What Really Drives Better Health: Exercise, Stress, and Diet Matter Most

By shrijeetverma13 · June 16, 2026

We analyzed 10,000 people's health, lifestyle, and wellness data to identify which factors actually improve outcomes. Early morning risers have better…

A data table was generated to help identify which factors most strongly relate to higher Sleep Quality Scores across the population. The table provides a structured view of the available factors and their relationships to sleep quality.

Early Wakers tend to have better Health Scores and Energy Levels compared to Non-Early Wakers. The analysis compared 10,000 individuals split into two groups. Early Wakers (n=4,158) averaged a Health Score of 75.36 and an Energy Level Score of 7.87, while Non-Early Wakers (n=5,842) averaged 73.38 and 7.57 respectively. Two visualizations were generated: a grouped bar chart showing the average scores side-by-side for each group, and a box plot showing the full distribution of Health Scores across both groups.

Exercise frequency has a meaningful positive impact on both Productivity Score and Wellness Category. Two charts were generated showing these relationships clearly. People who exercise 7 days a week average a productivity score of 7.78, compared to just 6.14 for those who don't exercise at all — a notable improvement. The correlation between exercise frequency and productivity is 0.37, indicating a moderate positive relationship. On the wellness side, 74.3% of people exercising 7 days a week fall into the 'Excellent' wellness category, and more frequent exercise consistently shifts people toward higher wellness categories.

The analysis reveals a clear divide between how Stress Level and Screen Time Before Bed relate to Anxiety and Depression risk scores. Stress Level is a strong predictor of both mental health outcomes, while screen time shows virtually no relationship. Three visualizations and supporting data tables were generated to illustrate these findings.

The analysis reveals clear patterns in Hypertension, Diabetes, and Cardiovascular risk across both Occupation and Age groups. Two bar charts were generated showing the breakdown visually. Among occupations, Nurses stand out with the highest Hypertension risk (47.4% High) and Cardiovascular risk (6.9% High), while Students show the highest Diabetes risk (6.7% High). For age groups, the 61-80 bracket dominates all three risk categories: 84.7% High for Hypertension, 10.1% High for Diabetes, and 18.2% High for Cardiovascular risk — significantly higher than younger age groups.

The analysis reveals that Fast Food Meals Per Week and Sugary Drinks Per Week have different effects on BMI and Health Score. Three charts and supporting data tables were generated to visualize these relationships. Fast food has a notable negative impact on Health Score (correlation: -0.207), meaning more fast food meals are associated with lower health scores. In contrast, neither fast food nor sugary drinks show a meaningful relationship with BMI (correlations near zero: 0.004 and -0.009 respectively). Sugary drinks also show virtually no correlation with Health Score (0.004). Looking at the extremes, people eating 0 fast food meals/week average a BMI of 25.6 and Health Score of 77.1, while those eating 14 meals/week average a BMI of 26.3 and Health Score of 67.3 — a notable 10-point drop in health score.

The analysis successfully produced a grouped bar chart and data tables comparing the top 10 occupations by average Sleep Quality Score against their Stress Level, Fatigue Level Score, and Working Hours Per Day. The visualization shows each occupation side by side with four color-coded bars, making it easy to spot which roles maintain good sleep despite demanding schedules and which are most affected by stress-related sleep disruption.

Using the IQR method, outliers were detected across all five physical health metrics — BMI, Resting Heart Rate, Systolic BP, Diastolic BP, and Blood Sugar Level. A total of 241 outlier instances were identified, with Blood Sugar Level having the most (64), followed by Diastolic BP (51), BMI (45), Resting Heart Rate (43), and Systolic BP (38). Box plots and a bar chart visualize these distributions and counts. Cross-tabulation against risk categories reveals that outlier individuals are dramatically overrepresented in high-risk groups compared to normal individuals.

The analysis explored how Wellness Categories (Excellent, Good, Fair, Poor) are distributed across different Fitness Level groups, and how physical fitness correlates with Health, Mood, and Life Satisfaction scores. Four data tables were successfully generated, breaking down the wellness distribution and average scores across fitness levels.

The analysis successfully produced a correlation heatmap for 7 cardiovascular and metabolic health indicators (Resting Heart Rate, Systolic BP, Diastolic BP, Cholesterol Level, Blood Sugar Level, BMI, and Health Score), along with a grouped bar chart showing how lifestyle factors relate to these markers. Two visualizations and multiple data tables were generated to capture these relationships.