التفاصيل البيبلوغرافية
| العنوان: |
Correlates of physical activity behavior in adults:a data mining approach |
| المؤلفون: |
Farrahi, V. (Vahid), Niemelä, M. (Maisa), Kärmeniemi, M. (Mikko), Puhakka, S. (Soile), Kangas, M. (Maarit), Korpelainen, R. (Raija), Jämsä, T. (Timo) |
| بيانات النشر: |
Springer Nature |
| سنة النشر: |
2020 |
| المجموعة: |
Jultika - University of Oulu repository / Oulun yliopiston julkaisuarkisto |
| مصطلحات موضوعية: |
CHAID, Classification, Decision tree, Multilevel model, Prediction |
| الوصف: |
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 ... |
| نوع الوثيقة: |
article in journal/newspaper |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
info:eu-repo/grantAgreement/EC/H2020/713645/EU/Biomedical Engineering and Medical Physics/BioMEP |
| الاتاحة: |
http://urn.fi/urn:nbn:fi-fe2020120499467 |
| Rights: |
info:eu-repo/semantics/openAccess ; © The Author(s). 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. ; https://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: |
edsbas.EDE68852 |
| قاعدة البيانات: |
BASE |