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- 🧠 ADHD Predicts Recovery Timing in High School Athletes; How Jump Tests Predict Water Performance
🧠 ADHD Predicts Recovery Timing in High School Athletes; How Jump Tests Predict Water Performance
🐍🍎 Using Python to Analyze Athlete Recovery Patterns
Haresh Suppiah
August 11, 2025 • Estimated Reading Time: 13 minutes
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Hello there,
In today’s edition:
• ADHD and recovery from sports-related concussions in high school athletes
• Jump force characteristics and rowing performance relationship
• Hamstring muscle activation patterns and injury risk factors
• Core engagement effects on squat performance and stability
• Breath-holding techniques for enhanced athletic performance
• Youth soccer passing skills vs small-sided games training
and several more…
In focus: The Impact of ADHD on Concussion Recovery in High School Athletes
Attention-Deficit/Hyperactivity Disorder (ADHD) significantly influences the recovery process from sports-related concussions among high school athletes, with recent 2025 research providing crucial insights for treatment and management protocols. The most comprehensive evidence comes from a landmark study analyzing 935 high school athletes, which found that those with ADHD experienced substantially longer recovery times—taking an average of nearly 13 days to return to the classroom compared to 11 days for non-ADHD peers, and almost 21 days to return to sport versus 18 days for controls. The 2025 Journal of Athletic Training research demonstrates that ADHD athletes are also nearly twice as likely to have sustained prior concussions (14.1% vs 7.8%), indicating a complex relationship between attention disorders and concussion vulnerability.
The neuropsychological mechanisms underlying these prolonged recovery patterns are becoming clearer through recent investigations. Research in the Journal of International Neuropsychological Society identifies ADHD as a significant risk factor for prolonged recovery, while findings from the NCAA-DOD CARE Consortium reveal an intriguing therapeutic consideration: athletes with ADHD who use psychostimulant medications did not demonstrate prolonged recovery times compared to controls, suggesting that proper medication management may normalize recovery trajectories.
For sports medicine practitioners, these findings necessitate immediate changes in concussion management protocols. The 2024 National Athletic Trainers’ Association Bridge Statement now emphasizes the critical importance of screening for pre-existing mental health conditions and establishing comprehensive referral networks including neuropsychologists and school counselors. Athletes with ADHD require individualized recovery plans with extended timelines, enhanced symptom monitoring, and specialized return-to-learn protocols that account for their unique cognitive processing patterns.
-Haresh 🤙
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When managing athlete concussions, sports medicine professionals need to analyze recovery patterns to identify athletes who may need additional support. Based on the research we’ve discussed, athletes with conditions like ADHD often have longer recovery times. Let’s use Python to help identify these patterns in recovery data.
def average_recovery_time(recovery_days):
# This line creates a new function called 'average_recovery_time'
# The function takes one input: a list of recovery times called 'recovery_days'
# This line adds up all the recovery times and divides by how many athletes there are
return sum(recovery_days) / len(recovery_days)
# This line creates a list of recovery times (in days) for 5 different athletes
athlete_recovery = [12, 15, 10, 8, 14]
# This line calls our function with the athlete data and prints the result
print("Average Recovery Time:", average_recovery_time(athlete_recovery))Your Task: Modify the function to also return a list of recovery times that are above average. This will help identify athletes who took longer than expected to recover.
Here’s what that might look like:
def average_recovery_time(recovery_days):
# This line creates a new function called 'average_recovery_time'
# The function takes one input: a list of recovery times called 'recovery_days'
# This line adds up all recovery times and divides by the count to get the average
average_time = sum(recovery_days) / len(recovery_days)
# This line creates a new list containing only recovery times that are above average
# It uses a 'list comprehension' - a Python shortcut for filtering data
# It checks each 'time' in 'recovery_days' and only keeps it if 'time > average_time'
above_average = [time for time in recovery_days if time > average_time]
# This line returns both pieces of information: the average and the above-average times
return average_time, above_average
# This line creates our sample data - recovery times for 5 athletes
athlete_recovery = [12, 15, 10, 8, 14]
# This line calls our function and separates the two returned values
# 'avg_time' gets the average, 'above_avg_times' gets the list of longer recoveries
avg_time, above_avg_times = average_recovery_time(athlete_recovery)
# These lines print both results in a readable format
print("Average Recovery Time:", avg_time, "days")
print("Athletes with Above-Average Recovery Times:", above_avg_times, "days")This code helps identify athletes like those with ADHD who, according to recent research, typically need 13+ days to return to academics and 21+ days to return to sport. By flagging longer recovery times, medical staff can provide more targeted support and adjust treatment protocols accordingly.
Key finding:
High school athletes with ADHD experience significantly longer recovery times from concussions compared to those without ADHD.
How they did it:
Methodology: The study involved 935 high school athletes diagnosed with sports-related concussions, with 78 (8.3%) self-reporting a history of attention-deficit/hyperactivity disorder (ADHD). Data were collected from 60 schools over eight years using a standardized concussion management protocol, including a computerized neuropsychological assessment.
