Risk and protective factors for musculoskeletal injury in youth track and field: a methodological scoping review

Abstract

Background

Musculoskeletal injuries are widespread among youth track and field athletes, largely due to the combined effects of growth, maturation, and escalating training loads. Yet, inconsistent definitions and measurement approaches for injuries, and for the factors that may increase or reduce risk, limit the comparability of findings across studies and impede progress toward effective prevention strategies.

Objectives

To identify risk and protective factors for musculoskeletal injuries in youth track and field athletes, and to examine how these injuries and associated factors have been defined, measured, and aligned with consensus methodological standards.

Methods

This scoping review was conducted in accordance with Joanna Briggs Institute methodology and PRISMA-ScR reporting standards. Five databases (PubMed^®, Web of Science^®, Scopus^®, SPORTDiscus^®, and Embase^®) were systematically searched from inception to March 2026. Studies were eligible if they included youth track and field athletes (mean age 15-24 years) and reported injury characteristics and/or risk or protective factors. Data were extracted on injury definitions, surveillance methods, exposure metrics, and factor measurement approaches. Methodological alignment with the athletics consensus statement by Timpka et al. was assessed using a structured framework.

Results

A total of 54 studies were included. Injury definitions varied widely, encompassing time-loss, medical-attention, symptom-based, and diagnosis-confirmed approaches, with substantial heterogeneity in surveillance methods and exposure quantification (48 studies). Factors associated with musculoskeletal injury outcomes spanned intrinsic (e.g., previous injury, biomechanics, strength, bone health) and extrinsic domains (e.g., training load, footwear, environment), with diverse measurement techniques (27 studies). Reporting of consensus-based methodological components varied across studies. Injury-definition elements were reported in a greater proportion of studies than exposure and surveillance elements. Variation was observed in the reporting of exposure metrics, prospective monitoring approaches, and the inclusion of overuse injuries across studies.

Conclusions

Considerable methodological heterogeneity exists in youth track and field injury research. Greater adoption of consensus-based definitions, standardised exposure measures, and robust surveillance systems may improve comparability and support the development of injury prevention strategies.

Review registration

Open Science Framework (Registration DOI: https://doi.org/10.17605/OSF.IO/U4BSY).

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s13102-026-01824-4.

Introduction

Track and field comprise a diverse range of disciplines including sprinting, middle- and long-distance running, jumping, throwing and combined events, each placing unique physical and physiological demands on athletes [1]. These activities require maximal force production, repetitive high-impact movements, and complex neuromuscular coordination [2], all of which contribute to a high risk of sport-related injury [3]. A recent systematic review and meta‑analysis reported an injury prevalence of 69.7% and pooled incidence rates of 68.8 injuries per 1,000 registered athletes across training and competition [4]. This elevated injury burden has multidimensional consequences, including persistent physical symptoms and recurrent time‑loss [5], psychological distress [6], and social disruption affecting academic and peer engagement, which may contribute to sport withdrawal [7].

Youth track and field athletes may be particularly vulnerable to musculoskeletal injuries due to the interaction of biological growth, maturation, and increasing training and competition demands during adolescence [8]. Periods of rapid growth may result in temporary imbalances in strength, flexibility, and neuromuscular control, potentially increasing injury susceptibility [9]. These injuries can result in substantial time‑loss from training and competition, negatively affecting athlete development and performance during a particularly sensitive developmental period [10].

Musculoskeletal injury risk in youth athletes is widely recognised as multifactorial, arising from a complex interaction of intrinsic and extrinsic determinants [11]. Intrinsic factors include biological and individual characteristics such as age, sex, growth and maturation status, biomechanical characteristics, muscle strength, flexibility, and previous injury history [12]. During adolescence, rapid growth and skeletal immaturity may increase vulnerability to injury due to physiological changes in bone, muscle, and connective tissues, particularly around periods of peak growth velocity [13]. Extrinsic factors related to the training and competition environment may also influence injury risk. These include training load, sport specialization, coaching practices, equipment use, and competition schedules [12]. High training volumes and intensities have been associated with greater injury occurrence in youth track and field athletes [14]. Huxley and colleagues reported that injured youth athletes undertook higher-intensity and higher-volume training than their uninjured peers [14].

Epidemiological research on musculoskeletal injuries in youth track and field is fundamentally shaped by how injuries are defined and measured [15], yet the literature demonstrates substantial variability in operational definitions [16]. Commonly used definitions include time‑loss injuries, medical‑attention injuries, and all‑complaint or self‑reported injuries [17], each capturing different aspects of the injury spectrum. Surveillance methods also differ widely [4], ranging from athlete self-report to medical-record and coach-report systems, resulting in differences in data capture and accuracy. Exposure quantification similarly lacks uniformity, with studies differing in the metrics used and in surveillance windows, limiting comparability of injury incidence and risk estimates.

To address such heterogeneity, sport science researchers have developed consensus statements recommending standardized definitions and surveillance procedures. For track and field specifically, the consensus statement by Timpka et al., [18] provides explicit definitions, structured guidance for characterizing injury nature and cause, and recommended procedures for exposure and severity recording, including baseline variables and appropriate denominators for calculating incidence and prevalence [18]. Adoption of these procedures may improve the comparability and utility of epidemiological evidence for injury prevention [19]. However, recent systematic and meta‑analytic work [4] indicates that many track and field studies continue to use heterogeneous definitions and inconsistent surveillance methods, with few explicitly adhering to the Timpka et al. recommendations [18].

Despite the growing body of research on musculoskeletal injuries in youth track and field athletes, the literature remains fragmented, with limited comprehensive mapping of risk and protective factors, injury definitions, and measurement approaches. This scoping review therefore aims to systematically map the tools, systems, and methodological approaches used to collect injury epidemiological data and associated factors in this population. Specifically, it aims to (i) identify reported risk and protective factors, (ii) examine how injuries and associated factors are defined and measured, (iii) assess alignment with consensus methodological recommendations, and (iv) identify key gaps requiring standardisation in youth track and field injury research.

Methods

Methodological approach

The protocol for this scoping review was prepared in accordance with the Joanna Briggs Institute (JBI) guidance for scoping reviews [20]. The protocol was prospectively published on the Open Science Framework (registration DOI: https://doi.org/10.17605/OSF.IO/U4BSY), and no deviations from the preregistered protocol were made during the conduct of this review. The review followed the methodological framework proposed by Arksey and O’Malley [21], with further enhancements suggested by Levac and colleagues [22]. The review was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (Supplementary Material 1) [23].

Research objectives

The following research objectives, framed according to the Population, Concept, and Context (PCC) framework [20], were used to guide the scoping review:

1. Map risk and protective factors for musculoskeletal injury in youth track and field athletes and describe how these factors are conceptualised and categorised across studies.

2. Summarise injury definitions, diagnostic criteria, and measurement approaches used in youth track and field research.

3. Examine how risk and protective factors are defined, operationalised, and measured, including variability in constructs, instruments, and analytical methods.

4. Evaluate the alignment of injury definitions, exposure metrics, and surveillance methods with the athletics injury surveillance consensus statement by Timpka et al. [18].

5. Identify methodological gaps, inconsistencies, and areas requiring standardisation to improve future epidemiological research in this field.

Eligibility criteria

• Population: Youth athletes participating in track and field (sprints, hurdles, middle/long-distance, jumps, throws, and combined events) with a mean age of 15-24 years, consistent with the United Nations definition of youth [24]. Inclusion up to 24 years reflects the prolonged physical, biological, and athletic development in track and field athletes [25], including ongoing musculoskeletal maturation, event-specific specialisation, and delayed peak performance in several disciplines [26]. Athletes within this age range often compete in youth or under-23 pathways and demonstrate distinct training loads, exposure patterns, and injury risk profiles compared with senior athletes [27]. Studies with broader age ranges were included only if youth-specific data were reported or extractable. Findings should therefore be interpreted across a developmental continuum rather than as a single homogeneous age group.

• Concept: Empirical investigation of risk factors, protective factors, or both, in relation to musculoskeletal injuries (acute or overuse) and/or injury-related outcomes (incidence, prevalence, burden, or time-loss).

• Context: Any setting (school, club, regional, national), training or competition, any country.

Study designs

Any study reporting injury characteristics and/or risk or protective factors using any injury data collection method was eligible. Narrative reviews, editorials, letters, case reports, conference abstracts without full texts, and animal or in vitro studies were excluded. Only English-language studies published from database inception to 1 March 2026 were included.

