It sounds like you have a substantial amount of personal exercise and health data accumulated over 150 days. This data can provide valuable insights into your fitness journey and overall well-being. Here are some suggestions on how you can analyze and make the most of this information:

Exercise Types:

Identify the types of exercises you've been engaging in. Categorize them into cardiovascular, strength training, flexibility, and other categories. Note the frequency and duration of each type of exercise.

Intensity Levels: Assess the intensity of your workouts. This can be measured in terms of heart rate, perceived exertion, or weight lifted. Determine if there are patterns in intensity levels over time.

Progress and Setbacks: Look for trends in your progress. Are you consistently improving, or have you encountered any setbacks? Identify factors that contribute to your success or challenges.

Rest and Recovery: Analyze your rest days and recovery strategies. Ensure that you're allowing your body enough time to recover between intense workouts. Look for patterns in your energy levels and performance related to rest.

Nutrition and Hydration: Correlate your exercise data with your nutrition and hydration habits. Consider whether certain eating patterns impact your workouts positively or negatively.

Sleep Patterns: Examine your sleep data if available. Adequate sleep is crucial for recovery and overall health. Identify any correlations between your sleep patterns and exercise performance.

Mood and Stress Levels: Reflect on your mood and stress levels on different days. Exercise can have a significant impact on mental well-being. Consider whether there are connections between your exercise routine and your emotional state.

Injury Analysis: If you've experienced any injuries during this period, analyze the circumstances surrounding them. This can help in understanding potential risk factors.

Goal Alignment: Evaluate whether your exercise routine aligns with your initial goals. Are you progressing toward your desired outcomes?

Adjustment of Exercise Routine: Based on the analysis, consider adjustments to your exercise routine. This might involve modifying the types of exercises, intensity, or frequency.

Remember, the goal of analyzing this data is to make informed decisions about your fitness routine, identify areas of improvement, and celebrate your successes. If you have specific questions about the data or need guidance on certain aspects, feel free to provide more details for personalized advice.

In January 2025, the leading mobile fitness and workout apps recorded over 25 million downloads worldwide. The month of January regularly sees a seasonal surge in downloads of fitness and workout mobile apps. January 2021 recorded roughly 26.31 million downloads of leading fitness and workout apps, representing a 30 percent increase from the previous year. Between 2022 and 2023, the trend appears to have normalized, with downloads of the most popular mobile fitness apps experiencing a slowing growth. In recent years, fitness and workout mobile apps have become increasingly popular thanks to their convenience over gym memberships and the ability of app publishers to increase both quality and quantity of available in-app features. In 2024, apps in the eServices fitness market are forecasted to generate revenues for almost 1.8 million U.S. dollars in the United States alone.

Key Fitness App StatisticsTop Fitness AppsHealth & Fitness App Market LandscapeFitness App RevenueFitness Revenue by AppFitness App UsersFitness App Market ShareFitness App DownloadsTracking...

A September 2023 survey on exercise habits in the United States revealed that a lack of energy was the most common barrier to engaging in fitness. Moreover, time constraints were an issue for 27 percent of respondents when it came to taking part in fitness activities.

Dataset Overview

FitLife360 is a synthetic dataset that simulates real-world health and fitness tracking data from 3,000 participants over a one-year period. The dataset captures daily activities, vital health metrics, and lifestyle factors, making it valuable for health analytics and predictive modeling.

Features Description

Demographic Information

participant_id: Unique identifier for each participant age: Age of participant (18-65 years) gender: Gender (M/F/Other) height_cm: Height in centimeters weight_kg: Weight in kilograms bmi: Body Mass Index calculated from height and weight

Activity Metrics

activity_type: Type of exercise (Running, Swimming, Cycling, etc.) duration_minutes: Length of activity session intensity: Exercise intensity (Low/Medium/High) calories_burned: Estimated calories burned during activity daily_steps: Daily step count

Health Indicators

avg_heart_rate: Average heart rate during activity resting_heart_rate: Resting heart rate blood_pressure_systolic: Systolic blood pressure blood_pressure_diastolic: Diastolic blood pressure health_condition: Presence of health conditions smoking_status: Smoking history (Never/Former/Current)

Lifestyle Metrics

hours_sleep: Hours of sleep per night stress_level: Daily stress level (1-10) hydration_level: Daily water intake in liters fitness_level: Calculated fitness score based on cumulative activity

Potential Use Cases

1. Health Outcome Prediction

Predict risk of health conditions based on activity patterns Forecast potential life expectancy based on health metrics Identify early warning signs of health issues

2. Weight Management Analysis

Develop personalized weight loss prediction models Analyze effectiveness of different activities for weight loss Study the relationship between sleep, stress, and weight management

