A small-scale randomised controlled trial of home telemonitoring in patients with severe chronic obstructive pulmonary disease.

Introduction This was a pilot study to examine the effects of home telemonitoring (TM) of patients with severe chronic obstructive pulmonary disease (COPD). Methods A randomised controlled 12-month trial of 42 patients with severe COPD was conducted. Home TM of oximetry, temperature, pulse, electrocardiogram, blood pressure, spirometry, and weight with telephone support and home visits was tested against a control group receiving only identical telephone support and home visits. Results The results suggest that TM had a reduction in COPD-related admissions, emergency department presentations, and hospital bed days. TM also seemed to increase the interval between COPD-related exacerbations requiring a hospital visit and prolonged the time to the first admission. The interval between hospital visits was significantly different between the study arms, while the other findings did not reach significance and only suggest a trend. There was a reduction in hospital admission costs. TM was adopted well by most patients and eventually, also by the nursing staff, though it did not seem to change patients' psychological well-being. Discussion Ability to draw firm conclusions is limited due to the small sample size. However the trends of reducing hospital visits warrant a larger study of a similar design. When designing such a trial, one should consider the potential impact of the high quality of care already made available to this patient cohort.

Review: Are we stumbling in our quest to find the best predictor? Over-optimism in sensor-based models for predicting falls in older adults.

The field of fall risk testing using wearable sensors is bustling with activity. In this Letter, the authors review publications which incorporated features extracted from sensor signals into statistical models intended to estimate fall risk or predict falls in older people. A review of these studies raises concerns that this body of literature is presenting over-optimistic results in light of small sample sizes, questionable modelling decisions and problematic validation methodologies (e.g. inherent problems with the overly-popular cross-validation technique, lack of external validation). There seem to be substantial issues in the feature selection process, whereby researchers select features before modelling begins based on their relation to the target, and either perform no validation or test the models on the same data used for their training. This, together with potential issues related to the large number of features and their correlations, inevitably leads to models with inflated accuracy that are unlikely to maintain their reported performance during everyday use in relevant populations. Indeed, the availability of rich sensor data and many analytical options provides intellectual and creative freedom for researchers, but should be treated with caution, and such pitfalls must be avoided if we desire to create generalisable prognostic tools of any clinical value.

Validation of an accelerometer-based fall prediction model.

Falls are a common and serious problem faced by older populations. There is a growing interest in estimating the risk of falling for older people using body-worn sensors and simple movement tasks, allowing appropriate fall prevention programs to be administered in a timely manner to the high risk population. This study investigated the capability and validity of using a waist-mounted triaxial accelerometer (TA) and a directed routine (DR) that includes three movement tasks to discriminate between fallers and non-fallers and between multiple fallers and non-multiple fallers. Data were collected from 98 subjects who were stratified into two separate groups, one for model training and the other for model validation. Logistic regression models were constructed using the TA features from the entire DR and from each single DR task, and were validated using unseen data. The best models were obtained using features from the alternate step test to classify between fallers and non-fallers with κ = 0.34-0.41, sensitivity = 68%-71% and specificity = 63%-73%. However, the overall validation performances were poor. The study emphasizes the importance of independent validation in fall prediction studies.

Prediction of chronic obstructive pulmonary disease exacerbation using physiological time series patterns.

Chronic obstructive pulmonary disease (COPD) is responsible for significant morbidity and mortality worldwide. Recent clinical research has indicated a strong association between physiological homeostasis and the onset of COPD exacerbation. Thus the analysis of these variables may yield a means of predicting a COPD exacerbation in the near future. However, the accuracy of existing prediction methods based on statistical analysis of periodic snapshots of physiological variables is still far from satisfactory, due to lack of integration of long-term and interactive effects of the physiological variables. Therefore, developing a relatively accurate method for predicting COPD exacerbation is an outstanding challenge. In this paper, a regression-based machine learning technique was developed, using trend pattern variables extracted from COPD patients' longitudinal physiological records, to classify subjects into "low-risk" and "high-risk" categories, indicating their risk of suffering a COPD exacerbation event. Experimental results from cross validation assessment of the classifier model show an average accuracy of 79.27% using this method.

Home telecare study for patients with chronic lung disease in the Sydney West Area Health Service.

Information and communication technologies may be used to provide health care services to people living at home. The term "home telecare" has been coined for this service. The elderly and patients with chronic pulmonary conditions, heart disease and diabetes have been thought to be obvious beneficiaries. The evidence base supporting home telecare is growing; however, there is a need for studies of long-term deployment and integration with existing health system processes. We discuss the experiences gained from one such pilot conducted in the Sydney West Area Health Service, which examines the integration of home telecare within the framework of an existing respiratory ambulatory care service. Interim results demonstrate high levels of reliability and positive patient attitude towards use of home monitoring. Clinical staff acceptance levels appeared lower. Effects on health burden, such as hospital admissions and nurse workload, were not significantly altered. The study results have been essential in developing local telecare knowledge within the health care community.

Telehealth technologies for managing chronic disease - experiences from Australia and the UK.

In developed countries, chronic disease now accounts for more than 75% of health care expenditure and nearly an equivalent percentage of disease-related deaths [1]. The burden of chronic disease (often, but not exclusively, associated with ageing) includes congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), hypertension and diabetes. Over the past several decades there has been an epidemiological shift in disease burden from acute to chronic diseases that has rendered acute care models of health service delivery inadequate to address population health needs.