Comparison of U.S., Canadian, and Australian participants' performance on the Algase Wandering Scale-Version 2 (AWS-V2)

Background Wandering represents a major problem in the management of patients with Alzheimer’s disease (AD). In this study we examined the utility of the Algase Wandering Scale (AWS), a newly developed psychometric instrument that asks caregivers to assess the likelihood of wandering behavior. Methods The AWS was administered to the caregivers of 40 AD patients and total and subscale scores were examined in relation to measures of mental and functional status, depressive symptoms and medication usage. Results AWS scores were comparable, though slightly lower, than those normative values previously published. Higher scores were associated with more severe dementia. The Negative Outcome subscale showed a significant increase in reported falls or injuries in association with anti-depressant use. Conclusions These data provide some construct validation for the AWS as a potentially useful scale to assess wandering behaviors in AD.

Validation of the Algase Wandering Scale (Version 2) in a cross cultural sample

This study examined the psychometric properties of an expanded version of the Algase Wandering Scale (Version 2) (AWS-V2) in a cross-cultural sample. A cross-sectional survey design was used. Study subjects were 172 English-speaking persons with dementia (PWD) from long-term care facilities in the USA, Canada, and Australia. Two or more facility staff rated each subject on the AWS-V2. Demographic and cognitive data (MMSE) were also obtained. Staff provided information on their own knowledge of the subject and of dementia. Separate factor analyses on data from two samples of raters each explained greater than 66% of the variance in AWS-V2 scores and validated four (persistent walking, navigational deficit, eloping behavior, and shadowing) of five factors in the original scale. Items added to create the AWS-V2 strengthened the shadowing subscale, failed to improve the routinized walking subscale, and added a factor, attention shifting as compared to the original AWS. Evidence for validity was found in significant correlations and ANOVAs between the AWS-V2 and most subscales with a single item indicator of wandering and with the MMSE. Evidence of reliability was shown by internal consistency of the AWS-V2 (0.87, 0.88) and its subscales (range 0.88 to 0.66), with Kappa for individual items (17 of 27 greater than 0.4), and ANOVAs comparing ratings across rater groups (nurses, nurse aids, and other staff). Analyses support validity and reliability of the AWS-V2 overall and for persistent walking, spatial disorientation, and eloping behavior subscales. The AWS-V2 and its subscales are an appropriate way to measure wandering as conceptualized within the Need-driven Dementia-compromised Behavior Model in studies of English-speaking subjects. Suggestions for further strengthening the scale and for extending its use to clinical applications are described.

Initial psychometric evaluation of the Wayfinding Effectiveness Scale

This study evaluates three versions of the Wayfinding Effectiveness Scale (WES), developed to differentiate problems of wayfinding and wandering behavior of community-residing elders with dementia (EWD), in 266 dyads (EWD and caregiver) recruited from Alzheimer's Association chapters. Factor analyses yield a five-factor solution (explained variance = 62.6%): complex wayfinding goals, analytic strategies, global strategies, simple wayfinding goals, and being stimulus bound. Overall, internal consistencies are high: WES (.94-.95), and subscales are stable across all versions. Testretest reliability is acceptable for the overall WES and two subscales (complex and simple wayfinding goals) for the care recipient current behavior version. Construct validity is supported by the pattern of correlations among subscales and analyses of variance (ANOVAs) showing significant differences among the care recipient (current vs. prior behavior) and caregiver versions overall and for all subscales. Results support the WES as a valid and reliable measure of wayfinding effectiveness in persons with dementia.

