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

Fitness Trackers and Smart Pedometers/Accelerometers (561) Wearable Devices and Sensors (888) Research Instruments, Questionnaires, and Tools (576) mHealth for Data Collection and Research (955) Evaluation and Research Methodology for mHealth (208)

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 (22) Tweetations (23) Metrics

Journals

  1. Guo W, Leroux A, Shou H, Cui L, Kang S, Strippoli M, Preisig M, Zipunnikov V, Merikangas K. Processing of Accelerometry Data with GGIR in Motor Activity Research Consortium for Health. Journal for the Measurement of Physical Behaviour 2022:1 View
  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
  3. Lai D, Cheng E, Lim H, So B, Lam W, Cheung D, Wong D, Cheung J. Computer-aided screening of aspiration risks in dysphagia with wearable technology: a Systematic Review and meta-analysis on test accuracy. Frontiers in Bioengineering and Biotechnology 2023;11 View
  4. Clevenger K, McKee K, McNarry M, Mackintosh K, Berrigan D. Association of Recess Provision With Accelerometer-Measured Physical Activity and Sedentary Time in a Representative Sample of 6- to 11-Year-Old Children in the United States. Pediatric Exercise Science 2024;36(2):83 View
  5. Dooley E, Chen L, Ghazi L, Hornikel B, Martinez-Amezcua P, Palta P, Bowling C, Muntner P, Lewis C, Pettee Gabriel K. Multimorbidity is associated with lower total 24-hour movement activity among US adults. Preventive Medicine Reports 2023;36:102483 View
  6. Lee D. The importance of exercise and physical activity in older adults with HIV. AIDS 2023;37(12):1905 View
  7. Lin Y, Wang M, Lei J, He H. Perception and Interaction of Urban Medical Space from the Perspective of Technical Geography: A Case Study of Guangzhou, China. Journal of Urban Planning and Development 2023;149(4) View
  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;81(9):911 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
  16. Marino F, Wu H, Etzkorn L, Rooney M, Soliman E, Deal J, Crainiceanu C, Spira A, Wanigatunga A, Schrack J, Chen L. Associations of Physical Activity and Heart Rate Variability from a Two-Week ECG Monitor with Cognitive Function and Dementia: The ARIC Neurocognitive Study. Sensors 2024;24(13):4060 View
  17. Neishabouri A, Nguyen J, Patterson M, Pilkar R, Guo C. A Walkthrough of ActiGraph Counts. Journal for the Measurement of Physical Behaviour 2024;7(1) View
  18. Tackney M, Carpenter J, Villar S. Unleashing the full potential of digital outcome measures in clinical trials: eight questions that need attention. BMC Medicine 2024;22(1) View
  19. Ghosal R, Matabuena M. Multivariate Scalar on Multidimensional Distribution Regression With Application to Modeling the Association Between Physical Activity and Cognitive Functions. Biometrical Journal 2024;66(7) View
  20. Gao Y, Zhang Q, Wang Y, Gao Y, Xu Y. Association between physical activity based on wearables and self-reported health status among adolescents: NHANES 2011–2014. BMC Public Health 2024;24(1) View
  21. Rowlands A, Troiano R. Comment on “Development of an accelerometer age- and sex-specific approach based on population-standardized values for physical activity surveillance: A proof of concept”. Journal of Sport and Health Science 2024:101004 View
  22. Aguiar E, Turner D, Pleuss J, Zheng P, Benitez C, Ducharme S. Daily and Peak Monitor Independent Movement Summary (MIMS) Values Associated With Metabolic Syndrome: NHANES 2011–12 and 2013–14. Scandinavian Journal of Medicine & Science in Sports 2024;34(11) View