The OnTrack Diabetes web-based program for type 2 diabetes and dysphoria self-management: A randomized controlled trial protocol

Background: The prevalence of type 2 diabetes is rising with the majority of patients practicing inadequate disease self-management. Depression, anxiety, and diabetes-specific distress present motivational challenges to adequate self-care. Health systems globally struggle to deliver routine services that are accessible to the entire population, in particular in rural areas. Web-based diabetes self-management interventions can provide frequent, accessible support regardless of time and location Objective: This paper describes the protocol of an Australian national randomized controlled trial (RCT) of the OnTrack Diabetes program, an automated, interactive, self-guided Web program aimed to improve glycemic control, diabetes self-care, and dysphoria symptoms in type 2 diabetes patients. Methods: A small pilot trial is conducted that primarily tests program functionality, efficacy, and user acceptability and satisfaction. This is followed by the main RCT, which compares 3 treatments: (1) delayed program access: usual diabetes care for 3 months postbaseline followed by access to the full OnTrack Diabetes program; (2) immediate program: full access to the self-guided program from baseline onward; and (3) immediate program plus therapist support via Functional Imagery Training (FIT). Measures are administered at baseline and at 3, 6, and 12 months postbaseline. Primary outcomes are diabetes self-care behaviors (physical activity participation, diet, medication adherence, and blood glucose monitoring), glycated hemoglobin A1c (HbA1c) level, and diabetes-specific distress. Secondary outcomes are depression, anxiety, self-efficacy and adherence, and quality of life. Exposure data in terms of program uptake, use, time on each page, and program completion, as well as implementation feasibility will be conducted. Results: This trial is currently underway with funding support from the Wesley Research Institute in Brisbane, Australia. Conclusions: This is the first known trial of an automated, self-guided, Web-based support program that uses a holistic approach in targeting both type 2 diabetes self-management and dysphoria. Findings will inform the feasibility of implementing such a program on an ongoing basis, including in rural and regional locations.

Practical analysis of big acoustic sensor data for environmental monitoring

Monitoring the environment with acoustic sensors is an effective method for understanding changes in ecosystems. Through extensive monitoring, large-scale, ecologically relevant, datasets can be produced that can inform environmental policy. The collection of acoustic sensor data is a solved problem; the current challenge is the management and analysis of raw audio data to produce useful datasets for ecologists. This paper presents the applied research we use to analyze big acoustic datasets. Its core contribution is the presentation of practical large-scale acoustic data analysis methodologies. We describe details of the data workflows we use to provide both citizen scientists and researchers practical access to large volumes of ecoacoustic data. Finally, we propose a work in progress large-scale architecture for analysis driven by a hybrid cloud-and-local production-grade website.

Board monitoring and the balanced scorecard in nonprofits : challenges in realising potential

Directors of nonprofits in most countries have legal responsibility for monitoring organisational performance (Brody 2010), although there is typically little guidance on how this should occur. The balanced scorecard (BSC) (Kaplan & Norton, 1996, 2001) potentially provides boards with a monitoring tool (Kaplan $ Norton, 2006; Lorsch, 2002). The BSC is intended to help integrate performance measurement, performance management and strategy implmentation (Kaplan 2009). The scorecards is balanced in that it should incorporate both financial and non-financial measures, external and internal perspectives, short and long-term objectives and both lagging and leading indicators. It is a relatively simple tool, but with potentially profound implications for directing board attention and sbusequent action (Ocasio, 1997; Salterio, 2012).

A continental-scale tool for acoustic identification of European bats

Summary 1. Acoustic methods are used increasingly to survey and monitor bat populations. However, the use of acoustic methods at continental scales can be hampered by the lack of standardized and objective methods to identify all species recorded. This makes comparable continent-wide monitoring difficult, impeding progress towards developing biodiversity indicators, transboundary conservation programmes and monitoring species distribution changes. 2. Here we developed a continental-scale classifier for acoustic identification of bats, which can be used throughout Europe to ensure objective, consistent and comparable species identifications. We selected 1350 full-spectrum reference calls from a set of 15 858 calls of 34 European species, from EchoBank, a global echolocation call library. We assessed 24 call parameters to evaluate how well they distinguish between species and used the 12 most useful to train a hierarchy of ensembles of artificial neural networks to distinguish the echolocation calls of these bat species. 3. Calls are first classified to one of five call-type groups, with a median accuracy of 97·6%. The median species-level classification accuracy is 83·7%, providing robust classification for most European species, and an estimate of classification error for each species. 4. These classifiers were packaged into an online tool, iBatsID, which is freely available, enabling anyone to classify European calls in an objective and consistent way, allowing standardized acoustic identification across the continent. 5. Synthesis and applications. iBatsID is the first freely available and easily accessible continental- scale bat call classifier, providing the basis for standardized, continental acoustic bat monitoring in Europe. This method can provide key information to managers and conservation planners on distribution changes and changes in bat species activity through time.

