A COMPARISON OF THE REY COMPLEX FIGURE AND THE WECHSLER SCALES

This study was conducted to determine the correlation among the Wechsler Intelligence Scales and the Rey Complex Figure Test, a measure of visual memory, in a clinical sample. The purpose was to determine the point at which a difference between cognitive ability scores (at the overall ability level or at the index level) and visual memory scores would be statistically meaningful. Participants in this study were selected from clinical client folders with completed variables of interest. The mean age of the 64 participants was 21 years (SD = 12.6). Statistically significant correlations were found among three of the four Wechsler indices and the three RCF indices. The Perceptual Reasoning Index accounted for the bulk of the variance. All three correlations were statistically significant at p = .01 or less. Given the degree of correlation between the Wechsler Scales and the RCF, these results generated a predictable confidence band allowing practitioners to determine when a difference between obtained visual memory scores and IQ scores is unexpected

An ontology for formal representation of medication adherence-related knowledge : case study in breast cancer

Indiana University-Purdue University Indianapolis (IUPUI)Medication non-adherence is a major healthcare problem that negatively impacts the health and productivity of individuals and society as a whole. Reasons for medication non-adherence are multi-faced, with no clear-cut solution. Adherence to medication remains a difficult area to study, due to inconsistencies in representing medicationadherence behavior data that poses a challenge to humans and today’s computer technology related to interpreting and synthesizing such complex information. Developing a consistent conceptual framework to medication adherence is needed to facilitate domain understanding, sharing, and communicating, as well as enabling researchers to formally compare the findings of studies in systematic reviews. The goal of this research is to create a common language that bridges human and computer technology by developing a controlled structured vocabulary of medication adherence behavior—“Medication Adherence Behavior Ontology” (MAB-Ontology) using breast cancer as a case study to inform and evaluate the proposed ontology and demonstrating its application to real-world situation. The intention is for MAB-Ontology to be developed against the background of a philosophical analysis of terms, such as belief, and desire to be human, computer-understandable, and interoperable with other systems that support scientific research. The design process for MAB-Ontology carried out using the METHONTOLOGY method incorporated with the Basic Formal Ontology (BFO) principles of best practice. This approach introduces a novel knowledge acquisition step that guides capturing medication-adherence-related data from different knowledge sources, including adherence assessment, adherence determinants, adherence theories, adherence taxonomies, and tacit knowledge source types. These sources were analyzed using a systematic approach that involved some questions applied to all source types to guide data extraction and inform domain conceptualization. A set of intermediate representations involving tables and graphs was used to allow for domain evaluation before implementation. The resulting ontology included 629 classes, 529 individuals, 51 object property, and 2 data property. The intermediate representation was formalized into OWL using Protégé. The MAB-Ontology was evaluated through competency questions, use-case scenario, face validity and was found to satisfy the requirement specification. This study provides a unified method for developing a computerized-based adherence model that can be applied among various disease groups and different drug categories

EFFECTIVENESS OF AUTOGENIC TRAINING ON REDUCING ANXIETY DISORDERS: A COMPREHENSIVE REVIEW AND META-ANALYSIS

Background: Autogenic training (AT) is a relaxation technique that has garnered attention for its potential to reduce anxiety and improve psychological well-being. Objectives: This study aims to synthesize the findings from a diverse range of studies investigating the relationship between autogenic training and anxiety disorder across different populations and settings. Methods: A comprehensive review of 162 studies, including randomised controlled trials (RCTs), non-randomized controlled trials (N-RCTs), surveys, and meta-analysis, was conducted out of these 29 studies were selected which is directly related to the objectives of the studies. Participants in the studies had conditions such as cancer patients, bulimia nervosa, stroke survivors, coronary angioplasty, nursing students, healthy volunteers, athletes, and so on. Anxiety levels were measured before and after the AT intervention using a variety of anxiety assessment scales, including the State Trait Anxiety Inventory (STAI) and the Hospital Anxiety and Depression Scale (HADS). The formats, duration, and delivery of the interventions varied, with some studies utilising guided sessions by professionals and other self-administered practises. Results: The combined findings of these studies revealed consistent trends in the beneficial effects of autogenic training on anxiety reduction. AT was found to be effective in reducing anxiety symptoms across a wide range of populations and settings. Following AT interventions, participants reported reduced anxiety, improved mood states, and improved coping mechanisms. AT was found to be superior to no treatment or a comparable intervention in a number of cases. Conclusion: The body of evidence supports autogenic training as a non-pharmacological approach to reducing anxiety and improving psychological well-being. Despite differences in methodology and participant profiles, the studies show that AT has a positive impact on a wide range of populations. The findings merit further investigation and highlight AT's potential contribution to anxiety management strategies.  Article visualizations

The Use of Explicit, Non-Evocative Print Referencing with Preschool Children At-Risk: Implications for Increasing Print Concept Knowledge

