Yale Food Addiction Scale Analytics
Discover how your relationship with food compares to others. Gain context for your own experiences and see the patterns that emerge across populations struggling with food-related behaviours.
About This Data
This page presents aggregated, anonymised data from our Yale Food Addiction Scale (YFAS 2.0) test. The information displayed is based on 6439 individual calculator completions.
Important Note: This data represents a self-selecting cohort of individuals who actively sought out and completed our YFAS calculator. As such, it is not representative of the general population and should not be interpreted as prevalence data for food addiction in the broader community.
Total Completions
6,439
Average Symptom Count
4.8
out of 11 possible symptoms
Median Symptom Count
5
Clinical Significance Present
48.3%
of completions
Note: responses collected before the criterion-7 (withdrawal) scoring fix were scored under a different definition of that criterion. Figures from before and after the fix are not directly comparable. Read more about this change.
Diagnosis Category Distribution
The YFAS 2.0 classifies results into four categories based on symptom count and clinical significance: No Food Addiction, Mild, Moderate, and Severe Food Addiction.
Diagnosis Severity by Country
Distribution Pattern
The diagnosis distribution shows a striking bimodal pattern - people either report no food addiction (53.1%) or severe food addiction (37.2%), with mild and moderate categories combined representing only 9.8% of completions. This suggests food addiction may function more as a threshold phenomenon than a gradual spectrum. The YFAS diagnostic structure requires both clinical significance (clinically significant distress or impairment) and multiple symptoms for a diagnosis, which may contribute to this pattern.
Geographic Variation
Severe food addiction rates vary substantially by country:
- India: 46.5% severe food addiction (n=129)
- Norway: 25.7% severe food addiction (n=101)
This represents a 1.8-fold difference in severe food addiction rates (p < 0.001). These top 10 countries represent 4,747 completions.
Note:
Geographic differences may reflect cultural factors, dietary patterns, demographic composition, or help-seeking behaviours. Caution is advised when interpreting cross-country comparisons due to potential selection bias.
Symptom Count Distribution
This chart shows the distribution of symptom counts (0-11) across all calculator completions. The YFAS 2.0 measures 11 possible symptoms of food addiction.
Distribution of Symptom Counts
Distribution Pattern
The symptom count distribution reveals a zero-inflated, relatively uniform pattern. Notably, 20.6% of people report zero symptoms, followed by a sharp decline to 8.6% with exactly one symptom. However, for people reporting 2-11 symptoms, the distribution is remarkably flat, ranging from 6.3% to 7.8%.
Key Finding
This pattern is particularly striking because:
- People are evenly distributed across symptom counts 2-11 (approximately 10% in each category)
- There is no clustering at either low or high symptom counts
- This contradicts the bimodal diagnosis pattern observed in diagnosis categories (mostly 'No FA' or 'Severe FA')
What This Reveals
The contrast between uniform symptom distribution and bimodal diagnosis distribution demonstrates how the YFAS diagnostic structure transforms symptom counts into clinical categories. Many people with 2-5 symptoms are classified as 'No Food Addiction' due to lacking clinical significance (clinically significant distress or impairment), while those with 6+ symptoms are more likely to meet both symptom and clinical significance thresholds, resulting in 'Severe Food Addiction' diagnoses. This threshold effect creates the bimodal pattern in diagnoses despite the underlying symptom distribution being relatively uniform.
The Zero-Symptom Peak
The 20.6% of completions with zero symptoms likely represents individuals exploring the assessment out of curiosity, health professionals familiarizing themselves with the tool, or people seeking confirmation that their eating patterns are within typical ranges.
Note: responses collected before the criterion-7 (withdrawal) scoring fix were scored under a different definition of that criterion. Figures from before and after the fix are not directly comparable. Read more about this change.
Demographic Patterns
This section explores how food addiction symptoms vary across different demographic groups. Understanding these patterns can help identify populations at higher risk and inform targeted interventions.
Completions with Demographics
3,105
users provided age, sex, and BMI
Highest Risk Group
6.9
Obese Class III BMI
Strongest Predictor
BMI Category
shows clearest correlation
Data Completeness
51.5%
provided BMI information
Distribution Across Demographics
These charts show both the number of completions and average symptom count for each demographic group. The dual y-axes allow comparison of population size (left) and symptom severity (right).
Sex Distribution
Age Distribution
BMI Category Distribution
Clinical Significance Comparison
This chart compares the percentage of people with clinical significance (distress/impairment) across all demographic groups. Higher percentages indicate groups at greater risk.
