Relationships among dietary intake, dietary patterns, dietary quality and nutrient adequacy and substance use in Latina mother/daughter dyads

This study examined the relationships among dietary intake, substance use, socioeconomic and acculturation-related factors among Latinas in Miami-Dade County. Substance abuse is rising among Latinas. A fuller understanding of this problem is needed given the rise of the Hispanic population and the role of women in Latin society. A better understanding between substance use and dietary intake can guide nutrition interventions to reduce negative substance-related health consequences. A purposeful sample of 320 Latina mother/daughter dyads were recruited and interviewed face-to-face as part of the Latino Women's Study. Dietary intake was collected via a 24-hour recall and examined by (1) nutrient intake, (2) dietary patterns using cluster analysis, (3) quality of diet using the Healthy Eating Index (HEI) and (4) the Dietary Reference Intakes to determine nutrient adequacy. Substance use was measured with the Drug Use Frequency and the Healthy and Daily Living Form. Acculturation was measured with the Cultural Identity Scale. Three dietary patterns emerged based on the number of servings from the food groups established in MyPyramid. None were associated with substance use. Latinas who reported using cannabis, cocaine, sedatives without prescription and/or more than five alcoholic drinks on an occasion at least once a month during the previous twelve months had significantly lower HEI scores (64 vs. 60; F = 7.8, p = .005) and consumed fewer fruits (F = 16, p < .001) than non-users. Latinas classified as mothers whom reported consuming cannabis at least 1-7 times a week had significantly lower HEI scores (F = 4.23, p = .015, η2 = .027) than daughters with the same frequency of substance use. One dimension of acculturation, greater familiarity with Latin culture, was associated with good dietary quality (β = .142, p = .012) regardless of any type of substance used or income level. There was a high prevalence of inadequacy of folic acid intake (50-75%) regardless of substance use. Substance users consumed significantly more energy (1,798 vs. 1,615; p = .027) than non-users. Although effect sizes were small, associations between dietary intake and substance use among Latinas deserve further exploration while acknowledging the combined association with acculturation. ^

Glycemic control: Risk factors, quality of life, workforce participation, and mortality among US adults with type 2 diabetes

Despite research showing the benefits of glycemic control, it remains suboptimal among adults with diabetes in the United States. Possible reasons include unaddressed risk factors as well as lack of awareness of its immediate and long term consequences. The objectives of this study were to, using cross-sectional data, (1) ascertain the association between suboptimal (Hemoglobin A1c (HbA1c) .7%), borderline (HbA1c 7-8.9%), and poor (HbA1c .9%) glycemic control and potentially new risk factors (e.g. work characteristics), and (2) assess whether aspects of poor health and well-being such as poor health related quality of life (HRQOL), unemployment, and missed-work are associated with glycemic control; and (3) using prospective data, assess the relationship between mortality risk and glycemic control in US adults with type 2 diabetes. Data from the 1988-1994 and 1999-2004 National Health and Nutrition Examination Surveys were used. HbA1c values were used to create dichotomous glycemic control indicators. Binary logistic regression models were used to assess relationships between risk factors, employment status and glycemic control. Multinomial logistic regression analyses were conducted to assess relationships between glycemic control and HRQOL variables. Zero-inflated Poisson regression models were used to assess relationships between missed work days and glycemic control. Cox-proportional hazard models were used to assess effects of glycemic control on mortality risk. Using STATA software, analyses were weighted to account for complex survey design and non-response. Multivariable models adjusted for socio-demographics, body mass index, among other variables. Results revealed that being a farm worker and working over 40 hours/week were risk factors for suboptimal glycemic control. Having greater days of poor mental was associated with suboptimal, borderline, and poor glycemic control. Having greater days of inactivity was associated with poor glycemic control while having greater days of poor physical health was associated with borderline glycemic control. There were no statistically significant relationships between glycemic control, self-reported general health, employment, and missed work. Finally, having an HbA1c value less than 6.5% was protective against mortality. The findings suggest that work-related factors are important in a person’s ability to reach optimal diabetes management levels. Poor glycemic control appears to have significant detrimental effects on HRQOL.^

Healthy Eating Index scores associated with symptoms of depression in Cuban-Americans with and without type 2 diabetes: a cross sectional study

