Objective
Randomised controlled trial (RCT) in adults with anorexia nervosa (AN) showed that Cognitive Remediation Therapy (CRT) enhances cognitive flexibility, abstract thinking and quality‐of‐life. Despite inconsistent findings, CRT has the potential as an adjunct treatment for young people (YP) with AN. A feasibility RCT was conducted in an inpatient setting. The study will also consider the effect of CRT in YP with AN and autistic symptoms.
Methods
Participants were randomly allocated to the Immediate or Delayed condition to receive individual CRT sessions, in addition to standard treatment. A repeated measures design was conducted.
Results
Eighty participants were recruited. The neuropsychological measures were feasible for evaluating individual CRT in YP. Significant improvements in set‐shifting and central coherence were found, with no main effect between immediate and delayed condition. Significant interactions were found between the condition, and autism spectrum condition (ASC) and No‐ASC subgroup, with significant positive impact of CRT on set‐shifting in the No‐ASC subgroup. There was some evidence that for the No‐ASC subgroup, CRT was more effective if delivered at the start of the treatment; and for the ASC subgroup, that CRT was more effective if delivered at the later stage of treatment.
Conclusions
These findings suggest that the overall positive effect of CRT in set‐shifting and central coherence alongside standard treatment. They also indicate the importance of screening for the presence of ASC which could require tailored CRT.
In this paper we propose a wide class of truncated stochastic approximation procedures with moving random bounds. While we believe that the proposed class of procedures will find its way to a wider range of applications, the main motivation is to accommodate applications to parametric statistical estimation theory. Our class of stochastic approximation procedures has three main characteristics: truncations with random moving bounds, a matrix valued random step-size sequence, and dynamically changing random regression function. We establish convergence and consider several examples to illustrate the results.
We consider estimation procedures which are recursive in the sense that each successive estimator is obtained from the previous one by a simple adjustment. The model considered in the paper is very general as we do not impose any preliminary restrictions on the probabilistic nature of the observation process and cover a wide class of nonlinear recursive procedures. In this paper we study asymptotic behaviour of the recursive estimators. The results of the paper can be used to determine the form of a recursive procedure which is expected to have the same asymptotic properties as the corresponding non-recursive one defined as a solution of the corresponding estimating equation.
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