Baseline quality of life as a predictor of exercise intervention adherence among endometrial cancer survivors

Background: The objective of this analysis is to test whether baseline quality of life (QOL) measurements, body mass index (BMI) and prior exercise behavior are significantly associated with (1) telephone counseling adherence, and (2) activity at the final assessment, in a physical activity promoting intervention among endometrial cancer survivors.^ Methods: One hundred endometrial cancer survivors not currently meeting physical activity guidelines completed baseline QOL and anthropometric assessments to measure general physical and mental health [Medical Outcomes Survey (SF-36)], sleep patterns and sleep quality [Pittsburgh Sleep Quality Index (PSQI)], perceived stress [Perceived Stress Scale (PSS)], cancer-specific concerns of long-term survivors [Quality of Life in Adult Cancer Survivors (QLACS)], and psychological distress [Brief Symptom Inventory-18 (BSI-18)]. Survivors were counseled by telephone during the 6-month intervention and their completion rate determined their adherence. The primary variables of interest included age, baseline BMI, baseline activity level, time since diagnosis, education, treatment received, and the SF-36 physical and mental component scores.^ Results: Final activity was most closely linked with baseline activity (p<.001) and less invasive surgery, being leaner and older, and experiencing less pain and more vitality. Telephone counseling was also predicted well by baseline activity, working less and having better overall cancer-related functioning.^ Conclusion: Above and beyond the QOL measures, baseline activity was the strongest predictor of both final activity and telephone counseling adherence. While education, surgery treatment type and bodily pain were important predictors for final exercise and employment status and cancer-related quality of life were important predictors for telephone counseling adherence, considering adaptive exercise interventions that focus heavily on engaging inactive participants may be a way to produce better exercise-related outcomes in the endometrial cancer survivor population.^

Quantitative Determination of the Relationship between Internal Validity and Bias in Software Engineering Experiments: Consequences for Systematic Literature Reviews

Quality assessment is one of the activities performed as part of systematic literature reviews. It is commonly accepted that a good quality experiment is bias free. Bias is considered to be related to internal validity (e.g., how adequately the experiment is planned, executed and analysed). Quality assessment is usually conducted using checklists and quality scales. It has not yet been proven;however, that quality is related to experimental bias. Aim: Identify whether there is a relationship between internal validity and bias in software engineering experiments. Method: We built a quality scale to determine the quality of the studies, which we applied to 28 experiments included in two systematic literature reviews. We proposed an objective indicator of experimental bias, which we applied to the same 28 experiments. Finally, we analysed the correlations between the quality scores and the proposed measure of bias. Results: We failed to find a relationship between the global quality score (resulting from the quality scale) and bias; however, we did identify interesting correlations between bias and some particular aspects of internal validity measured by the instrument. Conclusions: There is an empirically provable relationship between internal validity and bias. It is feasible to apply quality assessment in systematic literature reviews, subject to limits on the internal validity aspects for consideration.

Changing the paradigm: endoscopic video analysis and information extraction for safer surgeries

Background: Minimally invasive surgery creates two technological opportunities: (1) the development of better training and objective evaluation environments, and (2) the creation of image guided surgical systems.

Energy Management and Optimization for Video Decoders based on Functional-oriented Reconfiguration

In recent years, the increasing sophistication of embedded multimedia systems and wireless communication technologies has promoted a widespread utilization of video streaming applications. It has been reported in 2013 that youngsters, aged between 13 and 24, spend around 16.7 hours a week watching online video through social media, business websites, and video streaming sites. Video applications have already been blended into people daily life. Traditionally, video streaming research has focused on performance improvement, namely throughput increase and response time reduction. However, most mobile devices are battery-powered, a technology that grows at a much slower pace than either multimedia or hardware developments. Since battery developments cannot satisfy expanding power demand of mobile devices, research interests on video applications technology has attracted more attention to achieve energy-efficient designs. How to efficiently use the limited battery energy budget becomes a major research challenge. In addition, next generation video standards impel to diversification and personalization. Therefore, it is desirable to have mechanisms to implement energy optimizations with greater flexibility and scalability. In this context, the main goal of this dissertation is to find an energy management and optimization mechanism to reduce the energy consumption of video decoders based on the idea of functional-oriented reconfiguration. System battery life is prolonged as the result of a trade-off between energy consumption and video quality. Functional-oriented reconfiguration takes advantage of the similarities among standards to build video decoders reconnecting existing functional units. If a feedback channel from the decoder to the encoder is available, the former can signal the latter changes in either the encoding parameters or the encoding algorithms for energy-saving adaption. The proposed energy optimization and management mechanism is carried out at the decoder end. This mechanism consists of an energy-aware manager, implemented as an additional block of the reconfiguration engine, an energy estimator, integrated into the decoder, and, if available, a feedback channel connected to the encoder end. The energy-aware manager checks the battery level, selects the new decoder description and signals to build a new decoder to the reconfiguration engine. It is worth noting that the analysis of the energy consumption is fundamental for the success of the energy management and optimization mechanism. In this thesis, an energy estimation method driven by platform event monitoring is proposed. In addition, an event filter is suggested to automate the selection of the most appropriate events that affect the energy consumption. At last, a detailed study on the influence of the training data on the model accuracy is presented. The modeling methodology of the energy estimator has been evaluated on different underlying platforms, single-core and multi-core, with different characteristics of workload. All the results show a good accuracy and low on-line computation overhead. The required modifications on the reconfiguration engine to implement the energy-aware manager have been assessed under different scenarios. The results indicate a possibility to lengthen the battery lifetime of the system in two different use-cases.

