The Sport Journal – A peer-reviewed journal of sports, published by the United States Sports University

The Sport Journal – A peer-reviewed journal of sports, published by the United States Sports University
Skip to content
Search Our Database of Sports Related Articles
U.S. Sports Academy
2026-02-13T16:39:50-06:00
Submission policy and pricing changes have been made as of February 13, 2026. The
Information For Authors
page and
Submit Your Paper
page now have the latest information.
Latest Articles
Troy Palmer
2025-10-08T12:16:04-05:00
A Manual Therapy Treatment for Headache Pain
Troy Palmer
2025-10-08T12:16:04-05:00
April 15th, 2026
Concussions
General
Research
Sports Health & Fitness
Sports Medicine
Authors: Lindsay C. Luinstra
, Dan Sigley
, Heidi A. VanRavenhorst-Bell
Corresponding Author:
Dr. Lindsay Luinstra, DAT, MS, LAT, ATC
1845 Fairmount Street,
Box 16,
Wichita, KS 67260
[email protected]
(316) 978-5440
Department 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 Root
Location of Pain
Special Characteristics
C1
Anywhere on the head, this is determined when we do the ‘yes/no’ test.
If their head is ‘rotated only,’ it is C1.
C2
Top of the head, sides of the head, front and back of the head.
No lower than the occiput.
C3
In 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 Criteria
Exclusion 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
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
Bogduk, N. (2004). The neck and headaches.
Neurologic Clinics, 22
(1), 151–171. https://doi.org/10.1016/j.ncl.2003.11.006
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
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
The International Classification of Headache Disorders, 3rd edition. (2018).
Cephalalgia, 38
(1), 1–211. https://doi.org/10.1177/0333102417738202
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
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
Haldeman, S., & Dagenais, S. (2001). Cervicogenic headaches.
The Spine Journal, 1
(1), 31–46. https://doi.org/10.1016/S1529-9430(01)00017-2
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
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
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
Uriarte, M. (2004).
MyoKinesthetic system upper body training manual.
MyoKinesthetic Institute.
Moy, B. (2015). Case study detail – The MyoKinesthetic Institute (MYK).
MyoKinesthetic Institute
. Retrieved August 18, 2021, from
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
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
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
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
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
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
NDI
HIT-6
NPRS
ASSESSMENT
Score
Ranking
Score
Ranking
Pre-
Score
Post-
Score
Mean of
Raw
Initial
14/50
Mild
58
Substantial
3.75
Discharge
15/50
Moderate
54
Some
Mean
57.6
Substantial
30-Day
Mild
46
Little to no impact
60-Day
Mild
38
Little to  no impact
.666
Table 5
Goniometric measurement mean normative data for cervical range of motion taken from Norkin et al.
Cervical Ran
ge of Motion
Movement
Normative Data
Pre-treatment
Post-treatment (change)
30-Day Follow Up
60-Day Follow Up
Flexion
40° ± 12
45°
40° (-5°)
47.3°
46°
Extension
50° ± 14
53°
60° (7°)
41.6°
37°
Left Rotation
49° ± 9
53°
54.6° (1.6°)
55.6°
51°
Right Rotation
51° ± 11
60°
61.6° (1.6°)
58.6°
62°
Troy Palmer
2026-04-09T15:25:29-05:00
Accreditation, Curriculum, and Competition: An Explanatory Case Study of Sport Sales Education in Undergraduate Sport Management Programs
Troy Palmer
2026-04-09T15:25:29-05:00
April 9th, 2026
Contemporary Sports Issues
Leadership
Research
Sports Coaching
Sports Studies and Sports Psychology
Authors
Joshua S. Greer
, Nicholas Zoroya
, and Tim Wilson
Cumberland University
Wayne State University
Middle Tennessee State University
Corresponding Author:
Joshua S. Greer
[email protected]
Joshua S. Greer. https://orcid.org/0009-0005-2890-1673
We have no known conflict of interest to disclose.
ABSTRACT
This explanatory mixed-methods case study explored the relationship between accreditation, curriculum design, and student performance in sport sales education within undergraduate sport management programs. Using archival data from the 2024–2025 National Collegiate Sports Sales Championship (NCSSC), the study compared outcomes among 25 institutions, including COSMA- and non-accredited programs. Quantitative analysis found no significant relationship between accreditation status and Top-10 finishes in either the Ticket Sales or Corporate Partnerships divisions (p > .05). Qualitative findings indicated that student performance was more closely associated with experiential learning depth, faculty expertise, and the integration of customer relationship management and analytics tools. Grounded in Experiential Learning Theory, Competency-Based Education, Human Capital Theory, and Communities of Practice, the study concludes that accreditation provides useful structure but does not independently predict competitive success. Program-level factors such as applied pedagogy, simulation-based learning, and industry partnerships appear to be stronger indicators of professional readiness and employability in sport sales.
KEYWORDS:
Experiential Learning Theory, Competency-Based Education, Human Capital Theory, Communities of Practice
INTRODUCTION
The goal of supporting positive outcomes for younger people (i.e., generativity; Erikson, 1950) is one that is both widely and cross-culturally relevant, yet despite this, the understanding for how to best support young people and the strategies employed to do so are still in flux. Only recently have developmental psychology and social research begun to place an emphasis on fostering positive outcomes for youth, as opposed to the prevention of negative outcomes and problematic behaviors (Larson, 2000). Within the areas of social and developmental research, this emphasis has led to the creation of diverse approaches to and philosophies of developmental youth programming (Lerner et al., 2011), which often provide opportunities for life skill development (i.e., explicit positive youth development). That said, the translation of such knowledge to spaces where youth development is view as a secondary priority, such as sport, tends to be challenging (Jones et al., 2011).  The primary aim of the present pilot study was to test a grounded theory of implicit positive youth development through sport by examining the impact of peer, coach, and parental relationships on youth sport experiences in a small, single-organization sample. In doing so, the present study offers a novel examination of the collective social climate (i.e., PYD climate) and its relationship to athlete developmental outcomes. We hypothesized the following:
Athletes’ perceptions of positive outcomes obtained through sport participation (e.g., social skills, goal setting skills) will be predicted by positive changes to the ratings of the coach-athlete relationship, peer cohesion, and parental involvement across a sport season.
At two time points (e.g., beginning of the season, end of the season), athletes’ ratings of their relationships with their coach, peer cohesion, and parental involvement were collected.  Subsequently, athletes’ perceptions of skill development across four areas (e.g., personal and social skills, cognitive skills, goal setting, initiative) were regressed on changes to the relationship variables. Both the coach-athlete relationship and parental involvement were shown to significantly predict social skill development, not only offering partial support for a theory of implicit PYD through sport and underscoring the critical developmental role of relationship building in sport but also pointing to the need for stakeholders to prioritize a high-quality social climate in the sport context to better support youth development.
LITERATURE REVIEW
Historically, adolescence and adolescent development has been regarded as a period during which youth are at risk and laden with problematic behaviors (Benson et al., 2006), therefore implying that the role of adults was to manage and prevent the problems that arise from adolescent development, also known as a deficit-focused approach to youth development (Clonan et al., 2004; Lerner, 2005). However, preventing such problems through a focus on treatment or intervention often failed to yield positive results (Catalano et al., 2008). Appearing concurrently with positive psychology’s focus on human strengths and flourishing, positive youth development theory offered that youth are “resources to be developed,” presenting a path toward positive youth outcomes through youth enrichment and the promotion of adolescent strengths (Lerner, Almerigi, et al., 2005). Positive youth development is a broad term, but generally refers to “processes, approaches, and instances” (Lerner et al., 2011) which seek to optimally prepare young people for adulthood, with the targeted outcomes being well-being and the fulfillment of their potential (Catalano et al., 2008). Contexts which aim to support positive youth development vary widely, to include agricultural programming (Lerner, Lerner, et al., 2005), volunteer and service programming (McBride et al., 2011), tutoring (Worker et al., 2019), aquatics (Storm et al., 2017), adventure-based programming (Sibthorp & Morgan, 2011), and sport (Bruner et al., 2021).
Youth sports are generally touted as tools for healthy and positive development, yet research aimed at validating this claim or understanding the processes by which it occurs is ambiguous (Holt et al., 2017). PYD theory was developed outside of the sport context (Lerner, Lerner, et al., 2005) and researchers have struggled to apply PYD models and measures to sporting contexts (Jones et al., 2011). One reason for this may be that PYD researchers have failed to acknowledge keyfeatures of the sport environment (Holt et al., 2017). In a systematic review of qualitative data, Holt and colleagues (2017) proposed that PYD through sport occurs via two distinct pathways. In the first, programs offer explicit education to youth sport participants aimed at life skill development. In the second pathway, PYD occurs implicitly via positive relationships with coaches, peers, and parents (i.e., the creation of a ‘PYD climate’). Holt and colleagues concluded that further research is needed to not only investigate the validity of this framework but also understand additional nuances for when and how PYD may occur through explicit and implicit factors. The need for further research was bolstered by a systematic review of sport-based PYD programming, conducted by Whitley and colleagues (2019), who concluded the benefit of explicit PYD programming in sport is not clear enough to support the implementation of a standardized intervention. Therefore, while the field’s understanding of how to best implement explicit PYD programming through sport is still evolving, there also exists a need to test the proposed model of implicit PYD through positive relationships within sport. While the specific role positive relationships play in supporting PYD within sport is unclear, it is generally accepted that these relationships are all valuable, if not necessary, for positive athlete outcomes (Burns et al., 2019).
Coach-Athlete Relationship
Arguably the primary relationship in the sporting context (Jowett, 2017), the dyadic relationship between coach and athlete has been shown to be instrumental to numerous athlete outcomes. In a systematic review of the coach-athlete relationship literature, Nikolina and Đorić (2023) reported that a positive coach-athlete relationship was not only predictive of increased motivation, satisfaction, and performance, but also protective from athlete stress, burnout, and negative affect. Davis and Jowett (2014) have reported that the quality of the coach-athlete relationship is directly related to athlete positive and negative affect. Furthermore, in a systematic review of the literature, McShan and Moore (2023) found that a positive coach-athlete relationship, as reported by coaches, was associated with coach’s beliefs of fostering an environment supportive of athlete life skill development. In Holt and colleague’s (2017) grounded theory of implicit PYD, the authors posit that strong, positive relationships between athletes and coaches can create a developmentally supportive social environment.
Peer Cohesion
Paralleling the coach-athlete relationship research, research on the role of peer relationships in the sport environment have shown these relationships to be highly influential on athlete experiences and outcomes (Smith & Ullrich-French, 2020).  Peer support has been shown to be related to elite sport participation, athlete motivation, and reduced withdrawal from sport (Sheridan et al., 2014). Additionally, researchers have shown that peer cohesion is not only associated with performance (Carron et al., 2002; Filho et al., 2014), but also athlete need satisfaction and learning (Erikstad et al., 2018). Furthermore, Smith and Ulrich-French (2020) have posited that peer relationships in the sport context are likely to be influential to individual athlete development, to include character, moral, social, and life skill development. In proposing strong peer relationships as influential of an implicit PYD climate, Holt and colleagues (2017) highlighted how strong peer relationships in the sport context often result in feelings of belongingness and support, which may provide developmental benefit.
Parental Involvement
While not always directly involved in the training environment, researchers have shown that parents are highly influential to youth athletes’ experiences and outcomes in sport. Youth who perceive their parents as satisfied with their performance and who experience low parental pressure are more likely to report sport enjoyment and positive affect (Dorsch et al., 2021). Additionally, parental involvement has also been associated with youth sport enjoyment, perceptions of competence, and self-esteem (Dorsch et al., 2021). Parental involvement in sport has also been found to be associated with youth athlete need satisfaction (Felber Charbonneau & Camiré, 2020). Furthermore, parental involvement in sport has also been connected to athletes’ development, to include socialization and value adoption (Danioni et al., 2017). In their grounded theory model, Holt and colleagues (2017) highlighted the reinforcing role that parental involvement plays to creating a PYD climate; while coaches may be responsible for delivering lessons and values to athletes in the sport context, the authors noted that it is important that parents support, not contradict, these messages.
Study Aims
In their grounded theory model, Holt and colleagues (2017) posited that these three relationships (i.e., coaches, peers, parents) collectively create a social climate supportive of implicit positive youth development. Therefore, the primary aim of the present study was to examine the impact of peer, coach, and parental relationships on youth sport experiences and youth athletes’ perceptions of developmental skills gained, thereby piloting a test of Holt and colleagues’ (2017) grounded theory model. Should these relationships be predictive of positive youth development, it could be expected that athletes who experience positive changes to these relationships (e.g., increased peer cohesion, increased parental involvement) across a sport season should also receive increased benefit from their participation compared to athletes whose relationships did not improve. As such, we hypothesized that athletes’ perceptions of positive outcomes obtained through sport participation (e.g., social skills, goal setting skills) would be predicted by positive changes to the ratings of their peer relationships, coach-athlete relationships, and parental involvement across a sport season.
METHODS
Participants
Participants included 67 youth athletes from a competitive soccer club in the northwest region of the United States. In total, 41 athletes (
age
= 11.85) completed data collection at both time points. Participants represented 13 teams from four separate age categories. Additionally, 65.9% of the athletes identified as white and 61.0% of the athletes identified as boys.
Measures
Coach-Athlete Relationship Questionnaire (CART-Q)
To measure athlete perceptions of their relationship with their coach, the Coach-Athlete Relationship Questionnaire (CART-Q; Jowett & Ntoumanis, 2004) was utilized. The 11-item scale measured the nature of the athlete’s relationship with their coach (a = 0.97). Using a seven-point Likert scale, athletes rated their agreement with statements such as, “I trust my coach.”
Youth Sport Environment Questionnaire (YSEQ)
Athletes’ perceptions of their relationship with teammates were measured utilizing the Youth Sport Environment Questionnaire (YSEQ; Eys et al., 2009). The scale, which has been shown to be both valid and reliable, measured group cohesion and peer relationship quality. The YSEQ contains 16 statements, such as, “I am happy with my team’s level of desire to win” (a = 0.93). Athletes rated their agreement with these statements utilizing a seven-point Likert scale.
Parental Involvement in Sport Questionnaire (PISQ)
The Parental Involvement in Sport Questionnaire (PISQ; Lee & MacLean, 1997) is a valid and reliable 19-item scale (a = 0.87), which captures athletes’ perceptions of parental involvement across three subscales: directive behavior, praise and understanding, and active involvement. Utilizing a five-point Likert scale, athletes rated their level of agreement with statements such as, “Do your parents push you to practice harder?”
Youth Experience Survey for Sport (YES-S)
Employed only at the second time point, the short form Youth Experience Survey for Sport (YES-S; MacDonald et al., 2012; Sullivan et al., 2015) is 16-item scale that measured the perceptions of athletes’ experiences participating in sport across the previous season, and was utilized in the present study to operationalize PYD. The scale measures whether athletes perceived any benefit to their participation across four subscales: personal and social skills (a = 0.78), cognitive skills (a = 0.78), goal setting (a = 0.81), and initiative (a = 0.71). Athletes rated their agreement with statements such as, “I learned to push myself” on a five-point Likert scale.
Procedure
Ahead of the start of the summer season, the first author attended the club’s tryouts and parent meetings to share information about the study and recruit participants. During this time, parental consent was obtained through the completion of a written consent form and household demographic survey. The first survey was completed electronically one month into the summer season.  Subsequently, 14 weeks later, the research team returned to conduct the second survey during the final week of the fall season. At both time points, the surveys collected demographic information, athlete perceptions of relationships with their coach, peer cohesion, and parental involvement. At the second time point, the survey collected measurements of athletes’ perceptions of their experiences playing sport across the previous season, particularly focused on skills gained.
The dataset contained 0.3% missingness, and results of an MCAR test were not significant (
(1386) = 0.00,
= 1.00), suggesting data was missing at random. For cases with missingness, scales were prorated based on completed items. Descriptive statistics were calculated for each scale and notable demographic differences are reported in Table 1. For each of the relationship variables (i.e., CART-Q, PISQ, YSEQ), a difference score was calculated (M
T2
– M
T1
) to measure changes in these relationships across the season. While the utilization of difference scores has been criticized for its negative, summative impact on reliability (Edwards, 1994), researchers have noted that difference scores can be an appropriate choice in research, particularly for nonrandomized, theory-driven analyses (Castro-Schilo & Grimm, 2018). Assumptions testing revealed issues regarding multicollinearity as there was a high correlation between coach-athlete relationship and the peer cohesion change scores (
= 0.801), which resulted in unstable beta coefficients. This instability indicated that the presence of the peer cohesion variable in the model was distorting the estimation of other predictors, undermining the reliability and interpretability of the model. As such, the peer cohesion variable was removed from primary analyses. Following this, we regressed the four subscales of the YES-S (i.e., personal and social skills, cognitive skills, goal setting skills, initiative) on changes in relationship quality across the season, while controlling for age, race, and gender.
Table 1
Sample Characteristics and Descriptive Statistics
CART-Q
YSEQ
PISQ
YES-S Social Skills
YES-S Cog. Skills
YES-S Goal Setting
YES-S Initiative
Variable
T1 –
M(SD)
T2 –
M(SD)
T1 –
M(SD)
T2 –
M(SD)
T1 –
M(SD)
T2 –
M(SD)
T2 –
M(SD)
T2 –
M(SD)
T2 –
M(SD)
T2 –
M(SD)
Age
10
7.3
5.61(1.24)*
5.97(1.47)*
4.25(2.01)*
5.08(1.98)*
2.39(0.18)
2.91(0.45)
3.58(0.52)
3.67(0.58)
4.25(0.58)
4.58(0.52)
11
22.0
5.46(1.73)
6.36(0.39)
4.74(1.52)
5.53(0.83)
3.02(0.60)
3.13(0.52)
4.00(0.60)
3.69(1.05)
4.00(0.85)
4.50(0.45)
12
20
48.8
6.10(0.40)
5.96(0.85)
5.10(0.75)
5.30(0.91)*
2.92(0.60)
3.25(0.74)*
4.17(0.75)
3.53(1.16)
3.93(0.90)
4.25(0.59)
13
22.0
5.71(1.04)*
5.15(1.26)*
4.69(1.40)
4.89(1.18)
3.16(0.69)
3.30(0.58)
4.03(0.57)
3.56(0.69)
4.25(0.57)
4.43(0.66)
Gender
Boy
25
61.0
6.03(0.61)
6.17(0.69)
4.91(1.03)*
5.26(0.89)*
2.92(0.62)*
3.24(0.66)*
4.12(0.66)
3.72(0.85)
4.11(0.71)
4.43(0.41)
Girl
16
39.0
5.56(1.43)
5.41(0.99)
4.81(1.42)
5.22(1.25)
3.01(0.62)
3.17(0.62)
3.96(0.89)
3.35(1.19)
3.90(0.94)
4.27(0.76)
Race
White
27
65.9
5.77(1.13)
5.97(0.83)
4.75(1.21)*
5.24(1.03)*
2.95(0.61)
3.14(0.57)
4.07(0.69)
3.52(1.04)
3.99(0.85)
4.43(0.54)
Black
2.4
Asian
9.8
5.50(1.38)
5.41(1.85)
4.77(1.85)*
5.30(1.64)*
2.74(0.90)
3.29(0.90)
4.00(0.35)
3.94(0.43)
3.94(0.43)
3.94(0.66)
Hispanic
9.8
6.27(0.45)
5.86(1.12)
5.50(0.89)
5.55(0.74)
2.99(0.57)
3.41(0.83)
4.50(0.41)
4.25(0.54)
4.69(0.47)
4.63(0.32)
Other
12.2
6.13(0.31)
5.65(1.20)
5.05(0.83)
4.99(1.04)
2.99(0.45)
3.31(0.52)
3.80(0.89)
3.15(1.29)
3.80(0.94)
4.15(0.74)
Total
41
100.0
5.84(1.02)
5.87(0.99)
4.87(1.18)*
5.24(1.03)*
2.96(0.62)*
3.21(0.64)*
4.06(0.67)
3.58(1.00)
4.03(0.80)
4.37(0.57)
Notes
= 41; CART-Q = Coach-Athlete Relationship; PISQ = Parental Involvement; YSEQ = Ratings of Peer Cohesion; YES-S = Perceptions of Developmental Experiences, *Difference is significant between time points;
Difference is significant between groups.
