Walking in hospital is associated with a shorter length of stay in older medical inpatients

Evidence suggests that inactivity during a hospital stay is associated with poor health outcomes in older medical inpatients. We aimed to estimate the associations of average daily step-count (walking) in hospital with physical performance and length of stay in this population. Medical in-patients aged ⩾65 years, premorbidly mobile, with an anticipated length of stay ⩾3 d, were recruited. Measurements included average daily step-count, continuously recorded until discharge, or for a maximum of 7 d (Stepwatch Activity Monitor); co-morbidity (CIRS-G); frailty (SHARE F-I); and baseline and end-of-study physical performance (short physical performance battery). Linear regression models were used to estimate associations between step-count and end-of-study physical performance or length of stay. Length of stay was log transformed in the first model, and step-count was log transformed in both models. Similar models were used to adjust for potential confounders. Data from 154 patients (mean 77 years, SD 7.4) were analysed. The unadjusted models estimated for each unit increase in the natural log of stepcount, the natural log of length of stay decreased by 0.18 (95% CI −0.27 to −0.09). After adjustment of potential confounders, while the strength of the inverse association was attenuated, it remained significant (βlog(steps) = −0.15, 95%CI −0.26 to −0.03). The back-transformed result suggested that a 50% increase in step-count was associated with a 6% shorter length of stay. There was no apparent association between step-count and end-of-study physical performance once baseline physical performance was adjusted for. The results indicate that step-count is independently associated with hospital length of stay, and merits further investigation.

Effect of Performance Feedback on Perceived Knowledge and Likelihood to Pursue Continuing Education

The purpose of this study was to assess the effect of performance feedback on Athletic Trainers’ (ATs) perceived knowledge (PK) and likelihood to pursue continuing education (CE). The investigation was grounded in the theories of “the definition of the situation” (Thomas & Thomas, 1928) and the “illusion of knowing,” (Glenberg, Wilkinson, & Epstein, 1982) suggesting that PK drives behavior. This investigation measured the degree to which knowledge gap predicted CE seeking behavior by providing performance feedback designed to change PK. A pre-test post-test control-group design was used to measure PK and likelihood to pursue CE before and after assessing actual knowledge. ATs (n=103) were randomly sampled and assigned to two groups, with and without performance feedback. Two independent samples t-tests were used to compare groups on the difference scores of the dependent variables. Likelihood to pursue CE was predicted by three variables using multiple linear regression: perceived knowledge, pre-test likelihood to pursue CE, and knowledge gap. There was a 68.4% significant difference (t101= 2.72, p=0.01, ES=0.45) between groups in the change scores for likelihood to pursue CE because of the performance feedback (Experimental group=13.7% increase; Control group= 4.3% increase). The strongest relationship among the dependent variables was between pre-test and post-test measures of likelihood to pursue CE (F2,102=56.80, p<0.01, r=0.73, R2=0.53). The pre- and post-test predictive relationship was enhanced when group was included in the model. In this model [YCEpost=0.76XCEpre-0.34 Xgroup+2.24+E], group accounted for a significant amount of unique variance in predicting CE while the pre-test likelihood to pursue CE variable was held constant (F3,102=40.28, p<0.01,: r=0.74, R2=0.55). Pre-test knowledge gap, regardless of group allocation, was a linear predictor of the likelihood to pursue CE (F1,102=10.90, p=.01, r=.31, R2=.10). In this investigation, performance feedback significantly increased participants’ likelihood to pursue CE. Pre-test knowledge gap was a significant predictor of likelihood to pursue CE, regardless if performance feedback was provided. ATs may have self-assessed and engaged in internal feedback as a result of their test-taking experience. These findings indicate that feedback, both internal and external, may be necessary to trigger CE seeking behavior.

Chlorine Contribution to Quantitative Structure and Activity Relationship Models of Disinfection By-Products' Quantum Chemical Descriptors and Toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: 1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (ELUMO) via QSAR modelling and analysis; 2) to validate the models by using internal and external cross-validation techniques; 3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: 1) Linear or Multi-linear Regression (MLR); 2) Partial Least Squares (PLS); and 3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: 1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; 2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; 3) ELUMO are shown to correlate highly with the NCl for several classes of DBPs; and 4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.