Dataset Information

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

ABSTRACT: The current study applied innovative data mining techniques to a community survey dataset to develop prediction models for two aspects of physical activity (i.e., active transport and screen time) in a sample of urban, primarily Hispanic, older adults (N=2,514). Main predictors for active transport (accuracy=69.29%, precision=0.67, recall=0.69) were immigrant status, high level of anxiety, having a place for physical activity, and willingness to make time for physical activity. The main predictors for screen time (accuracy=63.13%, precision=0.60, recall=0.63) were willingness to make time for exercise, having a place for exercise, age, and availability of family support to access health information on the Internet. Data mining methods were useful to identify intervention targets and inform design of customized interventions.

SUBMITTER: Yoon S

PROVIDER: S-EPMC4580373 | biostudies-other | 2015 Jul

REPOSITORIES: biostudies-other

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

Yoon Sunmoo S Suero-Tejeda Niurka N Bakken Suzanne S

Journal of gerontological nursing 20150507 7

The current study applied innovative data mining techniques to a community survey dataset to develop prediction models for two aspects of physical activity (i.e., active transport and screen time) in a sample of urban, primarily Hispanic, older adults (N=2,514). Main predictors for active transport (accuracy=69.29%, precision=0.67, recall=0.69) were immigrant status, high level of anxiety, having a place for physical activity, and willingness to make time for physical activity. The main predicto ...[more]

PMID: 25941800

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Project description:BackgroundEngaging in regular physical activity (PA) as an older adult has been associated with numerous physical and mental health benefits. The aim of this study is to directly compare how individual-level cognitive factors (self-efficacy for PA, self-determined motivation for PA, self-concept for PA) and neighborhood perceptions of the social factors (neighborhood satisfaction, neighborhood social life) impact moderate-to-vigorous physical activity (MVPA) longitudinally among older African American adults.MethodsData were analyzed from a sub-set of older African American adults (N = 224, M age = 63.23 years, SD = 8.74, 63.23% female, M Body Mass Index = 32.01, SD = 7.52) enrolled in the Positive Action for Today's Health trial. MVPA was assessed using 7-day accelerometry-estimates and psychosocial data (self-efficacy for PA, self-determined motivation for PA, self-concept for PA, neighborhood satisfaction, neighborhood social life) were collected at baseline, 12-, 18-, and 24-months.ResultsMultilevel growth modeling was used to examine within- and between-person effects of individual-level cognitive and social environmental factors on MVPA. At the between-person level, self-concept (b = 0.872, SE = 0.239, p < 0.001), and neighborhood social life (b = 0.826, SE = 0.176, p < 0.001) predicted greater MVPA, whereas neighborhood satisfaction predicted lower MVPA (b = -0.422, SE = 0.172, p = 0.015). Among the between-person effects, only average social life was moderated by time (b = 0.361, SE = 0.147, p = 0.014), indicating that the impact of a relatively positive social life on MVPA increased across time. At the within-person level, positive increases in self-concept (b = 0.294, SE = 0.145, p = 0.043) and neighborhood social life (b = 0.270, SE = 0.113, p = 0.017) were associated with increased MVPA.ConclusionsThese results suggest that people with a higher average self-concept for PA and a more positive social life engaged in greater average MVPA. Additionally, changes in perceptions of one's neighborhood social life and one's self-concept for PA were associated with greater MVPA over 2 years. These factors may be particularly relevant for future interventions targeting long-term change and maintenance of MVPA in older African Americans.Trial registrationClinicalTrials.Gov # NCT01025726 registered 1 December 2009.

Project description:BACKGROUND:Reduced mobility may be responsible for functional decline and acute sarcopenia in older hospitalised patients. The drivers of reduced in-hospital mobility are poorly understood, especially during the early phase of acute hospitalisation. We investigated predictors of in-hospital activity during a 24-h period in the first 48?h of hospital admission in older adults. METHODS:This was a secondary analysis of a prospective repeated measures cohort study. Participants aged 75?years or older were recruited within the first 24?h of admission. At recruitment, patients underwent a baseline assessment including measurements of pre-morbid functional mobility, cognition, frailty, falls efficacy, co-morbidity, acute illness severity, knee extension strength and grip strength, and consented to wear accelerometers to measure physical activity during the first 7?days (or until discharge if earlier). In-hospital physical activity was defined as the amount of upright time (standing or walking). To examine the predictors of physical activity, we limited the analysis to the first 24?h of recording to maximise the sample size as due to discharge from hospital there was daily attrition. We used a best subset analysis including all baseline measures. The optimal model was defined by having the lowest Bayesian information criterion in the best-subset analyses. The model specified a maximum of 5 covariates and used an exhaustive search. RESULTS:Seventy participants were recruited but eight were excluded from the final analysis due to lack of accelerometer data within the first 24?h after recruitment. Patients spent a median of 0.50?h (IQR: 0.21; 1.43) standing or walking. The optimal model selected the following covariates: functional mobility as measured by the de Morton Mobility Index and two measures of illness severity, the National Early Warning Score, and serum C-reactive protein. CONCLUSIONS:Physical activity, particularly in the acute phase of hospitalisation, is very low in older adults. The association between illness severity and physical activity may be explained by symptoms of acute illness being barriers to activity. Interdisciplinary approaches are required to identify early mobilisation opportunities.

Dataset Information

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

Publications

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

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