Influence of pre-processing criteria on analysis of accelerometry-based physical activity.
<h4>Background</h4>Accelerometers are widely adopted for physical activity (PA) measurement. Accelerometry data require pre-processing before entering formal statistical analyses. Many pre-processing criteria may influence PA outcomes and the processed sample, impacting results in subseq...
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0316357 |
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| author | Bing Han Lilian Perez Deborah A Cohen Rachana Seelam Kathryn P Derose |
| author_facet | Bing Han Lilian Perez Deborah A Cohen Rachana Seelam Kathryn P Derose |
| author_sort | Bing Han |
| collection | DOAJ |
| description | <h4>Background</h4>Accelerometers are widely adopted for physical activity (PA) measurement. Accelerometry data require pre-processing before entering formal statistical analyses. Many pre-processing criteria may influence PA outcomes and the processed sample, impacting results in subsequent statistical analyses.<h4>Aim</h4>To study the implications of pre-processing criteria for accelerometer data on outputs of interest in physical activity studies.<h4>Methods</h4>We used the ActiGraph hip-worn accelerometry data from 538 adult Latino participants. We studied four most important domains of pre-processing criteria (wear-time, minimum wear-time, intensity level, and modified bouts). We examined the true sample size in pre-processed data, the moderate-to-vigorous physical activity (MVPA) outcome, and regression coefficients of age and gender predicting MVPA.<h4>Results</h4>Many pre-processing criteria have minimum impact to the output of interest. However, requirements for minimum wear-time can have high influence on subsequent analyses for MVPA. High requirements for wear-time (e.g., minimum of 5 days with more than 12 hours of wear-time per day) lead to weakened statistical efficiency in estimating the relationship between potential predictors and the MVPA outcome. Intensity levels using vector magnitude triaxial counts yielded drastically different results than those using conventional vertical axis counts.<h4>Conclusion</h4>Moderate changes in minimum wear-time can yield notably different output data and subsequently influence analyses assessing the impacts of interventions on MVPA behaviors. Processed data using vector magnitude and conventional vertical axis counts are not directly comparable. Sensitivity analyses using alternative pre-processing scenarios are highly recommended to verify the robustness of analyses for accelerometry data. |
| format | Article |
| id | doaj-art-179ab218fac8487c9f67cd01c7dd4634 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS ONE |
| spelling | doaj-art-179ab218fac8487c9f67cd01c7dd46342025-01-08T05:31:54ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031635710.1371/journal.pone.0316357Influence of pre-processing criteria on analysis of accelerometry-based physical activity.Bing HanLilian PerezDeborah A CohenRachana SeelamKathryn P Derose<h4>Background</h4>Accelerometers are widely adopted for physical activity (PA) measurement. Accelerometry data require pre-processing before entering formal statistical analyses. Many pre-processing criteria may influence PA outcomes and the processed sample, impacting results in subsequent statistical analyses.<h4>Aim</h4>To study the implications of pre-processing criteria for accelerometer data on outputs of interest in physical activity studies.<h4>Methods</h4>We used the ActiGraph hip-worn accelerometry data from 538 adult Latino participants. We studied four most important domains of pre-processing criteria (wear-time, minimum wear-time, intensity level, and modified bouts). We examined the true sample size in pre-processed data, the moderate-to-vigorous physical activity (MVPA) outcome, and regression coefficients of age and gender predicting MVPA.<h4>Results</h4>Many pre-processing criteria have minimum impact to the output of interest. However, requirements for minimum wear-time can have high influence on subsequent analyses for MVPA. High requirements for wear-time (e.g., minimum of 5 days with more than 12 hours of wear-time per day) lead to weakened statistical efficiency in estimating the relationship between potential predictors and the MVPA outcome. Intensity levels using vector magnitude triaxial counts yielded drastically different results than those using conventional vertical axis counts.<h4>Conclusion</h4>Moderate changes in minimum wear-time can yield notably different output data and subsequently influence analyses assessing the impacts of interventions on MVPA behaviors. Processed data using vector magnitude and conventional vertical axis counts are not directly comparable. Sensitivity analyses using alternative pre-processing scenarios are highly recommended to verify the robustness of analyses for accelerometry data.https://doi.org/10.1371/journal.pone.0316357 |
| spellingShingle | Bing Han Lilian Perez Deborah A Cohen Rachana Seelam Kathryn P Derose Influence of pre-processing criteria on analysis of accelerometry-based physical activity. PLoS ONE |
| title | Influence of pre-processing criteria on analysis of accelerometry-based physical activity. |
| title_full | Influence of pre-processing criteria on analysis of accelerometry-based physical activity. |
| title_fullStr | Influence of pre-processing criteria on analysis of accelerometry-based physical activity. |
| title_full_unstemmed | Influence of pre-processing criteria on analysis of accelerometry-based physical activity. |
| title_short | Influence of pre-processing criteria on analysis of accelerometry-based physical activity. |
| title_sort | influence of pre processing criteria on analysis of accelerometry based physical activity |
| url | https://doi.org/10.1371/journal.pone.0316357 |
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