3. Fitness Progress Tracking

Track fitness level progression over time Analyze the impact of consistent exercise on health metrics Study recovery patterns and optimal training frequencies

4. Healthcare Analytics

Analyze the relationship between lifestyle choices and health outcomes Study the impact of smoking on fitness performance Investigate correlations between sleep patterns and health metrics

5. Personal Training Applications

Develop personalized exercise recommendations Optimize workout intensity based on individual characteristics Create targeted fitness programs based on health conditions

6. Research Applications

Study seasonal patterns in exercise behavior Analyze the relationship between stress and physical activity Research the impact of hydration on exercise performance

Dataset Overview:

This dataset contains simulated (hypothetical) but almost realistic (based on AI) data related to sleep, heart rate, and exercise habits of 500 individuals. It includes both pre-exercise and post-exercise resting heart rates, allowing for analyses such as a dependent t-test (Paired Sample t-test) to observe changes in heart rate after an exercise program. The dataset also includes additional health-related variables, such as age, hours of sleep per night, and exercise frequency.

The data is designed for tasks involving hypothesis testing, health analytics, or even machine learning applications that predict changes in heart rate based on personal attributes and exercise behavior. It can be used to understand the relationships between exercise frequency, sleep, and changes in heart rate.

File: Filename: heart_rate_data.csv File Format: CSV

- Features (Columns):

Age: Description: The age of the individual. Type: Integer Range: 18-60 years Relevance: Age is an important factor in determining heart rate and the effects of exercise.

Sleep Hours: Description: The average number of hours the individual sleeps per night. Type: Float Range: 3.0 - 10.0 hours Relevance: Sleep is a crucial health metric that can impact heart rate and exercise recovery.

Exercise Frequency (Days/Week): Description: The number of days per week the individual engages in physical exercise. Type: Integer Range: 1-7 days/week Relevance: More frequent exercise may lead to greater heart rate improvements and better cardiovascular health.

Resting Heart Rate Before: Description: The individual’s resting heart rate measured before beginning a 6-week exercise program. Type: Integer Range: 50 - 100 bpm (beats per minute) Relevance: This is a key health indicator, providing a baseline measurement for the individual’s heart rate.

Resting Heart Rate After: Description: The individual’s resting heart rate measured after completing the 6-week exercise program. Type: Integer Range: 45 - 95 bpm (lower than the "Resting Heart Rate Before" due to the effects of exercise). Relevance: This variable is essential for understanding how exercise affects heart rate over time, and it can be used to perform a dependent t-test analysis.

Max Heart Rate During Exercise: Description: The maximum heart rate the individual reached during exercise sessions. Type: Integer Range: 120 - 190 bpm Relevance: This metric helps in understanding cardiovascular strain during exercise and can be linked to exercise frequency or fitness levels.

Potential Uses: Dependent T-Test Analysis: The dataset is particularly suited for a dependent (paired) t-test where you compare the resting heart rate before and after the exercise program for each individual.

Exploratory Data Analysis (EDA):Investigate relationships between sleep, exercise frequency, and changes in heart rate. Potential analyses include correlations between sleep hours and resting heart rate improvement, or regression analyses to predict heart rate after exercise.

Machine Learning: Use the dataset for predictive modeling, and build a beginner regression model to predict post-exercise heart rate using age, sleep, and exercise frequency as features.

Health and Fitness Insights: This dataset can be useful for studying how different factors like sleep and age influence heart rate changes and overall cardiovascular health.

License: Choose an appropriate open license, such as:

CC BY 4.0 (Attribution 4.0 International).

Inspiration for Kaggle Users: How does exercise frequency influence the reduction in resting heart rate? Is there a relationship between sleep and heart rate improvements post-exercise? Can we predict the post-exercise heart rate using other health variables? How do age and exercise frequency interact to affect heart rate?

Acknowledgments: This is a simulated dataset for educational purposes, generated to demonstrate statistical and machine learning applications in the field of health analytics.

This Fitness and Health Tracking Dataset provides a comprehensive collection of daily health metrics for a variety of users over a five-year period. It includes measurements such as age, gender, height, weight, steps taken, calories burned, sleep hours, water intake, active minutes, heart rate, workout type, stress level, and mood. Each record corresponds to a specific date, enabling detailed time-series analysis.

This dataset is ideal for:

Data scientists and machine learning enthusiasts looking to build predictive models. Health professionals and fitness analysts aiming to understand behavioral patterns. Anyone interested in analyzing trends in personal health and fitness data.

