Longitudinal Association of Built Environment Pattern with Physical Activity in a Community-Based Cohort of Elderly Hong Kong Chinese: A Latent Profile Analysis

Lin, Jing; Chan, Faye Ya-Fen; Leung, Jason; Yu, Blanche; Lu, Zhonghua; Woo, Jean; Kwok, Timothy; Lau, Kevin Ka-Lun

doi:10.3390/ijerph17124275

Cited by 6 publications

(5 citation statements)

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“…There was some variation in adjustment for confounding variables between studies, but most adjusted for sociodemographic and neighbourhood contextual variables. Over half of the studies (n = 7) additionally adjusted for health status, including BMI, physical functioning and chronic diseases [82,84,85,87,[89][90][91]. However, no patterns between confounding and statistically significant findings could be identified.…”

Section: Relationship Between Green Space and Physical Activitymentioning

confidence: 99%

“…Such data sources are often incomplete and provide a varying degree of accuracy, which increases the difficulty of sourcing enough data to measure green space both at baseline and follow-up. Very often, green space exposures in longitudinal studies are measured only at one point in time with the assumption that the presence of vegetation doesn't change drastically over time [51,53,54,58,59,61,63,65,66,70,71,74,76,80,[83][84][85][86][87][88][89]91]. However, urban areas undergoing regeneration or expansion may experience drastic changes in the amount and availability of greenery [100].…”

Section: Relationship Between the Natural Environment And Healthmentioning

confidence: 99%

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Relationship between Green and Blue Spaces with Mental and Physical Health: A Systematic Review of Longitudinal Observational Studies

Geneshka

Coventry

Cruz

et al. 2021

IJERPH

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There is growing interest in the ways natural environments influence the development and progression of long-term health conditions. Vegetation and water bodies, also known as green and blue spaces, have the potential to affect health and behaviour through the provision of aesthetic spaces for relaxation, socialisation and physical activity. While research has previously assessed how green and blue spaces affect mental and physical wellbeing, little is known about the relationship between these exposures and health outcomes over time. This systematic review summarised the published evidence from longitudinal observational studies on the relationship between exposure to green and blue space with mental and physical health in adults. Included health outcomes were common mental health conditions, severe mental health conditions and noncommunicable diseases (NCDs). An online bibliographic search of six databases was completed in July 2020. After title, abstract and full-text screening, 44 eligible studies were included in the analysis. Depression, diabetes and obesity were the health conditions most frequently studied in longitudinal relationships. The majority of exposures included indicators of green space availability and urban green space accessibility. Few studies addressed the relationship between blue space and health. The narrative synthesis pointed towards mixed evidence of a protective relationship between exposure to green space and health. There was high heterogeneity in exposure measures and adjustment for confounding between studies. Future policy and research should seek a standardised approach towards measuring green and blue space exposures and employ theoretical grounds for confounder adjustment.

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Section: Relationship Between Green Space and Physical Activitymentioning

confidence: 99%

Section: Relationship Between the Natural Environment And Healthmentioning

confidence: 99%

Relationship between Green and Blue Spaces with Mental and Physical Health: A Systematic Review of Longitudinal Observational Studies

Geneshka

Coventry

Cruz

et al. 2021

IJERPH

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“…LCA proved to be a valuable method for categorizing individuals based on their distinguishable response patterns. 21 We conducted a range of models with one to four classes to determine the optimal number of classes. The selection of the best model was based on various model fit indicators such as the Bayesian information criterion (BIC) and Akaike information criterion (AIC), where a decreasing number indicates a better model fit.…”

Section: Methodsmentioning

confidence: 99%

Development of Machine Learning Models for Predicting Osteoporosis in Patients with Type 2 Diabetes Mellitus—A Preliminary Study

