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Association of Physical Activity from Wearable Devices and Chronic Disease Risk: Insights from the All of Us Research Program

Hou, Y.; Cui, E.; Ikramuddin, S.; Zhang, R.

2024-11-12 health informatics
10.1101/2024.11.11.24317124 medRxiv
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BackgroundPhysical activity is widely recognized as a key modifiable factor for reducing the risk of chronic diseases. Wearable devices such as Fitbit offer a unique opportunity to objectively measure physical activity metrics, providing insights into the association between different types of physical activity and chronic disease risk. ObjectiveThis study aims to examine the association between physical activity metrics derived from Fitbit devices and the incidence of various chronic diseases among participants from the All of Us Research Program. MethodsPhysical activity metrics included daily steps, elevation gain, and activity durations at different intensities (e.g., very active, lightly active, and sedentary). Cox proportional hazards models and multiple regression models were used to assess the relationship between these metrics and the incidence of chronic diseases represented by Phecodes. Age, sex, and body mass index (BMI) were included as covariates. ResultsA total of 15,538 participants provided Fitbit activity data, of which 9,320 also had electronic health records (EHR). Increased daily step count, elevation gain, and very active minutes were significantly associated with a reduced risk of several chronic conditions, including obesity, Type 2 diabetes, and major depressive disorder. Conversely, increased sedentary time was linked to higher risks for conditions such as obesity, Type 2 diabetes, and essential hypertension. Multiple regression analyses confirmed these associations. ConclusionOur findings highlight the beneficial effects of increased physical activity, particularly daily steps and elevation gain, on reducing the risk of chronic diseases. Conversely, sedentary behavior remains a significant risk factor for the development of several conditions. These insights may inform personalized activity recommendations aimed at reducing disease burden and improving population health outcomes.

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