Fitness Wearable User Behaviour Survey

Columns

• Timestamp: Records the exact time and date when the survey response was submitted.
• What is your age?: Indicates the age group of the respondent (e.g., 18-24, 25-34).
• What is your gender?: Specifies the gender of the respondent (e.g., Female, Male).
• What is your highest level of education?: Details the respondent's highest educational attainment (e.g., Bachelor's degree, High school diploma).
• What is your current occupation?: Describes the respondent's current profession (e.g., Student, Self-employed).
• How often do you exercise in a week?: Records the frequency of exercise per week as stated by the respondent (e.g., 3-4 times a week, 1-2 times a week).
• How long have you been using a fitness wearable?: Indicates the duration of fitness wearable usage by the respondent (e.g., Less than 6 months, 6-12 months).
• How frequently do you use your fitness wearable?: States how often the respondent uses their fitness wearable (e.g., 3-4 times a week, Daily).
• How often do you track fitness data using wearable?: Details the frequency with which the respondent tracks fitness data using their wearable (e.g., Every day, Once a week).
• How has the fitness wearable impacted your fitness routine?: Describes the respondent's perceived impact of the wearable on their fitness routine (e.g., Positively impacted my fitness routine).
• Has the fitness wearable helped you stay motivated to exercise?: Measures the respondent's agreement on whether the wearable helped them stay motivated to exercise (e.g., Strongly agree, Agree).
• Do you think that the fitness wearable has made exercising more enjoyable?: Measures the respondent's agreement on whether the wearable made exercising more enjoyable (e.g., Strongly agree, Agree).
• How engaged do you feel with your fitness wearable?: Gauges the respondent's level of engagement with their fitness wearable (e.g., Somewhat engaged, Very engaged).
• Does using a fitness wearable make you feel more connected to the fitness community?: Assesses whether using a wearable fosters a feeling of connection to the fitness community (e.g., Agree, Strongly agree).
• How has the fitness wearable helped you achieve your fitness goals?: Describes the extent to which the wearable assisted in achieving fitness goals (e.g., Helped me achieve my goals somewhat more quickly).
• How has the fitness wearable impacted your overall health?: Details the respondent's perceived impact of the wearable on their overall health (e.g., Improved my overall health somewhat, Improved my overall health significantly).
• Has the fitness wearable improved your sleep patterns?: Measures the respondent's agreement on whether the wearable improved their sleep patterns (e.g., Agree, Strongly agree).
• Do you feel that the fitness wearable has improved your overall well-being?: Measures the respondent's agreement on whether the wearable improved their overall well-being (e.g., Strongly agree, Agree).
• Has using a fitness wearable influenced your decision? [To exercise more?]: Assesses if the wearable influenced the decision to exercise more (e.g., Agree, Strongly agree).
• Has using a fitness wearable influenced your decision? [To purchase other fitness-related products?]: Assesses if the wearable influenced the decision to purchase other fitness-related products (e.g., Agree, Neutral).
• Has using a fitness wearable influenced your decision? [To join a gym or fitness class?]: Assesses if the wearable influenced the decision to join a gym or fitness class (e.g., Agree, Strongly agree).
• Has using a fitness wearable influenced your decision? [To change your diet?]: Assesses if the wearable influenced the decision to change diet (e.g., Agree, Strongly agree).

Distribution

Usage

• Exploratory data analysis (EDA), particularly suitable for beginners due to its clear structure and manageable size.
• Creating various data visualisations to explore insights into customer demographics, exercise habits, and the perceived benefits of fitness wearables.
• Analysing the impact of fitness wearables on users' routines, motivation levels, enjoyment of exercise, and sense of connection to the fitness community.
• Investigating how wearables contribute to achieving fitness goals, improving overall health, sleep patterns, and general well-being.
• Understanding the influence of wearable technology on lifestyle decisions, such as increasing exercise, purchasing other fitness products, joining gyms, or modifying diet.

Coverage

License

Who Can Use It

• Beginners in data science looking for practical experience with survey data for exploratory analysis.
• Researchers focused on consumer behaviour, health technology adoption, and the psychological effects of fitness wearables.
• Data analysts and statisticians interested in current trends in health and fitness technologies.
• Students undertaking academic projects involving data visualisation and questionnaire analysis.
• Professionals in the fitness and wellness industry seeking insights into user engagement and product effectiveness.

Dataset Name Suggestions

• Fitness Wearable User Behaviour Survey
• Digital Wellness Impact Dataset
• Consumer Fitness Technology Insights
• Wearable Device Usage and Perceptions
• Health Wearable Consumer Study

Attributes