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Jon Wright

Publications and source records attributed to Jon Wright.

4 recordsLinked to original sources
PubMed2004

Occupational balance of women with rheumatoid arthritis: a qualitative study.

OBJECTIVE: Occupational balance has been shown to be an important factor in maintaining health. Rheumatoid arthritis (RA) reduces functional ability and quality of life and may thus reduce occupational balance. The aim of this qualitative pilot study was to explore occupational balance in women who have RA. METHODS: Nine women with RA with past, but not current, paid work experience, no other confounding neuro-motor disease and with disease duration of 0.75-31 years were selected from an Austrian rheumatology outpatient clinic. Age range of the participants was 28-68 years. A semi-structured interview was conducted with each participant and transcribed verbatim. Data were analysed by the constant comparative method from an occupational perspective. RESULTS: Three main categories emerged: (1) The participants experienced a process of change that affected their occupational balance. (2) This new state of occupational balance was characterized by changed levels of involvement in physical, mental, social and rest occupations and by a certain level of unpredictability of symptoms. (3) Overall, the new state of occupational balance was valued differently: positively, indifferently or negatively. CONCLUSION: RA was found to have a considerable impact on occupational balance. The experience is not invariably seen as negative as previous literature would suggest. Further research should explore the longitudinal dimension of occupational balance in people with RA.

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PubMed1995

Monitoring changes in time of chaotic nonlinear systems.

We extend our method for classifying signals from chaotic nonlinear dynamical systems to the problem of monitoring chaotic nonlinear dynamical systems with the goal of detecting that the state of a system has changed. One potential application would be to systems where the changes are not easily detectable by spectral analysis or other linear techniques. The method is expected to be most useful in comparison to other techniques when there are other signals or noise present, some of which have a broad band frequency spectrum, and the signal of interest is associated with either a low dimensional dynamical system or a low dimensional chaotic attractor. The method is applied to data from a laboratory model of a fluidized bed reactor and to data from a gyroscope as well as to numerically generated signals from mathematical models. For the dynamical systems considered in the paper, the proposed method provides significantly better discrimination than spectral analysis. (c) 1995 American Institute of Physics.

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PubMed1993

Recognition and classification of nonlinear chaotic signals.

We propose a method of identifying and classifying signals generated by nonlinear, chaotic processes even when there are other signals and noise present. The method compares probability densities constructed from signals with a library of known densities. We note that there are many different invariant densities that can be constructed and that the characteristic functional of these densities factorizes into a product of characteristic functionals. Each member of the product is the characteristic functional of one of the signals present. This factorization provides a means of identifying the presence of a particular signal. Simple examples are given to demonstrate the method.

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