Exercise training in juvenile dermatomyositis

Objective. To investigate the effects of a supervised exercise training program on health parameters, physical capacity, and health-related quality of life in patients with mild and chronic juvenile dermatomyositis (DM). Methods. This was a prospective longitudinal study following 10 children with mild and chronic juvenile DM (disease duration >1 year). The exercise program consisted of twice-a-week aerobic and resistance training. At baseline and after the 12-week intervention, we assessed muscle strength and function, aerobic conditioning, body composition, juvenile DM scores, and health-related quality of life. Results. Child self-report and parent proxy-report Pediatric Quality of Life Inventory scores were improved after the intervention (-40.3%; P = 0.001 and -48.2%; P = 0.049, respectively). Importantly, after exercise, the Disease Activity Score was reduced (-26.9%; P = 0.026) and the Childhood Muscle Assessment Scale was improved (+2.5%; P = 0.009), whereas the Manual Muscle Test presented a trend toward statistical significance (+2.2%; P = 0.081). The peak oxygen consumption and time-to-exhaustion were increased by 13.3% (P = 0.001) and 18.2% (P = 0.003), respectively, whereas resting heart rate was decreased by 14.7% (P = 0.006), indicating important cardiovascular adaptations to the exercise program. Upper and lower extremity muscle strength and muscle function were also significantly improved after the exercise training (P < 0.05). Both the whole-body and the lumbar spine bone mineral apparent density were significantly increased after training (1.44%; P = 0.044 and 2.85%; P = 0.008, respectively). Conclusion. We showed for the first time that a 12-week supervised exercise program is safe and can improve muscle strength and function, aerobic conditioning, bone mass, disease activity, and health-related quality of life in patients with active and nonactive mild and chronic juvenile DM with near normal physical function and quality of life.

Semi-supervised dimensionality reduction based on partial least squares for visual analysis of high dimensional data

Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces for high dimensional data or to mapping data directly into 2D or 3D spaces. Although techniques have evolved to improve data segregation on reduced or visual spaces, they have limited capabilities for adjusting the results according to user's knowledge. In this paper, we propose a novel approach to handling both dimensionality reduction and visualization of high dimensional data, taking into account user's input. It employs Partial Least Squares (PLS), a statistical tool to perform retrieval of latent spaces focusing on the discriminability of the data. The method employs a training set for building a highly precise model that can then be applied to a much larger data set very effectively. The reduced data set can be exhibited using various existing visualization techniques. The training data is important to code user's knowledge into the loop. However, this work also devises a strategy for calculating PLS reduced spaces when no training data is available. The approach produces increasingly precise visual mappings as the user feeds back his or her knowledge and is capable of working with small and unbalanced training sets.

Diaphragmatic Breathing Training Program Improves Abdominal Motion During Natural Breathing in Patients With Chronic Obstructive Pulmonary Disease: A Randomized Controlled Trial

Yamaguti WP, Claudino RC, Neto AP, Chammas MC, Gomes AC, Salge TM, Moriya HT, Cukier A, Carvalho CR. Diaphragmatic breathing training program improves abdominal motion during natural breathing in patients with chronic obstructive pulmonary disease: a randomized controlled trial. Arch Phys Med Rehabil 2012;93:571-7. Objective: To investigate the effects of a diaphragmatic breathing training program (DBTP) on thoracoabdominal motion and functional capacity in patients with chronic obstructive pulmonary disease. Design: A prospective, randomized controlled trial. Setting: Academic medical center. Participants: Subjects (N=30; forced expiratory volume in Is, 4270 +/- 13% predicted) were randomly allocated to either a training group (TG) or a control group (CG). Interventions: Subjects in the TG completed a 4-week supervised DBTP (3 individualized weekly sessions), while those in the CG received their usual care. Main Outcome Measures: Effectiveness was assessed by amplitude of the rib cage to abdominal motion ratio (RC/ABD ratio) (primary outcome) and diaphragmatic mobility (secondary outcome). The RC/ABD ratio was measured using respiratory inductive plethysmography during voluntary diaphragmatic breathing and natural breathing. Diaphragmatic mobility was measured by ultrasonography. A 6-minute walk test and health-related quality of life were also evaluated. Results: Immediately after the 4-week DBTP, the TG showed a greater abdominal motion during natural breathing quantified by a reduction in the RC/ABD ratio when compared with the CG (F=8.66; P<.001). Abdominal motion during voluntary diaphragmatic breathing after the intervention was also greater in the TG than in the CG (F=4.11; P<.05). The TG showed greater diaphragmatic mobility after the 4-week DBTP than did the CG (F=15.08; P<.001). An improvement in the 6-minute walk test and in health-related quality of life was also observed in the TG. Conclusions: DBTP for patients with chronic obstructive pulmonary disease induced increased diaphragm participation during natural breathing, resulting in an improvement in functional capacity.

