Correlates of physical activity behavior in adults: a data mining approach
Abstract Purpose A data mining approach was applied to establish a multilevel hierarchy predicting physical activity (PA) behavior, and to methodologically identify the correlates of PA behavior. Methods Cross-sectional data from the population-based Northern Finland Birth Cohort 1966 study, collect...
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ftdoajarticles:oai:doaj.org/article:67b44416b3bf4011a2017a3b857db421 2023-05-15T17:42:53+02:00 Correlates of physical activity behavior in adults: a data mining approach Vahid Farrahi Maisa Niemelä Mikko Kärmeniemi Soile Puhakka Maarit Kangas Raija Korpelainen Timo Jämsä 2020-07-01T00:00:00Z https://doi.org/10.1186/s12966-020-00996-7 https://doaj.org/article/67b44416b3bf4011a2017a3b857db421 EN eng BMC http://link.springer.com/article/10.1186/s12966-020-00996-7 https://doaj.org/toc/1479-5868 doi:10.1186/s12966-020-00996-7 1479-5868 https://doaj.org/article/67b44416b3bf4011a2017a3b857db421 International Journal of Behavioral Nutrition and Physical Activity, Vol 17, Iss 1, Pp 1-14 (2020) Decision tree CHAID Multilevel model Prediction Classification Nutritional diseases. Deficiency diseases RC620-627 Public aspects of medicine RA1-1270 article 2020 ftdoajarticles https://doi.org/10.1186/s12966-020-00996-7 2022-12-30T22:58:13Z Abstract Purpose A data mining approach was applied to establish a multilevel hierarchy predicting physical activity (PA) behavior, and to methodologically identify the correlates of PA behavior. Methods Cross-sectional data from the population-based Northern Finland Birth Cohort 1966 study, collected in the most recent follow-up at age 46, were used to create a hierarchy using the chi-square automatic interaction detection (CHAID) decision tree technique for predicting PA behavior. PA behavior is defined as active or inactive based on machine-learned activity profiles, which were previously created through a multidimensional (clustering) approach on continuous accelerometer-measured activity intensities in one week. The input variables (predictors) used for decision tree fitting consisted of individual, demographical, psychological, behavioral, environmental, and physical factors. Using generalized linear mixed models, we also analyzed how factors emerging from the model were associated with three PA metrics, including daily time (minutes per day) in sedentary (SED), light PA (LPA), and moderate-to-vigorous PA (MVPA), to assure the relative importance of methodologically identified factors. Results Of the 4582 participants with valid accelerometer data at the latest follow-up, 2701 and 1881 had active and inactive profiles, respectively. We used a total of 168 factors as input variables to classify these two PA behaviors. Out of these 168 factors, the decision tree selected 36 factors of different domains from which 54 subgroups of participants were formed. The emerging factors from the model explained minutes per day in SED, LPA, and/or MVPA, including body fat percentage (SED: B = 26.5, LPA: B = − 16.1, and MVPA: B = − 11.7), normalized heart rate recovery 60 s after exercise (SED: B = -16.1, LPA: B = 9.9, and MVPA: B = 9.6), average weekday total sitting time (SED: B = 34.1, LPA: B = -25.3, and MVPA: B = -5.8), and extravagance score (SED: B = 6.3 and LPA: B = − 3.7). Conclusions Using data mining, we ... Article in Journal/Newspaper Northern Finland Directory of Open Access Journals: DOAJ Articles International Journal of Behavioral Nutrition and Physical Activity 17 1 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Decision tree CHAID Multilevel model Prediction Classification Nutritional diseases. Deficiency diseases RC620-627 Public aspects of medicine RA1-1270 |
spellingShingle |
Decision tree CHAID Multilevel model Prediction Classification Nutritional diseases. Deficiency diseases RC620-627 Public aspects of medicine RA1-1270 Vahid Farrahi Maisa Niemelä Mikko Kärmeniemi Soile Puhakka Maarit Kangas Raija Korpelainen Timo Jämsä Correlates of physical activity behavior in adults: a data mining approach |
