How Fitbit data are being made available to registered researchers in <i>All of Us</i> Research Program

Master, Hiral; Kouame, Aymone; Marginean, Kayla; Basford, Melissa A.; Harris, Paul A.; Holko, Michelle

doi:10.1142/9789811270611_0003

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…A substantial majority of Americans across a range of demographic groups own a smartphone [ 9 ] and nearly 90% (n=1800) of users look at their phone within 1 hour of waking and throughout the day [ 38 ], providing opportunities to engage adults with monitoring HR, daily steps, and other health metrics in both urban and rural locations where health care access may be challenging. Moreover, many large research programs such as the All of Us research program are using wearables to gather HR and physical activity data with the ability to link the data to health outcomes available in electronic health records [ 39 , 40 ]. By demonstrating relations of CRF to 2 measures that are relatively straightforward to assess via wearable devices, this investigation provides potentially useful metrics to monitor with the goals of maintaining or improving CRF.…”

Section: Discussionmentioning

confidence: 99%

Association of Smartwatch-Based Heart Rate and Physical Activity With Cardiorespiratory Fitness Measures in the Community: Cohort Study

Zhang,

Wang,

Pathiravasan

et al. 2024

J Med Internet Res

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Background Resting heart rate (HR) and routine physical activity are associated with cardiorespiratory fitness levels. Commercial smartwatches permit remote HR monitoring and step count recording in real-world settings over long periods of time, but the relationship between smartwatch-measured HR and daily steps to cardiorespiratory fitness remains incompletely characterized in the community. Objective This study aimed to examine the association of nonactive HR and daily steps measured by a smartwatch with a multidimensional fitness assessment via cardiopulmonary exercise testing (CPET) among participants in the electronic Framingham Heart Study. Methods Electronic Framingham Heart Study participants were enrolled in a research examination (2016-2019) and provided with a study smartwatch that collected longitudinal HR and physical activity data for up to 3 years. At the same examination, the participants underwent CPET on a cycle ergometer. Multivariable linear models were used to test the association of CPET indices with nonactive HR and daily steps from the smartwatch. Results We included 662 participants (mean age 53, SD 9 years; n=391, 59% women, n=599, 91% White; mean nonactive HR 73, SD 6 beats per minute) with a median of 1836 (IQR 889-3559) HR records and a median of 128 (IQR 65-227) watch-wearing days for each individual. In multivariable-adjusted models, lower nonactive HR and higher daily steps were associated with higher peak oxygen uptake (VO2), % predicted peak VO2, and VO2 at the ventilatory anaerobic threshold, with false discovery rate (FDR)–adjusted P values <.001 for all. Reductions of 2.4 beats per minute in nonactive HR, or increases of nearly 1000 daily steps, corresponded to a 1.3 mL/kg/min higher peak VO2. In addition, ventilatory efficiency (VE/VCO2; FDR-adjusted P=.009), % predicted maximum HR (FDR-adjusted P<.001), and systolic blood pressure-to-workload slope (FDR-adjusted P=.01) were associated with nonactive HR but not associated with daily steps. Conclusions Our findings suggest that smartwatch-based assessments are associated with a broad array of cardiorespiratory fitness responses in the community, including measures of global fitness (peak VO2), ventilatory efficiency, and blood pressure response to exercise. Metrics captured by wearable devices offer a valuable opportunity to use extensive data on health factors and behaviors to provide a window into individual cardiovascular fitness levels.

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Section: Discussionmentioning

confidence: 99%

Association of Smartwatch-Based Heart Rate and Physical Activity With Cardiorespiratory Fitness Measures in the Community: Cohort Study

Zhang,

Wang,

Pathiravasan

et al. 2024

J Med Internet Res

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“…Daily SDANN was calculated for AoU participants that had provided minute-level heart rate Fitbit measurements for at least one full day (1,440 minutes) in a Bring-Your-Own-Device model (24). Fitbit uses photoplethysmography to derive heart rate measurements (25)Data extraction and SDANN calculation were conducted using R (Methods in Supplement 1).…”

Section: Study Populationmentioning

confidence: 99%

Integrating Genome-wide information and Wearable Device Data to Explore the Link of Anxiety and Antidepressants with Heart Rate Variability