Background Low diet quality and depression symptoms are independently associated with poor glycemic control in subjects with type 2 diabetes (T2D); however, the relationship between them is unclear. The aim of this study was to determine the association between diet quality and symptoms of depression among Cuban-Americans with and without T2D living in South Florida. Methods Subjects (n = 356) were recruited from randomly selected mailing list. Diet quality was determined using the Healthy Eating Index-2005 (HEI-05) score. Symptoms of depression were assessed using the Beck Depression Inventory (BDI). Both linear and logistic regression analyses were run to determine whether or not these two variables were related. Symptoms of depression was the dependent variable and independent variables included HEI-05, gender, age, marital status, BMI, education level, A1C, employment status, depression medication, duration of diabetes, and diabetes status. Analysis of covariance was used to test for interactions among variables. Results An interaction between diabetes status, gender and HEI-05 was found (P = 0.011). Among males with a HEI-05 score ≤ 55.6, those with T2D had a higher mean BDI score than those without T2D (11.6 vs. 6.6 respectively, P = 0.028). Among males and females with a HEI-05 score ≤ 55.6, females without T2D had a higher mean BDI score compared to males without T2D (11.0 vs. 6.6 respectively, P = 0.012) Conclusions Differences in symptoms of depression according to diabetes status and gender are found in Cuban-Americans with low diet quality.

Glycemic Control: Risk Factors, Quality of Life, Workforce Participation, and Mortality Among US Adults with Type 2 Diabetes

Despite research showing the benefits of glycemic control, it remains suboptimal among adults with diabetes in the United States. Possible reasons include unaddressed risk factors as well as lack of awareness of its immediate and long term consequences. The objectives of this study were to, using cross-sectional data, 1) ascertain the association between suboptimal (Hemoglobin A1c (HbA1c) ≥7%), borderline (HbA1c 7-8.9%), and poor (HbA1c ≥9%) glycemic control and potentially new risk factors (e.g. work characteristics), and 2) assess whether aspects of poor health and well-being such as poor health related quality of life (HRQOL), unemployment, and missed-work are associated with glycemic control; and 3) using prospective data, assess the relationship between mortality risk and glycemic control in US adults with type 2 diabetes. Data from the 1988-1994 and 1999-2004 National Health and Nutrition Examination Surveys were used. HbA1c values were used to create dichotomous glycemic control indicators. Binary logistic regression models were used to assess relationships between risk factors, employment status and glycemic control. Multinomial logistic regression analyses were conducted to assess relationships between glycemic control and HRQOL variables. Zero-inflated Poisson regression models were used to assess relationships between missed work days and glycemic control. Cox-proportional hazard models were used to assess effects of glycemic control on mortality risk. Using STATA software, analyses were weighted to account for complex survey design and non-response. Multivariable models adjusted for socio-demographics, body mass index, among other variables. Results revealed that being a farm worker and working over 40 hours/week were risk factors for suboptimal glycemic control. Having greater days of poor mental was associated with suboptimal, borderline, and poor glycemic control. Having greater days of inactivity was associated with poor glycemic control while having greater days of poor physical health was associated with borderline glycemic control. There were no statistically significant relationships between glycemic control, self-reported general health, employment, and missed work. Finally, having an HbA1c value less than 6.5% was protective against mortality. The findings suggest that work-related factors are important in a person’s ability to reach optimal diabetes management levels. Poor glycemic control appears to have significant detrimental effects on HRQOL.

Quality of life and general health in patients with temporomandibular disorders

The aim of this study was to associate minor psychiatric disorders (general health) and quality of life with temporomandibular disorders (TMD) in patients diagnosed with different TMD classifications and subclassifications with varying levels of severity. Among 150 patients reporting TMD symptoms, 43 were included in the present study. Fonseca's anamnestic index was used for initial screening while axis I of the Research Diagnostic Criteria for Temporomandibular Disorders (RDC-TMD) was used for TMD diagnosis (muscle-related, joint-related or muscle and joint-related). Minor psychiatric disorders were evaluated through the General Health Questionnaire (GHQ) and quality of life was assessed using the World Health Organization Quality Of Life-Brief Version (WHOQOL-BREF). An association was found between minor psychiatric disorders and TMD severity, except for stress. A stronger association was found with mild TMD. Considering TMD classifications and severity together, only the item "death wish" from the GHQ was related to severe muscle-related TMD (p = 0.049). For quality of life, an association was found between disc displacement with reduction and social domain (p = 0.01). Physical domains were associated with TMD classifications and severity and the association was stronger for muscle and joint-related TMD (p = 0.37) and mild TMD (p = 0.042). It was concluded that patients with TMD require multiple focuses of attention since psychological indicators of general health and quality of life are likely associated with dysfunction.

