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

Fitness Trackers and Smart Pedometers/Accelerometers (539) Wearable Devices and Sensors (811) Research Instruments, Questionnaires, and Tools (540) mHealth for Data Collection and Research (907) Evaluation and Research Methodology for mHealth (193)

Published on in Vol 10, No 7 (2022): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38077, first published .
Comparison of Accelerometry-Based Measures of Physical Activity: Retrospective Observational Data Analysis Study

Comparison of Accelerometry-Based Measures of Physical Activity: Retrospective Observational Data Analysis Study

Comparison of Accelerometry-Based Measures of Physical Activity: Retrospective Observational Data Analysis Study

Authors of this article:

Marta Karas1 Author Orcid Image ;   John Muschelli1 Author Orcid Image ;   Andrew Leroux2 Author Orcid Image ;   Jacek K Urbanek3 Author Orcid Image ;   Amal A Wanigatunga4 Author Orcid Image ;   Jiawei Bai1 Author Orcid Image ;   Ciprian M Crainiceanu1 Author Orcid Image ;   Jennifer A Schrack3, 4 Author Orcid Image
Article Authors Cited by (15) Tweetations (23) Metrics

Journals

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  2. Fairclough S, Rowlands A, del Pozo Cruz B, Crotti M, Foweather L, Graves L, Hurter L, Jones O, MacDonald M, McCann D, Miller C, Noonan R, Owen M, Rudd J, Taylor S, Tyler R, Boddy L. Reference values for wrist-worn accelerometer physical activity metrics in England children and adolescents. International Journal of Behavioral Nutrition and Physical Activity 2023;20(1) View
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  6. Lee D. The importance of exercise and physical activity in older adults with HIV. AIDS 2023;37(12):1905 View
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  8. Bianchim M, McNarry M, Barker A, Williams C, Denford S, Thia L, Evans R, Mackintosh K. A Machine Learning Approach for Physical Activity Recognition in Cystic Fibrosis. Measurement in Physical Education and Exercise Science 2024;28(2):172 View
  9. Lisi E, Abellan J. Statistical analysis of actigraphy data with generalised additive models. Pharmaceutical Statistics 2024;23(3):308 View
  10. Davoudi A, Urbanek J, Etzkorn L, Parikh R, Soliman E, Wanigatunga A, Gabriel K, Coresh J, Schrack J, Chen L. Validation of a Zio XT Patch Accelerometer for the Objective Assessment of Physical Activity in the Atherosclerosis Risk in Communities (ARIC) Study. Sensors 2024;24(3):761 View
  11. Musa S, Barua A, Stanley K, Basset F, Mamyia H, Mongeon K, Fuller D. Comparing Accelerometer Processing Metrics and Hyperparameter Optimization for Physical Activity Classification Accuracy Using Machine Learning Methods. Journal for the Measurement of Physical Behaviour 2024;7(1) View
  12. Xue Y, Diep C, Zhao H, Wijeysundera D, Clarke H, Ladha K. Recent Cannabis Use and Accelerometer-Measured Physical Activity and Sedentary Behavior Among Young-to-Midlife Adults: An Analysis of the National Health and Nutrition Examination Survey from 2011 to 2014. Cannabis and Cannabinoid Research 2024 View
  13. Karas M, Olsen J, Straczkiewicz M, Johnson S, Burke K, Iwasaki S, Lahav A, Scheier Z, Clark A, Iyer A, Huang E, Berry J, Onnela J. Tracking amyotrophic lateral sclerosis disease progression using passively collected smartphone sensor data. Annals of Clinical and Translational Neurology 2024;11(6):1380 View
  14. Kang S, Leroux A, Guo W, Dey D, Strippoli M, Di J, Vaucher J, Marques-Vidal P, Vollenweider P, Preisig M, Merikangas K, Zipunnikov V. Integrative Modeling of Accelerometry-Derived Sleep, Physical Activity, and Circadian Rhythm Domains With Current or Remitted Major Depression. JAMA Psychiatry 2024 View
  15. Etzkorn L, Davoudi A, Dooley E, Gabriel K, Chen L, Crainiceanu C, Schrack J, Wanigatunga A. Physical activity cut-points for older adults using the Zio XT onboard accelerometer. BMC Digital Health 2024;2(1) View