Clinical Significance Across Demographics
Diagnosis Severity Distribution
These stacked bar charts show the percentage of people in each diagnosis category (No Food Addiction, Mild, Moderate, or Severe Food Addiction) across demographic groups. Each bar represents 100% of the population within that demographic category, allowing direct comparison of severity patterns.
Diagnosis Categories by Sex
Diagnosis Categories by Age
Diagnosis Categories by BMI
Interpreting These Patterns
⚖️ BMI Correlation
BMI shows the strongest correlation with food addiction symptoms, with over double the symptom count in higher BMI categories. Those with higher BMI categories consistently show higher symptom counts and clinical significance rates, suggesting a strong bidirectional relationship between weight status and food-related behaviours.
📉 Age Trend
Symptom counts decrease with age, with young adults (18-24) showing the highest rates. This may reflect differences in eating patterns, stress levels, food environment, or biological factors across life stages. The decline with age could indicate improved coping strategies or changing food relationships over time.
⚧️ Sex Differences
Females show approximately 22% higher symptom counts than males. This pattern is consistent with research on eating disorders and may reflect biological factors (hormonal influences on appetite and reward), psychological factors (emotion regulation, body image concerns), or social factors (cultural pressures, different food relationships).
📊 Combined Risk
Risk factors compound: under 18, young females show the highest symptom counts. Understanding these intersections helps identify the most vulnerable populations and can inform targeted prevention and intervention strategies. Multiple risk factors appearing together may indicate cumulative effects that warrant particular attention.
Data Quality & Statistical Significance
Demographic information was provided voluntarily by participants:
- Age: 48.2% of participants provided age information
- Sex: 55.6% of participants provided sex information
- BMI: 51.5% of participants provided height/weight for BMI calculation
Charts exclude "not specified" categories to focus on interpretable patterns. The substantial sample sizes provide reliable insights into demographic trends within this self-selecting population.
Statistical Note: P-values (shown in parentheses where applicable) indicate the statistical significance of observed differences. For group comparisons (e.g., males vs females), p < 0.05 is used as the significance threshold. For trend tests across ordered categories with small sample sizes (e.g., age groups, BMI categories), p < 0.10 is used to account for reduced statistical power. P < 0.01 indicates strong evidence, and p < 0.001 indicates very strong evidence for the observed pattern.
Explore the Data Interactively
Go beyond these charts with our interactive explorer. Filter by demographics, compare specific groups side-by-side, and discover patterns in the data with advanced visualizations.
🔥 Interactive Heatmaps
📊 Group Comparisons
🎯 Dynamic Filtering
DSM Criteria Met
The YFAS 2.0 assesses 11 DSM-5 substance-related and addictive disorders criteria adapted for food. This chart shows which criteria were most commonly met.
Most Commonly Met DSM Criteria
DSM Criteria Descriptions:
- Withdrawal symptoms
- Characteristic withdrawal symptoms; substance taken to relieve withdrawal
- Persistent desire to quit
- Persistent desire or repeated unsuccessful attempts to quit
- Larger amounts than intended
- Substance taken in larger amount and for longer period than intended
- Continued despite problems
- Use continues despite knowledge of adverse consequences (e.g., emotional problems, physical problems)
- Time spent obtaining / using
- Much time/activity to obtain, use, recover
- Craving
- Craving, or a strong desire or urge to use
- Tolerance
- Tolerance (marked increase in amount; marked decrease in effect)
- Use in hazardous situations
- Use in physically hazardous situations
- Activities given up
- Important social, occupational, or recreational activities given up or reduced
- Social or interpersonal problems
- Continued use despite social or interpersonal problems
- Failure to fulfill obligations
- Failure to fulfill major role obligation (e.g., work, school, home)
Note: responses collected before the criterion-7 (withdrawal) scoring fix were scored under a different definition of that criterion. Figures from before and after the fix are not directly comparable. Read more about this change.
