A successful lifestyle intervention model replicated in diverse clinical settings

Lifestyle interventions (LIs) can treat metabolic syndrome and prevent type 2 diabetes mellitus, but they remain underutilised in routine practice. In 2010, an LI model was created in a rural primary care practice and spread with few resources to four other rural practices. A retrospective chart review evaluated changes in health indicators in two practice environments by following 372 participants, mainly women (mean age 52  years). Participants had a mean body mass index of 37 kg/m2 at baseline and lost an average of 12% of their initial body weight as a result of the intervention. Among  participants at the first intervention site for whom cardiometabolic data were available, the prevalence of metabolic syndrome decreased from 58% at baseline to 19% at follow-up. Taken as a whole, our experience suggests that LIs are feasible and deliver meaningful results in routine primary care practice

Columbus State University Honors College: Senior Theses, Spring 2020

This is a collection of senior theses written by honors students at Columbus State University in Spring 2020.https://csuepress.columbusstate.edu/honors_theses/1001/thumbnail.jp

April 5, 2018

The Breeze is the student newspaper of James Madison University in Harrisonburg, Virginia

Data mining Twitter for cancer, diabetes, and asthma insights

Twitter may be a data resource to support healthcare research. Literature is still limited related to the potential of Twitter data as it relates to healthcare. The purpose of this study was to contrast the processes by which a large collection of unstructured disease-related tweets could be converted into structured data to be further analyzed. This was done with the objective of gaining insights into the content and behavioral patterns associated with disease-specific communications on Twitter. Twelve months of Twitter data related to cancer, diabetes, and asthma were collected to form a baseline dataset containing over 34 million tweets. As Twitter data in its raw form would have been difficult to manage, three separate data reduction methods were contrasted to identify a method to generate analysis files, maximizing classification precision and data retention. Each of the disease files were then run through a CHAID (chi-square automatic interaction detector) analysis to demonstrate how user behavior insights vary by disease. Chi-square Automatic Interaction Detector (CHAID) was a technique created by Gordon V. Kass in 1980. CHAID is a tool used to discover the relationship between variables. This study followed the standard CRISP-DM data mining approach and demonstrates how the practice of mining Twitter data fits into this six-stage iterative framework. The study produced insights that provide a new lens into the potential Twitter data has as a valuable healthcare data source as well as the nuances involved in working with the data

Exploring Holistic Comfort in Children who Experience a Clinical Venipuncture Procedure

Children often experience the uncomfortable effects of invasive procedures as a part of primary health supervision and during times of illness. Inadequate procedural comfort management can lead to numerous lasting harmful effects including distrust of healthcare providers, future intensified pain responses, negative cognitive and emotional experiences, and psychosocial health problems (Czarnecki et al. 2011). Holistic comfort has been well documented in adult literature but little research exists on the understanding of holistic procedural comfort from the child’s perspective. The purpose of this study was to explore perspectives of children age 4 to 7 years and their caregivers regarding procedural holistic comfort. A qualitative descriptive design described by Sandelowski (2000; 2010) was used with the philosophical underpinnings of naturalistic inquiry (Guba & Lincoln, 1982). Purposive and convenience sampling with a flyer was used to recruit participants from an outpatient hospital laboratory. The sample included 13 child participants and 15 caregiver participants who were interviewed using a semi-structured format. Traditional thematic content analysis described by Hsieh and Shannon (2005) was implemented to interpret four overarching themes of holistic comfort related to venipuncture procedures in children: Body Comfort, Cognitive and Emotional Comfort, Comfort in the Procedure Surroundings, and Comfort Play. Numerous recommendations for future research as well as implications for nursing and related science practice, organizational/administrative management, invasive procedures, theory, and methods are discussed

MODELING TWITTER SENTIMENT AS A FUNCTION OF PARTICULATE MATTER 2.5 FOR COMMUNITIES IMPACTED BY WILDFIRE ACROSS MONTANA AND IDAHO

Fine particulate matter (PM2.5) is a known pollutant with clinically detrimental physiological and behavioral effects. We consider Twitter sentiment as a potential indicator for well-being in communities impacted by wildfire-associated PM2.5 across Montana and Idaho spanning 5 years (2014-2018). From these geospatial air quality data and geo-tagged tweets, we trained county level models to examine the power of Twitter sentiment as a function of PM2.5. For all 24 counties sampled, we found between 1 and 8 affective dimensions where a positive 2 was detected with a significant F-statistic ( \u3c 0.05). Specifically, we show that sentiment for anticipation in the wildfire-prone county of Missoula, MT yielded respective training/test set 2 of 0.0958 and 0.0686 with a p-value for the F-statistic of 3.09E-07. These analyses support social media sentiment as a potential public health metric by showing one of the first observations of a relationship between PM2.5 and Twitter sentiment

To Thine Own Self Be True? Incentive Problems in Personalized Law

Recent years have seen an explosion of scholarship on “personalized law.” Commentators foresee a world in which regulators armed with big data and machine learning techniques determine the optimal legal rule for every regulated party, then instantaneously disseminate their decisions via smartphones and other “smart” devices. They envision a legal utopia in which every fact pattern is assigned society’s preferred legal treatment in real time. But regulation is a dynamic process; regulated parties react to law. They change their behavior to pursue their preferred outcomes— which often diverge from society’s—and they will continue to do so under personalized law: They will provide regulators with incomplete or inaccurate information. They will attempt to manipulate the algorithms underlying personalized laws by taking actions intended to disguise their true characteristics. Personalized law can also (unintentionally) encourage regulated parties to act in socially undesirable ways, a phenomenon known as moral hazard. Moreover, regulators seeking to combat these dynamics will face significant constraints. Regulators will have imperfect information, both because of privacy concerns and because regulated parties and intermediaries will muddle regulators’ data. They may lack the authority or the political will to respond to regulated parties’ behavior. The transparency requirements of a democratic society may hinder their ability to thwart gamesmanship. Concerns about unintended consequences may further lower regulators’ willingness to personalize law. Taken together, these dynamics will limit personalized law’s ability to optimally match facts to legal outcomes. Personalized law may be a step forward, but it will not produce the utopian outcomes that some envision