Actions to Improve Physical Activity Surveillance in the United States

Int J Behav Nutr Phys Act

et al. 2020

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:

Section: Introductionmentioning

confidence: 99%

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

Int J Behav Nutr Phys Act

et al. 2020

“…Many studies have investigated the association of various factors including personal, societal, and environmental factors with different PA behavior indices such as the daily amount of moderate-to-vigorous PA (MVPA) or sedentariness (5)(6)(7). Despite much progress in research into correlates, only a few studies have followed analytical approaches that account for both the existence of several levels of influence (5,7,8) and the complexity and multimodality of PA behavior (5,9,10). To further advance correlates research, there has been calls for more research using both sophisticated statistical assessment that can capture the multilevel nature of correlates (4,5) and PA behavior definitions that better reflect everyday life rather than unidimensional metrics such as daily MVPA (1,5,9,10).…”

Section: Introductionmentioning

confidence: 99%

Correlates of Physical Activity Behavior in Adults: A Data Mining Approach

et al. 2020

Preprint

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 depending on participants’ 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 4,582 participants with valid accelerometer data at the latest follow-up, 2,701 and 1,881 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 seconds 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.

“…Despite the existence of a large number of studies on correlates of PA, most PA promotion strategies have remained unsuccessful (4). This may be because previous studies have often neglected to account for the complexity and multimodality of PA behavior (4,(6)(7)(8), and also the existence of several levels of influence in their analytical approaches (6,9).…”

Section: Introductionmentioning

confidence: 99%

Correlates of Physical Activity Behavior in Adults: A Data Mining Approach

et al. 2020

Preprint

Purpose A data mining approach was applied to establish a multilevel hierarchy explaining physical activity (PA) behavior, and to methodologically identify the correlates of PA behavior. MethodsThe 46-year follow-up data from the population-based Northern Finland Birth Cohort 1966 were used to create a hierarchy using Chi-square Automatic Interaction Detection (CHAID) decision tree technique for predicting PA behavior. The study's subjects were classified as physically active or physically inactive based on their activity profiles derived from objective measurement of PA. The variables were a wide list of potentially modifiable factors including self-reported, clinical, and environmental measures. We then analyzed the association of the factors emerging from the model with three PA metrics including sedentary (SED), light PA (LPA), and moderate-to-vigorous PA (MVPA) minutes per day. ResultsModel fitting was performed using a total of 168 factors as input variables to classify the PA behavior of 2,701 physically active and 1,881 physically inactive subjects. The decision tree selected a total of 36 factors of different domains by which 54 subgroups of subjects were formed. Factors emerging from the model were associated with the PA metrics, including body fat percentage (SED: B=26.5, LPA: B=-16.1, and MVPA: B=-11.7), normalized heart rate recovery 60 seconds 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). ConclusionsUsing data mining, a data-driven model was established from empirical data that can be potentially utilized to identify subgroups for multilevel intervention allocation. An extensive set of factors was methodologically discovered that can be a basis for additional hypothesis testing in PA correlates research.