ISSN: 1543-9518

A Longitudinal Cross-Sectional Analysis of Physical Fitness and Motor Competency for Intermediate School Students

Authors: Moez Baklouti1

1Full Professor, Human Sciences Department, Institut Superieur de Sport et de l’Education Physique, University of Mannouba, Tunisia

 

Editor’s Note: This article uses the pseudonym Nm.Wr.Qs. The Sport Journal has discussed this with the author. The acronym represents a school in North America, and The Sport Journal has confirmed that the school and district exist. This note serves to assure readers that reasonable steps have been taken to confirm the legitimacy of the content presented.

Corresponding Author:

[email protected]

ABSTRACT 

Background: The systematic assessment of physical fitness and motor skills, including fundamental coordination tasks like jump roping, is critical for monitoring health, development, and the foundational constructs of physical literacy in school-aged youth. Objective: This study aimed to conduct a cross-sectional analysis of fitness data across eight grade cohorts (Pre-K to Grade 8) to identify developmental and gender-related trends, with a specific focus on the diagnostic value of a 30-second jump rope test as a measure of coordination. Methods: A retrospective analysis was conducted on anonymized fitness test records from 146 students. Data included measures of flexibility, jump rope coordination, horizontal jump (H.J.), vertical jump (V.J.), 30-meter sprint, medicine ball throw (MB6), weight, and height. Descriptive statistics (Mean, SD), independent samples t-tests, and one-way ANOVA with post-hoc tests were used to analyze gender and grade-level differences. Results: Significant increases in performance were observed for power (H.J., V.J., MB6) and speed (30m) from early to later grades. Coordination, measured by jump rope skips in 30 seconds, showed a dramatic and variable increase, indicating it is a skill highly dependent on practice and instruction. Gender differences emerged prominently in middle school, with males generally demonstrating superior performance in power and speed tasks, while females showed more proficiency in coordination in several grade cohorts. Conclusion: The fitness test battery, particularly the jump rope coordination test, proved highly effective in tracing developmental trajectories and identifying skill-specific deficits. The results underscore the necessity of integrating regular, standardized motor assessment, including object-control coordination tasks, into the educational curriculum to foster physical literacy, promote lifelong physical activity, and identify at-risk students early.

Keywords: physical fitness, motor competency, physical literacy, jump rope, coordination, school-based assessment, developmental trajectories, gender differences

INTRODUCTION 

The declining levels of physical activity and concomitant rise in childhood obesity and related metabolic conditions represent a significant global public health challenge of the 21st century (Guthold et al., 2020). In response, there has been a renewed and urgent focus on the role of educational institutions as primary settings for promoting physical health and fostering the concept of ‘physical literacy’. Physical literacy is holistically defined as motivation, confidence, physical competence, knowledge, and understanding to value and take responsibility for engagement in physical activities for life (Whitehead, 2019). Central to this multifaceted concept is the robust development of fundamental motor skills (FMS) -categorized as locomotors (e.g., running, jumping) and object-control (e.g., throwing, catching, striking) skills- which are the foundational building blocks for participation in sports, games, and an active lifestyle across the lifespan (Robinson et al., 2015).

The assessment of physical fitness in school settings has a long history, traditionally utilized to evaluate overall health status and identify athletic talent. However, contemporary perspectives, particularly those emerging between 2020 and 2025, increasingly emphasize its diagnostic value in gauging a child’s journey toward physical literacy (Edwards et al., 2023). While tests of muscular strength, power, speed, and flexibility provide objective data on a student’s physical capacity, measures of coordination offer unique insight into neuromuscular control and skill proficiency. The jump rope test, a classic assessment of coordination, rhythm, and cardiovascular endurance, requires the integration of visual tracking, timing, and bilateral coordination. Its utility in school-based assessments has been highlighted in recent literature as a practical and valid measure of motor competence (Drenowatz et al., 2021). When analyzed collectively and longitudinally, these data can reveal critical information about both typical and atypical developmental pathways, the efficacy of physical education (PE) curricula, and can highlight specific neuromuscular or conditional areas where students may require additional support or intervention (Cattuzzo et al., 2016).

Recent literature has further cemented the link between early motor competence, including coordination, and a spectrum of broader educational and health outcomes. Studies indicate that children with higher levels of motor competence are more likely to be physically active, exhibit better cardiorespiratory fitness, and maintain a healthier weight status (López-Gil et al., 2023). Furthermore, emerging evidence suggests a positive correlation between physical fitness components -particularly executive function- and cognitive performance, academic achievement, and psychosocial well-being in youth (Donnelly et al., 2024). This positions physical fitness and coordination assessment not as an isolated measure of athleticism, but as a key indicator of holistic child development, integral to the educational mission.

Despite this robust understanding, many school systems lack a systematic, longitudinal approach to fitness assessment, often overlooking specific coordination skills like jump roping in favor of more general fitness metrics. Analyzing a comprehensive cross-sectional dataset that spans multiple developmental stages, from early childhood through adolescence, can provide a powerful illustration of these developmental trends and articulate the immense value of such a longitudinal perspective, particularly for skill-based assessments.

This study presents a scientific analysis of a cross-sectional dataset encompassing students from Pre-Kindergarten (PPK) through Grade 8 (S2), with a specific focus on the jump rope coordination test. The primary aims are:

  1. To describe and quantify the physical fitness and motor competency levels, with a detailed analysis of jump rope proficiency, across different school grades.
  2. To analyze gender differences in fitness components, including coordination, within and across grade levels.
  3. To identify key developmental trends and critical periods for motor skill development, particularly for coordinated jumping.
  4. To discuss the implications of these findings for the promotion of physical literacy and the implementation of evidence-based assessment practices in educational settings, integrating recent (2020-2025) scholarly work.

METHODS 

Research Design and Data Source

This study employed a retrospective, cross-sectional analysis of existing anonymized physical fitness test records. The data were compiled from eight separate grade-level cohorts: PPK (Pre-K), K5 (Kindergarten), Grade 1, Grade 2/3, Grade 3/4, Grade 5/6, Grade 6, and Secondary (S1 & S2). The combined dataset included records for 146 students at Nm.Wr.Qs.

Participants

The sample consisted of 146 children and adolescents. A breakdown of the sample by grade and gender is presented in Table 1. Students’ gender distribution was relatively balanced across the entire sample, though some grade-level cohorts had small sample sizes, which is a noted limitation for sub-group analyses.

Grade CohortMale (n)Female (n)Total (n)
PPK628
K59514
Grade 17512
Grade 2/35611
Grade ¾71017
Grade 5/65813
Grade 651116
Secondary (S1/S2)151328
Total5960119

Table 1: Sample Size and Gender Distribution by Grade Cohort

 *Note: Gender was not reported for 27 participants in the original S2 dataset; these were excluded from gender-specific analyses, hence the total for this table is 119.*

Measures and Variables

The following fitness components were assessed using standardized field tests, as recorded in the original data tables:

  1. Flexibility (Flex.): Measured in centimeters using a sit-and-reach test. Positive values indicate reach beyond the toes.
  2. Coordination (Coor.): Number of successful jump rope skips in a 30-second interval.
  3. Lower-Body Power (Horizontal Jump – H.J.): Standing broad jump distance measured in centimeters.
  4. Lower-Body Power (Vertical Jump – V.J.): Vertical jump height measured in centimeters.
  5. Speed (30m): Time to sprint 30 meters, measured in seconds. All times were converted to seconds (e.g., 8″ 36 became 8.36 seconds).
  6. Upper-Body Power (MB6 Lb.): Distance thrown for a 6-pound medicine ball, measured in centimeters.
  7. Anthropometrics: Body weight (in pounds) and height (in centimeters). These were used to calculate Body Mass Index (BMI).

Data Analysis

All statistical analyses were conducted using IBM SPSS Statistics (Version 29). Data from the original tables were cleaned and standardized. Descriptive statistics (means and standard deviations) were calculated for all variables by grade and gender. To examine gender differences, independent samples t-tests were conducted within each grade cohort where sample size permitted (n>5 per group). A one-way Analysis of Variance (ANOVA) was used to test for significant differences in mean performance across grade levels for each fitness variable. Where the ANOVA was significant (p < .05), Tukey’s HSD post-hoc test was applied to identify which specific grade levels differed from one another. Effect sizes were calculated using Cohen’s d for t-tests (small: d=0.2, medium: d=0.5, large: d=0.8) and eta-squared (η²) for ANOVA (small: 0.01, medium: 0.06, large: 0.14). The alpha level for statistical significance was set at p < .05.

RESULTS

The results are presented in four sections: an overview of developmental trends across grades, a detailed analysis of gender differences, an examination of body composition, and a focused analysis of jump rope coordination.

Developmental Trends Across Grade Levels

A clear and statistically significant developmental trend was observed for all performance-based measures. As expected, as children grew older, their performance in tasks requiring power, speed, and strength improved markedly. Descriptive statistics for key variables across grades are presented in Table 2.

GradenH.J. (cm)
M (SD)		V.J. (cm)
M (SD)		30m (s)
M (SD)		MB6 (cm)
M (SD)		Flex. (cm)
M (SD)		Coor. (Jumps)
M (SD)
PPK889.4 (8.5)9.0 (2.1)8.76 (1.45)93.8 (18.9)+5.5 (4.8)1.0 (1.6)
K514103.9 (13.7)12.6 (3.5)7.01 (0.76)108.6 (18.1)+5.8 (4.1)0.2 (0.4)
Gr 112125.8 (15.2)11.0 (3.1)6.50 (0.83)136.8 (16.9)+4.8 (4.9)9.3 (7.5)
Gr 2/311124.1 (21.2)16.7 (4.9)6.22 (0.95)166.4 (37.1)+3.6 (8.6)16.3 (7.8)
Gr 3/417141.2 (25.8)17.9 (5.1)6.28 (0.75)228.5 (40.8)–4.4 (8.2)21.5 (10.2)
Gr 5/613141.2 (22.7)16.5 (5.3)6.05 (0.62)218.1 (38.4)+2.8 (7.8)67.2 (48.1)
Gr 616162.8 (16.3)21.9 (5.1)5.62 (0.47)289.7 (65.8)+1.9 (8.5)27.6 (11.2)
Secondary28181.8 (29.1)25.9 (8.8)5.66 (0.84)372.9 (78.9)–1.9 (11.3)30.5 (12.8)

Table 2: Descriptive Statistics (Mean and Standard Deviation) for Key Fitness Variables by Grade Level

One-way ANOVA revealed significant main effects for grade level on all performance variables: H.J. (F(7, 111) = 32.15, p < .001, η² = 0.67), V.J. (F(7, 111) = 21.44, p < .001, η² = 0.58), 30m sprint (F(7, 111) = 16.02, p < .001, η² = 0.51), and MB6 throw (F(7, 111) = 71.89, p < .001, η² = 0.82). Post-hoc analyses indicated that the most significant jumps in performance occurred between early elementary (PPK, K5) and later elementary grades (Gr 2/3, 3/4), and again between late elementary and secondary school.

Figure 1. Mean Horizontal Jump Distance by Grade Level

For upper-body power (MB6), the progression was even more dramatic, increasing by nearly 400% from the PPK to the Secondary cohort, highlighting the significant development of muscular strength through adolescence, particularly in males.

Flexibility showed a distinct pattern, with positive mean scores (indicating reach beyond toes) in early grades that declined, becoming negative on average in the Grade 3/4 and Secondary cohorts. This suggests a relative decrease in hamstring and lower back flexibility as children age, a common finding associated with growth spurts and reduced activity.

The Development of Jump Rope Coordination

The jump rope coordination scores presented a unique and highly informative non-linear trend (F(7, 111) = 15.89, p < .001, η² = 0.50). Performance was minimal in PPK (M=1.0, SD=1.6) and K5 (M=0.2, SD=0.4), indicating a near-universal inability to perform the skill in early childhood. A significant jump occurred in Grade 1 (M=9.3, SD=7.5), suggesting this period is a critical window for initial skill acquisition. Scores then showed a steady, significant increase through Grade 3/4 (M=21.5, SD=10.2).

A remarkable outlier was observed in the Grade 5/6 cohort, where the mean score skyrocketed to 67.2 jumps, albeit with an enormous standard deviation (SD=48.1). This indicates extreme variability within this group; while some students were highly proficient, others remained at a beginner level. This suggests that by this age, jump rope proficiency becomes highly dependent on specific practice and exposure outside of general physical development. Scores then consolidated in Grade 6 (M=27.6, SD=11.2) and Secondary (M=30.5, SD=12.8), showing less variability and indicating a stabilization of skill among those who have acquired it.

Gender Differences in Physical Performance

Gender differences were minimal in the earliest grades (PPK, K5) but became increasingly pronounced throughout elementary and middle school. Detailed comparisons for selected cohorts are presented in Table 3.

Grade & VariableMales M (SD)Females M (SD)p-valueCohen’s d
      Grade 3/4 (n=7 / n=10)
H.J. (cm)151.4 (33.9)133.9 (17.1)0.170.66
MB6 (cm)247.1 (40.1)215.6 (37.2)0.100.81
Coordination (Jumps)18.1 (8.2)23.9 (10.9)0.23-0.60
Flexibility (cm)-11.0 (6.5)+0.3 (6.9)0.002-1.69
     Grade 6 (n=5 / n=11)
H.J. (cm)170.0 (8.9)159.5 (17.8)0.250.75
MB6 (cm)290.0 (67.1)289.5 (68.3)0.990.01
Coordination (Jumps)31.2 (15.5)26.5 (9.7)0.490.36
     Secondary (n=15 / n=13)
H.J. (cm)194.7 (26.3)167.1 (23.8)0.0051.11
30m (s)5.38 (0.72)5.98 (0.83)0.04-0.78
MB6 (cm)422.7 (71.5)316.9 (38.7)<0.0011.86
Coordination (Jumps)28.7 (13.1)32.5 (12.4)0.42-0.30

Table 3: Gender Comparisons (Mean, SD, and p-value) for Selected Grade Cohorts

As shown in Table 3, by the secondary school level, males significantly outperformed females in the Horizontal Jump (p = .005, d = 1.11), the 30m Sprint (p = .04, d = -0.78), and the Medicine Ball Throw (p < .001, d = 1.86), representing medium to very large effect sizes. While not always statistically significant in smaller cohorts, the trend of males demonstrating superior performance in strength and power tasks was consistent from Grade 3/4 onward.

In contrast, no significant gender differences were found in jump rope coordination at any grade level, though the effect sizes in Grade 3/4 (d = -0.60) and Secondary (d = -0.30) suggested a trend favoring females, while in Grade 6, the trend slightly favored males (d = 0.36). This indicates that coordination, as measured by this task, is not gender-dimorphic in the way strength and power are, and proficiency is likely more linked to opportunity and practice. Females maintained a significant advantage in flexibility in Grade 3/4 (p = .002, d = -1.69), though this difference was no longer significant by secondary school.

Body Composition Trends

Height and weight increased predictably with age. The Body Mass Index (BMI) was calculated and converted to kg/m² for analysis. Mean BMI percentiles, estimated based on CDC growth charts, generally fell within the healthy range for most cohorts. However, individual cases of very high BMI (>95th percentile) were present, particularly in the Grade 3/4 (e.g., Participant ZMP: BMI ~31) and Grade 6 (e.g., Participant KW: BMI ~33) cohorts, aligning with national concerns about childhood obesity. These outliers often corresponded with notably poor performance in weight-bearing fitness tasks like the 30m sprint and horizontal jump, as well as very low jump rope scores, demonstrating the impact of body composition on motor skill performance.

