RESUMO
BACKGROUND: Clinical symptoms and treatment adherence are one of the most important problems in dialysis patients. Psychological treatments can be effective in reducing the problems of these patients. Therefore, this study aimed at investigating the effectiveness of acceptance and commitment therapy (ACT) on clinical symptoms and treatment adherence in these patients. MATERIALS AND METHOD: This study was a quasi-experimental study with the experimental and control groups in the dialysis clinic of Torbat-e Heydarieh City in 2012. The sample consisted of 40 people who were referred to the dialysis clinic, and the available sampling method was used to randomly assign participants to the experimental and control groups. In the experimental group, ACT was performed in eight sessions of 90 minutes. Questionnaires of Depression, Anxiety, and Stress Scale (DASS-21) and general adherence scale were used. Data were analyzed using Statistical Package for the Social Sciences (SPSS 21) software and multivariate analysis of covariance (MANCOVA) test. RESULTS: There was a significant difference between the mean scores of clinical symptoms and treatment adherence variables in the experimental and control groups (P < 0.05). The effect of this treatment on reducing the clinical symptoms score was 48%, and on increasing the treatment, the adherence score was 44%. CONCLUSION: ACT can reduce clinical symptoms and increase treatment adherence in dialysis patients, so it is suggested to use this intervention in the design of treatment plans for dialysis patients.
RESUMO
A semisupervised deformed kernel function, using low-rank representation with consideration of local geometrical structure of data, is presented for the classification of hyperspectral images. The proposed method incorporates the wealth of unlabeled information to deal with the limited labeled samples situation as a common case in HSIs applications. The proposed kernel needs to be computed before training the classifier, e.g., a support vector machine, and it relies on combining the standard radial basis function kernel based on labeled information and the low-rank representation kernel obtained using all available (labeled and unlabeled) information. The low-rank representation kernel can overcome the difficulties of clustering methods that are used to construct the kernels such as bagged kernel and multi-scale bagged kernel. The experimental results of two well-known HSIs data sets demonstrate the effectiveness of the proposed method in comparison with cluster kernels obtained using traditional clustering methods and graph learning methods.
RESUMO
BACKGROUND: Back pain is one of the most prevalent health problems for which physicians are consulted. Back pain has many economic impacts, such as sickness absences and long-term disability. The prevalence of major depression in patients with chronic low back pain is approximately 3 to 4 times greater than the prevalence rate reported in the general population. OBJECTIVES: This study was designed to evaluate the depression and disability improvement after lumbar discectomy compared with presurgery levels in patients with chronic low back pain and radicular leg pain. PATIENTS AND METHODS: One hundred forty-eight patients with chronic low back pain and radicular leg pain were included in this analytic observational study. The study evaluated several main variables, including age; sex; educational level; job; height; weight; and patient history of abortion, leg pain, back pain, smoking, trauma, number of previous pregnancies, driving, long-term sitting, lifting heavy bodies, and disability and depression before and 6 and 12 months after laminectomy. RESULTS: The depression and disability scores of patients before lumbar discectomy significantly decreased after surgery. CONCLUSIONS: Our results indicate that lumbar discectomy surgery significantly improved depression and disability in patients with chronic herniated discs.
