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Updated: Jun 12, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

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Published on: July 27, 2018

Clustering Accelerometer Activity Patterns from the UK Biobank Cohort.

Stephen Clark¹, Nik Lomax¹, Michelle Morris²

¹Leeds Institute for Data Analytics and School of Geography, University of Leeds, Leeds LS2 9JT, UK.

Sensors (Basel, Switzerland)

|December 28, 2021

Summary

Researchers identified nine distinct physical activity profiles from UK Biobank accelerometer data. These patterns, like "Active 9 to 5" and "Live for the Weekend", offer new insights into health outcomes.

Keywords:

accelerometer clustering personal activity profiling wearables

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Area of Science:

* Biomedical data science
* Physical activity research
* Wearable sensor technology

Background:

* Wrist-worn accelerometers are increasingly used to objectively measure personal activity levels.
* Existing methods often summarize activity data using averages and variances.
* A need exists for methods that capture the complexity of daily activity patterns.

Purpose of the Study:

* To develop a clustering method utilizing the whole activity profile from accelerometer data.
* To identify distinct physical activity patterns within a large population dataset.
* To explore the associations between identified activity profiles and other health-related data.

Main Methods:

* Application of k-medoids clustering to over 90,000 whole activity profiles.
* Utilisation of data from the UK Biobank study.
* Analysis of sociodemographic, socioeconomic, health, and circadian rhythm data.

Main Results:

* Identification of nine distinct physical activity profiles, including 'Active 9 to 5', 'Morning Movers', 'Live for the Weekend', 'Sedate', and 'Inactive'.
* These profiles capture variations in weekly activity patterns and intensity.
* Activity profiles are differentiated by sociodemographic, socioeconomic, and health data.

Conclusions:

* The identified activity profiles provide a nuanced understanding of physical activity beyond simple summary measures.
* These typified patterns can help explain variations in health and morbidity outcomes, such as BMI and COVID-19.
* This research offers a valuable resource for future studies utilizing UK Biobank data.