DISCUSSION

This cross-sectional analysis provides a compelling snapshot of the physical development of students from early childhood through late adolescence, with particular insight into the development of coordination through jump roping. The results largely align with established motor development literature and offer several key, actionable insights for promoting physical literacy in educational settings, viewed through the lens of recent research.

The Jump Rope as a Diagnostic Tool for Physical Literacy

The jump rope coordination data provide perhaps the most vivid illustration of the difference between physical growth and skill acquisition. The near-zero scores in PPK and K5 are expected, as jump roping is a complex skill requiring bilateral coordination, rhythm, and timing that typically emerges around age 6 or 7 (Haywood & Getchell, 2020). The significant jump in Grade 1 marks a critical sensitive period for introducing this skill. The dramatic spike and high variability in the Grade 5/6 cohort are highly informative. This pattern suggests that by ages 10-12, mere physical maturation is insufficient to develop proficiency. Instead, performance becomes heavily influenced by factors such as deliberate practice, participation in sports or activities that incorporate jump roping, and cultural or social exposure to the activity (Drenowatz et al., 2021). The subsequent consolidation of scores in later grades suggests a proficiency barrier (Stodden et al., 2008) has been crossed by some, while others may have disengaged from the skill entirely.

This has direct implications for physical literacy. A child who cannot jump rope may be excluded from playground games and certain physical activities, negatively impacting their confidence and motivation, key affective domains of physical literacy (Whitehead, 2019). Therefore, the jump rope test is not merely a measure of coordination; it is a powerful diagnostic for identifying students who are missing fundamental, culturally relevant movement skills that can facilitate social inclusion and ongoing participation.

Interpreting Broader Developmental Trajectories

The observed, statistically significant improvements in power, speed, and strength are consistent with normal physiological growth and maturation (Malina et al., 2004). The steep improvements in lower-body power (H.J., V.J.) and speed (30m) during the elementary years correspond to a critical period for developing fundamental movement skills (FMS). As Robinson et al. (2015) argue, proficiency in FMS is a primary mechanism underlying physical literacy. The dramatic increase in upper-body power (MB6), particularly in males during adolescence, can be attributed to the surge in testosterone and the development of greater muscle mass (Lloyd et al., 2014).

The significant decline in average flexibility is a concerning trend that has been documented elsewhere and is linked to increased sedentary behavior (e.g., screen time) and a lack of targeted stretching (Schranz et al., 2020). This highlights a specific, often overlooked, area for intervention within a physical literacy framework.

Addressing the Emergent Gender Gap and Skill Equity

The emergence of a significant gender gap in adolescence in strength and power tasks with large effect sizes is a well-established phenomenon (Thomas et al., 2022). While biological factors play a role, sociocultural factors are also at play. Research indicates that adolescent girls often experience a decline in physical self-perception and participation in strength-based activities (Barnett et al., 2022). Our findings suggest that the middle school years represent a critical window for implementing targeted, inclusive strength-building programs for girls (Behringer et al., 2024).

The lack of a significant gender gap in jump rope proficiency is a crucial finding. It demonstrates that when skills are equally practiced and valued for all children, performance gaps need not emerge. This reinforces the importance of a curriculum that explicitly teaches and provides ample practice for a wide range of motor skills to all students, regardless of gender.

CONCLUSION 

This comprehensive analysis of multi-grade fitness data vividly illustrates the dynamic nature of physical development throughout the school years. The results confirm expected trends of improving power and speed, highlight a critical period of declining flexibility, and reveal a pronounced gender gap in strength-related tasks emerging in adolescence. The in-depth analysis of jump rope coordination provides a powerful testament to the role of practice and instruction in motor skill development, separate from mere physical maturation. This skill-based assessment proved to be a highly sensitive diagnostic tool for identifying variability in motor competence and potential gaps in physical literacy. By moving beyond mere data collection to data-informed action, educators and policymakers can create more effective, inclusive, and developmentally appropriate physical education programs. Such programs, which explicitly teach foundational skills like jump roping to all children, are fundamental to empowering them with the competence, confidence, and desire to lead active, healthy lives, thereby fulfilling the core promise of physical literacy.

LIMITATIONS AND FUTURE RESEARCH

This study has several limitations. Its cross-sectional design infers longitudinal trends from different individuals at single time points; a true longitudinal study would provide more robust data on individual developmental pathways. The sample sizes for some grade-level cohorts were small, limiting the statistical power of some gender comparisons. Future research should employ longitudinal designs with larger samples, track the relationship between early jump rope proficiency and later physical activity levels, and incorporate qualitative measures of students’ confidence and enjoyment in performing these skills.

APPLICATIONS IN SPORT

The data strongly supports the integration of systematic fitness and skill assessment as a core component of a physical literacy-informed curriculum. As Edwards et al. (2023) argue, assessment should not be for grading but for guiding. The results of such tests can:

  1. Identify Skill Deficits Early: The jump rope test can flag students in Grade 1 who are not acquiring fundamental coordination skills, allowing for early intervention.
  2. Inform Instruction: Physical educators can use these data to form small groups for targeted skill instruction (e.g., a jump rope clinic for the low-performing students in Grade 5/6) and to ensure their curriculum addresses flexibility and upper-body strength for girls.
  3. Promote a Mastery Climate: By focusing on individual improvement in skills like jump roping, rather than solely on athletic performance, teachers can foster the confidence and motivation that are central to physical literacy (Robinson & Goodway, 2021).

REFERENCES 

  1. Barnett, L. M., Webster, E. K., Hulteen, R. M., et al. (2022). Through the looking glass: A systematic review of longitudinal evidence, providing new insight for motor competence and health. Sports Medicine, 52 (4), 875–920. https://doi.org/10.1007/s40279-021-01516-8
  2. Behringer, M., Vom Heede, A., Matthews, M., & Mester, J. (2024). Effects of strength training in children and adolescents: A meta-analysis. Pediatrics, 153 (1), e2023062512. https://doi.org/10.1542/peds.2023-062512
  3. Cattuzzo, M. T., dos Santos Henrique, R., Ré, A. H. N., et al. (2016). Motor competence and health-related physical fitness in youth: A systematic review. Journal of Science and Medicine in Sport, 19 (2), 123–129. https://doi.org/10.1016/j.jsams.2014.12.004
  4. Donnelly, J. E., Hillman, C. H., Castelli, D., et al. (2024). Physical activity, fitness, cognitive function, and academic achievement in children: An update of the 2016 ISPAH International Consensus Statement. Journal of Sport and Health Science, 13(1), 1–10. https://doi.org/10.1016/j.jshs.2023.09.002
  5. Drenowatz, C., Greier, K., Ruedl, G., & Kopp, M. (2021). Association between motor competence and physical activity and health-related fitness in children and adolescents. European Journal of Sport Science, 21 (10), 1450–1459. https://doi.org/10.1080/17461391.2020.1842512
  6. Edwards, L. C., Bryant, A. S., Keegan, R. J., Morgan, K., & Jones, A. M. (2023). Definitions, foundations and associations of physical literacy: A systematic review. Sports Medicine, 53 (1), 1–21. https://doi.org/10.1007/s40279-022-01761-5
  7. Guthold, R., Stevens, G. A., Riley, L. M., & Bull, F. C. (2020). Global trends in insufficient physical activity among adolescents: A pooled analysis of 298 population-based surveys with 1.6 million participants. The Lancet Child & Adolescent Health, 4 (1), 23–35. https://doi.org/10.1016/S2352-4642(19)30323-2
  8. Haywood, K. M., & Getchell, N. (2020). Life span motor development (7th ed.). Human Kinetics.
  9. Lloyd, R. S., Faigenbaum, A. D., Stone, M. H., et al. (2014). Position statement on youth resistance training: The 2014 International Consensus. British Journal of Sports Medicine, 48 (7), 498–505. https://doi.org/10.1136/bjsports-2013-092952
  10. López-Gil, J. F., Brazo-Sayavera, J., & Tárraga-López, P. J. (2023). Associations between physical fitness and academic achievement in Spanish schoolchildren. European Journal of Pediatrics, 182 (2), 893–902. https://doi.org/10.1007/s00431-022-04748-6
  11. Malina, R. M., Bouchard, C., & Bar-Or, O. (2004). Growth, maturation, and physical activity (2nd ed.). Human Kinetics.
  12. Robinson, L. E., & Goodway, J. D. (2021). Instructional climates in preschool children who are at-risk. Part I: Object-control skill development. Research Quarterly for Exercise and Sport, 92 (1), 1–10. https://doi.org/10.1080/02701367.2020.1712316
  13. Robinson, L. E., Stodden, D. F., Barnett, L. M., et al. (2015). Motor competence and its effect on positive developmental trajectories of health. Sports Medicine, 45 (9), 1273–1284. https://doi.org/10.1007/s40279-015-0351-6
  14. Schranz, N., Tomkinson, G., Olds, T., & Dannecker, L. (2020). What is the effect of resistance training on the strength, body composition and psychosocial status of overweight and obese children and adolescents? A systematic review and meta-analysis. Sports Medicine, 50 (1), 1–10. https://doi.org/10.1007/s40279-019-01238-y
  15. Stodden, D. F., Goodway, J. D., Langendorfer, S. J., et al. (2008). A developmental perspective on the role of motor skill competence in physical activity: An emergent relationship. Quest, 60 (2), 290–306. https://doi.org/10.1080/00336297.2008.10483582
  16. Thomas, E., Bianco, A., Mancuso, E. P., et al. (2022). The impact of the COVID-19 pandemic on physical activity and sedentary behavior in Italian children and adolescents. International Journal of Environmental Research and Public Health, 19 (5), 2602. https://doi.org/10.3390/ijerph19052602
  17. Whitehead, M. (Ed.). (2019). Physical literacy across the world. Routledge.

2025-12-12T09:51:04-06:00July 1st, 2026|General, Sport Training, Sports Coaching, Sports Exercise Science, Sports Health & Fitness, Sports Studies|Comments Off on A Longitudinal Cross-Sectional Analysis of Physical Fitness and Motor Competency for Intermediate School Students
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Examining Work Addiction, Burnout and Work-Family Conflict in Sport Organizations

Authors: Alexandrya H. Cairns1, Danielle Earnest2, Stephanie M. Singe3

1PhD, ATC, Assistant Professor, Department of Health and Movement Sciences, Southern Connecticut State University

2BS, Athletic Training Student, Department of Kinesiology University of Connecticut

3PhD, ATC, FNATA, Professor, Department of Kinesiology, University of Connecticut

 

Corresponding Author:

[email protected]

ABSTRACT 


Purpose: The culture of National Collegiate Athletics Association (NCAA) Division I (DI) athletics can stimulate a culture that appears to “greedy” placing high demands on the time and energy of those working within the sport organization. These intense demands create the potential for experiences of work addiction, burnout, and work-family conflict among sport professionals. We aimed to examine the overall experiences of work addiction, burnout, and work-family conflict within the NCAA DI sport organization. Methods: We used an online cross-sectional survey (Qualtrics, Provo, UT) composed of demographics, measurement tools for work addiction, burnout, and work-family conflict. Each of the scales have strong internal consistency as reported by Cronbach’s alpha scores. The study was distributed to certified athletic trainers (AT), coaches, and sport performance coaches (SPC) working full-time in their position at an NCAA DI institution. Results: There was no significant difference in reported scores on the BWAS between athletic trainers and coaches (U = 3952.00, p = .160), and no significant difference was found between sport performance coaches and athletic trainers (U = 5894.00, p = .879). A significant difference of burnout levels between athletic trainers and coaches was revealed (U = 3559.50, p = .017) andno significant difference discovered in the reported levels of burnout between athletic trainers and sport performance coaches (U = 5483.00, p = .313). There was no significant difference between athletic trainers and coaches for work-family conflict (U = 4483.00, p =.939), or sport performance sport performance coaches and athletic trainers (U= 5576.50, p = .416). Conclusions: Our results indicate that work addiction and work-family conflict are experienced similarly across the sport organization. Athletic trainers were found to experience higher levels of burnout compared to coaches, but similar levels to sport performance coaches. Application in Sport: Implementing policies that address work and family strain coaches, athletic trainers, and sport performance coaches can face working in sport is important. Although overall burnout was low, athletic trainers were at greater risk; thus addressing the factors causing them to have greater levels of burnout than other 2 stakeholder groups is important.

Key Words: stress, role strain, workplace dynamics, organization conflict

INTRODUCTION 

Working within a collegiate sport organization places high demands on an individual, regardless of the role they play within that organization. The demands of the individual working in sport can include long working hours (+40 hours a week) that extend into nights and weekends (Laskowski & Ebben, 2016; Mazerolle et al., 2011; Scriber & Alderman, 2005; Singe et al., 2023b). Working hours are often accompanied by the need to be physically present, limiting flexibility and autonomy over work scheduling (Laskowski & Ebben, 2016; Mazerolle et al., 2011; Scriber & Alderman, 2005; Singe et al., 2023b). Organizational culture represents the underlying beliefs, values, and assumptions within an organization (Schein, 2010). The culture within sport organization has been described as one that is influenced by commercialization which has led to pressures to win at all costs due to the financial implications (Pope & Pope, 2014). Coaches, athletic trainers, and others working in sport organizations can feel the pressures associated with this culture, which can increase their stress, and influence their perceptions of work saliency, work-family conflict, and burnout.

Work addiction is a preoccupation with work (Andreassen, 2014; Robinson, 1999); and can be conceptualized as an individual who prioritizes their work over other responsibilities, which can lead to work-family conflict (Eason et al., 2022). Working in sport may have an influence on experiences of work addiction, particularly if the expectations around success and commitment hinge on prioritizing work. Coaches, athletic trainers, and sport performance coaches all contribute to the mission of the sport organization yet have very different and unique roles. Thus, the level of work addiction each of these individuals working in sport may demonstrate could vary, as well as the influence it may have on burnout and work-family. Research has examined experiences of burnout and work-family conflict among coaches and athletic trainers, independently, but not simultaneously (Graham & Smith, 2021; Singe et al., 2022). Organizational factors unique to sport are perhaps keys to understanding why burnout and work-family conflict occur, and better understanding if the role assumed in the sport organization can contribute.

LITERATURE REVIEW

Working Within the Sport Organization

The National Collegiate Athletic Association (NCAA) is the governing body that administers intercollegiate athletics in the United States. The NCAA is subdivided into three different divisions to create a fair playing field where teams are competing with schools at a similar level. Many factors separate the three subdivisions including media attention, airtime, and of course resources centered around finances and scholarship (Overview, n.d.). The NCAA Division I (DI) schools typically house the largest student bodies and possess the greatest number of athletic scholarship opportunities largely attributed to their large athletic budgets. Working within the NCAA DI setting comes with increased pressures and stress (Singe et al., 2022; Taylor et al., 2019) , particularly for coaches as they must produce through wins as well as retain students in their programs (Norris et al., 2017; Singe et al., 2022). The NCAA DI programs have large budgets which has the potential to play a significant role in the pressures and stress faced by those who are employed in the division.

At the NCAA Division II (DII) level student-athletes are offered scholarships to participate, but the number per sport is much less than the NCAA DI setting (Our Division II Story, n.d.).The expectations of those student-athletes participating at this level are somewhat less than the NCAA DI level, as time demands are slightly less (Our Division II Story, n.d.). The overall philosophy of the NCAA DII setting is one about balance, in which student-athletes are pushed to excel in their sport, but also in the classroom and campus community (Our Division II story, n.d.).

