Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of MIE 2025 |
| Pages | 1185-1189 |
| Number of pages | 5 |
| ISBN (electronic) | 978-1-64368-596-0 |
| Publication status | Published - 15 May 2025 |
Publication series
| Name | Studies in health technology and informatics |
|---|---|
| Volume | 327 |
| ISSN (Print) | 0926-9630 |
| ISSN (electronic) | 1879-8365 |
Abstract
Accelerometer data obtained with wearable devices over extended periods of time provides objective, valuable information on activity behavior. Building on previous work to derive easy-to-interpret activity parameters - the Activity Types from Long-term Accelerometric Sensor data (ATLAS) index - from such data, we aim to investigate whether this approach is feasible with high-quality, extensive data from the UK Biobank, for identifying activity behavior groups, and if exemplary, clinically relevant parameters differ between these groups. A sample of 6,400 subjects' raw accelerometer data was chosen to be processed for computation of the ATLAS index parameters 'regularity', 'intensity' and 'duration' of moderate-intensity, 15+-minute physical activity events. Subsequently, hierarchical clustering was applied, and differences in HDL cholesterol, BMI and C-Reactive Protein (CRP) lab data levels were evaluated. Clustering yielded five distinct activity clusters, and statistically significant differences in HDL cholesterol, BMI and CRP were found between several clusters. The use of the ATLAS index parameters allows for physical activity group identification from objective accelerometer data. These groups differ in physiologically relevant outcome parameters. More research is necessary to uncover potential causal relationships, e.g., by using causal inference methods.
Keywords
- accelerometry, clustering, cohort study, machine learning, physical activity, physiology
ASJC Scopus subject areas
- Engineering(all)
- Biomedical Engineering
- Medicine(all)
- Health Informatics
- Health Professions(all)
- Health Information Management
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
Proceedings of MIE 2025. 2025. p. 1185-1189 (Studies in health technology and informatics; Vol. 327).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Processing UK Biobank High Resolution Accelerometry Data for Unsupervised Identification of Activity Profiles and Their Differences in Clinically Relevant Outcome Parameters
T2 - The ATLAS Index Revisited
AU - Li, Jiaru
AU - Beitlich, Jessica M.
AU - Nejdl, Wolfgang
AU - Zacharias, Helena U.
AU - Marschollek, Michael
PY - 2025/5/15
Y1 - 2025/5/15
N2 - Accelerometer data obtained with wearable devices over extended periods of time provides objective, valuable information on activity behavior. Building on previous work to derive easy-to-interpret activity parameters - the Activity Types from Long-term Accelerometric Sensor data (ATLAS) index - from such data, we aim to investigate whether this approach is feasible with high-quality, extensive data from the UK Biobank, for identifying activity behavior groups, and if exemplary, clinically relevant parameters differ between these groups. A sample of 6,400 subjects' raw accelerometer data was chosen to be processed for computation of the ATLAS index parameters 'regularity', 'intensity' and 'duration' of moderate-intensity, 15+-minute physical activity events. Subsequently, hierarchical clustering was applied, and differences in HDL cholesterol, BMI and C-Reactive Protein (CRP) lab data levels were evaluated. Clustering yielded five distinct activity clusters, and statistically significant differences in HDL cholesterol, BMI and CRP were found between several clusters. The use of the ATLAS index parameters allows for physical activity group identification from objective accelerometer data. These groups differ in physiologically relevant outcome parameters. More research is necessary to uncover potential causal relationships, e.g., by using causal inference methods.
AB - Accelerometer data obtained with wearable devices over extended periods of time provides objective, valuable information on activity behavior. Building on previous work to derive easy-to-interpret activity parameters - the Activity Types from Long-term Accelerometric Sensor data (ATLAS) index - from such data, we aim to investigate whether this approach is feasible with high-quality, extensive data from the UK Biobank, for identifying activity behavior groups, and if exemplary, clinically relevant parameters differ between these groups. A sample of 6,400 subjects' raw accelerometer data was chosen to be processed for computation of the ATLAS index parameters 'regularity', 'intensity' and 'duration' of moderate-intensity, 15+-minute physical activity events. Subsequently, hierarchical clustering was applied, and differences in HDL cholesterol, BMI and C-Reactive Protein (CRP) lab data levels were evaluated. Clustering yielded five distinct activity clusters, and statistically significant differences in HDL cholesterol, BMI and CRP were found between several clusters. The use of the ATLAS index parameters allows for physical activity group identification from objective accelerometer data. These groups differ in physiologically relevant outcome parameters. More research is necessary to uncover potential causal relationships, e.g., by using causal inference methods.
KW - accelerometry
KW - clustering
KW - cohort study
KW - machine learning
KW - physical activity
KW - physiology
UR - http://www.scopus.com/inward/record.url?scp=105005816797&partnerID=8YFLogxK
U2 - 10.3233/SHTI250577
DO - 10.3233/SHTI250577
M3 - Conference contribution
C2 - 40380682
AN - SCOPUS:105005816797
T3 - Studies in health technology and informatics
SP - 1185
EP - 1189
BT - Proceedings of MIE 2025
ER -