RESULTS
The model examining personal and social skills was significant and explained 45.4% of variance in the outcome (
= 0.454,
(5,34) = 5.664,
< 0.001).
Regression Results for Perceptions of Social Skills Gained by Athletes
95% CI
Variable
SE
LL
UL
Intercept
0.774
1.268
-1.802
3.350
0.546
Gender
-0.129
-0.175
0.184
-0.550
0.199
0.348
Age
0.369
0.291
0.110
0.067
0.515
0.012
Race
-0.024
-0.008
0.042
-0.094
0.078
0.858
DCART-Q
0.482
0.250
0.074
0.099
0.400
0.002
DPISQ
0.326
0.382
0.160
0.580
0.707
0.022
Notes
= 41;
= 0.454,
(5,34) = 5.664,
< 0.001; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement.
**
When ran independently due to the existing multicollinearity, change to peer cohesion was also a significant predictor of personal and social skills (
= 0.317,
(4,35) = 4.063,
= 0.008).
Within this model, both changes to the coach-athlete relationships (b= 0.482,
= 0.002) and changes to parental involvement (b= 0.326,
= 0.022) across the season were significant predictors of personal and social skills. Additionally, the covariate age was also a significant predictor of personal and social skills (b
0.369,
= 0.012). The model examining cognitive skills explained 25.1% of the variance, however was only marginally significant (
= 0.251,
(5,34) = 2.275,
= 0.069). Within this model the change in coach-athlete relationship was a statistically significant predictor (b= 0.403,
= 0.022), whereas changes to parental involvement was not (b= 0.158,
= 0.330).
Table 3
Regression Results for Perceptions of Cognitive Skills Gained by Athletes
95% CI
Variable
SE
LL
UL
Intercept
2.048
2.221
-2.465
6.561
0.363
Gender
-0.155
-0.315
0.323
-0.972
0.342
0.337
Age
0.143
0.169
0.193
-0.224
0.561
0.389
Race
-0.066
-0.032
0.074
-0.182
0.119
0.670
DCART-Q
0.403
0.312
0.130
0.048
0.576
0.022
DPISQ
0.158
0.277
0.280
-0.292
0.845
0.330
Notes
= 41;
= 0.251,
(5,34) = 2.275,
= 0.069; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement.
** When ran independently due to the existing multicollinearity, change to peer cohesion was not a significant predictor of cognitive skills.
The models predicting goal setting skills (
= 0.183,
(5,34) = 1.528,
= 0.207) and initiative (
= 0.185,
(5,34) = 1.542,
= 0.203) were not statistically significant.
DISCUSSION
The present study provides partial support to Holt and colleague’s (2017) proposition that there is an implicit pathway of PYD in sport that takes place through positive relationships. In particular, changes to the coach-athlete relationship significantly predicted youth athletes’ perceptions of social skills and cognitive skills gained; and changes to perceptions of parental involvement also predicted social skills gained. Additionally, when analyzed separately due to issues of multicollinearity, changes to peer cohesion also significantly predicted social skill perceptions. As such, data in the current study reinforce the importance of relationships within the sport environment, and extend previous research by highlighting their value to the specific area of PYD through sport.
While research has shown the coach-athlete relationship to be associated with motivation (Adie & Jowett, 2010), collective-efficacy (Hampson & Jowett, 2014), and team cohesion (Turman, 2003), its role in the social and cognitive development of athletes is less understood. That said, research has shown that coaches seem to intuitively understand the developmental value of a positive coach-athlete relationship as coaches have reported a positive relationship with their athletes led to social and emotional development and resilience (White & Bennie, 2015). Furthermore, Davis and colleagues (2019) proposed a bidirectional relationship between communication skills and the coach-athlete relationship, where communication skills not only helped to improve the relationship, but also improved as a product of a high-quality coach-athlete relationship. When examining the more expansive literature on the impact of a high-quality relationships, researchers have documents that teacher-student relationships can promote cognitive development (Davis, 2003) and social adjustment (Dong et al., 2021) through positive and trusting learning environments. Data in the current study suggest coaches hold a responsibility to ensure the development and sustainment of positive relationships in the sport environment to support similarly positive developmental outcomes for youth athletes. This is particularly important as social skills have been shown to be associated with academic performance (Sung & Chang, 2010), increased mental health (Greenberg et al., 2003), wellbeing (Sancassiani et al., 2015), and self-esteem (Riggio et al., 1990).
The present study also highlights the important yet specific role that parents play in positive youth development through sport. Parental styles have been shown to be associated with social skill development; youth with democratic and permissive parents have been shown to score higher on social skills measures than those with neglectful or authoritative parents (Salavera et al., 2022). As such, it could be hypothesized that parents with more developmentally supportive parenting styles are more likely to be involved in their child’s sport and supportive of their child’s social skills. That said, data in the current study suggests the need to delineate the roles of parents and coaches, as these relationships may provide different benefits for youth. For example, Knight and colleagues (2011) reported that athletes consistently prefer parents to fill a supportive and encouraging role, as opposed to a coaching role. This is supported by data in the current study in that while change to parental involvement predicted athletes’ perceptions of social skill development, it did not predict their cognitive skill perceptions.
Finally, it is important to note that girls rated their relationship with their coach significantly lower than their peers who identified as boys; and older athletes were also significantly less likely to rate their coach-relationships higher than younger athletes. As such, should there exist any developmental benefit to high-quality, coaching relationships, the present findings would suggest that girls and older youth athletes are less likely to receive those benefits. Given that a positive coach-athlete relationship can be protective from poor mental health outcomes for girl athletes specifically (Massey et al., 2024), it is important that positive coach-athlete relationships are prioritized for female athletes, particularly adolescent female athletes. Furthermore, it is generally accepted that as athletes get older, the sporting environment shifts from a focus on fun to a focus on competition. Be that as it may, research has shown that the true shift lies within how athletes are treated; Kipp and Bolter (2020) found that while both older and younger athletes equally perceived their sporting environments to be focused on effort and learning, older athletes were more likely to report being punished or disciplined for mistakes. It is possible that such climates explain the decreasing trend of the coach-athlete relationship observed in the present study. Speaking strictly to the proposed developmental role of the coach-athlete relationship within sport, the present findings would offer that sports become less beneficial and developmentally supportive over time.
Despite the present study’s value to the literature base on PYD through sport, its small, homogenous sample limits its generalizability. In addition to being predominantly white, the sample derived from a singular, pay-to-play soccer organization within an affluent community. Additionally, the present sample predominantly identified as boys, which may parallel youth sport participation trends, but limits the generalizability of the findings to non-boy athlete populations. The age rage of the sample was also limited, clustered into the soccer organizations U11 and U13 age groupings, and as such, the findings may be in part reflective of the natural development occurring in this age range.
Furthermore, most athletes in the present study were satisfied with their relationship with their coach and peers, and the mean parental involvement score was slightly above the midpoint of the scale. Depending on sport or community context, it is possible that more athletes would report more dissatisfaction with these relationships or less parental involvement, thereby affecting the nature of the findings. With respect to age and gender differences, it is possible that these differences could be explained by confounding variables, such as coach gender, competition level, or position, which could not be differentiated in the present study due to the small sample size. Lastly, while multicollinearity necessitated the removal of the peer cohesion variable from the analyses, it should be acknowledged that doing so also limits the completeness of the model by excluding a theoretically important dimension of the sport environment, and one which should continue to be examined in this line of research.  As such, future studies should not only continue to examine the nuanced roles of parents and coaches in sport-based PYD, but also peer relationships, and doing so in larger and more diverse samples.
CONCLUSION
The social context of the sport environment, which includes coaches, parents, and peers, plays a significant role in shaping athletes’ perceived development through sport. In the present study, athletes’ perceived social skill development was significantly predicted by positive changes to the coach-athlete relationship and parental involvement. The quality of the coach-athlete relationship also emerged as a meaningful predictor of athletes’ perceived cognitive development, highlighting the broader developmental impact of adult figures in the sport context. Furthermore, while peer cohesion was omitted in analyses due to multicollinearity, its interconnectedness with the coach-athlete relationship should be acknowledged, and researchers should continue to utilize it as a variable of interest as theory would dictate. Taken together, these findings underscore the importance of considering the full network of sport-based relationships when seeking to support athletes’ development through sport participation.
APPLICATIONS IN SPORT
In addition to providing support for Holt and colleagues’ (2017) theory of implicit PYD through sport, the present study highlights the interconnected nature of youth sport’s social context. We offer the following recommendations to stakeholders seeking to utilize these findings to develop their youth sport organization’s PYD climate:
Provide coaches with education and training that supports their development of communication and relationship-building skills (see Barnett et al., 1992; Jowett & Cockerill, 2003).
Provide education and clear expectations for parents’ involvement in the organization, as well as opportunities for involvement (see Knight et al., 2011).
Prioritize relationship building and psychological safety at the outset of the season, to include team-building activities and the development of team norms, rituals, and goals (see Carron et al., 1997; Senécal et al., 2008).
REFERENCES
1. Adie, J. W., & Jowett, S. (2010). Meta‐perceptions of the coach–athlete relationship, achievement goals, and intrinsic motivation among sport participants.
Journal of Applied Social Psychology
40
(11), 2750-2773.
2. Barnett, N. P., Smoll, F. L., & Smith, R. E. (1992). Effects of enhancing coach-athlete relationships on youth sport attrition.
The Sport Psychologist, 6
(2), 111-127.
3. Benson, P. L., Scales, P. C., Hamilton, S. F., & Sesma Jr., A. (2006). Positive youth development so far: Core hypotheses and their implications for policy and practice.
Search Institute Insights & Evidence 3
(1), 1-13.
4. Bruner, M. W., McLaren, C. D., Sutcliffe, J. T., Gardner, L. A., Lubans, D. R., Smith, J. J., & Vella, S. A. (2021). The effect of sport-based interventions on positive youth development: A systematic review and meta-analysis.
International Review of Sport and Exercise Psychology
16
(1), 368-395.
5. Burns, L., Weissensteiner, J. R., & Cohen, M. (2019). Supportive interpersonal relationships: a key component to high-performance sport.
British Journal of Sports Medicine
53
(22), 1386-1389.
6. Carron, A. V., Colman, M. M., Wheeler, J., & Stevens, D. (2002). Cohesion and performance in sport: A meta analysis.
Journal of Sport and Exercise Psychology, 24
(2), 168–188. https://doi.org/10.1123/jsep.24.2.168
7. Carron, A. V., Spink, K. S., & Prapavessis, H. (1997). Team building and cohesiveness in the sport and exercise setting: Use of indirect interventions.
Journal of Applied Sport Psychology, 9(
1), 61–72. https://doi.org/10.1080/10413209708415384
8. Castro-Schilo, L., & Grimm, K. J. (2018). Using residualized change versus difference scores for longitudinal research.
Journal of Social and Personal Relationships, 35
(1), 32-58.
9. Catalano, R. F., Toumbourou, J. W., & Hawkins, J. D. (2008). Positive youth development in the United States: History, efficacy, and links to moral and character education. In L. Nucci & T. Krettenauer (Eds.)
Handbook of moral and character education
. Routledge.
10. Christiansen, E. D., & von Eye, A. (2005). Positive youth development, participation in community youth development programs, and community contributions of fifth-grade adolescents: Findings from the first wave of the 4-H study of positive youth development.
The Journal of Early Adolescence
25
(1), 17-71.
10. Clonan, S. M., Chafouleas, S. M., McDougal, J. L., & Riley-Tillman, T. C. (2004). Positive psychology goes to school: Are we there yet?
Psychology in the Schools, 41
(1), 101–110.
11. Danioni, F., Barni, D., & Rosnati, R. (2017). Transmitting sport values: The importance of parental involvement in children’s sport activity.
Europe’s Journal of Psychology, 13
(1), 75–92.
12. Davis, H. A. (2003). Conceptualizing the role and influence of student-teacher relationships on children’s social and cognitive development.
Educational Psychologist
38
(4), 207-234.
13. Davis, L., & Jowett, S. (2014). Coach–athlete attachment and the quality of the coach–athlete relationship: Implications for athlete’s well-being.
Journal of Sports Sciences, 32
(15), 1454–1464.
14. Davis, L., Jowett, S., & Tafvelin, S. (2019). Communication strategies: The fuel for quality coach-athlete relationships and athlete satisfaction.
Frontiers in Psychology
10
, 2156.
15. Dong, Y., Wang, H., Luan, F., Li, Z., & Cheng, L. (2021). How children feel matters: Teacher–student relationship as an indirect role between interpersonal trust and social adjustment.
Frontiers in Psychology
11
, 581235.
16. Dorsch, T. E., Wright, E., Eckardt, V. C., Elliott, S., Thrower, S. N., & Knight, C. J. (2021). A history of parent involvement in organized youth sport: A scoping review.
Sport, Exercise, and Performance Psychology
10
(4), 536.
17. Edwards, J. R. (1994). Regression analysis as an alternative to difference scores.
Journal of Management, 20
(3), 683-689.
18. Erikson, E. H. (1950).
Childhood and society.
W. W. Norton & Co.
19. Erikstad, M. K., Martin, L. J., Haugen, T., & Høigaard, R. (2018). Group cohesion, needs satisfaction, and self-regulated learning: A one-year prospective study of elite youth soccer players’ perceptions of their club team.
Psychology of Sport and Exercise, 39
, 171–178.
20. Eys, M., Loughead, T., Bray, S. R., & Carron, A. V. (2009). Development of a cohesion questionnaire for youth: The Youth Sport Environment Questionnaire.
Journal of Sport and Exercise Psychology
31
(3), 390-408.
21. Felber Charbonneau, E., & Camiré, M. (2020). Parental involvement in sport and the satisfaction of basic psychological needs: Perspectives from parent–child dyads.
International Journal of Sport and Exercise Psychology, 18
(5), 655–671.
22. Filho, E., Dobersek, U., Gershgoren, L., Becker, B., & Tenenbaum, G. (2014). The cohesion–performance relationship in sport: A 10-year retrospective meta-analysis.
Sport Sciences for Health, 10
(3), 165–177.
23. Greenberg, M. T., Weissberg, R. P., O’Brien, M. U., Zins, J. E., Fredericks, L., Resnik, H., & Elias, M. J. (2003). Enhancing school-based prevention and youth development through coordinated social, emotional, and academic learning.
American Psychologist
58
(6-7), 466.
24. Hampson, R., & Jowett, S. (2014). Effects of coach leadership and coach–athlete relationship on collective efficacy.
Scandinavian Journal of Medicine & Science in Sports
24
(2), 454-460.
25. Holt, N. L., Neely, K. C., Slater, L. G., Camiré, M., Côté, J., Fraser-Thomas, J., MacDonald, D., Strachan, L., & Tamminen, K. A. (2017). A grounded theory of positive youth development through sport based on results from a qualitative meta-study.
International Review of Sport and Exercise Psychology
10
(1), 1-49.
26. Jones, M. I., Dunn, J. G., Holt, N. L., Sullivan, P. J., & Bloom, G. A. (2011). Exploring the ‘5Cs’ of positive youth development in sport.
Journal of Sport Behavior
34
(3).
27. Jowett, S. (2017). Coaching effectiveness: The coach–athlete relationship at its heart.
Current Opinion in Psychology, 16,
154–158. https://doi.org/10.1016/j.copsyc.2017.05.006
28. Jowett, S., & Cockerill, I. M. (2003). Olympic medalists’ perspective of the athlete–coach relationship.
Psychology of Sport and Exercise, 4
(4), 313–331.
29. Jowett, S., & Ntoumanis, N. (2004). The Coach–Athlete Relationship Questionnaire (CART‐Q): Development and initial validation.
Scandinavian Journal of Medicine & Science in Sports
14
(4), 245-257.
30. Kipp, L. E., & Bolter, N. D. (2020). Motivational climate, psychological needs, and personal and social responsibility in youth soccer: Comparisons by age group and competitive level.
Psychology of Sport and Exercise, 51.
31. Knight, C. J., Neely, K. C., & Holt, N. L. (2011). Parental behaviors in team sports: How do female athletes want parents to behave?
Journal of Applied Sport Psychology
23
(1), 76-92.
32. Larson, R. W. (2000). Toward a psychology of positive youth development.
American Psychologist, 55
(1), 170–183. https://doi.org/10.1037/0003-066X.55.1.170
33. Lee, M., & MacLean, S. (1997). Sources of parental pressure among age group swimmers.
European Journal of Physical Education
(2), 167-177.
34. Lerner, R. M. (2005).
Promoting positive youth development: Theoretical and empirical bases.
Washington, DC: National Academies of Sciences.
35. Lerner, R. M., Almerigi, J. B., Theokas, C., & Lerner, J. V. (2005). Positive youth development A view of the issues.
The Journal of Early Adolescence, 25
(1), 10–16.
36. Lerner, R. M., Lerner, J. V., Almerigi, J. B., Theokas, C., Phelps, E., Gestsdottir, S., Naudeau, S., Jelicic, H., Alberts, A., Ma, L., Smith, L. M., Bobek, D. L., Richman-Raphael, D., Simpson, I., 37.
37. Lerner, R. M., Lerner, J. V., Lewin-Bizan, S., Bowers, E. P., Boyd, M. J., Mueller, M. K., Schmid, K. L., & Napolitano, C. M. (2011). Positive youth development: Processes, orograms, and problematics.
Journal of Youth Development, 6
(3), 38–62.
38. MacDonald, D. J., Côté, J., Eys, M., & Deakin, J. (2012). Psychometric properties of the youth experience survey with young athletes.
