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

Portable and Mobile Technologies for Rehabilitation (142) Fitness Trackers and Smart Pedometers/Accelerometers (541)

Published on in Vol 7, No 1 (2020): Jan-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/14059, first published .
Accuracy and Precision of Three Consumer-Grade Motion Sensors During Overground and Treadmill Walking in People With Parkinson Disease: Cross-Sectional Comparative Study

Accuracy and Precision of Three Consumer-Grade Motion Sensors During Overground and Treadmill Walking in People With Parkinson Disease: Cross-Sectional Comparative Study

Accuracy and Precision of Three Consumer-Grade Motion Sensors During Overground and Treadmill Walking in People With Parkinson Disease: Cross-Sectional Comparative Study

Authors of this article:

Byron Lai1 Author Orcid Image ;   Jeffer E Sasaki1, 2 Author Orcid Image ;   Brenda Jeng1 Author Orcid Image ;   Katie L Cederberg1 Author Orcid Image ;   Marcas M Bamman3, 4 Author Orcid Image ;   Robert W Motl1, 3 Author Orcid Image
Article Authors Cited by (19) Tweetations (1) Metrics

Journals

  1. Artusi C, Imbalzano G, Sturchio A, Pilotto A, Montanaro E, Padovani A, Lopiano L, Maetzler W, Espay A. Implementation of Mobile Health Technologies in Clinical Trials of Movement Disorders: Underutilized Potential. Neurotherapeutics 2020;17(4):1736 View
  2. Jeng B, Cederberg K, Lai B, Sasaki J, Bamman M, Motl R. Step‐rate threshold for physical activity intensity in Parkinson’s disease. Acta Neurologica Scandinavica 2020;142(2):145 View
  3. Bacanoiu M, Mititelu R, Danoiu M, Olaru G, Buga A. Functional Recovery in Parkinson’s Disease: Current State and Future Perspective. Journal of Clinical Medicine 2020;9(11):3413 View
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  5. Cederberg K, Jeng B, Sasaki J, Lai B, Bamman M, Motl R. Accuracy and precision of wrist-worn actigraphy for measuring steps taken during over-ground and treadmill walking in adults with Parkinson's disease. Parkinsonism & Related Disorders 2021;88:102 View
  6. Waddell K, Patel M, Wilkinson J, Burke R, Bravata D, Koganti S, Wood S, Morley J. Deploying Digital Health Technologies for Remote Physical Activity Monitoring of Rural Populations With Chronic Neurologic Disease. Archives of Rehabilitation Research and Clinical Translation 2023;5(1):100250 View
  7. De Calheiros Velozo J, Habets J, George S, Niemeijer K, Minaeva O, Hagemann N, Herff C, Kuppens P, Rintala A, Vaessen T, Riese H, Delespaul P. Designing daily-life research combining experience sampling method with parallel data. Psychological Medicine 2024;54(1):98 View
  8. Shokouhi N, Khodakarami H, Fernando C, Osborn S, Horne M. Accuracy of Step Count Estimations in Parkinson’s Disease Can Be Predicted Using Ambulatory Monitoring. Frontiers in Aging Neuroscience 2022;14 View
  9. Chevance G, Golaszewski N, Tipton E, Hekler E, Buman M, Welk G, Patrick K, Godino J. Accuracy and Precision of Energy Expenditure, Heart Rate, and Steps Measured by Combined-Sensing Fitbits Against Reference Measures: Systematic Review and Meta-analysis. JMIR mHealth and uHealth 2022;10(4):e35626 View
  10. Jeng B, Cederberg K, Lai B, Sasaki J, Bamman M, Motl R. Wrist-based accelerometer cut-points for quantifying moderate-to-vigorous intensity physical activity in Parkinson’s disease. Gait & Posture 2022;91:235 View
  11. Alberts J, Rosenfeldt A, Lopez-Lennon C, Suttman E, Jansen A, Imrey P, Dibble L. Effectiveness of a Long-Term, Home-Based Aerobic Exercise Intervention on Slowing the Progression of Parkinson Disease: Design of the Cyclical Lower Extremity Exercise for Parkinson Disease II (CYCLE-II) Study. Physical Therapy 2021;101(11) View
  12. Sokas D, Paliakaitė B, Rapalis A, Marozas V, Bailón R, Petrėnas A. Detection of Walk Tests in Free-Living Activities Using a Wrist-Worn Device. Frontiers in Physiology 2021;12 View
  13. Bianchini E, Caliò B, Alborghetti M, Rinaldi D, Hansen C, Vuillerme N, Maetzler W, Pontieri F. Step-Counting Accuracy of a Commercial Smartwatch in Mild-to-Moderate PD Patients and Effect of Spatiotemporal Gait Parameters, Laterality of Symptoms, Pharmacological State, and Clinical Variables. Sensors 2022;23(1):214 View
  14. Ginis P, Goris M, De Groef A, Blondeel A, Gilat M, Demeyer H, Troosters T, Nieuwboer A. Validation of Commercial Activity Trackers in Everyday Life of People with Parkinson’s Disease. Sensors 2023;23(8):4156 View
  15. Brandenbarg P, Hoekstra F, Barakou I, Seves B, Hettinga F, Hoekstra T, van der Woude L, Dekker R, Krops L. Measurement properties of device-based physical activity instruments in ambulatory adults with physical disabilities and/or chronic diseases: a scoping review. BMC Sports Science, Medicine and Rehabilitation 2023;15(1) View
  16. Wang F. Wearable sensor-based on exercise monitoring system for disabled the individuals using a multi-attribute fuzzy evaluation mode. Journal of Intelligent & Fuzzy Systems 2024:1 View
  17. Jiang M, Tian Z, Yu C, Shi Y, Liu L, Peng T, Hu X, Yu F. Intelligent 3D garment system of the human body based on deep spiking neural network. Virtual Reality & Intelligent Hardware 2024;6(1):43 View
  18. Lozano-García M, Doheny E, Mann E, Morgan-Jones P, Drew C, Busse-Morris M, Lowery M. Estimation of Gait Parameters in Huntington’s Disease Using Wearable Sensors in the Clinic and Free-living Conditions. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2024;32:2239 View

Books/Policy Documents

  1. Sasaki J, Motl R. Encyclopedia of Sensors and Biosensors. View