Integrating Screening and Clinical Interviews: Advancing the Assessment of Exercise Addiction in Athletes

This study demonstrates that while high scores on the Exercise Dependence Scale are common among endurance athletes, only a subset meet the stricter ICD-11 criteria for clinical addiction, highlighting the necessity of combining quantitative screening with structured clinical interviews to distinguish normative high involvement from pathological exercise addiction.

The Gist

Imagine you are a coach looking at a team of marathon runners. You notice that many of them run 20 hours a week, skip family dinners to train, and feel terrible if they miss a run. You might think, "Wow, these people are addicted to running!"

But is it really an addiction, or are they just incredibly dedicated athletes?

This is the exact puzzle researchers in this study tried to solve. They wanted to figure out the difference between being a super-competitive athlete and having a clinical addiction to exercise.

Here is the story of their research, broken down simply:

1. The "False Alarm" Problem

The researchers started with a common tool called the Exercise Dependence Scale (EDS). Think of this like a metal detector at an airport.

• How it works: It beeps loudly if you have a lot of metal (or in this case, if you have high scores on questions about running too much, feeling bad when you stop, etc.).
• The problem: A metal detector beeps for a belt buckle, a watch, and a gun. It can't tell the difference between a harmless accessory and a dangerous weapon. Similarly, the EDS "beeps" for both the dedicated athlete who loves their sport and the person with a dangerous addiction.

In this study, they scanned 342 endurance athletes. About 18% of them triggered the "high risk" alarm on the questionnaire.

2. The "Detective Interview"

To find out who was actually in trouble, the researchers didn't just trust the metal detector. They invited the 63 "high-risk" athletes for a structured clinical interview.

Think of this as a detective sitting down with a suspect. Instead of just asking, "Do you run a lot?" the detective asked deep questions:

• "Does your running ruin your life?"
• "Do you feel physically sick or mentally panicked if you miss a run?"
• "Are you running to cope with depression or anxiety?"
• "Do you have a history of other mental health struggles?"

3. The Big Surprise

Here is the twist: Only about 70% of the people who set off the "high risk" alarm actually had a clinical addiction.

The other 30% were just very dedicated athletes. They ran a lot and felt bad if they stopped, but their lives weren't falling apart, and they didn't have the deep psychological scars that come with addiction. The "metal detector" had flagged them, but the "detective interview" cleared them.

4. What Actually Separated the Two Groups?

The researchers compared the "Addicted" group against the "Just Dedicated" group. They found that how much they ran didn't matter. Both groups ran the same amount of hours.

Instead, the difference was in their hearts and minds:

• The "Just Dedicated" Group: They ran for the joy of it, for performance, or for health. They were happy with their lives overall.
• The "Addicted" Group: They ran to escape pain. They had lower life satisfaction. They were often running to control their weight or to silence negative feelings.
• The "Withdrawal" Clue: The addicted group suffered much more when they couldn't run. It was like a drug withdrawal; they felt physically and mentally sick.
• The "History" Clue: The addicted group had a much higher rate of past mental health struggles (like anxiety, depression, or eating disorders). It seemed like their running was a way to self-medicate old wounds.

5. The "Magic Formula"

The researchers used a special computer program (called LASSO) to find the best way to predict who was truly addicted. They found that if you want to know if someone has an exercise addiction, don't just ask, "How many miles do you run?"

Instead, ask these three questions:

1. Do you feel sick or panicked when you stop? (Withdrawal symptoms)
2. Do you have a history of other mental health issues? (Comorbidities)
3. Is your running a way to cope with emotional pain? (Anamnesis/History)

If the answer is "Yes" to these, it's likely a real addiction, not just a hobby.

The Bottom Line

This study teaches us that high volume doesn't equal high danger.

Just because an athlete runs 20 hours a week doesn't mean they are sick. Many are just passionate professionals. True addiction is quieter and darker: it's when exercise stops being a joy and becomes a desperate, painful compulsion used to hide from emotional pain.

The Takeaway: We shouldn't label every marathon runner as "addicted." Instead, we need to look deeper at why they run and how they feel when they stop. If they are running to escape their life, that's when we need to step in and help.

Technical Summary

1. Problem Statement

Exercise addiction (EA) is a dysfunctional behavior characterized by excessive exercise, withdrawal symptoms, and negative consequences. However, it lacks a distinct diagnostic category in major classification systems (DSM-5, ICD-11), often being subsumed under "Disorders due to addictive behaviors" (6C5Y).

• The Core Issue: Current research relies heavily on self-report questionnaires like the Exercise Dependence Scale (EDS). While useful for screening, these tools struggle to distinguish between exercise dependence (high, potentially normative involvement in athletes) and clinically relevant exercise addiction (pathological behavior causing functional impairment).
• The Gap: High EDS scores in endurance athletes may reflect professional dedication rather than psychopathology. Previous studies (e.g., Müller et al., 2014) showed only moderate agreement between EDS screening and clinical interviews but did not deeply characterize the psychological or motivational differences between those who met clinical criteria and those who did not.

2. Methodology

The study employed a two-step mixed-methods design involving 342 endurance athletes.