Key Features: User Demographics: Information such as age, gender, height, and weight. Daily Activity: Steps taken, calories burned, and active minutes. Health Metrics: Average heart rate, hours slept, and water intake. Mood and Stress: Daily mood tracking and stress level on a scale from 1 to 10. Workout Types: Categorized workouts (Cardio, Strength, Yoga, etc.). Missing Data: Intentional missing values (e.g., sleep hours, water intake) to allow data scientists to practice handling incomplete data. Possible Uses: Visualization: This dataset is ideal for creating various visualizations, such as bar charts, line graphs, and pie charts to demonstrate fitness and health trends. Machine Learning: Use the data for predictive modeling to identify trends in user activity or detect patterns in heart rate and stress levels. Data Cleaning: Contains missing values, providing opportunities to practice data cleaning techniques. Tags: health fitness tracking wearables time-series machine learning data analysis visualization Note: This dataset includes missing values in columns such as Hours_Slept, Water_Intake, and Heart_Rate to provide a realistic scenario for data cleaning and analysis. Users are encouraged to explore this dataset to uncover interesting insights or develop health-related models.

Dataset

This dataset was created by Nargis Karimova

Released under CC0: Public Domain

Contents

Context

The motivation behind collecting this data-set was personal, with the objective of answering a simple question, “does exercise/working-out improve a person’s activeness?”. For the scope of this project a person’s activeness was the measure of their daily step-count (the number of steps they take in a day). Mood was measured in either "Happy", "Neutral" or "Sad" which were given numeric values of 300, 200 and 100 respectively. Feeling of activeness was measured in either "Active" or "Inactive" which were given numeric values of 500 and 0 respectively. I had noticed for a while that during the months when I was exercising regularly I felt more active and would move around a lot more. As opposed to when I was not working out, i would feel lethargic. I wanted to know for sure what the connection between exercise and activeness was. I started compiling the data on 6th October with the help Samsung Health application that was recording my daily step count and the number of calories burned. The purpose of the project was to establish through two sets of data (control and experimental) if working-out/exercise promotes an increase in the daily step-count or not.

Content

Date Step Count Calories Burned Mood Hours of Sleep Feeling or Activeness or Inactiveness Weight

Acknowledgements

Special thanks to Samsung Health that contributed to the set by providing daily step count and the number of calories burned.

Inspiration

"Does exercise/working-out improve a person’s activeness?”

The health and fitness club market in the United States was estimated to grow at an annual rate of **** percent between 2018 and 2024. This meant that the industry was predicted to be worth over *** billion U.S. dollars by 2024.

Childhood obesity has risen and is one of the most important global problems of our time, and school physical education programs are the key to ameliorating it. In American schools, physical fitness scores have declined; yet, global, national, state, and local concerns for the overall health, physical fitness, and wellbeing of children are at an all-time high. The lack of safe and affordable options for physical activity coupled with the significant decrease in physical activity rates among most American children underscores the need for programs, data, and research on physical fitness in schools, where children spend a significant amount of their time. The purpose of this brief research report is to call the federal government and states to mandate physical fitness programs and to increase data collection capacity on physical fitness in schools. Subsequently, this study asks researchers to study physical fitness in schools in the U.S. to increase its importance to policy makers and educational stakeholders and advance our understanding of educational inequities in school physical fitness. As an example, using descriptive analyses, we have provided policymakers, educational stakeholders, and researchers with a first look at California’s physical fitness data which shows how our findings complement prior literature as well as extend them. Implications for the research and practice are discussed.

Introduction

Fitness Industry Statistics: The fitness industry has experienced significant growth over the past few years, driven by the increasing importance of fitness, exercise, mental health, and hobbies. Most of the younger generation prefer to work out at gyms. With iconic personalities such as Arnold Schwarzenegger and Franco Colombo, people are willing to follow in their footsteps.

Since the pandemic, new trends are evolving that support online fitness training. Let’s see what these recent Fitness Industry Statistics hold in terms of recent developments all over the world.

Comprehensive YouTube channel statistics for fitness in gym, featuring 1,310,000 subscribers and 102,655,263 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Lifestyle category and is based in EG. Track 319 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Dataset Summary: Gym Check-ins and User Metadata

This synthetic dataset represents gym check-ins and user metadata, split across four CSV files. It simulates gym activity across 10 different locations, featuring user details, gym attributes, and check-in history. The dataset now also includes information about different subscription plans.

Data Description

Users Data

This file contains detailed information about users who visit the gyms.

