This paper is in the following e-collection/theme issue:

Evaluation and Research Methodology for mHealth (207) Fitness Trackers and Smart Pedometers/Accelerometers (560) Fitness Trackers and Smart Pedometers/Accelerometers (560) Wearable Devices and Sensors (887) mHealth for Data Collection and Research (954) mHealth for Wellness, Behavior Change and Prevention (2867) Innovations and Technology for Physical Activity Education (740) Mobile Health (mhealth) (2936) Mobile Health (mhealth) (2936) mHealth for Data Collection and Research (954) Wearable Devices and Sensors (887) Innovations and Technology for Physical Activity Education (740) mHealth for Wellness, Behavior Change and Prevention (2867)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50663, first published .
Physical Activity Pattern of Adults With Metabolic Syndrome Risk Factors: Time-Series Cluster Analysis

Physical Activity Pattern of Adults With Metabolic Syndrome Risk Factors: Time-Series Cluster Analysis

Physical Activity Pattern of Adults With Metabolic Syndrome Risk Factors: Time-Series Cluster Analysis

Authors of this article:

Junhyoung Kim1 Author Orcid Image ;   Jin-Young Choi1 Author Orcid Image ;   Hana Kim1 Author Orcid Image ;   Taeksang Lee1 Author Orcid Image ;   Jaeyoung Ha1 Author Orcid Image ;   Sangyi Lee1 Author Orcid Image ;   Jungmi Park1 Author Orcid Image ;   Gyeong-Suk Jeon2 Author Orcid Image ;   Sung-il Cho1, 3 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Tóth K, Takács P, Balatoni I. Users’ Expectations of Smart Devices during Physical Activity—A Literature Review. Applied Sciences 2024;14(8):3518 View
  2. 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