Correlates of Pedometer Use: Results from a Community-based Physical Activity Intervention Trial (10,000 Steps Rockhampton)

Background Pedometers have become common place in physical activity promotion, yet little information exists on who is using them. The multi-strategy, community-based 10,000 Steps Rockhampton physical activity intervention trial provided an opportunity to examine correlates of pedometer use at the population level. Methods Pedometer use was promoted across all intervention strategies including: local media, pedometer loan schemes through general practice, other health professionals and libraries, direct mail posted to dog owners, walking trail signage, and workplace competitions. Data on pedometer use were collected during the 2-year follow-up telephone interviews from random population samples in Rockhampton, Australia, and a matched comparison community (Mackay). Logistic regression analyses were used to determine the independent influence of interpersonal characteristics and program exposure variables on pedometer use. Results Data from 2478 participants indicated that 18.1% of Rockhampton and 5.6% of Mackay participants used a pedometer in the previous 18-months. Rockhampton pedometer users (n = 222) were more likely to be female (OR = 1.59, 95% CI: 1.11, 2.23), aged 45 or older (OR = 1.69, 95% CI: 1.16, 2.46) and to have higher levels of education (university degree OR = 4.23, 95% CI: 1.86, 9.6). Respondents with a BMI > 30 were more likely to report using a pedometer (OR = 1.68, 95% CI: 1.11, 2.54) than those in the healthy weight range. Compared with those in full-time paid work, respondents in 'home duties' were significantly less likely to report pedometer use (OR = 0.18, 95% CI: 0.06, 0.53). Exposure to individual program components, in particular seeing 10,000 Steps street signage and walking trails or visiting the website, was also significantly associated with greater pedometer use. Conclusion Pedometer use varies between population subgroups, and alternate strategies need to be investigated to engage men, people with lower levels of education and those in full-time 'home duties', when using pedometers in community-based physical activity promotion initiatives.

Executive coaching : investigating effects of leader-empowering behaviours and psychological empowerment

Executive coaching is a rapidly expanding approach to leadership development which has grown at a rate that warrants extensive examination of its effects (Wasylyshyn, 2003). This thesis has therefore examined both behavioural and psychological effects based on a nine month executive coaching intervention within a large not-for-profit organisation. The intervention was a part of a larger ongoing integrated organisational strategy to create an organisational coaching culture. In order to examine the effectiveness of the nine month executive coaching intervention two studies were conducted. A quantitative study used a pre and post questionnaire to examine leaders and their team members‘ responses before and after the coaching intervention. The research examined leader-empowering behaviours, psychological empowerment, job satisfaction and affective commitment. Significant results were demonstrated from leaders‘ self-reports on leader-empowering behaviours and their team members‘ self-reports revealed a significant flow on effect of psychological empowerment. The second part of the investigation involved a qualitative study which explored the developmental nature of psychological empowerment through executive coaching. The examination dissected psychological empowerment into its widely accepted four facets of meaning, impact, competency and self-determination and investigated, through semi-structured interviews, leaders‘ perspectives of the effect of executive coaching upon them (Spreitzer, 1992). It was discovered that a number of the common practices within executive coaching, including goal-setting, accountability and action-reflection, contributed to the production of outcomes that developed higher levels of psychological empowerment. Careful attention was also given to organisational context and its influence upon the outcomes.

An exercise intervention for women undergoing chemotherapy for ovarian cancer : feasibility and preliminary outcomes

Exercise interventions during adjuvant cancer treatment have been shown to increase functional capacity, relieve fatigue and distress and in one recent study, assist chemotherapy completion. These studies have been limited to breast, prostate or mixed cancer groups and it is not yet known if a similar intervention is even feasible among women diagnosed with ovarian cancer. Women undergoing treatment for ovarian cancer commonly have extensive pelvic surgery followed by high intensity chemotherapy. It is hypothesized that women with ovarian cancer may benefit most from a customised exercise intervention during chemotherapy treatment. This could reduce the number and severity of chemotherapy-related side-effects and optimize treatment adherence. Hence, the aim of the research was to assess feasibility and acceptability of a walking intervention in women with ovarian cancer whilst undergoing chemotherapy, as well as pre-post intervention changes in a range of physical and psychological outcomes. Newly diagnosed women with ovarian cancer were recruited from the Royal Brisbane and Women’s Hospital (RBWH), to participate in a walking program throughout chemotherapy. The study used a one group pre- post-intervention test design. Baseline (conducted following surgery but prior to the first or second chemotherapy cycles) and follow-up (conducted three weeks after the last chemotherapy dose was received) assessments were performed. To accommodate changes in side-effects associated with treatment, specific weekly walking targets with respect to frequency, intensity and duration, were individualised for each participant. To assess feasibility, adherence and compliance with prescribed walking sessions, withdrawals and adverse events were recorded. Physical and psychological outcomes assessed included functional capacity, body composition, anxiety and depression, symptoms experienced during treatment and quality of life. Chemotherapy completion data was also documented and self-reported program helpfulness was assessed using a questionnaire post intervention. Forty-two women were invited to participate. Nine women were recruited, all of whom completed the program. There were no adverse events associated with participating in the intervention and all women reported that the walking program was helpful during their neo-adjuvant or adjuvant chemotherapy treatment. Adherence and compliance to the walking prescription was high. On average, women achieved at least two of their three individual weekly prescription targets 83% of the time (range 42% to 94%). Positive changes were found in functional capacity and quality of life, in addition to reductions in the number and intensity of treatment-associated symptoms over the course of the intervention period. Functional capacity increased for all nine women from baseline to follow-up assessment, with improvements ranging from 10% to 51%. Quality of life improvements were also noted, especially in the physical well-being scale (baseline: median 18; follow-up: median 23). Treatment symptoms reduced in presence and severity, specifically, in constipation, pain and fatigue, post intervention. These positive yet preliminary results suggest that a walking intervention for women receiving chemotherapy for ovarian cancer is safe, feasible and acceptable. Importantly, women perceived the program to be helpful and rewarding, despite being conducted during a time typically associated with elevated distress and treatment symptoms that are often severe enough to alter or cease chemotherapy prescription.