topic_facet |
Decision tree CHAID Multilevel model Prediction Classification Nutritional diseases. Deficiency diseases RC620-627 Public aspects of medicine RA1-1270 |
description |
Abstract Purpose A data mining approach was applied to establish a multilevel hierarchy predicting physical activity (PA) behavior, and to methodologically identify the correlates of PA behavior. Methods Cross-sectional data from the population-based Northern Finland Birth Cohort 1966 study, collected in the most recent follow-up at age 46, were used to create a hierarchy using the chi-square automatic interaction detection (CHAID) decision tree technique for predicting PA behavior. PA behavior is defined as active or inactive based on machine-learned activity profiles, which were previously created through a multidimensional (clustering) approach on continuous accelerometer-measured activity intensities in one week. The input variables (predictors) used for decision tree fitting consisted of individual, demographical, psychological, behavioral, environmental, and physical factors. Using generalized linear mixed models, we also analyzed how factors emerging from the model were associated with three PA metrics, including daily time (minutes per day) in sedentary (SED), light PA (LPA), and moderate-to-vigorous PA (MVPA), to assure the relative importance of methodologically identified factors. Results Of the 4582 participants with valid accelerometer data at the latest follow-up, 2701 and 1881 had active and inactive profiles, respectively. We used a total of 168 factors as input variables to classify these two PA behaviors. Out of these 168 factors, the decision tree selected 36 factors of different domains from which 54 subgroups of participants were formed. The emerging factors from the model explained minutes per day in SED, LPA, and/or MVPA, including body fat percentage (SED: B = 26.5, LPA: B = − 16.1, and MVPA: B = − 11.7), normalized heart rate recovery 60 s after exercise (SED: B = -16.1, LPA: B = 9.9, and MVPA: B = 9.6), average weekday total sitting time (SED: B = 34.1, LPA: B = -25.3, and MVPA: B = -5.8), and extravagance score (SED: B = 6.3 and LPA: B = − 3.7). Conclusions Using data mining, we ... |
format |
Article in Journal/Newspaper |
author |
Vahid Farrahi Maisa Niemelä Mikko Kärmeniemi Soile Puhakka Maarit Kangas Raija Korpelainen Timo Jämsä |
author_facet |
Vahid Farrahi Maisa Niemelä Mikko Kärmeniemi Soile Puhakka Maarit Kangas Raija Korpelainen Timo Jämsä |
author_sort |
Vahid Farrahi |
title |
Correlates of physical activity behavior in adults: a data mining approach |
title_short |
Correlates of physical activity behavior in adults: a data mining approach |
title_full |
Correlates of physical activity behavior in adults: a data mining approach |
title_fullStr |
Correlates of physical activity behavior in adults: a data mining approach |
title_full_unstemmed |
Correlates of physical activity behavior in adults: a data mining approach |
title_sort |
correlates of physical activity behavior in adults: a data mining approach |
publisher |
BMC |
publishDate |
2020 |
url |
https://doi.org/10.1186/s12966-020-00996-7 https://doaj.org/article/67b44416b3bf4011a2017a3b857db421 |
genre |
Northern Finland |
genre_facet |
Northern Finland |
op_source |
International Journal of Behavioral Nutrition and Physical Activity, Vol 17, Iss 1, Pp 1-14 (2020) |
op_relation |
http://link.springer.com/article/10.1186/s12966-020-00996-7 https://doaj.org/toc/1479-5868 doi:10.1186/s12966-020-00996-7 1479-5868 https://doaj.org/article/67b44416b3bf4011a2017a3b857db421 |
op_doi |
https://doi.org/10.1186/s12966-020-00996-7 |
container_title |
International Journal of Behavioral Nutrition and Physical Activity |
container_volume |
17 |
container_issue |
1 |
_version_ |
1766144816378281984 |