A Data Mining Approach for Examining Predictors of Physical Activity Among Urban Older Adults

Yoon, Sunmoo; Suero-Tejeda, Niurka; Bakken, Suzanne

doi:10.3928/00989134-20150420-01

Cited by 12 publications

(14 citation statements)

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“…The potential for self-management of these chronic diseases has implications for the creation of realistic, achievable goals and setting targets for health disparity interventions. 7 , 8 , 16 , 26 , 36 …”

Section: Discussionmentioning

confidence: 99%

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Trends in Poor Health Indicators Among Black and Hispanic Middle-aged and Older Adults in the United States, 1999-2018

et al. 2020

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IMPORTANCEAdults who belong to racial/ethnic minority groups are more likely than White adults to receive a diagnosis of chronic disease in the United States. OBJECTIVE To evaluate which health indicators have improved or become worse among Black and Hispanic middle-aged and older adults since the Minority Health and Health Disparities Research and Education Act of 2000. DESIGN, SETTING, AND PARTICIPANTS In this repeated cross-sectional study, a total of 4 856 326 records were extracted from the Behavioral Risk Factor Surveillance System from January 1999 through December 2018 of persons who self-identified as Black (non-Hispanic), Hispanic (non-White), or White and who were 45 years or older. EXPOSUREThe 1999 legislation to reduce racial/ethnic health disparities. MAIN OUTCOMES AND MEASURESPoor health indicators and disparities including major chronic diseases, physical inactivity, uninsured status, and overall poor health. RESULTS Among the 4 856 326 participants (2 958 041 [60.9%] women; mean [SD] age, 60.4 [11.8] years), Black adults showed an overall decrease indicating improvement in uninsured status (β = −0.40%; P < .001) and physical inactivity (β = −0.29%; P < .001), while they showed an overall increase indicating deterioration in hypertension (β = 0.88%; P < .001), diabetes (β = 0.52%; P < .001), asthma (β = 0.25%; P < .001), and stroke (β = 0.15%; P < .001) during the last 20 years.The Black-White gap (ie, the change in β between groups) showed improvement (2 trend lines converging) in uninsured status (−0.20%; P < .001) and physical inactivity (−0.29%; P < .001), while the Black-White gap worsened (2 trend lines diverging) in diabetes (0.14%; P < .001), hypertension (0.15%; P < .001), coronary heart disease (0.07%; P < .001), stroke (0.07%; P < .001), and asthma (0.11%; P < .001). Hispanic adults showed improvement in physical inactivity (β = −0.28%; P = .02) and perceived poor health (β = −0.22%; P = .001), while they showed overall deterioration in hypertension (β = 0.79%; P < .001) and diabetes (β = 0.50%; P < .001). The Hispanic-White gap showed improvement in coronary heart disease (−0.15%; P < .001), stroke (−0.04%; P < .001), kidney disease (−0.06%; P < .001), asthma (−0.06%; P = .02), arthritis (−0.26%; P < .001), depression (−0.23%; P < .001), and physical inactivity (−0.10%; P = .001), while the Hispanic-White gap worsened in diabetes (0.15%; P < .001), hypertension (0.05%; P = .03), and uninsured status (0.09%; P < .001).

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Section: Discussionmentioning

confidence: 99%

“… 40 , 41 Our findings also support the need for culturally sensitive social support in the realms of mental health care and promoting physical activity. 6 , 7 , 8 , 36 …”

Section: Discussionmentioning

confidence: 99%

Trends in Poor Health Indicators Among Black and Hispanic Middle-aged and Older Adults in the United States, 1999-2018

et al. 2020

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“…All participants have been willing to participate in the study and have granted permission for the use of their information in unidentifiable format for the purposes of scientific research. Current smoker 447 (18) 335 (19) Former smoker 702 (27) 458 (26) Values are numbers (%) if not otherwise stated, SD=standard deviation. Table 3: Associations with the whole study population (N=4,582) between the continuous factors emerged in the decision tree model and time spent in sedenteriness (SED), light physical activity (LPA), and moderate-tovigorous physical activity (MVPA).…”

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confidence: 99%

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

Farrahi

Niemelä

Kärmeniemi

et al. 2020

Preprint

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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.

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“…These principles also regard the field of PA research, in which there is a need for more complex approaches to identify the next generation of PA behavior correlates, understand their relative importance, and capture the complex interrelations among the factors at different levels [5,6,8]. Several studies have applied data mining approaches [16][17][18][19] mostly to establish datadriven correlate hierarchies [16,17] but using a limited number of factors and self-reported measurement of PA or sedentary behavior.…”

Section: Introductionmentioning

confidence: 99%

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

Farrahi

Niemelä

Kärmeniemi

et al. 2020

Int J Behav Nutr Phys Act

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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:

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A Data Mining Approach for Examining Predictors of Physical Activity Among Urban Older Adults

Cited by 12 publications

References 28 publications

Trends in Poor Health Indicators Among Black and Hispanic Middle-aged and Older Adults in the United States, 1999-2018

Trends in Poor Health Indicators Among Black and Hispanic Middle-aged and Older Adults in the United States, 1999-2018

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

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

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