Wu¹,

Zhai²,

Chang³

et al. 2023

DMSO

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Purpose Diagnosing osteoporosis in T2DM based on bone mineral density (BMD) remains challenging. We sought to develop prediction models employing machine learning algorithms for use as screening instruments for osteoporosis in T2DM patients. Patients and Methods Data were collected from 433 participants and analyzed using nine categorical machine learning algorithms to select features based on demographic and clinical variables. Multiple classification models were compared using the area under the receiver operating characteristic curve (ROC-AUC), accuracy, sensitivity, specificity, the average precision (AP), precision, F1 score, precision-recall curves, calibration plots, and decision curve analysis (DCA) to determine the best model. In addition, 5-fold cross-validation was utilized to optimize the model, followed by an evaluation of feature significance using Shapley Additive exPlanations (SHAP). Using latent class analysis (LCA), distinct subpopulations were identified by constructing several discrete clusters. Results In this study, nine feature variables were identified to construct predictive models for osteoporosis in individuals with T2DM. The machine learning algorithms achieved an AP range of 0.444–1.000. The XGBoost model was selected as the final prediction model with an AUROC of 0.940 in the training set, 0.772 in the validation set for 5-fold cross-validation, and 0.872 in the test set. Using SHAP methodology, 25(OH)D was identified as the most important risk factor. Additionally, a 3-Class model was constructed using LCA, which categorized individuals into high, medium, and low-risk groups. Conclusion Our study developed a predictive model with high accuracy and clinical validity for predicting osteoporosis in type 2 diabetes patients. We also identified three subpopulations with varying osteoporosis risk using clustering. However, limited sample size warrants cautious interpretation of results, and validation in larger cohorts is needed.

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“…The premise is that since people practice PA in a physical space, specific characteristics of the built environment are fundamental to encourage the PA and pattern development [ 18 – 20 ]. Physical environments include natural environment features, such as geography and weather conditions, as well as the built environment’s characteristics, including the design of the neighborhoods and available resources such as structures, green spaces, buildings, and objects, which are created or altered by man [ 18 , 21 , 22 ]. All of these can have a specific influence in each PA context or domain (transportation, occupation, leisure, household chores) [ 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

“…A characteristic of studies of this nature is that many evaluate the neighborhood’s individual characteristics and its relations with PA [ 22 ]. Still, few studies analyze the neighborhood’s characteristics as a whole [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

Adolescent’s movement behaviors and built environment: a latent class analysis

et al. 2021

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Background Latent class analysis (LCA) is an alternative and innovative approach to verify the relation of the various combinations of the constructed environment and movement behavior (levels of physical activity, sedentary behavior, and sleep) characteristics. This study aimed to identify latent classes based on the characteristics of the neighborhood environment perceived by adolescents and their association with gender, socioeconomic status (SS), body composition and movement behaviors. Methods This cross-sectional study includes 309 Brazilian adolescents (14 to 16 years old, 57% female). The characteristics of the neighborhood environment perceived were analyzed by the Neighborhood Walkability for Youth Scale. Accelerometers were used for a week to evaluate the movement behaviors. Questionnaires assessed the screen times, total sitting time (TST), and sociodemographic characteristics. LCA was used for modeling the “Perceived Enviroment” variable, having been conducted in the poLCA (Polychromous Variable Latent Class Analysis) package of the R statistical software. Results Three classes were recognized: class 1, “Best Perceived Environment” with 23.03% of adolescents; class 2, “Moderate Perceived Environment”, 63.33%; and class 3, “Worst Perceived Environment”, 13.67%. Light physical activity (LPA), TST, and SS were associated with class prevalence. The adolescents with medium and low SS were, respectively, 3.42 (95% CI 1.62–7.21) and 4.18 (95% CI 1.66–10.50) more likely to belong to class 2, and those with low SS were 5.21 (95% CI 1.35–20.13) more likely to belong to class 3. Class 1 adolescents were associated with a lower chance (OR: 0.09, 95% CI 0.02–0.55) of involvement in ‘adequate LPA time’ compared to class 3. Class 1 adolescents were associated with a lower chance (OR: 0.31, 95% CI 0.12–0.79) of involvement in ‘adequate TST’ compared to class 2. There was a difference between the LPA and TST classes; class 3 presented a longer time in LPA than class 1; class 1 had higher TST than the other classes. Conclusion The findings highlight the influence of neighborhood classes on adolescents’ LPA and TST.

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Longitudinal Association of Built Environment Pattern with Physical Activity in a Community-Based Cohort of Elderly Hong Kong Chinese: A Latent Profile Analysis

Cited by 6 publications

References 50 publications

Relationship between Green and Blue Spaces with Mental and Physical Health: A Systematic Review of Longitudinal Observational Studies

Relationship between Green and Blue Spaces with Mental and Physical Health: A Systematic Review of Longitudinal Observational Studies

Development of Machine Learning Models for Predicting Osteoporosis in Patients with Type 2 Diabetes Mellitus—A Preliminary Study

Adolescent’s movement behaviors and built environment: a latent class analysis

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