Activity Tracker-Based Metrics as Digital Markers of Cardiometabolic Health in Working Adults: Cross-Sectional Study

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.

Keywords: Fitbit; circadian rhythms; fitness trackers; heart rate; metabolic cardiovascular syndrome; mobile health; physical activity; sedentary behavior; steps; wearable electronic devices.

Figure 1

Steps tracking charts. Individuals with relatively high (A) and low (B) interdaily stability but similar total daily steps. x-axis: time in days. y-axis: steps count. IS: interdaily stability of locomotor activity rhythm.

Figure 2

Participants flow diagram. BG: blood glucose; DBP: diastolic blood pressure; HDL: high-density lipoprotein; LDL: low-density lipoprotein; SBP: systolic blood pressure; TG: triglyceride; WC: waist circumference; WHR: waist-to-hip ratio.

Figure 3

Visualization of normalized average 24-hour activity profiles by clusters. A: first cluster (N=35); B: second cluster (N=34); C: third cluster (N=14). Each line represents an individual within a cluster. x-axis: hours in a daily cycle. y-axis: average number of steps normalized by an individual sum of steps in average 24-hour profile.

Figure 4

Correlation network of cardiometabolic disease biomarkers and activity tracker metrics. Only associations significant at P<.05 were displayed. Width of lines is proportional to absolute value of Spearman coefficients that vary between 0.22 and 0.38. BG: blood glucose; DayHR: daytime heart rate; DBP: diastolic blood pressure; dRHR: delta of resting heart rate; HDL: high-density lipoprotein; HR: heart rate; IS: interdaily stability of locomotor activity rhythm; IV: interdaily variation of locomotor activity; LDL: low-density lipoprotein; MVPA: moderate-to-vigorous physical activity; NightHR: nighttime heart rate; PA: physical Activity; RHR: resting heart rate; SBP: systolic blood pressure; TG: triglyceride; WC: waist circumference; WHR: waist-to-hip ratio.