Description:
The Australian Fitness Industry Risk Management (AFIRM) Project was set up to explore the operation of rules and regulations for the delivery of safe fitness services. This article summarises the results of recent focus group research and a national survey of risk management practices by the AFIRM Project. Our focus group research in four States identified the following most important concerns: (1) the competency of fitness professionals; (2) the effectiveness of pre-exercise screening and the management of de-conditioned clients; (3) poor supervision of fitness service users and incorrect use of equipment; (4) fitness trainers failing to remain within their scope of practice; (5) equipment misuse (as distinct from incorrect use); and (6) poor fitness training environments. This information was then used to develop 45 specific items for a questionnaire that was disseminated throughout the fitness industry. The survey, which is the largest ever conducted in the Australian fitness industry (n = 1,178), identified similar concerns. Our research indicates that efforts to improve risk management in the fitness industry should focus, first and foremost, on the development and monitoring of safety policy, and improvements in the education and training of fitness instructors to ensure that they can incorporate risk management practices.
Anxiety online-A virtual clinic: Preliminary outcomes following completion of five fully automated treatment programs for anxiety disorders and symptoms
Description:
Objectives: COAST (Chiropractic Observation and Analysis Study) aimed to describe the clinical practices of chiropractors in Victoria, Australia. Design: Cross-sectional study using the BEACH (Bettering the Evaluation and Care of Health) methods for general practice. Setting and participants: 180 chiropractors in active clinical practice in Victoria were randomly selected from the list of 1298 chiropractors registered on Chiropractors Registration Board of Victoria. Twenty-four chiropractors were ineligible, 72 agreed to participate, and 52 completed the study. Main outcome measures: Each participating chiropractor documented encounters with up to 100 consecutive patients. For each chiropractor-patient encounter, information collected included patient health profile, patient reasons for encounter, problems and diagnoses, and chiropractic care. Results: Data were collected on 4464 chiropractor-patient encounters from 52 chiropractors between 11 December 2010 and 28 September 2012. In most (71%) encounters, patients were aged 25-64 years; 1% of encounters were with infants (age < 1 year; 95% CI, 0.3%-3.2%). Musculoskeletal reasons for encounter were described by patients at a rate of 60 per 100 encounters (95% CI, 54-67 encounters) and maintenance and wellness or check-up reasons were described at a rate of 39 per 100 encounters (95% CI, 33-47 encounters). Back problems were managed at a rate of 62 per 100 encounters (95% CI, 55-71 encounters). The most frequent care provided by the chiropractors was spinal manipulative therapy and massage. Conclusions: A range of conditions are managed by chiropractors in Victoria, Australia, but most commonly these conditions are musculoskeletal-related. These results can be used by stakeholders of the chiropractic profession in workforce development, education and health care policy.
Description:
Objective: To investigate the feasibility, effectiveness, and short-term effects of an exercise intervention using a novel exercise park in improving seniors’ balance, physical function, and quality of life. Method: Randomized controlled trial with pre- and post-intervention design (baseline and 18-week intervention) was used. Outcome measures included measures of balance, strength, and function, as well as quality of life and fear of falling. MANCOVA was used to assess differences between groups (control and exercise intervention) over time. Results: Intervention group showed significant improvement on single leg stance (p = .02, 95% confidence interval [CI] = [−8.35, −0.549]), knee strength (p < .01, 95% CI = [−29.14, −5.86]), 2-min walk (p = 0.02, 95% CI = [−19.13, −0.859]), and timed sit to stand (p = .03, 95% CI = [−2.26, −0.143]) tests. Discussion: The exercise park program improved physical function and had high adherence and participation rate. Such intervention has been shown to be safe and therefore might enhance participation in exercise programs for older adults.
