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Cited by 29Physical Activity and HealthContext-Aware Activity Recognition SystemsMobile Health and mHealth Applications

Activity Tracker–Based Metrics as Digital Markers of Cardiometabolic Health: Cross-Sectional Study

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Rykov, Yuri, Thach, Thuan-Quoc, Dunleavy, Gerard, Roberts, Adam Charles, Christopoulos, George, Soh, Chee Kiong, Car, Josip·2020·JMIR mHealth and uHealth
DOI 10.2196/16409PMC PMC7055791
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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 independently 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.

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  • Further data analyses were conducted in a data-driven manner with artificial intelligence, such as k-means or unsupervised cluster analysis, recursive feature elimination, rotation random forest classifier, and supervised machine learning algorithms.

    “Further data analyses were conducted in a data-driven manner [223] with artificial intelligence, such as k-means [176] or unsupervised cluster analysis [172], recursive feature elimination technique [170], rotation random forest classifier [130], and supervised machine learning algorithms using logistic regression, decision tree, and random forest [215].”

    From The impact of wearable technologies in health research: scoping review (2022) · cites this paper as [176]

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Authors
Rykov, Y., Thach, T.-Q., Dunleavy, G., Roberts, A. C., Christopoulos, G., Soh, C. K., & Car, J.
Affiliations
Nanyang Technological University (SG)
Corresponding author
Rykov, Yuri
ORCID
Venue
JMIR mHealth and uHealth
Publisher
JMIR Publications Inc.
Published
2020-01-27
Volume
8
Issue
1
Pages
e16409
ISSN
2291-5222 (ISSN-L)
DOI
10.2196/16409
PMC
PMC7055791
OpenAlex
W3003551560
Cited by
29
References
62
Citations by year
2026: 5 · 2025: 5 · 2024: 4 · 2023: 5 · 2022: 5 · 2021: 4 · 2020: 1
FWCI
2.26
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR mhealth and uhealth (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DR-NTU (Nanyang Technological University) (submittedVersion, cc-by)
Topics
  • Health Sciences › Medicine › Physiology › Physical Activity and Health
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Keywords
wearable activity trackers, cardiometabolic health, cardiovascular risk prediction, HDL cholesterol, triglycerides, body mass index, waist circumference, waist-to-hip ratio, sedentary time, energy expenditure, heart rate, Fitbit, working adults, biomarker prediction, population health screening
MeSH terms
Fitness Trackers, Adult, Cardiovascular Diseases, Cross-Sectional Studies, Female, Humans, Male, Middle Aged, Biomarkers, Benchmarking

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