Long-tailed bats' use of a Pinus radiata stand in Kinleith Forest : Recommendations for monitoring

Targeted monitoring of threatened species within plantations is becoming more important due to forest certification programmes’ requirement to consider protection of threatened species, and to increase knowledge of the distribution of species. To determine patterns of long-tailed bat (Chalinolobus tuberculatus) activity in different habitat structures, with the aim of improving the likelihood of detection by targeting monitoring, we monitored one stand of 26 year-old Pinus radiata over seven months between December 2007 and June 2008 in Kinleith Forest, an exotic plantation forest centred around Tokoroa, South Waikato, New Zealand. Activity was determined by acoustic recording equipment, which is able to detect and record bats’ echolocation calls. We monitored activity from sunset to sunrise along a road through the stand, along stand edges, and in the interior of the stand. Bats were recorded on 80% of the 35 nights monitored. All activity throughout the monitoring period was detected on the edge of the stand or along the road. No bats were detected within the interior of the stand. Bat activity was highest along the road through the stand (40.4% of all passes), followed by an edge with stream running alongside (35.2%), along the road within a skidsite (19.8%), and along an edge without a stream (4.6%). There was a significant positive relationship between bat pass rate (bat passes h-1) and the feeding buzz rate (feeding buzzes h-1) indicating that bat activity was associated with feeding and not just commuting. Bat feeding activity was also highest along the road through the stand (59.2% of feeding buzzes), followed by the road within the skidsite (30.6%), and along the stream-side edge (10.2%). No feeding buzzes were recorded in either the interior or along the edge without the stream. Differences in overall feeding activity were significant only between the road and edge and between edges with and without a stream. Bat activity was detected each month and always by the second night of monitoring, and in this stand was highest during April. We recommend targeted monitoring for long-tailed bats be focused on road-side and stand edge habitat, and along streams, and that monitoring take place for at least three nights to maximise probability of detection.

Non-monetised multi-objective decision making system for road management

This paper presents a performance-based optimisation approach for conducting trade-off analysis between safety (roads) and condition (bridges and roads). Safety was based on potential for improvement (PFI). Road condition was based on surface distresses and bridge condition was based on apparent age per subcomponent. The analysis uses a non-monetised optimisation that expanded upon classical Pareto optimality by observing performance across time. It was found that achievement of good results was conditioned by the availability of early age treatments and impacted by a frontier effect preventing the optimisation algorithm from realising of the long-term benefits of deploying actions when approaching the end of the analysis period. A disaggregated bridge condition index proved capable of improving levels of service in bridge subcomponents.

Nutritional challenges for the family caregiver and person with dementia dyad

ABSTRACT Background: The majority of people with dementia live at home until quite late in the disease trajectory, supported by family caregivers who typically take increasing responsibility for providing nutrition. Caregiving is highly stressful and thus both dyad partners are at risk of nutritional issues. Objective: This study evaluated the nutritional status of both dyad members and the associations between these. Design Descriptive, correlational Setting Community Participants 26 dyads of persons with dementia and caregivers Measurements: The nutritional status of each dyad member was evaluated at home using a comprehensive battery of measures including the Mini-Nutritional Assessment, Corrected Arm Muscle Area and a 3-day food diary. Stage of dementia and functional eating capacity was measured for the person with dementia. Caregivers completed a brief burden scale. Results: Of those with dementia (n = 26), a large proportion had nutritional issues (one was malnourished and another 16 were at risk). Six of the caregivers were at risk of malnutrition. In addition, fifteen of the people with dementia did not meet their recommended daily energy requirements. A moderate and significant positive correlation between functional eating skills and nutritional status (MNA score) among participants with dementia was found (r =.523, n = 26, p.006). Conclusion: These findings suggest that a dyadic perspective of nutritional status provides important insights into risk in this vulnerable group. Specifically, monitoring of the functional eating independence skills of the person with dementia is critical, along with assisting caregivers to be aware of their own eating patterns and intake.

Structural damage detection method using frequency response functions

Structural damage detection using measured dynamic data for pattern recognition is a promising approach. These pattern recognition techniques utilize artificial neural networks and genetic algorithm to match pattern features. In this study, an artificial neural network–based damage detection method using frequency response functions is presented, which can effectively detect nonlinear damages for a given level of excitation. The main objective of this article is to present a feasible method for structural vibration–based health monitoring, which reduces the dimension of the initial frequency response function data and transforms it into new damage indices and employs artificial neural network method for detecting different levels of nonlinearity using recognized damage patterns from the proposed algorithm. Experimental data of the three-story bookshelf structure at Los Alamos National Laboratory are used to validate the proposed method. Results showed that the levels of nonlinear damages can be identified precisely by the developed artificial neural networks. Moreover, it is identified that artificial neural networks trained with summation frequency response functions give higher precise damage detection results compared to the accuracy of artificial neural networks trained with individual frequency response functions. The proposed method is therefore a promising tool for structural assessment in a real structure because it shows reliable results with experimental data for nonlinear damage detection which renders the frequency response function–based method convenient for structural health monitoring.