Are team personality and climate related to satisfaction and software quality? Aggregating results from a twice replicated experiment

Research into software engineering teams focuses on human and social team factors. Social psychology deals with the study of team formation and has found that personality factors and group processes such as team climate are related to team effectiveness. However, there are only a handful of empirical studies dealing with personality and team climate and their relationship to software development team effectiveness. Objective We present aggregate results of a twice replicated quasi-experiment that evaluates the relationships between personality, team climate, product quality and satisfaction in software development teams. Method Our experimental study measures the personalities of team members based on the Big Five personality traits (openness, conscientiousness, extraversion, agreeableness, neuroticism) and team climate factors (participative safety, support for innovation, team vision and task orientation) preferences and perceptions. We aggregate the results of the three studies through a meta-analysis of correlations. The study was conducted with students. Results The aggregation of results from the baseline experiment and two replications corroborates the following findings. There is a positive relationship between all four climate factors and satisfaction in software development teams. Teams whose members score highest for the agreeableness personality factor have the highest satisfaction levels. The results unveil a significant positive correlation between the extraversion personality factor and software product quality. High participative safety and task orientation climate perceptions are significantly related to quality. Conclusions First, more efficient software development teams can be formed heeding personality factors like agreeableness and extraversion. Second, the team climate generated in software development teams should be monitored for team member satisfaction. Finally, aspects like people feeling safe giving their opinions or encouraging team members to work hard at their job can have an impact on software quality. Software project managers can take advantage of these factors to promote developer satisfaction and improve the resulting product.

Evidence of the presence of bias in subjective metrics: analysis within a family of experiments

Context: Measurement is crucial and important to empirical software engineering. Although reliability and validity are two important properties warranting consideration in measurement processes, they may be influenced by random or systematic error (bias) depending on which metric is used. Aim: Check whether, the simple subjective metrics used in empirical software engineering studies are prone to bias. Method: Comparison of the reliability of a family of empirical studies on requirements elicitation that explore the same phenomenon using different design types and objective and subjective metrics. Results: The objectively measured variables (experience and knowledge) tend to achieve more reliable results, whereas subjective metrics using Likert scales (expertise and familiarity) tend to be influenced by systematic error or bias. Conclusions: Studies that predominantly use variables measured subjectively, like opinion polls or expert opinion acquisition.

Quality certification, performance and size in hotel chains

This paper examines the relationship between quality certification and performance, and quality certification and size in hotel chains operating in Spain. In an initial phase, a quantitative study is made with secondary and objective data to analyse these relationships. In a second phase, a qualitative analysis is applied to reach a better understanding of the quantitative results. The findings show that chains with certified hotels achieve better performance levels; that better performance levels increase with the percentage of certified hotels within the chain; and that quality certification has positive effects on some performance variables. In addition, size is not a key factor for certification, although it could be an enabler.

Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.

Federal workforce quality : measurement and improvement : a report to the U.S. Merit Systems Protection Board and the U.S. Office of Personnel Management.

Shipping list no.: 92-0508-P.

Water quality in the Potomac River basin, Maryland, Pennsylvania, Virginia, West Virginia, and the District of Columbia, 1992-96 /

Mode of access: Internet.