Responsiveness and clinical utility of the geriatric self-efficacy index for urinary incontinence

OBJECTIVES: To report on the responsiveness testing and clinical utility of the 12-item Geriatric Self-Efficacy Index for Urinary Incontinence (GSE-UI). DESIGN: Prospective cohort study. SETTING: Six urinary incontinence (UI) outpatient clinics in Quebec, Canada. PARTICIPANTS: Community-dwelling incontinent adults aged 65 and older. MEASUREMENTS: The abridged 12-item GSE-UI, measuring older adults' level of confidence for preventing urine loss, was administered to all new consecutive incontinent patients 1 week before their initial clinic visit, at baseline, and 3 months posttreatment. At follow-up, a positive rating of improvement in UI was ascertained from patients and their physicians using the Patient's and Clinician's Global Impression of Improvement scales, respectively. Responsiveness of the GSE-UI was calculated using Guyatt's change index. Its clinical utility was determined using receiver operating curves. RESULTS: Eighty-nine of 228 eligible patients (39.0%) participated (mean age 72.6+5.8, range 65–90). At 3-month follow-up, 22.5% of patients were very much better, and 41.6% were a little or much better. Guyatt's change index was 2.6 for patients who changed by a clinically meaningful amount and 1.5 for patients having experienced any level of improvement. An improvement of 14 points on the 12-item GSE-UI had a sensitivity of 75.1% and a specificity of 78.2% for detecting clinically meaningful changes in UI status. Mean GSE-UI scores varied according to improvement status (P<.001) and correlated with changes in quality-of-life scores (r=0.7, P<.001) and reductions in UI episodes (r=0.4, P=.004). CONCLUSION: The GSE-UI is responsive and clinically useful.

Advanced Techniques for Image Quality Assessment of Modern X-ray Computed Tomography Systems

X-ray computed tomography (CT) imaging constitutes one of the most widely used diagnostic tools in radiology today with nearly 85 million CT examinations performed in the U.S in 2011. CT imparts a relatively high amount of radiation dose to the patient compared to other x-ray imaging modalities and as a result of this fact, coupled with its popularity, CT is currently the single largest source of medical radiation exposure to the U.S. population. For this reason, there is a critical need to optimize CT examinations such that the dose is minimized while the quality of the CT images is not degraded. This optimization can be difficult to achieve due to the relationship between dose and image quality. All things being held equal, reducing the dose degrades image quality and can impact the diagnostic value of the CT examination.

A recent push from the medical and scientific community towards using lower doses has spawned new dose reduction technologies such as automatic exposure control (i.e., tube current modulation) and iterative reconstruction algorithms. In theory, these technologies could allow for scanning at reduced doses while maintaining the image quality of the exam at an acceptable level. Therefore, there is a scientific need to establish the dose reduction potential of these new technologies in an objective and rigorous manner. Establishing these dose reduction potentials requires precise and clinically relevant metrics of CT image quality, as well as practical and efficient methodologies to measure such metrics on real CT systems. The currently established methodologies for assessing CT image quality are not appropriate to assess modern CT scanners that have implemented those aforementioned dose reduction technologies.

Thus the purpose of this doctoral project was to develop, assess, and implement new phantoms, image quality metrics, analysis techniques, and modeling tools that are appropriate for image quality assessment of modern clinical CT systems. The project developed image quality assessment methods in the context of three distinct paradigms, (a) uniform phantoms, (b) textured phantoms, and (c) clinical images.

The work in this dissertation used the “task-based” definition of image quality. That is, image quality was broadly defined as the effectiveness by which an image can be used for its intended task. Under this definition, any assessment of image quality requires three components: (1) A well defined imaging task (e.g., detection of subtle lesions), (2) an “observer” to perform the task (e.g., a radiologists or a detection algorithm), and (3) a way to measure the observer’s performance in completing the task at hand (e.g., detection sensitivity/specificity).