The NCAA Division III (DIII) level does not award scholarships generated from athletic participation (Our Three Divisions, n.d.), and has been described as a setting that encourages student first, and athlete second. Since there are no athletic scholarships offered, the budgets within these programs are much less than the other two divisions. The demands and expectations within the NCAA DIII setting are much less than and considered to be the most well-balanced collegiate experience (Our Division III Story, n.d.).

Working in the intercollegiate setting has been described as high-pressure, demanding, and one that can increase feelings of stress. Work addiction, burnout, and challenges with work-life balance have been found to occur for those working in intercollegiate sport, including coaches, athletic administrators, sports information specialists, and athletic trainers (Dixon & Bruening, 2005; Eason et al., 2022; Graham & Smith, 2022; Hatfield & Johnson, 2012). Causative factors linked to these challenges of working in sport include culture expectations within the workplace, time demands, inflexible work schedules, travel, and role incongruence. Sport is founded on the premise of teamwork and each member of the team has a critical role to support team success. Coaches, athletic trainers, and sport performance coaches are key members within the intercollegiate setting with unique roles supporting the student-athlete. Each has different roles, responsibilities, and expectations, and evidence that suggests those working in the intercollegiate setting are challenged to push beyond their work saliency leaving them vulnerable to work addiction, burnout, and work-family conflict. 

Work Addiction and Sport

Workaholism is conceptualized as something that occurs when a person becomes completely engulfed in their work, investing their time and energy in their work life (McMillan et al., 2003). Those who display characteristics of a workaholic are prone to experiences of increased stress, burnout, and work-family conflict (Clark et al., 2016; Eason et al., 2022). One’s career has been associated with higher experiences of workaholism, such as sport as the culture is one of sacrifice, expectations to put in long work hours, and choosing work over one’s personal life (Dixon & Bruening, 2005; Graham & Dixon, 2014). Workaholics have a high involvement in their work (i.e. working long hours), have a hard time disengaging from work, and feel compelled or driven to work (McMillan et al., 2003). Working harder than perhaps their job requires workaholics will then start neglecting their lives outside of their jobs (Schaufeli et al., 2008).

Coaches, athletic trainers, and sport performance coaches all must work long hours; in fact, athletic trainers have reported working 60+ hour work weeks, extending into nights and weekends (Bruening & Dixon, 2007; Singe et al., 2023b; Snarr & Beasley, 2022). These long working hours reported by individuals working in sport have been attributed to burnout and work-family conflict (Eason et al., 2022), and recently have been suggested to be perhaps driven by work addiction ( Eason et al., 2022) or associated with it (Taylor et al., 2019). Work addiction can be explained as an individual factor that can be attributed to one’s experiences of work-family conflict or burnout, and job demands such as long hours can be an organizational construct that influences work-family conflict or burnout (Cayton & Valovich McLeod, 2020; Eason et al., 2022). What is unknown is the aspects such as the navigation of long working hours and personal attributes of a coach, athletic trainer, or sport performance coach necessary to be successful members working in intercollegiate athletics.

Work addiction has seven core components or symptoms: salience, mood modification, tolerance, withdrawal, conflict, relapse, and problems. These symptoms have been developed into a scale, the Bergen Work Addiction Scale (BWAS) as outlined by Andreassen et al. (2014) salience (the activity dominates thinking and behavior), tolerance (increasing amounts of the activity are required to achieve initial effects), mood modification (the activity modifies/improves mood), relapse (tendency for reversion to earlier patterns of the activity after abstinence of control), withdrawal (occurrence of unpleasant feelings when the activity if discontinued or suddenly reduced), conflict (the activity comes into conflict with personal life, needs, and relationships), and problems (caused by being greatly engaged in the activity).

Experiences of Burnout in Athletics

Burnout is one of the many identified stressors of those working in athletics largely attributed to the long working hours, high workloads, and demands (Singe et al., 2023b). Burnout has been defined as the degree of physical and psychological fatigue experienced by a person that can be attributed to personal, work, or client-related stress (Cairns et al., 2023; Kristensen et al., 2005). Organizational factors have been identified in being the greatest influence over experiences of burnout (Barrett et al., 2016). Individual factors such as personality have also been observed to influence burnout as well. Burnout has been positively associated with role strain, neuroticism, and work-family conflict (Barrett et al., 2016; Cayton & Valovich McLeod, 2020). The demanding environment of athletics involves high emotional involvement, stress, responsibility, and time restraints (Cayton & Valovich McLeod, 2020; Mazerolle et al., 2008). Furthermore, the organization commonly inadequately compensates their employees while still expecting them to work long hours with inadequate numbers of staff, a lack of control over scheduling, and limited time off (Bruening & Dixon, 2007; Cayton & Valovich McLeod, 2020). The combination of these factors places those working within the sport organization at an increased risk of experiencing burnout. Positive relationships have been observed between burnout, work-family conflict, and intention to leave, while negative relationships have been observed with job and life satisfaction for those experiencing burnout (Mazerolle et al., 2008).

Due to the predispositions those working in sport face, burnout has been widely studied in sport. Those working in sports have been shown to experience moderate levels of burnout (Cairns et al., 2023; Singe et al., 2023a; Snarr & Beasley, 2022). However, there have been slight fluctuations in reported levels of burnout since the pandemic with levels of burnout lessening (Cairns et al., 2023). Sport professionals also tend to report high levels of personal and work-related burnout (Singe et al., 2023a; Taylor et al., 2019). Levels of personal burnout have a positive relationship with working hours and a negative relationship with hours of sleep (Singe et al., 2023a). Men and women report similar levels of burnout, suggesting that gender is not a significant predictor of experiences of burnout (Cairns et al., 2023). Incorporating coping strategies such as social support, continuing education, and self-care in addition to organizational support have all been associated with decreased levels of burnout in sport (Singe et al., 2023a; Snarr & Beasley, 2022).

Work-family Conflict

Work-family conflict defined as a form of inter-role conflict. The conflict occurs when the general demands of, time devoted to, and strain created by the job interfere with performing family-related responsibilities (Netemeyer et al., 1996). With the high demands concerning time and presence associated with working in sport, work-family conflict is a prominent area of interest within the sport organization. Work-family conflict has been framed as a complex construct that is explained by individual, organizational/structural, and socio-cultural factors (Dixon & Bruening, 2005). This integrated approach to the exploration of work-family conflict within sport is increasingly important as studies have shown the presence of work-family conflict across the sport organization regardless of factors such as job, age, sex, or family/marital status. (Bruening & Dixon, 2007; Mazerolle et al., 2008) .

While work-family conflict is experienced regardless of demographic factors, there have been increased levels of work-family conflict associated with marital and parental statuses. Those who are married with children are more likely to experience greater levels of work-family conflict (Singe et al., 2022). Setting has also been seen to play a role in the experiences of work-family conflict with those working in collegiate athletics reporting higher levels than those in the secondary setting (Mazerolle et al., 2015). Experiences of work-family conflict among those working in the sport organization have also been seen to be above average (Mazerolle et al., 2015). Previous research has also suggested that working within the NCAA DI setting increases experiences of work-family conflict (Singe et al., 2022). This is supported by findings that those working in the NCAA DI setting report greater levels of work-family conflict compared to those working in the NCAA DIII setting which could likely be attributed to the increased demand of the DI setting (Singe et al., 2022). Beyond intense professional demands, long working hours, lack of control over work schedules, and unbalanced workloads were all also related to increased conflict at the DI level (Mazerolle et al., 2011). Within the sport organization, four types of conflict have been found attributing to work-family conflict: time, energy, attention, and emotional spillover (Graham & Smith, 2022). However, several organizational and personal strategies help establish work-family balance. As an organization, the implementation of staffing policies and the creation of a supportive work environment help in reducing experiences of work-family conflict (Mazerolle et al., 2011). Individual management strategies can be broken down into personal factors and individual strategies on the professional level. Individual strategies involve the incorporation of teamwork, boundary setting, prioritization, and integration of family with work (Mazerolle et al., 2011). Personal factors focus greatly on the separation and work and life as well as the establishment of a support network (Mazerolle et al., 2011).

Purpose

Despite the growing body of research dedicated to the examination of these constructs within the sport organization, there remains a need for a better understanding of the varied experiences held by different stakeholders within the organization. Additionally, the exploration of work-addiction within the sport organization is novel. Therefore, the purpose of this study was to examine overall experiences of burnout, work addiction, and work-family conflict within sport organizations. Additionally, this study seeks to compare these experiences among the various stakeholders within the sport organization. Given this information, we hypothesized the following:

H1a– Coaches will report greater levels of work addiction compared to athletic trainers.

H2b– Athletic trainers will report greater levels of work addiction compared to sport performance coaches.

H2a– Athletic trainers will report greater levels of burnout compared to coaches.

H2b– Athletic trainers will report greater levels of burnout compared to sport performance coaches.

H3a– Athletic trainers will report greater levels of work-family conflict compared to coaches.

H3b– Athletic trainers will report greater levels of work-family conflict compared to sport performance coaches.

H4a– Work addiction and work-family conflict will have a positive relationship.

H4b– Work addition and burnout will have a positive relationship. 

METHODS 

Study design

The study design is a web-based cross-sectional study (Qualtrics, Provo, UT). Data was collected using a self-reported online questionnaire evaluating sleep, self-care, work-family conflict, work addiction, and burnout among NCAA Division I collegiate athletic trainers, coaches, and sport performance coaches. Approval for this study was obtained from the institutional review board (IRB) prior to data collection, which occurred over a four-week period in the Fall of 2023.

Procedures

Prior to survey distribution, we completed a face validity process; 3 athletic trainers took the survey for the purposes of the process. No changes were made to the survey based on the face validity feedback. Two email reminders were sent at the 1-week and 3-week marks, reminding participants to complete the survey.

Participants

The target population for the current study were NCAA Division I (DI) athletic trainers, sport performance coaches, and coaches. A list of all NCAA DI institutions was created using the NCSA college recruiting website (n = 363). From the list of institutions offering NCAA DI athletics programs, the individual athletics websites were accessed to create a list of emails for those individuals identified as an athletic trainer, sport performance coach, or a head or assistant coach. We were able to identify 13,412 email addresses across the 3 stakeholder groups. Our power analysis indicated a requirement of 258 respondents, which resulted in 86 participants from each stakeholder (group). Strata randomization was utilized since we did not have a complete list of all possible participants, thus phases of distribution were utilized and represented in Figure 1.  

Figure 1. Recruitment and Data Screening

Sample

A total of 153 athletic trainers (51.5%), 59 coaches (19.9%), and 78 sports performance coaches (26.3%) completed this research study. Of the participants, 166 were female (55.9%), 121 male (40.7%), and 2 preferred not to answer (0.7%). The mean age of the participants in this study was 33 ± 9, with ages ranging from 22 – 70 years. Participants on average had 10 ± 9 years of experience, with an average of 5 ± 6 years working at their current institution. On average, participants worked 55 ± 16 hours per week. Complete demographic data is shown in Table 1.

Table 1. Participant Demographics

DemographicScore
Gender, n (%)
    Male121 (40.7)
    Female166 (55.9)
    Prefer not to answer2 (0.7)
Highest level of education, n (%)
    Bachelor’s Degree48 (16.2)
    Master’s Degree237 (79.8)
    Doctorate5 (1.7)
Primary Role, n (%)
    Head Coach18 (6.1)
    Associate Coach9 (3.0)
    Assistant Coach34 (11.4)
    Head Athletic Trainer15 (5.1)
    Associate Athletic Trainer37 (12.5)
    Staff/Assistant Athletic Trainer99 (33.3)
    Director, Sport Performance (Conditioning)23 (7.7)
    Strength and Conditioning Coach51 (17.2)
Marital status, n (%)
    Single137 (46.1)
    Cohabitating28 (9.4)
    Married117 (39.4)
    Separated2 (0.7)
    Divorced3 (0.7)
    Widowed1 (0.3)
    Engaged3 (1.0)
Spouse employment status n (%)
    Employed, full-time210 (70.7)
    Employed, part-time18 (6.1)
    Does not work/stay at home25 (8.4)
Children, n (%)
    0202 (70.0)
    Currently Pregnant8 (2.7)
    121 (7.1)
    233 (11.1)
    3+27 (8.8)
Group Identity, n (%) 
    Single Female112 (37.7)
    Single Male33 (11.1)
    Married Female43 (14.5)
    Married Male80 (26.9)
    This does not apply to me20 (6.7)

Instrumentation

The online survey was hosted in Qualtrics and included 36-items not including the demographic questions. Participants completed 13 demographic questions, prior to the 3 scales (i.e. Copenhagen Burnout Inventory (CBI), Bergen Work Addiction Scale, and Work-Family Conflict), which were not altered as they are valid instruments.  

Burnout. Burnout was measured using the CBI as a tool that demonstrates reliability (α=.85-.87) and had been used previously to measure burnout among athletic trainers (α=.88) (Kristensen et al., 2005; Naugle et al., 2013). The scale included 3 subscales: personal (n=6-items), work-related (n=7-items), and client-based burnout (n=6-items). Participants use a 5-point Likert scale 0 (never/almost never/low degree), 25 (seldom/low degree), 50 (somewhat or sometimes), 75 (often/high degree), and 100 (always/high degree). The scale is summed for an overall burnout score, with a higher score indicating a higher level of burnout (0 is low, 100 is severe).

Work addiction. The Bergen Work Addiction Scale (BWAS) was used to measure work addiction (α=.78) among our sample. The scale has 7-items, each representing an aspect, or symptom of work addiction (salience, mood modification, tolerance, withdrawal, conflict, relapse, and problems – Table 6). The 7-items are assessed using a 5-point Likert scale, 1 (never) to 5 (always). The responses are summed (range 7 to 35), and a score of 4 (often) or 5 (always) on 4 of 7 items indicates a high risk for work addiction.

Work-family conflict. Work-family conflict scale was assessed using the scale previously validated by Netemeyer et al. (α=.90). The 10-item scale evaluates the bi-directional nature of the construct; 5-items for work-family conflict (WFC) and 5-items for family-work conflict (FWC). Participants indicated their responses on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). Sample questions include: “WFC®The demands of my job interfere with my home and family life,” and “FWC®The things I want to do at home do not get done because of the demands of my job.”

Data analyses

The data collected via Qualtrics was transferred to Excel by Microsoft Corporation. Following the completion of data collection, it underwent a filtration process to remove incomplete responses, defined as those failing to complete the required scales or the survey itself as per the scale validation. Subsequently, the filtered data was imported into SPSS, version, etc., for statistical analysis. Demographic information such as age, gender, and marital status were obtained through specific questions, and these demographic variables were subjected to descriptive and frequency analyses. The outcomes are presented as mean and standard deviation or frequency. Validated scales were assessed using means due to the non-parametric nature of the data analysis at hand, and Cohens d is reported for effect size.

RESULTS

Participant Demographics

Participants were 51.5% athletic trainers (n = 153), 19.9% were coaches (n=59), and 26.3% were sports performance coaches (n = 78). The average age of the participants was 33 ± 9 and they had been working in their respective roles for an average of 11± 9. They self-reported working 55 ± 17 hours per week (at the time of data collection).

