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|Title:||Activity tracker-based metrics as digital markers of cardiometabolic health in working adults : cross-sectional study||Authors:||Rykov, Yuri
Roberts, Adam Charles
Christopoulos, George I.
|Keywords:||Science::Medicine||Issue Date:||2020||Source:||Rykov, Y., Thach, T.-Q., Dunleavy, G., Roberts, A. C., Christopoulos, G. I., Soh, C.-K., & Car, J. (2020). Activity tracker-based metrics as digital markers of cardiometabolic health in working adults : cross-sectional study. JMIR mHealth and uHealth, 8(1), e16409-. doi:10.2196/16409||Project:||L2NICCFP1-2013-2||Journal:||JMIR mHealth and uHealth||Abstract:||Background: Greater adoption of wearable devices with multiple sensors may enhance personalized health monitoring, facilitate early detection of some diseases, and further scale up population health screening. However, few studies have explored the utility of data from wearable fitness trackers in cardiovascular and metabolic disease risk prediction. Objective: This study aimed to investigate the associations between a range of activity metrics derived from a wearable consumer-grade fitness tracker and major modifiable biomarkers of cardiometabolic disease in a working-age population. Methods: This was a cross-sectional study of 83 working adults. Participants wore Fitbit Charge 2 for 21 consecutive days and went through a health assessment, including fasting blood tests. The following clinical biomarkers were collected: BMI, waist circumference, waist-to-hip ratio, blood pressure, triglycerides (TGs), high-density lipoprotein (HDL) and low-density lipoprotein cholesterol, and blood glucose. We used a range of wearable-derived metrics based on steps, heart rate (HR), and energy expenditure, including measures of stability of circadian activity rhythms, sedentary time, and time spent at various intensities of physical activity. Spearman rank correlation was used for preliminary analysis. Multiple linear regression adjusted for potential confounders was used to determine the extent to which each metric of activity was associated with continuous clinical biomarkers. In addition, pairwise multiple regression was used to investigate the significance and mutual dependence of activity metrics when two or more of them had significant association with the same outcome from the previous step of the analysis. Results: The participants were predominantly middle aged (mean age 44.3 years, SD 12), Chinese (62/83, 75%), and male (64/83, 77%). Blood biomarkers of cardiometabolic disease (HDL cholesterol and TGs) were significantly associated with steps-based activity metrics independent of age, gender, ethnicity, education, and shift work, whereas body composition biomarkers (BMI, waist circumference, and waist-to-hip ratio) were significantly associated with energy expenditure–based and HR-based metrics when adjusted for the same confounders. Steps-based interdaily stability of circadian activity rhythm was strongly associated with HDL (beta=5.4 per 10% change; 95% CI 1.8 to 9.0; P=.005) and TG (beta=−27.7 per 10% change; 95% CI −48.4 to −7.0; P=.01). Average daily steps were negatively associated with TG (beta=−6.8 per 1000 steps; 95% CI −13.0 to −0.6; P=.04). The difference between average HR and resting HR was significantly associated with BMI (beta=−.5; 95% CI −1.0 to −0.1; P=.01) and waist circumference (beta=−1.3; 95% CI −2.4 to −0.2; P=.03). Conclusions: Wearable consumer-grade fitness trackers can provide acceptably accurate and meaningful information, which might be used in the risk prediction of cardiometabolic disease. Our results showed the beneficial effects of stable daily patterns of locomotor activity for cardiometabolic health. Study findings should be further replicated with larger population studies.||URI:||https://hdl.handle.net/10356/145557||ISSN:||2291-5222||DOI:||10.2196/16409||Schools:||Lee Kong Chian School of Medicine (LKCMedicine)
School of Mechanical and Aerospace Engineering
Nanyang Business School
School of Civil and Environmental Engineering
|Research Centres:||Centre for Population Health Sciences||Rights:||© 2020 Yuri Rykov, Thuan-Quoc Thach, Gerard Dunleavy, Adam Charles Roberts, George Christopoulos, Chee-Kiong Soh, Josip Car. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 31.01.2020. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||LKCMedicine Journal Articles|
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