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Fitness trackers may help predict cardiometabolic disease risk

Pearl Toh
11 Feb 2020

Wearable fitness trackers can yield insightful information that may be predictive of cardiometabolic disease risk, according to a study by Singapore’s Nanyang Technological University (NTU).

“With wearables, people can monitor their biometrics and activity, enabling early detection of deviations in digital biomarkers. In addition, wearables can be used to increase control over modifiable behavioural and lifestyle risk factors of cardiometabolic disease,” said the researchers led by Dr Yuri Rykov of Lee Kong Chian School of Medicine, NTU, Singapore.

In the cross-sectional cohort study, 83 working adults (mean age 44.3 years, 77 percent male, 75 percent Chinese) in Singapore wore the Fitbit Charge 2 fitness tracker for 21 days consecutively and were assessed on various health measures, including clinical biomarkers based on fasting blood tests. [JMIR Mhealth Uhealth 2020;8:e16409]

The researchers found that steps-based activity measures were significantly correlated with blood biomarkers of cardiometabolic disease such as HDL cholesterol and triglyceride levels. Specifically, the greater the daily step counts, the lower the triglyceride levels (beta=-6.8 per 1,000 steps; p=0.04).

Similarly, steps-based regularity of circadian activity rhythm, indicated by interdaily stability, also showed significant positive association with HDL (beta=5.4 per 10 percent change; p=0.005) and negative association with triglyceride levels (beta=-27.7 per 10 percent change; p=0.01). The associations remained significant after adjusting for all sociodemographic confounding factors and different activity patterns such as shift work.

“The association between interdaily stability of circadian rhythm and HDL cholesterol and triglycerides did not depend on the overall activity level, which indicates that a stable activity rhythm may be beneficial for cardiometabolic health at any level of activity,” Rykov and co-authors noted. “This metric can be used for personalized risk prediction.”

On the other hand, measures based on energy expenditure or heart rate were associated with body composition biomarkers. For instance, participants who were more sedentary had a greater BMI (beta=0.1; p=0.047). In addition, a positive association was found for resting heart rate and waist-to-hip ratio (beta=0.02; p=0.04).

“Consumer-grade wearables are much more affordable and common among the general population and, therefore, have a higher potential value for public health, enabling predictive health monitoring on a population scale,” said the researchers.

“Apart from providing direct information about an individual’s physical health status [eg, body temperature], some of these physiological and behavioural characteristics can be considered as risk factors or markers related to different diseases,” they added. “Our results showed the beneficial effects of stable daily patterns of locomotor activity for cardiometabolic health.”  

Misalignment of circadian rhythm has been shown to adversely affect cardiometabolic functions in humans, for example, via increased glucose and arterial pressure, decreased leptin, and disrupted metabolism of triglycerides, the researchers pointed out.

“The molecular mechanisms underlying the effects of activity rhythms on the risk biomarkers of cardiometabolic disease require … [further studies] in other populations,” suggested Rykov and co-authors. “The development of highly accurate predictive algorithms that combine different data and digital markers in a single model is one of the main targets in this field.”

 

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