Purpose of the Fact‑Finding

This page summarises the fact‑finding aims for a workout physiology dataset focusing on human physical performance, cardiovascular responses, and caloric expenditure during exercise.

The analysis likely seeks to understand:

• How demographic and anthropometric factors (age, gender, weight, height) affect workout performance.
• The relationships between heart‑rate metrics (e.g., resting HR, average HR, peak HR), calories burned, and exercise type/intensity.
• How macronutrient intake (carbohydrates, protein, fat) and energy balance (intake vs. expenditure) influence fitness outcomes.

Data Collection and Sources

Outline and confirm the actual capture methods below to finalise this section.

• Capture methods: wearable devices (HR monitors/smartwatches), gym equipment logs (treadmill, cycle ergometer), and manual nutrition logs.
• Variables measured: workout type, duration, distance/pace, heart‑rate time series (rest/avg/peak), calories burned, RPE; demographics (age, gender), anthropometrics (height, weight); nutrition (carbs, protein, fat, total energy).
• Units & standards: heart rate (bpm), energy (kcal), distance (km), duration (min), mass (kg), height (cm).
• Quality controls: device calibration, duplicate removal, outlier screening (e.g., implausible HR > 220 bpm), and missing‑value handling.
• Ethics & privacy: de‑identification, consent documentation, and secure storage of personally identifiable information (PII).

Note: replace the generic bullets above with the exact devices, logging tools, and protocols used in your study.

Suggested Next Steps

• Confirm column names and units; standardise to consistent types.
• Compute derived metrics (e.g., HR reserve, kcal per minute, speed/pace, VO₂ proxies if available).
• Model outcomes (calories burned, average HR) against predictors (age, gender, weight, workout type) using regression.
• Visualise dose–response: intensity vs. calories; macronutrients vs. energy balance; HR profiles across exercise types.