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Signal processing on digitally sampled vowel sounds for the detection of pathological voices has been firmly established. This work examines compression artifacts on vowel speech samples that have been compressed using the adaptive multi-rate codec at various bit-rates. Whereas previous work has used the sensitivity of machine learning algorithm to test for accuracy, this work examines the changes in the extracted speech features themselves and thus report new findings on the usefulness of a particular feature. We believe this work will have potential impact for future research on remote monitoring as the identification and exclusion of an ill-defined speech feature that has been hitherto used, will ultimately increase the robustness of the system.

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BACKGROUND: Telehealth services based on at-home monitoring of vital signs and the administration of clinical questionnaires are being increasingly used to manage chronic disease in the community, but few statistically robust studies are available in Australia to evaluate a wide range of health and socio-economic outcomes. The objectives of this study are to use robust statistical methods to research the impact of at home telemonitoring on health care outcomes, acceptability of telemonitoring to patients, carers and clinicians and to identify workplace cultural factors and capacity for organisational change management that will impact on large scale national deployment of telehealth services. Additionally, to develop advanced modelling and data analytics tools to risk stratify patients on a daily basis to automatically identify exacerbations of their chronic conditions. METHODS/DESIGN: A clinical trial is proposed at five locations in five states and territories along the Eastern Seaboard of Australia. Each site will have 25 Test patients and 50 case matched control patients. All participants will be selected based on clinical criteria of at least two hospitalisations in the previous year or four or more admissions over the last five years for a range of one or more chronic conditions. Control patients are matched according to age, sex, major diagnosis and their Socio-Economic Indexes for Areas (SEIFA). The Trial Design is an Intervention control study based on the Before-After-Control-Impact (BACI) design. DISCUSSION: Our preliminary data indicates that most outcome variables before and after the intervention are not stationary, and accordingly we model this behaviour using linear mixed-effects (lme) models which can flexibly model within-group correlation often present in longitudinal data with repeated measures. We expect reduced incidence of unscheduled hospitalisation as well as improvement in the management of chronically ill patients, leading to better and more cost effective care. Advanced data analytics together with clinical decision support will allow telehealth to be deployed in very large numbers nationally without placing an excessive workload on the monitoring facility or the patient's own clinicians. TRIAL REGISTRATION: Registered with Australian New Zealand Clinical Trial Registry on 1st April 2013. Trial ID: ACTRN12613000635763.

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BACKGROUND: Lifespace is a multidimensional construct that describes the geographic area in which a person lives and conducts their activities, and reflects mobility, health, and well-being. Traditionally, it has been measured by asking older people to self-report the length and frequency of trips taken and assistance required. Global Positioning System (GPS) sensors on smartphones have been used to measure Lifespace of older people, but not with people with Parkinson's disease (PD). OBJECTIVE: The objective of this study was to investigate whether GPS data collected via smartphones could be used to indicate the Lifespace of people with PD. METHODS: The dataset was supplied via the Michael J Fox Foundation Data Challenge and included 9 people with PD and 7 approximately matched controls. Participants carried smartphones with GPS sensors over two months. Data analysis compared the PD group and the control group. The impact of symptom severity on Lifespace was also investigated. RESULTS: Visualization methods for comparing Lifespace were developed including scatterplots and heatmaps. Lifespace metrics for comparison included average daily distance, percentage of time spent at home, and number of trips into the community. There were no significant differences between the PD and the control groups on Lifespace metrics. Visual representations of Lifespace were organized based on the self-reported severity of symptoms, suggesting a trend of decreasing Lifespace with increasing PD symptoms. CONCLUSIONS: Lifespace measured by GPS-enabled smartphones may be a useful concept to measure the progression of PD and the impact of various therapies and rehabilitation programs. Directions for future use of GPS-based Lifespace are provided.

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BACKGROUND: The Australian Imaging, Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing is a prospective study of 1,112 individuals (211 with Alzheimer's disease (AD), 133 with mild cognitive impairment (MCI), and 768 healthy controls (HCs)). Here we report diagnostic and cognitive findings at the first (18-month) follow-up of the cohort. The first aim was to compute rates of transition from HC to MCI, and MCI to AD. The second aim was to characterize the cognitive profiles of individuals who transitioned to a more severe disease stage compared with those who did not. METHODS: Eighteen months after baseline, participants underwent comprehensive cognitive testing and diagnostic review, provided an 80 ml blood sample, and completed health and lifestyle questionnaires. A subgroup also underwent amyloid PET and MRI neuroimaging. RESULTS: The diagnostic status of 89.9% of the cohorts was determined (972 were reassessed, 28 had died, and 112 did not return for reassessment). The 18-month cohort comprised 692 HCs, 82 MCI cases, 197 AD patients, and one Parkinson's disease dementia case. The transition rate from HC to MCI was 2.5%, and cognitive decline in HCs who transitioned to MCI was greatest in memory and naming domains compared to HCs who remained stable. The transition rate from MCI to AD was 30.5%. CONCLUSION: There was a high retention rate after 18 months. Rates of transition from healthy aging to MCI, and MCI to AD, were consistent with established estimates. Follow-up of this cohort over longer periods will elucidate robust predictors of future cognitive decline.

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Behavioural mapping (BM) is a long established method of structured observational study used to understand where patients are and what they are doing within a hospital setting. BM is prominent in stroke rehabilitation research, where that research indicates patients spend most of their time at bed rest. We evaluate the technical feasibility of using the Microsoft Kinect to automate patient physical activity classification at bed rest.

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A large scale, long term clinical study faced significant quality issues with its medications use data which had been collected from participants using paper forms and manually entered into a data capture system. A method was developed that automatically mapped 72.2% of the unique medication names collected for the study to the AMT and SNOMED CT-AU using Ontoserver, a terminology server for clinical ontologies. These initial results are promising and, with further improvements to the algorithms and evaluation, are expected to greatly improve the analysis of medication data gathered from the study.

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A decline in cognition greater than expected with ageing and accompanied by subjective cognitive concerns or functional changes may be indicative of a dementing disorder. The capacity to correctly identify cognitive decline relies on comparisons with normative data from a suitably matched healthy reference group with relatively homogeneous demographic features. Formal assessment of cognition is usually performed by specialist neuropsychologists trained in administration and interpretation of psychometric tests. With a scarcity of normative data from large cohorts of older adults, Australian neuropsychologists commonly use representative data from small international studies. Data from 727 healthy older Australians participating in the Australian Imaging, Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing have been used to create a normative dataset. A web-based calculator was developed to simplify the time-consuming process of comparing cognitive performance scores with these representative data.

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