Stigma as a barrier to recovery: Adverse effects of perceived stigma on social adaptation of persons diagnosed with bipolar affective disorder.

OBJECTIVE: The purpose of this study was to evaluate the effect of concerns about stigma on social adaptation among persons with a diagnosis of bipolar affective disorder. METHODS: The sample comprised 264 persons who were consecutively admitted to a psychiatric inpatient or outpatient service at a university-affiliated hospital and who met research diagnostic criteria for bipolar I disorder, bipolar II disorder, or schizoaffective disorder, manic type. Patients were evaluated with use of the Schedule for Affective Disorders and Schizophrenia, Lifetime Version (SADS-L), the Brief Psychiatric Rating Scale (BPRS), and a measure of perceived stigma. Social adjustment was measured at baseline and seven months later with the Social Adjustment Scale (SAS). RESULTS: As predicted, patients who had concerns about stigma showed significantly more impairment at seven months on the social leisure subscale but not on the SAS extended family subscale, after baseline SAS score and symptom level had been controlled for. More refined models using SAS-derived factors as dependent variables indicated that concerns about stigma predicted higher avoidance of social interactions with persons outside the family and psychological isolation at seven-month follow-up, after baseline SAS and BPRS scores had been controlled for. CONCLUSIONS: Concerns about the stigma associated with mental illness reported by patients during an acute phase of bipolar illness predicted poorer social adjustment seven months later with individuals outside the patient's family. Greater attention to patients' concerns about stigma is needed from both researchers and clinicians.

Stochastic analysis of myoelectric temporal signatures for multifunctional single-site activation of prostheses and orthoses.

This paper is concerned with a stochastic time-series analysis of the temporal signatures of myoelectric (ME) signals including the determination of model order and sampling rate. The paper considers the use of time-series parameters for the activation of artificial limbs for high-level amputees, of stimulation electrodes or of powered braces for paralysed persons, in several degrees of freedom, from a single or two surface-electrode pairs at locations where considerable ME cross-talk exists. The multifunctional capability from a single site is based on the differences between the time-series (TS) parameters for different muscle activation patterns at the same ME site, these differences being thus used for limb function discrimination via easily trainable muscle activation patterns at the vicinity of the electrode site. Specifically, the analysis is in terms of identifying the AR parameters of a time-domain autoregressive (AR) signature model both for the complete ME spectrum and for parts thereof, and in terms of the autocorrelation of the signal and of the models residual. Determination of sampling rate and of model orders is discussed in detail. It is shown that, using online real-time analysis, differences in the AR time-series parameters can be observed for different trainable patterns of muscle activation, at the same electrode location, even at the same ME power levels, as long as considerable cross-talk exists at the electrode site. These parameter differences can be accentuated if one considers the AR parameters for lower-frequency spectral windows. A case is made in this paper for employing TS analysis to squeeze out information in a distinct but low-level ripple of the low frequency spectrum of the signal. This information tends to be ignored in frequency domain, but is all that the AR parameters care for in TS analysis, since they are not concerned, with a flat-average low-frequency spectrum, i.e., its white-noise-like part, which is the residual term of the AR Model and not an AR parameter. Discrimination between different functions from a single electrode-site, at even the same power level, is thus shown to require considerable cross-talk at the given site, and to require the consideration of only the low-frequency part of the spectrum.

Multifunctional prosthesis and orthosis control via microcomputer identification of temporal pattern differences in single-site myoelectric signals.

The paper discusses results of on-line tests on amputees and hemiplegics of multifunctional prostheses and orthoses control by identifying the parameters of single-site temporal EMG signal signatures. The results relate to tests on above-elbow amputees, on shoulder-disarticulation amputees (including a congenital disarticulation amputee) and on hemiplegics, varying from 5 to 50 years of age. The system employed is based on an 8-bit Intel 8080 microprocessor, when computation is in double precision, to obtain an effective 16-bit work-length. The system employs a sequential least-squares algorithm to identify a 4-parameter auto-regressive time-series model of the EMG signal, and a Bayesian rule discrimination algorithm.