Psychology of Sport and Exercise
13
(3), 332-340.
39. Massey, W. V., Veliz, P. T., Zarrett, N., & Farello, A. (2024).
Thriving through sport: The transformative impact on girls’ mental health.
Women’s Sport Foundation.
40. McBride, A. M., Johnson, E., Olate, R., & O’Hara, K. (2011). Youth volunteer service as positive youth development in Latin America and the Caribbean.
Children and Youth Services Review, 33
(1), 34–41.
41. Nikolina, K., & Đorić, V. (2023). Relations of coach-athlete relationship quality and athlete psychosocial functioning: A systematic review.
SportLogia
19
(1).
42. Riggio, R. E., Throckmorton, B., & Depaola, S. (1990). Social skills and self-esteem.
Personality and Individual Differences
11
(8), 799-804.
43. Salavera, C., Usán, P., & Quilez-Robres, A. (2022). Exploring the effect of parental styles on social skills: The mediating role of affects.
International Journal of Environmental Research and Public Health
19
(6), 3295.
44. Sancassiani, F., Pintus, E., Holte, A., Paulus, P., Moro, M. F., Cossu, G., Angermeyer, M. C., Carta, M. G., & Lindert, J. (2015). Enhancing the emotional and social skills of the youth to promote their wellbeing and positive development: A systematic review of universal school-based randomized controlled trials [Special issue].
Clinical Practice and Epidemiology in Mental Health,
11
, 21-40.
45. Senécal, J., Loughead, T. M., & Bloom, G. A. (2008). A season-long team-building intervention: Examining the effect of team goal setting on cohesion
. Journal of Sport and Exercise Psychology, 30
(2), 186-199.
46. Sheridan, D., Coffee, P., & Lavallee, D. (2014). A systematic review of social support in youth sport.
International Review of Sport and Exercise Psychology
(1), 198-228.
47. Sibthorp, J., & Morgan, C. (2011). Adventure‐based programming: Exemplary youth development practice.
New Directions for Youth Development,
(130), 105-119.
48. Smith, A. L., & Ullrich-French, S. (2020). Peers and the sport experience. In G. Tenenbaum & R. C. Eklund (Eds.)
Handbook of sport psychology
(pp. 410–428). John Wiley & Sons, Inc.
49. Storm, M. K., Williams, K. N., Shetter, E. M., Kaminsky, J., Lowery, C. M., Caldas, S. V., & Winch, P. J. (2017). Sink or swim: Promoting youth development through aquatics programs in Baltimore, Maryland.
Journal of Park & Recreation Administration, 35
(1), 66-79.
50. Sullivan, P. J., LaForge-MacKenzie, K., & Marini, M. (2015). Confirmatory factor analysis of the Youth Experiences Survey for Sport (YES-S).
Open Journal of Statistics
(05), 421.
51. Sung, Y. Y., & Chang, M. (2010). Which social skills predict academic performance of elementary school students.
Journal on Educational Psychology
(3), 23-34.
52. Turman, P. D. (2003). Coaches and cohesion: The impact of coaching techniques on team cohesion in the small group sport setting.
Journal of Sport Behaviour
26
(1), 86-104.
53. White, R. L., & Bennie, A. (2015). Resilience in youth sport: A qualitative investigation of gymnastics coach and athlete perceptions.
International Journal of Sports Science & Coaching
10
(2-3), 379-393.
54. Whitley, M. A., Massey, W. V., Camiré, M., Boutet, M., & Borbee, A. (2019). Sport-based youth development interventions in the United States: A systematic review.
BMC Public Health
19
, 1-20.
55. Worker, S. M., Iaccopucci, A. M., Bird, M., & Horowitz, M. (2019). Promoting positive youth development through teenagers-as-teachers programs.
Journal of Adolescent Research, 34
(1), 30-54.
Appendix A
Supplemental Materials
Table 4
Correlation Matrix of Study Variables
Variables
1. Age
2. CART-Q
-0.34*
3. PISQ
0.15
0.23
4. YSEQ
-0.14
0.66**
0.31*
5. Social Skills
0.14
0.62**
0.35*
0.47**
6. Cognitive Skills
-0.05
0.40*
0.16
0.16
0.66**
7. Goal Setting
0.03
0.43**
0.13
0.42**
0.57**
0.70**
8. Initiative
-0.10
0.53**
0.18
0.47**
0.51**
0.40*
0.70**
Notes.
* Correlation is significant at the 0.05 level (two-tailed). ** Correlation is significant at the 0.01 level (two-tailed); CART-Q = Coach-Athlete Relationship; PISQ = Parental Involvement, YSEQ = Peer Relationships
Table 5
Regression Results for Perceptions of Goal Setting Skills Gained by Athletes
95% CI for B
Variable
SE
LL
UL
Intercept
2.053
1.862
-1.731
5.836
0.278
Gender
-0.228
0.271
-0.779
0.322
-0.140
0.405
Age
0.186
0.162
-0.143
0.515
0.196
0.259
Race
0.011
0.062
-0.115
0.137
0.028
0.863
DCART-Q
0.230
0.109
0.008
0.451
0.369
0.042
DPISQ
0.175
0.235
-0.302
0.651
0.124
0.462
Notes
= 0.183,
= 0.207; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement
** When ran independently due to the existing multicollinearity, change to peer cohesion was not a significant predictor of cognitive skills.
Table 6
Regression Results for Perceptions of Initiative Gained by Athletes
95% CI for B
Variable
SE
LL
UL
Intercept
4.000
1.315
1.328
6.671
-0.120
3.043
Gender
-0.138
0.191
-0.527
0.250
0.062
-0.723
Age
0.042
0.114
-0.191
0.274
0.035
0.365
Race
0.010
0.044
-0.079
0.099
0.389
0.221
DCART-Q
0.171
0.077
0.015
0.327
0.087
2.224
DPISQ
0.086
0.166
-0.251
0.423
-0.120
0.520
Notes
= 0.185,
= 0.203; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement
** When ran independently due to the existing multicollinearity, change to peer cohesion was not a significant predictor of cognitive skills.
Troy Palmer
2025-10-01T13:40:31-05:00
The Role of Sport Relationships in Positive Youth Development
Troy Palmer
2025-10-01T13:40:31-05:00
March 18th, 2026
Leadership
Research
Sport Education
Sport Training
Sports Coaching
Sports Studies and Sports Psychology
Authors
Jim P. Arnold
and William V. Massey
Department of Kinesiology, College of Health, Oregon State University
Corresponding Author:
Jim P. Arnold
[email protected]
Jim P. Arnold https://orcid.org/0009-0004-2282-1915
William V. Massey, Ph.D. https://orcid.org/0000-0002-4002-3720
We have no known conflicts of interest to disclose.
ABSTRACT
Purpose.
Research on positive youth development (PYD) through sport remains unclear and speculative (Whitley et al., 2019). It has been suggested that sport-based PYD can occur implicitly through positive relationships (Holt et al., 2017). The present pilot study examined the impact of changes in the coach-athlete relationship, peer cohesion, and parental involvement on PYD outcomes across a sport season in a sample of youth soccer participants (
N =
41,
age
= 11.85, 61% boys).
Methods.
Athletes responded to surveys rating their relationships with coaches, parents, and peers at two time points, and additionally reported their perceptions of developmental skills gained across the sport season. A difference score was calculated for each relationship variable to measure change across the season. Four developmental outcomes (i.e., personal and social skills, cognitive skills, goal setting skills, initiative) were regressed on changes in relationship quality across the season, while controlling for age, race, and gender.
Results.
Changes to the coach-athlete relationship (b= 0.482,
= 0.002) and parental involvement (b= 0.326,
= 0.022) were significant predictors of perceptions of social skill development (
= 0.454,
(5,34) = 5.664,
< 0.001), supporting a relationship-based model of PYD in sport. Significant age and gender differences in ratings of the coach-athlete relationship were also discovered.
Conclusions.
The present study not only offers partial support to a Holt and colleagues’ (2017) theory of implicit PYD through sport but also highlights the need important developmental role of relationship building in in the sport context.
Applications in Sport.
Organizations should prioritize positive sport relationships through education, training, and programming, as poor or absent relationships may undermine the envisioned benefits of sport. In particular, the present study highlights the need for positive parental involvement, which may require stakeholders to work with parents to define their role expectations.
KEYWORDS:
youth sport, positive youth development, sport relationships, coaching, parental involvement
INTRODUCTION
The goal of supporting positive outcomes for younger people (i.e., generativity; Erikson, 1950) is one that is both widely and cross-culturally relevant, yet despite this, the understanding for how to best support young people and the strategies employed to do so are still in flux. Only recently have developmental psychology and social research begun to place an emphasis on fostering positive outcomes for youth, as opposed to the prevention of negative outcomes and problematic behaviors (Larson, 2000). Within the areas of social and developmental research, this emphasis has led to the creation of diverse approaches to and philosophies of developmental youth programming (Lerner et al., 2011), which often provide opportunities for life skill development (i.e., explicit positive youth development). That said, the translation of such knowledge to spaces where youth development is view as a secondary priority, such as sport, tends to be challenging (Jones et al., 2011).  The primary aim of the present pilot study was to test a grounded theory of implicit positive youth development through sport by examining the impact of peer, coach, and parental relationships on youth sport experiences in a small, single-organization sample. In doing so, the present study offers a novel examination of the collective social climate (i.e., PYD climate) and its relationship to athlete developmental outcomes. We hypothesized the following:
Athletes’ perceptions of positive outcomes obtained through sport participation (e.g., social skills, goal setting skills) will be predicted by positive changes to the ratings of the coach-athlete relationship, peer cohesion, and parental involvement across a sport season.
At two time points (e.g., beginning of the season, end of the season), athletes’ ratings of their relationships with their coach, peer cohesion, and parental involvement were collected.  Subsequently, athletes’ perceptions of skill development across four areas (e.g., personal and social skills, cognitive skills, goal setting, initiative) were regressed on changes to the relationship variables. Both the coach-athlete relationship and parental involvement were shown to significantly predict social skill development, not only offering partial support for a theory of implicit PYD through sport and underscoring the critical developmental role of relationship building in sport but also pointing to the need for stakeholders to prioritize a high-quality social climate in the sport context to better support youth development.
LITERATURE REVIEW
Historically, adolescence and adolescent development has been regarded as a period during which youth are at risk and laden with problematic behaviors (Benson et al., 2006), therefore implying that the role of adults was to manage and prevent the problems that arise from adolescent development, also known as a deficit-focused approach to youth development (Clonan et al., 2004; Lerner, 2005). However, preventing such problems through a focus on treatment or intervention often failed to yield positive results (Catalano et al., 2008). Appearing concurrently with positive psychology’s focus on human strengths and flourishing, positive youth development theory offered that youth are “resources to be developed,” presenting a path toward positive youth outcomes through youth enrichment and the promotion of adolescent strengths (Lerner, Almerigi, et al., 2005). Positive youth development is a broad term, but generally refers to “processes, approaches, and instances” (Lerner et al., 2011) which seek to optimally prepare young people for adulthood, with the targeted outcomes being well-being and the fulfillment of their potential (Catalano et al., 2008). Contexts which aim to support positive youth development vary widely, to include agricultural programming (Lerner, Lerner, et al., 2005), volunteer and service programming (McBride et al., 2011), tutoring (Worker et al., 2019), aquatics (Storm et al., 2017), adventure-based programming (Sibthorp & Morgan, 2011), and sport (Bruner et al., 2021).
Youth sports are generally touted as tools for healthy and positive development, yet research aimed at validating this claim or understanding the processes by which it occurs is ambiguous (Holt et al., 2017). PYD theory was developed outside of the sport context (Lerner, Lerner, et al., 2005) and researchers have struggled to apply PYD models and measures to sporting contexts (Jones et al., 2011). One reason for this may be that PYD researchers have failed to acknowledge keyfeatures of the sport environment (Holt et al., 2017). In a systematic review of qualitative data, Holt and colleagues (2017) proposed that PYD through sport occurs via two distinct pathways. In the first, programs offer explicit education to youth sport participants aimed at life skill development. In the second pathway, PYD occurs implicitly via positive relationships with coaches, peers, and parents (i.e., the creation of a ‘PYD climate’). Holt and colleagues concluded that further research is needed to not only investigate the validity of this framework but also understand additional nuances for when and how PYD may occur through explicit and implicit factors. The need for further research was bolstered by a systematic review of sport-based PYD programming, conducted by Whitley and colleagues (2019), who concluded the benefit of explicit PYD programming in sport is not clear enough to support the implementation of a standardized intervention. Therefore, while the field’s understanding of how to best implement explicit PYD programming through sport is still evolving, there also exists a need to test the proposed model of implicit PYD through positive relationships within sport. While the specific role positive relationships play in supporting PYD within sport is unclear, it is generally accepted that these relationships are all valuable, if not necessary, for positive athlete outcomes (Burns et al., 2019).
Coach-Athlete Relationship
Arguably the primary relationship in the sporting context (Jowett, 2017), the dyadic relationship between coach and athlete has been shown to be instrumental to numerous athlete outcomes. In a systematic review of the coach-athlete relationship literature, Nikolina and Đorić (2023) reported that a positive coach-athlete relationship was not only predictive of increased motivation, satisfaction, and performance, but also protective from athlete stress, burnout, and negative affect. Davis and Jowett (2014) have reported that the quality of the coach-athlete relationship is directly related to athlete positive and negative affect. Furthermore, in a systematic review of the literature, McShan and Moore (2023) found that a positive coach-athlete relationship, as reported by coaches, was associated with coach’s beliefs of fostering an environment supportive of athlete life skill development. In Holt and colleague’s (2017) grounded theory of implicit PYD, the authors posit that strong, positive relationships between athletes and coaches can create a developmentally supportive social environment.
Peer Cohesion
Paralleling the coach-athlete relationship research, research on the role of peer relationships in the sport environment have shown these relationships to be highly influential on athlete experiences and outcomes (Smith & Ullrich-French, 2020).  Peer support has been shown to be related to elite sport participation, athlete motivation, and reduced withdrawal from sport (Sheridan et al., 2014). Additionally, researchers have shown that peer cohesion is not only associated with performance (Carron et al., 2002; Filho et al., 2014), but also athlete need satisfaction and learning (Erikstad et al., 2018). Furthermore, Smith and Ulrich-French (2020) have posited that peer relationships in the sport context are likely to be influential to individual athlete development, to include character, moral, social, and life skill development. In proposing strong peer relationships as influential of an implicit PYD climate, Holt and colleagues (2017) highlighted how strong peer relationships in the sport context often result in feelings of belongingness and support, which may provide developmental benefit.
Parental Involvement
While not always directly involved in the training environment, researchers have shown that parents are highly influential to youth athletes’ experiences and outcomes in sport. Youth who perceive their parents as satisfied with their performance and who experience low parental pressure are more likely to report sport enjoyment and positive affect (Dorsch et al., 2021). Additionally, parental involvement has also been associated with youth sport enjoyment, perceptions of competence, and self-esteem (Dorsch et al., 2021). Parental involvement in sport has also been found to be associated with youth athlete need satisfaction (Felber Charbonneau & Camiré, 2020). Furthermore, parental involvement in sport has also been connected to athletes’ development, to include socialization and value adoption (Danioni et al., 2017). In their grounded theory model, Holt and colleagues (2017) highlighted the reinforcing role that parental involvement plays to creating a PYD climate; while coaches may be responsible for delivering lessons and values to athletes in the sport context, the authors noted that it is important that parents support, not contradict, these messages.
Study Aims
In their grounded theory model, Holt and colleagues (2017) posited that these three relationships (i.e., coaches, peers, parents) collectively create a social climate supportive of implicit positive youth development. Therefore, the primary aim of the present study was to examine the impact of peer, coach, and parental relationships on youth sport experiences and youth athletes’ perceptions of developmental skills gained, thereby piloting a test of Holt and colleagues’ (2017) grounded theory model. Should these relationships be predictive of positive youth development, it could be expected that athletes who experience positive changes to these relationships (e.g., increased peer cohesion, increased parental involvement) across a sport season should also receive increased benefit from their participation compared to athletes whose relationships did not improve. As such, we hypothesized that athletes’ perceptions of positive outcomes obtained through sport participation (e.g., social skills, goal setting skills) would be predicted by positive changes to the ratings of their peer relationships, coach-athlete relationships, and parental involvement across a sport season.
METHODS
Participants
Participants included 67 youth athletes from a competitive soccer club in the northwest region of the United States. In total, 41 athletes (
age
= 11.85) completed data collection at both time points. Participants represented 13 teams from four separate age categories. Additionally, 65.9% of the athletes identified as white and 61.0% of the athletes identified as boys.
Measures
Coach-Athlete Relationship Questionnaire (CART-Q)
To measure athlete perceptions of their relationship with their coach, the Coach-Athlete Relationship Questionnaire (CART-Q; Jowett & Ntoumanis, 2004) was utilized. The 11-item scale measured the nature of the athlete’s relationship with their coach (a = 0.97). Using a seven-point Likert scale, athletes rated their agreement with statements such as, “I trust my coach.”
Youth Sport Environment Questionnaire (YSEQ)
Athletes’ perceptions of their relationship with teammates were measured utilizing the Youth Sport Environment Questionnaire (YSEQ; Eys et al., 2009). The scale, which has been shown to be both valid and reliable, measured group cohesion and peer relationship quality. The YSEQ contains 16 statements, such as, “I am happy with my team’s level of desire to win” (a = 0.93). Athletes rated their agreement with these statements utilizing a seven-point Likert scale.
Parental Involvement in Sport Questionnaire (PISQ)
The Parental Involvement in Sport Questionnaire (PISQ; Lee & MacLean, 1997) is a valid and reliable 19-item scale (a = 0.87), which captures athletes’ perceptions of parental involvement across three subscales: directive behavior, praise and understanding, and active involvement. Utilizing a five-point Likert scale, athletes rated their level of agreement with statements such as, “Do your parents push you to practice harder?”
Youth Experience Survey for Sport (YES-S)
Employed only at the second time point, the short form Youth Experience Survey for Sport (YES-S; MacDonald et al., 2012; Sullivan et al., 2015) is 16-item scale that measured the perceptions of athletes’ experiences participating in sport across the previous season, and was utilized in the present study to operationalize PYD. The scale measures whether athletes perceived any benefit to their participation across four subscales: personal and social skills (a = 0.78), cognitive skills (a = 0.78), goal setting (a = 0.81), and initiative (a = 0.71). Athletes rated their agreement with statements such as, “I learned to push myself” on a five-point Likert scale.
Procedure
Ahead of the start of the summer season, the first author attended the club’s tryouts and parent meetings to share information about the study and recruit participants. During this time, parental consent was obtained through the completion of a written consent form and household demographic survey. The first survey was completed electronically one month into the summer season.  Subsequently, 14 weeks later, the research team returned to conduct the second survey during the final week of the fall season. At both time points, the surveys collected demographic information, athlete perceptions of relationships with their coach, peer cohesion, and parental involvement. At the second time point, the survey collected measurements of athletes’ perceptions of their experiences playing sport across the previous season, particularly focused on skills gained.
