Beyond Daily Step Count: Evaluation of an Individuals Physical Health Status From a Wearable Activity Sensor

Beyond Daily Step Count: Evaluation of an Individuals Physical Health Status From a Wearable Activity Sensor

According to the American Thoracic Society, health status is defined as “an individual's relative level of wellness and illness, taking into account the presence of biological or physiological dysfunction, symptoms, and functional impairment.”  Physical status is an important component of an individual’s overall health status and embodies an individual’s capacity to perform activities of daily life, and hence is intimately linked to the status of most of the major organ systems. Measurements of physical status are used to assess disease progression of recovery, and are associated with clinical outcomes.  Clinical tools to quantify physical status usually rely on patient reports or visual observation in clinical settings (e.g. timed walk tests, gait speed, etc.).  Since they are performed at clinic or physician visits, data are collected at low frequency. 

In contrast to assessment of an individual’s physical status, assessment of an individual’s health status at the molecular and organ levels are based on high information content data.  For example, modern sequencing methods provide reliable assessment of an individual’s genome (e.g. 1 GB – 1 TB), and a wide range of laboratory assays routinely provide important information on the levels of protein, hormones, and other small molecules in blood and other samples.  Similarly, at the organ level, current imaging technologies provide high image quality (e.g. 1 MB – 1 GB) at relatively high resolution.  Data from molecular level and organ level measurements have been critical to identification of disease subtypes and improving patient care. 

Walking, or ambulating, is a fundamental movement of daily life and ambulation metrics such as daily step count, timed walk tests, and gait speed and timed walk tests, have been found to be predictive of clinically relevant outcomes.  Historically, remote monitoring of an individual’s physical status has been challenging, however, advances in wearable technology have the potential to enable continuous assessment between clinic visits.  Key issues for widespread adoption include unit cost, form factor, battery life, and usability.  Although daily step count, remains the most common metric for remote assessment of physical activity, many devices record minute-to-minute step rate (i.e. steps per minute) and average heart rate (bpm).

In this paper we show that ambulation metrics, such as daily step count, represent the tip of the iceberg in terms of insight that can be obtained from common commercial wearable devices, such as a Fitbit.  These data enable a much more granular fingerprint of an individual’s activities of daily life.  In this paper we present results from analysis of 22 individuals with Pulmonary Arterial Hypertension who were provided a Fitbit devices between two clinic visits.  From minute-to-minute step rate and heart rate data we derived a range of parameters associated with physical activity during free-living, including metrics associated with the weekly heart rate and step rate distributions, parameters related to the intensity, length, and frequency of ambulations, an analog of the Physical Working Capacity test to assess fitness, a free-living 6-minute walk distance (FL6MWD), as well as weekly usage metrics.  We also define a metric associated with physical health status based on comparison of the FL6MWD to predicted values for healthy individuals with the same age, gender, and BMI as the subject. We used a thresholding approach to compare clinical parameters between the two subgroups and showed that many Fitbit-derived metrics could be used to identify subgroups with differences in clinical parameters associated with physical status, cardiovascular function, pulmonary function, as well as biomarkers from blood tests.  In addition, we showed that several of the Fitbit metrics were strongly correlated with continuous clinical parameters.  Overall, we show that Fitbit-derived metrics can provide insight into an individual’s activities of daily life, and have the potential to support clinical care.

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