Essays on Procurement, Scoring Auction, and Quality Manipulation Corruption

Thesis (Ph.D.)--University of Washington, 2016-06

Evaluation of clinically relevant changes in patient reported outcomes in knee and hip osteoarthritis: the minimal clinically important improvement

Background: In clinical trials, at the group level, results are usually reported as mean and standard deviation of the change in score, which is not meaningful for most readers. Objective: To determine the minimal clinically important improvement (MCII) of pain, patient's global assessment of disease activity, and functional impairment in patients with knee and hip osteoarthritis (OA). Methods: A prospective multicentre 4 week cohort study involving 1362 outpatients with knee or hip OA was carried out. Data on assessment of pain and patient's global assessment, measured on visual analogue scales, and functional impairment, measured on the Western Ontario McMaster Universities Osteoarthritis Index (WOMAC) function subscale, were collected at baseline and final visits. Patients assessed their response to treatment on a five point Likert scale at the final visit. An anchoring method based on the patient's opinion was used. The MCII was estimated in a subgroup of 814 patients ( 603 with knee OA, 211 with hip OA). Results: For knee and hip OA, MCII for absolute ( and relative) changes were, respectively, ( a) -19.9 mm (-40.8%) and -15.3 mm (-32.0%) for pain; ( b) -18.3 mm ( - 39.0%) and -15.2 mm ( -32.6%) for patient's global assessment; ( c) -9.1 ( -26.0%) and -7.9 ( -21.1%) for WOMAC function subscale score. The MCII is affected by the initial degree of severity of the symptoms but not by age, disease duration, or sex. Conclusion: Using criteria such as MCII in clinical trials would provide meaningful information which would help in interpreting the results by expressing them as a proportion of improved patients.

A rapid dietary assessment tool to determine intake of folate was developed and validated

Background and Objective: Estimates of dietary folate intake are currently of considerable interest, but no rapid tools are available to assess dietary intake of folate that are well suited to everyday health promotion activities, We developed and tested the reliability and validity of two prototypes of a rapid dietary assessment tool (a folate intake tool, FIT) to determine dietary intake of folate. Study Design and Setting: Five hundred and sixty eight men and women aged 33-93 years from Perth, Western Australia. Completed one of the two prototypes of the tool and gave a fasting blood sample for measurement of serum folate. A subset (n - 277) of participants completed the same tool on a second occasion 3-6 weeks later. Results: The Pearson correlations (r) between folate score from the tool and serum folate were moderately high for both prototypes (FIT-A r = 0.54-, FIT-B r = 0.49). The folate scores for the two prototypes were similar on repeat testing and correlated strongly (FIT-A r = 0.75; FIT-B r = 0.68). Conclusions: The rapid dietary assessment tool described here, FIT, provides a valid and reliable measurement of dietary intake of folate for both men and women. (c) 2005 Elsevier Inc. All rights reserved.

Modelling pre-clearing vegetation distribution using GIS-integrated statistical, ecological and data models: A case study from the wet tropics of Northeastern Australia

Traditional vegetation mapping methods use high cost, labour-intensive aerial photography interpretation. This approach can be subjective and is limited by factors such as the extent of remnant vegetation, and the differing scale and quality of aerial photography over time. An alternative approach is proposed which integrates a data model, a statistical model and an ecological model using sophisticated Geographic Information Systems (GIS) techniques and rule-based systems to support fine-scale vegetation community modelling. This approach is based on a more realistic representation of vegetation patterns with transitional gradients from one vegetation community to another. Arbitrary, though often unrealistic, sharp boundaries can be imposed on the model by the application of statistical methods. This GIS-integrated multivariate approach is applied to the problem of vegetation mapping in the complex vegetation communities of the Innisfail Lowlands in the Wet Tropics bioregion of Northeastern Australia. The paper presents the full cycle of this vegetation modelling approach including sampling sites, variable selection, model selection, model implementation, internal model assessment, model prediction assessments, models integration of discrete vegetation community models to generate a composite pre-clearing vegetation map, independent data set model validation and model prediction's scale assessments. An accurate pre-clearing vegetation map of the Innisfail Lowlands was generated (0.83r(2)) through GIS integration of 28 separate statistical models. This modelling approach has good potential for wider application, including provision of. vital information for conservation planning and management; a scientific basis for rehabilitation of disturbed and cleared areas; a viable method for the production of adequate vegetation maps for conservation and forestry planning of poorly-studied areas. (c) 2006 Elsevier B.V. All rights reserved.