Correlates of physical activity behavior in adults: a data mining approach

Autor: Raija Korpelainen, Maisa Niemelä, Maarit Kangas, Soile E. Puhakka, Mikko Kärmeniemi, Vahid Farrahi, Timo Jämsä
Rok vydání: 2020
Předmět:
Male
Population
Decision tree
Medicine (miscellaneous)
Physical Therapy
Sports Therapy and Rehabilitation
computer.software_genre
Multilevel model
Body fat percentage
Generalized linear mixed model
03 medical and health sciences
0302 clinical medicine
Heart Rate
Surveys and Questionnaires
Accelerometry
Data Mining
Humans
030212 general & internal medicine
education
lcsh:RC620-627
Exercise
Finland
Statistical hypothesis testing
education.field_of_study
Sitting Position
Nutrition and Dietetics
lcsh:Public aspects of medicine
Decision Trees
Methodology
lcsh:RA1-1270
030229 sport sciences
Middle Aged
Classification
CHAID
lcsh:Nutritional diseases. Deficiency diseases
Cross-Sectional Studies
Adipose Tissue
Female
Data mining
Sedentary Behavior
Psychology
Prediction
computer
Physical activity behavior
Algorithms
Follow-Up Studies
Zdroj: The International Journal of Behavioral Nutrition and Physical Activity
International journal of behavioral nutrition and physical activity
International Journal of Behavioral Nutrition and Physical Activity, Vol 17, Iss 1, Pp 1-14 (2020)
ISSN: 1479-5868
Popis: 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.
Databáze: OpenAIRE
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