subgroup in our cohort. Compared with riders, they were older and more oftenfemale, and had greater comorbidity burden, with higher rates ofTBI, ICU admission, and nonhome discharge. Injury severity wasgreater when pedestrians were struck by electric devices, despitesimilar neurological severity by GCS. These events frequentlyoccurred during evening hours, highlighting a distinct high-risksubgroup within urban micromobility trauma. Speed is a well-established determinant of injury severity intrauma. While electric devices can reach higher speeds, pedestrians struck by these devices demonstrated the highest rates ofTBI, craniofacial trauma, ICU admission, and nonhome discharge, reflecting their vulnerability in the absence of protectiveequipment or mass.22 These findings underscore the need for ### Page 6 ![Figure]() targeted infrastructure and enforcement strategies to mitigatepedestrian risk. Helmet use remains alarmingly low, with fewer than one thirdof riders wearing a helmet at the time of injury. Consistentwith the previous literature, helmet use was associated with significantly lower rates of TBI and craniofacial trauma andlower admission rates and less severe neurological injury.23-25These findings reinforce helmet use as a critical, modifiablefactor in reducing neurosurgical morbidity. Electric micromobility users wore helmets more frequently, possibly because of ![Figure]() ### Page 7 | TABLE 2. Covariate Balance Before and After Propensity Score Matching Between Electric and Mechanical Micromobility Device Users | | - | | Before matching After matching | | Variables | Means treated =Electric device users | Means control =Mechanical device users | SMD | Means treated =Electric device users | Means control =Mechanical device users | SMD | | Age | 38.0385 | 39.5647 | 0.1025 | 38.0385 | 36.9885 | 0.0705 | | Sex | 0.1269 | 0.1581 | 0.0935 | 0.1269 | 0.0231 | 0.0231 | | Non-White | 0.2346 | 0.3655 | 0.3089 | 0.2346 | 0.2423 | 0.0182 | | Helmet | 0.3308 | 0.2464 | 0.1793 | 0.3308 | 0.3077 | 0.0231 | | Comorbidities | 0.3769 | 0.3799 | 0.0061 | 0.3769 | 0.3577 | 0.0192 | 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 mechanicalmicromobility devices. Variables include age, sex, race, helmet use, and comorbidities. An SMD of <0.1 is generally considered indicative of good covariate balance. higher speeds or New York City regulations mandating helmetuse for commercial cyclists (eg, food delivery workers) since2007.26 A few jurisdictions (eg, Vancouver’s bike share) have experimented with providing helmets alongside shared micromobility devices, which may suggest a promising model forimproving helmet compliance.27 | TABLE 3. Comparison of Clinical Characteristics and Outcomes Between Mechanical and Electric Micromobility Device Users | | - | | Mechanicalmicromobilitydevice (n = 260) | Asymptotic | | Percentage | Electric micromobility | Percentage | P | Mann–Whitney | significance | | Category | (%) | device (n = 260) | (%) | value | U (Z-value) | 2-tailed | | Age (y) | 33.5 [20.0] | 35.0 [20.3] | 0.596 | 0.551 | | Sex: male | 226 | 86.9 | 227 | 87.3 | .606 | | Helmet | 80 | 30.8 | 86 | 33.1 | .126 | | GCS (mean) | 14.4 [2.0] | 14.7 [1.1] | 0.802 | 0.422 | | Alcohol intoxication | 50 | 19.2 | 46 | 17.7 | .883 | | TBI | 81 | 31.2 | 63 | 24.2 | .078 | | Craniofacial trauma | 61 | 23.5 | 60 | 23.1 | .917 | | Spine injury | 21 | 8.1 | 18 | 6.9 | .740 | | Other injury | 176 | 67.7 | 171 | 65.8 | .642 | | Surgical intervention | 138 | 53.1 | 134 | 51.5 | .725 | | LOS (d) | 3.0 [4.0] | 3.0 [3.0] | 0.085 | 0.932 | | Admission | 183 | 70.4 | 163 | 62.7 | .077 | | ICU | 51 | 19.6 | 45 | 17.3 | .498 | | Dispositionelsewhere thanhome | 19 | 7.3 | 11 | 4.2 | .187 | | Weekend | 89 | 34.2 | 81 | 31.2 | .455 | | Non-White race | 197 | 75.7 | 199 | 76.5 | .837 | 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 reflectcomparisons between groups using χ2 or Mann–Whitney U tests, with Z-values and asymptotic significance reported for nonparametric tests. ### Page 8 Alcohol intoxication was common and associated with higherrates of TBI, lower GCS, increased ICU utilization, and lowerhelmet use. These injury patterns parallel alcohol-related vehiculartrauma and highlight intoxication as an important, modifiable riskfactor 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 thistrend were associated with purely commuter activity, a bimodaldistribution with an additional peak during typical morningcommuting hours would be expected). Reduced visibility, trafficdensity, fatigue, and increased commercial cycling activity maycontribute to this