Multiple camera management using wide baseline matching

Camera calibration information is required in order for multiple camera networks to deliver more than the sum of many single camera systems. Methods exist for manually calibrating cameras with high accuracy. Manually calibrating networks with many cameras is, however, time consuming, expensive and impractical for networks that undergo frequent change. For this reason, automatic calibration techniques have been vigorously researched in recent years. Fully automatic calibration methods depend on the ability to automatically find point correspondences between overlapping views. In typical camera networks, cameras are placed far apart to maximise coverage. This is referred to as a wide base-line scenario. Finding sufficient correspondences for camera calibration in wide base-line scenarios presents a significant challenge. This thesis focuses on developing more effective and efficient techniques for finding correspondences in uncalibrated, wide baseline, multiple-camera scenarios. The project consists of two major areas of work. The first is the development of more effective and efficient view covariant local feature extractors. The second area involves finding methods to extract scene information using the information contained in a limited set of matched affine features. Several novel affine adaptation techniques for salient features have been developed. A method is presented for efficiently computing the discrete scale space primal sketch of local image features. A scale selection method was implemented that makes use of the primal sketch. The primal sketch-based scale selection method has several advantages over the existing methods. It allows greater freedom in how the scale space is sampled, enables more accurate scale selection, is more effective at combining different functions for spatial position and scale selection, and leads to greater computational efficiency. Existing affine adaptation methods make use of the second moment matrix to estimate the local affine shape of local image features. In this thesis, it is shown that the Hessian matrix can be used in a similar way to estimate local feature shape. The Hessian matrix is effective for estimating the shape of blob-like structures, but is less effective for corner structures. It is simpler to compute than the second moment matrix, leading to a significant reduction in computational cost. A wide baseline dense correspondence extraction system, called WiDense, is presented in this thesis. It allows the extraction of large numbers of additional accurate correspondences, given only a few initial putative correspondences. It consists of the following algorithms: An affine region alignment algorithm that ensures accurate alignment between matched features; A method for extracting more matches in the vicinity of a matched pair of affine features, using the alignment information contained in the match; An algorithm for extracting large numbers of highly accurate point correspondences from an aligned pair of feature regions. Experiments show that the correspondences generated by the WiDense system improves the success rate of computing the epipolar geometry of very widely separated views. This new method is successful in many cases where the features produced by the best wide baseline matching algorithms are insufficient for computing the scene geometry.

Quantitative evaluation of route allocation solutions at large-scale passenger stations

Routing trains within passenger stations in major cities is a common scheduling problem for railway operation. Various studies have been undertaken to derive and formulate solutions to this route allocation problem (RAP) which is particularly evident in mainland China nowadays because of the growing traffic demand and limited station capacity. A reasonable solution must be selected from a set of available RAP solutions attained in the planning stage to facilitate station operation. The selection is however based on the experience of the operators only and objective evaluation of the solutions is rarely addressed. In order to maximise the utilisation of station capacity while maintaining service quality and allowing for service disturbance, quantitative evaluation of RAP solutions is highly desirable. In this study, quantitative evaluation of RAP solutions is proposed and it is enabled by a set of indices covering infrastructure utilisation, buffer times and delay propagation. The proposed evaluation is carried out on a number of RAP solutions at a real-life busy railway station in mainland China and the results highlight the effectiveness of the indices in pinpointing the strengths and weaknesses of the solutions. This study provides the necessary platform to improve the RAP solution in planning and to allow train re-routing upon service disturbances.

Validation of the Chinese version of the Problem Areas in Diabetes (PAID-C) scale

OBJECTIVE To examine the psychometric properties of a Chinese version of the Problem Areas In Diabetes (PAID-C) scale. RESEARCH DESIGN AND METHODS The reliability and validity of the PAID-C were evaluated in a convenience sample of 205 outpatients with type 2 diabetes. Confirmatory factor analysis, Bland-Altman analysis, and Spearman's correlations facilitated the psychometric evaluation. RESULTS Confirmatory factor analysis confirmed a one-factor structure of the PAID-C (χ2/df ratio = 1.894, goodness-of-fit index = 0.901, comparative fit index = 0.905, root mean square error of approximation = 0.066). The PAID-C was associated with A1C (rs = 0.15; P < 0.05) and diabetes self-care behaviors in general diet (rs = −0.17; P < 0.05) and exercise (rs = −0.17; P < 0.05). The 4-week test-retest reliability demonstrated satisfactory stability (rs = 0.83; P < 0.01). CONCLUSIONS The PAID-C is a reliable and valid measure to determine diabetes-related emotional distress in Chinese people with type 2 diabetes.