We demonstrated initial support for the reliability and validity of the 27-item SESPRM-LC, as a developmentally appropriate instrument for assessing self-efficacy among lung cancer patients during postoperative rehabilitation.
The objective of this study was to evaluate the feasibility and preliminary efficacy of a self-efficacy enhancing intervention designed for pulmonary rehabilitation based on motivational interviewing (MI) for postsurgical non-small cell lung cancer (NSCLC) patients. This study was a 2-arm pilot randomized controlled trial and was conducted in two cardiothoracic surgery departments, a tertiary hospital in Fuzhou, China. 28 postsurgical NSCLC patients were randomized to a 3 month (6 session) self-efficacy enhancing intervention based on MI or usual care (UC). Data were measured at baseline and after intervention. The MI based self-efficacy enhancing intervention group was superior to the UC group for reducing anxiety and depression, improving self-efficacy, quality of life, confrontational coping, social support and functional capacity. However, no statistically significant difference was observed in subjective well-being, posttraumatic growth, body mass index and pulmonary function between the two groups. This pilot study demonstrated the feasibility of MI based self-efficacy enhancing intervention for postsurgical NSCLC patients. A larger randomized trial would demonstrate a more rigorous test of efficacy.
This paper deals with the adaptive nonlinear identification and trajectory tracking via dynamic multilayer neural network (NN) with different timescales. Two NN identifiers are proposed for nonlinear systems identification via dynamic NNs with different timescales including both fast and slow phenomenon. The first NN identifier uses the output signals from the actual system for the system identification. In the second NN identifier, all the output signals from nonlinear system are replaced with the state variables of the NNs. The online identification algorithms for both NN identifier parameters are proposed using Lyapunov function and singularly perturbed techniques. With the identified NN models, two indirect adaptive NN controllers for the nonlinear systems containing slow and fast dynamic processes are developed. For both developed adaptive NN controllers, the trajectory errors are analyzed and the stability of the systems is proved. Simulation results show that the controller based on the second identifier has better performance than that of the first identifier.
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