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Digital health technologies to strengthen patient-centred outcome assessment in clinical trials in inflammatory arthritis.
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Author Correction: Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality.
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Self-Supervised Machine Learning to Characterise Step Counts from Wrist-Worn Accelerometers in the UK Biobank.
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Correction: Device-measured movement behaviours in over 20,000 China Kadoorie Biobank participants.
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Device-Measured Physical Activity in 3506 Individuals with Knee or Hip Arthroplasty.
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Self-supervised learning for human activity recognition using 700,000 person-days of wearable data.
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A systematic review of the performance of actigraphy in measuring sleep stages.
Yuan H. et al, (2024), J Sleep Res
Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis.
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Feasibility and usability of remote monitoring in Alzheimer's disease.
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Characterising personal, household, and community PM2.5 exposure in one urban and two rural communities in China.
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Amount and intensity of physical activity and risk of incident cancer in the UK Biobank.
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Device-measured movement behaviours in over 20,000 China Kadoorie Biobank participants.
Chen Y. et al, (2023), Int J Behav Nutr Phys Act, 20
Dose-response of accelerometer-measured physical activity, step count, and cancer risk in the UK Biobank: a prospective cohort analysis.
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Prognostic Significance of Different Ventricular Ectopic Burdens During Submaximal Exercise in Asymptomatic UK Biobank Subjects.
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