Source of perceived social support and cognitive change: an 8-year prospective cohort study

OBJECTIVES

This study explored the longitudinal association between overall and individual sources of social support and global cognitive function in older adults.Methods:Data were drawn from three waves (2006, 2010 and 2014) of the Health and Retirement Study (HRS). The analytic sample included participants aged 65 and above who were married and had at least one child (N = 1319). Global cognitive function was measured through a 35-point Telephone Interview Cognitive Screen (TICS). Perceived social support was measured via questions across four sources of support (spouse, child, other family members, and friends). Analyses were conducted using Hierarchical Linear Modeling (HLM) with the addition of a cross-lagged panel model (CLPM).

RESULTS

There was no significant association between perceived overall social support and global cognitive function over time (β=-0.02, p=0.19). However, changes in perceived support from children were positively associated with changes in global cognition (β=0.05,p<0.01). In contrast, changes in perceived support from other family members were negatively associated with changes in cognitive function (β=-0.07,p<0.01).

CONCLUSIONS

Our findings highlight the importance of examining perceived social support from specific elements of one's social networks, rather than in an aggregated variable. Although the positive effect of perceived support from children may be small to moderate, the findings could provide a target for interventions to protect the cognitive function of older adults.

Decision Tree Application to Classification Problems with Boosting Algorithm

A personal credit evaluation algorithm is proposed by the design of a decision tree with a boosting algorithm, and the classification is carried out. By comparison with the conventional decision tree algorithm, it is shown that the boosting algorithm acts to speed up the processing time. The Classification and Regression Tree (CART) algorithm with the boosting algorithm showed 90.95% accuracy, slightly higher than without boosting, 90.31%. To avoid overfitting of the model on the training set due to unreasonable data set division, we consider cross-validation and illustrate the results with simulation; hypermeters of the model have been applied and the model fitting effect is verified. The proposed decision tree model is fitted optimally with the help of a confusion matrix. In this paper, relevant evaluation indicators are also introduced to evaluate the performance of the proposed model. For the comparison with the conventional methods, accuracy rate, error rate, precision, recall, etc. are also illustrated; we comprehensively evaluate the model performance based on the model accuracy after the 10-fold cross-validation. The results show that the boosting algorithm improves the performance of the model in accuracy and precision when CART is applied, but the model fitting time takes much longer, around 2 min. With the obtained result, it is verified that the performance of the decision tree model is improved under the boosting algorithm. At the same time, we test the performance of the proposed verification model with model fitting, and it could be applied to the prediction model for customers’ decisions on subscription to the fixed deposit business.

Motor Control and Achilles Tendon Adaptation in Adolescence: Effects of Sport Participation and Maturity

An important but unresolved research question in adolescent children is the following: “Does sport participation interact with maturation to change motor control and the mechanical and morphological properties of tendons?” Here, we address this important research question with a longitudinal study around the age of peak height velocity (PHV). Our purpose was to characterize the interactive effects of maturation and sports participation on motor control and the mechanical and morphological properties of the Achilles tendon (AT) in adolescent athletes and non-athletes. Twenty-two adolescent athletes (13.1 ± 1.1 years) and 19 adolescent non-athletes (12.8 ± 1.1 years) volunteered for this study. We quantified motor control as the coefficient of variation of torque during a ramp task. In addition, we quantified the AT morphological and mechanical properties using ultrasonography from 18 months before to 12 months after PHV. We found that motor control improved with maturation in both athletes and non-athletes. We found that athletes have a greater increase in body mass with maturation that relates to greater plantarflexion peak force and AT peak stress. Also, athletes have a thicker and longer AT, as assessed with resting cross-sectional area and length. Although the rate of increase in the morphological change with maturation was similar for athletes and non-athletes, the rate of increase in normalized AT stiffness was greater for athletes. This increased AT stiffness in athletes related to peak force and stress. In summary, maturation improves motor control in adolescent children. Further, we provide novel longitudinal evidence that sport participation interacts with maturation in adolescents to induce adaptive effects on the Achilles tendon morphology and mechanical properties. These findings have the potential to minimize the risk of injuries and maximize athletic development in talented adolescents.

Testing Categorized Bivariate Normality With Two-Stage Polychoric Correlation Estimates

Structural equation modeling (SEM) with ordinal indicators rely on an assumption of categorized normality. This assumption may be tested for pairs of variables using the likelihood ratio G2 or Pearson’s X2 statistics. For increased computational efficiency, SEM programs usually estimate polychoric correlations in two stages. However, two-stage polychoric estimates are not asymptotically efficient and G2 and X2 need not be asymptotically chi-square when the estimator is not efficient. Recently, Maydeu-Olivares and Joe (2005) have introduced a new statistic, Mn , that is asymptotically chi-square even for estimators that are not efficient. We investigate the behavior of G2, X2, and Mn when testing underlying bivariate normality with polychoric correlations estimated in two stages.