The Rhythm of Wellness | A Fitbit Case Study on Activity and Rest
BY DENISE BEHRMANN
Note: This is a fictional case study project. The dataset used is publicly available from Kaggle and is based on Fitbit user data. This analysis was independently written by me using RStudio.
Goal: This analysis aims to uncover insights into user behavior based on daily physical activity and sleep patterns. The goal is to support Bellabeat, a wellness-focused smart device company, in identifying trends that can inform product development and targeted marketing strategies.
Target Audience: This analysis is intended for the Bellabeat product and marketing teams, but it may also be relevant to wellness researchers and potential users seeking to understand lifestyle impacts on personal health.
Sources: Two CSV files were used from the Fitbit Fitness Tracker Data on Kaggle:
sleepDay_merged.csv: Tracks total time in bed and minutes asleep.
dailyActivity_merged.csv: Tracks steps, distance, and sedentary behavior.
ANALYTICS
Patterns Observed
A general inverse trend: higher daily step counts correlate with less sedentary time.
Sleep patterns show inefficiencies; time in bed does not always equate to time asleep.
Variability in tracking behavior may reflect different lifestyle routines or device usage habits.
Key Findings
- Activity vs. Inactivity:
Individuals with higher physical activity often report fewer sedentary minutes, although the correlation is not absolute.
- Sleep Efficiency:
There is notable inconsistency between time spent in bed and actual sleep, indicating potential areas for user education or intervention.
- User Behavior: Not all users consistently track both sleep and activity, which may impact the comprehensiveness of health insights.
Recommended Marketing Strategies
Target users with low activity and poor sleep efficiency through personalized campaigns.
Highlight Bellabeat’s ability to provide holistic insights by linking activity to rest patterns.
Product Feature Ideas:
Add sleep efficiency scores with tips to improve nighttime routines.
Develop smart nudges for users experiencing long sedentary periods.
Integrate personalized feedback that reflects both daytime and nighttime behavior.
References: Piwek, L., Ellis, D. A., Andrews, S., & Joinson, A. (2016). The rise of consumer health wearables: Promises and barriers.* * PLOS Medicine, 13(2), e1001953. * Tudor-Locke, C., Craig, C. L., Brown, W. J., et al. (2011). How many steps/day are enough? Int J Behav Nutr Phys Act, 8(1), 79. * Strath, S. J., Kaminsky, L. A., Ainsworth, B. E., et al. (2013). Guide to the assessment of physical activity. Circulation, 128(20), 2259–2279.*