ArticleSensors (Basel, Switzerland)2021
Clustering Accelerometer Activity Patterns from the UK Biobank Cohort.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Circadian rhythm profiles derived from accelerometer measures of the sleep-wake cycle in two cohort studies.Nature communications · 2025Article
- Beyond FITT: addressing density in understanding the dose-response relationships of physical activity with health-an example based on brain health.European journal of applied physiology · 2025Review
- An inclusive economy dataset for wards in Great Britain using administrative and synthetic data sources.Scientific data · 2025Article
- Negative binomial mixed effects location-scale models for intensive longitudinal count-type physical activity data provided by wearable devices.Biometrics · 2025Article
- Accelerometer-based physical activity, air pollution and risk of dementia subtypes: a prospective study using UK biobank.Frontiers in public health · 2025Article
- Assessing the impact of body location on the accuracy of detecting daily activities with accelerometer data.iScience · 2024Article
- Comparing Self-Reported Running Distance and Pace With a Commercial Fitness Watch Data: Reliability Study.JMIR formative research · 2024Article
- Weekend physical activity profiles and their relationship with quality of life: The SOPHYA cohort of Swiss children and adolescents.PloS one · 2024Article
- Association between circadian physical activity patterns and mortality in the UK Biobank.The international journal of behavioral nutrition and physical activity · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Many researchers are beginning to adopt the use of wrist-worn accelerometers to objectively measure personal activity levels. Data from these devices are often used to summarise such activity in terms of averages, variances, exceedances, and patterns within a profile. In this study, we report the development of a clustering utilising the whole activity profile. This was achieved using the robust clustering technique of k-medoids applied to an extensive data set of over 90,000 activity profiles, collected as part of the UK Biobank study. We identified nine distinct activity profiles in these data, which captured both the pattern of activity throughout a week and the intensity of the activity: "Active 9 to 5", "Active", "Morning Movers", "Get up and Active", "Live for the Weekend", "Moderates", "Leisurely 9 to 5", "Sedate" and "Inactive". These patterns are differentiated by sociodemographic, socioeconomic, and health and circadian rhythm data collected by UK Biobank. The utility of these findings are that they sit alongside existing summary measures of physical activity to provide a way to typify distinct activity patterns that may help to explain other health and morbidity outcomes, e.g., BMI or COVID-19. This research will be returned to the UK Biobank for other researchers to use.
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Registered trials
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