Exercise training in childhood-onset systemic lupus erythematosus: a controlled randomized trial

Abstract Introduction Exercise training has emerged as a promising therapeutic strategy to counteract physical dysfunction in adult systemic lupus erythematosus. However, no longitudinal studies have evaluated the effects of an exercise training program in childhood-onset systemic lupus erythematosus (C-SLE) patients. The objective was to evaluate the safety and the efficacy of a supervised aerobic training program in improving the cardiorespiratory capacity in C-SLE patients. Methods Nineteen physically inactive C-SLE patients were randomly assigned into two groups: trained (TR, n = 10, supervised moderate-intensity aerobic exercise program) and non-trained (NT, n = 9). Gender-, body mass index (BMI)- and age-matched healthy children were recruited as controls (C, n = 10) for baseline (PRE) measurements only. C-SLE patients were assessed at PRE and after 12 weeks of training (POST). Main measurements included exercise tolerance and cardiorespiratory measurements in response to a maximal exercise (that is, peak VO2, chronotropic reserve (CR), and the heart rate recovery (ΔHRR) (that is, the difference between HR at peak exercise and at both the first (ΔHRR1) and second (ΔHRR2) minutes of recovery after exercise). Results The C-SLE NT patients did not present changes in any of the cardiorespiratory parameters at POST (P > 0.05). In contrast, the exercise training program was effective in promoting significant increases in time-to-exhaustion (P = 0.01; ES = 1.07), peak speed (P = 0.01; ES = 1.08), peak VO2 (P = 0.04; ES = 0.86), CR (P = 0.06; ES = 0.83), and in ΔHRR1 and ΔHRR2 (P = 0.003; ES = 1.29 and P = 0.0008; ES = 1.36, respectively) in the C-SLE TR when compared with the NT group. Moreover, cardiorespiratory parameters were comparable between C-SLE TR patients and C subjects after the exercise training intervention, as evidenced by the ANOVA analysis (P > 0.05, TR vs. C). SLEDAI-2K scores remained stable throughout the study. Conclusion A 3-month aerobic exercise training was safe and capable of ameliorating the cardiorespiratory capacity and the autonomic function in C-SLE patients. Trial registration NCT01515163.

A semi-supervised classification technique based on interacting forces

Semi-supervised learning is a classification paradigm in which just a few labeled instances are available for the training process. To overcome this small amount of initial label information, the information provided by the unlabeled instances is also considered. In this paper, we propose a nature-inspired semi-supervised learning technique based on attraction forces. Instances are represented as points in a k-dimensional space, and the movement of data points is modeled as a dynamical system. As the system runs, data items with the same label cooperate with each other, and data items with different labels compete among them to attract unlabeled points by applying a specific force function. In this way, all unlabeled data items can be classified when the system reaches its stable state. Stability analysis for the proposed dynamical system is performed and some heuristics are proposed for parameter setting. Simulation results show that the proposed technique achieves good classification results on artificial data sets and is comparable to well-known semi-supervised techniques using benchmark data sets.