Information sources and search strategy

A three-step search strategy was employed: (i) an initial limited search to refine search terms, (ii) comprehensive searches of relevant databases, and (iii) reference and citation tracking of included studies. The search was conducted in the PubMed^® (U.S. National Library of Medicine, USA), Web of Science^® (v.5.4; Thomson Reuters, USA), SciVerse Scopus^® (Elsevier Properties S.A., USA), SPORTDiscus^® (EBSCOhost, USA) and Embase^® (Elsevier, USA). Both controlled vocabulary terms and free-text keywords were used. The search strategy was based on three core concepts: (i) population (track and field athletes), (ii) musculoskeletal injury, and (iii) epidemiology and risk-related constructs (incidence, prevalence, risk factors). In PubMed, Medical Subject Headings (MeSH) were combined with title and abstract keywords using Boolean operators to maximise search sensitivity. In Embase, equivalent Emtree terms were incorporated alongside free-text keywords. In Web of Science, the search was conducted using the Topic (TS) field, while in Scopus the TITLE-ABS-KEY fields were used. In SPORTDiscus, subject headings and keywords were combined using database-specific operators. Truncation, phrase searching, and Boolean logic were adapted for each database to ensure comprehensive retrieval of relevant literature. Complete, database‑specific search strings, including controlled vocabulary terms and free‑text keywords, are provided in Supplementary Material 2.

Study selection

Two reviewers (KW and RJ) independently conducted the study selection process. All retrieved records were first exported to Covidence (v.2024; Veritas Health Innovation, Melbourne, Australia), where duplicate entries were identified and removed. Screening then proceeded in two stages: initial title and abstract screening, followed by full-text assessment, both conducted in accordance with the above-mentioned eligibility criteria. Calibration exercises were undertaken prior to each screening phase to ensure consistency between reviewers. Discrepancies were resolved through discussion, with a third reviewer (AW, APH) consulted when consensus could not be reached.

Data extraction

Following full-text selection, eligible studies were imported into EndNote (v.2025.3.1; Clarivate Analytics, USA). A JBI-aligned data-charting form was piloted and refined iteratively. Two reviewers (KW and RJ) independently extracted data, with discrepancies resolved through discussion. Extracted data included: (i) bibliographic details (author, year, country, study design); (ii) participant characteristics (age, sex, athletic discipline, performance level, sample size); (iii) injury definitions (type, ascertainment source, anatomical location, pathology classification, and severity measures); (iv) exposure metrics (athlete exposures, training/competition hours, sessions, mileage); (v) risk and protective factors (definitions, measurement methods, timing, and thresholds); and (vi) outcomes (incidence, prevalence, burden, and reported effect estimates with confidence intervals). Where multiple publications reported on the same surveillance system or dataset, the most recent and/or methodologically comprehensive report was included.

Assessment of alignment with athletics consensus statement and methodological gaps requiring standardisation

Methodological alignment with the athletics consensus statement [18] was assessed using a structured framework across two components. Part I (Injury-related methodological standards) evaluated injury definitions, classification, and severity reporting, while Part II (Exposure, surveillance, and reporting standards) assessed exposure measurement, surveillance design, and reporting practices. Studies were categorised as aligned, partially aligned, or not aligned based on predefined criteria. All studies reporting epidemiological injury data were included in the alignment assessment, while studies not involving injury surveillance were excluded from scoring but retained in the descriptive synthesis. Two reviewers (KW, RJ) independently assessed each study, with discrepancies resolved through discussion. Inter-rater agreement was evaluated using Cohen’s kappa statistic (κ = 0.78). Detailed descriptions of the alignment framework, scoring criteria, and domain-level definitions are provided in Supplementary Material 3.

Methodological characteristics were examined by identifying domains that were not consistently reported or showed variation in alignment with consensus recommendations. These domains were grouped into key themes, including variation in injury definitions, differences in exposure metrics, approaches to overuse injury surveillance, and the use of classification systems. Findings were synthesised descriptively to summarise patterns in reporting and methodological approaches across studies.

Data synthesis

Extracted data were synthesised descriptively and narratively in accordance with JBI guidance for scoping reviews. Studies were grouped by design, injury definition and surveillance approach, exposure metrics, and investigated risk or protective factors. Injury definitions, measurement methods, and risk-factor operationalisation were compared to identify methodological approaches. These findings were integrated with the consensus-alignment assessment to characterise methodological patterns and areas of variability in approaches across the literature. Consistent with JBI and PRISMA-ScR guidance, no formal risk-of-bias or critical appraisal was undertaken, as the review aimed to map the breadth and methodological characteristics of the literature rather than evaluate causal effects. Given the heterogeneity in study designs, data sources, and outcome definitions, a single appraisal tool was not applied. Instead, methodological alignment with established athletics consensus standards was assessed to describe reporting practices and methodological characteristics across studies.

Results

Study selection

The database searches yielded a total of 9,135 records, comprising 3,391 from Scopus^®, 1,862 from PubMed^®, 1,286 from Web of Science^®, 1,661 from SPORTDiscus^®, and 935 from Embase^®. An additional seven records were identified through other sources, including citation searching (n = 5) and manual searching (n = 2). After removing 2,207 duplicate entries, 6,935 unique records proceeded to title and abstract screening. Of these, 3,639 studies were excluded at the title‑level screening stage, and 158 studies proceeded to full‑text review. Following detailed full‑text evaluation, 54 studies met all eligibility criteria and were included in the scoping review. Of the included studies, 47 reported injury‑characteristics data, while 27 examined risk or protective factors associated with youth track and field athletes (Fig. 1).

Fig. 1.

Preferred reporting items for scoping reviews flow diagram detailing the review filtering process

How musculoskeletal injuries have been defined and measured in studies involving youth track and field athletes

Across the included studies, injury definitions were grouped into five approaches: time-loss, medical-attention, combined time-loss/medical-attention, symptom-based, and diagnosis-confirmed definitions. Time-loss definitions were the most used and frequently incorporated severity classifications [28–30]. Medical-attention criteria were often applied alongside time-loss definitions, whereas diagnosis-confirmed studies primarily focused on specific conditions such as bone stress injuries (BSIs) and hamstring strains [31, 32]. Injury onset (acute or gradual) was commonly reported across both time-loss and symptom-based frameworks [11, 28, 31, 33]. Injury data were most frequently collected through athlete self-report systems, although several studies incorporated clinician verification [11, 33]. Exposure was variably quantified using athlete exposures [31, 34], training hours, running mileage, or athlete-hours [11, 35], while some studies did not report exposure measures [36, 37]. The characteristics of the included studies are presented in Tables 1 and 2, while detailed study-level information is provided in Supplementary Tables 1 and 2.

Table 1.

Injury definitions and data collection methods used in epidemiological studies of youth track and field