Description:
Postural control impairment is a key target for rehabilitation of older adults with high fall risk. To investigate whether anodal transcranial direct current stimulation (a-tDCS) over primary motor cortex (M1) or cerebellum can enhance the positive effects of postural training on balance and postural stability in older adults with high fall risk. In this randomised, double-blinded sham-controlled study, 65 participants were randomly assigned into five groups M1 a-tDCS with postural training, bilateral cerebellar a-tDCS with postural training, sham a-tDCS with postural training, postural training alone and cerebellar a-tDCS alone. Participants in the first two a-tDCS groups received 2 mA stimulation for 20 min concurrently with postural training. Postural training was conducted for three sessions of 20 min per week for two weeks. The Berg Balance Score (BBS) and the stability indices at both static and dynamic levels of the Biodex Balance System were evaluated before and after intervention. A general linear model repeated measure ANOVA was used to assess the effects of variables among groups. Simultaneous postural training with M1 or bilateral cerebellar a-tDCS significantly improved postural stability indices (p < 0.05) and BBS scores (p < 0.05). Besides, the effects of bilateral cerebellar a-tDCS were significantly higher than that of M1 stimulation on these indices (p < 0.05). Moreover, two weeks postural training alone or cerebellar a-tDCS alone is not an adequate intervention to improve the postural stability indices (p > 0.05). Postural training with M1 or bilateral cerebellar a-tDCS, especially bilateral cerebellar a-tDCS, can significantly improve postural control or balance in older adults with high fall risk, while two weeks of postural training alone or two weeks cerebellar a-tDCS alone is not a sufficient intervention. [Display omitted] •Balance did not improve after two weeks of postural training alone in older adults.•Balance did not improve after two weeks of cerebellar a-tDCS alone in older adults.•M1 or cerebellar a-tDCS can enhance the positive effects of postural training on balance in older adults.•Cerebellar more than M1 a-tDCS modulates dynamic balance in older adults with high fall risk.
Description:
Directional optical coherence tomography (D-OCT) allows the visualization of the Henle fiber layer (HFL) in vivo. Here, we used D-OCT to characterize the HFL and outer nuclear layer (ONL) in albinism and examine the relationship between true foveal ONL and peak cone density. Horizontal D-OCT B-scans were acquired, registered, and averaged for 12 subjects with oculocutaneous albinism and 26 control subjects. Averaged images were manually segmented to extract HFL and ONL thickness. Adaptive optics scanning light ophthalmoscopy was used to acquire images of the foveal cone mosaic in 10 subjects with albinism, from which peak cone density was assessed. Across the foveal region, the HFL topography was different between subjects with albinism and normal controls. In particular, foveal HFL thickness was thicker in albinism than in normal controls (P < 0.0001), whereas foveal ONL thickness was thinner in albinism than in normal controls (P < 0.0001). The total HFL and ONL thickness was not significantly different between albinism and controls (P = 0.3169). Foveal ONL thickness was positively correlated with peak cone density in subjects with albinism (r = 0.8061, P = 0.0072). Foveal HFL and ONL topography are significantly altered in albinism relative to normal controls. Our data suggest that increased foveal cone packing drives the formation of Henle fibers, more so than the lateral displacement of inner retinal neurons (which is reduced in albinism). The ability to quantify foveal ONL and HFL may help further stratify grading schemes used to assess foveal hypoplasia.
Description:
Directional optical coherence tomography (D-OCT) allows the visualization of the Henle fiber layer (HFL) in vivo. Here, we used D-OCT to characterize the HFL and outer nuclear layer (ONL) in albinism and examine the relationship between true foveal ONL and peak cone density. Horizontal D-OCT B-scans were acquired, registered, and averaged for 12 subjects with oculocutaneous albinism and 26 control subjects. Averaged images were manually segmented to extract HFL and ONL thickness. Adaptive optics scanning light ophthalmoscopy was used to acquire images of the foveal cone mosaic in 10 subjects with albinism, from which peak cone density was assessed. Across the foveal region, the HFL topography was different between subjects with albinism and normal controls. In particular, foveal HFL thickness was thicker in albinism than in normal controls (P < 0.0001), whereas foveal ONL thickness was thinner in albinism than in normal controls (P < 0.0001). The total HFL and ONL thickness was not significantly different between albinism and controls (P = 0.3169). Foveal ONL thickness was positively correlated with peak cone density in subjects with albinism (r = 0.8061, P = 0.0072). Foveal HFL and ONL topography are significantly altered in albinism relative to normal controls. Our data suggest that increased foveal cone packing drives the formation of Henle fibers, more so than the lateral displacement of inner retinal neurons (which is reduced in albinism). The ability to quantify foveal ONL and HFL may help further stratify grading schemes used to assess foveal hypoplasia.
