Overview and resources to aid using Fitbit data available on All of Us Researcher Workbench

  • Updated

Table of Contents

Authors

Goal

Data Types

Heart Rate

heart_rate_summary table

heart_rate_minute_level table

Sleep

sleep_daily_summary and sleep_level tables 

Activity

activity_summary table

steps_intraday table

Device

Ways to pre-process Fitbit data

Heart rate algorithm 

Step compliance definition 

Authors

Hiral Master, Aymone Kouame, Hunter Hollis, Melika Salehabadi, Sam Stewart

Goal

The goal of this article is provide details, links and resources to gain a better understanding around Fitbit data. We acknowledge that we can’t teach the exact steps on how to pre-process and analyze Fitbit data as the processes vary by study design and intent. This article is an overview of data elements available on the Researcher Workbench to help you get started using the All of Us Fitbit data. We hope it will be a helpful tool as you start using Fitbit data in your study on Researcher Workbench.

Note: The program simply provides Fitbit data values as generated by Fitbit webAPI (https://dev.fitbit.com/build/reference/web-api/) and no additional cleaning and processing has been done on the values. 

Data Types

Below table provides a high level overview of all the tables for the Fitbit data that are presented to the Researcher Workbench. You can find detailed data elements present in each table via the most recent CDR in the Data Dictionary under the “Wearables” tab here. You can also find the Fitbit Web API link in the table to get an overview on how the elements in these tables are generated.

Data Types BQ Tables  Fitbit web API link
Heart Rate heart_rate_summary https://dev.fitbit.com/build/reference/web-api/heartrate-timeseries/get-heartrate-timeseries-by-date/
  heart_rate_minute_level https://dev.fitbit.com/build/reference/web-api/intraday/get-heartrate-intraday-by-date/
Sleep sleep_daily_summary https://dev.fitbit.com/build/reference/web-api/sleep/get-sleep-log-by-date/
  sleep_level https://dev.fitbit.com/build/reference/web-api/sleep/get-sleep-log-by-date/
Activity activity_summary

https://dev.fitbit.com/build/reference/web-api/activity-timeseries/get-activity-timeseries-by-date/

  steps_intraday

https://dev.fitbit.com/build/reference/web-api/intraday/get-activity-intraday-by-date/

Device device

https://dev.fitbit.com/build/reference/web-api/devices/get-devices/

 

In the next sections, we provide a detailed overview of each of the tables and some links that might be useful to better understand these data types.

 

Heart Rate

On the Researcher Workbench, registered users can access participant-level heart rate data measured using the Fitbit device on daily summary as well as on intraday basis using heart_rate_summary and heart_rate_minute_level BQ tables, respectively.

heart_rate_summary table

  • We recommend users to visit Fitbit web API website to gain more understanding around data elements provided in heart_rate_summary table using this link: https://dev.fitbit.com/build/reference/web-api/heartrate-timeseries/get-heartrate-timeseries-by-date/ 
  • In this table, we provide heart rate values by zone names. Currently, the possible values for heart rate zone are: out of range, fat burn, cardio, and peak. More information on these default heart rate zone names can be found on Fitbit website. On a high level, here’s the brief summary definitions of the zone names available to researchers. Zones are defined as a percentage of maximum heart rate, which is (220 - Age).
      • Fat Burn Zone: Between 50% and 69% of participant’s maximum heart rate. In the fat burn zone, participants are likely in a moderate activity such as a brisk walk. 
      • Cardio Zone: Between 70% and 84% of your maximum heart rate. In the cardio zone, participants are likely doing a vigorous activity such as running or spinning.
      • Peak Zone: Greater than 85% of your maximum heart rate. In the peak zone, participants are likely doing a short, intense activity that improves performance and speed, such as sprinting or high-intensity interval training.
      • Out of range Zone: below fat burn zone, i.e. anything below 50% max heart rate. In this out of range zone, participants’ heart beat at a slower pace.

heart_rate_minute_level table

Sleep

sleep_daily_summary and sleep_level tables

  • Registered users can access participant-level sleep data sourced from Fitbit on daily summary as well as on sequence level using sleep_daily_summary and sleep_level BQ tables, respectively.
  • We recommend users to visit Fitbit web API website to gain more understanding around sleep data using this link: https://dev.fitbit.com/build/reference/web-api/sleep/get-sleep-log-by-date/
  • To get a better understanding of each sleep data that is being made available, refer to https://blog.fitbit.com/sleep-stages-explained/. The possible values for the sleep stage are light, deep, rem, wake. Based on experts this reference, summarizing the key takeaways to gain understanding around each sleep stage:
      • LIGHT: In this stage, the body processes memories and emotions and metabolism regulates itself. Breathing and heart rate typically decrease slightly during this stage.
      • DEEP: In this stage, participants become less responsive to outside stimuli. Breathing slows and muscles relax; heart rate usually becomes more regular.
      • REM: stage is when most dreaming happens and participant’s eyes move rapidly in different directions (hence the name). Heart rate increases and breathing becomes more irregular.
  • Refer to this blog :https://help.fitbit.com/articles/en_US/Help_article/1314.htm to get an overview on how fitbit tracks sleep. Providing a high-level summary here: There are a few scenarios where user might see sleep pattern (which shows time asleep, restless, and awake) instead of sleep stages (awake, light, deep, REM):
      • If a user slept in a position that prevented the device from getting a consistent heart-rate reading or if the device is worn too loosely.
        For best results, the device should be worn higher on the wrist (about 2-3 finger widths above wrist bone). The band should feel secure but not too tight.
      • If the user used the Begin Sleep Now option in the Fitbit app (instead of simply wearing your device to bed).
      • If the user slept for less than 3 hours.
      • If the device’s battery is critically low.

 

Activity

Registered users can access participant-level activity data measured using the Fitbit device on daily summary as well as on intraday basis using activity_summary and step_intraday BQ tables, respectively.

activity_summary table

steps_intraday table

Device

Ways to pre-process Fitbit Data

Currently, the Fitbit data provided on Researcher Workbench does not involve any specific and additional pre-processing or cleaning and the data simply represents what is being generated by the Fitbit device. Therefore, researchers might want to consider pre-processing or cleaning  the data to account for different types of biases present in wearables data. Detailed information around considerations to be accounted for using Fitbit data can be found in this article: Considerations while using Fitbit Data in the All of Us Research Program

One of the most common biases is adherence bias since Fitbit can’t always be collecting data, either due to users forgetting to wear the device or have removed the device to charge or take a shower.

Heart Rate algorithm

Heart rate algorithm is one of the approaches users can use to account for adherence bias. This algorithm uses the concept of “daily adherence level” that is derived using minute-level heart rate data. Specifically, daily adherence level is calculated by dividing the total count of minute level heart rate data collected within a day by total number of minutes (1440 minutes in a day). The optimal threshold for filtering can then be selected carefully based on the data type in use to avoid extensive data loss and to conserve variance in the data.

Step compliance definition

Evidence suggests that 10-hour or more wear time to define a valid day to estimate daily physical activity such as daily step counts during waking time. Therefore, another approach to account for adherence bias is to define whether day is valid or not. Valid day can then be defined as a participant wearing the Fitbit for at least 10 hours per day and reporting at least 100 steps per day. This approach has been implemented in prior study that leveraged All of Us data. Researchers can refer to this featured workspace to find out coding details on how to implement this algorithm on Researcher Workbench.

Other error mitigation strategies are described in this user support hub article: Considerations while using Fitbit Data in the All of Us Research Program.  

 

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