GGIR: A Research Community–Driven Open Source R Package for Generating Physical Activity and Sleep Outcomes from Multi-Day Raw Accelerometer Data
Open accessAbstract
Recent technological advances have transformed the research on physical activity initially based on questionnaire data to the most recent objective data from accelerometers. The shift to availability of raw accelerations has increased measurement accuracy, transparency, and the potential for data harmonization. However, it has also shifted the need for considerable processing expertise to the researcher. Many users do not have this expertise. The R package GGIR has been made available to all as a tool to convermulti-day high resolution raw accelerometer data from wearable movement sensors into meaningful evidence-based outcomes and insightful reports for the study of human daily physical activity and sleep. This paper aims to provide a one-stop overview of GGIR package, the papers underpinning the theory of GGIR, and how research contributes to the continued growth of the GGIR package. The package includes a range of literature-supported methods to clean the data and provide day-by-day, as well as full recording, weekly, weekend, and weekday estimates of physical activity and sleep parameters. In addition, the package also comes with a shell function that enables the user to process a set of input files and produce csv summary reports with a single function call, ideal for users less proficient in R. GGIR has been used in over 90 peer-reviewed scientific publications to date. The evolution of GGIR over time and widespread use across a range of research areas highlights the importance of open source software development for the research community and advancing methods in physical behavior research.
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Only a small body of evidence exists to indicate that raw accelerometer data are an accurate representation of body acceleration and most scientific evidence indicates that the validity of accelerometer data are dependent mostly on epoch averages.
“Unfortunately, only a small body of evidence exists to indicate that raw accelerometer data are an accurate representation of body acceleration and most scientific evidence indicates that the validity of accelerometer data are dependent mostly on epoch averages [192].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [192]
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References 34 · Cited by 955
- Authors
- Migueles, J. H., Rowlands, A. V., Huber, F., Sabia, S., & van Hees, V. T.
- Affiliations
- Universidad de Granada (ES)
- University of Leicester (GB)
- Netherlands eScience Center (NL)
- 5Inserm
- 4Netherlands eScience Center
- Venue
- Journal for the Measurement of Physical Behaviour
- Publisher
- Human Kinetics
- Published
- 2019-09-01
- Volume
- 2
- Issue
- 3
- Pages
- 188-196
- ISSN
- 2575-6605 (ISSN-L)
- 2575-6613
- OpenAlex
- W2945073425
- Cited by
- 955
- References
- 34
- Citations by year
- 2026: 156 · 2025: 201 · 2024: 184 · 2023: 132 · 2022: 146 · 2021: 89 · 2020: 41 · 2019: 5
- FWCI
- 48.34
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- other-oa
- Locations
- Journal for the Measurement of Physical Behaviour (publishedVersion, other-oa)
- UCL Discovery (University College London) (acceptedVersion)
- Figshare (submittedVersion, cc-by)
- Leicester Research Archive (University of Leicester) (submittedVersion, cc-by)
- Funders
- National Institute for Health and Care Research (MC_UU_12015/3, 2017-2022)
- Universidad de Granada (SOMM17/6107/UGR)
- Junta de Andalucía (SOMM17/6107/ UGR)
- National Institutes of Health (2017-2022)
- Directorate for Biological Sciences
- Medical Research Council (MC_UU_12015, MR/R024227/1, MC_UU_12015/3)
- Biotechnology and Biological Sciences Research Council
- Economic and Social Research Council (ES/K005987)
- European Regional Development Fund (SOMM17/6107/UGR)
- NIHR Leicester Biomedical Research Centre
- Topics
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Data Analysis with R
- Keywords
- accelerometer data processing, physical activity estimation, sleep estimation, R package, open source software, data cleaning, sleep-wake classification
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