• Phase 1: Quantitative Screening (Online Survey)

  • Participants: 342 endurance athletes (159 female, 183 male).
  • Instruments:
    • EDS (Exercise Dependence Scale): Used to identify "at-risk" athletes (Cut-off > 77).
    • Demographics & Training Data: Age, BMI, training volume, duration.
    • Psychological Measures: Life satisfaction, motivational factors (health, weight, performance, pleasure, social), and the SCL-90-R (Symptom Checklist-90) for general psychopathology.
  • Outcome: 63 athletes (18.4%) scored above the EDS cut-off and were invited for Phase 2.

• Phase 2: Structured Clinical Interviews

  • Participants: 34 athletes from the "at-risk" group participated (46% attrition rate).
  • Procedure: Semi-structured interviews based on ICD-11 criteria for "Disorders due to addictive behaviors" (6C5Y).
  • Classification Criteria: Athletes were classified as "Exercise Addicted" if they met ICD-11 features: impaired control, increasing priority of exercise, continuation despite harm, persistence for >12 months, and significant distress/impairment.
  • Coding: Two independent raters analyzed transcripts using MAXQDA (Interrater reliability: 97%).

• Statistical Analysis

  • Comparative Analysis: Mann-Whitney U-tests and t-tests compared "Addicted" vs. "Non-Addicted" groups on demographics, EDS subscales, SCL-90 scores, and motivation.
  • Predictive Modeling: A LASSO (Least Absolute Shrinkage and Selection Operator) logistic regression was used to identify the most discriminative predictors of ICD-11 classification.
  • Validation: A stratified bootstrap stability analysis (1,000 resamples) was conducted to assess model robustness given the small sample size.

3. Key Results

A. Diagnostic Concordance

• Of the 63 athletes screened as "at-risk" by the EDS, only 34 completed the interview.
• 70.6% (n=24) of the interviewed athletes met the ICD-11 criteria for exercise addiction.
• 29.4% (n=10) were classified as non-addicted despite high EDS scores.
• Implication: A significant portion of athletes flagged by questionnaires do not meet clinical criteria for addiction.

B. Group Differences (Addicted vs. Non-Addicted)

• Training Volume: No significant differences in weekly hours, workout frequency, or duration of athletic involvement.
• Demographics: Addicted athletes were significantly older (p = .028).
• Psychological & Motivational Factors:
  • Life Satisfaction: Significantly lower in the addicted group (p = .037).
  • Motivation: Addicted athletes showed higher weight-related motivation and lower performance-oriented motivation.
  • EDS Subscales: Addicted athletes scored significantly higher on Withdrawal and Continuation subscales. No differences were found in Tolerance or Lack of Control.
• Psychopathology (SCL-90-R): Addicted athletes exhibited significantly higher scores in:
  • Interpersonal Sensitivity (p = .006).
  • Global Severity Index (GSI), Positive Symptom Total (PST), and Positive Symptom Distress Index (PSDI).
• Comorbidities: Addicted athletes reported significantly more current (p = .012) and past (p < .001) psychiatric comorbidities.

C. Predictive Modeling (LASSO)

• The LASSO model achieved an AUC of 0.97 and 94.1% accuracy in distinguishing addicted from non-addicted athletes.
• Top Predictors:
  1. Interview-based withdrawal symptom sum (Highest stability: 81.3% selection probability).
  2. Past comorbidity sum score.
  3. EDS Withdrawal subscale.
  4. Age (emerged as a frequent predictor in bootstrap analysis).

4. Key Contributions

1. Refining the Diagnostic Boundary: The study empirically demonstrates that high EDS scores in endurance athletes often reflect normative dependence (high commitment) rather than pathological addiction. Only a subset meets ICD-11 clinical criteria.
2. Differentiation of Profiles: It identifies that the distinction between "at-risk" and "addicted" lies not in training volume, but in psychological vulnerability (interpersonal sensitivity, low life satisfaction), motivational shifts (from performance to weight control), and withdrawal symptoms.
3. Validation of Multi-Method Assessment: It supports the necessity of combining quantitative screening with structured clinical interviews to avoid over-pathologizing dedicated athletes.
4. Predictive Framework: It establishes that withdrawal symptoms and history of comorbidities are the strongest clinical indicators of true exercise addiction, suggesting EA often functions as a maladaptive coping mechanism for pre-existing psychological issues.

5. Significance and Clinical Implications

• Clinical Practice: Clinicians should not rely solely on EDS scores for diagnosis. A positive screen should trigger a structured interview focusing on functional impairment, withdrawal, and comorbid psychopathology.
• Treatment Planning: Since past comorbidities are strong predictors, treatment should address underlying emotional regulation difficulties and body image concerns rather than focusing solely on reducing exercise volume.
• Conceptual Clarity: The findings align with the Dualistic Model of Passion, suggesting that "addicted" athletes exhibit obsessive passion (driven by internal pressure and affect regulation), whereas high-scoring non-addicted athletes likely exhibit harmonious passion (volitional, integrated with identity).
• Future Research: Highlights the need for longitudinal studies to determine causality between comorbidities and exercise addiction, and the development of standardized diagnostic interviews for EA.

Conclusion: The study concludes that while questionnaires are useful for identifying high-involvement athletes, true exercise addiction is a distinct clinical entity characterized by withdrawal, functional impairment, and comorbid psychopathology, requiring a multi-method assessment approach for accurate diagnosis.