Self-efficacy as a predictor of ability to learn control of heart rate increase with and without biofeedback
Abstract The usefulness of self-efficacy as a predictor of success in controlling heart rate increase with and without biofeedback was investigated in an experimental study on 60 adult subjects. The first part of the study measured heart rate increase control without biofeedback training, the second part measured heart rate increase control with biofeedback training and a posttest measured subsequent heart rate increase control without biofeedback. The results revealed that higher levels of self-efficacy were predictive of success in controlling heart rate increase without biofeedback, but not with biofeedback.
It is the purpose of this paper to introduce a novel estimator for the extremal index of an instantaneous function {f(Xn)}n of a regenerative Harris Markov chain X, based on the renewal properties of the latter. The estimate proposed may be viewed as a "regenerative version" of the runs estimator, insofar as it measures the clustering tendency of high threshold exceedances within regeneration cycles. Strong consistency of this estimator is established under mild stochastic stability assumptions and a simulation result is displayed in the case when the underlying chain is the waiting process related to a simple M/M/1 queue.