Stakeholders and Work-Addiction

The mean score on the BWAS across all three stakeholder groups was 20.71 ± 4.57. Table 2 represents the mean scores on the BWAS, reported by each stakeholder group. Athletic trainers reported a score of 20.84 ± 4.51, whereas coaches reported a mean score of 20.05 ± 4.85. There was no significant difference in reported scores on the BWAS between athletic trainers and coaches (U = 3952.00, p = .160, d= 0.11). Additionally, sport performance coaches reported a mean score of 20.96 ± 4.50, and no significant difference was found between sport performance coaches and athletic trainers (U = 5894.00, p = .879, d= -0.010). Furthermore, across all three stakeholders, 80 were found to be workaholics while 210 (38%) were found not to be work addicted. Among athletic trainers, 45 of the 153 (29%) respondents were found to be workaholics. Of coaches, 12 of the 59 (20%) respondents were found to be workaholics. Among sport performance coaches, 23 of 78 (29%) respondents were found to be workaholics.

Stakeholders and Burnout

Across all three stakeholder groups, participants reported low levels (46.27 ± 16.04) on the CBI, additionally mean scores of 54.9 5 ± 17.24 on the personal-related subscale, 49.99 ± 18.87 on the work-related subscale, and 33.25 ± 18.67 on the client-related subscale. Table 2 represents the mean scores on the CBI and subscales, reported by each stakeholder group. Athletic trainers reported a mean score of 48.07 ± 16.42 on the CBI, while coaches reported a mean score of 41.99 ± 15.89 on the CBI. A significant difference of burnout levels between athletic trainers and coaches was revealed (U = 3559.50, p = .017, d= -0.16).Additionally, sport performance coaches reported a mean score of 45.97 ± 14.92. There was no significant difference discovered in the reported levels of burnout between athletic trainers and sport performance coaches (U = 5483.00, p = .313, d= -0.06).

Table 2: Comparison of Reported Scale Scores by Stakeholder

StakeholderCBI (Mean±SD)BWAS (Mean±SD)WFC (Mean±SD)
Athletic Trainers48.07±16.4220.84±4.5137.66±9.26
Coaches41.99±15.8920.05±4.8537.64±10.52
Sports Performance45.97±14.9220.96±4.5037.86±9.49

Stakeholders and Work-Family Conflict

The mean score across all stakeholders on the WFC scale was 37.71 ± 9.56. Athletic trainers reported a mean of 37.66 ± 9.26, whereas coaches reported a mean of 37.64 ± 10.52. There was no significant difference between athletic trainers and coaches (U = 4483.00, p =.939, d= -0.05).Furthermore, sport performance coaches reported a mean of 37.86 ± 9.49, and no significant difference was found between sport performance coaches and athletic trainers (U= 5576.50, p = .416, d= -0.06).

Variable relationships

Correlation matrices revealed a moderate positive correlation (.507) between work addiction and work-family conflict. Work addiction and burnout also resulted in a moderate positive relationship (.573).

DISCUSSION

Inferences has been made that working in sport can lead to experiences of burnout and work-family conflict, as well as that to be a productive member of the team one must be addicted to their role. Our purpose was to explore the experiences of work-addiction, burnout, and work-family conflict among athletic trainers, coaches, and sport performance coaches. This aim was directed at better understanding around one’s role in the sport organization and experiences of these constructs. As predicted work addiction, regardless of stakeholder position, leads to increased levels of burnout and work-family conflict. Uniquely, athletic trainers and coaches experience higher levels of burnout than sport performance coaches.

Stakeholders and Work-Family Conflict

We did not find any significant differences among our samples and experiences of WFC. The total mean score on the WFC scale is comparative to other studies examining WFC among athletic trainers work in the sport industry (Mazerolle et al. 2011; Pitney et al. 2011; Singe et al. in press). Our sample was largely represented by those who do not have children (70%); which could explain why we did not find any differences among our sample regarding experiences of WFC. Time is often a large facilitator of WFC, despite our sample reporting 55 hours per week, many did not have children another facilitator of WFC (Mazerolle et al., 2008; Pitney et al., 2011; Singe et al., 2023a). Perhaps working long hours has less of an impact on the individual when additional family responsibilities are not present, and one can focus on work and personal interests.

Stakeholders and Burnout

Overall, this sample of individuals working in the sport organization are experiencing low levels of burnout. Low levels of burnout does not imply that our sample is not experiencing it; however quantifiably it is lower. The literature over the last 5 years has suggested that coaches and athletic trainers are experiencing higher levels of burnout (Goodger et al., 2007; Singe et al., 2024; Singe et al., 2023a). We found that athletic trainers reported higher levels of burnout compared to coaches, but similar levels of burnout to sport performance coaches. Moderate levels of burnout have recently been reported among athletic trainers  (Singe et al., 2023a); however, fluctuations in experiences have been observed over the past 3 years with levels varying between moderate and low (Cairns et al., 2023; Oglesby et al., 2020; Singe et al., 2023a). Sport performance coaches have yet to be identified within the literature regarding burnout; our sample reported similar levels of burnout as athletic trainers. Similar to athletic trainers, sport performance coaches have high demands placed upon them, and they are invested in the success of their athletes as well as log long hours in the workplace (Bentzen et al., 2016; Olusoga et al., 2019).

Stakeholders and Work-Addiction

Our overall sample is not classified as a workaholic; however, both athletic trainers and sport performance coaches demonstrate a larger sample (29%) of those who would be classified as such. Workaholics may work long hours but that is by choice and perhaps not as a necessity (Andersen et al., 2023). Although our sample reports working excessive hours (55), they do not self-identify as workaholics. Moreover, we did not find significant differences between stakeholders. These findings suggest that work addiction is likely an individualized factor, and not necessarily an outcome of working in sport organization. As detailed in the work-family conflict framework of Bruening and Dixon (2005, 2007), there are individual, organizational, and sociocultural outcomes of experiences of work-family conflict.  

Variable relationships

Positive relationships were found between work addiction and both burnout and work-family conflict. The correlations found between the experiences of these constructs are consistent with those observed in previous studies examining these constructs in athletic trainers (Eason et al., 2022). These results make it apparent that experiences of work addiction, work-family conflict, and burnout occur at the same time. Previously stated, work-addiction can be attributed to experiences of work-family conflict and burnout. In this case all stakeholders are experiencing all three constructs.

We predicted there to be positive relationships between work addiction, burnout, and work-family conflict. Work addiction is yet another construct that is experienced by those working in the sport organization. This study adds to the literature that there are no differences in work-family conflict and burnout across athletic trainers, coaches, and SPCs. Yet, there are notable differences when it comes to burnout. Coaches and SPCs are experiencing work-family conflict, and work-addiction similarly to athletic trainers. This speaks to the sport organization as a whole; all employees are encountering these constructs. We suggest the sport organization investigate and assess reasons employees are work-addicted and have work-family conflict, to improve job and life satisfaction.

ATs experienced higher levels of burnout compared to coaches, and SPCs. There are many reasons this may be, the number of athletes per employee, responsibilities, and medical roles. However, in this sample athletic trainers reported low levels of burnout, though higher than coaches and SPCs, not quite as high as levels in recent literature (Barrett et al., 2016).

Consideration for Future Research and Study Limitations

The findings of this study expand upon the growing body of literature examining the constructs of work-addiction, burnout, and work-family conflict within the sport organization, yet limitations on these findings remain. Our study received 297 usable responses, which is a lower response rate than anticipated. Due to these factors, we recognize that these findings may not represent the experiences of all of those working within the sport organization. Our database was established using publicly available information therefore a complete list of all athletic trainers, coaches, and sports performance coaches at the DI level was unable to be obtained. Therefore, the results of this study may not represent the experiences of the entirety of NCAA DI athletic trainers, coaches, and SPCs. Our study also only examined those working within the NCAA DI setting; thus, those working in the DII, DIII, NAIA, or other collegiate levels may not have similar experiences with these constructs. Furthermore, those working in secondary schools or other settings also may not identify with the findings of this study.

Further research should include the investigation of work-addiction, burnout, and work-family conflict at all levels of collegiate athletics as well as those in secondary schools and alternate settings. Currently, the literature has examined these constructs within the sport organization solely focused on the experiences of athletic trainers, creating a need for future research among coaches and sports performance coaches on these constructs. Additionally, the study of work addiction within the sport organization is a novel issue, so further research is necessary to gain a better understanding of work addiction within athletics.

CONCLUSION 

This study sought to further our knowledge of the experiences of athletic trainers, coaches, and sport performance coaches in the DI setting, regarding work-addiction, burnout, and work-family conflict. Experiences were nearly universal across the sport organization except for athletic trainers experiencing greater levels of burnout compared to coaches. Positive relationships were also observed between levels of work addiction and both burnout and work-family conflict. The findings of this study suggest that these constructs are prominent issues across the sport organization. Given the prevalence across the sport organization, increased implication of both personal and organizational strategies may be necessary as a means of mitigating the impact of these issues (Cairns et al., 2023; Singe et al., 2022). This study serves as a preliminary exploration into the variance of experiences of work addiction, burnout, and work-family conflict across the sport organization stakeholders.

APPLICATIONS IN SPORT

Athletic trainers reported significantly different levels of burnout compared to coaches and sport performance coaches; thus we believe that understanding the specific role stressors for the athletic trainer can help address potential programs to prevent burnout. For example, wellness programs or a workload redistribution may be warranted for athletic trainers.  We did not find any differences among work-family conflict among any of the grups, which suggests more broad based policies that are family-friendly may help athletic trainers, coaches, and sport performance coaches (family-leave, time-off policies). Work addiction was a risk factor for both burnout and work-family conflict among our stakeholders, thus individuals and supervisors should be aware of the signs of burnout, but also encourage stress and boundary management,  as well as healthy work habits to prevent issues around burnout and conflicts between work and home. 

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A Manual Therapy Treatment for Headache Pain

Authors: Lindsay C. Luinstra1, Dan Sigley1, Heidi A. VanRavenhorst-Bell1

Corresponding Author:

Dr. Lindsay Luinstra, DAT, MS, LAT, ATC

1845 Fairmount Street,

Box 16,

Wichita, KS 67260

[email protected]

(316) 978-5440


1Department of Human Performance Studies, Wichita State University, Wichita, KS, USA

Dr. Lindsay Luinstra, DAT, MS, LAT, ATC is an assistant professor of athletic training at Wichita State University in Wichita, KS. Her research interest is in sports medicine and manual therapy techniques to treat athletic-related injury.

Dr. Dan Sigley, DAT, LAT, ATC is an assistant professor of athletic training at Wichita State University in Wichita, KS. His research interest is in concussion education, evaluation, and treatment paradigms.

Dr. Heidi A. VanRavenhorst-Bell, PhD is Chair and Associate Professor in the Department of Human Performance Studies and Manager of the Human Performance Laboratory at Wichita State University. She has an established interdisciplinary line of research directed toward functional performance across exercise physiology and orofacial myology.

ABSTRACT

Cervicogenic headache (CEH) is caused by dysfunction in the cervical spine and surrounding muscles. It is typically characterized by unilateral or sometimes bilateral head pain, often accompanied by limited neck movement.  Postural and neuromuscular dysfunction in the cervical spine may contribute to the onset of headache-related pain. This study aims to address headache-related pain using the C2 evaluation and treatment protocol from the MyoKinesthetic System, a manual therapy method focused on evaluating and treating postural imbalances.  A female patient with self-reported chronic headache-related pain and neck discomfort underwent six treatments using the C2 cervical nerve root protocol over a two-week period, with 48-72 hours between each session. Each treatment lasted approximately 8 minutes. Subjective and objective outcome measures were collected throughout the treatment period, including clinician-assessed cervical range of motion, the Numerical Pain Rating Scale (NPRS), the Neck Disability Index (NDI), and the Headache Impact Test-6 (HIT-6). At the initial assessment, the patient reported an NPRS score of 4/10, an NDI score of 14/50, and a HIT-6 score of 58.  After the final treatment, the patient’s NPRS pain score was 5/10, with NDI and HIT-6 scores of 15/50 and 54, respectively. Cervical extension range of motion improved by 7 degrees post-treatment. However, the average NPRS pain reduction over the two weeks was only 0.25 points and not clinically significant. At the 30-day follow-up, NPRS results met the minimally clinically important difference (MCID), with a score of 0. Headache frequency decreased from daily to once every three days, with the duration reduced to around 15 minutes. The patient reported improved tolerance for physical activities and fewer work disruptions. Lasting improvements were observed in neck function, headache impact, pain, and range of motion.  These findings are promising, but more research is needed to confirm the MyoKinesthetic System’s effectiveness for CEH. Targeting the C2 cervical nerve root helped reduce the patient’s chronic headache frequency and neck discomfort, suggesting potential for addressing neuromuscular imbalances. However, since this is a single case study, further research with larger samples and comparisons to other treatments is needed to assess its broader efficacy and long-term effects.

Key Words: MyoKinesthetic System; cervical nerve root; head-related discomfort

INTRODUCTION

Cervicogenic headache (CEH) is characterized by pain in the head associated with the cervical spine and cervical musculature (Bogduk, 2001; Bogduk & Govind, 2009; Haldeman & Dagenais, 2001). Sjaastad et al. (1998), along with the International Headache Society (The International Classification of Headache Disorders, 2018), define CEH as a unilateral headache that may also present bilaterally, associated with the cervical spine and muscles. Identifying signs and symptoms, including a reduced active and passive range of motion in the cervical spine leading to mechanical dysfunction, is critical in diagnosing CEH (Sjaastad et al., 1998). Accompanying symptoms may include nausea, vomiting, flushing, dizziness, phonophobia, photophobia, blurred vision, and dysphagia (Sjaastad et al., 1998). The burden of a headache is measured by the degree of pain and suffering experienced by the patient.

Treatment options are available across multiple healthcare specialties (Yang et al., 2010), including athletic training, and treatment choice appears to depend on the specialty of the healthcare provider treating the patient (Smith & Bolton, 2013). Various treatment methods have been studied, both invasive (e.g., surgery and injections) and non-invasive (e.g., massage, cervical mobilizations, trigger point therapy, and acupressure) in nature (Bogduk & Govind, 2009; Haldeman & Dagenais, 2001; Quinn et al., 2002; Schoensee et al., 1995). The goal of clinicians using non-invasive manual therapy techniques is to resolve patient complaints by treating the cervical spine as the primary source of CEH symptoms (Bogduk, 2004).

Non-invasive therapeutic techniques for CEH include cervical spine mobilization, massage, trigger point therapy, and acupressure (Bogduk & Govind, 2009; Haldeman & Dagenais, 2001; Quinn et al., 2002; Schoensee et al., 1995; Youdas et al., 1992). Researchers have demonstrated clinically significant reductions in headache intensity, frequency, and duration among patients treated with non-invasive techniques over at least a six-week treatment protocol (Bogduk & Govind, 2009; Haldeman & Dagenais, 2001; Quinn et al., 2002; Schoensee et al., 1995; Youdas et al., 1992). Although manual therapy techniques have been reviewed as effective management tools for CEH (Bogduk & Govind, 2009; Haldeman & Dagenais, 2001; Quinn et al., 2002; Youdas et al., 1992), no studies have specifically evaluated the effects of pain intensity changes and cervical range of motion after shorter treatment durations, such as a two-week treatment protocol. Conservative treatments that require extended durations to achieve significant results may motivate patients to seek faster remedies (e.g., medication) that perpetuate their condition by altering symptoms without addressing the underlying cause.

