Quantitative electroencephalographic comodulation : an investigation of patterns in chronic fatigue syndrome

Introduction. This is a pilot study of quantitative electro-encephalographic (QEEG) comodulation analysis, which is used to assist in identifying regional brain differences in those people suffering from chronic fatigue syndrome (CFS) compared to a normative database. The QEEG comodulation analysis examines spatial-temporal cross-correlation of spectral estimates in the resting dominant frequency band. A pattern shown by Sterman and Kaiser (2001) and referred to as the anterior posterior dissociation (APD) discloses a significant reduction in shared functional modulation between frontal and centro-parietal areas of the cortex. This research attempts to examine whether this pattern is evident in CFS. Method. Eleven adult participants, diagnosed by a physician as having CFS, were involved in QEEG data collection. Nineteen-channel cap recordings were made in five conditions: eyes-closed baseline, eyes-open, reading task one, math computations task two, and a second eyes-closed baseline. Results. Four of the 11 participants showed an anterior posterior dissociation pattern for the eyes-closed resting dominant frequency. However, seven of the 11 participants did not show this pattern. Examination of the mean 8-12 Hz amplitudes across three cortical regions (frontal, central and parietal) indicated a trend of higher overall alpha levels in the parietal region in CFS patients who showed the APD pattern compared to those who did not have this pattern. All patients showing the pattern were free of medication, while 71% of those absent of the pattern were using antidepressant medications. Conclusions. Although the sample is small, it is suggested that this method of evaluating the disorder holds promise. The fact that this pattern was not consistently represented in the CFS sample could be explained by the possibility of subtypes of CFS, or perhaps co-morbid conditions. Further, the use of antidepressant medications may mask the pattern by altering the temporal characteristics of the EEG. The results of this pilot study indicate that further research is warranted to verify that the pattern holds across the wider population of CFS sufferers.

The Melbourne Decision-Making Questionnaire : an instrument for measuring patterns for coping with decisional conflict

A study was conducted to examine the factorial validity of the Flinders Decision Making Questionnaire (Mann, 1982), a 31-item self-report inventory designed to measure tendencies to use three major coping patterns identified in the conflict theory of decision making (Janis and Mann, 1977): vigilance, hypervigilance, and defensive avoidance (procrastination, buck-passing, and rationalization). A sample of 2051 university students, comprising samples from Australia (n=262), New Zealand (n=260), the USA (n=475), Japan (n=359), Hong Kong (n=281) and Taiwan (n=414) was administered the DMQ. Factorial validity of the instrument was tested by confirmatory factor analysis with LISREL. Five different substantive models, representing different structural relationships between the decision-coping patterns had unsatisfactory fit to the data and could not be validated. A shortened instrument, containing 22 items, yielded a revised model comprising four identifiable factors-vigilance, hypervigilance, buck-passing, and procrastination. The revised model had adequate fit with data for each country sample and for the total sample, and was confirmed. It is recommended that the 22-item instrument, named the Melbourne DMQ, replace the Flinders DMQ for measurement of decision-coping patterns.

The effectiveness of using a simple ARIA based geographical classification to identify road crash patterns in rural and urban areas of Queensland

Research has noted a ‘pronounced pattern of increase with increasing remoteness' of death rates in road crashes. However, crash characteristics by remoteness are not commonly or consistently reported, with definitions of rural and urban often relying on proxy representations such as prevailing speed limit. The current paper seeks to evaluate the efficacy of the Accessibility / Remoteness Index of Australia (ARIA+) to identifying trends in road crashes. ARIA+ does not rely on road-specific measures and uses distances to populated centres to attribute a score to an area, which can in turn be grouped into 5 classifications of increasing remoteness. The current paper uses applications of these classifications at the broad level of Australian Bureau of Statistics' Statistical Local Areas, thus avoiding precise crash locating or dedicated mapping software. Analyses used Queensland road crash database details for all 31,346 crashes resulting in a fatality or hospitalisation occurring between 1st July, 2001 and 30th June 2006 inclusive. Results showed that this simplified application of ARIA+ aligned with previous definitions such as speed limit, while also providing further delineation. Differences in crash contributing factors were noted with increasing remoteness such as a greater representation of alcohol and ‘excessive speed for circumstances.' Other factors such as the predominance of younger drivers in crashes differed little by remoteness classification. The results are discussed in terms of the utility of remoteness as a graduated rather than binary (rural/urban) construct and the potential for combining ARIA crash data with census and hospital datasets.

Longitudinal patterns of weight in women diagnosed with breast cancer [Conference Abstract]

Introduction: Weight gain is a common concern following breast cancer and has been associated with negative health outcomes. As such, prevention of weight gain is of clinical interest. This work describes weight change between 6- and 18-months following a breast cancer diagnosis and explores the personal, treatment and behavioural characteristics associated with gains in weight. Methods: Body mass index was objectively assessed, at three-monthly intervals, on a population-based sample of women newly diagnosed with unilateral breast cancer (n=185). Changes in BMI between 6- and 18-months post-diagnosis were calculated, with gains of one or more being considered clinically detrimental to future health. Results: Approximately 60% of participants were overweight or obese at 6-months post-diagnosis. While BMI remained relatively stable across the testing period (range=27.3-27.8), 24% of participants experienced clinically relevant gains in BMI (median gains=1.9). Following adjustment for potential confounders, younger age (<45 years; Odds ratio, OR=9.8), being morbidly obese at baseline (OR=4.6) and receiving hormone therapy (OR=4.8) were characteristics associated with an increased odds (p<0.05) of gaining BMI. Other characteristics associated with gains in BMI were more extensive surgery and having a history of smoking, although these relationships were not supported statistically. In contrast, caring for younger children was associated with reduced risk of gaining BMI (OR=0.3, p=0.20). Conclusions: Clinically relevant weight gain between 6- and 18-months post-breast cancer diagnosis is an issue for one in four women, with certain subgroups being particularly susceptible. However, the majority of women diagnosed with breast cancer are overweight or obese and gains in body weight are common. Thus, interventions that address the importance of achieving and sustaining a healthy body weight, delivered to all women with breast cancer, may have greater public health impact than interventions targeting any specific breast cancer subgroup.