The aim of this study was to analyze the effects of neurofeedback training for reducing stress and enhancing self-regulation in late adolescence to identify the possibility of use for nursing intervention.
A nonequivalent control group pre-post quasi-experimental design was used. Participants were 78 late adolescents assigned to the experimental group (n=39) that received the neurofeedback training and the control group (n=39). Data were collected on heart rate variability (HRV) and skin conductance level (SCL) to assess stress-biomarker response. The questionnaire contained 164 items from: Positive and Negative Affect Schedule (PANAS), Symptom Checklist-90-Revised (SCL-90-R) and Self-regulatory Ability scale. The neurofeedback training was based on the general adaptation syndrome and body-mind medicine. The intervention was conducted in a total of 10 sessions for 30 minutes per session with high-beta, theta and sensory motor rhythm training on scalp at central zero.
There were significant difference in standard deviation of normal to normal interval (
The results indicated that the neurofeedback training is effective in stress-biomarkers, psy-choemotional stress response and self-regulation. Therefore, neurofeedback training using neuroscientific approach based on brain-mind-body model can be used as an effective nursing intervention for late adolescents in clinics and communities for effective stress responses.
The purpose of this study was to construct and test a hypothetical model of self-management in patients with hemodialysis based on the Self-Regulation Model and resource-coping perspective.
Data were collected from 215 adults receiving hemodialysis in 17 local clinics and one tertiary hospital in 2016. The Hemodialysis Self-management Instrument, the Revised Illness Perception Questionnaire, Herth Hope Index and Multidimensional Scale of Perceived Social Support were used. The exogenous variable was social context; the endogenous variables were cognitive illness representation, hope, self-management behavior, and illness outcome. For data analysis, descriptive statistics, Pearson correlation analysis, factor analysis, and structural equation modeling were performed.
The hypothetical model with six paths showed a good fitness to the empirical data: GFI=.96, AGFI=.90, CFI=.95, RMSEA=.08, SRMR=.04. The factors that had an influence on self-management behavior were social context (β=.84), hope and cognitive illness representation (β=.37 and β=.27) explaining 92.4% of the variance. Self-management behavior mediated the relationship between psychosocial coping resources and illness outcome.
This research specifies a more complete spectrum of the self-management process. It is important to recognize the array of clinical resources available to support patients' self-management. Healthcare providers can facilitate self-management through collaborative care and understanding the ideas and emotions that each patient has about the illness, and ultimately improve the health outcomes. This framework can be used to guide self-management intervention development and assure effective clinical assessment.