Author, Years, Country	Study design	Sample	Event	Injury definition	Exposure metric
Bennell & Crossley, 1996, Australia	Retrospective cohort	M, F, 20.3 ± 2.0 y, 95	T & F	Timeloss definition	Training hours
Bennell et al., 1995, Australia [38]	Retrospective cohort	F, 20.5 ±0.3 y, 53	T & F	Physiciandiagnosed, imagingconfirmed SFs	Training hours
Bennell et al., 1996, Australia [39]	Prospective cohort	M, F, 20.4 ± 2.1 y, 111	T & F	Imagingconfirmed SFs	NR
Boltz et al., 2021, USA [31]	Descriptive epidemiology	M, 18-24 y, 1,081 injuries	T&F	Medical attention definition	AE hours
Braçic et al., 2024, Spain [28]	Retrospective crosssectional	M, F, 16-22 y, 66	Throwing events	Time-loss & medical attention definition	AE hours
Carbuhn et al., 2022, USA [40]	Retrospective case-control	M, F, 20.0 ± 0.3 y 79	Distance running	Imagingconfirmed BSIs	NR
Carragher et al., 2019, Ireland [29]	Prospective cohort	M, F, 17.1 ± 0.8 y, 70	T&F	Time-loss definition	NR
Edouard et al.; 2012, France	Retrospective caseseries	M, F, 21.5 ± 5 y, 204	Heptathlon, Decathlon	Time-loss & medical attention definition	Injury rate per 100 athletes per season
Edouard et al., 2024, France [10]	Crosssectional, exploratory retrospective survey	M, F, 23.6 ± 4.7 y, 2020	T&F	Symptom-based definition	Training hours
Edouard et al., 2024; Africa [41]	Crosssectional (4 W prechampionship), prospective cohort; (inchampionship period)	M. F, 17.3± 1.0 y, 933	T&F	Symptom-based definition	Preparticipation: Prevalence of complaints, Inchampionship: incidence per 1000 athletes
Edouard et al., 2012, France	Prospective injury surveillance	M, F, 19.6 ± 3.7y, 112	Decathlon, Heptathlon	Medical attention (IOC) definition	Incidence per 1000 athletes
Edouard et al., 2019, France	Retrospective	M, F, 15-19 y, 651	Pole Vault	Symptom-based definition	Training hours
Ek et al., 2022, Sweden [42]	Prospective cohort, 52 W	M, F, 12-15 y, 113	T&F	Symptom-based & time-loss definition	Training hours
Enoki et al., 2022, Japan [36]	Prospective cross-sectional	M, 22.2 ± 3.2 y, 21	Pole vault	Time-loss definition	No exposure quantification
Enoki et al., 2021, Japan	Prospective cohort	M, F, 2016-17: 19.67 ± 0.95y, n=51, 2017-18), 19.78 ± 1.13 y, n=54	Jumping & pole vault	Consensus-based (IOC/IAAF) definition	AEs hours
Enoki et al., 2021, Japan	Prospective cohort	M & F, 19.6 ± 1.1 y, 27	Pole vault & decathlon	Symptom-based definition	No exposure quantification
FeddermannDemont et al., 2014, multi-nation sample	Prospective cohort	M, F, 16-19 y, 3,055	T&F	Consensus-based (IOC/IAAF) definition	Incidence per 1000 athletes
Goldman et al., 2022, USA [35]	Prospective cohort	M, F, 15± 1.96 y, 1,927	Marathon	Symptom-based definition	Training hours
Hopkins et al., 2024, USA	Prospective descriptive epidemiology	M, F, 14-18 y, 8,060 ED cases	T&F	Medical-attention (ED-based) definition	Incidence per 1000 athletes
Hopkins et al., 2022, USA [34, 43]	Prospective descriptive epidemiology	M, F, 18-23 y, 367,285 AEs	T&F	Medical attention definition	AE hours
Huxley et al., 2014, Australia [14]	Retrospective cohort	M, F, 17.7 ± 2.4 y, 103	T&F	Time-loss definition	Weekly and annual trainingloads
Jacobsson et al., 2013, Sweden [11]	Prospective cohort	M, F, 17± 0.1 y, 126	T&F	Time-loss & medical attention definition	Athlete hours
Kraus et al., 2019, USA [44]	Mixed retrospective + prospective cohort	M, 19.5 ± 1.3 y, 156	Middle and longdistance running	Physiciandiagnosed, imagingconfirmed SFs	Athlete yrs
Lempke et al., 2022, USA	Retrospective, crosssectional study	M, F, 15 y, 129	Longdistance running (≥800 m)	Medical attention definition	Training hours
Li et al., 2025, Republic of Korea [45]	Retrospective, cross-sectional	M, F, 14± 3.12 y, 257	T & F	Symptom-based (self-report) definition	NS
Luedke et al., 2016, USA	Prospective cohort	M, F, 16.2 ± 1.3 y, 68	Distance running	Time-loss definition	AEs hours
Malliaropoulos et al., 2018, Greece	Retrospective cohort, 17 y	M, F, 20.0 ± 3.2 y, 257	T & F	Diagnosis-specific definition	Incidence proportion
Mann et al., 2021, England (Prospective)	Prospective cohort, 24 W	M, F, 13–18 y, 136	Distance running, steeplechase	Consensus-based (IOC/IAAF) definition	Running hours
Mann et al., 2021, United Kingdom	Retrospective crosssectional	M, F, 15.9 ± 1.5y, 113	Distance running, steeplechase	Consensus-based (IOC/IAAF) definition	Training hours
MartínezSilván et al., 2021, Qatar [30]	Prospective cohort	M, 14.9 ± 1.8 y, 178	T&F	Time-loss definition	AE hours
Meyers et al., 2024, USA [37]	Prospective cohort	M, F, 16.1 ± 1.2 y, 51	Longdistance running	Time-loss definition	NR
Pierpoint et al., 2016, USA [32]	Retrospective descriptive epidemiology	M, F, 14-18 y, 2485	T&F	Time-loss definition	AE hours
Radel et al., 2023, USA	Retrospective descriptive epidemiology	M, F, 14.0 ± 2.2 y, 749	Hurdling	Medical-attention (ED-based) definition	NR
Rebella, 2015, USA [46]	Prospective cohort	M, F, 20 ± 1.4 y, 135	Pole vault	Time-loss definition	AE hours
Reid et al., 2012, USA	Retrospective descriptive epidemiology	M, F, 10-18y, 4496 ED cases	Sprinting, hurdles, relays	Medical-attention (ED-based) definition	NR
Rhim et al., 2025, USA	Retrospective descriptive epidemiology	M, F, 14-18y, 727	Jumping events & pole vault	Symptom-based & time-loss definition	AEs hours
Sugiura et al., 2022, Japan	Retrospective cohort	M, F, 18-24y, 613	Sprints	Symptom-based & time-loss definition	Incidence per 1000 athletes
Tenforde et al., 2011, USA [47]	Retrospective crosssectional	M, F, 13-18y, 748	Long distance running	Symptom-based (self-report) definition	Average weekly mileage, % miles on pavement/hills, workouts/week
Tokutake & Kuramochi, 2020, Japan	Retrospective descriptive epidemiology	M, F, 18-23y, 1078	T & F	Diagnosis-specific (consensus-based) definition	Persontime
Tsukahara et al., 2023, Japan	Crosssectional	F, 20.65 y, 84	Sprint, hurdles, long jump, triple jump, heptathlon, relays	Symptom-based (self-report) definition	No exposure quantification
Tsukahara et al., 2025, Japan	Crosssectional analytical	M, F, 18-23y, 1078	T & F	Physiciandiagnosed, imagingconfirmed SFs	No exposure quantification
Tyflidis et al., 2012, Greece	Prospective cohort	M, F, 12–15 y, 2045	T & F	Time-loss & medical attention definition	NR
Watson & DiMartino, 1987, USA	Prospective cohort	M, F, 15.8 ± 1.3 y, 257	T&F	Time-loss definition	Weekly training hours
Wen, 2022, China [48]	Descriptive cross-sectional survey	M, F, 15-24 y, 16	Jumping events & pole vault	NR	NR
Wilzman et al., 2022, USA [49]	Prospective cohort	M, F, 18-23 y, 39	Middledistance, distance running	Physiciandiagnosed, imagingconfirmed BSIs	NR
Wolff et al., 2025, USA	Prospective epidemiological	M, F, 18.7 ± 1.1 y, 221	Middle- & long-distance running	Physiciandiagnosed, imagingconfirmed BSIs	Athlete-years at risk per M
Yamamoto, 1993, Japan [50]	Prospective cohort	M, 18-23 y, 64	Sprint, long jump, triple jump, hurdles, decathlon	Diagnosis-specific definition	NR

Abbreviations: AEs Athlete Exposures, AT athletic trainers, BSI bone stress injuries, CE competition exposure, comp Competition, d days, Def Definition, E Elite, ED Emergency Department, HIPAA Health Insurance Portability and Accountability Act, HIS Hamstring Strain Injuries, hrs Hours, IPR injury proportion ratio, IRR Injury rate ratios, ISP Injury Surveillance Program, LBP Low Back Pain, M months, MSK musculoskeletal, NA Not applicable, NEISS National Electronic Injury Surveillance System, NS Not specified, NTLI non-time-loss injuries, OSTRC-H Oslo Sports Trauma Research Centre Questionnaire on Health Problems, PT Physiotherapist, RA Recreationally Active, ROM Range of motion, RR Relative risk, RRI Running-related injury, SLR Single Leg Raise, T&F track and field events, TE training exposure, TLI time-loss injuries, Trn Training, W weeks, y years

Table 2.

Summary of reported risk and protective factors for musculoskeletal injury in youth track and field athletes