First, this task-based image quality paradigm was implemented using a novel multi-sized phantom platform (with uniform background) developed specifically to assess modern CT systems (Mercury Phantom, v3.0, Duke University). A comprehensive evaluation was performed on a state-of-the-art CT system (SOMATOM Definition Force, Siemens Healthcare) in terms of noise, resolution, and detectability as a function of patient size, dose, tube energy (i.e., kVp), automatic exposure control, and reconstruction algorithm (i.e., Filtered Back-Projection– FPB vs Advanced Modeled Iterative Reconstruction– ADMIRE). A mathematical observer model (i.e., computer detection algorithm) was implemented and used as the basis of image quality comparisons. It was found that image quality increased with increasing dose and decreasing phantom size. The CT system exhibited nonlinear noise and resolution properties, especially at very low-doses, large phantom sizes, and for low-contrast objects. Objective image quality metrics generally increased with increasing dose and ADMIRE strength, and with decreasing phantom size. The ADMIRE algorithm could offer comparable image quality at reduced doses or improved image quality at the same dose (increase in detectability index by up to 163% depending on iterative strength). The use of automatic exposure control resulted in more consistent image quality with changing phantom size.

Based on those results, the dose reduction potential of ADMIRE was further assessed specifically for the task of detecting small (<=6 mm) low-contrast (<=20 HU) lesions. A new low-contrast detectability phantom (with uniform background) was designed and fabricated using a multi-material 3D printer. The phantom was imaged at multiple dose levels and images were reconstructed with FBP and ADMIRE. Human perception experiments were performed to measure the detection accuracy from FBP and ADMIRE images. It was found that ADMIRE had equivalent performance to FBP at 56% less dose.

Using the same image data as the previous study, a number of different mathematical observer models were implemented to assess which models would result in image quality metrics that best correlated with human detection performance. The models included naïve simple metrics of image quality such as contrast-to-noise ratio (CNR) and more sophisticated observer models such as the non-prewhitening matched filter observer model family and the channelized Hotelling observer model family. It was found that non-prewhitening matched filter observers and the channelized Hotelling observers both correlated strongly with human performance. Conversely, CNR was found to not correlate strongly with human performance, especially when comparing different reconstruction algorithms.

The uniform background phantoms used in the previous studies provided a good first-order approximation of image quality. However, due to their simplicity and due to the complexity of iterative reconstruction algorithms, it is possible that such phantoms are not fully adequate to assess the clinical impact of iterative algorithms because patient images obviously do not have smooth uniform backgrounds. To test this hypothesis, two textured phantoms (classified as gross texture and fine texture) and a uniform phantom of similar size were built and imaged on a SOMATOM Flash scanner (Siemens Healthcare). Images were reconstructed using FBP and a Sinogram Affirmed Iterative Reconstruction (SAFIRE). Using an image subtraction technique, quantum noise was measured in all images of each phantom. It was found that in FBP, the noise was independent of the background (textured vs uniform). However, for SAFIRE, noise increased by up to 44% in the textured phantoms compared to the uniform phantom. As a result, the noise reduction from SAFIRE was found to be up to 66% in the uniform phantom but as low as 29% in the textured phantoms. Based on this result, it clear that further investigation was needed into to understand the impact that background texture has on image quality when iterative reconstruction algorithms are used.

To further investigate this phenomenon with more realistic textures, two anthropomorphic textured phantoms were designed to mimic lung vasculature and fatty soft tissue texture. The phantoms (along with a corresponding uniform phantom) were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Scans were repeated a total of 50 times in order to get ensemble statistics of the noise. A novel method of estimating the noise power spectrum (NPS) from irregularly shaped ROIs was developed. It was found that SAFIRE images had highly locally non-stationary noise patterns with pixels near edges having higher noise than pixels in more uniform regions. Compared to FBP, SAFIRE images had 60% less noise on average in uniform regions for edge pixels, noise was between 20% higher and 40% lower. The noise texture (i.e., NPS) was also highly dependent on the background texture for SAFIRE. Therefore, it was concluded that quantum noise properties in the uniform phantoms are not representative of those in patients for iterative reconstruction algorithms and texture should be considered when assessing image quality of iterative algorithms.

The move beyond just assessing noise properties in textured phantoms towards assessing detectability, a series of new phantoms were designed specifically to measure low-contrast detectability in the presence of background texture. The textures used were optimized to match the texture in the liver regions actual patient CT images using a genetic algorithm. The so called “Clustured Lumpy Background” texture synthesis framework was used to generate the modeled texture. Three textured phantoms and a corresponding uniform phantom were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Images were reconstructed with FBP and SAFIRE and analyzed using a multi-slice channelized Hotelling observer to measure detectability and the dose reduction potential of SAFIRE based on the uniform and textured phantoms. It was found that at the same dose, the improvement in detectability from SAFIRE (compared to FBP) was higher when measured in a uniform phantom compared to textured phantoms.

