Fitbit Case Study Analysis

Author

Joseph Burch

Published

October 9, 2025

Executive Summary

This report provides insights from Fitbit data on daily activity and sleep patterns.

The analysis focuses on trends in steps taken, sedentary behavior, and sleep quality among participants.

**Key objectives for stakeholders:**

- Identify patterns in activity and sleep to guide product positioning.

- Highlight potential areas for marketing initiatives.

- Offer actionable insights to enhance user engagement and retention.

Data Overview

We analyzed two primary datasets from Fitbit:

  1. Daily Activity (dailyActivity_merged.csv)
    • Metrics: Total steps, total distance, sedentary minutes, calories burned.
  2. Sleep Data (sleepDay_merged.csv)
    • Metrics: Total minutes asleep, time in bed, sleep records.

Both datasets include the Id column to uniquely identify participants.

Participant Overview

  • Number of unique participants:
    • Daily Activity: 33
    • Sleep Data: 24
  • Observations in each dataset:
    • Daily Activity: 940
    • Sleep Data: 413

Observation: The daily activity dataset contains slightly more participants than the sleep dataset, indicating that not all users track sleep regularly.

   TotalSteps    TotalDistance    SedentaryMinutes
 Min.   :    0   Min.   : 0.000   Min.   :   0.0  
 1st Qu.: 3790   1st Qu.: 2.620   1st Qu.: 729.8  
 Median : 7406   Median : 5.245   Median :1057.5  
 Mean   : 7638   Mean   : 5.490   Mean   : 991.2  
 3rd Qu.:10727   3rd Qu.: 7.713   3rd Qu.:1229.5  
 Max.   :36019   Max.   :28.030   Max.   :1440.0  
 TotalSleepRecords TotalMinutesAsleep TotalTimeInBed 
 Min.   :1.000     Min.   : 58.0      Min.   : 61.0  
 1st Qu.:1.000     1st Qu.:361.0      1st Qu.:403.0  
 Median :1.000     Median :433.0      Median :463.0  
 Mean   :1.119     Mean   :419.5      Mean   :458.6  
 3rd Qu.:1.000     3rd Qu.:490.0      3rd Qu.:526.0  
 Max.   :3.000     Max.   :796.0      Max.   :961.0

Key Insights From Summary Statistics

  1. Daily Activity
    • Average steps per day indicate moderate activity levels.
    • Sedentary minutes are significant, highlighting opportunities to encourage more movement.
  2. Sleep Patterns
    • Most participants spend slightly more time in bed than asleep.
    • Variability in sleep duration suggests opportunities for sleep-improvement features.

These trends can inform product messaging, user challenges, and personalized recommendations.

Visual Insights

The following visualizations highlight key trends:

  • Steps vs Sedentary Minutes: Understand how movement and inactivity relate.
  • Minutes Asleep vs Time in Bed: Assess sleep efficiency and identify potential interventions.

Combined Activity & Sleep Analysis

By merging the two datasets, using the ID, we can examine the interplay between daily activity and sleep habits. We can see the total number of unique ID’s is 24.

Strategic Insights

  1. Activity & Sleep Correlation
    • Participants with higher steps may have slightly better sleep, though patterns vary.
    • This suggests potential for programs that link activity incentives to improved sleep.
  2. Marketing Opportunities
    • Segment users based on activity level and sleep patterns for targeted campaigns.
    • Promote features like daily challenges, step goals, or sleep coaching.
  3. Product Recommendations
    • Encourage low-activity users to take incremental steps to improve daily movement.
    • Offer insights to users on how sleep and activity are connected, enhancing engagement.

Overall, the data supports data-driven product positioning and personalized marketing initiatives to increase Fitbit engagement.

Conclusion

This analysis demonstrates that Fitbit users show diverse activity and sleep behaviors.
Insights from this data can help guide product strategy, marketing campaigns, and user engagement initiatives.

Future analyses could include: - Advanced segmentation of participants
- Trend analysis over time
- Integration with additional Fitbit datasets for holistic insights