Author, Year, Country	Study design	Sample	Factor	Category	Measurement	Key findings
Bennell & Crossley, 1996, Australia	Retrospective cohort	20-21 y, T & F, M, F	1. Older age	Risk, Intrinsic	Age groups	Older age associated with higher injury odds (OR≈3.6–4.2)
			2. Increased flexibility	Risk, Intrinsic	Flexibility score	Higher flexibility associated with greater injury risk (OR = 5.3)
			3. Menstrual disturbance	Risk, Intrinsic	Menstrual questionnaire	Menstrual disturbance more common in athletes with multiple injuries
			4. Recent training changes	Risk, Extrinsic	Self-reported	Most injuries linked to recent training changes
Bennell et al., 1995, Australia [38]	Retrospective cohort	20-21 y, T & F, F	1. Age at menarche (older)	Risk, Intrinsic	Menstrual questionnaire	Later menarche reported in athletes with stress fractures
			2. Menstrual Index (lower MI)	Risk, Intrinsic	Derived index	Lower menstrual index reported in injured athletes
			3. Menstrual disturbance ratio (higher)	Risk, Intrinsic	Derived ratio	Higher menstrual disturbance ratio reported in injured athletes
			4. Years with <9 menses/year (higher)	Risk, Intrinsic	Menstrual questionnaire	Greater duration of menstrual disturbance reported in injured athletes
			5. Oligomenorrhea history	Risk, Intrinsic	Menstrual questionnaire	More common in injured athletes; associated with increased injury risk
			6. Eating attitude	Risk, Intrinsic	EAT-40 questionnaire	Higher scores reported in injured athletes
			7. “Careful about weight”	Risk, Intrinsic	Dietary behaviour questionnaire	More frequent in injured athletes; associated with increased injury risk
			8. Dieting >1 month	Risk, Intrinsic	Dietary questionnaire	More common in injured athletes
Bennell et al., 1996, Australia [39]	Prospective cohort	20.4 ± 2.1 y, T & F, F	1. Higher lumbar spine & lower limb BMD	Protective, Intrinsic	DEXA (Hologic QDR-1000 W)	Lower BMD observed in injured athletes
			2. Higher total-body BMC	Protective, Intrinsic	DEXA	Lower BMC reported in injured athletes
			3. Higher lower limb LM	Protective, Intrinsic	DEXA	Lower LM reported in injured athletes
			4. Lower calf girth adjusted for subcutaneous fat	Risk, Intrinsic	Tape measure and skinfold calliper	Smaller calf girth associated with higher injury risk (OR≈4.0)
			5. Age at menarche (older)	Risk, Intrinsic	Menstrual questionnaire	Later menarche associated with higher injury risk (OR≈4.1)
			6. Fewer menses in prior year	Risk, Intrinsic	Menstrual questionnaire	Lower menstrual frequency reported in injured athletes
			7. Leg-length discrepancy (higher)	Risk, Intrinsic	Tape measurement	More common in injured athletes
			8. Lower dietary fat intake (g·kg⁻¹·day⁻¹)	Risk, Extrinsic	Dietary questionnaire	Lower fat intake reported in injured athletes
Carbuhn et al., 2020, United States	Retrospective case-control	19.8 ± 1.4 y, distance runners, M, F	1. Higher leg BMD	Protective, Intrinsic	DEXA	Higher leg BMD associated with lower injury risk
			2. Higher total upper body mass (TUB)	Risk, Intrinsic	DEXA	Higher TUB associated with increased injury risk
Carbuhn et al., 2022, USA [40]	Retrospective case-control	20.0 ± 0.3 y, distance runners, M, F	1. Higher total body BMD	Protective, Intrinsic	DEXA	Higher leg BMD associated with lower injury risk
			2. Leg/trunk length ratio (lower)	Risk, Intrinsic	Anthropometry	Lower leg-to-trunk ratio reported in injured athletes
			3. Higher total-body fat mass	Risk, Intrinsic	DEXA	Lower fat mass reported in injured athletes
			4. Arm length (shorter)	Risk, Intrinsic	Anthropometry	Shorter arm length reported in injured athletes
Edouard et al., 2019, France	Crosssectional	15-19 y, pole vaulters, M, F	1 Reduced grip hand height at takeoff	Risk, Intrinsic	High-speed video & Kinovea	Lower grip height associated with higher injury risk (OR = 0.817)
			2. Higher training volume per week (hrs)	Risk, Extrinsic	Selfreported	Higher training volume associated with increased injury risk
			3. Greater last stride adjustment (SLadj, cm)	Risk, Intrinsic	Optojump Next	Greater stride adjustment associated with higher injury risk
			4. Greater speed increase in last 5 m (1Spd, m·s⁻¹)	Risk, Intrinsic	Stalker Pro II radar	Greater speed increase associated with higher injury risk
			5. Longer ground contact time (tc, s)	Risk, Intrinsic	Optojump Next	Longer ground contact time associated with higher injury risk
			6. Greater stride length variability (SLvar, cm)	Risk, Intrinsic	Optojump Next	Greater stride variability associated with higher injury risk (OR = 1.378)
Ek et al., 2022, Sweden [42]	Prospective cohort	13.9 ± 0.9 y, T & F, M, F	1. Gender (male)	Risk, Intrinsic	Baseline questionnaire	Shorter time to first injury observed in girls compared with boys
			2. Training volume	Risk, Extrinsic	Training hours	Higher training volume associated with increased injury risk
			3. Training in track spikes	Risk, Extrinsic	Footwear exposure	Spike use associated with increased injury risk, particularly at lower training volumes (HR = 6.1)
Enoki et al., 2021, Japan	Prospective cohort	19.6 ± 1.1 y, pole vaulters & decathletes, M, F	1. Chronic LBP history	Risk, Intrinsic	Injury history	Higher injury occurrence reported in athletes with prior LBP
			2. Lower hip flexor strength	Risk, Intrinsic	Biodex System 3 isokinetic testing	Lower hip flexor strength associated with higher injury risk (HR = 3.805)
			3. Lower hip flexion ROM (take-off leg)	Risk, Intrinsic	Camera + ImageJ ROM analysis	Lower ROM associated with higher injury risk
			4. Lower hip flexion ROM (nontake-off leg)	Risk, Intrinsic	Camera + ImageJ ROM analysis	Lower ROM associated with higher injury risk
			5. Lower hip extension ROM (take-off leg)	Risk, Intrinsic	Camera + ImageJ ROM analysis	Lower ROM associated with higher injury risk
Goldman et al., 2022, USA [35]	Prospective cohort	15.0 y, marathoners, M, F	1. School status	Risk, Extrinsic	Baseline demographics	Higher injury occurrence reported in high school vs. middle school athletes
			2. Weekly running distance (training load)	Risk, Extrinsic	Coach-reported total mileage	Higher weekly running distance associated with increased injury risk
Huxley, et al., 2014, Australia [14]	Retrospective crosssectional	17.7 ± 2.4 y, T & F, M, F	1. High training load/intensity	Risk, Extrinsic	Training history	Higher training load associated with increased injury occurrence, particularly for overuse injuries
			2. Greater cumulative intense training load	Risk, Extrinsic	Training history	Greater cumulative training load associated with increased overuse injury occurrence
Jacobsson et al., 2013, Sweden [11]	Prospective cohort	17 y, T & F, M, F	1 Previous severe injury	Risk, Intrinsic	Injury history	Previous severe injury associated with higher injury risk
			2. Training Load Rank Index (TLRI)	Risk; extrinsic	Weekly training logs	Higher training load associated with increased injury risk
Kraus et al., 2019, USA [44]	Mixed retrospective + prospective cohort	19.5 ± 1.3 y, distance runners, M	1. Cumulative Triad Risk Score	Risk, Intrinsic	Clinical/physiological assessment	Higher triad risk score associated with increased injury risk
			2. History of bone stress injury	Risk, Intrinsic	Medical history	Previous bone stress injury associated with higher injury risk
Lempke et al., 2022, USA	Crosssectional	18-36 y, distance runners, M, F	1. Intentional weight-loss	Risk, Intrinsic	Questionnaire	Intentional weight loss reported more frequently in injured athletes
			2. Lowe neuromuscular endurance measured	Protective, Intrinsic	Singleleg bridge endurance test	Lower endurance reported in injured athletes
			3. Lower movement quality	Protective, Intrinsic	Standardized FMS™ score	Lower movement quality reported in injured athletes
Li et al., 2025, Republic of Korea [45]	Crosssectional	14.0 ± 3.12 y, T & F, M, F	Higher training load	Risk, Extrinsic	Self-reported	Higher training load associated with increased injury rates
Luedke et al., 2016, USA	Prospective cohort	16.2 ± 1.3 y, distance runners, M, F	1. Gender (male)	Risk, Intrinsic	Baseline questionnaire	Higher shin injury occurrence reported in males compared with females
			2. Lower step rate (cadence)	Risk, Intrinsic	Polar RCX5 + S3+ Stride Sensor	Lower step rate associated with higher injury risk (OR = 6.67)
Malliaropoulos et al., 2018, Greece	Retrospective cohort	20.0 ± 3.2 y, T & F, M, F	1. Previous ankle ligament injury	Risk, Intrinsic	Medical history	Previous ankle injury associated with higher risk of subsequent hamstring injury
			2. Athletic discipline	Risk, Extrinsic	Event group	Injury patterns varied across disciplines, with hamstring injuries more frequently reported in running-based events
Moffit et al., 2020, USA	Cross-sectional	20.0 ± 1.5 y, distance runners, M, F	Higher back-squat strength	Protective, Intrinsic	Laboratory 3D running analysis	Higher strength associated with knee mechanics linked to reduced injury risk
Sugiura et al., 2017, Japan	Descriptive epidemiology	18-24 y, sprinters, M	1. Multicomponent training programme	Protective, Extrinsic	Injury logs	Multicomponent programme (agility, flexibility, eccentric training) associated with lower injury occurrence
			2. Lower-fatigue exposure	Protective, Extrinsic	Training records	Lower fatigue exposure associated with reduced injury occurrence
Tenforde et al., 2011, USA [47]	Retrospective survey	15.4 ± 1.15 y, distance runners, M, F	1. Higher weekly mileage	Risk, Extrinsic	Training records	Higher weekly mileage reported in association with overuse injuries and stress fractures
			2. More running on pavement	Risk, Extrinsic	Training records	Greater running on pavement reported in association with injury occurrence
			3. Faster performance times	Risk, Intrinsic	Training records	Faster performance times reported in association with injury history
			4. Older age	Risk, Intrinsic	Demographics	Older age reported in association with injury history
Tokutake & Kuramochi, 2020, Japan	Retrospective cohort	18-22 y, T & F, M, F	1. Gender (Male)	Risk, Intrinsic	Baseline questionnaire	Higher injury occurrence reported in males
			2. Event group	Risk, Extrinsic	As above	Injury occurrence varied across disciplines, with higher rates in sprint and combined events
			3. Lowermiddle temperature (10.0–14.9 °C)	Risk, Extrinsic	Temperature from Meteorological Agency	Lower ambient temperature associated with increased injury occurrence
Tokutake et al., 2018, Japan [51]	Prospective nested case-control	19.6 ± 1.1 y, T&F, M, F	History of HSI	Risk, Intrinsic	Injury history	Previous hamstring injury associated with higher risk of subsequent injury.
Tsukahara et al., 2023, Japan	Cross-sectional, 1 competitive season	17.4 y T & F, M, F	1. Higher training hours per day	Risk, Extrinsic	Training records	Higher training volume associated with increased overtraining-related outcomes (OR = 1.74)
			2. Skipping meals	Risk, Extrinsic	Questionnaire	Meal skipping reported more frequently with overtraining-related outcomes (OR = 3.73)
			3. Snacks/light meals regularly	Protective, Extrinsic	Questionnaire	Regular snack consumption associated with lower overtraining-related outcomes (OR = 0.46)
			4. History of anaemia	Risk, Intrinsic	Questionnaire	Anaemia reported more frequently in affected athletes
			5. History of SF	Risk, Intrinsic	Injury history	Previous stress fracture more common in affected athletes
			6. Age (older)	Risk, Intrinsic	Questionnaire	Older age reported in association with injury outcomes
			7. Age at menarche (older)	Risk, Intrinsic	Questionnaire	Later menarche reported in association with injury outcomes (OR = 0.86)
			8. BMI (higher)	Risk, Intrinsic	Self-reported weight & height	Higher BMI reported in association with injury outcomes (OR = 0.85)
Tsukahara et al., 2025, Japan	Cross-sectional observational	20.7 ± 2.1 y, eumenorrheic sprinters & jumpers, F	1. Prolactin (higher)	Risk, Intrinsic	Blood biomarker	Higher prolactin levels reported in association with stress fracture history (OR = 1.104)
Wen, 2022, China [48]	Crosssectional	15-25 y, jumpers, M, F	1. Long jump event	Risk, Extrinsic	Questionnaire	Injury occurrence varied by event, with higher rates in certain jumping disciplines.
			2. Landing pattern: singleleg vs. twoleg landing	Risk, Intrinsic	Self-reported	Single-leg landing associated with higher injury occurrence
			3. Neuromuscular fatigue state	Risk, Intrinsic	Self-reported	Fatigue reported in association with increased injury occurrence
Wilzman et al., 2022, USA [49]	Prospective cohort, 4 y	18-23 y, distance runners, M, F	1. Higher hindfoot impulse during overground walking	Risk, Intrinsic	PedarX inshoe pressure insoles	Higher hindfoot loading associated with injury occurrence
			2. Higher triad cumulative risk score	Risk, Intrinsic	Baseline medical questionnaire	Higher triad risk score associated with multiple injuries
			3. Higher first metatarsal pressure during jump take-off	Risk, Intrinsic	PedarX insoles	Higher forefoot pressure associated with injury occurrence
			4. Lower hindfoot impulse during shuttle run	Risk, Intrinsic	PedarX insoles	Lower hindfoot loading associated with injury occurrence in specific contexts
			5. Higher medial midfoot peak pressure during shuttle run	Risk, Intrinsic	PedarX insoles	Higher midfoot pressure associated with injury occurrence
Yamamoto, 1993, Japan [50]	Prospective, 2 y follow-up	18-23 y, T & F, M	1. Lower hamstring strength	Risk, Intrinsic	Straingauge system	Lower hamstring strength associated with higher injury risk
			2. Lower Hamstring to Quadriceps strength ratio	Risk, Intrinsic	As above	Lower strength ratio associated with higher injury risk
			3. Higher bilateral strength imbalance-hip flexion	Risk, Intrinsic	As above	Greater strength asymmetry associated with injury occurrence
			4. Higher bilateral strength imbalance-knee extension	Risk, Intrinsic	As above	Greater bilateral imbalance associated with injury occurrence