Description:
Optical coherence tomography (OCT) based measurements of retinal layer thickness, such as the retinal nerve fibre layer (RNFL) and the ganglion cell with inner plexiform layer (GCIPL) are commonly employed for the diagnosis and monitoring of glaucoma. Previously, machine learning techniques have relied on segmentation-based imaging features such as the peripapillary RNFL thickness and the cup-to-disc ratio. Here, we propose a deep learning technique that classifies eyes as healthy or glaucomatous directly from raw, unsegmented OCT volumes of the optic nerve head (ONH) using a 3D Convolutional Neural Network (CNN). We compared the accuracy of this technique with various feature-based machine learning algorithms and demonstrated the superiority of the proposed deep learning based method. Logistic regression was found to be the best performing classical machine learning technique with an AUC of 0.89. In direct comparison, the deep learning approach achieved a substantially higher AUC of 0.94 with the additional advantage of providing insight into which regions of an OCT volume are important for glaucoma detection. Computing Class Activation Maps (CAM), we found that the CNN identified neuroretinal rim and optic disc cupping as well as the lamina cribrosa (LC) and its surrounding areas as the regions significantly associated with the glaucoma classification. These regions anatomically correspond to the well established and commonly used clinical markers for glaucoma diagnosis such as increased cup volume, cup diameter, and neuroretinal rim thinning at the superior and inferior segments.
Description:
Optical coherence tomography (OCT) based measurements of retinal layer thickness, such as the retinal nerve fibre layer (RNFL) and the ganglion cell with inner plexiform layer (GCIPL) are commonly employed for the diagnosis and monitoring of glaucoma. Previously, machine learning techniques have relied on segmentation-based imaging features such as the peripapillary RNFL thickness and the cup-to-disc ratio. Here, we propose a deep learning technique that classifies eyes as healthy or glaucomatous directly from raw, unsegmented OCT volumes of the optic nerve head (ONH) using a 3D Convolutional Neural Network (CNN). We compared the accuracy of this technique with various feature-based machine learning algorithms and demonstrated the superiority of the proposed deep learning based method. Logistic regression was found to be the best performing classical machine learning technique with an AUC of 0.89. In direct comparison, the deep learning approach achieved a substantially higher AUC of 0.94 with the additional advantage of providing insight into which regions of an OCT volume are important for glaucoma detection. Computing Class Activation Maps (CAM), we found that the CNN identified neuroretinal rim and optic disc cupping as well as the lamina cribrosa (LC) and its surrounding areas as the regions significantly associated with the glaucoma classification. These regions anatomically correspond to the well established and commonly used clinical markers for glaucoma diagnosis such as increased cup volume, cup diameter, and neuroretinal rim thinning at the superior and inferior segments.
Description:
Objective To examine the appropriateness of different statistical models in analysing falls count data. Methods Six count models (Poisson, negative binomial (NB), zero-inflated Poisson (ZIP), zero-inflated NB (ZINB), hurdle Poisson (HP) and hurdle NB (HNB)) were used to analyse falls count data. Empirical evaluation of the competing models was performed using model selection criteria and goodness-of-fit through simulation. Data used were from a prospective cohort study of women aged 40-80 years. Results Of the 465 women analysed, 330 (71%) did not fall at all. The analyses identified strong evidence of overdispersion in the falls data. The NB-based regression models (HNB, ZINB, NB) were better performed than the Poisson-based regression models (Poisson, ZIP, HP). Vuong tests favoured the HNB model over the NB and ZINB models and the NB model over the ZINB model. Model accuracy measures and Monte Carlo simulation of goodness-of-fit confirmed the lack of fit of the Poissonbased regression models and demonstrated the best fit for the HNB model with comparable good fit for the ZINB and NB models. Conclusions Falls count data consisting of a considerable number of zeros can be appropriately modelled by the NBbased regression models, with the HNB model offering he best fit. The evaluation procedure presented in this aper provides a defensible guideline to appropriately odel falls or similar count data with excess zeros.