Criterion 7: before vs. after the scoring fix
Comparison shown now that enough post-fix responses have been collected. Figures are ordered to follow the mechanism: criterion 7 changed first, and because symptomCount (which drives clinical significance) contains criterion 7, the symptom-count distribution and clinically-significant percentage move as a consequence.
| Scoring version | n | Criterion 7 endorsement | Mean symptom count (0-11) | Clinically significant |
|---|---|---|---|---|
| Before fix (v1) | 6,113 | 58.9% | 4.75 | 48.2% |
| After fix (v2) | 326 | 52.8% | 5.02 | 50.6% |
| Symptom count | Before fix (v1) | After fix (v2) |
|---|---|---|
| 0 | 20.8% | 16.6% |
| 1 | 8.6% | 8.3% |
| 2 | 7.2% | 8.9% |
| 3 | 6.3% | 6.1% |
| 4 | 6.7% | 8.6% |
| 5 | 6.5% | 6.7% |
| 6 | 6.7% | 8.0% |
| 7 | 7.4% | 6.4% |
| 8 | 7.9% | 6.4% |
| 9 | 7.8% | 5.8% |
| 10 | 6.9% | 7.1% |
| 11 | 7.3% | 11.0% |
Trends Over Time
These charts show how results have changed over time. Data is aggregated by month.
Average Symptom Count Over Time
Diagnosis Category Distribution Over Time
Geographic Distribution
This section shows how results vary by geographic location. All location data is anonymised and aggregated.
Top Countries by Completion Count
| Country | Region | Entries | Avg. Symptom Count | Clinical Significance % | No Addiction % | Mild % | Moderate % | Severe % |
|---|---|---|---|---|---|---|---|---|
| US - United States of America | - | 2004 | 5.2 | 56.1% | 45.5% | 3.4% | 7.8% | 43.3% |
| GB - United Kingdom | - | 1497 | 4.7 | 42.6% | 58.5% | 2.7% | 5.7% | 33.0% |
| CA - Canada | - | 284 | 5.3 | 57.0% | 45.1% | 2.8% | 6.7% | 45.4% |
| AU - Australia | - | 215 | 5.4 | 52.1% | 48.8% | 3.7% | 7.4% | 40.0% |
| DE - Germany | - | 149 | 4.4 | 46.3% | 55.0% | 3.4% | 6.0% | 35.6% |
| FR - France | - | 141 | 4.5 | 40.4% | 61.0% | 1.4% | 8.5% | 29.1% |
| IN - India | - | 129 | 5.4 | 51.9% | 48.1% | 3.1% | 2.3% | 46.5% |
| PL - Poland | - | 120 | 3.6 | 43.3% | 57.5% | 4.2% | 10.8% | 27.5% |
| NZ - New Zealand | - | 107 | 5.2 | 49.5% | 51.4% | 4.7% | 6.5% | 37.4% |
| NO - Norway | - | 101 | 3.7 | 33.7% | 68.3% | 1.0% | 5.0% | 25.7% |
| FI - Finland | - | 99 | 4.2 | 50.5% | 52.5% | 4.0% | 3.0% | 40.4% |
| NL - Netherlands | - | 85 | 4.3 | 41.2% | 60.0% | 2.4% | 5.9% | 31.8% |
| IL - Israel | - | 77 | 3.1 | 28.6% | 74.0% | 2.6% | 3.9% | 19.5% |
| BE - Belgium | - | 76 | 3.4 | 39.5% | 61.8% | 3.9% | 9.2% | 25.0% |
| IE - Ireland | - | 69 | 3.5 | 34.8% | 68.1% | 1.4% | 4.3% | 26.1% |
Top Regions by Completion Count
| Country | Region | Entries | Avg. Symptom Count | Clinical Significance % | No Addiction % | Mild % | Moderate % | Severe % |
|---|---|---|---|---|---|---|---|---|
| GB - United Kingdom | England | 1353 | 4.6 | 41.8% | 59.3% | 2.6% | 5.5% | 32.6% |
| US - United States of America | California | 198 | 5.1 | 55.1% | 46.5% | 3.5% | 5.6% | 44.4% |
| US - United States of America | New York | 145 | 5.2 | 53.8% | 47.6% | 4.8% | 3.4% | 44.1% |
| US - United States of America | Texas | 132 | 5.3 | 53.8% | 47.0% | 3.0% | 6.1% | 43.9% |
| CA - Canada | Ontario | 116 | 5.2 | 56.9% | 45.7% | 2.6% | 9.5% | 42.2% |
| US - United States of America | Florida | 98 | 5.7 | 66.3% | 35.7% | 4.1% | 9.2% | 51.0% |
| GB - United Kingdom | Scotland | 89 | 5.0 | 50.6% | 50.6% | 6.7% | 9.0% | 33.7% |
| US - United States of America | Illinois | 85 | 5.0 | 54.1% | 47.1% | 3.5% | 7.1% | 42.4% |
| US - United States of America | Ohio | 82 | 4.5 | 50.0% | 52.4% | 3.7% | 9.8% | 34.1% |
| US - United States of America | Pennsylvania | 80 | 5.4 | 56.3% | 43.8% | 1.3% | 5.0% | 50.0% |
Top Cities by Completion Count
| Country | Region | City | Entries | Avg. Symptom Count | Clinical Significance % | No Addiction % | Mild % | Moderate % | Severe % |
|---|---|---|---|---|---|---|---|---|---|