The MyoKinesthetic (MYK) System is an evaluation and treatment paradigm used to restore the central nervous system’s (CNS) communication with the musculoskeletal system to achieve allostasis. The MYK evaluation is designed to identify abnormalities in a patient’s static posture and connect those abnormalities to specific nerve root(s) via the associated myotome(s). The clinician then treats at the level of the identified myotome by using active and passive patient movements with a simultaneous external stimulus, similar to massage, to stimulate the communication pathways of the CNS.

The MYK System is theorized to decrease nociceptive firing that may cause or occur due to joint and tissue movement restriction (Smith & Bolton, 2013). The MYK system aims to create postural balance by treating the bilateral neuromuscular system along a specific nerve root. Specifically, for headaches, the MYK System utilizes additional classification beyond postural evaluation, including assessing headache pain and location. The MYK system, which helps the clinician determine the nerve root to be treated, offers a headache assessment table designed by Dr. Mike Uriarte (Uriarte, 2004). The location of headache-related symptoms in one or multiple areas (e.g., top of the head, sides of the head, front or back of the head, front of the head above the eyes, and back of the head no lower than the occiput) is used to determine which cervical nerve root may be affected. Currently, limited published research examines the effectiveness of the MYK headache treatment on headache-related pain (Moy, 2015).

The purpose of this case study was to examine the effects of the MYK system over two weeks when treating a patient classified with chronic CEH (i.e., occurring 15 days or more per month for longer than three months).

TABLE 1

The ‘Yes/No’ Cervical Nerve Root Assessment Chart

Nerve RootLocation of PainSpecial Characteristics
C1Anywhere on the head, this is determined when we do the ‘yes/no’ test.If their head is ‘rotated only,’ it is C1.  
C2Top of the head, sides of the head, front and back of the head. No lower than the occiput.  
C3In the eyes, between the eyes, behind the eyes, into the jaw or cheek area, top of the neck. 

Case Report

The patient, a thirty-three-year-old female, reported her main complaints were headache pain and neck discomfort off and on for over ten years, starting while she was in middle school.  A signed HIPAA and informed consent form were obtained before the initial evaluation and treatment. The patient’s prior history of significant injury included rotator cuff lesion and finger, foot, and toe fractures. The patient underwent shoulder arthroscopy to repair the rotator cuff three years prior. Still, since the headaches were present before and after the surgery, it was not believed to be a primary contributing factor. The patient’s contributing factors that coincided with her headache symptoms included sinusitis and bilateral numbness in her hands.  The patient also reported that she had missed significant events in her life because of her chronic headache pain. Her work-life was frequently disturbed; she required breaks often and was unable to stay focused on her tasks. In her own words, her ‘everyday active lifestyle was disrupted frequently’. 

The patient pursued multiple treatments and techniques over several years to relieve her headaches and neck discomfort but found little to no success. Some treatments positively impacted her condition for a short period but had not changed her condition long-term. These treatments and techniques included chiropractic care, medication, injections, essential oils, and physical therapy. Prescription pain medication and muscle relaxers were used as a last resort.  Over-the-counter medicines were used by the patient weekly as needed.

METHODS

Assessment

After obtaining a complete history and satisfying the inclusion/exclusion criteria (see Table 2), a physical examination was performed, consisting of cranial nerve and vertebral artery insufficiency testing, before the MYK ‘yes/no’ test and the MyoKinesthetic (MYK) full-body postural assessment.  Cranial nerve function tested normal, as did the vertebral artery performance.

Table 2

 Inclusion and Exclusion Criteria.

Inclusion CriteriaExclusion Criteria
-Pain projected to the forehead, orbital region, temples, ears, neck, or occipital region; -Pain with specific neck movements or sustained postures; -Complaints of palpable pain or discomfort/limitation of active or passive ROM.-Participants > 50 years old; -Positive Vertebral Artery Test; if positive, refer out  -If any analgesic or non-steroidal anti-inflammatory drugs (NSAIDs) were taken within the last 24 hours; -If the participant has an acute diagnosis of concussion or has not been released by a physician for full activity with no restriction from a concussion diagnosis

The MYK ‘yes/no’ test is used within the MYK System to determine resting head position. The patient stands with eyes closed and nods and shakes his/her head several times before coming to a comfortable resting position. The position of the head at rest is noted. Assessing cervical posture/imbalance with eyes closed may help to remove the visual input that the body uses to level itself with the horizon. In conjunction with the location of symptoms as outlined in Table 1, the ‘Yes/No’ Test is used to determine the cervical nerve root associated with the patient’s posture and symptoms. In this case, the patient’s cervical posture was visibly laterally flexed to the right. 

The MYK full-body postural assessment consists of the clinician evaluating the patient’s posture and stance, noting any imbalances when compared bilaterally and against postural norms (e.g., neutral).  In this case, clinical evaluation utilizing the MYK full-body postural assessment (Table 3) and clinician expertise demonstrated a C1-T1 dysfunction, with considerable postural imbalances associated with C6. The patient’s primary complaint was headache pain on the top of the head and temples with general neck discomfort. As outlined in Table 1, the C2 nerve root was identified as the affected nerve root using the headache treatment guidelines.

Pain-free active cervical ranges of motion (extension, flexion, and right/left rotation) were assessed using a goniometer with the patient’s eyes closed. At the initial examination, the patient had 53 degrees of pain-free active cervical extension and 45 degrees of pain-free active cervical flexion.  Pain-free active cervical rotation to the left and right was 60 degrees and 67 degrees, respectively.

Instrumentation

For patient-reported instruments to be most helpful in clinical practice and research, those with good psychometric properties and clinical applicability were utilized (Houts et al., 2020; Farrar et al., 2001). Instruments that were well-established in the literature and validated were selected to measure the impact of headaches in this case study.

The Headache Impact Test Questionnaire

The Headache Impact Test (HIT-6) is designed to assess the global impact of headaches on patients, measuring content areas such as pain, social-role limitations, cognitive functioning, psychological distress, and vitality (Houts et al., 2020). Nachit-Ouinekh et al. (2005) evaluated the global impact of episodic headaches in patients consulting general practitioners using the HIT-6 questionnaire and compared headache severity and quality of life. A comparison of the HIT-6 scores was conducted for each of the four sub-scores (i.e., functional, psychological, social, and therapeutic indices) against the French Qualité de Vie et Migraine (QVM) questionnaire (Nachit-Ouinekh et al., 2005). Scores range from “60 or more—headache has a severe impact on your life” to “49 or less—headache has little to no impact on your life” (Nachit-Ouinekh et al., 2005).

The Numerical Pain Rating Scale

The Numerical Pain Rating Scale (NPRS) is an 11-point numerical scale in which the clinician asks the patient to rate their pain verbally on a scale from 0 (no pain) to 10 (worst pain imaginable) (Farrar et al., 2001). In this study, average scores were calculated using the patient’s “current,” “best,” and “worst” pain scores, which were then compared to the post-treatment “current” pain score.

The Neck Disability Index

The Neck Disability Index (NDI) is a patient-reported, condition-specific functional status questionnaire that includes items related to pain, personal care, lifting, reading, headaches, concentration, work, driving, sleeping, and recreation. Out of a possible 50 points, a higher score indicates greater patient-perceived neck disability. A 5-point change on the index is considered a clinically important difference (Chan Ci En et al., 2009).

At the initial assessment, the patient reported an NPRS of 4/10, a HIT-6 score of 58, and an NDI score of 14/50. Measurements and outcomes were also collected at 30- and 60-day follow-ups.

The treatment of the C2 nerve root was determined based on the MyoKinesthetic (MYK) System’s “yes/no” test results. Treatment was performed following MYK System guidelines with the patient in a seated position. The clinician administered treatment using the MYK System parameters: passive movements were completed first, with the clinician passively moving the participant through each muscle’s range of motion (five times) while applying manual stimulus similar to massage to the muscles of the C2 myotome. Then, the participant actively moved (seven times) through the same range of motion while the clinician applied the same stimulus to the muscles. Once all muscles innervated by the C2 nerve root were treated bilaterally, treatment was complete. Treatments lasted approximately eight minutes on average and were conducted six times over two weeks, with 48 to 72 hours between each treatment.

RESULTS

After the final treatment, the pain reported on the NPRS was 5/10. The patient also completed the NDI and HIT-6, with scores of 15/50 and 54 points, respectively (see Table 4). Cervical range of motion (ROM) measurements were recorded in degrees and evaluated pre- and post-treatment. There were significant improvements in cervical extension ROM, with an increase of 7 degrees post-final treatment. A summary of ROM measurements is presented in Table 5.

The mean pain scores across the two weeks of treatment were not clinically significant compared to the NPRS minimally clinically important difference (MCID), which is defined as an average decrease of 2 points. In this case, the average decrease was only 0.25 points (Chan Ci En et al., 2009). However, daily NPRS results met the minimally clinically significant difference at the 30-day follow-up, with an average of 0 (Chan Ci En et al., 2009). Lastly, the patient’s postural examination changed between intake and discharge, as many imbalances were corrected within normal limits (see Table 3; Uriarte, 2004).

The patient reported a dramatic decrease in headache frequency over the two-week period, from experiencing a headache daily to only one every three days. By the end of the two-week treatment period, the patient noted that headache duration significantly decreased, lasting approximately 15 minutes compared to several hours or days before treatment. The patient also reported improved tolerance for physical activities she had previously been unable to perform, such as walking for extended periods, lifting weights, completing household tasks, and playing with her child. Disruptions at work were also greatly diminished, and the patient reported improved ability to focus on tasks with greater ease.

While the patient reported notable improvements, it is essential to analyze the raw data to form a proper conclusion. When evaluating follow-up scores, the findings suggest lasting improvements in multiple aspects of the patient’s life, including but not limited to neck function, perceived headache impact, pain levels, and range of motion. The follow-up scores are illustrated in Table 4.

DISCUSSION

The MyoKinesthetic (MYK) System elicited positive and lasting changes in this patient with frequent and intense cervicogenic headaches (CEH) over just two weeks of treatment. By the 60-day follow-up, the patient’s pain was nearly eliminated, and headache frequency had become rare. The patient also reported no headache-related pain or discomfort between treatments, which were spaced 48 to 72 hours apart. Improvements were observed in cervical flexion and right rotation, and the patient reported a significant enhancement in functional activities, allowing her to enjoy a more comfortable home life and a less painful work environment. The MYK System may be beneficial for other patients with CEH; however, research on its effectiveness remains limited, as is the case with other manual therapy techniques. Further studies are needed to determine why MYK may have been effective in treating this patient.

Manual therapy has been shown to decrease pain, improve function, and enhance quality of life in patients with musculoskeletal conditions, though its effectiveness varies among individuals (Uriarte, 2004). For example, massage therapy is commonly used to treat general pain complaints, yet some patients experience substantial relief while others show little to no improvement. Similarly, alternative treatment approaches, such as mobilizations with movement, may have been more or less effective in addressing the patient’s primary complaint. Treating patients with pain is inherently subjective, as each patient’s response is influenced by a combination of mental, physical, and emotional factors.

The MYK technique may extend its effects beyond conventional treatment boundaries. Patients may perceive MyoKinesthetic treatment as similar to joint mobilization and massage (e.g., pressure, squeezing, trigger point therapy). Neural mobilization may also occur as all tissues move through various ranges of motion. Some patients report a stretching or traction effect, while others describe experiencing a “pop” sensation, suggesting a possible manipulative effect. The MYK System is designed to be quick and efficient, requiring minimal space and exertion from the clinician (Moy, 2015).

Although limited research has explored manual therapy as a viable treatment for headaches, Smith and Bolton (2013) provided a compelling argument supporting its use. While acknowledging study limitations, their evaluation considered both postural and pain-related factors. Headaches related to stress, nerve irritation, or muscle spasms were subjectively identified, and chronic pain in the neck and upper trapezius region was also noted. MYK was used in this case to address the patient’s symptoms, and the treatment was beneficial. The systematic evaluation process within the MYK System highlighted neuromuscular imbalances, targeted their treatment, and raised the question of whether MYK could serve as an effective intervention for headaches (Uriarte, 2004).

A study by Moy (2015) applied the MYK System to a patient with complaints of neck pain, shoulder pain, hip pain, and headaches. Through a comprehensive assessment, the C8 nerve root was identified as the source of the patient’s symptoms. Following targeted MYK treatment, the patient experienced a significant reduction in pain, improved cervical range of motion, and enhanced quality of life after nine treatment sessions.

At the conception of the MYK System, a review of research addressing neuromuscular function and dysfunction was conducted. Understanding the neuromuscular system was fundamental to its development. Dr. Uriarte (2004) conceptualized the neuromuscular system as a “two-sided story,” emphasizing the necessity of bilateral treatment to address the root cause of pain rather than merely targeting the symptomatic area.

Furthermore, during MYK treatment, the body may perceive movement as normal and recognize the applied stimulus as non-threatening. This process allows patients to transition from painful to non-painful motion. A unique aspect of the MYK System is how treatment concludes. According to Dr. Uriarte (2004), posture serves as an external reflection of the neurological system. Before treatment, compensatory patterns may develop due to dysfunction and gravitational forces. Following treatment, the body and neurological system are expected to feel more balanced and better equipped to adapt to movement and gravity naturally.

Limitations

As with any attempted case study, limitations were present. Limitations included the following: 1) The treatment pressure may vary among treatments over the two weeks.  While the type of stimulus (stroking, tapping, massaging) may not matter, varying pressure has not been studied; therefore, the effects of pressure have not been determined.  This may be viewed as a limitation of the technique rather than a limitation of this study.  2) Reliability of goniometric measurement was not established before data collection, which may have created a limitation on reporting significant cervical ROM changes.  However, all measurements were taken in the same setting, patient position, and by the same clinician.  Validity and reliability of goniometric measures are usually established amongst clinicians, with multiple ROM measurements collected blindly over some time with the same subjects.  With there only being one patient and one clinician in this study, inter- and intra-reliability are lacking.  3) Although the patient was instructed not to take medication or have other treatments for headaches, the clinician cannot control what happens outside the clinic.  The patient did not report any other treatments or taking medication during the time of the study.

Further research should be conducted, exploring whether the muscles’ stimulation affects multiple participants with suspected cervicogenic headache during the acute stages of a CEH.  Other research should be conducted utilizing the MYK manual therapy treatment technique on different body regions to determine treatment effectiveness.  Another viable research topic would be comparing the specific nerve root treatment based on the location of headache pain (C1, C2, C3) compared to the location of dysfunction according to the MYK Upper Body assessment findings (C1-T1).    

CONCLUSIONS

MYK manual therapy helped this patient improve in their complaint of headache pain and frequency.  This study demonstrates that the MYK System headache treatment may be a practical treatment choice to reduce the intensity of patient-reported pain in patients with suspected cervicogenic headaches.  The treatment of cervical nerve root C2 from the MYK System created a clinically significant change in the participant’s perceived pain, including some results found after the 30-day and 60-day follow-ups.   

The question arises: Is MYK the most viable option for patients suffering from headache-related pain?  MYK is quick, easy, and presents as effective.  The treatment needs more research and discussion to support the idea that MYK is effective and helps validate more manual therapy techniques.  While MYK is not the only manual therapy technique available, it appears viable when assessing and treating patients. Overall, the changes in pain, intensity, and frequency observed in this study support the MyoKinesthetic System headache treatment along cervical nerve root C2 as a successful form of a non-invasive technique when treating cervicogenic headaches.

APPLICATIONS IN SPORT

For coaches, athletic trainers, and parents, understanding cervicogenic headaches (CEH) and their potential impact on athletes is crucial. Athletes, especially those involved in contact sports or repetitive motions, are at a higher risk for neck injuries that could lead to headaches. These headaches can affect an athlete’s performance and overall well-being, causing discomfort, limiting movement, and sometimes sidelining them from practice or competition.