The final trajectory of this project aimed at developing methods to mathematically model lesions, as a means to help assess image quality directly from patient images. The mathematical modeling framework is first presented. The models describe a lesion’s morphology in terms of size, shape, contrast, and edge profile as an analytical equation. The models can be voxelized and inserted into patient images to create so-called “hybrid” images. These hybrid images can then be used to assess detectability or estimability with the advantage that the ground truth of the lesion morphology and location is known exactly. Based on this framework, a series of liver lesions, lung nodules, and kidney stones were modeled based on images of real lesions. The lesion models were virtually inserted into patient images to create a database of hybrid images to go along with the original database of real lesion images. ROI images from each database were assessed by radiologists in a blinded fashion to determine the realism of the hybrid images. It was found that the radiologists could not readily distinguish between real and virtual lesion images (area under the ROC curve was 0.55). This study provided evidence that the proposed mathematical lesion modeling framework could produce reasonably realistic lesion images.

Based on that result, two studies were conducted which demonstrated the utility of the lesion models. The first study used the modeling framework as a measurement tool to determine how dose and reconstruction algorithm affected the quantitative analysis of liver lesions, lung nodules, and renal stones in terms of their size, shape, attenuation, edge profile, and texture features. The same database of real lesion images used in the previous study was used for this study. That database contained images of the same patient at 2 dose levels (50% and 100%) along with 3 reconstruction algorithms from a GE 750HD CT system (GE Healthcare). The algorithms in question were FBP, Adaptive Statistical Iterative Reconstruction (ASiR), and Model-Based Iterative Reconstruction (MBIR). A total of 23 quantitative features were extracted from the lesions under each condition. It was found that both dose and reconstruction algorithm had a statistically significant effect on the feature measurements. In particular, radiation dose affected five, three, and four of the 23 features (related to lesion size, conspicuity, and pixel-value distribution) for liver lesions, lung nodules, and renal stones, respectively. MBIR significantly affected 9, 11, and 15 of the 23 features (including size, attenuation, and texture features) for liver lesions, lung nodules, and renal stones, respectively. Lesion texture was not significantly affected by radiation dose.

The second study demonstrating the utility of the lesion modeling framework focused on assessing detectability of very low-contrast liver lesions in abdominal imaging. Specifically, detectability was assessed as a function of dose and reconstruction algorithm. As part of a parallel clinical trial, images from 21 patients were collected at 6 dose levels per patient on a SOMATOM Flash scanner. Subtle liver lesion models (contrast = -15 HU) were inserted into the raw projection data from the patient scans. The projections were then reconstructed with FBP and SAFIRE (strength 5). Also, lesion-less images were reconstructed. Noise, contrast, CNR, and detectability index of an observer model (non-prewhitening matched filter) were assessed. It was found that SAFIRE reduced noise by 52%, reduced contrast by 12%, increased CNR by 87%. and increased detectability index by 65% compared to FBP. Further, a 2AFC human perception experiment was performed to assess the dose reduction potential of SAFIRE, which was found to be 22% compared to the standard of care dose.

In conclusion, this dissertation provides to the scientific community a series of new methodologies, phantoms, analysis techniques, and modeling tools that can be used to rigorously assess image quality from modern CT systems. Specifically, methods to properly evaluate iterative reconstruction have been developed and are expected to aid in the safe clinical implementation of dose reduction technologies.

Automated Assessment of Image Quality and Dose Attributes in Clinical CT Images

Computed tomography (CT) is a valuable technology to the healthcare enterprise as evidenced by the more than 70 million CT exams performed every year. As a result, CT has become the largest contributor to population doses amongst all medical imaging modalities that utilize man-made ionizing radiation. Acknowledging the fact that ionizing radiation poses a health risk, there exists the need to strike a balance between diagnostic benefit and radiation dose. Thus, to ensure that CT scanners are optimally used in the clinic, an understanding and characterization of image quality and radiation dose are essential.