The dataset contained 0.3% missingness, and results of an MCAR test were not significant (
(1386) = 0.00,
= 1.00), suggesting data was missing at random. For cases with missingness, scales were prorated based on completed items. Descriptive statistics were calculated for each scale and notable demographic differences are reported in Table 1. For each of the relationship variables (i.e., CART-Q, PISQ, YSEQ), a difference score was calculated (M
T2
– M
T1
) to measure changes in these relationships across the season. While the utilization of difference scores has been criticized for its negative, summative impact on reliability (Edwards, 1994), researchers have noted that difference scores can be an appropriate choice in research, particularly for nonrandomized, theory-driven analyses (Castro-Schilo & Grimm, 2018). Assumptions testing revealed issues regarding multicollinearity as there was a high correlation between coach-athlete relationship and the peer cohesion change scores (
= 0.801), which resulted in unstable beta coefficients. This instability indicated that the presence of the peer cohesion variable in the model was distorting the estimation of other predictors, undermining the reliability and interpretability of the model. As such, the peer cohesion variable was removed from primary analyses. Following this, we regressed the four subscales of the YES-S (i.e., personal and social skills, cognitive skills, goal setting skills, initiative) on changes in relationship quality across the season, while controlling for age, race, and gender.
Table 1
Sample Characteristics and Descriptive Statistics
CART-Q
YSEQ
PISQ
YES-S Social Skills
YES-S Cog. Skills
YES-S Goal Setting
YES-S Initiative
Variable
T1 –
M(SD)
T2 –
M(SD)
T1 –
M(SD)
T2 –
M(SD)
T1 –
M(SD)
T2 –
M(SD)
T2 –
M(SD)
T2 –
M(SD)
T2 –
M(SD)
T2 –
M(SD)
Age
10
7.3
5.61(1.24)*
5.97(1.47)*
4.25(2.01)*
5.08(1.98)*
2.39(0.18)
2.91(0.45)
3.58(0.52)
3.67(0.58)
4.25(0.58)
4.58(0.52)
11
22.0
5.46(1.73)
6.36(0.39)
4.74(1.52)
5.53(0.83)
3.02(0.60)
3.13(0.52)
4.00(0.60)
3.69(1.05)
4.00(0.85)
4.50(0.45)
12
20
48.8
6.10(0.40)
5.96(0.85)
5.10(0.75)
5.30(0.91)*
2.92(0.60)
3.25(0.74)*
4.17(0.75)
3.53(1.16)
3.93(0.90)
4.25(0.59)
13
22.0
5.71(1.04)*
5.15(1.26)*
4.69(1.40)
4.89(1.18)
3.16(0.69)
3.30(0.58)
4.03(0.57)
3.56(0.69)
4.25(0.57)
4.43(0.66)
Gender
Boy
25
61.0
6.03(0.61)
6.17(0.69)
4.91(1.03)*
5.26(0.89)*
2.92(0.62)*
3.24(0.66)*
4.12(0.66)
3.72(0.85)
4.11(0.71)
4.43(0.41)
Girl
16
39.0
5.56(1.43)
5.41(0.99)
4.81(1.42)
5.22(1.25)
3.01(0.62)
3.17(0.62)
3.96(0.89)
3.35(1.19)
3.90(0.94)
4.27(0.76)
Race
White
27
65.9
5.77(1.13)
5.97(0.83)
4.75(1.21)*
5.24(1.03)*
2.95(0.61)
3.14(0.57)
4.07(0.69)
3.52(1.04)
3.99(0.85)
4.43(0.54)
Black
2.4
Asian
9.8
5.50(1.38)
5.41(1.85)
4.77(1.85)*
5.30(1.64)*
2.74(0.90)
3.29(0.90)
4.00(0.35)
3.94(0.43)
3.94(0.43)
3.94(0.66)
Hispanic
9.8
6.27(0.45)
5.86(1.12)
5.50(0.89)
5.55(0.74)
2.99(0.57)
3.41(0.83)
4.50(0.41)
4.25(0.54)
4.69(0.47)
4.63(0.32)
Other
12.2
6.13(0.31)
5.65(1.20)
5.05(0.83)
4.99(1.04)
2.99(0.45)
3.31(0.52)
3.80(0.89)
3.15(1.29)
3.80(0.94)
4.15(0.74)
Total
41
100.0
5.84(1.02)
5.87(0.99)
4.87(1.18)*
5.24(1.03)*
2.96(0.62)*
3.21(0.64)*
4.06(0.67)
3.58(1.00)
4.03(0.80)
4.37(0.57)
Notes
= 41; CART-Q = Coach-Athlete Relationship; PISQ = Parental Involvement; YSEQ = Ratings of Peer Cohesion; YES-S = Perceptions of Developmental Experiences, *Difference is significant between time points;
Difference is significant between groups.
RESULTS
The model examining personal and social skills was significant and explained 45.4% of variance in the outcome (
= 0.454,
(5,34) = 5.664,
< 0.001).
Regression Results for Perceptions of Social Skills Gained by Athletes
95% CI
Variable
SE
LL
UL
Intercept
0.774
1.268
-1.802
3.350
0.546
Gender
-0.129
-0.175
0.184
-0.550
0.199
0.348
Age
0.369
0.291
0.110
0.067
0.515
0.012
Race
-0.024
-0.008
0.042
-0.094
0.078
0.858
DCART-Q
0.482
0.250
0.074
0.099
0.400
0.002
DPISQ
0.326
0.382
0.160
0.580
0.707
0.022
Notes
= 41;
= 0.454,
(5,34) = 5.664,
< 0.001; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement.
**
When ran independently due to the existing multicollinearity, change to peer cohesion was also a significant predictor of personal and social skills (
= 0.317,
(4,35) = 4.063,
= 0.008).
Within this model, both changes to the coach-athlete relationships (b= 0.482,
= 0.002) and changes to parental involvement (b= 0.326,
= 0.022) across the season were significant predictors of personal and social skills. Additionally, the covariate age was also a significant predictor of personal and social skills (b
0.369,
= 0.012). The model examining cognitive skills explained 25.1% of the variance, however was only marginally significant (
= 0.251,
(5,34) = 2.275,
= 0.069). Within this model the change in coach-athlete relationship was a statistically significant predictor (b= 0.403,
= 0.022), whereas changes to parental involvement was not (b= 0.158,
= 0.330).
Table 3
Regression Results for Perceptions of Cognitive Skills Gained by Athletes
95% CI
Variable
SE
LL
UL
Intercept
2.048
2.221
-2.465
6.561
0.363
Gender
-0.155
-0.315
0.323
-0.972
0.342
0.337
Age
0.143
0.169
0.193
-0.224
0.561
0.389
Race
-0.066
-0.032
0.074
-0.182
0.119
0.670
DCART-Q
0.403
0.312
0.130
0.048
0.576
0.022
DPISQ
0.158
0.277
0.280
-0.292
0.845
0.330
Notes
= 41;
= 0.251,
(5,34) = 2.275,
= 0.069; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement.
** When ran independently due to the existing multicollinearity, change to peer cohesion was not a significant predictor of cognitive skills.
The models predicting goal setting skills (
= 0.183,
(5,34) = 1.528,
= 0.207) and initiative (
= 0.185,
(5,34) = 1.542,
= 0.203) were not statistically significant.
DISCUSSION
The present study provides partial support to Holt and colleague’s (2017) proposition that there is an implicit pathway of PYD in sport that takes place through positive relationships. In particular, changes to the coach-athlete relationship significantly predicted youth athletes’ perceptions of social skills and cognitive skills gained; and changes to perceptions of parental involvement also predicted social skills gained. Additionally, when analyzed separately due to issues of multicollinearity, changes to peer cohesion also significantly predicted social skill perceptions. As such, data in the current study reinforce the importance of relationships within the sport environment, and extend previous research by highlighting their value to the specific area of PYD through sport.
While research has shown the coach-athlete relationship to be associated with motivation (Adie & Jowett, 2010), collective-efficacy (Hampson & Jowett, 2014), and team cohesion (Turman, 2003), its role in the social and cognitive development of athletes is less understood. That said, research has shown that coaches seem to intuitively understand the developmental value of a positive coach-athlete relationship as coaches have reported a positive relationship with their athletes led to social and emotional development and resilience (White & Bennie, 2015). Furthermore, Davis and colleagues (2019) proposed a bidirectional relationship between communication skills and the coach-athlete relationship, where communication skills not only helped to improve the relationship, but also improved as a product of a high-quality coach-athlete relationship. When examining the more expansive literature on the impact of a high-quality relationships, researchers have documents that teacher-student relationships can promote cognitive development (Davis, 2003) and social adjustment (Dong et al., 2021) through positive and trusting learning environments. Data in the current study suggest coaches hold a responsibility to ensure the development and sustainment of positive relationships in the sport environment to support similarly positive developmental outcomes for youth athletes. This is particularly important as social skills have been shown to be associated with academic performance (Sung & Chang, 2010), increased mental health (Greenberg et al., 2003), wellbeing (Sancassiani et al., 2015), and self-esteem (Riggio et al., 1990).
The present study also highlights the important yet specific role that parents play in positive youth development through sport. Parental styles have been shown to be associated with social skill development; youth with democratic and permissive parents have been shown to score higher on social skills measures than those with neglectful or authoritative parents (Salavera et al., 2022). As such, it could be hypothesized that parents with more developmentally supportive parenting styles are more likely to be involved in their child’s sport and supportive of their child’s social skills. That said, data in the current study suggests the need to delineate the roles of parents and coaches, as these relationships may provide different benefits for youth. For example, Knight and colleagues (2011) reported that athletes consistently prefer parents to fill a supportive and encouraging role, as opposed to a coaching role. This is supported by data in the current study in that while change to parental involvement predicted athletes’ perceptions of social skill development, it did not predict their cognitive skill perceptions.
Finally, it is important to note that girls rated their relationship with their coach significantly lower than their peers who identified as boys; and older athletes were also significantly less likely to rate their coach-relationships higher than younger athletes. As such, should there exist any developmental benefit to high-quality, coaching relationships, the present findings would suggest that girls and older youth athletes are less likely to receive those benefits. Given that a positive coach-athlete relationship can be protective from poor mental health outcomes for girl athletes specifically (Massey et al., 2024), it is important that positive coach-athlete relationships are prioritized for female athletes, particularly adolescent female athletes. Furthermore, it is generally accepted that as athletes get older, the sporting environment shifts from a focus on fun to a focus on competition. Be that as it may, research has shown that the true shift lies within how athletes are treated; Kipp and Bolter (2020) found that while both older and younger athletes equally perceived their sporting environments to be focused on effort and learning, older athletes were more likely to report being punished or disciplined for mistakes. It is possible that such climates explain the decreasing trend of the coach-athlete relationship observed in the present study. Speaking strictly to the proposed developmental role of the coach-athlete relationship within sport, the present findings would offer that sports become less beneficial and developmentally supportive over time.
Despite the present study’s value to the literature base on PYD through sport, its small, homogenous sample limits its generalizability. In addition to being predominantly white, the sample derived from a singular, pay-to-play soccer organization within an affluent community. Additionally, the present sample predominantly identified as boys, which may parallel youth sport participation trends, but limits the generalizability of the findings to non-boy athlete populations. The age rage of the sample was also limited, clustered into the soccer organizations U11 and U13 age groupings, and as such, the findings may be in part reflective of the natural development occurring in this age range.
Furthermore, most athletes in the present study were satisfied with their relationship with their coach and peers, and the mean parental involvement score was slightly above the midpoint of the scale. Depending on sport or community context, it is possible that more athletes would report more dissatisfaction with these relationships or less parental involvement, thereby affecting the nature of the findings. With respect to age and gender differences, it is possible that these differences could be explained by confounding variables, such as coach gender, competition level, or position, which could not be differentiated in the present study due to the small sample size. Lastly, while multicollinearity necessitated the removal of the peer cohesion variable from the analyses, it should be acknowledged that doing so also limits the completeness of the model by excluding a theoretically important dimension of the sport environment, and one which should continue to be examined in this line of research.  As such, future studies should not only continue to examine the nuanced roles of parents and coaches in sport-based PYD, but also peer relationships, and doing so in larger and more diverse samples.
CONCLUSION
The social context of the sport environment, which includes coaches, parents, and peers, plays a significant role in shaping athletes’ perceived development through sport. In the present study, athletes’ perceived social skill development was significantly predicted by positive changes to the coach-athlete relationship and parental involvement. The quality of the coach-athlete relationship also emerged as a meaningful predictor of athletes’ perceived cognitive development, highlighting the broader developmental impact of adult figures in the sport context. Furthermore, while peer cohesion was omitted in analyses due to multicollinearity, its interconnectedness with the coach-athlete relationship should be acknowledged, and researchers should continue to utilize it as a variable of interest as theory would dictate. Taken together, these findings underscore the importance of considering the full network of sport-based relationships when seeking to support athletes’ development through sport participation.
APPLICATIONS IN SPORT
In addition to providing support for Holt and colleagues’ (2017) theory of implicit PYD through sport, the present study highlights the interconnected nature of youth sport’s social context. We offer the following recommendations to stakeholders seeking to utilize these findings to develop their youth sport organization’s PYD climate:
Provide coaches with education and training that supports their development of communication and relationship-building skills (see Barnett et al., 1992; Jowett & Cockerill, 2003).
Provide education and clear expectations for parents’ involvement in the organization, as well as opportunities for involvement (see Knight et al., 2011).
Prioritize relationship building and psychological safety at the outset of the season, to include team-building activities and the development of team norms, rituals, and goals (see Carron et al., 1997; Senécal et al., 2008).
REFERENCES
1. Adie, J. W., & Jowett, S. (2010). Meta‐perceptions of the coach–athlete relationship, achievement goals, and intrinsic motivation among sport participants.
Journal of Applied Social Psychology
40
(11), 2750-2773.
2. Barnett, N. P., Smoll, F. L., & Smith, R. E. (1992). Effects of enhancing coach-athlete relationships on youth sport attrition.
The Sport Psychologist, 6
(2), 111-127.
3. Benson, P. L., Scales, P. C., Hamilton, S. F., & Sesma Jr., A. (2006). Positive youth development so far: Core hypotheses and their implications for policy and practice.
Search Institute Insights & Evidence 3
(1), 1-13.
4. Bruner, M. W., McLaren, C. D., Sutcliffe, J. T., Gardner, L. A., Lubans, D. R., Smith, J. J., & Vella, S. A. (2021). The effect of sport-based interventions on positive youth development: A systematic review and meta-analysis.
International Review of Sport and Exercise Psychology
16
(1), 368-395.
5. Burns, L., Weissensteiner, J. R., & Cohen, M. (2019). Supportive interpersonal relationships: a key component to high-performance sport.
British Journal of Sports Medicine
53
(22), 1386-1389.
6. Carron, A. V., Colman, M. M., Wheeler, J., & Stevens, D. (2002). Cohesion and performance in sport: A meta analysis.
Journal of Sport and Exercise Psychology, 24
(2), 168–188. https://doi.org/10.1123/jsep.24.2.168
7. Carron, A. V., Spink, K. S., & Prapavessis, H. (1997). Team building and cohesiveness in the sport and exercise setting: Use of indirect interventions.
Journal of Applied Sport Psychology, 9(
1), 61–72. https://doi.org/10.1080/10413209708415384
8. Castro-Schilo, L., & Grimm, K. J. (2018). Using residualized change versus difference scores for longitudinal research.
Journal of Social and Personal Relationships, 35
(1), 32-58.
9. Catalano, R. F., Toumbourou, J. W., & Hawkins, J. D. (2008). Positive youth development in the United States: History, efficacy, and links to moral and character education. In L. Nucci & T. Krettenauer (Eds.)
Handbook of moral and character education
. Routledge.
10. Christiansen, E. D., & von Eye, A. (2005). Positive youth development, participation in community youth development programs, and community contributions of fifth-grade adolescents: Findings from the first wave of the 4-H study of positive youth development.
The Journal of Early Adolescence
25
(1), 17-71.
10. Clonan, S. M., Chafouleas, S. M., McDougal, J. L., & Riley-Tillman, T. C. (2004). Positive psychology goes to school: Are we there yet?
Psychology in the Schools, 41
(1), 101–110.
11. Danioni, F., Barni, D., & Rosnati, R. (2017). Transmitting sport values: The importance of parental involvement in children’s sport activity.
Europe’s Journal of Psychology, 13
(1), 75–92.
12. Davis, H. A. (2003). Conceptualizing the role and influence of student-teacher relationships on children’s social and cognitive development.
Educational Psychologist
38
(4), 207-234.
13. Davis, L., & Jowett, S. (2014). Coach–athlete attachment and the quality of the coach–athlete relationship: Implications for athlete’s well-being.
Journal of Sports Sciences, 32
(15), 1454–1464.
14. Davis, L., Jowett, S., & Tafvelin, S. (2019). Communication strategies: The fuel for quality coach-athlete relationships and athlete satisfaction.
Frontiers in Psychology
10
, 2156.
15. Dong, Y., Wang, H., Luan, F., Li, Z., & Cheng, L. (2021). How children feel matters: Teacher–student relationship as an indirect role between interpersonal trust and social adjustment.
Frontiers in Psychology
11
, 581235.
16. Dorsch, T. E., Wright, E., Eckardt, V. C., Elliott, S., Thrower, S. N., & Knight, C. J. (2021). A history of parent involvement in organized youth sport: A scoping review.
Sport, Exercise, and Performance Psychology
10
(4), 536.
17. Edwards, J. R. (1994). Regression analysis as an alternative to difference scores.
Journal of Management, 20
(3), 683-689.
18. Erikson, E. H. (1950).
Childhood and society.
W. W. Norton & Co.
19. Erikstad, M. K., Martin, L. J., Haugen, T., & Høigaard, R. (2018). Group cohesion, needs satisfaction, and self-regulated learning: A one-year prospective study of elite youth soccer players’ perceptions of their club team.
Psychology of Sport and Exercise, 39
, 171–178.
20. Eys, M., Loughead, T., Bray, S. R., & Carron, A. V. (2009). Development of a cohesion questionnaire for youth: The Youth Sport Environment Questionnaire.
Journal of Sport and Exercise Psychology
31
(3), 390-408.
21. Felber Charbonneau, E., & Camiré, M. (2020). Parental involvement in sport and the satisfaction of basic psychological needs: Perspectives from parent–child dyads.
International Journal of Sport and Exercise Psychology, 18
(5), 655–671.
22. Filho, E., Dobersek, U., Gershgoren, L., Becker, B., & Tenenbaum, G. (2014). The cohesion–performance relationship in sport: A 10-year retrospective meta-analysis.
Sport Sciences for Health, 10
(3), 165–177.
