The Working After Cancer Study (WACS): a population-based study of middle-aged workers diagnosed with colorectal cancer and their return to work experiences

Background The number of middle-aged working individuals being diagnosed with cancer is increasing and so too will disruptions to their employment. The aim of the Working After Cancer Study is to examine the changes to work participation in the 12 months following a diagnosis of primary colorectal cancer. The study will identify barriers to work resumption, describe limitations on workforce participation, and evaluate the influence of these factors on health-related quality of life. Methods/Design An observational population-based study has been designed involving 260 adults newly-diagnosed with colorectal cancer between January 2010 and September 2011 and who were in paid employment at the time they were diagnosed. These cancer cases will be compared to a nationally representative comparison group of 520 adults with no history of cancer from the general population. Eligible cases will have a histologically confirmed diagnosis of colorectal cancer and will be identified through the Queensland Cancer Registry. Data on the comparison group will be drawn from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. Data collection for the cancer group will occur at 6 and 12 months after diagnosis, with work questions also asked about the time of diagnosis, while retrospective data on the comparison group will be come from HILDA Waves 2009 and 2010. Using validated instruments administered via telephone and postal surveys, data will be collected on socio-demographic factors, work status and circumstances, and health-related quality of life (HRQoL) for both groups while the cases will have additional data collected on cancer treatment and symptoms, work productivity and cancer-related HRQoL. Primary outcomes include change in work participation at 12 months, time to work re-entry, work limitations and change in HRQoL status. Discussion This study will address the reasons for work cessation after cancer, the mechanisms people use to remain working and existing workplace support structures and the implications for individuals, families and workplaces. It may also provide key information for governments on productivity losses.

Validation of scales measuring self-efficacy and outcome expectancy in evidence-based practice

Background: Evidence-based practice (EBP) is embraced internationally as an ideal approach to improve patient outcomes and provide cost-effective care. However, despite the support for and apparent benefits of evidence-based practice, it has been shown to be complex and difficult to incorporate into the clinical setting. Research exploring implementation of evidence-based practice has highlighted many internal and external barriers including clinicians’ lack of knowledge and confidence to integrate EBP into their day-to-day work. Nurses in particular often feel ill-equipped with little confidence to find, appraise and implement evidence. Aims: The following study aimed to undertake preliminary testing of the psychometric properties of tools that measure nurses’ self-efficacy and outcome expectancy in regard to evidence-based practice. Methods: A survey design was utilised in which nurses who had either completed an EBP unit or were randomly selected from a major tertiary referral hospital in Brisbane, Australia were sent two newly developed tools: 1) Self-efficacy in Evidence-Based Practice (SE-EBP) scale and 2) Outcome Expectancy for Evidence-Based Practice (OE-EBP) scale. Results: Principal Axis Factoring found three factors with eigenvalues above one for the SE-EBP explaining 73% of the variance and one factor for the OE-EBP scale explaining 82% of the variance. Cronbach’s alpha for SE-EBP, three SE-EBP factors and OE-EBP were all >.91 suggesting some item redundancy. The SE-EBP was able to distinguish between those with no prior exposure to EBP and those who completed an introductory EBP unit. Conclusions: While further investigation of the validity of these tools is needed, preliminary testing indicates that the SE-EBP and OE-EBP scales are valid and reliable instruments for measuring health professionals’ confidence in the process and the outcomes of basing their practice on evidence.

Threat appraisal for harm from falls: Insights for development of education-based intervention

Objective: This study aims to describe how patients perceive the threat of falls in hospitals, to identify patient characteristics that are associated with greater or lesser perceptions of the threat of falls, and to examine whether there is a discord between the risk that patients perceive in general and the risk that they perceive for themselves personally. Method: A cross-sectional survey amongst geriatric rehabilitation inpatients in Brisbane, Australia, was implemented. The first component of the survey dealt with the ‘general’ nature of in-hospital falls and falls related risks while the second component of the survey was directed at identifying whether the patient held the same belief for themselves. Results: A total of 21 out of 125 participants (17%) indicated that they felt that they were at risk of falling during their hospitalisation and 28 (22%) felt that they would injure themselves if they were to fall. Self-perceived risk of falls was associated with decreasing age and lower cognitive function (Functional Independence Measure Cognitive score). A majority of patients felt that falls most commonly occur in the bathroom [n=67 (54%)] and that if they were to fall, they would fall in the bathroom [n=56 (45%)]. Discussion: Patients generally do not think they are at risk of falling while in hospital and this may contribute to poor adherence to falls prevention strategies. It is possible that raising patient perception of the risk of falls and injury from falls in hospitals may help improve adherence to falls prevention strategies in this setting.

Development and feasibility of a smartphone, ECG and GPS based system for remotely monitoring exercise in cardiac rehabilitation

Background Despite its efficacy and cost-effectiveness, exercise-based cardiac rehabilitation is undertaken by less than one-third of clinically eligible cardiac patients in every country for which data is available. Reasons for non-participation include the unavailability of hospital-based rehabilitation programs, or excessive travel time and distance. For this reason, there have been calls for the development of more flexible alternatives. Methodology and Principal Findings We developed a system to enable walking-based cardiac rehabilitation in which the patient's single-lead ECG, heart rate, GPS-based speed and location are transmitted by a programmed smartphone to a secure server for real-time monitoring by a qualified exercise scientist. The feasibility of this approach was evaluated in 134 remotely-monitored exercise assessment and exercise sessions in cardiac patients unable to undertake hospital-based rehabilitation. Completion rates, rates of technical problems, detection of ECG changes, pre- and post-intervention six minute walk test (6 MWT), cardiac depression and Quality of Life (QOL) were key measures. The system was rated as easy and quick to use. It allowed participants to complete six weeks of exercise-based rehabilitation near their homes, worksites, or when travelling. The majority of sessions were completed without any technical problems, although periodic signal loss in areas of poor coverage was an occasional limitation. Several exercise and post-exercise ECG changes were detected. Participants showed improvements comparable to those reported for hospital-based programs, walking significantly further on the post-intervention 6 MWT, 637 m (95% CI: 565–726), than on the pre-test, 524 m (95% CI: 420–655), and reporting significantly reduced levels of cardiac depression and significantly improved physical health-related QOL. Conclusions and Significance The system provided a feasible and very flexible alternative form of supervised cardiac rehabilitation for those unable to access hospital-based programs, with the potential to address a well-recognised deficiency in health care provision in many countries. Future research should assess its longer-term efficacy, cost-effectiveness and safety in larger samples representing the spectrum of cardiac morbidity and severity.

A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.