The state-of-the-art in both image quality characterization and radiation dose estimation in CT are dependent on phantom based measurements reflective of systems and protocols. For image quality characterization, measurements are performed on inserts imbedded in static phantoms and the results are ascribed to clinical CT images. However, the key objective for image quality assessment should be its quantification in clinical images; that is the only characterization of image quality that clinically matters as it is most directly related to the actual quality of clinical images. Moreover, for dose estimation, phantom based dose metrics, such as CT dose index (CTDI) and size specific dose estimates (SSDE), are measured by the scanner and referenced as an indicator for radiation exposure. However, CTDI and SSDE are surrogates for dose, rather than dose per-se.

Currently there are several software packages that track the CTDI and SSDE associated with individual CT examinations. This is primarily the result of two causes. The first is due to bureaucracies and governments pressuring clinics and hospitals to monitor the radiation exposure to individuals in our society. The second is due to the personal concerns of patients who are curious about the health risks associated with the ionizing radiation exposure they receive as a result of their diagnostic procedures.

An idea that resonates with clinical imaging physicists is that patients come to the clinic to acquire quality images so they can receive a proper diagnosis, not to be exposed to ionizing radiation. Thus, while it is important to monitor the dose to patients undergoing CT examinations, it is equally, if not more important to monitor the image quality of the clinical images generated by the CT scanners throughout the hospital.

The purposes of the work presented in this thesis are threefold: (1) to develop and validate a fully automated technique to measure spatial resolution in clinical CT images, (2) to develop and validate a fully automated technique to measure image contrast in clinical CT images, and (3) to develop a fully automated technique to estimate radiation dose (not surrogates for dose) from a variety of clinical CT protocols.

Prospective Estimation of Radiation Dose and Image Quality for Optimized CT Performance

X-ray computed tomography (CT) is a non-invasive medical imaging technique that generates cross-sectional images by acquiring attenuation-based projection measurements at multiple angles. Since its first introduction in the 1970s, substantial technical improvements have led to the expanding use of CT in clinical examinations. CT has become an indispensable imaging modality for the diagnosis of a wide array of diseases in both pediatric and adult populations [1, 2]. Currently, approximately 272 million CT examinations are performed annually worldwide, with nearly 85 million of these in the United States alone [3]. Although this trend has decelerated in recent years, CT usage is still expected to increase mainly due to advanced technologies such as multi-energy [4], photon counting [5], and cone-beam CT [6].

Despite the significant clinical benefits, concerns have been raised regarding the population-based radiation dose associated with CT examinations [7]. From 1980 to 2006, the effective dose from medical diagnostic procedures rose six-fold, with CT contributing to almost half of the total dose from medical exposure [8]. For each patient, the risk associated with a single CT examination is likely to be minimal. However, the relatively large population-based radiation level has led to enormous efforts among the community to manage and optimize the CT dose.

As promoted by the international campaigns Image Gently and Image Wisely, exposure to CT radiation should be appropriate and safe [9, 10]. It is thus a responsibility to optimize the amount of radiation dose for CT examinations. The key for dose optimization is to determine the minimum amount of radiation dose that achieves the targeted image quality [11]. Based on such principle, dose optimization would significantly benefit from effective metrics to characterize radiation dose and image quality for a CT exam. Moreover, if accurate predictions of the radiation dose and image quality were possible before the initiation of the exam, it would be feasible to personalize it by adjusting the scanning parameters to achieve a desired level of image quality. The purpose of this thesis is to design and validate models to quantify patient-specific radiation dose prospectively and task-based image quality. The dual aim of the study is to implement the theoretical models into clinical practice by developing an organ-based dose monitoring system and an image-based noise addition software for protocol optimization.

More specifically, Chapter 3 aims to develop an organ dose-prediction method for CT examinations of the body under constant tube current condition. The study effectively modeled the anatomical diversity and complexity using a large number of patient models with representative age, size, and gender distribution. The dependence of organ dose coefficients on patient size and scanner models was further evaluated. Distinct from prior work, these studies use the largest number of patient models to date with representative age, weight percentile, and body mass index (BMI) range.

With effective quantification of organ dose under constant tube current condition, Chapter 4 aims to extend the organ dose prediction system to tube current modulated (TCM) CT examinations. The prediction, applied to chest and abdominopelvic exams, was achieved by combining a convolution-based estimation technique that quantifies the radiation field, a TCM scheme that emulates modulation profiles from major CT vendors, and a library of computational phantoms with representative sizes, ages, and genders. The prospective quantification model is validated by comparing the predicted organ dose with the dose estimated based on Monte Carlo simulations with TCM function explicitly modeled.