23. Greenberg, M. T., Weissberg, R. P., O’Brien, M. U., Zins, J. E., Fredericks, L., Resnik, H., & Elias, M. J. (2003). Enhancing school-based prevention and youth development through coordinated social, emotional, and academic learning.
American Psychologist
58
(6-7), 466.
24. Hampson, R., & Jowett, S. (2014). Effects of coach leadership and coach–athlete relationship on collective efficacy.
Scandinavian Journal of Medicine & Science in Sports
24
(2), 454-460.
25. Holt, N. L., Neely, K. C., Slater, L. G., Camiré, M., Côté, J., Fraser-Thomas, J., MacDonald, D., Strachan, L., & Tamminen, K. A. (2017). A grounded theory of positive youth development through sport based on results from a qualitative meta-study.
International Review of Sport and Exercise Psychology
10
(1), 1-49.
26. Jones, M. I., Dunn, J. G., Holt, N. L., Sullivan, P. J., & Bloom, G. A. (2011). Exploring the ‘5Cs’ of positive youth development in sport.
Journal of Sport Behavior
34
(3).
27. Jowett, S. (2017). Coaching effectiveness: The coach–athlete relationship at its heart.
Current Opinion in Psychology, 16,
154–158. https://doi.org/10.1016/j.copsyc.2017.05.006
28. Jowett, S., & Cockerill, I. M. (2003). Olympic medalists’ perspective of the athlete–coach relationship.
Psychology of Sport and Exercise, 4
(4), 313–331.
29. Jowett, S., & Ntoumanis, N. (2004). The Coach–Athlete Relationship Questionnaire (CART‐Q): Development and initial validation.
Scandinavian Journal of Medicine & Science in Sports
14
(4), 245-257.
30. Kipp, L. E., & Bolter, N. D. (2020). Motivational climate, psychological needs, and personal and social responsibility in youth soccer: Comparisons by age group and competitive level.
Psychology of Sport and Exercise, 51.
31. Knight, C. J., Neely, K. C., & Holt, N. L. (2011). Parental behaviors in team sports: How do female athletes want parents to behave?
Journal of Applied Sport Psychology
23
(1), 76-92.
32. Larson, R. W. (2000). Toward a psychology of positive youth development.
American Psychologist, 55
(1), 170–183. https://doi.org/10.1037/0003-066X.55.1.170
33. Lee, M., & MacLean, S. (1997). Sources of parental pressure among age group swimmers.
European Journal of Physical Education
(2), 167-177.
34. Lerner, R. M. (2005).
Promoting positive youth development: Theoretical and empirical bases.
Washington, DC: National Academies of Sciences.
35. Lerner, R. M., Almerigi, J. B., Theokas, C., & Lerner, J. V. (2005). Positive youth development A view of the issues.
The Journal of Early Adolescence, 25
(1), 10–16.
36. Lerner, R. M., Lerner, J. V., Almerigi, J. B., Theokas, C., Phelps, E., Gestsdottir, S., Naudeau, S., Jelicic, H., Alberts, A., Ma, L., Smith, L. M., Bobek, D. L., Richman-Raphael, D., Simpson, I., 37.
37. Lerner, R. M., Lerner, J. V., Lewin-Bizan, S., Bowers, E. P., Boyd, M. J., Mueller, M. K., Schmid, K. L., & Napolitano, C. M. (2011). Positive youth development: Processes, orograms, and problematics.
Journal of Youth Development, 6
(3), 38–62.
38. MacDonald, D. J., Côté, J., Eys, M., & Deakin, J. (2012). Psychometric properties of the youth experience survey with young athletes.
Psychology of Sport and Exercise
13
(3), 332-340.
39. Massey, W. V., Veliz, P. T., Zarrett, N., & Farello, A. (2024).
Thriving through sport: The transformative impact on girls’ mental health.
Women’s Sport Foundation.
40. McBride, A. M., Johnson, E., Olate, R., & O’Hara, K. (2011). Youth volunteer service as positive youth development in Latin America and the Caribbean.
Children and Youth Services Review, 33
(1), 34–41.
41. Nikolina, K., & Đorić, V. (2023). Relations of coach-athlete relationship quality and athlete psychosocial functioning: A systematic review.
SportLogia
19
(1).
42. Riggio, R. E., Throckmorton, B., & Depaola, S. (1990). Social skills and self-esteem.
Personality and Individual Differences
11
(8), 799-804.
43. Salavera, C., Usán, P., & Quilez-Robres, A. (2022). Exploring the effect of parental styles on social skills: The mediating role of affects.
International Journal of Environmental Research and Public Health
19
(6), 3295.
44. Sancassiani, F., Pintus, E., Holte, A., Paulus, P., Moro, M. F., Cossu, G., Angermeyer, M. C., Carta, M. G., & Lindert, J. (2015). Enhancing the emotional and social skills of the youth to promote their wellbeing and positive development: A systematic review of universal school-based randomized controlled trials [Special issue].
Clinical Practice and Epidemiology in Mental Health,
11
, 21-40.
45. Senécal, J., Loughead, T. M., & Bloom, G. A. (2008). A season-long team-building intervention: Examining the effect of team goal setting on cohesion
. Journal of Sport and Exercise Psychology, 30
(2), 186-199.
46. Sheridan, D., Coffee, P., & Lavallee, D. (2014). A systematic review of social support in youth sport.
International Review of Sport and Exercise Psychology
(1), 198-228.
47. Sibthorp, J., & Morgan, C. (2011). Adventure‐based programming: Exemplary youth development practice.
New Directions for Youth Development,
(130), 105-119.
48. Smith, A. L., & Ullrich-French, S. (2020). Peers and the sport experience. In G. Tenenbaum & R. C. Eklund (Eds.)
Handbook of sport psychology
(pp. 410–428). John Wiley & Sons, Inc.
49. Storm, M. K., Williams, K. N., Shetter, E. M., Kaminsky, J., Lowery, C. M., Caldas, S. V., & Winch, P. J. (2017). Sink or swim: Promoting youth development through aquatics programs in Baltimore, Maryland.
Journal of Park & Recreation Administration, 35
(1), 66-79.
50. Sullivan, P. J., LaForge-MacKenzie, K., & Marini, M. (2015). Confirmatory factor analysis of the Youth Experiences Survey for Sport (YES-S).
Open Journal of Statistics
(05), 421.
51. Sung, Y. Y., & Chang, M. (2010). Which social skills predict academic performance of elementary school students.
Journal on Educational Psychology
(3), 23-34.
52. Turman, P. D. (2003). Coaches and cohesion: The impact of coaching techniques on team cohesion in the small group sport setting.
Journal of Sport Behaviour
26
(1), 86-104.
53. White, R. L., & Bennie, A. (2015). Resilience in youth sport: A qualitative investigation of gymnastics coach and athlete perceptions.
International Journal of Sports Science & Coaching
10
(2-3), 379-393.
54. Whitley, M. A., Massey, W. V., Camiré, M., Boutet, M., & Borbee, A. (2019). Sport-based youth development interventions in the United States: A systematic review.
BMC Public Health
19
, 1-20.
55. Worker, S. M., Iaccopucci, A. M., Bird, M., & Horowitz, M. (2019). Promoting positive youth development through teenagers-as-teachers programs.
Journal of Adolescent Research, 34
(1), 30-54.
Appendix A
Supplemental Materials
Table 4
Correlation Matrix of Study Variables
Variables
1. Age
2. CART-Q
-0.34*
3. PISQ
0.15
0.23
4. YSEQ
-0.14
0.66**
0.31*
5. Social Skills
0.14
0.62**
0.35*
0.47**
6. Cognitive Skills
-0.05
0.40*
0.16
0.16
0.66**
7. Goal Setting
0.03
0.43**
0.13
0.42**
0.57**
0.70**
8. Initiative
-0.10
0.53**
0.18
0.47**
0.51**
0.40*
0.70**
Notes.
* Correlation is significant at the 0.05 level (two-tailed). ** Correlation is significant at the 0.01 level (two-tailed); CART-Q = Coach-Athlete Relationship; PISQ = Parental Involvement, YSEQ = Peer Relationships
Table 5
Regression Results for Perceptions of Goal Setting Skills Gained by Athletes
95% CI for B
Variable
SE
LL
UL
Intercept
2.053
1.862
-1.731
5.836
0.278
Gender
-0.228
0.271
-0.779
0.322
-0.140
0.405
Age
0.186
0.162
-0.143
0.515
0.196
0.259
Race
0.011
0.062
-0.115
0.137
0.028
0.863
DCART-Q
0.230
0.109
0.008
0.451
0.369
0.042
DPISQ
0.175
0.235
-0.302
0.651
0.124
0.462
Notes
= 0.183,
= 0.207; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement
** When ran independently due to the existing multicollinearity, change to peer cohesion was not a significant predictor of cognitive skills.
Table 6
Regression Results for Perceptions of Initiative Gained by Athletes
95% CI for B
Variable
SE
LL
UL
Intercept
4.000
1.315
1.328
6.671
-0.120
3.043
Gender
-0.138
0.191
-0.527
0.250
0.062
-0.723
Age
0.042
0.114
-0.191
0.274
0.035
0.365
Race
0.010
0.044
-0.079
0.099
0.389
0.221
DCART-Q
0.171
0.077
0.015
0.327
0.087
2.224
DPISQ
0.086
0.166
-0.251
0.423
-0.120
0.520
Notes
= 0.185,
= 0.203; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement
** When ran independently due to the existing multicollinearity, change to peer cohesion was not a significant predictor of cognitive skills.
Troy Palmer
2025-09-30T16:08:05-05:00
Positional Differences and Workload Requirements of a 3-5-2 Formation in Women’s Soccer
Troy Palmer
2025-09-30T16:08:05-05:00
March 4th, 2026
Research
Sport Education
Sport Training
Sports Coaching
Women and Sports
Authors
Asher L. Flynn
, Joanne Spalding
, and Sellena Dixon
Department of Sport & Exercise Science, Lincoln Memorial University, Harrogate, TN, USA
Department of Health Sciences, Georgia College & State University, Milledgeville, GA, USA
Athletics Department, Lincoln Memorial University, Harrogate, TN, USA
Corresponding Author:
Asher L. Flynn, PhD, CSCS
6965 Cumberland Gap Parkway
[email protected]
423.869.6828
Asher L Flynn, PhD, CSCS is an Assistant Professor of Sport and Exercise Science at Lincoln Memorial University, TN. His research interests focus on fatigue and athlete monitoring in colligate athletes, and aspects of women’s soccer performance.
Joanne Spalding, PhD, is an Assistant Professor in Exercise Science at Georgia College & State University, GA. Her research interests include athlete monitoring and long term athlete development in female athletes.
Sellena Dixon, BS, is the graduate assistant for the women’s soccer team at Lincoln Memorial University, TN.
ABSTRACT
The purpose of this research was to investigated the match demands of 24 female soccer players over one season (15 conference matches) playing in a 3-5-2 formation to determine the match demands and work rates of each position. In order to determine formation specific workload, positions were grouped as center-backs (CB), wingbacks (WB), midfielders (MF), and forwards (FW). Velocity bands used to compare distances and work rates were total distance (TD), 2-3 m/s, 3-4 m/s, 4-5 m/s, 5-6 m/s, and >6 m/s. Results revealed that MF covered significantly more total distance (TD; 10743 ±1520 m) and distance between 2-3 m/s (3444 ± 423 m) than all other positions (CB TD: 8549 ± 1106 m, 2-3: 2497 ± 276 m; WB TD: 8860 ± 1216 m, 2-3: 2324 ± 344 m; FW TD: 8069 ± 1286 m) and greater distance between 3-4 m/s (2515 ± 382m) compared to CB (1574 ± 214 m) and FW (1270 ± 281 m), but not WB (1814 ± 258 m). There were no significant differences between any of the higher velocity bands (>4 m/s). These results can be useful for the coaching staff as descriptive data for the expected distances covered and work rates of Division 2 Women’s soccer teams playing a 3-5-2 formation. These data can be used to determine practice plans in season and for off-season training plans, to prepare athletes for reporting for preseason in appropriate condition.
KEYWORDS:
GPS, Sport Science, Athlete Monitoring, Fitness, NCAA
Abbreviations:
CB: Center-back, WB: Wingback, MF: Midfield, FW: Forward
GPS: Global Positioning System, MD: Match Day, vVO
2max
: Velocity at VO
2max
INTRODUCTION
There is a growing interest in the physical demands of women’s soccer, but the majority of research has focused on the top levels of competition (International and Professional; 23). Although this information is important, it is likely to have limited use for lower levels of competition. It has been reported that “top class” females perform more high intensity running compared to “high-level” females (16, 17, 19). Currently, there is a lack of research investigating match demands at other levels of women’s soccer, especially at lower levels of collegiate soccer in the USA.
The majority of the literature investigating the demands of lower levels of women’s soccer has focused on the match demands of NCAA Division I (2, 5, 12, 20, 21), two articles focused on NCAA Division II athletes (9, 11), and one article focused on NCAA DIII (22), with no studies investigating the match demands of women’s NAIA soccer. Gentles et al (2018) observed that NCAA DII female players covered approximately 5480 m in 45 minutes of match play, while NCAA Division I players covered between 9486m and 9930 m in 90 minutes (20, 22). With little evidence exploring the activity profiles of the lower divisions of collegiate women’s soccer, further investigation is needed.
Many of the studies to date, at any level, have been conducted using only a few matches (3, 17), which, due to the high standard deviations (approximately 10 – 40%; 3, 11, 13, 20, 21-23), raise the question of whether a small number of matches truly reflects the average match demands. Previous studies have also shown differences in match demands based on position (20, 21). On average, defenders covered less distance than midfielders and attackers (forwards), and forwards covered more distance than midfielders (20, 21). Another complication when extrapolating information from current research is that the formation type may also alter the match demands (4, 6, 7). These limitations can lead to complications when inferring information from current research for use in a practical setting with different teams.
Factors that alter expected match demands, level of play and formation used, imply that it would be improper for the coaching staff to use data from a different level of play and unknown formation to determine the expected demands for their specific situation; that is, a high school coach should not be implementing a training plan based on data from college, professional, or international level teams. As such, the purpose of this research was to observe the match demands (distances and work rates) of an NCAA Division II women’s soccer team playing in a 3-5-2 formation, and to determine if there are significant differences in distances covered and/or work rates between positions.
METHODS
Participants
Retrospective data from 24 field players (age: 19.90 ± 1.56 years old; height: 163.43 ± 6.18 cm, weight: 59.35 ± 7.20 kg,) on the same team were included in this study. A total of 194 observations were included in the analysis (center back (CB),
= 49; wingback (WB),
= 36; midfield (MF),
= 61; and forward (FW),
= 48). This study was approved by the institutional review board of Lincoln Memorial University.
Procedures
Match data from an NCAA division II women’s soccer team playing a 3-5-2 formation were collected during a single competitive season. Only conference regular season matches, conference tournament, and national tournament matches were included, with 15 matches used for analysis.
Global Positioning System (GPS) devices (TITAN sports, Houston, TX, USA), sampling at 10 Hz, were used to track player movement duringcompetition. GPS units were activated at least 20-minutes prior to kick-off and were worn in a provided chest halter that secured the device between the shoulder blades under their game uniform. GPS devices were provided to all athletes (starters and substitutes) during the 10-minute window after the team warm-up and before the start of the match, and were collected again after the final whistle. All data from the duration of the match (substitute warm-up and half-time warm-up) were included in the analysis.
For positional analysis, distances accumulated by all athletes who played in a specific position for a match were summed and then divided by the number of positions in the formation. For example, in the forward position, if one athlete was substituted for a portion of the match, the total distance covered by all three athletes playing in the forward position was summed and then divided by two for the two forward positions in the 3-5-2 system. For the work rate analysis, meters covered per minute at each threshold (distances divided by total match time) were averaged for all players in that position (20). Due to the different substitution rules in college soccer, this analysis was deemed optimal to determine the distances and work rate of each position, instead of specific players. Variables of interest included total distance and distances covered in velocity thresholds; 2 – 3 m/s (7.2 – 10.8 km·h
-1
), 3 – 4 m/s (10.8 – 14.4 km·h
-1
), 4 – 5 m/s (14.4 – 18.0 km·h
-1
), 5 – 6 m/s (18 – 21.6 km·h
-1
), and >6 m/s (>21.6 km·h
-1
).
All matches analyzed were official NCAA matches consisting of two 45-minute halves with a 15-minute half-time period, with a maximum of two 10-minute extra-time periods with a 2-minute intermission, with extra time being stopped in the event of a goal if the competition was tied at the end of the normal 90-minute match.
Data Analyses
Two Repeated Measures ANOVA analyses with Bonferroni corrections were performed to determine whether there was a significant difference in distances and work rates for each position at each threshold (TD, 2 – 3 m/s, 3 – 4 m/s, 4 – 5 m/s, 5 – 6 m/s, and >6 m/s). Statistical analyses were performed using JASP (Version 0.16.2). The alpha level was set at 0.05.
RESULTS
The first RM-ANOVA revealed significantly higher distances (F(3.37, 62.82) = 11.96, p < 0.001) covered in the MF position compared to all other positions for TD and 2 – 3 m/s. Midfielders also covered significantly more distances than CBs and FWs, but not WBs, at 3 – 4 m/s. The only other significant difference observed was between the WB and FW positions in TD covered. There were no significant differences between any other positions at any other threshold. Descriptive statistics are provided in Table 1.
Table 1
Mean distance covered by position in each velocity zone (meters).
CB
WB
MF
FW
TD
8549 ± 1106 (6467 – 11066)
8860 ± 1216# (5443 – 10854)
10743 ± 1520* (7060 – 12864)
8069 ± 1286# (6204 – 10537)
7.2 – 10.8 km·h-1
2497 ± 276 (1827 – 2972)
2324 ± 344 (1389 – 2842)
3444 ± 423* (2180 – 3916)
2301 ± 400 (1668 – 2942)
10.8 – 14.4 km·h-1
1574 ± 214 ǂ (1191 – 1959)
1814 ± 258 (1068 – 2218)
2515 ± 382# ǂ (1771 – 3129)
1270 ± 281# (759 – 1688)
14.4 – 18.0 km·h-1
607 ± 113 (449 – 769)
878 ± 135 (639 – 1124)
1092 ± 211 (792 – 1453)
587 ± 109 (395 – 734)
18.0 – 21.6 km·h-1
246 ± 58 (155 – 342)
404 ± 86 (252 – 552)
381 ± 79 (241 – 533)
272 ± 60 (149 – 357)
>21.6 km·h-1
96 ± 35 (45 – 175)
200 ± 102 (68 – 426)
120 ± 55 (68 – 295)
180 ± 112 (45 – 530)
Note:
Data are presented as mean ± Standard Deviation (Range).
