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different populations have different averages of life-space assessment scores and defining cutoff values of clinical significance by each population should take into consideration. Different cutoffs to define restricted life space have been reported. The most common is a score of 60 points. There are other cutoffs derived from receiver operating characteristic (ROC) analysis and used to classify older adults according to their ability in activities of daily living (ADLs) (52.3 points) or instrumental activities of daily living (IADLs) (56 points); other cutoffs are specialized for people with cognitive impairment (26.75 points) or people with spinal cord injury who need mobility aids (78.5 points). The aims were to identify cutoff points of Life Space Assessment (LSA) in older adults in different sites and to determine the relationship of the cutoff scores with mobility disability and depression. The study population was composed of community-dwelling adults aged 65–74 years who were not institutionalized. An ROC analysis was constructed, and the area under the curve (AUC) was calculated to identify the optimal cutoff that discriminates life-space restriction for each city. Logistic regressions were executed by site to comprehend the association among restricted LSA and mobility disability and depression. In total, 1890 participants were included in the analyses (52.38% women, 37.19% mobility disability and 21.32% had depression). Canada cities had the highest cutoff, while Tirana and Natal had the lowest cutoff (< 50). Kingston was the site with the highest association between life-space restriction and mobility disability (OR 5.4, 95% CI 2.9–10). Saint-Hyacinthe, Tirana, and Manizales had significant associations between depression and restricted life space (OR 3.25, 95% CI 1.53–6.89, OR 3.14, 95% CI 1.88–5.24, and OR 3.99, 95% CI 2.28–6.98, respectively). Different cutoffs to define restricted life-space have been identified in elderly people at different sites. The analysis of the relationship between the restricted life space and personal characteristics like depression and mobility disability supported these findings. The groupings produced by the cutoff points for each site showed notable variations. These findings emphasize the importance of population-based cutoffs to improve the general applicability of LSM criteria and take into consideration the importance of site-specific approaches. [ABSTRACT FROM AUTHOR]

Background: Some studies have employed machine learning (ML) methods for mobility prediction modeling in older adults. ML methods could be a helpful tool for life-space mobility (LSM) data analysis. Aim: This study aimed to evaluate the predictive value of ML algorithms for the restriction of life-space mobility (LSM) among elderly people and to identify the most important risk factors for that prediction model. Methods: A 2-year LSM reduction prediction model was developed using the ML-based algorithms decision tree, random forest, and eXtreme gradient boosting (XGBoost), and tested on an independent validation cohort. The data were collected from the International Mobility in Aging Study (IMIAS) from 2012 to 2014, comprising 372 older patients (≥ 65 years of age). LSM was measured by the Life-Space Assessment questionnaire (LSA) with five levels of living space during the month before assessment. Results: According to the XGBoost algorithm, the best model reached a mean absolute error (MAE) of 10.28 and root-mean-square error (RMSE) of 12.91 in the testing portion. The variables frailty (39.4%), mobility disability (25.4%), depression (21.9%), and female sex (13.3%) had the highest importance. Conclusion: The model identified risk factors through ML algorithms that could be used to predict LSM restriction; these risk factors could be used by practitioners to identify older adults with an increased risk of LSM reduction in the future. The XGBoost model offers benefits as a complementary method of traditional statistical approaches to understand the complexity of mobility. [ABSTRACT FROM AUTHOR]

Despite the efficiency produced by long-distance commuting (LDC) as an adjustment mechanism between local labor markets, the impact that it has on the equilibrium of labor markets has not been studied in depth. This paper uses the case of Chile, since in the last two decades the LDC has increased its importance as a strategy of labor mobility for workers in this country. We demonstrate, both theoretically and empirically, that LDC generates wage differences in the labor markets that receive commuters, as a function of the market equilibrium of these workers' place of origin. These differences are not only related to labor productivity and/or employment, but also to the wage expectations of commuters in their place of origin. [ABSTRACT FROM AUTHOR]