Application of higher order spectra to identify epileptic EEG

Epilepsy is characterized by the spontaneous and seemingly unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic system that detects seizure onsets would allow patients or the people near them to take appropriate precautions, and could provide more insight into this phenomenon. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, we made a comparative study of the performance of Gaussian mixture model (GMM) and Support Vector Machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Results show that the selected HOS based features achieve 93.11% classification accuracy compared to 88.78% with features derived from the power spectrum for a GMM classifier. The SVM classifier achieves an improvement from 86.89% with features based on the power spectrum to 92.56% with features based on the bispectrum.

Pattern recognition using invariants defined from higher order spectra : 2-D image inputs

A new algorithm for extracting features from images for object recognition is described. The algorithm uses higher order spectra to provide desirable invariance properties, to provide noise immunity, and to incorporate nonlinearity into the feature extraction procedure thereby allowing the use of simple classifiers. An image can be reduced to a set of 1D functions via the Radon transform, or alternatively, the Fourier transform of each 1D projection can be obtained from a radial slice of the 2D Fourier transform of the image according to the Fourier slice theorem. A triple product of Fourier coefficients, referred to as the deterministic bispectrum, is computed for each 1D function and is integrated along radial lines in bifrequency space. Phases of the integrated bispectra are shown to be translation- and scale-invariant. Rotation invariance is achieved by a regrouping of these invariants at a constant radius followed by a second stage of invariant extraction. Rotation invariance is thus converted to translation invariance in the second step. Results using synthetic and actual images show that isolated, compact clusters are formed in feature space. These clusters are linearly separable, indicating that the nonlinearity required in the mapping from the input space to the classification space is incorporated well into the feature extraction stage. The use of higher order spectra results in good noise immunity, as verified with synthetic and real images. Classification of images using the higher order spectra-based algorithm compares favorably to classification using the method of moment invariants

Shape discrimination using invariants defined from higher-order spectra

An approach to pattern recognition using invariant parameters based on higher-order spectra is presented. In particular, bispectral invariants are used to classify one-dimensional shapes. The bispectrum, which is translation invariant, is integrated along straight lines passing through the origin in bifrequency space. The phase of the integrated bispectrum is shown to be scale- and amplification-invariant. A minimal set of these invariants is selected as the feature vector for pattern classification. Pattern recognition using higher-order spectral invariants is fast, suited for parallel implementation, and works for signals corrupted by Gaussian noise. The classification technique is shown to distinguish two similar but different bolts given their one-dimensional profiles

A general procedure for the derivation of principal domains of higher-order spectra

A general procedure to determine the principal domain (i.e., nonredundant region of computation) of any higher-order spectrum is presented, using the bispectrum as an example. The procedure is then applied to derive the principal domain of the trispectrum of a real-valued, stationary time series. These results are easily extended to compute the principal domains of other higher-order spectra

Position, rotation, and scale invariant recognition of images using higher-order spectra

A new approach to recognition of images using invariant features based on higher-order spectra is presented. Higher-order spectra are translation invariant because translation produces linear phase shifts which cancel. Scale and amplification invariance are satisfied by the phase of the integral of a higher-order spectrum along a radial line in higher-order frequency space because the contour of integration maps onto itself and both the real and imaginary parts are affected equally by the transformation. Rotation invariance is introduced by deriving invariants from the Radon transform of the image and using the cyclic-shift invariance property of the discrete Fourier transform magnitude. Results on synthetic and actual images show isolated, compact clusters in feature space and high classification accuracies