TD: Total distance, CB: Center back, WB: Wingback, MF: Midfield, FW: Forward
* = p < 0.05 compared to all other positions in that velocity range
# = p < 0.05 compared to the other indicated positions in that velocity range
ǂ = p < 0.05 compared to the other indicated positions in that velocity range
The RM-ANOVA performed to determine differences in work rates at each threshold revealed significantly higher work rates (F(3.47, 64.82) = 6.05, p < 0.001) over the entire match (TD) for the MF position compared to all other positions, and a significantly higher work rate from the MF position in the 3 – 4 m/s velocity range compared to the FW position. There were no other significant differences between any of the other positions at any other threshold. The results are presented in Table 2.
Table 2
Mean work rate by position in each velocity zone (meters per minute).
CB
WB
MF
FW
TD
96 ± 8 (88 – 123)
101 ± 7 (85 – 117)
119 ± 20 * (88 – 167)
101 ± 24 (72 – 156)
7.2 – 10.8 km·h
-1
28 ± 2 (25 – 33)
26 ± 3 (21 – 35)
36 ± 3 (29 – 40)
27 ± 5 (19 – 35)
10.8 – 14.4 km·h
-1
18 ± 2 (14 – 21)
21 ± 2 (18 – 26)
27 ± 3 # (21 – 32)
16 ± 4 # (8 – 21)
14.4 – 18.0 km·h
-1
7 ± 1 (5 – 10)
10 ± 1 (8 – 12)
12 ± 3 (9 – 19)
8 ± 3 (4 – 14)
18.0 – 21.6 km·h
-1
3 ± 1 (2 – 4)
5 ± 1 (4 – 6)
4 ± 1 (2 – 7)
4 ± 2 (2 – 9)
>21.6 km·h-1
1.3 ± 1 (1 – 3)
2.3 ± 1 (1 – 4)
1.3 ± 1 (1 – 3)
3.4 ± 4 (1 – 13)
Note.
Data are presented as mean ± Standard Deviation (Range).
TD: Total distance, CB: Center back, WB: Wingback, MF: Midfield, FW: Forward
* = p < 0.05 compared to all other positions in that velocity range
# = p < 0.05 compared to the other indicated positions in that velocity range
DISCUSSION
The purpose of this study was to examine the external demands of NCAA DII women’s college soccer playing in a 3-5-2 formation. The most interesting finding from these data was that the WB position was only significantly higher in total distance covered compared to the FW position (p = 0.044), but there were no other significant differences in distances or work rates compared to other positions. This was interesting, given that the WB position is commonly accepted as the most demanding position. Another interesting result was that the MF position had significantly higher distances at lower speeds (2-3 m/s, p < 0.001; 3-4 m/s, p < 0.001) only when compared to FW position. Other studies have reported that different positions have significantly different total distances covered in a match (1, 14). Abbot et al.(2018) reported that central midfielders covered the greatest distance (11,570 ± 469 m), followed by wide attackers (10,918 ± 353 m), wide defenders (10,747 ± 420 m), strikers (10,320 ± 420 m), and central defenders covering the least amount of distance (9,830 ± 428 m), while Lago-Peñas (2009) reported significant differences between nearly every position for each threshold, except for total distance (11.1 – 14 km·h
-1
, 14.1 – 19 km·h
-1
, 19.1 – 23 km·h
-1
, > 23 km·h
-1
). Both these studies observed high-level male soccer teams (U23 English Premier League, Professional Spanish Premier League) and as such they would not be expected to mimic the results of this study and highlight the importance of sex- and level-specific research.
In a more direct comparison with other women’s college soccer research, Sausaman et al(2019) and Alaxander et al (2014) reported significant differences in distances covered by position, whereas Corrales (2020) reported no differences, regardless of position. Sausaman et al (2019) reported that attackers covered more high-speed (> 15 km·h
-1
) and sprint (> 18 km·h
-1
) distances than midfielders and defenders, with no difference between midfielders and defenders. Alexander et al (2104) reported that central defenders covered the least amount of total distance (8041.2 ± 371.0 m), followed by central attacking midfielders (9236.1 ± 491.3 m), fullbacks (9306.2 ± 367.8 m), and wide midfielders (9500.4 ± 847.0 m), with central defensive midfielders (9947.4 ± 577.9 m) covering the greatest amount of total distance. Fullbacks (1321.5 ± 173.7 m) and wide midfielders (1208.2 ± 314.1 m) covered significantly more distance at high speed (> 15 km·h
-1
) compared to central defensive midfielders (847.7 ± 234.9 m), central attacking midfielders (747.64 ± 196.5 m), and central defenders (614.1 ± 98.9 m). The difference in results between these studies and the current investigation could be due to a different level of competition (NCAA Division 1), a possible difference in formation (not reported), or due to the current studies banding velocity zones (i.e. 4 -5 m/s, 5 – 6 m/s) instead of summing distances above thresholds (> 15 km·h
-1
).
CONCLUSION
This present study provides information on the expected work and work rates of division 2 women’s soccer. Data analysis revealed minimal differences based on position, with the midfield position being the only position with significant differences and only at low intensity thresholds (TD, 2-3 m/s). All other positions and intensities were not statistically different, highlighting the possibility that training for these positions likely does not need to be modified to fit each position but rather each athlete.
APPLICATIONS IN SPORT
The primary application of this research is to allow the coaching staff to determine the appropriate fitness, conditioning, and practice workloads for their team with respect to their level of competition, formation, and style of play. Using typical tactical periodization plans for match-day preparation (Match day (MD) +1, MD -2, MD -1, etc.), position-specific workloads can be determined and monitored to ensure optimal loading during each practice session. This information can also be used to determine fitness testing requirements. Since there was a significant drop (43.7%) in distance covered and work rate (approximately 43% decrease) at intensities above 4 m/s (14.4 km·h
-1
) observed from this study, minimum criteria for aerobic fitness tests could be set at 15.5 km·h
-1
, which would allow players to do the majority of the work expected below their estimated lactate threshold (85% vVO2max; 8, 18)
In addition, these data can be used to determine the appropriate time requirements for different conditioning drills. For example, making a time criterion of 3:40 for an 800 m run would meet the Long Interval definition for an individual with a vVO2max of 15.5 km·h
-1
, but if a team had higher/lower requirements, adjusting time cut offs would be suggested (15). These data can also be used to create game-specific conditioning drills, such as creating an interval training exercise (100m active running, 100m recovery jog; Table 3) that would provide about 30 – 35% of game distances at the higher end of expected game work rates.
Table 3
Interval conditioning exercise.
Speed Level
Reps
Time
Work Rate
Accumulated Distance
8.0 km·h
-1
22
45s
89 m/min
2200
12.0 km·h
-1
10
30s
36 m/min
1000
15.0 km·h
-1
24s
20 m/min
500
20.0 km·h
-1
18s
8 m/min
200
>21.6 km·h
-1
<16s
4 m/min
100
Note
: Work Rate was calculated as the total distance covered in each velocity threshold divided by the total exercise time (24.5 min).
When retroactively analyzing team distances and work rates to create a training plan, it is important to note that since the majority of the total distance covered during a match is at low intensities (<3 m/s; 11), focusing on “running” the observed TD is likely unnecessary. Lower speed distances (<4 m/s) would be expected to be accumulated through daily technical drills and exercises. Higher speed distances (>4 m/s) could be accumulated in any manner chosen by the coach, such as a mixture of soccer technical drills and/or conditioning drills.
When using workload data in this manner, this would allow the coaching staff to create training plans that develop physical characteristics in a manner appropriate to the athletes level and expected match play requirements instead of arbitrarily spending time and effort developing a specific characteristic beyond projected usefulness. For example, since the majority of work is performed below 14.4 kph, spending the time and training effort for a vVO2max above 16 kph would be counterproductive. The extra focus could be better spent on improving other training targets to improve performance (technical, tactical, sprint, acceleration, change of direction)
ACKNOWLEDGMENTS
The authors declare no conflicts of interest, and no funding was received for this research.
REFERENCES
1.   Abbott, W., Brickley, G., & Smeeton, N. J. (2018). Positional differences in GPS outputs and perceived exertion during soccer training games and competition.
The Journal of Strength & Conditioning Research
32
(11), 3222-3231.
2.   Alexander, RP. (2014)
Physical and Technical Demands of Women’s Collegiate Socce
r (Publication No. 2421). [Doctoral dissertation, East Tennessee State University], Digital Commons.
3.   Andersson, H. Å., Randers, M. B., Heiner-Møller, A., Krustrup, P., & Mohr, M. (2010). Elite female soccer players perform more high-intensity running when playing in international games compared with domestic league games.
The Journal of Strength & Conditioning Research
24
(4), 912-919.
4.   Aquino, R., Vieira, L. H. P., Carling, C., Martins, G. H., Alves, I. S., & Puggina, E. F. (2017). Effects of competitive standard, team formation and playing position on match running performance of Brazilian professional soccer players.
International Journal of Performance Analysis in Sport
17
(5), 695-705.
5.   Bozzini, B. N., McFadden, B. A., Walker, A. J., & Arent, S. M. (2020). Varying demands and quality of play between in-conference and out-of-conference games in division i collegiate women’s soccer.
The Journal of Strength & Conditioning Research
34
(12), 3364-3368.
6.   Bradley, P. S., Carling, C., Archer, D., Roberts, J., Dodds, A., Di Mascio, M., Paul, D., Gomez Diaz, A., Peart D., & Krustrup, P. (2011). The effect of playing formation on high-intensity running and technical profiles in English FA Premier League soccer matches.
Journal of sports sciences
29
(8), 821-830.
7.   Carling, C., Espié, V., Le Gall, F., Bloomfield, J., & Jullien, H. (2010). Work-rate of substitutes in elite soccer: A preliminary study.
Journal of science and medicine in sport
13
(2), 253-255.
8.   Cerezuela-Espejo, V., Courel-Ibáñez, J., Morán-Navarro, R., Martínez-Cava, A., & Pallarés, J. G. (2018). The relationship between lactate and ventilatory thresholds in runners: validity and reliability of exercise test performance parameters.
Frontiers in physiology
, 1320.
9.   Choice, E. E., Tufano, J. J., Jagger, K. L., & Cochrane-Snyman, K. C. (2022). Match-play external load and internal load in NCAA division II women’s soccer.
The Journal of Strength & Conditioning Research
, 10-1519.
10. Corrales, I. (2020).
The Physical Demands of Women’s Collegiate Soccer Matches Assessed Using GPS Devices
[master’s thesis, California State University, Fullerton], https://scholarworks.calstate.edu/.
11. Gentles, J. A., Coniglio, C. L., Besemer, M. M., Morgan, J. M., & Mahnken, M. T. (2018). The demands of a women’s college soccer season.
Sports
(1), 16.
12. Ishida, A., Travis, S. K., Draper, G., White, J. B., & Stone, M. H. (2022). Player position affects relationship between internal and external training loads during Division I collegiate female soccer season.
The Journal of Strength & Conditioning Research
36
(2), 513-517.
13. Jagim, A. R., Murphy, J., Schaefer, A. Q., Askow, A. T., Luedke, J. A., Erickson, J. L., & Jones, M. T. (2020). Match demands of women’s collegiate soccer.
Sports
(6), 87.
14. Lago-Peñas, C., Rey, E., Lago-Ballesteros, J., Casais, L., & Dominguez, E. (2009). Analysis of work-rate in soccer according to playing positions.
International Journal of Performance Analysis in Sport
(2), 218-227.
15. Laursen, P., & Buchheit, M. (2019).
Science and application of high-intensity interval training
. Human kinetics.
16. Martínez-Lagunas, V., Niessen, M., & Hartmann, U. (2014). Women’s football: Player characteristics and demands of the game.
Journal of Sport and Health Science
(4), 258-272.
17. Mohr, M., Krustrup, P., Andersson, H., Kirkendal, D., & Bangsbo, J. (2008). Match activities of elite women soccer players at different performance levels.
The Journal of Strength & Conditioning Research
22
(2), 341-349..
18. Parpa, K., & Michaelides, M. A. (2025). Ventilatory thresholds in professional female soccer players.
International Journal of Sports Medicine
46
(02), 97-103.
19. Ramos, G. P., Nakamura, F. Y., Penna, E. M., Wilke, C. F., Pereira, L. A., Loturco, I., Capelli, L., Mahseredjian F., Silami-Garcia E., & Coimbra, C. C. (2019). Activity profiles in U17, U20, and senior women’s Brazilian national soccer teams during international competitions: are there meaningful differences?.
The Journal of Strength & Conditioning Research
33
(12), 3414-3422.
20. 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), 73.
21. Vescovi, J. D. (2014). Motion characteristics of youth women soccer matches: female athletes in motion (FAiM) study.
International journal of sports medicine
35
(02), 110-117.
22. Vescovi, J. D. (2015). Physical demands of regular season and playoff matches in professional women’s soccer: a pilot from the Female Athletes in Motion (FAiM) study. In
International research in science and soccer II
(pp. 95-106). Routledge.
23. Vescovi, J. D., & Falenchuk, O. (2019). Contextual factors on physical demands in professional women’s soccer: female athletes in motion study.
European journal of sport science
19
(2), 141-146.
Troy Palmer
2025-09-25T16:05:07-05:00
Supplemental lessons to the Peak Health and Performance curriculum: Nutritional considerations for injury, energy management, and gastrointestinal issues
Troy Palmer
2025-09-25T16:05:07-05:00
February 18th, 2026
Research
Sport Education
Sport Training
Sports Medicine
Sports Nutrition
Authors
Tyler B. Becker
12
, Ronald L. Gibbs, Jr
Department of Food Science and Human Nutrition, Michigan State University, East Lansing, MI, USA
Michigan State University Extension, Health and Nutrition Institute, Michigan State University, East Lansing, Michigan, USA
Corresponding Author:
Tyler B. Becker, PhD, CSCS
469 Wilson Road, Room 125
East Lansing, MI 48824
[email protected]
517-353-3338
Tyler B. Becker, PhD, CSCS is an Associate Professor of Nutritional Sciences at Michigan State University in East Lansing, MI. His research interests focus on sports nutrition practices and strategies in youth athletes and higher education andragogy.
Ronald L. Gibbs, Jr, PhD, MCHES is currently a Program Evaluation Specialist for Michigan State University in East Lansing, MI. His research interests focus on coach and athlete education, long-term athlete development (LTAD), psychosocial aspects of sports and physical activity, adolescent nutrition and physical activity behavior change through sport participation, sports performance, and reducing childhood obesity.
ABSTRACT
Youth sports injuries are quite common in sport and have several negative consequences, including healthcare costs, loss of playing time, and producing mental stress. Nutritional strategies have been suggested to improve recovery from sports-related injuries. The Peak Health and Performance (PHP) youth-sports curriculum was developed to use sport to promote healthy eating behaviors. Six additional lessons on nutrition for recovery from injury, energy management, and gastrointestinal issues have been added as addendums to PHP. Lesson A discusses the importance of key nutrients (eg., protein, complex carbohydrates, unsaturated fatty acids, water) for promoting tissue healing following an injury. Lesson B describes several micronutrients and the possible role of nitrates for aiding in injury recovery. Lesson C discusses the implications of low energy availability, including how to identify and prevent it. In Lesson D, several nutritional strategies for addressing mild traumatic brain injuries are explored. Lesson E discusses the importance of sleep for injury recovery and describes nutritional strategies for improving sleep quality. The final lesson (Lesson F) describes various gastrointestinal issues encountered in sport and how to prevent them. Future research will examine youth athlete knowledge of nutritional strategies for recovering from a sports-related injury following these lessons.
KEYWORDS:
adolescent sports; sports nutrition; injury management
INTRODUCTION
Sports-related injuries are a significant concern among adolescent athletes, with prevalence rates ranging from 34.1% to 65% (2). Certain groups, including female athletes, obese athletes, and those participating in contact sports, are at particularly high risk.  In the US, the rate of injuries in sports, recreation, and leisure activities is 117.1 per 1000 children and adolescents aged 12-17 years of age (57). These injuries impose substantial financial burdens; for example, over a 5-year period in Florida, inpatient care costs for pediatric sports injuries totaled $24.55 million, while emergency care expenses reached $87 million (61). Beyond the economic impact, sports-related injuries also incur both physical and mental challenges to the athlete, including lost playing time, with female athletes averaging 10 days of missed competition per injury (5). This contributes to social isolation and depressive symptoms in adolescent athletes during recovery (63). In addition, gastrointestinal (GI) problems, including diarrhea, vomiting, and abdominal injuries are common place among athletes (12,73), and can contribute to decreases in performance and a loss of playing time (39). Given these multifaceted challenges, there is a critical need to optimize injury prevention and rehabilitation strategies to support young athletes’ physical and psychological well-being.
Proper nutritional intake plays a critical role in injury prevention and rehabilitation among youth athletes (3). In addition to supporting overall health and well-being, adequate nutrition is essential during adolescence–a period marked by rapid growth and development–and contributes to athletic performance and post-workout recovery (15, 66). Despite its importance, many adolescent athletes demonstrate a lack of knowledge regarding both general and sport-specific nutritional practices (6).  A recent systematic review by Hulland et al. (2023) revealed that adolescent athletes are more familiar with general than sport-specific dietary strategies (32), while Gibbs and Becker (2025) found that both male and female adolescent athletes scored below 50% on assessments covering both areas (24). These findings underscore a significant gap in nutritional literacy among youth athletes, indicating the need for targeted education to optimize their health development and athletic outcomes.
Several nutritional strategies have emerged highlighting the importance of it for injury rehabilitation primarily in adult athletes (26, 54, 65). For example, kilocaloric and protein needs often increase following injury due to a need for recovery and maintenance of lean body mass resulting from disuse (58, 65). However, the application of nutritional strategies for recovering from injury for youth athletes remains understudied. Alcock et al. (2024) offered a comprehensive overview of injury rehabilitation strategies for youth, indicating practical applications; however there remains a critical gap in understanding how adolescent athletes perceive and apply nutrition during recovery (3). To date, no research has directly examined youth athletes’ knowledge of nutrition for injury rehabilitation, but existing evidence suggests they are likely deficient in this area as well (46). Research suggests that poor food literacy and nutrition knowledge could theoretically contribute to increased injury risk (3, 15). This reinforces the urgency of developing age-appropriate interventions that address both performance and recovery nutrition, particularly in the context of injury.
The Peak Health and Performance (PHP) curriculum was designed from a collaboration by faculty and staff at Michigan State University, Division of Sports and Cardiovascular Nutrition, College of Osteopathic Medicine, East Lansing, MI and Spartan Performance Training, East Lansing, MI (25). This curriculum incorporates various sports nutrition best practices from several areas of literature providing sports nutrition recommendations (17, 66, 69). Fruit and vegetable intake significantly increased in 290 children and adolescents who completed the PHP curriculum (4). Due to the success of the program in modifying nutrition behaviors, additional lessons were created to educate youth on nutritional strategies for injury recovery, energy management, and managing GI issues. These topics include nutritional strategies for musculoskeletal and mild traumatic brain injury (mTBI) recovery, and other nutritional considerations around injury risk and recovery including sleep, low energy availability (LEA), and GI issues. This manuscript describes the rationality and creation of these addendum lessons for the PHP curriculum.
