Farrahi, V., Niemelä, M., Kärmeniemi, M. et al. Correlates of physical activity behavior in adults: a data mining approach. Int J Behav Nutr Phys Act 17, 94 (2020). https://doi.org/10.1186/s12966-020-00996-7
Correlates of physical activity behavior in adults : a data mining approach
|Author:||Farrahi, Vahid1; Niemelä, Maisa1; Kärmeniemi, Mikko2,3,4;|
1Research Unit of Medical Imaging, Physics and Technology, University of Oulu, P.O. 5000, FI-90014, Oulu, Finland
2Medical Research Center, Oulu University Hospital and University of Oulu, Oulu, Finland
3Center for Life Course Health Research, University of Oulu, Oulu, Finland
4Department of Sports and Exercise Medicine, Oulu Deaconess Institute Foundation sr, Oulu, Finland
5Geography Research Unit, University of Oulu, Oulu, Finland
6Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 3.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020120499467
|Publish Date:|| 2020-12-04
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 established a data-driven model composed of 36 different factors of relative importance from empirical data. This model may be used to identify subgroups for multilevel intervention allocation and design. Additionally, this study methodologically discovered an extensive set of factors that can be a basis for additional hypothesis testing in PA correlates research.
International journal of behavioral nutrition and physical activity
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3142 Public health care science, environmental and occupational health
NFBC1966 received financial support from University of Oulu Grant no. 24000692, Oulu University Hospital Grant no. 24301140, ERDF European Regional Development Fund Grant no. 539/2010 A31592. This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska–Curie grant agreement no. 713645, the Ministry of Education and Culture in Finland [grant numbers OKM/86/626/2014, OKM/43/626/2015, OKM/17/626/2016, OKM/54/626/2019], Infotech Oulu, Finland, and Northern Ostrobothnia Hospital District. The funders played no role in designing the study, or collecting, analyzing, and interpreting the data, or writing the manuscript.
|EU Grant Number:||
(713645) BioMEP - Biomedical Engineering and Medical Physics
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