دورية أكاديمية

Identifying multilevel predictors of behavioral outcomes like park use: A comparison of conditional and marginal modeling approaches.

التفاصيل البيبلوغرافية
العنوان: Identifying multilevel predictors of behavioral outcomes like park use: A comparison of conditional and marginal modeling approaches.
المؤلفون: Wende, Marilyn E., Hughey, S. Morgan, McLain, Alexander C., Hallum, Shirelle, Hipp, J. Aaron, Schipperijn, Jasper, Stowe, Ellen W., Kaczynski, Andrew T.
المصدر: PLoS ONE; 4/16/2024, Vol. 19 Issue 4, p1-18, 18p
مصطلحات موضوعية: MULTILEVEL models, PARK use, GENERALIZED estimating equations, DEMOGRAPHIC characteristics, HEALTH behavior, ETHNICITY
مصطلحات جغرافية: GREENVILLE (S.C.), BROOKLYN (New York, N.Y.), RALEIGH (N.C.)
مستخلص: This study compared marginal and conditional modeling approaches for identifying individual, park and neighborhood park use predictors. Data were derived from the ParkIndex study, which occurred in 128 block groups in Brooklyn (New York), Seattle (Washington), Raleigh (North Carolina), and Greenville (South Carolina). Survey respondents (n = 320) indicated parks within one half-mile of their block group used within the past month. Parks (n = 263) were audited using the Community Park Audit Tool. Measures were collected at the individual (park visitation, physical activity, sociodemographic characteristics), park (distance, quality, size), and block group (park count, population density, age structure, racial composition, walkability) levels. Generalized linear mixed models and generalized estimating equations were used. Ten-fold cross validation compared predictive performance of models. Conditional and marginal models identified common park use predictors: participant race, participant education, distance to parks, park quality, and population >65yrs. Additionally, the conditional mode identified park size as a park use predictor. The conditional model exhibited superior predictive value compared to the marginal model, and they exhibited similar generalizability. Future research should consider conditional and marginal approaches for analyzing health behavior data and employ cross-validation techniques to identify instances where marginal models display superior or comparable performance. [ABSTRACT FROM AUTHOR]
Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
قاعدة البيانات: Complementary Index
الوصف
تدمد:19326203
DOI:10.1371/journal.pone.0301549