Chapter 5 aims to implement the organ dose-estimation framework in clinical practice to develop an organ dose-monitoring program based on a commercial software (Dose Watch, GE Healthcare, Waukesha, WI). In the first phase of the study we focused on body CT examinations, and so the patient’s major body landmark information was extracted from the patient scout image in order to match clinical patients against a computational phantom in the library. The organ dose coefficients were estimated based on CT protocol and patient size as reported in Chapter 3. The exam CTDIvol, DLP, and TCM profiles were extracted and used to quantify the radiation field using the convolution technique proposed in Chapter 4.

With effective methods to predict and monitor organ dose, Chapters 6 aims to develop and validate improved measurement techniques for image quality assessment. Chapter 6 outlines the method that was developed to assess and predict quantum noise in clinical body CT images. Compared with previous phantom-based studies, this study accurately assessed the quantum noise in clinical images and further validated the correspondence between phantom-based measurements and the expected clinical image quality as a function of patient size and scanner attributes.

Chapter 7 aims to develop a practical strategy to generate hybrid CT images and assess the impact of dose reduction on diagnostic confidence for the diagnosis of acute pancreatitis. The general strategy is (1) to simulate synthetic CT images at multiple reduced-dose levels from clinical datasets using an image-based noise addition technique; (2) to develop quantitative and observer-based methods to validate the realism of simulated low-dose images; (3) to perform multi-reader observer studies on the low-dose image series to assess the impact of dose reduction on the diagnostic confidence for multiple diagnostic tasks; and (4) to determine the dose operating point for clinical CT examinations based on the minimum diagnostic performance to achieve protocol optimization.

Chapter 8 concludes the thesis with a summary of accomplished work and a discussion about future research.

Responsiveness and clinical utility of the geriatric self-efficacy index for urinary incontinence

OBJECTIVES: To report on the responsiveness testing and clinical utility of the 12-item Geriatric Self-Efficacy Index for Urinary Incontinence (GSE-UI). DESIGN: Prospective cohort study. SETTING: Six urinary incontinence (UI) outpatient clinics in Quebec, Canada. PARTICIPANTS: Community-dwelling incontinent adults aged 65 and older. MEASUREMENTS: The abridged 12-item GSE-UI, measuring older adults' level of confidence for preventing urine loss, was administered to all new consecutive incontinent patients 1 week before their initial clinic visit, at baseline, and 3 months posttreatment. At follow-up, a positive rating of improvement in UI was ascertained from patients and their physicians using the Patient's and Clinician's Global Impression of Improvement scales, respectively. Responsiveness of the GSE-UI was calculated using Guyatt's change index. Its clinical utility was determined using receiver operating curves. RESULTS: Eighty-nine of 228 eligible patients (39.0%) participated (mean age 72.6+5.8, range 65–90). At 3-month follow-up, 22.5% of patients were very much better, and 41.6% were a little or much better. Guyatt's change index was 2.6 for patients who changed by a clinically meaningful amount and 1.5 for patients having experienced any level of improvement. An improvement of 14 points on the 12-item GSE-UI had a sensitivity of 75.1% and a specificity of 78.2% for detecting clinically meaningful changes in UI status. Mean GSE-UI scores varied according to improvement status (P<.001) and correlated with changes in quality-of-life scores (r=0.7, P<.001) and reductions in UI episodes (r=0.4, P=.004). CONCLUSION: The GSE-UI is responsive and clinically useful.

Mobilizing capacity of environmental information: the importance of quality journalism to achieve proenvironmental attitudes

Citizens say they are very concerned about the environment, and they know the role they play in their deterioration; but there is a gap between this proclaimed interest and the mobilization against environmental problems. Several news published between 2010 and 2011 about the Spanish energy policy and Doñana have economic and social aspects, that sometimes are confused with environmental aspects. It is worthy of study, therefore, to analyze how the press reflects that citizen interest; and how a critical issue as the quality of the information can influence the attitude of citizens in issues related to the environment. If the journalistic practice does not meet quality its function, it will condition the social participation.

Some Remarks on Quality in Interpreting, Interpreting Modes and the Interpreter’s Role

Quality in interpreting is a hotly debated issue whose complexity is determined by a mix of factors. In this article I analyze it in the light of the role played by interpreters, stressing how the constraints imposed by the different interpreting modes, the different roles actually played by professionals (who become more or less visible, even within the same assignment) and the expectations they generate require the adoption of a flexible perspective when it comes to identifying and assessing quality criteria and drafting professional codes that are open enough to adjust to diverse communicative settings and to the dynamic character of quality.