The purpose of this research study was to investigate the learning of print concepts (PCs) by preschool children at risk for literacy problems using an experimental treatment: explicit, non-evocative print referencing. Children from low socio-economic status (SES) families have been determined to be at-risk for literacy learning problems including a reduced knowledge of print concepts. The study incorporated a multiple group (experimental and control) time series design with persistent insertion of treatment to those subjects who were assigned to the experimental condition. Participants included 25 children at-risk, ages 4:0- 4:11 (years: months) who qualified for pre-school services and for subsidized childcare (low SES). Participants received eligibility pre-testing and a standardized test of print concept knowledge (PCK).The children were randomly assigned to the experimental or control condition. Children in the experimental condition received three treatment sequences of two illustrated story books read to them each day for three days with the adult reader using the experimental treatment of verbal descriptions and gestures to point out PCs. At the end of each treatment sequence the children were tested for PCK. This intermittent testing helped determine which concepts were learned using this treatment and at what level of dosage of the treatment. Children in the control condition were periodically tested for their PCK and only receive the “business as usual” class room references to print. Results of data analysis indicated a significant increase in the learning of print concepts by the children enrolled in the experimental condition compared to those in the control condition and suggested that some print concepts were more easily learned using this intervention than others

Bottom-Up Modeling of Permissions to Reuse Residual Clinical Biospecimens and Health Data

Consent forms serve as evidence of permissions granted by patients for clinical procedures. As the recognized value of biospecimens and health data increases, many clinical consent forms also seek permission from patients or their legally authorized representative to reuse residual clinical biospecimens and health data for secondary purposes, such as research. Such permissions are also granted by the government, which regulates how residual clinical biospecimens may be reused with or without consent. There is a need for increasingly capable information systems to facilitate discovery, access, and responsible reuse of residual clinical biospecimens and health data in accordance with these permissions. Semantic web technologies, especially ontologies, hold great promise as infrastructure for scalable, semantically interoperable approaches in healthcare and research. While there are many published ontologies for the biomedical domain, there is not yet ontological representation of the permissions relevant for reuse of residual clinical biospecimens and health data. The Informed Consent Ontology (ICO), originally designed for representing consent in research procedures, may already contain core classes necessary for representing clinical consent processes. However, formal evaluation is needed to make this determination and to extend the ontology to cover the new domain. This dissertation focuses on identifying the necessary information required for facilitating responsible reuse of residual clinical biospecimens and health data, and evaluating its representation within ICO. The questions guiding these studies include: 1. What is the necessary information regarding permissions for facilitating responsible reuse of residual clinical biospecimens and health data? 2. How well does the Informed Consent Ontology represent the identified information regarding permissions and obligations for reuse of residual clinical biospecimens and health data? We performed three sequential studies to answer these questions. First, we conducted a scoping review to identify regulations and norms that bear authority or give guidance over reuse of residual clinical biospecimens and health data in the US, the permissions by which reuse of residual clinical biospecimens and health data may occur, and key issues that must be considered when interpreting these regulations and norms. Second, we developed and tested an annotation scheme to identify permissions within clinical consent forms. Lastly, we used these findings as source data for bottom-up modelling and evaluation of ICO for representation of this new domain. We found considerable overlap in classes already in ICO and those necessary for representing permissions to reuse residual clinical biospecimens and health data. However, we also identified more than fifty classes that should be added to or imported into ICO. These efforts provide a foundation for comprehensively representing permissions to reuse residual clinical biospecimens and health data. Such representation fills a critical gap for developing applications which safeguard biospecimen resources and enable querying based on their permissions for use. By modeling information about permissions in an ontology, the heterogeneity of these permissions at a range of levels (e.g., federal regulations, consent forms) can be richly represented using entity-relationship links and embedded rules of inference and inheritance. Furthermore, by developing this content in ICO, missing content will be added to the Open Biological and Biomedical Ontology (OBO) Foundry, enabling use alongside other widely adopted ontologies and providing a valuable resource for biospecimen and information management. These methods may also serve as a model for domain experts to interact with ontology development communities to improve ontologies and address gaps which hinder successful uptake.PHDNursingUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162937/1/eliewolf_1.pd

Computational Approaches to Assisting Patients\u27 Medical Comprehension from Electronic Health Records

Patient-centered care has been established as a fundamental approach to improve the quality of health care in a seminal report by the Institute of Medicine published at the start of the century. Improved access to health information and demand for greater transparency contributed to its move into the mainstream. Research has also demonstrated that actively involving patients in the management of their own health can lead to better outcomes, and potentially lower costs. However, despite the efforts in many areas of medicine to embrace patient-centered care, engaging patients is still considered a challenge. One of the barriers is the lack of effective tools to help patients understand their health conditions, options and their consequences. Patient portals are now widely adopted by hospitals and other healthcare practices to provide patients with the capabilities to view their own Electronic Health Records. They are a rich resource of information for patients. However, the language in the records are generally difficult for patients without training in medicine to understand. Furthermore, the amount of information can often be overwhelming as well. In this work, we propose computational approaches to foster patient engagement from three aspects by exploiting the rich information in the medical records. First, we design a framework to automatically generate health literacy instruments to measure a patient\u27s literacy levels. This framework exploits readily available large scale corpora to generate instruments in a commonly used test format. Second, we investigate methods that can determine the readability of complex documents such as health records. We propose to rank document readability, instead of assigning a grade level or a pre-defined difficulty category. Lastly, we examine the problem of finding targeted educational materials to facilitate patient comprehension of medical notes. We study methods to formulate effective queries from specialized and long clinical narratives. In addition, we propose a neural network based method to identify medical concepts that are important to patients. The three aspects of this work address the issues of the overabundance and technical complexity of medical language in health records. We demonstrate that our approaches are effective with various experiments and evaluation metric