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Step 2: Prepare

Guidelines from the assignment

  • Guiding questions
  • Where is your data stored?
  • How is the data organized? Is it in long or wide format?
  • Are there issues with bias or credibility in this data? Does your data ROCCC?
  • How are you addressing licensing, privacy, security, and accessibility?
  • How did you verify the data's integrity?
  • How does it help you answer your question?
  • Are there any problems with the data?
  • Key tasks
  1. Download data and store it appropriately.
  2. Identify how it's organized.
  3. Sort and filter the data.
  4. Determine the credibility of the data.
  • Deliverable
  • A description of all data sources used

Fitbit Fitness Tracker data (Fitabase)

This dataset is located in Kaggle, seems like a well-documented and widely used dataset posted here [1]. Even though the dataset is in Kaggle, the metadata is not great. Looking more into the discussion section, I found a better description of the dataset and variables in this pdf (thanks to Laimis).

This dataset is organized in many different CSV files, split according to the activity register in the gadget.

Without looking into the details of the dataset, I already see some possible sources of bias. First, the dataset is not current, and therefore the findings of this project can be outdated. Although it is original and cited, it is indicated that the data was taken from survey respondents without specifying genre or location. The data can be comprehensive for the 30 individuals surveyed but cannot be extrapolated to an entire population.

This dataset can be limited and biased for Bellabeat, who is interested in women's health. Therefore the findings of this project should specify this.

The original dataset is located in the Zenodo repository.

The Fitabase dataset contains the following files:

  • dailyActivity_merged.csv
  • heartrate_seconds_merged.csv
  • hourlyCalories_merged.csv
  • hourlyIntensities_merged.csv
  • hourlySteps_merged.csv
  • minuteCaloriesNarrow_merged.csv
  • minuteIntensitiesNarrow_merged.csv
  • minuteMETsNarrow_merged.csv
  • minuteSleep_merged.csv
  • minuteStepsNarrow_merged.csv
  • weightLogInfo_merged.csv

Other data sources

I identified other cited and reliable open datasets that might be helpful:

  • A dataset for 46 participants using apple watch and Fitbit charge [2], I will call this dataset AppleFitbitData. This dataset contains the following files:
    • aw_fb_data.csv
    • data_for_weka_aw.csv
    • data_for_weka_fb.csv

I found the preprint related to the dataset here. It is helpful to understand the data. After looking at that paper, I know that each row corresponds to one minute of recorded activity. Participants were asked to do six different tasks in 65 minutes. In the files, I can find 49 participants (23 men and 26 women), which is different from what the website's dataset indicated.

  • A dataset of first-year college students using Fitbit to study the effects in academic performance in Fall 2017 [3]. I will call this dataset FitbitsAndGradesData. This dataset can be helpful because it contains data split into gender; I found the paper from this dataset, and the link is here.

References

[1] Furberg, R., Brinton, J., Keating, M., & Ortiz, A. (2016). Crowd-sourced Fitbit datasets 03.12.2016-05.12.2016 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.53894

[2] Fuller, Daniel, 2020, "Replication Data for: Using machine learning methods to predict physical activity types with Apple Watch and Fitbit data using indirect calorimetry as the criterion.", https://doi.org/10.7910/DVN/ZS2Z2J, Harvard Dataverse, V1

[3] Broaddus, Allie; Jaquis, Brandon; Jones, Colt; Jost, Scarlet; Lang, Andrew; Li, Ailin; et al. (2018): Dataset: Fitbits, field-tests, and grades. The effects of a healthy and physically active lifestyle on the academic performance of first year college students.. figshare. Dataset. https://doi.org/10.6084/m9.figshare.7218497.v1