Abbreviations: 1RM 1-repetition maximum, Avg Average, BMD Bone Mineral Density, BSI bone stress injury, BW Body weight, d days, d Cohen’s d, DEXA DualEnergy XRay Absorptiometry, ER External rotation, FFQ Food frequency questionnaire, FMS™ Functional Movement Screen™, HIPAA Health Insurance Portability and Accountability Act, HIS Hamstring Strain Injury, H/Q ratio Hamstring to Quadriceps strength ratio, hrs hours, IR Internal rotation, IRR Incidence rate ratio, LBP low back pain, LEA Low energy availability, LM Lean Mass, M months, MI Menstrual Index, MT1 First metatarsal, MVC Maximum Voluntary Contraction, NR Not reported, OTS Overtraining syndrome, ROC Receiver Operating Characteristic, ROM Range of motion, RRI Running related injuries, SF stress-fracture, SLadj last-stride adjustment, SLvar stride length variability, T & F track & field, TLRI Training Load Rank Index, TUB Total upper body mass

What are the risk and protective factors for musculoskeletal injury, and how have they been measured in youth track and field athletes?

Protective factors

Protective factors were reported less frequently than risk factors and included physiological, neuromuscular, biomechanical, and behavioural characteristics (Table 2). Greater bone mineral density (BMD), bone mineral content (BMC), and lean mass were consistently associated with lower occurrence of BSIs and stress fractures [38–40]. Higher lower-body strength, improved movement quality, and greater trunk and hip endurance were also associated with lower injury rates [38–40]. Multicomponent injury-prevention programmes [11] and regular meal or snack consumption [44] were also associated with lower injury occurrence.

Risk factors

Reported risk factors were classified as intrinsic or extrinsic (Table 2). A history of prior injury was consistently reported as being associated with injury occurrence during follow‑up. Risk factors included previous injury [44, 51], unfavourable anthropometric characteristics [33, 38, 39], reduced lower-limb strength, strength asymmetries [50], faster race performances [47] and menstrual-health-related factors [38, 39]. Extrinsic factors were dominated by training-load variables, including greater training volume, mileage, and high-intensity exposure [11, 14, 47]. Footwear and environmental factors, such as spike use [42] and training on harder surfaces [47], were also associated with increased injury occurrence.

How these risk and protective factors have been measured?

Risk and protective factors were assessed using a combination of objective and self-report measures. Physiological, anthropometric, and bone-health variables were commonly evaluated using DEXA, anthropometric assessments, and strength-testing protocols. Neuromuscular and biomechanical characteristics were assessed using dynamometry, endurance tests, motion-analysis technologies, wearable sensors, force platforms, video-based techniques, Functional Movement Screen, landing-error scoring systems, and pressure insoles [12, 49]. Menstrual, nutritional, behavioural, injury-history, and training-related variables were primarily collected through questionnaires, athlete self-reports, training logs, coach reports, athlete diaries, and session-RPE measures. Study designs used to investigate these factors are summarised in Fig. 2.

Fig. 2.

Study design × factor category heatmap in youth track and field injury studies

To what extent do injury data collection methods in youth track and field literature adhere to methodological standards and what gaps exist in current practice?

Reporting of consensus-recommended methodological components varied across studies (Tables 3 and 4).

Table 3.