pattern.7 Sociodemographic disparities were evident. Non-White ridersand pedestrians had higher rates of TBI, ICU admission, andnonhome discharge. These disparities may reflect differences ininfrastructure, access to safety equipment, and exposure to highrisk environments.28 From a neurosurgical standpoint, the burdenof severe head and spinal injuries in these populations highlightsthe need for equitable investment in road safety and public healthintervention. Although our data did not demonstrate worse outcomes inelectric vs mechanical device users, the dramatic increase inelectric micromobility use may contribute to the rising absolutenumber of severe injuries. Nearly half of injuries in our cohortresulted from motor vehicle collisions, underscoring critical infrastructure deficiencies. Only 3% of Manhattan’s bike lanes areprotected, and in 2023, 94% of ccling fatalities occurred onroads without protected bike lanes.29 Infrastructure redesign—including separated lanes and intersection protections—mayoffer the most immediate opportunity to reduce neurosurgicaltrauma burden. This study has limitations. As a single-center, retrospectiveanalysis, findings may not be generalizable nationwide althoughthey align with national and international trends.-,, Reliance on registry and electronic medical record data might haveresulted in missing information, particularly regarding crashcircumstances, rider behavior, and environmental factors. Fatalities at the scene were not captured in this data set. According tothe New York City Department of Transportation, 2023 was thedeadliest year for bike accidents and 23 of the 30 bike riders whodied were on electric bikes.30 Alcohol intoxication may confoundneurological 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 willincreasingly encounter these injuries—from mild TBI to operativecranial and spinal trauma—within the emergency setting. Mitigation will require coordinated strategies spanning infrastructuredesign, helmet policy, intoxication enforcement, and equitableresource allocation. As micromobility adoption continues to expand globally, especially in densely populated urban centers, understanding theneurosurgical implications will be essential. Future prospective,multicenter studies should assess mechanisms of injury in greaterdetail, evaluate helmet compliance and enforcement efforts, anddetermine the impact of targeted interventions on clinical outcomes including operative and neurocritical care needs. ## CONCLUSION Micromobility-related trauma represents a growing neurosurgicalburden, accounting for nearly 1 in 15 trauma admissions at our Level 1 center. Injury severity varied widely, with over two thirds of patientsrequiring hospital admission, nearly one third requiring ICU care, andhalf undergoing a surgical intervention or procedure. The medianhospital stay was short, yet nonhome discharge and mortality—thoughinfrequent—were concentrated among patients with TBI and multisystem injury. While riders of electric and mechanical devices hadcomparable injury severity and outcomes, pedestrians—particularlythose 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 inmanaging micromobility-related head and spine trauma and highlightactionable targets for prevention, including helmet compliance, intoxication reduction, and infrastructure redesign. ### Funding This study did not receive any funding or financial support. ### Disclosures The authors have no personal, financial, or institutional interest in anyof the drugs, materials, or devices described in this article. ## REFERENCES - 1. Berto F. Bicycle. In: Encyclopedia Britannica; 2024. Accessed August 28, 2024. - 2. The Future of Micromobility: Ridership and Revenue After a Crisis. McKinsey.Accessed August 28, 2024. - 3. Namiri NK, Lui H, Tangney T, Allen IE, Cohen AJ, Breyer BN. 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Accessed August 29, 2024. ### Acknowledgments Author contributions: H.W. conceived and designed the study, supervised datacollection and analysis, and drafted the manuscript. R.B., M.B., N.K., and C.O.contributed to data acquisition and interpretation. C.B. assisted with data interpretation and critically revised the manuscript for important intellectual content.P.P.H. provided senior oversight, contributed to study design and interpretation ofresults, and critically revised the manuscript. All authors reviewed and approved thefinal manuscript. Supplemental digital content is available for this article at. Supplemental Digital Content 1: Figure 1. Love plot demonstrating covariatebalance before and after