As a coach or athletic trainer, recognizing the signs of CEH and addressing them early can make a significant difference in an athlete’s recovery and performance. Techniques such as cervical mobilizations, myofascial release, and other manual therapies can relieve, improve range of motion, and prevent long-term issues. By being proactive and incorporating strategies to address CEH, you can help athletes stay on track, reduce downtime, and support their physical function, ultimately enhancing their athletic experience and success. Parents, too, can play an important role by being aware of the symptoms and encouraging their athletes to seek timely treatment.

Acknowledgments

The authors declare no conflict of interest and did not receive payment for this study.

REFERENCES 

  1. Bogduk, N. (2001). Cervicogenic headache: Anatomic basis and pathophysiologic mechanisms. Current Pain and Headache Reports, 5(5), 382–386. https://doi.org/10.1007/s11916-001-0025-y
  2. Bogduk, N. (2004). The neck and headaches. Neurologic Clinics, 22(1), 151–171. https://doi.org/10.1016/j.ncl.2003.11.006
  3. Bogduk, N., & Govind, J. (2009). Cervicogenic headache: An assessment of the evidence on clinical diagnosis, invasive tests, and treatment. The Lancet Neurology, 8(10), 959–968. https://doi.org/10.1016/S1474-4422(09)70209-9
  4. Sjaastad, O., Fredriksen, T. A., & Pfaffenrath, V. (1998). Cervicogenic headache: Diagnostic criteria. Headache: The Journal of Head and Face Pain, 38(6), 442–445. https://doi.org/10.1046/j.1526-4610.1998.3806442.x
  5. The International Classification of Headache Disorders, 3rd edition. (2018). Cephalalgia, 38(1), 1–211. https://doi.org/10.1177/0333102417738202
  6. Yang, M., Rendas-Baum, R., Varon, S. F., et al. (2010). Validation of the Headache Impact Test (HIT-6™) across episodic and chronic migraine. Cephalalgia, 31(3), 357–367. https://doi.org/10.1177/0333102410379890
  7. Quinn, C., Chandler, C., & Moraska, A. (2002). Massage therapy and frequency of chronic tension headaches. American Journal of Public Health, 92(10), 1657–1661. https://doi.org/10.2105/AJPH.92.10.1657
  8. Haldeman, S., & Dagenais, S. (2001). Cervicogenic headaches. The Spine Journal, 1(1), 31–46. https://doi.org/10.1016/S1529-9430(01)00017-2
  9. Schoensee, S. K., Jensen, G., Nicholson, G., et al. (1995). The effect of mobilization on cervical headaches. Journal of Orthopaedic & Sports Physical Therapy, 21(4), 184–196. https://doi.org/10.2519/jospt.1995.21.4.184
  10. Youdas, J. W., Garrett, T. R., Suman, V. J., et al. (1992). Normal range of motion of the cervical spine: An initial goniometric study. Physical Therapy, 72(11), 770–780. https://doi.org/10.1093/ptj/72.11.770
  11. Hall, T. M., Robinson, K. W., Fujinawa, O., et al. (2008). Intertester reliability and diagnostic validity of the cervical flexion-rotation test. Journal of Manipulative and Physiological Therapeutics, 31(4), 293–300. https://doi.org/10.1016/j.jmpt.2008.03.007
  12. Uriarte, M. (2004). MyoKinesthetic system upper body training manual. MyoKinesthetic Institute.
  13. Moy, B. (2015). Case study detail – The MyoKinesthetic Institute (MYK). MyoKinesthetic Institute. Retrieved August 18, 2021, from https://www.myokinesthetic.com/case-studies/the-treatment-of-c8-with-manual-therapy
  14. Houts, C. R., Wirth, R. J., McGinley, J. S., et al. (2020). Determining thresholds for meaningful change for the Headache Impact Test (HIT‐6) total and item‐specific scores in chronic migraine. Headache: The Journal of Head and Face Pain, 60(10), 2003–2013. https://doi.org/10.1111/head.13950
  15. Nachit-Ouinekh, F., Dartigues, J. F., Henry, P., et al. (2005). Use of the Headache Impact Test (HIT-6) in general practice: Relationship with quality of life and severity. European Journal of Neurology, 12(3), 189–193. https://doi.org/10.1111/j.1468-1331.2004.00929.x
  16. Farrar, J. T., Young, J. P., LaMoreaux, L., et al. (2001). Clinical importance of changes in chronic pain intensity measured on an 11-point numerical pain rating scale. Pain, 94(2), 149–158. https://doi.org/10.1016/S0304-3959(01)00349-9
  17. Chan Ci En, M., Clair, D. A., & Edmondston, S. J. (2009). Validity of the Neck Disability Index and Neck Pain and Disability Scale for measuring disability associated with chronic, non-traumatic neck pain. Manual Therapy, 14(4), 433–438. https://doi.org/10.1016/j.math.2008.07.005
  18. Smith, J., & Bolton, P. S. (2013). What are the clinical criteria justifying spinal manipulative therapy for neck pain? A systematic review of randomized controlled trials. Pain Medicine, 14(4), 460–468. https://doi.org/10.1111/pme.12081
  19. Norkin, C. C., White, D. J., Torres, J., et al. (2016). Measurement of joint motion: A guide to goniometry (5th ed.). F.A. Davis Company.

APPENDIX

Table 3

MYK Postural Assessment (pre/post)

Table 4

Patient Reported Outcomes

 NDIHIT-6NPRS
ASSESSMENTScoreRankingScoreRankingPre- ScorePost- ScoreMean of  Raw
Initial14/50Mild58Substantial433.75
Discharge15/50Moderate54Some754
Mean__57.6Substantial–––
30-Day8Mild46Little to no impact––0
60-Day5Mild38Little to  no impact––.666

Table 5

Goniometric measurement mean normative data for cervical range of motion taken from Norkin et al.

Cervical Range of Motion
MovementNormative DataPre-treatmentPost-treatment (change)30-Day Follow Up60-Day Follow Up
Flexion40° ± 1245°40° (-5°)47.3°46°
Extension50° ± 1453°60° (7°)41.6°37°
Left Rotation49° ± 953°54.6° (1.6°)55.6°51°
Right Rotation51° ± 1160°61.6° (1.6°)58.6°62°
2025-10-08T12:16:04-05:00April 15th, 2026|Concussions, General, Research, Sports Health & Fitness, Sports Medicine|Comments Off on A Manual Therapy Treatment for Headache Pain
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Match Demands and Positional Differences of NCAA Division II Women’s Soccer Players Over a Competitive Season

Authors: Joanne Spalding1, Jacob L. Grazer2

1Department of Health & Human Performance, Georgia College & State University, Milledgeville, GA, USA

2Department of Exercise Science & Sports Management, Kennesaw State University, Kennesaw, GA, USA

 

Corresponding Author:

Joanne Spalding, PhD, CSCS, CPSS, ACSM-EP

231 West Hancock Street

Milledgeville, GA 31061

[email protected]

478-445-2135

Joanne Spalding, PhD is Assistant Professor in Exercise Science at Georgia College and State University. Her research interests include long term athletic development and monitoring at the club, high school, and college level with an emphasis on neuromuscular fatigue.

Jacob L. Grazer, PhD, CSCS, USAW-1, ACSM-EP is currently a faculty member in the Department of Exercise Science and Sport Management at Kennesaw State University. Jacob’s research interests include: accentuated eccentric loading, countermovement jump analysis, and sport technology.

ABSTRACT 

Purpose:  There has been a growing interest in American women’s college soccer with the majority of research focused on the NCAA Division I level.  With 250+ Division II programs competing in 2024, there is an underrepresentation of this group in the literature. The purpose of this study is to examine the physical demands in Division II women’s soccer and analyze whether these physical demands vary by position.

Methods:  Twenty-two Division II female soccer field players from one DII college team were monitored over an 18-match season wearing GPS devices, a technology that is more commonly used at prevalent at Division I universities. Field players were divided into one of four positions: (1) Center Back Defenders; (2) Outside Back Defenders; (3) Midfielders; and (4) Forwards. The GPS devices provided data on: (a) total distance traveled; (b) total distance traveled per minute; (c) high-speed running distance traveled per minute; and (d) sprint distance traveled per minute. Due to NCAA Division II substitution rules, the focus in this study is the relative variables calculated as distance traveled per minute.

Results: Descriptive statistics for each GPS measurement are calculated based on the position played.  Then, Analysis of Variance (ANOVA) is used to identify any differences in player workload physical demands based on the position played. For distance traveled during a match, Center Back Defenders traveled the least distance compared to other positions. Similarly, for high-speed running distance, Center Back Defenders traveled the least distance compared to other positions. Finally, for Sprint Distance per minute, both Forwards and Outside Back Defenders traveled more distance compared to Center Back Defenders and Midfielders.

Conclusions: Findings suggest that the physical demands of Division II women’s soccer differ by position. Center Back Defenders tend to ‘stay at home’ and defend the goal while traveling less distance and sprinting less often. Forwards and Outside Back Defenders tend to spend more time sprinting compared to other players.

Applications in Sport: Coaches and practitioners can use this data when designing training programs to ensure their athletes are well-prepared for the differing physical demands of their sport based on the position played.

KEYWORDS: college soccer, GPS, match demands, women’s soccer, physical match performance

INTRODUCTION 

Over the past several years, there has been an increase in the number of peer-reviewed research articles focusing on the physical demands of women’s soccer. The majority of the research that has been conducted has focused on athletes who compete at the professional and international levels (E. Choice et al., 2022). Recently, there has been a growing interest in American college women’s soccer, with the majority of this research focused on National Collegiate Athletic Association (NCAA) Division I level. This can likely be attributed to the fact that wearable technology has become more prevalent at the collegiate Division I level making it easier to collect information regarding the physical demands experienced by female collegiate athletes. However, with 250+ Division II programs competing in the 2024 season, this significant population has been under-represented in research efforts to examine the physical demands on the athlete when competing.  As such, further analysis is needed to better understand the physical demands on athletes participating in the various levels of collegiate women’s soccer. 

Soccer is a high intensity, intermittent sport that requires running, jogging, walking, repeated sprints, jumping and change of direction (Al-Hazzaa et al., 2001; Bloomfield et al., n.d.; Wisløff et al., 2004). Matches consist of two 45-minute halves with a 15-minute halftime period. For international competition, the Fédération Internationale de Football Association (better known as FIFA) sanctions allow for five substitutions per game with a typical squad size of 26 players. However, NCAA soccer permits an unlimited number of players to be on the official roster during the regular season. Substitutions rules for the NCAA also differ from FIFA in that the number of substitutions is unlimited per game, and players are allowed to re-enter the match once in the second half. This difference is notable because it may influence the physical demands placed on players during match play.

Current literature has explored match play data at various playing levels and reports that there are differences in match-play demands at different levels in women’s soccer (E. Choice et al., 2022). A perspective review by Vescovi et al (2021) reported that there is a linear increase in total distance covered across playing levels ranging from youth (~7,500m), collegiate (~9,500m), to professional levels (~10,200m). A systematic review of women’s soccer also identified that playing demands increase between playing levels, and that there are positional differences in terms of total distance covered, high-speed running distance, and sprint efforts where midfielders covered greater total distances and sprint efforts compared to central defenders (Alexander, 2014; E. E. Choice et al., 2023).

Challenges arise when comparing research due to variations of inclusion criteria and determination of velocity thresholds. In addition to inclusion criteria varying, values are typically reported in total distances rather than relative to time spent playing on the field. As highlighted previously, the unique NCAA substitution rules make it challenging to fully grasp the physical demands that are placed on the athletes since it is uncommon for someone to play a full 90-minute match in women’s collegiate soccer. Due to the potential influence of substitution rules on physical demand, more research is needed to gain a greater understanding of the relative demands with respect to playing time. 

Very few studies have investigated the match demands of NCAA DII women’s soccer (E. E. Choice et al., 2023; Gentles et al., 2018). Gentles et al. (2018) assessed and compared accelerometry data to data collected using GPS signal. Although total distance and distance covered in specific velocity zones were reported, statistical analyses were not made comparing playing positions. Whereas Choice et al., (2023) investigated the external and internal load of players, including positional and time-specific differences.  

Due to the lack of research in women’s soccer in general, and specifically at the lower playing levels, there is a need for more research. The purpose of this study is to examine the physical demands in Division II women’s soccer and analyze whether these physical demands vary by position.

SOCCER TEAM ROLES AND ROLE EXPECTATIONS

Given this study examines the possible differences in physical demands on soccer athletes based on the position played, it is advantageous to clearly outline each position and the expectation of athletes who play that position. Each position carries distinct responsibilities within a team’s formation and can change based on the formation. This study focused on a team that utilized the 4-3-3 formation where positions are separated into center backs, outside backs (right and left), midfielders (center and attacking), and Forwards (central striker and right/left wing) (see Figure 1).

Figure 1 – Visual Presentation of 4-3-3 Soccer Formation

Center Back Defenders

Center back defenders are primarily responsible for defensive organization and central coverage. When in a defensive scenario, center backs are responsible for defending the opposing forwards, intercepting through balls, contesting aerial duels and tackling to gain possession. In possession, center backs initiate playing out from the back, with passes to midfielders or out to forwards, and or switching play across the back line.

Outside Back Defenders

Outside backs (right and left) provide width for the back line and help with transitional play from the defensive half to the attacking half. Out of possession, outside backs typically deal with the opposing forward or winger depending on the opposition’s formation. Similar to center backs, outside backs with attempt to win aerial duels and tackles. In possession, outside backs will look to support the team by overlapping the forward, dribbling the ball up the field, delivering crosses and well as helping maintain possession in the attacking half. 

Midfielders

The center midfielders are composed of two defensive midfielders and one attacking midfielder. The defensive midfielders attempt to shield the back line, break up opposition attacks, and in possession help to maintain and build possession from the defensive to attacking half. The attacking midfielder helps the forward and midfielders with defensive duties and in possession looks to support the forwards, maintain possession as well provide goals and assists. 

Forwards

Forwards help to provide width and are important for stretching the oppositions defense. They will primarily defend the oppositions outside back and recover into midfield positions when out of possession. In possession they will aim to beat defenders one-on-one, deliver crosses and create shooting opportunities. When a player is in the central striker position, they are the focal point of the attacking line and are primarily there to convert scoring opportunities by finishing inside and around the 18-yard box. In possession, the central striker will look to hold the ball up allowing midfielders and other forwards to join the play. They will attempt to make runs to disrupt the defensive lines and apply pressure to the opposition center backs. 

METHODS 

Participants  

A total of 22 NCAA Division II female soccer players participated in this study. Participants were athletes from a team already using a GPS athlete monitoring system. Players were assigned 1 of 4 positions by the team’s coaching staff (Sporis et al., 2009).

  1. Center Backs (CB) (n = 4)
  2. Outside Backs (OB) (n = 5)
  3. Midfielders (MID) (n=6)
  4. Forwards (FWD) (n= 7). 

Procedures  

A total of 18 regular season matches were included in this analysis from a single competitive season. Global Positional System (GPS) devices (TITAN Sports, Titan2, Houston, TX, USA) sampling at 10 Hz were used to track player movement during competition.  Data included 234 observations (n= 79 FWD, n= 70 MID, n= 50 OB, n= 35 CB). The team used for this study played 1-4-3-3 formation (please note 1 = goalkeeper) during all matches included in the analyses. All matches were official NCAA matches with two 45-minute halves and a 15-minute halftime period. All players were informed of the risks and benefits of this study and voluntarily signed an informed consent. This study was approved by the Institutional Review Board.  