Results: Athletes with ADHD had a mean return-to-learn (RTL) duration of 12.86 days compared to 11.43 days for non-ADHD athletes, indicating ADHD status contributed to a significant longer RTL (risk ratio [RR] = 1.16, P < .001). The return-to-sport (RTS) duration was also longer in the ADHD group (20.82 days) than in the non-ADHD group (18.03 days), with ADHD status showing significant impact (RR = 1.17, P < .001).
Innovations: The study utilized a Poisson regression model to analyze the impact of ADHD status, sex, and age on recovery outcomes, allowing for robust predictions of RTL and RTS durations in a large sample of high school athletes.
Clinical Implications: The findings underscore the importance of accounting for ADHD when managing concussed athletes, as those with ADHD and female athletes appear to have longer recovery times. This necessitates tailored guidance from health care providers and athletic trainers regarding return-to-learn and return-to-sport timelines.
Participant Demographics: The ADHD group comprised predominantly male athletes (83.3%), contrasting with a more balanced gender distribution in the non-ADHD group, highlighting the need for further exploration into the impact of sex differences on concussion recovery.
Why it matters:
Understanding how Attention-Deficit/Hyperactivity Disorder (ADHD) impacts recovery times from sports-related concussions is crucial for coaches and athletic trainers. This study revealed that high school athletes with ADHD take, on average, 1.16 times longer to return to school and 1.17 times longer to return to sport compared to their peers without ADHD, emphasizing the need for tailored support strategies. Being aware of these differences allows sports professionals to plan more effectively and provide necessary accommodations throughout the recovery process.
This correlation matrix visualizes the relationships between various force-time variables from the countermovement jump (CMJ) and rowing performance measured in watts during a 2,000-meter ergometer test. The strongest positive relationship with rowing performance is observed for Positive Take-off Impulse (r = 0.71), indicating that greater impulse during the jump correlates with higher rowing power.
Key finding:
Positive Take-off Impulse strongly correlates with improved 2,000-m rowing performance, suggesting targeted training can enhance results.
How they did it:
Methodology: A total of 30 rowers (27 male, 3 female) participated in a study at the 2023 USRowing Atlantic City Indoor National Championships, where they completed a 2,000-meter rowing ergometer time trial and a countermovement jump (CMJ) test on force plates. The athletes’ performance data, including 2k times and CMJ force-time characteristics, were collected and analyzed for correlations.
Results: Positive Take-off Impulse (N•s) showed a strong correlation (r = 0.71, p < 0.001) with 2k rowing erg performance, while concentric mean force (N) and peak power (W) exhibited moderate correlations (r = 0.63, p < 0.001 and r = 0.55, p = 0.003, respectively). Interestingly, jump height did not correlate significantly with rowing performance (r = -0.13, p = 0.518).
Impact of Low Back Pain: Self-reported rowing-related low back pain (LBP) significantly altered the relationships of concentric mean force with 2k performance; a strong relationship (r = 0.74, p < 0.001) was found when controlling for current LBP.
Innovations: This study is the first to identify that CMJ variable of positive take-off impulse relates strongly to 2k erg performance, emphasizing the importance of impulse as a measure of athlete power during rowing, which can guide strength and conditioning programs.
Application for Training: The findings advocate for monitoring CMJ force-time characteristics as they may reflect changes in neuromuscular function that directly correlate with rowing performance, allowing coaches to tailor strength and power training to enhance 2k erg results.
Why it matters:
The findings from this study highlight the significant role of positive take-off impulse in predicting performance during a 2,000-meter rowing ergometer race, showing a strong correlation (r = 0.71). This information is invaluable for coaches aiming to customize training programs, as focusing on strength and impulse characteristics in counter-movement jumps could lead to improved power output and better race times for rowers.
Biomechanics
-Proximal Biceps femoris shows lower muscle activation homogeneity and amplitude, potentially increasing injury risk during sport.
Biomechanics
-Core engagement during deep squats enhances lower limb muscle activation and stability, potentially reducing injury risk.
Biomechanics
-The fast knee flexion test reliably assesses acute knee flexor fatigue in young football players on the field.
Gender and Sex Differences in Sport
-Sex differences in double poling performance among youth athletes are mainly due to differences in upper-body strength and aerobic power.
Motor Skills
-Children practicing Parkour outperform team sports athletes in specific motor skills and physical fitness measures.
Neuromuscular Function
-Localized neuromuscular fatigue significantly impairs postural control, while general fatigue has more transient effects.
Physical Education and Pedagogy
-Adult physical fitness in China shows improvement, but physical activity and obesity rates are still growing concerns.
Skill acquisition
-Passing-skill training significantly improves side-foot kick accuracy in youth female soccer players compared to small-sided games.
Sport Physiology
-Breath-holding can enhance athletic performance, but requires tailored protocols and further research for optimal results.
Strength and Conditioning
-Velocity and acceleration-based plyometric training significantly enhances physical performance in young basketball players.
Youth Athlete
-Agility training significantly reduces sedentary behavior and improves mood and stress levels in adolescents in Pakistan.
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