Data

Actigraphy and associated data on free-living assessments of movement in people with Amyotrophic Lateral Sclerosis (ALS)

The University of Queensland
Associate Professor Frederik Steyn (Aggregated by) Associate Professor Shyuan Ngo (Aggregated by) Associate Professor Shyuan Ngo (Aggregated by) Dr Frederik Steyn (Aggregated by) Mr Cory Holdom (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.48610/d75ce7e&rft.title=Actigraphy and associated data on free-living assessments of movement in people with Amyotrophic Lateral Sclerosis (ALS)&rft.identifier=RDM ID: 061926bc-fdf4-469a-92ee-7f2ad13da8ae&rft.publisher=The University of Queensland&rft.description=This dataset includes raw and processed actigraphy data collected from individuals diagnosed with Amyotrophic Lateral Sclerosis (ALS), recruited through the Royal Brisbane and Women's Hospital between 2017 and 2021. Participants wore ActiGraph GT9X Link devices on their non-dominant wrist for 8 consecutive days at multiple timepoints over follow-up periods of up to 36 months. The dataset includes summary features derived using ActiLife and the open-source R package GGIR. These features include daily averages of Euclidean Norm Minus One (ENMO), time spent in moderate-to-vigorous physical activity (MVPA), step counts, and peak 6-minute activity measures. Activity count-derived metrics and epoch-level outputs are also provided. Matched phenotypic data includes ALS Functional Rating Scale-Revised (ALSFRS-R) scores and calculated motor subdomains, demographic details (age in years, sex), and durations since disease onset and diagnosis. These covariates are included in accompanying phenotype files to support replication and secondary analyses. The dataset was used to evaluate accelerometer-derived outcomes as candidate digital biomarkers for functional decline, disease monitoring, and powering of clinical trials in MND&rft.creator=Associate Professor Frederik Steyn&rft.creator=Associate Professor Shyuan Ngo&rft.creator=Associate Professor Shyuan Ngo&rft.creator=Dr Frederik Steyn&rft.creator=Mr Cory Holdom&rft.date=2025&rft_rights= http://guides.library.uq.edu.au/deposit_your_data/terms_and_conditions&rft_subject=eng&rft_subject=Clinical outcome measures&rft_subject=Remote monitoring&rft_subject=Actigraphy&rft_subject=Neurology and neuromuscular diseases&rft_subject=Neurosciences&rft_subject=BIOMEDICAL AND CLINICAL SCIENCES&rft.type=dataset&rft.language=English Access the data
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[email protected]
Australian Institute for Bioengineering and Nanotechnology

Full description

This dataset includes raw and processed actigraphy data collected from individuals diagnosed with Amyotrophic Lateral Sclerosis (ALS), recruited through the Royal Brisbane and Women's Hospital between 2017 and 2021. Participants wore ActiGraph GT9X Link devices on their non-dominant wrist for 8 consecutive days at multiple timepoints over follow-up periods of up to 36 months. The dataset includes summary features derived using ActiLife and the open-source R package GGIR. These features include daily averages of Euclidean Norm Minus One (ENMO), time spent in moderate-to-vigorous physical activity (MVPA), step counts, and peak 6-minute activity measures. Activity count-derived metrics and epoch-level outputs are also provided. Matched phenotypic data includes ALS Functional Rating Scale-Revised (ALSFRS-R) scores and calculated motor subdomains, demographic details (age in years, sex), and durations since disease onset and diagnosis. These covariates are included in accompanying phenotype files to support replication and secondary analyses. The dataset was used to evaluate accelerometer-derived outcomes as candidate digital biomarkers for functional decline, disease monitoring, and powering of clinical trials in MND

Issued: 2025

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Subjects
Actigraphy | Biomedical and Clinical Sciences | Clinical outcome measures | Neurology and Neuromuscular Diseases | Neurosciences | Remote monitoring | eng |

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Other Information
Identification of passive wrist-worn accelerometry outcomes for improved disease monitoring and trial design in motor neuron disease

local : UQ:75e2718

Holdom, Cory J., Pilkar, Rakesh, Guo, Christine C., Eijk, Ruben P.A van, Sethi, Nadia, Henderson, Robert D., Ngo, Shyuan T. and Steyn, Frederik J. (2025). Identification of passive wrist-worn accelerometry outcomes for improved disease monitoring and trial design in motor neuron disease. eBioMedicine, 117 105779, 105779-117. doi: 10.1016/j.ebiom.2025.105779

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local : UQ:289097

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