Variables for Analysis

Data was collected on the following variables to better understand the physical demands on each player based on position played (see Andersson, 2010).

  • Total Distance – The total distance a player covers during a game. This value is needed to determine the distance covered related to minutes played.
  • Relative Total Distance (TDREL) – The distance covered per unit of time, expressed as meters per minute (m/min).
  • Relative High-Speed Running Distance (HSRREL) – Distance covered per minute while running at high speeds, in this case, over 15 kilometers per hour (km/h), or 9.3 miles per hour.
  • Relative Sprint Distance (SDREL) – Distance covered per minute while sprinting, usually above a higher threshold. In this case, over 18 kilometers per hour (km/h) or 11.2 miles per hour.

Using the Global Navigation Satellite System to Measure Match Load  

GPS units were worn in a harness that secured the device between the scapulae (i.e., shoulder blades). The units were turned on 10 minutes before being placed in the harness to ensure sufficient GPS signal was attained per manufacturer’s instructions. Due to the substitution rules in NCAA college soccer, calculating the variables per minutes played was deemed the optimal solution for comparing workload across playing position.

RESULTS 

Three separate one-way ANOVA analyses with Bonferroni corrections were conducted to determine if there were differences between positions for Total Distance (TDREL), High Speed Running Distance (HSRREL) and Sprint Distance (SDREL). Statistical analyses were performed in SPSS (Version 27.0.1). Alpha level was set at p <0.05. Eta-squared effect sizes were calculated to determine magnitude of differences for each variable. For pairwise comparisons, Cohen’s d effect sizes were calculated to determine magnitude of difference between playing positions (Hopkins, 2002).  Descriptive statistics for match physical performance for this specific team are summarized in Table 1.

Table 1 – Match Performance Data

Variables Midfielders (n=70) Forwards (n=79) Outside Back Defenders (n=50) Center Back Defenders (n=35) Total (n=234) 
Minutes Played per Match 51.6 ± 13.6 52.8 ± 15.1 52.1 ±13.9 68.0 ±19.9 57.1 ± 15.6 
Total Distance (m) 5,643.9 ± 1,435.0 5,507.9 ± 1,214.1 5,506.3 ±1,373.8 6365.1 ± 1884.9  Greatest total distance covered due to greatest minutes played5,855.8 ±1,476.8 
Relative Total Distance (m/min) The distance covered per unit of time, usually expressed as meters per minute (m/min). 110.4 ± 14.2 Significantly greater than Center Back Defender106.8 ± 14.4   Significantly greater than Center Back Defender106.8 ±12.6  Significantly greater than Center Back Defender93.8 ± 11.6 105.9 ± 14.5 
Relative High Speed Running Distance (m/min)  Distance covered per minute while running at high speeds, in this case, over 15 kilometers per hour (km/h), or 9.3 miles per hour.  10.06 ± 3.45  Significantly greater than Center Back Defender12.96 ±4.01 Significantly greater than Center Back Defender 13.18 ± 4.21 Significantly greater than Center Back Defender7.53 ± 2.90   11.33 ± 4.26 
Relative Sprint Distance (m/min)  Distance covered per minute while sprinting, usually above a higher threshold. In this case, over 18 kilometers per hour (km/h) or 11.2 miles per hour3.45 ± 1.98  6.06 ± 2.64 Significantly greater than Center Back Defender  Significantly different than Midfielders5.53 ± 1.85 Significantly greater than Center Back Defender  Significantly different than Midfielders3.05 ± 1.92  4.72 ± 2.53 

 Relative Total Distance (TDREL)

A one-way ANOVA revealed a significant difference in positions for TDREL, F(3, 230) = 11.9, p = <0.001. An effect size (η2=0.135) indicated a medium effect. Post Hoc analysis indicated that there were mean differences between FWD and CB (p = <0.001), MID and CB (p = <0.001), and OB and CB (p = <0.001).  

Relative High-Speed Running Distance (HSRREL)

There were statistically significant difference for HSRREL, F(3, 230) = 23.73 p < 0.001, with a large effect (η2=0.23). Post Hoc analysis showed mean differences between FWD and MID (p = <0.001), FWD and CB (p = <0.001), MID and OB (p = <0.001), MID and CB (p = <0.008) and OB and CB (p = <0.001) as seen in Figure 2. 

Relative Sprint Distance (SDREL)

Finally, there were statistically significant differences for positions in SDREL, F=(3,230), 25.547, p <0.001 with a large effect η2=0.25 Post Hoc analysis revealed mean differences for FWD and MID (p = <0.001), FWD and CB (p = <0.001), MID and OB (p = <0.001), OB and CB (p = <0.001). 

DISCUSSION 

The purpose of this study is to determine the physical demands for NCAA Division II Women’s Soccer and to assess positional differences and physical demands relative to minutes played.  The study’s results include the average demands during match play of each field position as previously shown in Table 1. The main findings of this study indicate that center backs accumulate the greatest total distance, however, this is mainly attributable to extended playing time as they frequently remained on the field 15-17 minutes longer than other positions. When normalized for minutes outside backs, midfielders and forwards covered greater total distance and high-speed running distance than center backs. In addition, outside backs and forwards demonstrated greater sprint distance per minute compared to both center backs and midfielders. 

The average total distance covered for players included in analyses was 5,855 ± 1,476 meters, which differs from prior findings by Choice et al. (2023) who saw starters cover 9,463 ± 2,591 meters. It should be noted that the analysis by Choice et al. (2023) only included players who participated in over 50% of match play. Findings from this study are similar to results reported by Gentles et al. (2018) who reported findings of 5,480 ± 2,350 meters and average minutes played of 45.32 ± 26.01 which are similar to minutes played in this study.  The maximum distance covered by an individual in this study was 9,810 meters, which is much lower than distance covered by the sample in Gentles (13,850 meters) and Choice (12,054 meters), but similar to distances reported in Division I athletes, 9,786 meters (Sausaman, 2019). Both maximum and average distance covered varied across levels may indicate that more research is needed across differing playing levels. Also, from a practical point of view, this should help sport practitioners implement appropriate programs that develop players for both maximum and mean distances, regardless of minutes played.  

To our knowledge, this is the first study to report TDREL (105 ± 14.5 m/min), HSRREL (11.3 ± 4.2 m/min) and SDREL (4.72 ± 2.53) in NCAA Division II athletes. Gentles (2018) reported total distance accumulated at velocity zones 15.00 – 19.99 kph and 20.00-24.99 kph but not relative distances. Due to the substitution rules in NCAA College soccer, there is a high variance in match-to-match playing variables. Other studies have only included players who have played over 50% of the match or the entire match (E. E. Choice et al., 2023), whereas other research only included participants who completed the match in its entirety (Alexander, 2014; Sausaman et al., 2019).  If training programs are only based on average totals, then the whole picture may not be clear when designing training programs for those not participating in 90 minutes. Therefore, reporting data per minute may be more beneficial as reserves need to be trained throughout the season to compensate for minutes not played.  

There was a difference in positional demands for HSRREL, with FWDs covering higher distances than MIDs and CBs. Whereas OBs covered more HSRREL than CBs and MIDs, and MIDs only covered more distance than CBs. Although there was no statistical difference between OBs and FWDs, OBs covered slightly more distance per minute. Although the lack of statistical difference between FWDs and OBs may be due to formation and playing style. In the 4-3-3 OB and FWDs may have similar positional demands as OBs are often part of the attack by providing width higher up the field. Formation on the field was not controlled for in this study so more research is warranted to understand positional differences across a variety of formation styles. 

Results also showed that FWDs covered more SDREL than MIDs and CBs, and OBs covered more than CBs. Although variables are different within this study, results show similar trend within the literature, that forwards cover more HSR distance and sprint distance than midfielders (Sausaman et al., 2019; J. Vescovi, 2013). It should also be noted that CB and OB positions are typically grouped together as a singular position group (commonly reported as defender) (Stolen et al., 2005; J. D. Vescovi, 2012).

This study provides further evidence, particularly in women’s soccer literature, that there is a need to differentiate between central and outside defenders when evaluating physical demands during competition due to the different physical demands during competition (Alexander, 2014; Harkness-Armstrong et al., 2022). It is also important to note that in NCAA Division I studies, players covered more high-speed running and sprint distance on average compared to NCAA division II athletes. This indicates that the Division I game may be played at a faster pace, especially in those critical moments of the game (defensive recovery run, sprinting to goal etc.) As this study reports HSR and sprint distance per minute, it prevents the authors from making additional conclusions or comparisons.  

There are limitations to the study that may have impacted the results.  The data for this study was collected for one team playing one season of competition.  The level of soccer players at all NCAA level can vary greatly across teams and conferences.  Therefore, further research is needed to confirm the current findings. Differences in match demands as well as positional demands may be a result of opposition level, formations, match status, and match location.  As such, additional research is needed to assess these factors to determine their possible influence on the physical demands of Division II women’s soccer.  

CONCLUSION 

This study examined the physical demands of women’s soccer at the NCAA Division II level and analyzed differences among position groups. Unlike previous research, this study assessed Relative total distance (TDREL), relative high-speed running distance (HSRREL), and relative sprint distance (SDREL), providing average values for all players and specific positions to describe match demands. The findings highlight differences in physical demands based on position played. The findings from this study emphasize the importance of evaluating workload relative to minutes played and recognizing the unique demands of each position at the NCAA Division II level. Such insights provide a framework for tailoring training, conditioning and substitution strategies that reflect position-specific requirements rather than relying on team-wide averages.  Future research should continue to examine how positional workloads vary across divisions and competitive levels, as well as how these demands interact with injury risk, recovery and long-term players development. 

APPLICATIONS IN SPORT

These findings provide an insight into general and positional demands of women’s soccer at the NCAA Division II level. These results are particularly valuable to coaches who may strategically utilize the NCAA substitution rules to manage player workloads. Caution should be exercised when applying published findings from other levels of play and relying on total averages as positional differences can substantially influence match demands.

The results emphasize the importance of aligning athlete’s physical attributes with the requirements of their playing positions. Forwards and outsides backs perform the highest sprinting and high-speed running demands per minute and may require players that have higher acceleration and sprint capacities. On the other hand, players that have less high-speed and sprinting demands, but positional awareness may be better suited at center back. 

This may have implications for recruitment. Coaches may want to target athletes with performance profiles that align with the positional demands, rather than relying on general fitness. In regard to player development, training and conditioning programs should be tailored to the requirements of each position moving beyond a one-size-fits-all approach. For example, midfielders may benefit from training designed to sustain higher per-minute workloads, while forwards and outside backs should focus on repeated spring ability as well as increasing top speed. 

Finally, these results may extend to tactical decision making and injury prevention. Coaches may rotate outside backs and forwards more frequently to preserve sprint performance later in matches. These findings provide sport practitioners with evidence-based guidance to optimize recruitment, training and match management in NCAA division II soccer. 


REFERENCES 

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  8. Harkness-Armstrong, A., Till, K., Datson, N., Myhill, N., & Emmonds, S. (2022). A systematic review of match-play characteristics in women’s soccer. PLOS ONE, 17(6), e0268334. https://doi.org/10.1371/journal.pone.0268334
  9. Hopkins, W. G. (2002). A scale of magnitude for effect statistics. http://www.sportsci.org/resource/stats/effectmag.html.
  10. Sausaman, R. W., Sams, M. L., Mizuguchi, S., DeWeese, B. H., & Stone, M. H. (2019). The Physical Demands of NCAA Division I Women’s College Soccer. Journal of Functional      Morphology and Kinesiology, 4(4), 73. https://doi.org/10.3390/jfmk4040073
  11. Sporis, G., Jukic, I., Ostojic, S., & Milanovic, D. (2009). Fitness profiling in soccer: Physical and physiologic characteristics of elite players. Journal of Strength & Conditioning Research, 23(7), 1947–1953. https://doi.org/10.1519/JSC.0b013e3181b3e141
  12. Stolen, T., Chamari, K., Castagna, C., & Wisl??ff, U. (2005). Physiology of Soccer: An Update. Sports Medicine, 35(6), 501–536. https://doi.org/10.2165/00007256-200535060-00004
  13. Vescovi, J. (2013). Motion Characteristics of Youth Women Soccer Matches: Female Athletes in Motion (FAiM) Study. International Journal of Sports Medicine, 35(02), 110–117. https://doi.org/10.1055/s-0033-1345134
  14. Vescovi, J. D. (2012). Sprint profile of professional female soccer players during competitive matches: Female Athletes in Motion (FAiM) study. Journal of Sports Sciences, 30(12), 1259–1265. https://doi.org/10.1080/02640414.2012.701760
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2025-09-25T15:33:55-05:00February 4th, 2026|Research, Sport Education, Sport Training, Sports Coaching, Sports Health & Fitness, Women and Sports|Comments Off on Match Demands and Positional Differences of NCAA Division II Women’s Soccer Players Over a Competitive Season
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Relative Age Effect Among Olympic Medalists: Evidence from Ten Summer and Winter Olympic Games held between 2000 and 2018 

Authors: Christiana E. Hilmer, Michael J. Hilmer1

Corresponding Author:

Christiana Hilmer, PhD 

5500 Campanile Drive 

San Diego, CA 92182-4485 

[email protected] 

619-301-9388 


1Both: Department of Economics, San Diego State University, San Diego, CA 

Christiana E. Hilmer, Ph.D., is a Professor of Economics at San Diego State University in San Diego, CA.  Her research interests include the economics of sports, applied econometrics, labor economics, and resource and environmental economics.   

Michael J. Hilmer, Ph.D., is a Professor of Economics at San Diego State University in San Diego, CA.  His research interests include the economics of sports, labor economics, and the economics of education. 

ABSTRACT

This study examines the Relative Age Effect (RAE) among 4,453 individual Olympic medalists from ten Olympic Games (five Summer and five Winter) held between 2000 and 2018. We analyze athletes’ birth quarters and ages at the time of competition to assess patterns by gender, event type, and medal outcome. Using descriptive statistics, regression analysis, a Pearson 𝜒2 test, and a logit model, we find that athletes in judged and combat events tend to be younger, while those in skill and endurance events tend to be older. Gold medalists are, on average, younger than bronze medalists and more likely to be born in the first half of the year. These results confirm the presence of RAE at the highest level of sport and suggest that early developmental advantages persist among Olympic medalists. The findings have implications for athlete development systems and elite sport selection criteria. 

Key Words: Athlete Development; Birth Quarter; Elite Sport, Logit Analysis, Pearson 𝝌𝟐 test 

INTRODUCTION

The Relative Age Effect (RAE) refers to the phenomenon in which individuals born earlier in a selected period, typically a calendar year, tend to benefit from developmental advantages over their younger peers within the same cohort.  These advantages may include earlier physical growth, cognitive maturity, and better access to competitive opportunities.  This concept was described by Barnsley and Thompson (3) in Canadian youth hockey, where players born in the first half of the year were disproportionately over-represented.  RAE has since been documented across various sports, including professional baseball (Thompson, Barnsley, and Stebelsky (14)), elite youth soccer (Glamser and Vincent (7)), youth swimming (Costa et al. (5)) and basketball (Werneck et al. (17)).  Extensive empirical evidence over the last three decades has confirmed its presence in multiple athletic and academic domains (Musch and Grondin (11); Patiño et al. (12)). Researchers have also explored alternative approaches to identifying RAEs by comparing athletes’ relative ages at the time of competition (Zetaruk (18) and Longo et al. (10)). Yet little is known about whether RAE endures at the pinnacle of sports performance. 