| GB - United Kingdom | England | London | 518 | 4.4 | 37.1% | 63.9% | 1.9% | 4.4% | 29.7% |
| US - United States of America | New York | New York City | 84 | 4.7 | 50.0% | 51.2% | 4.8% | 4.8% | 39.3% |
| AU - Australia | Victoria | Melbourne | 68 | 5.6 | 51.5% | 48.5% | 2.9% | 7.4% | 41.2% |
| AU - Australia | New South Wales | Sydney | 59 | 5.2 | 44.1% | 57.6% | 3.4% | 8.5% | 30.5% |
| CA - Canada | Ontario | Toronto | 58 | 4.8 | 46.6% | 56.9% | 1.7% | 6.9% | 34.5% |
| PL - Poland | Mazovia | Warsaw | 57 | 3.5 | 40.4% | 61.4% | 3.5% | 10.5% | 24.6% |
| NZ - New Zealand | Auckland | Auckland | 56 | 5.0 | 48.2% | 51.8% | 5.4% | 7.1% | 35.7% |
| IE - Ireland | Leinster | Dublin | 55 | 3.2 | 30.9% | 72.7% | 1.8% | 3.6% | 21.8% |
| FI - Finland | Uusimaa | Helsinki | 55 | 4.5 | 50.9% | 50.9% | 0.0% | 5.5% | 43.6% |
| JO - Jordan | Amman | Amman | 55 | 4.0 | 36.4% | 67.3% | 3.6% | 7.3% | 21.8% |
Location Explorer
Select a country and region to explore more detailed location data.
Methodology Notes
The Yale Food Addiction Scale 2.0 is a validated tool to assess food addiction based on DSM-5 criteria for substance use disorders. It consists of 35 questions about eating behaviors over the past 12 months.
Key Metrics Explained:
- Symptom Count: The number of DSM-5 criteria met (0-11).
- Clinical Significance: Whether food-related problems cause significant distress.
- Diagnosis Categories:
- No Food Addiction: Clinical significance not present, or fewer than 2 symptoms.
- Mild Food Addiction: Clinical significance present + 2-3 symptoms.
- Moderate Food Addiction: Clinical significance present + 4-5 symptoms.
- Severe Food Addiction: Clinical significance present + 6 or more symptoms.
For a detailed explanation of how YFAS 2.0 works and what your results mean, visit our article The Yale Food Addiction Scale 2.0 Explained.
Data Privacy:
All data presented here is anonymised and aggregated. No personally identifiable information is collected or displayed. Location data is derived from anonymised IP addresses and is only shown when sufficient entries exist to protect privacy.
Data Filtering Methodology:
To ensure the data presented here reflects genuine user interactions with the calculator and is suitable for analysis, we apply several filtering criteria before including submissions in the aggregated statistics:
- Speed Checks: Submissions completed unrealistically quickly (e.g., faster than a human could reasonably read and answer the questions) are automatically removed as likely automated entries.
- Duplicate Prevention: Measures are in place to detect and exclude unintended duplicate submissions from the same user, such as those resulting from accidental double-clicks or form re-submissions.
- Automated Submission Detection: We employ techniques (such as analysing submission patterns and utilizing anti-bot measures) to identify and filter out submissions from bots or automated scripts rather than human users.
- Completion Requirements: Only submissions where all necessary questions in the questionnaire were answered are included in the aggregate data.
These data quality control steps help maintain the integrity and relevance of the dataset used for generating the statistics displayed.
Citing This Page
If you reference these analytics in your work, please cite:
APA 7th Edition
Creative Touch Aesthetics Ltd. (2025). Yale Food Addiction Scale 2.0 demographic analytics. Retrieved September 14, 2026, from https://creativetouchrotherham.co.uk/calculators/food-addiction/analytics
BibTeX
@misc{creative_touch_2025,
author = {{Creative Touch Aesthetics Ltd}},
title = {Yale Food Addiction Scale 2.0 Demographic Analytics},
year = {2025},
howpublished = {\url{https://creativetouchrotherham.co.uk/calculators/food-addiction/analytics}},
note = {Accessed: 2026-09-14}
}