Introduction
In today’s fast-paced digital world, people are increasingly conscious about health and wellness. With the rise of wearable devices such as smartwatches and fitness trackers, individuals now have access to detailed data about their daily activities. The Fitness & Health Tracking dataset is designed to capture essential aspects of a person’s lifestyle, including activity levels, calorie burn, workout duration, and sleep patterns. Combined with categorical details such as activity type, diet preference, gender, and age group, this dataset enables health researchers, fitness coaches, and wellness companies to analyze patterns and make data-driven decisions.
Dataset Overview
The dataset contains 350 rows of anonymized health-tracking data. Each record represents a snapshot of an individual’s daily activity. The dataset has 8 columns in total:
- Numerical Columns:
- Daily Steps – The total number of steps walked by a person in a day. Steps are an essential indicator of physical activity.
- Calories Burned – Estimated calories burned through all activities, including workouts and daily movements.
- Workout Duration (minutes) – The time spent in structured physical exercise, such as gym workouts, running, yoga, or sports.
- Hours of Sleep – The amount of rest an individual gets, which is vital for recovery and overall wellness.
- Categorical Columns:
5. Activity Type – The main activity for the day (e.g., Running, Walking, Gym, Yoga, Cycling).
6. Diet Preference – Whether the person follows a Vegetarian, Non-Vegetarian, Vegan, or Keto diet.
7. Gender – Male, Female, or Other.
8. Age Group – Segmented into categories like 18–25, 26–35, 36–50, and 51+.
Purpose of the Dataset
The dataset serves multiple purposes:
- Health Analysis – Identify lifestyle patterns that contribute to better fitness outcomes, such as higher calorie burn or better sleep quality.
- Segmentation Studies – Compare habits across genders, diet preferences, and age groups. For example, do younger individuals walk more steps than older ones?
- Predictive Modeling – Build models to predict calorie burn based on steps, workout duration, and demographics.
- Behavioral Insights – Understand how diet choices and activity preferences influence overall wellness.
- Business Applications – Fitness apps, gyms, and healthcare providers can use such datasets to tailor services to customer needs.
Importance of Such Data
Tracking fitness data is not just about numbers. It empowers individuals to take charge of their health and motivates them to improve daily habits. On a larger scale, organizations can use this dataset to design personalized health programs, create targeted wellness campaigns, and monitor population health trends.
📈 Five KPIs to Create
- Average Daily Steps – Measures the mean number of steps taken across all users.
KPI Use: Helps understand the activity level of the population. - Calories Burned per Workout Minute –
Avg Calories per Workout Minute = DIVIDE(AVERAGE(Fitness[Calories_Burned]), AVERAGE(Fitness[Workout_Duration]), 0)KPI Use: Evaluates workout efficiency. - Sleep Quality Index – % of users sleeping more than 7 hours daily.
KPI Use: Indicates overall restfulness. - Workout Participation Rate – % of users with Workout Duration > 0 minutes.
KPI Use: Measures consistency of active lifestyle. - Average Additional Burn by Activity Type – Compare calorie differences across activities (Running vs Yoga).
KPI Use: Identifies high-impact activities.
📊 Eight Visualization Questions in Power BI
- Which age group walks the most average daily steps?
- How do diet preferences impact average calorie burn?
- What is the gender distribution across activity types?
- Which activity type leads to the highest average workout duration?
- Is there a correlation between steps walked and hours of sleep?
- Which country (if extended with location data) shows healthier lifestyle patterns?
- How does sleep duration vary across different age groups?
- What percentage of users sleep less than 6 hours but burn more than 2500 calories daily?