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Rosemary Walmsley

Showing results (11-20 of 17) with videos related to

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International Journal of Epidemiology|February 20, 2026
Reproducibility and associated regression dilution bias of accelerometer-derived physical activity and sleep in UK BiobankCharilaos Zisou, Catherine Calvin, Hannah Taylor, et al.
Medrxiv : the Preprint Server for Health Sciences|July 18, 2023
Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortalityHang Yuan, Tatiana Plekhanova, Rosemary Walmsley, et al.
NPJ Digital Medicine|May 20, 2024
Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortalityHang Yuan, Tatiana Plekhanova, Rosemary Walmsley, et al.
Medrxiv : the Preprint Server for Health Sciences|May 19, 2023
Development and Validation of a Machine Learning Wrist-worn Step Detection Algorithm with Deployment in the UK BiobankScott R Small, Shing Chan, Rosemary Walmsley, et al.
Medicine and Science in Sports and Exercise|May 20, 2024
Self-Supervised Machine Learning to Characterize Step Counts from Wrist-Worn Accelerometers in the UK BiobankScott R Small, Shing Chan, Rosemary Walmsley, et al.
British Journal of Sports Medicine|April 13, 2021
GRANADA consensus on analytical approaches to assess associations with accelerometer-determined physical behaviours (physical activity, sedentary behaviour and sleep) in epidemiological studiesJairo H Migueles, Eivind Aadland, Lars Bo Andersen, et al.
Plos One|September 7, 2022
Your best day: An interactive app to translate how time reallocations within a 24-hour day are associated with health measuresDorothea Dumuid, Timothy Olds, Melissa Wake, et al.
Pageof 2

Showing results (11-20 of 17) with videos related to

Sort By:
Pageof 2
You have reached the last page of results.This site can display upto 17 results.
International Journal of Epidemiology|February 20, 2026
Reproducibility and associated regression dilution bias of accelerometer-derived physical activity and sleep in UK BiobankCharilaos Zisou, Catherine Calvin, Hannah Taylor, et al.
Medrxiv : the Preprint Server for Health Sciences|July 18, 2023
Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortalityHang Yuan, Tatiana Plekhanova, Rosemary Walmsley, et al.
NPJ Digital Medicine|May 20, 2024
Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortalityHang Yuan, Tatiana Plekhanova, Rosemary Walmsley, et al.
Medrxiv : the Preprint Server for Health Sciences|May 19, 2023
Development and Validation of a Machine Learning Wrist-worn Step Detection Algorithm with Deployment in the UK BiobankScott R Small, Shing Chan, Rosemary Walmsley, et al.
Medicine and Science in Sports and Exercise|May 20, 2024
Self-Supervised Machine Learning to Characterize Step Counts from Wrist-Worn Accelerometers in the UK BiobankScott R Small, Shing Chan, Rosemary Walmsley, et al.
British Journal of Sports Medicine|April 13, 2021
GRANADA consensus on analytical approaches to assess associations with accelerometer-determined physical behaviours (physical activity, sedentary behaviour and sleep) in epidemiological studiesJairo H Migueles, Eivind Aadland, Lars Bo Andersen, et al.
Plos One|September 7, 2022
Your best day: An interactive app to translate how time reallocations within a 24-hour day are associated with health measuresDorothea Dumuid, Timothy Olds, Melissa Wake, et al.
Pageof 2