• user_id: Unique identifier for each user.
• first_name: First name of the user.
• last_name: Last name of the user.
• age: Age of the user.
• gender: Gender of the user (Male, Female, Non-binary).
• birthdate: Date of birth of the user.
• sign_up_date: Date when the user signed up for the gym membership.
• user_location: City where the user lives.
• subscription_plan: The user's gym subscription plan (Basic, Pro, Student).

Gym Locations Data

This file describes the gyms and their locations.

• gym_id: Unique identifier for each gym.
• location: Real-world city where the gym is located (e.g., New York, Los Angeles).
• gym_type: The type of gym (Premium, Standard, Budget).
• facilities: List of facilities available at the gym (e.g., Swimming Pool, Sauna, Yoga Classes).

Check-in/Checkout History

This file tracks user check-ins and check-outs at the gyms.

• user_id: ID of the user who checked in.
• gym_id: ID of the gym where the check-in occurred.
• checkin_time: Timestamp of when the user checked in.
• checkout_time: Timestamp of when the user checked out.
• workout_type: Type of workout performed during the visit (e.g., Cardio, Weightlifting, Yoga).

• calories_burned: Estimated number of calories burned during the workout.

  Subscription Plans

  This file provides a description of the different subscription plans available to gym members.

• subscription_plan: The name of the subscription plan (Basic, Pro, Student).

• price_per_month: Price per month in Dollar

• features: Which features are present in this subsription

Acknowledgement

Foto von Danielle Cerullo auf Unsplash

In 2024, it was found that 23.6 percent of men in the United States participated in sports, exercise, and recreational activities daily, compared to only 19.4 percent of women. These statistics highlight a notable difference in the daily engagement of different genders in sporting activities. Other factors influencing this participation include socioeconomic status, age, disability, ethnicity, geography, personal interests, and societal expectations. These barriers can prevent individuals from having equal access to, and opportunities for, sport participation. What role does gender play in sports participation? Historically, many sports have been segregated by gender, with men and women participating in separate leagues and competitions. This segregation has led to a lack of opportunities for women and girls to participate in sports at the same level as men and boys. Additionally, societal attitudes and stereotypes about gender can discourage women and girls from participating in sports or limit their access to resources and support for their athletic pursuits. This often results in fewer women and girls participating in sports and a lack of representation of women and girls in leadership roles within the sports industry. However, in recent years, there has been an increased focus on promoting gender equality in sports and providing equal opportunities for men and women to participate in sports. This includes initiatives to increase funding and support for women's sports, as well as efforts to challenge gender stereotypes and discrimination in the athletic world.

Fitness-QA

This is a synthetic dataset for fitness content based on "neuml/txtai-wikipedia" embedding index. The generation of statements from context uses txtinstruct. This dataset contains questions generated from contexts using the statement generator "flan-t5-base" trained on SQuAD dataset. Each context includes generated questions with coherent relevant answers, and the irrelevant questions with (I don't have data on that). Fitness data is pulled from wikipedia data stored… See the full description on the dataset page: https://huggingface.co/datasets/hammamwahab/fitness-qa.

The National Health and Nutrition Examination Survey’s (NHANES) National Youth Fitness Survey (NNYFS) was conducted in 2012 to collect nationally representative data on physical activity and fitness levels for U.S. children and adolescents aged 3-15 years, through household interviews and fitness tests conducted in mobile examination centers.
The NNYFS interview includes demographic, socioeconomic, dietary, and health-related questions. The fitness tests included standardized measurements of core, upper, and lower body muscle strength, and gross motor skills, as well as a measurement of cardiovascular fitness by walking and running on a treadmill. A total of 1,640 children and adolescents aged 3-15 were interviewed and 1,576 were examined.
This set of restricted data files contains indirect identifying and/or sensitive information collected in NNYFS. For NNYFS public use files, please visit NNYFS 2012 at: https://wwwn.cdc.gov/nchs/nhanes/search/nnyfs12.aspx. For more information on the survey design, implementation, and data analysis, see the NNYFS Analytic Guidelines at: https://www.cdc.gov/nchs/nnyfs/analytic_guidelines.htm. For more information on NHANES, visit the NHANES - National Health and Nutrition Examination Survey Homepage at: https://www.cdc.gov/nchs/nhanes/index.htm.

Financial overview and grant giving statistics of Fitness on Main

** percent of U.S. respondents answer our survey on "Fitness and health service purchases" with ****************. The survey was conducted in 2025, among 13,689 consumers. Looking to gain valuable insights about consumers of health and fitness services worldwide? Check out our reports about gym & fitness club members worldwide. These reports offer the readers a comprehensive overview of gym goers: who they are; what they like; what they think; and how to reach them.

Statistics illustrates consumption, production, prices, and trade of Gym and Fitness Equipment in France from 2007 to 2024.