This study examined the effectiveness of the Happy Mother mobile app developed for self-management of postpartum depression, based on cognitive behavioural therapy. A randomized controlled trial, with a pre- and a post-test design, was conducted in South Korea. Effectiveness was analysed using repeated measures ANOVA and Wilcoxon Signed Rank Test. We confirmed that the experimental group performed significantly more health promoting behaviours than the control group (F = 5.15, p = 0.007). However, there was no significant difference in postpartum depression, knowledge of depression, maladaptive beliefs, social support, sleep quality, and stress-coping behaviours between the two groups. The experimental group’s mood score increased by 1.79 ± 2.51 points, resulting in significant differences before and after the intervention (Z = −2.81, p = 0.005). The quality of sleep score in the experimental group increased by 1.48 ± 1.70 points and was also significantly different after the intervention (Z = −3.23, p = 0.001). The activity practice rate of the experimental group significantly increased by 30.27 ± 29.27% after using the app (Z = −2.81, p = 0.005). We found the app to be effective in promoting mothers’ health behaviour and improving their depressive mood.
Artificial lights, which are powered by alternating current (AC), are ubiquitous nowadays. The intensity of these lights fluctuates dynamically depending on the AC power. In contrast to previous color constancy methods that exploited the spatial color information, we propose a novel deep learning-based color constancy method that exploits the temporal variations exhibited by AC-powered lights. Using a highspeed camera, we capture the intensity variations of AC lights. Then, we use these variations as an important cue for illuminant learning. We propose a network composed of spatial and temporal branches to train the model with both spatial and temporal features. The spatial branch learns the conventional spatial features from a single image, whereas the temporal branch learns the temporal features of AC-induced light intensity variations in a high-speed video. The proposed method calculates the temporal correlation between the highspeed frames to extract the effective temporal features. The calculations are done at a low computational cost and the output is fed into the temporal branch to help the model concentrate on illuminant-attentive regions. By learning both spatial and temporal features, the proposed method performs remarkably under a complex illuminant environment in a real world scenario in which color constancy is difficult to investigate. The experimental results demonstrate that the proposed method produces lower angular error than the previous state-of-the-art by 30%, and works exceptionally well under various illuminants, including complex ambient light environments.INDEX TERMS Temporal color constancy, temporal correlation, AC light, high-speed video.
The results of the study indicate that in order to promote primipara breastfeeding the amount of supplementary feeding immediately after the birth should be limited and an environment that encourages exclusive breastfeeding in the hospital should be provided. The results also suggest it is necessary to provide nursing interventions that increase the intention to breastfeed and the perceived effectiveness of breastfeeding.
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