BackgroundChinese calligraphic handwriting (CCH) has demonstrated a new role in health and therapy. Meanwhile, meditation is an traditional and effective method for coping with stress and staying healthy. This study compared the effectiveness of CCH and meditation as distinctive and parallel stress reduction interventions.MethodsThirty graduate students and academic staff members in Taiwan who suffered from stress were selected by the General Health Questionnaire and randomly assigned to one of three treatment groups, ie, a CCH group, a meditation group, or a control group, for 8 consecutive weeks. Changes in physiological parameters were measured before, during, and after treatment.ResultsCCH and meditation showed their strength in the respective indices of stress. There was a significant difference in respiratory rate, heart rate, and electromyographic scores between the groups. Comparing pre- and post-effects, a decrease in heart rate and an increase in skin temperature was seen in subjects who practiced CCH. Increased skin temperature and decreased respiratory rate were also seen in subjects who practiced meditation, along with reduced muscle tension and heart rate.ConclusionCCH and meditation have good effects in stress reduction. CCH is a particularly promising new approach to reducing stress.

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This study examined the mediating roles of both positive and negative affects in the relationship between sleep quality and self-control. A sample of 1,507 Chinese adults (37% men; mean age = 32.5 years) completed self-report questionnaires measuring sleep quality, positive and negative emotions, and self-control. Poor sleep quality was positively correlated with negative affect and negatively correlated with positive affect and self-control. Positive affect was positively correlated with self-control, while negative affect was negatively correlated with self-control. Both positive and negative affects significantly mediated the relationship between sleep quality and self-control. Improving individuals’ sleep qualities may lead to more positive emotions and less negative emotion, and these mood changes may increase resources for self-control. Regulating positive and negative affects may reduce the negative effects of poor sleep quality on self-control.

This paper proposes a simple fully data-driven version of Powell's (2001) two-step semiparametric estimator for the sample selection model. The main feature of the proposal is that the bandwidth used to estimate the infinite-dimensional nuisance parameter is chosen by minimizing the mean squared error of the fitted semiparametric model. We formally justify data-driven inference. We introduce the concept of asymptotic normality, uniform in the bandwidth, and show that the proposed estimator achieves this property for a wide range of bandwidths. The method of proof is different from that in Powell (2001) and permits straightforward extensions to other semiparametric or even fully nonparametric specifications of the selection equation. The results of a small Monte Carlo suggest that our estimator has excellent finite sample performance, comparing well with other competing estimators based on alternative choices of smoothing parameters.