LESSON CONTENT
The original PHP curriculum consists of six lessons labeled as: Lesson 1- Nutrition Basics; Lesson 2- Athletes Performance Plates; Lesson 3- Timing of Intake; Lesson 4- Hydration, Energy Drinks, and Sugary Beverages; Lesson 5- Convenience Foods; and Lesson 6- More Than a Game (25). Further information on PHP learning objectives and topics inclusion can be found in Gibbs & Becker (25). The new additional lessons and their learning objectives can be found in Table 1.  These additional lessons are meant to serve as their own lesson series, a single lesson session, or as supplemental lessons to the original PHP lessons.
Table 1.
Additional Lessons for the Peak Health and Performance Curriculum: Learning Objectives
Lesson
Learning Objectives
A: Macronutrients for Injury Rehabilitation
• Explain the four phases of an injury
• Understand the importance of consuming enough calories following an injury
• Explain why protein is needed during the health process and recall amounts needed
• Explain the role that carbohydrates have during the healing process
• Describe what role unsaturated fatty acids, such as omega-3s, have while healing an injury.
• Understand the importance of water during the healing process
B: Micronutrients for Injury Rehabilitation
• Explain the importance of choosing food sources of vitamins and minerals over dietary supplements
• List and understand the roles that vitamins A, C, D, and E have in injury healing
• Identify good food sources of vitamins A, C, D, and E
• List and understand the roles that calcium, zinc, and iron have in injury healing
• Identify good food sources of calcium, zine, and iron
• Explain why foods high in nitrates may promote injury healing and identify good food sources of them
C: Low Energy Availability
• Explain what low energy availability is
• Identify what causes low energy availability
• Understand how low energy availability negatively impacts performance and recovery
• Explain how low energy availability may lead to other negative health outcomes
• Recognize the symptoms of low energy availability
• Describe prevention and treatment strategies for low energy availability
D: Nutrition for Head Injuries
• Explain what happens during a head injury in sport
• List the different phases of concussion recovery
• Explain the benefits of creatine, magnesium, and flavonoids for head injury recovery
• Identify good food sources of creatine, magnesium, and flavonoids
• Identify other nutritional considerations to have when recovering from a head injury
E: Nutrition and Sleep for Injury Reduction and Recovery
• Explain why sleep is important for performance and reducing and healing injuries
• Identify how much sleep an athlete should be getting each night
• Explain the benefits of melatonin and serotonin rich foods for improving sleep quality
• Identify other nutrients of interest that are related to sleep quality
• Identify foods to avoid prior to sleep
• List strategies to set up an ideal bedtime routine
F: Gastrointestinal Issues and Sport
• Understand how vomiting and nausea symptoms may appear during practice and sport
• Provide strategies to reduce vomiting and nausea symptoms during practice and sport
• Explain how diarrhea can happen during practice and sport
• Identify strategies to prevent diarrhea during practice and sport
• Explain how probiotics and prebiotics are important for gut health
Each of these six lessons will be discussed in detail in the next section. These supplemental lessons were created in a manner to instruct participants to refer back to the original lessons for further information.
Lesson A: Macronutrients for Injury Prevention
This lesson begins by describing how musculoskeletal injuries heal and the importance of proper caloric intake and macronutrients during recovery from sports-related injuries. Each macronutrient is then highlighted to show its main role in providing both energy and nutritional needs to promote recovery. Macronutrient roles and responsibilities are described in detail in PHP Lesson 1 of the original curriculum (25).
Caloric Intake:
The following section of the lesson describes the importance of meeting kilocalorie (kcal) needs to help heal an injury. Research on adult athletes suggest increasing kcal consumption by 10-15% during injury and recovery (58). Additionally, to offset sarcopenia in adults resulting from injury and disuse, energy intake should be between 25-40 kcal/kg of bodyweight per day (54). Independent of injury status, growth and development demands of children aged 9 and up typically require 60-65 kcal/kg of bodyweight per day (21). Taking energy needs during injury into account, coupled with normal demands for growth and development (21), an injured adolescent would need slightly more than the recommended 60-65 kcal/kg of bodyweight per day.
Protein:
Following injury, protein requirements are significantly elevated to offset bodily stress incurred from the injury (65). Additionally, protein intake helps offset muscle atrophy due to disuse (47). Protein requirements for adult athletes and recreationally active adults is between 1.2 to 2.0 g/kg of bodyweight per day (69), with protein recommendations for adolescent athletes being in a similar range (15, 41). Following injury, it is suggested to increase daily intake of protein to 2.0 to 3.0 g/kg of bodyweight in athletic adults (65), which likely suffices for protein requirements for adolescent athletes.
Carbohydrates:
Carbohydrates can provide a  source of energy while healing through an injury (65), and aid in muscle adaptations and recovery (69). Due to a decrease in the amount of high-intensity exercise that can be performed while injured, carbohydrate needs are not as large as what is needed in an uninjured athlete (65). Thus, to meet demand while recovering from an injury, up to 60% of daily kcals should come from carbohydrates (65), with an emphasis on complex carbohydrates.  Additionally, fatty acids are important in the recovery process as they synthesize several hormones and aid in the absorption of several vitamins (3,  27). Unsaturated fatty acids, such as omega-3 fatty acids may reduce inflammation, thereby making their need instrumental during the recovery process (27). It is recommended to consume good sources of omega-3 fatty acids including fatty fish, walnuts, flaxseed, and avocado, which this lessons includes as suggested food sources (27, 65).
Fluid Intake:
Hydration for performance is covered in Lesson 4 of the original PHP, but in this lesson, it is explored in more detail pertaining to injury risk and recovery. Over half of US children are inadequately hydrated (37), and being in this state can increase risk of injury and prolong recovery (10). Muscles on average are 75% water with bones comprising 25% of it, suggesting that a lowered consumption of it could further exacerbate healing of injuries to these structures (27). Males aged 9 to 13 years need at least 8 cups of fluid per day, while females of the same age need at least 7 cups per day (34). Adolescent males aged 14 to 18 years of age, need at least 11 cups of fluid per day, while females of the same age need 8 cups. Thus, it could be hypothesized that an injured youth athlete should strive to meet and exceed these recommendations for fluid consumption.
Lesson B: Micronutrients for Injury Rehabilitation
Lesson B highlights the importance of specific micronutrients that provide a key role in injury rehabilitation (3, 26). Consuming adequate nutrients, including micronutrients, from whole food sources, is a major goal of the PHP curriculum (21). This lesson begins with a discussion on the concerns with the use of dietary supplements to meet micronutrient recommendations such as issues with regulation (20), and possible contamination (40). Each section of the lesson describes how the micronutrient of interest is implicated in the recovery process, how much is needed, other important functions it provides in the body, and suggested foods that are good sources for the micronutrient of interest.
Vitamin D and Calcium:
As summarized in Alcock et al. (2024) micronutrients of interest for bone injury rehabilitation include vitamin D and calcium (3). Calcium is needed to increase bone mineral density and bone remodeling such as when following an injury (27). Vitamin D is needed for calcium absorption and maintenance. Children and adolescents between 9 and 18 years old, need 1,300 mg of calcium every day (23). Adolescents between 14- and 18-years old need at least 15 mcg (600 IUs) of vitamin D daily. Food sources of calcium listed in the lesson include milk, yogurt, salmon, fortified fruit juice, and collard greens (27). Good food sources of vitamin D suggested in the lesson includes salmon, fortified milk, tuna, and cashews.
Zinc and Iron:
Other micronutrients of interest for muscle injury also include zinc and iron (3). Zinc and iron are both trace minerals that have several important functions in the human body (27). Zinc is involved in hundreds of functions in the body, such as involvement in DNA synthesis and wound healing, and immune system function (27). Zinc is needed for protein synthesis and iron is needed for the transport of oxygen to several tissues in the body which would increase healing (27). Youth aged 9 to 13 years, need 8 mg of zinc per day (23). Male adolescents aged 14-18 years of age need 11 mg of zinc per day, while females of the same age require 9 mg each day.  Children aged 9-13 years of age need 8 mg of iron per day (23). Males aged 14-18 years of age need 11 mg of iron per day, and females of the same age need 15 mg per day. Good sources of zinc include dark meat, legumes, shrimp, and nuts (27). Good food sources of iron includes dark meat, and also spinach and cashews.
Vitamins A, C, and E:
Vitamin C plays a pivotal role in the synthesis of collagen (3). Similar to vitamin C, vitamin A aids in collagen formation, specifically the laying down of new collagen (65). Vitamin E can reduce muscle breakdown and promote muscle repair (27). Each of these vitamins can reduce oxidative stress and inflammation and improve tissue healing (27). Children aged 9 to 13 years old need 1,200 mg of vitamin C every day, and adolescents aged 14 to 18 years old, need 1,800 mg per day (23). Good food sources of vitamin C include kiwis, green peppers, strawberries, and cantaloupe (27). Youth aged 9-13 years need 600 mcg of retinol activity equivalents (vitamin A) per day, while adolescents aged 14-18 years need 600 mcg of retinol activity equivalents each day (23). Youth aged 9-13 years of age need 11 mg of vitamin E per day, while adolescents over the age of 14 need 15 mg per day (23). Dietary sources of vitamin A include sweet potatoes, pumpkins, spinach, and squash, while good sources of vitamin E include sunflower seeds, apricots, avocados, and almonds (65).
Although not a micronutrient, eating foods high in nitrates, like beets, could theoretically help heal an injury (76). About 20% of the nitrates consumed in food is converted to nitrite by bacteria found in the oral cavity (76). In turn, the stomach transforms this nitrite into nitrous oxide which can cause vasodilation. Thus, more oxygen and nutrients are transported to the injured area, supporting the healing process. A recent systematic review examined nine studies and concluded that short-term consumption of beetroot may accelerate the recovery of muscle soreness and various functional markers due to its antioxidant and inflammatory properties likely exerted by its nitrate content and several phenolic compounds (60). Therefore, it could be assumed that consuming foods high in nitrates and phenolic compounds could expedite the injury healing process. Aside from beets, good food sources of nitrates include spinach, radishes, celery, and rhubarb (36).
Lesson C: Low Energy Availability
Energy availability is the amount of energy available after energy expenditure, that is used for bodily functions (9). Thus, LEA is the state of inadequate energy intake relative to energy expenditure (9) and the prevalence for LEA in athletes ranges from 22% to 58% in a given sport (44). LEA can lead to several negative impacts on performance including decreased muscular strength, decreased endurance performance, and decreased responses to training responses and adaptations (50, 70). Additionally, there is an increased injury risk with LEA (29,  56).
The next section of this lesson discusses how LEA can negatively impact the growth and development of a child or adolescent, potentially resulting in poor bone health, delayed puberty, short stature, and menstrual irregularities (15). It also highlights several signs and symptoms felt by an athlete that could indicate LEA (9, 70).
LEA, with or without the presence of an eating disorder, is a characteristic of the Female Athlete Triad, which is a condition that also includes decreased bone mineral density, and menstrual dysfunction (53, 59). The concept of Relative energy deficiency in sport (RED-S) expands upon the Female Athlete Triad by recognizing a broader range of health consequences including disruptions to the endocrine system, immune system, and cardiovascular health (9). Raising awareness of these signs and symptoms is essential, especially given that knowledge of LEA remains low among both athletes and coaches (44). The lesson concludes with evidence-based strategies to prevent LEA, as well as treatment options to address its underlying causes (9).
Lesson D: Nutrition for Head Injuries
This lesson discusses various nutritional considerations to assist in the healing process for someone who has had a concussion, or other types of mTBI (22, 62). Current concussion rates in youth sports are 4.17 cases per 10,000 athlete exposures (38). There are several nutritional aspects that may support brain health among those recovering from mTBIs (22, 62). Although several macronutrients are considered nutrients of interest during this process (22, 62), this lesson discusses other nutrients and micronutrients (aside from those discussed in previous lessons) that may have a place while recovering from a mTBI, including creatine, magnesium, and flavonoids.
Creatine:
Creatine is a compound that is formed in protein metabolism and works to recycle adenosine triphosphate (ATP) for energy metabolism (42). It has been shown that creatine content in the brain is diminished after a mTBI, and increasing its intake could maintain ATP levels in the brain (1, 65). This could help offset injury sustained from the mTBI, such as decreasing protease activation that degrades axon structures (1). Good food sources of creatine listed in this lesson includes lean red meats, fatty fish, pork, and wild game (72).
Magnesium:
Magnesium is a trace mineral that has several functions within the body (27). In the brain, magnesium is involved in efficient nerve signaling and maintaining the blood brain barrier (45). Following a mTBI, magnesium levels decrease in the brain (67), and low magnesium levels have been associated with neuroinflammation and neurodegeneration, including several diseases such as Alzheimer’s and Parkinson’s diseases (67). Research has revealed that magnesium supplementation can reduce concussion symptoms in adolescents following injury (67). Youth aged 9 to 13 years of age need 240 mg of magnesium per day (23). Older adolescent, males aged 14-18 years of age need 410 mg or magnesium per day while their female counterparts need 360 mg per day. Good food sources of magnesium include almonds, cashews, peanut butter, and spinach (27).
Flavonoids:
Lastly, flavonoids are phytochemicals found in many fruits and vegetables, that have anti-inflammatory and antioxidant effects, which may reduce swelling after a mTBI (28). Blueberries contain high amounts of flavonoids including anthocyanins, which contribute to the blueberry’s dark color (11). Anthocyanins could lower brain inflammation and stress caused by mTBI (30). Laboratory studies have shown beneficial effects from blueberry supplementation on various cognitive performance outcomes and symptoms following a mTBI (43, 68). Therefore, consuming foods high in flavonoids, including blueberries, could offer a benefit for healing from a head injury.
This lesson concludes with additional nutritional considerations for those recovering from a mTBI. For example, it is suggested to eliminate the consumption of caffeine following a mTBI (65). Other suggestions include taking note of any foods or drinks that cause vomiting or feelings of nausea, and reducing their consumption for a period of time while mTBI symptoms decrease (72).
Lesson E: Nutrition and Sleep for Injury Reduction and Recovery
This lesson highlights the importance of sleep for performance and injury recovery (19, 49). Youth athletes not getting enough sleep are 1.7 times more likely to get injured (52). School-aged children need 9-11 hours of sleep each night, while teenagers need 8-10 hours of sleep per night (31). It is likely an injured athlete should aim for the upper amount of sleep needed per day. Currently, adolescents aged 13-18 years of age are getting on average 7.7 hours of sleep per night, slightly less than the minimum amount needed (48). Several nutrients have been identified that can naturally aid in hormone regulation associated with sleep (55).
Melatonin:
Melatonin is a hormone secreted by the pineal gland that is involved in circadian rhythm and increases total sleep time and may reduce time to fall asleep (13, 55). It is found naturally in several foods including tart cherries (18, 51). In addition, tart cherries include other constituents that have anti-inflammatory and antioxidant effects, which may aid in sleep and recovery (8). Other foods with a high melatonin content include milk, pineapples, oranges, and bananas (18, 55).
Serotonin:
Serotonin is another hormone involved in sleep by synthesizing hypogenic substances that influence sleep quality (7, 55). Kiwi fruits are a good source of serotonin and contain several minerals, dietary fiber, and phytochemicals that also may aid in sleep (18, 55).
This section of the lesson also includes other nutritional considerations for quality sleep. For example, some foods that contain caffeine, can make it difficult to fall asleep and the recommendation is to reduce or eliminate its intake closer to bedtime (33). This lesson concludes with tips on how to establish an effective sleep routine such as minimizing screen time before it (33).
Lesson F: Gastrointestinal Issues and Sport
This lesson addresses common GI issues encountered in sport and concludes with practical applications for maintaining gut health.
Nausea and Vomiting:
Nausea and vomiting are frequent complaints among athletes across various disciplines (77). These symptoms may result from elevated levels of norepinephrine reducing splanchnic blood flow to the gut, delayed gastric emptying, or increased production of gastric bile acids (77). This lesson outlines several risk factors that may contribute to these symptoms along with simple strategies to help prevent them.
Diarrhea:
Diarrhea is a common condition experienced by athletes, particularly among endurance athletes (77). Proposed mechanisms include the secretion of vasoactive intestinal peptide which relaxes smooth muscle in the digestive system (35), and changes in gut motility (77). Many of the risk factors associated with diarrhea overlap with those linked to nausea and vomiting. This section concludes with evidence-informed approaches for minimizing the risk of diarrhea during training and competition.
Heartburn:
Heartburn is another GI issue sometimes encountered by athletes during exercise and sport and can be caused by increased abdominal pressure, changes in posture, and changes in exercise intensity (74). Additionally, consuming large meals prior to exercise, not being properly hydrated, and having high levels of stress or anxiety can also trigger heartburn. Chronic heartburn could be caused by gastroesophageal reflux disease or GERD (74). This section provides strategies to prevent heartburn during practice or a game, with an emphasis on taking note of such foods that sometimes cause heartburn in an individual.
This lesson concludes by discussing several strategies to maintain gut health and gut microbiota which may impact immunological function and thus injury risk and recovery from them (75). Rationale for its inclusion within this lesson is from the US Olympic & Paralympic Committee sports nutrition handout on nutrients for GI injury (71). Consuming foods high in probiotics may maintain digestion and absorption while also preventing several GI issues described in this lesson (71). Prebiotic fibers are a type of fermentable fiber that stimulates intestinal bacteria growth and activity (64). In addition, prebiotic fiber consumption is associated with several other benefits including increasing the absorption of calcium, improving cognitive health, and reducing risk of some diseases (14). Therefore, it is important to incorporate prebiotic fibers into one’s diet.
CONCLUSIONS
Nutrition is a cornerstone of health and performance for adolescent athletes not only supporting their growth and development but also their ability to train, compete, and recover effectively (15). Integrating sound nutrition practices into youth athlete development programs is essential for promoting lifelong well-being and optimal athletic potential (16). In addition to enhancing performance, proper nutrition can play a key role in preventing injuries and accelerating recovery when injuries occur (3). To emphasize these critical areas, several new lesson have been added as targeted addendums to the PHP curriculum (25). When combined with the original PHP content, these additions aim to strengthen both general and sport-specific nutrition behaviors, equipping young athletes with the knowledge and habits needed to thrive on and off the field.
Following an injury, it is important to consume adequate kcals from protein, carbohydrates, and unsaturated fatty acids, along with being properly hydrated to facilitate recovery (3). Emphasizing certain micronutrients from food may also improve recovery from injury (3). Additionally, nutritional support is needed for athletes recovering from an mTBI (65). LEA is a common problem in youth sports and understanding its consequences and how to prevent it are important for reducing injury risk (9). Getting adequate sleep is important not only for athletic performance, but also injury prevention and healing from an injury (19, 49). Although not a direct injury caused by sport, GI issues can occur during it, and can be prevented using evidence-based nutritional strategies (77). Next steps are to examine adolescent knowledge of nutritional best practices for recovering from sports-induced injuries.
