PURPOSE: The purpose of this study was to identify the effects of exhalation breathing exercises using expirometer and that of inhalation breathing exercises using incentive spirometry on pulmonary function and complications in elderly patients with upper-abdominal surgery. METHODS: The research design was a nonequivalent control group non-synchronized design. Participants were 63 patients who underwent upper-abdominal surgery under general anesthesia (32 in experiment group, 31 in control group). They were recruited at P university hospital from August 1 to November 30, 2015. Effects were evaluated by measuring pulmonary functions (Forced Vital Capacity [FVC], Forced Expiratory Volume in 1 second [FEV1]) and pulmonary complications. Data were analyzed using SPSS/WIN 18.0 program. RESULTS: There was no difference in FVC between the experimental group and the control group, but FEV1 in the experimental group increased significantly compared to the control group by time change (p=.001). Also, there were no pulmonary complications in the experimental group but there were 5 cases (16.1%)(p=.018) in the control group. CONCLUSION: Findings indicate that exhalation breathing exercises by elderly patients following upper-abdominal surgery is an effective nursing intervention in enhancing pulmonary function and preventing pulmonary complications.
Aged* ; Anesthesia, General ; Breathing Exercises* ; Exhalation* ; Forced Expiratory Volume ; Humans ; Inhalation* ; Motivation ; Nursing ; Research Design ; Respiration* ; Spirometry ; Vital Capacity

PURPOSE: This study compared climacteric symptoms, knowledge of menopause and menopausal management of middle aged women living in urban and rural areas. METHOD: The study subjects included 287 women aged 40-64 years in P city and G town. The instruments used in this study were a climacteric symptoms scale and knowledge of menopause and a menopausal management scale. Data was analyzed with SPSS Win 10.0. RESULT: The mean age of middle-aged women living in urban areas was 47.9 years and that of women in rural areas was 48.0 years. The mean score of the climacteric symptoms of middle-aged women living in urban and rural areas was 48.8 and 50.4 respectively, and was not significantly different. The mean score of the knowledge of menopause of middle-aged women living in urban areas was higher than that of women in rural areas (p=.017). In addition, the mean score of the menopausal management of middle-aged women living in rural areas was higher than that of women in urban areas. CONCLUSION: This study suggests that not only general characteristics but also living areas should be considered in developing nursing interventions to manage the climacteric symptoms of middle aged women.
Climacteric* ; Female ; Humans ; Menopause* ; Middle Aged* ; Nursing

Objective: The development of individual subtypes based on biomarkers offers a cost-effective and timely avenue to comprehending individual differences pertaining to mental health, independent from individuals’ subjective insights. Incorporating 2-channel electroencephalography (EEG) and photoplethysmogram (PPG), we sought to establish a subtype classification system with clinical relevance. Methods: One hundred healthy participants and 99 patients with psychiatric disorders were recruited. Classification thresholds were determined using the EEG and PPG data from 2,278 individuals without mental disorders, serving to classify subtypes in our sample of 199 participants. Multivariate analysis of variance was applied to examine psychological distinctions among these subtypes. K-means clustering was employed to verify the classification system. Results: The distribution of subtypes differed between healthy participants and those with psychiatric disorders. Cognitive abilities were contingent upon brain subtypes, while mind subtypes exhibited significant differences in symptom severity, overall health, and cognitive stress. K-means clustering revealed that the results of our theory-based classification and data-driven classification are comparable. The synergistic assessment of both brain and mind subtypes was also explored. Conclusion Our subtype classification system offers a concise means to access individuals’ mental health. The utilization of EEG and PPG signals for subtype classification offers potential for the future of digital mental healthcare.