Extracting Patterns from Educational Traces via Clustering and Associated Quality Metrics

Clustering algorithms, pattern mining techniques and associated quality metrics emerged as reliable methods for modeling learners’ performance, comprehension and interaction in given educational scenarios. The specificity of available data such as missing values, extreme values or outliers, creates a challenge to extract significant user models from an educational perspective. In this paper we introduce a pattern detection mechanism with-in our data analytics tool based on k-means clustering and on SSE, silhouette, Dunn index and Xi-Beni index quality metrics. Experiments performed on a dataset obtained from our online e-learning platform show that the extracted interaction patterns were representative in classifying learners. Furthermore, the performed monitoring activities created a strong basis for generating automatic feedback to learners in terms of their course participation, while relying on their previous performance. In addition, our analysis introduces automatic triggers that highlight learners who will potentially fail the course, enabling tutors to take timely actions.

The influence of socio-demographic, psychological and knowledge-related variables alongside perceived cooking and food skills abilities in the prediction of diet quality in adults: a nationally representative cross-sectional study

Background: Interventions to increase cooking skills (CS) and food skills (FS) as a route to improving overall diet are popular within public health. This study tested a comprehensive model of diet quality by assessing the influence of socio-demographic, knowledge- and psychological-related variables alongside perceived CS and FS abilities. The correspondence of two measures of diet quality further validated the Eating Choices Index (ECI) for use in quantitative research.
Methods: A cross-sectional survey was conducted in a quota-controlled nationally representative sample of 1049 adults aged 20–60 years drawn from the Island of Ireland. Surveys were administered in participants’ homes via computer-assisted personal interviewing (CAPI) assessing a range of socio-demographic, knowledge- and psychological-related variables alongside perceived CS and FS abilities. Regression models were used to model factors influencing diet quality. Correspondence between 2 measures of diet quality was assessed using chi-square and Pearson correlations.
Results: ECI score was significantly negatively correlated with DINE Fat intake (r = -0.24, p < 0.001), and ECI score was significantly positively correlated with DINE Fibre intake (r = 0.38, p < 0.001), demonstrating a high agreement. Findings indicated that males, younger respondents and those with no/few educational qualifications scored significantly lower on both CS and FS abilities. The relative influence of socio-demographic, knowledge, psychological variables and CS and FS abilities on dietary outcomes varied, with regression models explaining 10–20 % of diet quality variance. CS ability exerted the strongest relationship with saturated fat intake (β = -0.296, p < 0.001) and was a significant predictor of fibre intake (β = -0.113, p < 0.05), although not for healthy food choices (ECI) (β = 0.04, p > 0.05).
Conclusion: Greater CS and FS abilities may not lead directly to healthier dietary choices given the myriad of other factors implicated; however, CS appear to have differential influences on aspects of the diet, most notably in relation to lowering saturated fat intake. Findings suggest that CS and FS should not be singular targets of interventions designed to improve diet; but targeting specific sub-groups of the population e.g. males, younger adults, those with limited education might be more fruitful. A greater understanding of the interaction of factors influencing cooking and food practices within the home is needed.

Development of an instrument measuring the quality of residential care facilities for older people

Aims. To validate the Swedish version of the Sheffield Care Environment Assessment Matrix (S-SCEAM). The instrument’s items measure environmental elements important for supporting the needs of older people, and conceptualized within eight domains. Methods. Item relevance was assessed by a group of experts and measured using content validity index (CVI). Test-retest and inter-rater reliability tests were performed.  The domain structure was assessed by the inter-rater agreement of a second group of experts, and measured using Fleiss kappa. Results. All items attained a CVI above 0.78, the suggested criteria for excellent content validity. Test-retest reliability showed high stability (96% and 95% for two independent raters respectively), and inter-rater reliability demonstrated high levels of agreement (95% and 94% on two separate rating occasions). Kappa values were very good for test-retest (κ = 0.903 and 0.869) and inter-rater reliability (κ = 0.851 and 0.832). Domain structure was good,  Fleiss’ kappa was 0.63 (range 0.45 to 0.75).    Conclusion. The S-SCEAM of 210 items and eight domains showed good content validity and construct validity. The instrument is suggested for use in measuring of the quality of the physical environment in residential care facilities for older persons.