Alignment with consensus statement part I: Injury related methodological standards

Author, Year, Country	Recordable incident definition used?	Overuse + gradualonset included?	Timeloss vs. medicalattention specified?	Body region coded according to consensus?	Injury type classified (e.g., strain, tendon, BSI)?	Onset mode recorded (sudden/gradual)?	Cause recorded (contact, noncontact, overuse)?	Severity reported in days lost?	Score for part I (out of 8)
Bennell & Crossley, 1996, Australia	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	P (0.5)	A (1.0)	7.5/8
Bennell et al., 1995, Australia (Retrospective) [38]	A (1.0)	A (1.0)	P (0.5)	A (1.0)	A (1.0)	N (0)	N (0)	N (0)	4.5/8
Bennell et al., 1996, Australia (Prospective) [39]	A (1.0)	A (1.0)	P (0.5)	A (1.0)	A (1.0)	N (0)	N (0)	N (0)	4.5/8
Boltz et al., 2021, USA [31]	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	P (0.5)	A (1.0)	P (0.5)	7.0/8
Braçic et al., 2024, Spain [28]	P (0.5)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	N (0)	N (0)	A (1.0)	5.5/8
Carbuhn et al., 2022, USA [40]	A (1.0)	A (1.0)	P (0.5)	A (1.0)	A (1.0)	N (0)	N (0)	N (0)	4.5/8
Carbuhn et al., 2020, USA	A (1.0)	A (1.0)	P (0.5)	A (1.0)	A (1.0)	N (0)	N (0)	N (0)	4.5/8
Carragher et al., 2019, Ireland [29]	P (0.5)	A (1.0)	P (0.5)	P (0.5)	P (0.5)	A (1.0)	N (0)	N (0)	4.0/8
Edouard et al.; 2012, France (Retrospective)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	4.5/8
Edouard et al., 2012, France (Prospective)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	P (0.5)	7.5/8
Edouard et al., 2024; Africa [41]	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	P (0.5)	7.5/8
Edouard et al., 2024; France [10]	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	P (0.5)	A (1.0)	7.5/8
Ek et al., 2022, Sweden [42]	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	8.0/8
Enoki et al., 2021, Japan (Prospective)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	P (0.5)	A (1.0)	7.5/8
FeddermannDemont et al., 2014, multi-nation sample	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	P (0.5)	7.5/8
Goldman et al., 2022, USA [35]	A (1.0)	A (1.0)	A (1.0)	A (1.0)	P (0.5)	P (0.5)	N (0)	A (1.0)	6.0/8
Hopkins et al., 2022, USA (All injuries) [34]	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	8.0/8
Hopkins et al., 2022, USA (Recurrent Injuries) [43]	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	8.0/8
Huxley et al., 2014, Australia [14]	A (1.0)	A (1.0)	A (1.0)	P (0.5)	A (1.0)	P (0.5)	P (0.5)	A (1.0)	6.5/8
Jacobsson et al., 2013, Sweden [11]	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	8.0/8
Kraus et al., 2019, USA [44]	A (1.0)	A (1.0)	P (0.5)	P (0.5)	A (1.0)	A (1.0)	A (1.0)	N (0)	6.0/8
Li et al., 2025, Republic of Korea [45]	N (0)	P (0.5)	N (0)	P (0.5)	P (0.5)	N (0)	A (1.0)	N (0)	2.5/8
Luedke et al., 2016, USA	A (1.0)	A (1.0)	A (1.0)	P (0.5)	A (1.0)	A (1.0)	P (0.5)	A (1.0)	7.0/8
Malliaropoulos et al., 2018, Greece	A (1.0)	N (0)	P (0.5)	P (0.5)	A (1.0)	A (1.0)	P (0.5)	N (0)	4.5/8
Mann et al., 2021, England (Prospective)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	P (0.5)	A (1.0)	P (0.5)	A (1.0)	7.0/8
Mann et al., 2021, United Kingdom (Retrospective)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	P (0.5)	A (1.0)	A (1.0)	A (1.0)	7.5/8
MartínezSilván et al., 2021, Qatar [30]	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	P (0.5)	A (1.0)	7.5/8
Meyers et al., 2024, USA [37]	P (0.5)	A (1.0)	N (0)	A (1.0)	A (1.0)	N (0)	N (0)	N (0)	3.5/8
Pierpoint et al., 2016, USA [32]	A (1.0)	A (1.0)	P (0.5)	A (1.0)	A (1.0)	N (0)	A (1.0)	A (1.0)	6.5/8
Rebella, 2008, USA	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	N (0)	A (1.0)	A (1.0)	7.0/8
Rebella, 2015, USA [52]	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	8.0/8
Rhim et al., 2024, USA	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	N (0)	A (1.0)	A (1.0)	7.0/8
Rhim et al., 2025, USA	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	N (0)	A (1.0)	A (1.0)	7.0/8
Tenforde et al., 2011, USA [47]	N (0)	P (0.5)	N (0)	P (0.5)	P (0.5)	N (0)	N (0)	N (0)	1.5/8
Tyflidis et al., 2012, Greece	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	N (0)	N (0)	N (0)	5.0/8
Watson & DiMartino, 1987, USA	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	N (0)	P (0.5)	A (1.0)	6.5/8
Wen, 2022, China	N (0)	P (0.5)	N (0)	A (1.0)	P (0.5)	N (0)	P (0.5)	N (0)	2.5/8
Wolff et al., 2025, USA	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	N (0)	N (0)	A (1.0)	6.0/8

Coding scheme:

1 mark= (A) Aligned: The study explicitly adhered to the recommended methodological standard

0.5 mark = (P) Partially aligned: The study addressed the domain but did not fully meet consensus recommendations (e.g., incomplete detail, partial adherence, or modified definition)

0 = (N) Not aligned: The study did not address the domain, or the reported method clearly deviated from consensus guidance

0 =(U) Unclear: The study did not provide sufficient information to determine alignment

Table 4.

Alignment with consensus statement part II: Exposure, surveillance, and reporting standards

Author, Year, Country	Training vs. competition exposure separated?	Exposure quantified (hours, AE, sessions, km)?	Prospective/retrospective design with regular reporting?	Regular reporting frequency (weekly/eventbased/daily/none)?	Medical staff involved in case confirmation?	Selfreport validated by clinician?	Standard coding system used (OSICS/ICD/others)?	Baseline data complete (age, sex, event group, training load)?	STROBEaligned reporting?	Score for part II (out of 9)
Bennell & Crossley, 1996, Australia	P (0.5)	A (1.0)	N (0)	N (0)	P (0.5)	N (0)	N (0)	A (1.0)	P (0.5)	3.5/9
Bennell et al., 1995, Australia (Retrospective) [38]	N (0)	P (0.5)	N (0)	N (0)	A (1.0)	A (1.0)	N (0)	A (1.0)	P (0.5)	4.0/9
Bennell et al., 1996, Australia (Prospective) [39]	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	6.0/9
Boltz et al., 2021, USA [31]	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	9.0/9
Braçic et al., 2024, Spain [28]	A (1.0)	A (1.0)	N (0)	N (0)	P (0.5)	N (0)	P (0.5)	A (1.0)	P (0.5)	4.5/9
Carbuhn et al., 2022, USA [40]	N (0)	P (0.5)	N (0)	N (0)	A (1.0)	A (1.0)	A (1.0)	N (0)	P (0.5)	4.0/9
Carbuhn et al., 2020, USA	N (0)	P (0.5)	N (0)	N (0)	A (1.0)	A (1.0)	N (0)	A (1.0)	P (0.5)	4.0/9
Carragher et al., 2019, Ireland [29]	N (0)	N (0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	N (0)	A (1.0)	A (1.0)	6.0/9
Edouard et al.; 2012, France (Retrospective)	N (0)	P (0.5)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	N (0)	A (1.0)	P (0.5)	6.0/8
Edouard et al., 2012, France (Prospective)	N (0)	P (0.5)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	N (0)	A (1.0)	P (0.5)	6.0/9
Edouard et al., 2024; Africa [41]	P (0.5)	N (0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	6.5/9
Edouard et al., 2024; France [10]	N (0)	P (0.5)	N (0)	N (0)	N (0)	P (0.5)	A (1.0)	A (1.0)	P (0.5)	3.0/9
Ek et al., 2022, Sweden [49]	A (1.0)	A (1.0)	A (1.0)	A (1.0)	P (0.5)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	8.5/9
Enoki et al., 2021, Japan	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	9.0/9
FeddermannDemont et al., 2014, multi-nation sample	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	9.0/9
Goldman et al., 2022, USA [35]	A (1.0)	A (1.0)	A (1.0)	A (1.0)	N (0)	N (0)	N (0)	A (1.0)	P (0.5)	5.5/9
Hopkins et al., 2022, USA (All injuries)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	P (0.5)	A (1.0)	A (1.0)	8.5/9
Hopkins et al., 2022, USA (Recurrent Injuries) [34]	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	P (0.5)	A (1.0)	A (1.0)	8.5/9
Huxley et al., 2014, Australia [14]	N (0)	N (0)	N (0)	N (0)	P (0.5)	N (0)	N (0)	A (1.0)	P (0.5)	2.0/9
Jacobsson et al., 2013, Sweden [11]	A (1.0)	A (1.0)	A (1.0)	A (1.0)	P (0.5)	P (0.5)	A (1.0)	A (1.0)	A (1.0)	8.0/9
Kraus et al., 2019, USA [44]	N (0)	N (0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	N (0)	A (1.0)	A (1.0)	4.0/9
Li et al., 2025, Republic of Korea [45]	N (0)	N (0)	N (0)	N (0)	N (0)	N (0)	N (0)	A (1.0)	P (0.5)	1.5/9
Luedke et al., 2016, USA	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	N (0)	A (1.0)	A (1.0)	8.0/9
Malliaropoulos et al., 2018, Greece	N (0)	N (0)	N (0)	N (0)	A (1.0)	A (1.0)	N (0)	A (1.0)	P (0.5)	3.5/9
Mann et al., 2021, England (Prospective)	P (0.5)	A (1.0)	A (1.0)	A (1.0)	N (0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	7.5/9
Mann et al., 2021, United Kingdom (Retrospective)	N (0)	P (0.5)	N (0)	N (0)	N (0)	N (0)	A (1.0)	P (0.5)	A (1.0)	3.0/9
Martínez Silván et al., 2021, Qatar [30]	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	9.0/9
Meyers et al., 2024, USA [37]	N (0)	N (0)	N (0)	N (0)	N (0)	N (0)	N (0)	A (1.0)	P (0.5)	1.5/9
Pierpoint et al., 2016, USA [32]	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	P (0.5)	A (1.0)	A (1.0)	8.5/9
Rebella, 2008, USA	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	9.0/9
Rebella, 2015, USA [52]	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	9.0/9
Rhim et al., 2024, USA	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	P (0.5)	A (1.0)	A (1.0)	8.5/9
Rhim et al., 2025, USA	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	P (0.5)	A (1.0)	A (1.0)	8.5/9
Tenforde et al., 2011, USA [47]	N (0)	N (0)	N (0)	N (0)	N (0)	N (0)	N (0)	A (1.0)	P (0.5)	4.0/9
Tyflidis et al., 2012, Greece	N (0)	N (0)	A (1.0)	A (1.0)	P (0.5)	P (0.5)	N (0)	A (1.0)	P (0.5)	4.5/9
Watson & DiMartino, 1987, USA	N (0)	N (0)	A (1.0)	P (0.5)	A (1.0)	A (1.0)	N (0)	A (1.0)	P (0.5)	5.0/9
Wen, 2022, China [48]	N (0)	N (0)	N (0)	N (0)	N (0)	N (0)	N (0)	P (0.5)	P (0.5)	1.0/9
Wolff et al., 2025, USA	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	A (1.0)	P (0.5)	A (1.0)	A (1.0)	8.5/9