Friligkou

Koller

Pathak

et al. 2023

Preprint

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Background: Anxiety disorders are associated with decreased heart rate variability (HRV), but the underlying mechanisms remain elusive.Methods: We selected individuals with whole-genome sequencing, Fitbit, and electronic health record data (N=920; 61,333 data points) from the All of Us Research Program. Anxiety PRS were derived with PRS-CS after meta-analyzing anxiety genome-wide association studies from three major cohorts- UK Biobank, FinnGen, and the Million Veterans Program (N Total =364,550). The standard deviation of average RR intervals (SDANN) was calculated using five-minute average RR intervals over full 24-hour heart rate measurements. Antidepressant exposure was defined as an active antidepressant prescription at the time of the HRV measurement in the EHR. The associations of daily SDANN measurements with the anxiety PRS, antidepressant classes, and antidepressant substances were tested. Participants with lifetime diagnoses of cardiovascular disorders, diabetes mellitus, and major depression were excluded in sensitivity analyses. One-sample Mendelian randomization (MR) was employed to assess potential causal effect of anxiety on SDANN.Results: Anxiety PRS was independently associated with reduced SDANN (beta=-0.08; p=0.003). Of the eight antidepressant medications and four classes tested, venlafaxine (beta=-0.12, p=0.002) and bupropion (beta=-0.071, p=0.01), tricyclic antidepressants (beta=-0.177, p=0.0008), selective serotonin reuptake inhibitors (beta=-0.069; p=0.0008) and serotonin and norepinephrine reuptake inhibitors (beta=-0.16; p=2x10-6) were associated with decreased SDANN. One-sample MR indicated an inverse effect of anxiety on SDANN (beta=-2.22, p=0.03).Conclusions: Anxiety and antidepressants are independently associated with decreased HRV, and anxiety appears to exert a causal effect on HRV. Our observational findings provide novel insights into the impact of anxiety on HRV.

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“…As of 2023, over 15,000 All of Us participants have shared Fitbit device data [5]. The Researcher Workbench provides access to these data, including Fitbit-defined heart rate by zones that are based on percentages of estimated maximum heart rate, minute-level heart rate, daily activity summaries, minute-level intraday steps, daily sleep summaries, and sleep levels [6].…”

Section: Introductionmentioning

confidence: 99%

“…In this viewpoint paper, we examine the reliability of Fitbit device data in the context of the AoURP's BYOD program. We focus on Fitbit devices, given their wide market share [8,9], the ongoing collection of data from Fitbit users in the AoURP (eg, BYOD program) [2,10], and these data's availability to registered Researcher Workbench users [5]. Data are currently not available from the AoURP regarding the Fitbit device models included in their data set.…”

Section: Introductionmentioning

confidence: 99%

The Importance of Data Quality Control in Using Fitbit Device Data From the Research Program

Lederer,

Breton,

Jeong

et al. 2023

JMIR Mhealth Uhealth

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Wearable digital health technologies (DHTs) have become increasingly popular in recent years, enabling more capabilities to assess behaviors and physiology in free-living conditions. The All of Us Research Program (AoURP), a National Institutes of Health initiative that collects health-related information from participants in the United States, has expanded its data collection to include DHT data from Fitbit devices. This offers researchers an unprecedented opportunity to examine a large cohort of DHT data alongside biospecimens and electronic health records. However, there are existing challenges and sources of error that need to be considered before using Fitbit device data from the AoURP. In this viewpoint, we examine the reliability of and potential error sources associated with the Fitbit device data available through the AoURP Researcher Workbench and outline actionable strategies to mitigate data missingness and noise. We begin by discussing sources of noise, including (1) inherent measurement inaccuracies, (2) skin tone–related challenges, and (3) movement and motion artifacts, and proceed to discuss potential sources of data missingness in Fitbit device data. We then outline methods to mitigate such missingness and noise in the data. We end by considering how future enhancements to the AoURP’s Fitbit device data collection methods and the inclusion of new Fitbit data types would impact the usability of the data. Although the reliability considerations and suggested literature are tailored toward Fitbit device data in the AoURP, the considerations and recommendations are broadly applicable to data from wearable DHTs in free-living conditions.

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How Fitbit data are being made available to registered researchers in All of Us Research Program

Cited by 4 publications

References 3 publications

Association of Smartwatch-Based Heart Rate and Physical Activity With Cardiorespiratory Fitness Measures in the Community: Cohort Study

Association of Smartwatch-Based Heart Rate and Physical Activity With Cardiorespiratory Fitness Measures in the Community: Cohort Study

Integrating Genome-wide information and Wearable Device Data to Explore the Link of Anxiety and Antidepressants with Heart Rate Variability

The Importance of Data Quality Control in Using Fitbit Device Data From the Research Program

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