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

Journals

1. Mohammadi R, Atif M, Centi A, Agboola S, Jethwani K, Kvedar J, Kamarthi S. Neural Network–Based Algorithm for Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers: Retrospective Observation and Algorithm Development Study. JMIR mHealth and uHealth 2020;8(9):e18142 View
2. Hoopes E, Witman M, D’Agata M, Berube F, Brewer B, Malone S, Grandner M, Patterson F. Rest-activity rhythms in emerging adults: implications for cardiometabolic health. Chronobiology International 2021;38(4):543 View
3. Lee K, Gan W, Christopoulos G. Biomarker-Informed Machine Learning Model of Cognitive Fatigue from a Heart Rate Response Perspective. Sensors 2021;21(11):3843 View
4. Hahm J, Choi H, Matsuoka H, Kim J, Byon K. Understanding the relationship between acceptance of multifunctional health and fitness features of wrist-worn wearables and actual usage. International Journal of Sports Marketing and Sponsorship 2023;24(2):333 View
5. Harms R, Ferrari A, Meier I, Martinkova J, Santus E, Marino N, Cirillo D, Mellino S, Catuara Solarz S, Tarnanas I, Szoeke C, Hort J, Valencia A, Ferretti M, Seixas A, Santuccione Chadha A. Digital biomarkers and sex impacts in Alzheimer’s disease management — potential utility for innovative 3P medicine approach. EPMA Journal 2022;13(2):299 View
6. Jeong H, Jeong Y, Park Y, Kim K, Park J, Kang D. Applications of deep learning methods in digital biomarker research using noninvasive sensing data. DIGITAL HEALTH 2022;8:205520762211366 View
7. Huhn S, Axt M, Gunga H, Maggioni M, Munga S, Obor D, Sié A, Boudo V, Bunker A, Sauerborn R, Bärnighausen T, Barteit S. The Impact of Wearable Technologies in Health Research: Scoping Review. JMIR mHealth and uHealth 2022;10(1):e34384 View
8. Zhou W, Chan Y, Foo C, Zhang J, Teo J, Davila S, Huang W, Yap J, Cook S, Tan P, Chin C, Yeo K, Lim W, Krishnaswamy P. High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study. Journal of Medical Internet Research 2022;24(7):e34669 View
9. Viana J, Edney S, Gondalia S, Mauch C, Sellak H, O'Callaghan N, Ryan J. Trends and gaps in precision health research: a scoping review. BMJ Open 2021;11(10):e056938 View
10. Seffah K, Zaman M, Awais N, Satnarine T, Haq A, Hernandez G, Khan S. Exploring the Role of Wearable Electronic Medical Devices in Improving Cardiovascular Risk Factors and Outcomes Among Adults: A Systematic Review. Cureus 2023 View
11. BOCHIMOTO H, ISHIMARU T, NAKANO A, HASEGAWA K, KIMURA E, TAJIMA S, YOSHIKAWA T, NEMOTO H. Association Between Workplace Social Support and Use of Health-Promoting Wearable Devices: A Prospective Cohort Study of Japanese Employees. Journal of UOEH 2023;45(2):95 View
12. Nguyen L, Ngwenyama O, Bandyopadhyay A, Nallaperuma K. Realising the potential of digital health communities: a study of the role of social factors in community engagement. European Journal of Information Systems 2024;33(6):1033 View
13. Park S, Lee S, Woo S, Webster-Dekker K, Chen W, Veliz P, Larson J. Sedentary behaviors and physical activity of the working population measured by accelerometry: a systematic review and meta-analysis. BMC Public Health 2024;24(1) View
14. Mun S, Park K, Kim J, Kim J, Lee S. Assessment of heart rate measurements by commercial wearable fitness trackers for early identification of metabolic syndrome risk. Scientific Reports 2024;14(1) View