How Supplements, Gender, and Age Shape Fitness Gains

By shrijeetverma13 · June 13, 2026

We analyzed data from supplement users to see which combinations work best for weight gain and strength improvement. The findings reveal surprising…

The supplement that produces the greatest average weight change is 'Both' (combining multiple supplements), with an average weight gain of 8.04 kg from initial to final weight. A bar chart has been generated showing the average weight change for each supplement group, making it easy to compare their effectiveness.

The analysis compared average Strength Gain percentages across all supplements in the dataset. A bar chart and data tables have been generated showing the ranked performance of each supplement. You can see the full breakdown in the visualization above, which ranks supplements from highest to lowest average strength gain.

The Primary Benefit distribution varies clearly across the three supplement types. Two grouped bar charts and supporting data tables were generated to show both percentage and count distributions. Each supplement type has a different dominant benefit: users of 'Both' supplements most commonly seek Cognitive Support (23.0%), Creatine Monohydrate users most commonly aim for Strength Gain (22.7%), and Mass Gainer users most commonly target Weight Gain (22.4%).

The analysis compared weight change and strength gain outcomes across five age groups (18-25, 26-35, 36-45, 46-55, and 56-65). Two bar charts were generated showing average weight change and average strength gain by age group, making it easy to visually compare performance across different life stages.

Yes, males and females do respond differently to each supplement, and two bar charts have been generated to show exactly how. The first chart displays average weight gain (in kg) by supplement and gender side by side, while the second shows average strength gain (%) broken down the same way. These grouped bar charts make it easy to spot which supplements produce larger gains for one gender versus the other.

Program duration in weeks has virtually no meaningful relationship with either weight change or strength gain. The correlation between weeks and weight change is essentially zero (-0.001), and the correlation with strength gain is also very weak (0.070). This means that longer programs don't necessarily lead to better outcomes — other factors likely play a bigger role. Two charts and supporting data tables were generated to visualize these relationships.

The analysis breaks down Primary Benefit outcomes by Gender and visualizes how benefit categories are distributed within each gender group. A grouped bar chart was generated showing the percentage of participants achieving each Primary Benefit category for Female, Male, and Non-Binary identities. Data tables were also produced with the detailed counts and percentages for each gender-benefit combination.

The analysis successfully ranked all supplement types by their average Strength Gain percentage. A horizontal bar chart was generated showing each supplement's average strength gain from highest to lowest, along with supporting data tables that include median strength gain and the proportion of participants achieving above 20% strength gain for each supplement type.

The Pearson correlation matrix was successfully computed for all five variables — Age, Weeks, Initial WT, Final WT, and Strength Gain — and visualized as an annotated heatmap. The results reveal that most variable pairs have very weak relationships, with one standout exception: Initial Weight and Final Weight are very strongly correlated (r = 0.975), meaning participants who started heavier also ended heavier. All other correlations are near zero, suggesting that Age, Weeks of training, and Strength Gain are largely independent of each other and of weight in this dataset.

The analysis of weight change (Final WT minus Initial WT) across all participants reveals a well-behaved, nearly normal distribution. On average, participants gained 5.45 kg, with a median of 5.60 kg — the close alignment of mean and median confirms the distribution is symmetric. The middle 50% of participants (IQR) experienced weight changes between 2.00 kg and 8.20 kg. A histogram with KDE overlay and a box plot were generated to visualize the distribution shape and spread.