APPLICATIONS IN SPORT
These supplemental lessons are to serve as adjunct lessons to the PHP curriculum and to provide youth athletes with knowledge on injury management and other sports nutrition topics not otherwise discussed in athletic circles. Additionally, the hope is to encourage further research in this understudied area and add to the growing body of literature examining nutrition practices for injury management in youth athletes.
REFERENCES
1. Ainsley Dean, P. J., Arikan, G., Opitz, B., & Sterr, A. (2017). Potential for use of creatine supplementation following mild traumatic brain injury.
Concussion
(2), Cnc34.
2. Al-Qahtani, M. A., Allajhar, M. A., Alzahrani, A. A., Asiri, M. A., Alsalem, A. F., Alshahrani, S. A., & Alqahtani, N. M. (2023). Sports-related injuries in adolescent athletes: A systematic review.
Cureus
15
(11), e49392.
3. Alcock, R., Hislop, M., Vidgen, H. A., & Desbrow, B. (2024). Youth and adolescent athlete musculoskeletal health: Dietary and nutritional strategies to optimise injury prevention and support recovery.
Journal of Functional Morphology and Kinesiology
(4).
4. Becker, T. B., & Gibbs, R. L. (2024). Changes in nutrition, food safety, and physical activity behaviors: A comparison between the Peak Health and Performance and Teen Cuisine Curricula.
Journal of Human Sciences and Extension
12
(3), 10.
5. Beech, J., Jones, B., Hughes, T., & Emmonds, S. (2024). Injury profile in youth female athletes: A systematic review and meta-analysis.
Sports Medicine
54
(5), 1207-1230.
6. Bird, S. P., & Rushton, B. D. (2020). Nutritional knowledge of youth academy athletes.
BMC Nutrition
, 35.
7. Cespuglio, R. (2018). Serotonin: its place today in sleep preparation, triggering or maintenance.
Sleep Medicine
49
, 31-39.
8. Chai, S. C., Davis, K., Zhang, Z., Zha, L., & Kirschner, K. F. (2019). Effects of tart cherry juice on biomarkers of inflammation and oxidative stress in older adults.
Nutrients
11
(2).
9. Charlton, B. T., Forsyth, S., & Clarke, D. C. (2022). Low energy availability and relative energy deficiency in sport: What coaches should know.
International Journal of Sports Science & Coaching
17
(2), 445-460.
10. Chodkowski, J. (2024). The role of nutrition and hydration in injury prevention and recovery: A review.
Journal of Educcation Health and Sport
71
, 56117.
11. Conti, F., McCue, J. J., DiTuro, P., Galpin, A. J., & Wood, T. R. (2024). Mitigating traumatic brain injury: A narrative review of supplementation and dietary protocols.
Nutrients
16
(15), 2430.
12. Costa, R. J. S., Snipe, R. M. J., Kitic, C. M., & Gibson, P. R. (2017). Systematic review: exercise-induced gastrointestinal syndrome-implications for health and intestinal disease.
Aliment Pharmacolology and Therapeutics
46
(3), 246-265.
13. Costello, R. B., Lentino, C. V., Boyd, C. C., O’Connell, M. L., Crawford, C. C., Sprengel, M. L., & Deuster, P. A. (2014). The effectiveness of melatonin for promoting healthy sleep: a rapid evidence assessment of the literature.
Nutrition Journal
13
, 106.
14. Davani-Davari, D., Negahdaripour, M., Karimzadeh, I., Seifan, M., Mohkam, M., Masoumi, S. J., Berenjian, A., & Ghasemi, Y. (2019). Prebiotics: Definition, types, sources, mechanisms, and clinical applications.
Foods
(3).
15. Desbrow, B. (2021). Youth athlete development and nutrition.
Sports Medicine
51
(Suppl 1), 3-12.
16. Desbrow, B., Burd, N. A., Tarnopolsky, M., Moore, D. R., & Elliott-Sale, K. J. (2019). Nutrition for special populations: Young, female, and masters athletes.
International Journal of Sport Nutrition and Exercise Metabolism
29
(2), 220-227.
17. Desbrow, B., McCormack, J., Burke, L. M., Cox, G. R., Fallon, K., Hislop, M., Logan, R., Marino, N., Sawyer, S. M., Shaw, G., Star, A., Vidgen, H., & Leveritt, M. (2014). Sports Dietitians Australia position statement: sports nutrition for the adolescent athlete.
International Journal of Sport Nutrition and Exercise Metabolism
24
(5), 570-584.
18. Doherty, R., Madigan, S., Warrington, G., & Ellis, J. (2019). Sleep and nutrition interactions: Implications for athletes.
Nutrients
11
(4).
19. Dwivedi, S., Boduch, A., Gao, B., Milewski, M. D., & Cruz, A. I. J. (2019). Sleep and injury in the young athlete.
The Journal of Bone and Joint Surgery Reviews
(9), e1.
20. Dwyer, J. T., Coates, P. M., & Smith, M. J. (2018). Dietary supplements: Regulatory challenges and research resources.
Nutrients
10
(1).
21. Faizan, U., & Rouster, A. S. (2024). Nutrition and hydration requirements in children and adults. In
StatPearls
. StatPearls Publishing. Copyright © 2024, StatPearls Publishing LLC.
22. Finnegan, E., Daly, E., Pearce, A. J., & Ryan, L. (2022). Nutritional interventions to support acute mTBI recovery.
Frontiers in Nutrition
, 977728.
23. Food and Nutrition Board, & Institute of Medicine. (2006).
Dietary Reference Intakes: The essential guide to nutrient requirements.
National Academies Press.
24. Gibbs, R. L., & Becker, T. B. (2025). General and sport-specific nutrition knowledge and behaviors of adolescent athletes.
Journal of the Internation Society of Sports Nutrition
22
(1), 2477060.
25. Gibbs, R. L., Jr., & Becker, T. B. (2021). An evidence-based sports nutrition curriculum for youth.
The Sport Journal
24
(6), 6-unpaginated. ://CABI:20210342440
26. Giraldo-Vallejo, J. E., Cardona-Guzmán, M., Rodríguez-Alcivar, E. J., Kočí, J., Petro, J. L., Kreider, R. B., Cannataro, R., & Bonilla, D. A. (2023). Nutritional strategies in the rehabilitation of musculoskeletal injuries in athletes: A systematic integrative review.
Nutrients
15
(4).
27. Gropper, S. S., Smith, J. L., & Carr, T. P. (2018).
Advanced nutrition and human metabolism.
(7 ed.). Cengage Learning.
28. Hasan, S., Khatri, N., Rahman, Z. N., Menezes, A. A., Martini, J., Shehjar, F., Mujeeb, N., & Shah, Z. A. (2023). Neuroprotective potential of flavonoids in brain disorders.
Brain Sciences
13
(9).
29. Heikura, I. A., Uusitalo, A. L. T., Stellingwerff, T., Bergland, D., Mero, A. A., & Burke, L. M. (2018). Low energy availability is difficult to assess but outcomes have large impact on bone injury rates in elite distance athletes.
International Journal of Sport Nutrition and Exercise Metabolism
28
(4), 403-411.
30. Henriques, J. F., Serra, D., Dinis, T. C. P., & Almeida, L. M. (2020). The anti-neuroinflammatory role of anthocyanins and their metabolites for the prevention and treatment of brain disorders.
International Journal of Molecular Sciences
21
(22).
31. Hirshkowitz, M., Whiton, K., Albert, S. M., Alessi, C., Bruni, O., DonCarlos, L., Hazen, N., Herman, J., Adams Hillard, P. J., Katz, E. S., Kheirandish-Gozal, L., Neubauer, D. N., O’Donnell, A. E., Ohayon, M., Peever, J., Rawding, R., Sachdeva, R. C., Setters, B., Vitiello, M. V., & Ware, J. C. (2015). National Sleep Foundation’s updated sleep duration recommendations: Final report.
Sleep Health
(4), 233-243.
32. Hulland, S. C., Trakman, G. L., & Alcock, R. D. (2023). Adolescent athletes have better general than sports nutrition knowledge and lack awareness of supplement recommendations: a systematic literature review.
British Journal of Nutrition
, 1-15.
33. Imbergamo, C. M., Patankar, A. G., & Milewski, M. D. (2021). Sleep optimization in the young athlete.
Journal of the Pediatric Orthopaedic Society of North America
(3), 306.
34. Institute of Medicine of the National Academies. (2006).
Dietary Reference Intakes.  The essential guide to nutrient requirements.
The National Academies Press.
35. Iwasaki, M., Akiba, Y., & Kaunitz, J. D. (2019). Recent advances in vasoactive intestinal peptide physiology and pathophysiology: focus on the gastrointestinal system.
F1000Research
36. Karwowska, M., & Kononiuk, A. (2020). Nitrates/nitrites in food-risk for nitrosative stress and benefits.
Antioxidants (Basel)
(3).
37. Kenney, E. L., Long, M. W., Cradock, A. L., & Gortmaker, S. L. (2015). Prevalence of inadequate hydration among US children and disparities by gender and race/ethnicity: National Health and Nutrition Examination Survey, 2009-2012.
American Journal of Public Health
105
(8), e113-118.
38. Kerr, Z. Y., Chandran, A., Nedimyer, A. K., Arakkal, A., Pierpoint, L. A., & Zuckerman, S. L. (2019). Concussion Incidence and Trends in 20 High School Sports.
Pediatrics
144
(5).
39. Koon, G., Atay, O., & Lapsia, S. (2017). Gastrointestinal considerations related to youth sports and the young athlete.
Translational Pediatrics
(3), 129-136.
40. Kozhuharov, V. R., Ivanov, K., & Ivanova, S. (2022). Dietary supplements as source of unintentional doping.
BioMed Research International
2022
, 8387271.
41. Krabak, B. J., Roberts, W. O., Tenforde, A. S., Ackerman, K. E., Adami, P. E., Baggish, A. L., Barrack, M., Cianca, J., Davis, I., D’Hemecourt, P., Fredericson, M., Goldman, J. T., Harrast, M. A., Heiderscheit, B. C., Hollander, K., Kraus, E., Luke, A., Miller, E., Moyer, M.,…Wasfy, M. M. (2021). Youth running consensus statement: minimising risk of injury and illness in youth runners.
British Journal of Sports Medicine
55
(6), 305-318.
42. Kreider, R. B., Kalman, D. S., Antonio, J., Ziegenfuss, T. N., Wildman, R., Collins, R., Candow, D. G., Kleiner, S. M., Almada, A. L., & Lopez, H. L. (2017). International Society of Sports Nutrition position stand: safety and efficacy of creatine supplementation in exercise, sport, and medicine.
Journal of the International Society of Sports Nutrition
14
, 18.
43. Krishna, G., Ying, Z., & Gomez-Pinilla, F. (2019). Blueberry supplementation mitigates altered brain plasticity and behavior after traumatic brain injury in fats.
Molecular Nutrition & Food Research
63
(15), e1801055.
44. Logue, D. M., Madigan, S. M., Melin, A., Delahunt, E., Heinen, M., Donnell, S. M., & Corish, C. A. (2020). Low energy availability in athletes 2020: An updated narrative review of prevalence, risk, within-day energy balance, knowledge, and impact on sports performance.
Nutrients
12
(3).
45. Maier, J. A. M., Locatelli, L., Fedele, G., Cazzaniga, A., & Mazur, A. (2022). Magnesium and the brain: A focus on neuroinflammation and neurodegeneration.
International Journal of Molecular Sciences
24
(1).
46. Manore, M. M., Patton-Lopez, M. M., Meng, Y., & Wong, S. S. (2017). Sport nutrition knowledge, behaviors and beliefs of high school soccer players.
Nutrients
(4).
47. Marshall, R. N., Smeuninx, B., Morgan, P. T., & Breen, L. (2020). Nutritional strategies to offset disuse-induced skeletal muscle atrophy and anabolic resistance in older adults: From whole-foods to isolated ingredients.
Nutrients
12
(5).
48. Maslowsky, J., & Ozer, E. J. (2014). Developmental trends in sleep duration in adolescence and young adulthood: evidence from a national United States sample.
Journal of Adolescent Health
54
(6), 691-697.
49. Mason, L., Connolly, J., Devenney, L. E., Lacey, K., O’Donovan, J., & Doherty, R. (2023). Sleep, nutrition, and injury risk in adolescent athletes: A narrative review.
Nutrients
15
(24).
50. Melin, A. K., Areta, J. L., Heikura, I. A., Stellingwerff, T., Torstveit, M. K., & Hackney, A. C. (2024). Direct and indirect impact of low energy availability on sports performance.
Scandinavian Journal of Medicine & Science in Sports
34
(1), e14327.
51. Meng, X., Li, Y., Li, S., Zhou, Y., Gan, R. Y., Xu, D. P., & Li, H. B. (2017). Dietary sources and bioactivities of melatonin.
Nutrients
(4).
52. Milewski, M. D., Skaggs, D. L., Bishop, G. A., Pace, J. L., Ibrahim, D. A., Wren, T. A., & Barzdukas, A. (2014). Chronic lack of sleep is associated with increased sports injuries in adolescent athletes.
Journal of Pediatric Orthopaedics
34
(2), 129-133.
53. Nazem, T. G., & Ackerman, K. E. (2012). The female athlete triad.
Sports Health
(4), 302-311.
54. Papadopoulou, S. K. (2020). Rehabilitation nutrition for injury recovery of athletes: The Role of macronutrient intake.
Nutrients
12
(8).
55. Pattnaik, H., Mir, M., Boike, S., Kashyap, R., Khan, S. A., & Surani, S. (2022). Nutritional elements in sleep.
Cureus
14
(12), e32803.
56. Prus, D., Mijatovic, D., Hadzic, V., Ostojic, D., Versic, S., Zenic, N., Jezdimirovic, T., Drid, P., & Zaletel, P. (2022). (Low) energy availability and its association with injury occurrence in competitive dance: Cross-sectional analysis in female dancers.
Medicina (Kaunas)
58
(7).
57. QuickStats: Rates of injury from sports, recreation, and leisure activities among children and adolescents aged 1–17 years, by age group — National Health Interview Survey, United States, 2015–2017. (2019).
Morbidity and Mortality Weekly Report
68
, 466.
58. Quintero, K. J., Resende, A. d. S., Leite, G. S. F., & Lancha Junior, A. H. (2018). An overview of nutritional strategies for recovery process in sports-related muscle injuries.
Nutrire
43
(1), 27.
59. Raj, M., Creech, J., & Rogol, A. (2024).
Female athlete triad.
StatPearls Publishing. Retrieved February 3 from
60. Rojano-Ortega, D., Peña Amaro, J., Berral-Aguilar, A. J., & Berral-de la Rosa, F. J. (2022). Effects of beetroot supplementation on recovery after exercise-induced muscle damage: A systematic review.
Sports Health
14
(4), 556-565.
61. Ryan, J. L., Pracht, E. E., & Orban, B. L. (2019). Inpatient and emergency department costs from sports injuries among youth aged 5-18 years.
BMJ Open Sport & Exercise Medicine
(1), e000491.
62. Ryan, T., Nagle, S., Daly, E., Pearce, A. J., & Ryan, L. (2023). A potential role exists for nutritional interventions in the chronic phase of mild traumatic brain injury, concussion and sports-related concussion: A systematic review.
Nutrients
15
(17).
63. Sheehan, N., Summersby, R., Bleakley, C., Caulfield, B., Matthews, M., Klempel, N., & Holden, S. (2024). Adolescents’ experience with sports-related pain and injury: A systematic review of qualitative research.
Physical Therapy in Sport
68
, 7-21.
64. Slavin, J. (2013). Fiber and prebiotics: mechanisms and health benefits.
Nutrients
(4), 1417-1435.
65. Smith-Ryan, A. E., Hirsch, K. R., Saylor, H. E., Gould, L. M., & Blue, M. N. M. (2020). Nutritional considerations and strategies to facilitate injury recovery and rehabilitation.
Journal of Athletic Training
55
(9), 918-930.
66. Smith, J. W., Holmes, M. E., & McAllister, M. J. (2015). Nutritional considerations for performance in young athletes.
Journal of Sports Medicine (Hindawi Publ Corp)
2015
, 734649.
67. Standiford, L., O’Daniel, M., Hysell, M., & Trigger, C. (2021). A randomized cohort study of the efficacy of PO magnesium in the treatment of acute concussions in adolescents.
The American Journal of Emergency Medicine
44
, 419-422.
68. Sweeney, M. I., Kalt, W., MacKinnon, S. L., Ashby, J., & Gottschall-Pass, K. T. (2002). Feeding rats diets enriched in lowbush blueberries for six weeks decreases ischemia-induced brain damage.
Nutritional Neuroscience
(6), 427-431.
69. Thomas, D. T., Erdman, K. A., & Burke, L. M. (2016). Position of the Academy of Nutrition and Dietetics, Dietitians of Canada, and the American College of Sports Medicine: Nutrition and athletic performance.
Journal of the Academy of Nutrition and Dietetics
116
(3), 501-528.
70. United States Olypmic & Paralymipic Committee Sport Nutrition Team. (2020a).
Low energy availability
. Retrieved December 17 from chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/
71. United States Olypmic & Paralymipic Committee Sport Nutrition Team. (2020b).
Nutrients for GI injury
. Retrieved December 17 from
72. United States Olypmic & Paralymipic Committee Sport Nutrition Team. (2020c).
Nutrients for head injury recovery
. Retrieved December 17 from
73. Wardenaar, F. C., Schott, K. D., Mohr, A. E., Ortega-Santos, C. P., & Connolly, J. E. (2023). An exploratory study investigating the prevalence of gastrointestinal symptoms in collegiate division I American football athletes.
International Journal of Environ Research and Public Health
20
(15).
74. Waterman, J. J., & Kapur, R. (2012). Upper gastrointestinal issues in athletes.
Current Sports Medicine Reports
11
(2), 99-104.
75. Wiertsema, S. P., van Bergenhenegouwen, J., Garssen, J., & Knippels, L. M. J. (2021). The interplay between the gut microbiome and the immune system in the context of infectious diseases throughout life and the role of nutrition in optimizing treatment strategies.
Nutrients
13
(3).
76. Zamani, H., de Joode, M. E. J. R., Hossein, I. J., Henckens, N. F. T., Guggeis, M. A., Berends, J. E., de Kok, T. M. C. M., & van Breda, S. G. J. (2021). The benefits and risks of beetroot juice consumption: a systematic review.
Critical Reviews in Food Science and Nutrition
61
(5), 788-804.
77. Zaslow, T., & Mitrovich, C. (2023). Chapter 16 – Gatrointestinal and abdominal issues. In B. J. Krabak & A. Brooks (Eds.),
The Youth Athlete
(pp. 159-172). Academic Press.
Next
Article Categories
Page load link
The Sport Journal uses
Accessibility Checker
to monitor our website's accessibility.
Loading Comments...
Go to Top