Coding scheme:

1 mark= (A) Aligned: The study explicitly adhered to the recommended methodological standard

0.5 mark = (P) Partially aligned: The study addressed the domain but did not fully meet consensus recommendations (e.g., incomplete detail, partial adherence, or modified definition)

0 = (N) Not aligned: The study did not address the domain, or the reported method clearly deviated from consensus guidance

0 =(U) Unclear: The study did not provide sufficient information to determine alignment

Studies, including Ek et al. [42], Hopkins et al. [43], Jacobsson et al. [11], Edouard et al. [12, 53], Martínez-Silván et al. [30], and Rebella [46, 52], reported multiple components from Part I of the consensus statement. Several retrospective and cross-sectional studies, including Tenforde et al. [47], Li et al. [45] and Wen [48], reported a smaller range of injury-definition and reporting elements described in the Athletics Consensus Statement, such as injury definitions, injury onset classification, injury cause, and severity reporting. Overall alignment scores are presented in Table 5, with domain-level percentages shown in Fig. 3.

Table 5.

Overall score rating the extent of alignment of each study’s methodology with the consensus statement

Author, Year, Country	Score for part I (out of 8)	Score for part II (out of 9)	Total score (out of 17)	Overall comment
Bennell & Crossley, 1996, Australia	7.5	3.5	11.0	Strong alignment with injury definitions and coding; weak exposure/surveillance reporting
Bennell et al., 1995, Australia [38]	4.5	4.0	8.5	Partial coding + limited severity & exposure documentation
Bennell et al., 1996, Australia [39]	4.5	6.0	10.5	Strong exposure methods; incomplete onset/cause reporting
Boltz et al., 2021, USA [31]	7.0	9.0	16.0	Excellent compliance across both parts; minor partial areas only
Braçic et al., 2024, Spain [28]	5.5	4.5	10.0	Good injury classification; limited surveillance and coding
Carbuhn et al., 2022, USA [40]	4.5	4.0	8.5	Limited onset/cause reporting; exposure only partially addressed
Carbuhn et al., 2020, USA	4.5	4.0	8.5	Similar to 2022: modest alignment across domains
Carragher et al., 2019, Ireland [29]	4.0	6.0	10.0	Prospective design; limited classification and coding
Edouard et al., 2012, France (Retrospective)	4.5	6.0	10.5	Strong injury classification; partial exposure quantification
Edouard et al., 2012, France (Prospective)	7.5	6.0	13.5	High methodological alignment but limited coding in surveillance
Edouard et al., 2024 (Africa) [41]	7.5	6.5	13.5	Very strong injury methodology; moderate exposure alignment
Edouard et al., 2024 (France) [10]	7.5	3.0	10.5	High coding detail; limited exposure/surveillance
Ek et al., 2022, Sweden [42]	8.0	8.5	16.5	Nearperfect alignment across all domains
Enoki et al., 2021, Japan	7.5	9.0	16.5	Excellent surveillance & exposure reporting; minor partiality
FeddermannDemont et al., 2014	7.5	9.0	16.5	Comprehensive adherence across almost all categories
Goldman et al., 2022, USA [35]	6.0	5.5	11.5	Good injury coding; limited medical verification & coding
Hopkins et al., 2022 (All injuries) [34]	8.0	8.5	16.5	Strong prospective monitoring and complete reporting
Hopkins et al., 2022 (Recurrent) [34]	8.0	8.5	16.5	Same strengths as above; excellent completeness
Huxley et al., 2014, Australia [14]	6.5	2.0	8.5	Good injury coding; poor exposure/surveillance
Jacobsson et al., 2013, Sweden [11]	8.0	8.0	16.0	High-quality reporting across all domains
Kraus et al., 2019, USA [44]	6.0	4.0	10.0	Strong injury details; limited exposure quantification
Li et al., 2025, Korea [45]	2.5	1.5	4.0	Minimal alignment with consensus standards
Luedke et al., 2016, USA	7.0	8.0	15.0	High completeness; minor coding inconsistencies
Malliaropoulos et al., 2018, Greece	4.5	3.5	8.0	Limited injury definition detail; minimal surveillance detail
Mann et al., 2021, England (Prospective)	7.0	7.5	14.5	Good exposure & classification; minor partial items
Mann et al., 2021, UK (Retrospective)	7.5	3.0	10.5	Strong injury reporting; weak exposure reporting
MartínezSilván et al., 2021, Qatar [30]	7.5	9.0	16.5	Very strong compliance; complete exposure/outcome data
Meyers et al., 2024, USA [37]	3.5	1.5	5.0	Low adherence across domains; minimal exposure detail
Pierpoint et al., 2016, USA [32]	6.5	8.5	15.0	Excellent exposure tracking; minor limitations in onset coding
Rebella, 2008, USA	7.0	9.0	16.0	Strong compliance in all methodological areas
Rebella, 2015, USA [52]	8.0	9.0	17.0	Perfect/near-perfect alignment with standards
Rhim et al., 2024, USA	7.0	8.5	15.5	High-quality injury and surveillance coding
Rhim et al., 2025, USA	7.0	8.5	15.5	Same strengths as 2024 study
Tenforde et al., 2011, USA [47]	1.5	4.0	5.5	Low detail overall; minor exposure documentation
Tyflidis et al., 2012, Greece	5.0	4.5	9.5	Moderate alignment; key exposure metrics missing
Watson & DiMartino, 1987, USA	6.5	5.0	11.5	Good injury reporting; incomplete exposure tracking
Wen, 2022, China [48]	2.5	1.0	3.5	Very limited methodological alignment
Wolff et al., 2025, USA	6.0	8.5	14.5	Good injury coding + excellent exposure reporting

Abbreviations: AE Athlete exposure, BSI Bone stress injury, ICD International Classification of Diseases, km kilometres, OSTRC Oslo Sports Trauma Research Center, OSICS Orchard Sports Injury Classification System, STROBE Strengthening the Reporting of Observational Studies in Epidemiology

Fig. 3.

Percentage alignment with consensus methodological standards across each domain in youth track and field injury studies

Table 6 summarises methodological gaps across the included studies. Standardised exposure denominators (~ 45%), prospective surveillance systems (~ 40%), and injury severity reporting components (~ 60%) were reported in a proportion of studies.

Table 6.