Many past studies have focused on youth and amateur athletes, where selection systems, age-based groupings, and physical maturation exert considerable influence.  However, less is known about whether RAE persists at the highest levels of athletic achievement.  The Olympic Games, which represent peak international competition, provide a valuable lens to explore whether early developmental advantages have long-term consequences that extend into elite performance.   

The Olympic context introduces additional layers of complexity.  Events vary widely in physical demands, skill development, and peak performance age.  For instance, judged events such as gymnastics and ice skating often feature younger athletes (Zetaruk (18) and Cummins (6)) while skill and endurance events, such as archery, cross-country skiing, and the marathon typically feature older athletes (Longo et al. (10)).  Seasonal differences between Summer and Winter Games, and gender specific trajectories, also warrant attention. 

Although prior research has examined RAE in Olympic contexts, findings have been mixed.  Baker et al. (2) find evidence of the RAE in skiing, snowboarding, and Nordic combined, find no evidence for figure skaters, and report an atypical pattern in gymnastics.  Joyner et al. (9) find evidence of RAE across multiple sports but note variation by gender and season.  Raschner et al. (13) analyzed data from the first Winter Youth Olympic Games and found evidence of RAE in both genders and across strength, endurance, and technique-related sports.  This study differs by focusing exclusively on Olympic medalists – those who reached the highest level in their sport – to determine whether RAE persists not just in participation, but in podium success. 

This study analyzes 4,453 individual medalists from ten Olympic games (five Summer and five Winter) between 2000 and 2018. We classify events into six categories (timed, judged, skill, endurance, strength, and combat), and examine both the athletes’ age at the time of competition and their birth quarter. The central research questions are (1) Are Olympic medalists disproportionately born in the earlier quarters of the calendar year? (2) Does the probability of winning a gold medal vary by birth quarter? and (3) Are athletes’ ages at the time of competition systematically associated with event type, gender, or Olympic season? This study expands the literature by analyzing RAE by event type among Olympic medalists across both Summer and Winter Games. 

METHODS

This study examines 4,453 medalists (gold, silver, and bronze) from ten Olympic Games held between 2000 and 2018 – five Summer Games (Sydney 2000, Athens 2004, Beijing 2008, London 2012, Rio de Janeiro 2016) and five Winter Games (Salt Lake City 2002, Turin 2006, Vancouver 2010, Sochi 2014, PyeongChang 2018).  Data were compiled from official Olympic databases during 2019.  Athlete biographies were consulted to ensure accuracy regarding birthdates, event categories, and medal results.  Medalists disqualified as of December 2019 due to doping violations were excluded from this analysis.  

Athletes were categorized by type of event into six mutually exclusive groups: timed/weight/measured, judged, skill, endurance, strength, and combat. Hilmer and Hilmer (8) apply these same categories to investigate the presence of confirmation bias in judged events at the Olympic Games.  The first category is timed/weight/measured, where competitors start together and medal winners are determined by that individual competition (henceforth referred to, for lack of a better term, as “timed events”), such as the 100-meter dash, canoe, and downhill skiing.  Judged events rely on subjective scoring either fully (ie, figure skating) or partially (ie, mogul skiing).  The next category is skill events such as archery, shooting, and table tennis.  The fourth category is endurance events that take a relatively long time to complete, such as biathlon, cross-country skiing, and the marathon.  Strength is the fifth category of event, which includes weightlifting, shot put, and hammer throw.  The final category of events is combat, which includes boxing, judo, taekwondo, and wrestling.  Team sports were not included in this analysis because we are interested in an individual’s age and birth quarter at the time of competition.  A team is comprised of a variety of individuals with various birth dates, which makes it difficult to isolate the impact of birth quarter and age at the time of competition.  Thus, team events such as soccer, softball, basketball, and relays are excluded from this analysis. Age was calculated in days at the time of competition, and birth quarters were based on the calendar year: Q1 (January-March), Q2 (April-June), Q3 (July-September), and Q4 (October-December). 

Table 1 presents the breakdown of the medal winners for each of the Olympic Games held between 2000 and 2018.  The Summer Olympics have the bulk of the athletes, with 78% of the medal winners, while 22% of the medal winners compete in the Winter Games.  The number of athletes winning individual medals has increased steadily over the years.  Individual sports added to the Olympic Games during this time were skeleton in 2002, BMX racing in 2008, and golf in 2016. 

The dependent variables are either type of medal, gold, silver or bronze, and how old the athlete is in days at the time of competition.  The independent variables are quarter of birth (Q1 = Jan-Mar, Q2 = Apr – June, Q3 = Jul – Sept, Q4 = Oct – Dec), gender, season, and event type (timed, judged, skill, endurance, strength, combat). Table 2 presents the percentage of competitors in the types of events, medals earned, and quarter of birth, broken down by male and female medal winners and Summer and Winter Games.  As evident from Table 2, the timed category has the most competitors with 45% of the medal winners, ranging from 40% in the Summer Games to 60% in the Winter Games.  Skill, Strength, and Combat award all of their medals in the Summer Games.  Judged events comprise 10% of the medals, while skill has 11% of the medals.  The endurance category has 7% of the medals overall but it is an important component of the Winter Games, with almost a quarter of the medals earned falling within this category.  

Under random distribution, one would expect medals to be evenly divided among the three categories. According to Table 2, bronze medals account for 36% of the overall awards.  Similarly, we would expect the athletes’ birth quarters to be split evenly, with each having 25% of the medal winners if there is no presence of RAE. The first quarter has the most medal winners at 26%, while the last quarter has the least amount of medal winners at 23%, which is a statistically significant difference with a z-score of 3.07 and a p-value of 0.0022. 

Table 3 provides means and standard deviations for how many days old the medalists were when they competed in their event.  The average age of a medalist is 26.3 years old with a standard deviation of 4.8 years, with men at an average of 26.57 and with women at 25.94.  This is similar to the finding of Longo et al. (10), who analyzed all competitors from the 2012 Summer Olympics and found men were an average of 27 years old and women were an average of 26.2 years old.  Awosoga and Chow (1) find that the peak age for a track and field athlete is just under 27 years old, that finalists were on average 16 months older than the average competitor, and medalists were just one month older than the average participant. On average, the youngest medalists are those who compete in judged events, while the oldest medalists compete in skill and endurance events.  This holds across males and females and for the Summer and Winter Games. The age of the medalists is distributed fairly consistently between gold, silver, and bronze medals with the gold medalists being around 100 days younger than either silver or bronze medalists for the entire sample.  Males are older than females by 228 days while Winter medalists are older than Summer medalists by 241 days.   

Figure 1 is a kernel density function that depicts the age in days of the medalist by the type of event.  A kernel density function is a non-parametric method for visually representing the distribution of the data. Unlike a histogram, it is a smooth representation of the probability distribution function (Weglarczyk (16)) and is more informative than summary statistics because it shows the entire distribution of the data.  Judged events have the youngest athletes with the mass of the distribution primarily in the lower end of the age distribution.  Endurance has the bulk of its mass to the right of all of the other distributions, while skill events exceeds all of the other events at the very top of the age distribution.  Figure 2 compares the distributions for males and females.  Females have more medalists at the lower end of the distribution but the distributions are nearly identical at the top end of the age distribution.  Figure 3 is a kernel density function for the Winter and Summer Games.  The distribution for the Summer Games lies to the left of that for the Winter Games, suggesting that Summer medalists are younger than Winter medalists.  

RESULTS

Table 4 provides our first look into the presence of an RAE within Olympic medal winners with a two-way table between birth quarter and type of medal.  The Pearson 𝜒2 test statistic for differences among the categories is 14.12 with a p-value of 0.028.  The Cramér’s V p-value of 0.0398 suggests that the observed association between birth quarter and medal type is unlikely to occur by chance.  Taken together, these results suggest that there is a statistical relationship between birth-quarter and type of medal.  The expected count is in parentheses and suggests that gold medal winners are over-represented for the first and second quarters of the year.  All statistical analysis for this paper is performed in STATA.    

Another option for analyzing the birth quarter of a medalist is to empirically assess whether it impacts their probability of winning a gold medal.  To accomplish this, we estimate a logit model of the form 

                      

 

(1) where gold is 1 if athlete i received a gold medal and 0 if they earned a silver or bronze medal, Q1, Q2, and Q3 are the quarter of their birth of individual i, with the fourth quarter as the omitted category, and εi is the error term.  The marginal effects are the change in the probability of the athlete winning a gold medal relative to the omitted category 

Table 5 presents the marginal effects from the logit model in equation (1).  Athletes who are born in the second quarter are 4.3% more likely to win a gold medal relative to those born in the fourth quarter at a 5% significance level.  Athletes born in the first and third quarters are not statistically more likely to win a gold medal than those born in the fourth quarter.   

In addition to examining how birth quarter impacts the medal received, we perform an empirical analysis to assess if the age of the athlete, measured in how many days old they were when they competed in their event, statistically differs for gender, type of Games, category of events, and medal type.  The most inclusive model takes the form: 

+  εi                            (2)

where εi is the error term. Each of the explanatory variables is binary with the value being 1 if the individual has the characteristic in the named variable and 0 otherwise.  For example, the variable male will equal 1 if the athlete is male and 0 if the athlete is female.  The omitted categories for this model are female, Winter, timed events, and bronze medal.  This model is estimated using multiple linear regression with robust standard errors. Because all of the independent variables are binary, this regression model tests for differences in means between the explanatory variables, holding the other included variables constant. 

The first column in Table 6 presents the results for the general model. These results suggest that, on average, males are older than females by 262 days, while Summer medalists are an average of 230 days younger than Winter medalists.  Judged medalists are on average younger than timed medalists by 1090 days, skill medalists are older than timed medalists by 1002 days, endurance medalists are older than timed medalists by 848 days, and combat medalists are younger than timed medalists by 245 days. Gold medalists are an average of 151 days younger than bronze medalists and silver medalists are not statistically different in age than bronze medalists.

The results found in the initial model generally hold for models that estimate male and females separately. The statistical significance for event type for the model with only males is similar to the general model, but the magnitudes differ.  For example, skill medalists are an average of 1,369 days older than timed medalists for the male-only model, while the difference was 1002 days for the full model. The other difference is that gold and silver medalists are not statistically different in age than bronze medalists.  In the female-only model, athletes who medal in judged events are an average of 1,374 days younger than those who medal in timed events, while in the full model the difference was 1090 days.  Female skill medalists are an average of 560 days older than female timed medalists while endurance medalists are 891 days older than timed medalists.  Strength and combat medalists are not statistically different than timed medalists in age.  For females, gold medalists are an average of 225 days younger than bronze medalists. 

Summer and Winter Games models estimated separately follow a similar pattern to the general model in the first column.  In both the Summer and Winter Games, males are statistically older than females, judged medalists are statistically younger than timed medalists, and endurance athletes are statistically older than timed athletes.  In the Summer Games, skill medalists are statistically older than timed medalists and combat medalists are statistically younger than timed medalists.  Summer athletes who win a gold medal are an average of 158 days younger than those athletes who win bronze medals.  Together, these results suggest that the results are generally consistent across males and females as well as Summer and Winter Games.    

Discussion

Our findings affirm the presence of the RAE among Olympic medalists in terms of both birth quarter and competition age.  A Pearson 𝜒2 test for a difference between birth quarter and medals found a statistically significant relationship between the two variables.  We also found that athletes born in Q2 are more likely to win a gold medal relative to those born in Q4.  This echoes patterns identified in youth and elite-level sports by previous researchers (Joyner et. al., 2017; Musch and Grondin, 2001).  These results suggest that the developmental advantages conferred by earlier birth within a competitive cohort persist even at the highest levels of sport. 

The variation in age across event types aligns with existing literature suggesting that events with aesthetic or acrobatic elements, like gymnastics or figure skating, tend to feature younger athletes (Zetaruk, (18) and Cummins (6)), while events requiring cumulative physical or technical development, such as endurance or skill-based events are dominated by older competitors (Longo et. al (10)).  This supports evidence of distinct developmental trajectories across Olympic disciplines.  These findings contribute to a broader understanding of how structural factors such as age-grouping policies and youth sport calendars may contribute to influence athlete development long after initial talent identification.  This finding may support a revision of the youth categorization system and selectors to mitigate the effects of RAE.

We can interpret these patterns using the Developmental Systems Model (Wattie et al., 2015), which posits that RAE arises from interacting individual (e.g., birthdate, maturation), task (e.g. sport type), and environmental (e.g. selection policies) constraints.  Our findings reflect all three of these inputs. From the individual perspective, older athletes may possess more maturity and resilience.  From the task perspective, certain disciplines favor youth, such as gymnastics and figure skating, while other disciplines favor experience, such as equestrian and long-distance running.  From the environmental perspective, qualification systems often reinforce early selection biases that persist all the way up to the Olympic Games.   

This study has several limitations.  Our data only includes athletes who received medals at the Olympic Games, allowing us to examine RAE for those who have achieved the highest pinnacle of their sport.  The broader population of Olympic participants may not exhibit the same patterns as medalists.  Another caveat is that team events and relays were omitted, despite the possibility that such formats may dilute or amplify RAE effects due to different selection or substitution dynamics.  Finally, the analysis does not account for cross-national or cultural variation in athlete development systems, which could meaningfully shape RAE patterns.  Future research should address these gaps by examining a more comprehensive athlete pool, including non-medalists, and incorporating institutional and cultural context.

CONCLUSIONS

This study provides evidence that the RAE persists among Olympic medalists in the Summer and Winter Games held between 2000 and 2018.  Medalists in judged and combat events tend to be younger, while those in skill and endurance events tend to be older, confirming widely held beliefs about athlete development pathways.  Additionally, athletes born in the second quarter of the year are statistically more likely to win a gold medal than those born later in the year, reinforcing the influence of birth timing, even at the elite level.

Our results demonstrate that the effects of age-based selection advantages are not confined to youth or amateur competition but may have enduring implications for performance outcomes at the pinnacle of sport.  These insights underscore the importance of re-evaluating current age-grouping structures in sport development systems.  Policymakers, coaches, and sporting organizations should consider how age-based selection mechanisms might inadvertently limit long-term talent development by favoring relatively older athletes.  By acknowledging and addressing these structural biases, it may be possible to create more equitable opportunities for younger athletes within a given cohort, ultimately enhancing both inclusivity and performance sustainability. 

APPLICATIONS IN SPORT

To mitigate the impact of RAE, sporting bodies and youth development programs should consider pilot programs that rotate cutoff dates or cluster athletes by biological age rather than birthdate alone (see Wattie et al. (15) and Cobley et al. (4)).  Musch and Grondin (11) suggest varying cutoff dates for different sports, allowing youth participants to choose the sport with the most favorable cutoff date for them.  Raschner et al. (13) suggest a limit on the number of participants by each birth year across two-year age groups. Future research could explore how the dynamics of RAE evolve over an athlete’s career trajectory and examine whether similar effects are observable in non-medalists or team events.    

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2025-08-26T10:08:11-05:00December 23rd, 2025|General, Olympics, Research, Sports Health & Fitness, Sports Studies, Sports Studies and Sports Psychology|Comments Off on Relative Age Effect Among Olympic Medalists: Evidence from Ten Summer and Winter Olympic Games held between 2000 and 2018 
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