propensity score matching. ## COMMENTS T his study analyzes injuries associated with “micro-mobility” bicyclesand scooters, both mechanical and electrical, with a focus on neurotrauma, particularly head injury, over a 5-year period (2018-2023) at asingle institution (Bellevue Hospital). The study is important for several reasons. First, it describes a growingsource of neurologic injuries, although other general trauma is obviouslyinvolved. Secondly, in any urban environment, it should be obvious toany observer that electrical scooters and electric-assisted bicycles haveincreased in use and number significantly over the past decade. Thirdly,the risk of injury seems intuitive, as these vehicles speed along unprotected with traffic, and even occasionally on sidewalks, with no protectionfor the operator. This study found that the proportion of accidents due toelectric-driven or assisted “micro-mobility devices” has increased ratherdramatically in parallel with the increased use of these devices in NewYork City, even though the total number of reported incidents per quarter(or year) has not. This either means that motorized devices have a higherrisk than their nonmotorized analogues or motorized bikes and scootersare replacing more standard pedal bicycles. Other observations are thatthe proportion of accidents involving collision with motor vehicles issignificant (50%), and another major source of injury is motorizedscooter/bike vs pedestrian, with the pedestrian being the loser in theencounter. Even though the study is a retrospective survey study of electronicmedical records from a single urban institution, and thus may be uniqueto the locale and not generalizable to other urban areas or states, it seemsto reflect a growing trend in general and neuro-trauma and deservesattention, not because the injuries are unique, but because the cause ofinjury as a demographic trend warrants public exposure. James R. BeanLexington, Kentucky, USA ### Page 10 T he authors describe a retrospective cohort of neurosurgical injurytrends related to the micromobility boom in Manhattan. As theauthors mention, there has been a 35-fold increase in micromobility device usage since 2010, making this an important area to investigate, andthe paper is well-written. The authors find micromobility-related traumais a growing neurosurgical burden, with two-thirds of patients requiringhospital admission, one-third requiring ICU care, and one-half requiringsurgical intervention or bedside procedures (eg, central line placement).The authors identify modifiable risk factors and areas requiring further investigation.As the authors state, this study is the largest single-center analysis todate concerning the epidemiology of micromobility-related neurosurgicaltrauma. The study was conducted over a 5-year period and includesinjuries associated with both electric and mechanical micromobilitydevice (eg, bicycle) use. Additional strengths include the unselectednature of the cohort, which reduces bias, provides superior (longitudinal)data quality, and strengthens internal validity compared to studies of lessgranular (ecological) national databases or cohorts selected by injurypattern or severity. While the authors found no differences in outcomes between electricand mechanical micromobility device users following traumatic injury (thereby challenging assumptions in prior literature), they did find thatpedestrians struck by micromobility devices (and in the evening) sustained the most severe injuries. The authors suggest interventions including helmet compliance, intoxication reduction, infrastructureredesign, and addressing disparities may reduce neurosurgical injuryburden associated with micromobility devices. The author’s conclusions echo the broader conclusion of studies ofimpact biomechanics and mishap epidemiology across a variety of fields(eg, plane crash investigations), which is that the vast majority of accidents are caused not by technology but by human factors includingfatigue, intoxication, and distraction, and that proven safety devices suchas seatbelts, helmets, and traffic controls can reduce injury severity.Likewise, the advent of motorized micromobility devices offers newopportunities for targeted interventions addressing human error andtechnological factors to reduce preventable neurosurgical disease burden.This study represents a significant step in characterizing the epidemiologyof these types of injuries and in identifying suitable targets for intervention in a densely-populated urban environment. Christopher Carr, Abbas Jarahi, John VenderAugusta, Georgia, USA