Domain level identification of methodological gaps across included studies

Domain	% Studies aligned (Aligned/Aligned +Partially aligned)	Key issue identified	Gap Identified
1. Injury definition Clarity and completeness of the injury definition used (time‑loss, medical attention, complaint‑based)	~70%	Inconsistent definitions; older studies omit overuse	Underreporting and heterogeneity
2. Overuse/gradual onset injuries Inclusion and differentiation of overuse or gradual‑onset injuries within the case definition.	~65%	Many omit overuse or don’t specify onset	Under recognition of overuse injuries
3. Time‑loss vs medical attention criteria Specification of whether injury definition relies on time‑loss, medical attention, or dual‑criterion models.	~60%	Inconsistent definitions	Poor comparability across studies
4. Body region coding Use of standardized anatomical region coding consistent with consensus recommendations.	~80%	Some use non-standard anatomy	Limited standardisation
5. Injury type classification Classification of injury types (e.g., strain, sprain, tendinopathy, BSI) using recognised taxonomy.	~85%	Mostly aligned, but older studies lack detail	Mild inconsistency in coding depth
6. Onset model Documentation of onset mechanism, distinguishing acute traumatic vs gradual‑onset/overuse injuries.	~55%	Sudden vs gradual rarely reported	Limits causal inference
7. Cause/mechanism of injury Recording of injury mechanism (contact, non‑contact, overuse, equipment/surface related).	~55%	Contact vs non-contact missing	Hinders prevention strategy development
8. Severity measurement Reporting of injury severity using standardised metrics (e.g., days lost, OSTRC severity score).	~50%	Days lost often missing	Poor injury burden estimation
9. Training vs competition exposure Separation of exposure data for training and competition contexts.	~45%	Not routinely separated	Cannot calculate specific incidence rates
10. Exposure quantification Use of quantifiable exposure metrics (hours, athlete‑exposures, distance, sessions, km).	~50%	Heterogeneity in exposure metrics used (hours, AEs, km)	No standard denominator
11. Study design and regular reporting Use of prospective vs retrospective methods and presence of structured continuous surveillance.	~65%	Many followed retrospective designs	Weak longitudinal surveillance
12. Reporting frequency Frequency and consistency of data collection (daily, weekly, event‑based, one‑time recall).	~40%	Lack of weekly/event-based reporting	Misses time trends & injury evolution
13. Medical staff involvement in injury verification Involvement of qualified medical personnel in case confirmation and injury diagnosis.	~55%	Reliance on athletes’ self-report	Inconsistent injury verification
14. Validation of self‑reported injuries Whether athlete‑reported injuries were validated by clinicians or triangulated with medical records.	~45%	Limited clinician confirmation	Risk of misclassification
15. Use of standardised diagnostic/coding systems (e.g., OSICS, ICD) Application of recognised injury‑classification or diagnostic coding frameworks.	~40%	Many studies use no standard system	Limits cross-study comparability
16. Baseline data completeness Availability of key baseline data (age, sex, event group, training load, anthropometrics).	~70%	Missing load or demographic data	Confounds incidence estimates
17. Alignment with STROBE reporting standards Adherence to established observational‑study reporting guidelines (STROBE).	~60%	Common omissions in bias discussion and missing data	Lower transparency and reproducibility

Stratified patterns in methodological approaches

Several methodological patterns emerged when studies were examined according to study characteristics.

Study design: Prospective cohort studies more frequently reported standardised injury definitions, quantified exposure, and implemented regular surveillance protocols. In contrast, retrospective and cross-sectional studies less frequently reported exposure denominators, and several exposure and surveillance components recommended by the Athletics Consensus Statement were not reported.

Athlete population and competitive level: Studies involving elite or highly trained athletes more commonly included clinician verification, structured surveillance systems, and standardised reporting frameworks. In contrast, studies conducted in school or recreational settings more frequently relied on questionnaire-based approaches and retrospective recall.

Athletic discipline: Endurance-based disciplines more frequently incorporated overuse injury definitions and prospective symptom-based surveillance approaches, whereas sprint and jumping disciplines more commonly focused on acute or diagnosis-specific injuries.

Discussion

Novelty and key findings

To the best of our knowledge, this is the first scoping review to comprehensively synthesise how youth track and field injury studies define musculoskeletal injury, measure injury burden, and operationalise risk and protective factors, while also examining methodological characteristics and alignment with the Athletics Consensus Statement [18]. Previous reviews have focused on injury definitions and surveillance systems across all sports disciplines [16, 54] or specifically on running-related injuries [55], whereas this review uniquely maps the methodological aspects used in sport injury epidemiology research in youth track and field.

Injury definitions and measurement approaches

A central finding is the marked variation in how injuries are defined and measured, which is consistent with findings from the broader athletics literature [41]. Additionally, time-loss definitions appear to underestimate overuse injuries compared with symptom-based approaches such as the Oslo Sports Trauma Research Centre (OSTRC) questionnaire [17, 56]. Similarly, Bahr [57] highlighted that traditional surveillance models fail to capture the burden of gradual-onset conditions, which are highly prevalent in endurance-based sports such as athletics. Consequently, symptom-based surveillance may identify injuries that are not captured by time-loss definitions.

Risk and protective factors: definition and measurement

This review identified a range of intrinsic and extrinsic risk factors reported in association with injury outcomes. These findings are consistent with those reported in the narrative review by Edouard and colleagues [41], where injury-related risk factors were also classified as intrinsic modifiable (performance level, maladaptive coping strategies such as self-blame), intrinsic non-modifiable (age, previous injury, biomechanical pole vaulting patterns), and extrinsic factors (training volume, spikes in training load, and environmental temperature). Among these risk factors, training load was the most frequently examined extrinsic factor domain. Similarly, the consistent reporting of previous injury aligns with the broader sports injury literature, where it is recognised as frequently associated with subsequent injury occurrence [58, 59], potentially reflecting incomplete recovery or premature return to sport [60]. The prominence of bone health and menstrual-related factors, particularly in female athletes, highlights the importance of energy availability and physiological maturation in this population, especially in relation to BSIs [61]. In contrast, other domains such as lower energy availability, Relative Energy Deficiency in Sport (RED-S) and psychological factors recognised as critical in endurance athletes [62], remain underexplored in youth cohorts.

Alignment with consensus standards and methodological gaps

Although many studies reported core injury-definition components in accordance with the Athletics Consensus Statement [18], exposure measurement and surveillance practices varied across studies. This imbalance has also been reported in broader injury surveillance research, where prospective designs with standardised exposure metrics were less commonly reported [16, 54] including in studies specifically focused on running-related injuries [55]. A common study design approach involved retrospective and cross-sectional methods that frequently relied on self-reported injury data without exposure-adjusted outcomes. Similar concerns have been raised in youth sports epidemiology, where methodological characteristics may influence the interpretation of injury-risk relationships [63].

Strengths and limitations

A key strength of this scoping review is the comprehensive synthesis of 54 studies spanning injury surveillance and risk-factor research in youth track and field. The review systematically evaluates injury definitions, measurement approaches, and alignment with consensus standards, providing a structured overview of current methodological practices. Inclusion of both intrinsic and extrinsic risk factors, along with their measurement methods, enables a multidimensional assessment of injury epidemiology in this population.

Key limitations include heterogeneity in study design and reporting, which may influence comparability across studies. Publication bias is possible due to reliance on peer-reviewed literature, with limited inclusion of grey literature and non-English studies. Although a structured framework and independent dual review process were used to assess consensus alignment, some subjectivity in interpretation remains. Finally, as a scoping review, this study does not estimate effect sizes or causal relationships; therefore, all reported associations should be interpreted as non-causal. Alignment scores reflect adherence to reporting and surveillance standards rather than overall study quality or evidence certainty.

Recommendations

Future studies should prioritise prospective surveillance systems and standardised exposure metrics. Incorporating clinician involvement and standard diagnostic coding systems may improve diagnostic validity, while more consistent measurement and reporting of neuromuscular, biomechanical, and training-load factors may enhance reproducibility and facilitate comparisons across studies.

Conclusions

This scoping review identified substantial variation in the methods used to define, measure, and report musculoskeletal injuries and associated risk and protective factors among youth track and field athletes. Variation in injury definitions, exposure measurement, and surveillance approaches may affect comparability across studies. Greater alignment with consensus-informed methodological practices may improve the quality and comparability of future research and support the advancement of injury epidemiology and prevention efforts in this population.

Authors’ contributions

KW conceived and designed the study. KW and RJ searched databases. KW and RJ were involved in retrieving data. KW and RJ drafted the manuscript. APH, AW and RJ revised the paper. All authors provided critical feedback on the manuscript. All authors read and approved the final manuscript.

Funding

Not applicable.

Data availability

Data availability does not apply to this article as no datasets were generated or analysed during the current study. All data supporting this review are derived from publicly available published articles, which are properly cited in the reference list.

Declarations

Ethics approval and consent to participate

Not applicable. This study is a scoping review based exclusively on data from previously published literature and did not involve human participants, human tissue, or identifiable personal data. Therefore, ethical approval and informed consent were not required. The review was conducted in accordance with the Joanna Briggs Institute (JBI) methodology for scoping reviews and reported following the PRISMA-ScR guidelines.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.