High-quality nursing research is important to healthcare and is precipitated by successful participant recruitment. Young adults aged 18 to 30 years are particularly difficult to recruit due to transitions during this time, which makes it more problematic to locate these individuals and may make it more difficult for them to prioritize the need for participation. This paper includes data from two cross-sectional survey design pilot studies that aimed to enroll young adults with congenital heart disease using a variety of recruitment methods. The number of participants enrolled in these two pilot studies (7 and 22) was much lower than expected but the recruitment challenges encountered were consistent with other research studies that have recruited young adult populations. After presenting these data and a discussion of the relevant literature, we conclude with proposed strategies for research recruitment of young adults for nurse scientists who directly impact evidence-based literature and practice with research contributions.
Area of muscle, fat, and bone is often measured in thigh CT scans when tissue composition is a key outcome. SliceOmatic software is commonly referenced for such analysis but published methods may be insufficient for new users. Thus, a quick start guide to calculating thigh composition using SliceOmatic has been developed.CT images of the thigh were collected from older (69 ± 4 yrs, N = 24) adults before and after 12-weeks of resistance training. SliceOmatic was used to segment images into seven density regions encompassing fat, muscle, and bone from -190 to +2000 Hounsfield Units [HU]. The relative contributions to thigh area and the effects of tissue density overlap for skin and marrow with muscle and fat were determined.The largest contributors to the thigh were normal fat (-190 to -30 HU, 29.1 ± 7.4%) and muscle (35 to 100 HU, 48.9 ± 8.2%) while the smallest were high density (101 to 150 HU, 0.79 ± 0.50%) and very high density muscle (151 to 200 HU, 0.07 ± 0.02%). Training significantly (P<0.05) increased area for muscle in the very low (-29 to -1 HU, 5.5 ± 7.9%), low (0 to 34 HU, 9.6 ± 16.8%), normal (35 to 100 HU, 4.2 ± 7.9%), and high (100 to 150 HU, 70.9 ± 80.6%) density ranges for muscle. Normal fat, very high density muscle and bone did not change (P>0.05). Contributions to area were altered by ~1% or less and the results of training were not affected by accounting for skin and marrow.When using SliceOmatic to calculate thigh composition, accounting for skin and marrow may not be necessary. We recommend defining muscle as -29 to +200 HU but that smaller ranges (e.g. low density muscle, 0 to 34 HU) can easily be examined for relationships with the health condition and intervention of interest.Clinicaltrials.gov NCT02261961.
We consider hierarchical Bayes analyses of an experiment conducted to enable calibration of a set of mass-produced resistance temperature devices (RTDs). These were placed in batches into a liquid bath with a precise NIST-approved thermometer, and resistances and temperatures were recorded approximately every 30 seconds. Under the assumptions that the thermometer is accurate and each RTD responds linearly to temperature change, we use hierarchical Bayes methods to estimate the parameters of the linear calibration equations. Predictions of the parameters for an untested RTD of the same type, and interval estimates of temperature based on a realized resistance reading are also available (both for the tested RTDs and for an untested one produced under the same production process conditions).
Objective This study examined practices for monitoring metabolic side effects of antipsychotics at 32 Veterans Affairs (VA) facilities. Methods This retrospective cohort analysis included outpatients receiving a new antipsychotic prescription from April 2008 through March 2009 in Veterans Integrated Service Networks 18–22 (N=12,009). Data from national and regional VA data sources were used to examine the extent to which weight, glucose (or hemoglobin A1c), and low-density lipoprotein (LDL) cholesterol were monitored within 30 days of the new prescription (baseline) and 60–120 days thereafter, consistent with American Diabetes and American Psychiatric Association consensus recommendations. Repeated-measures analysis using the generalized estimating equation for binary variables examined the association of patient characteristics with likelihood of monitoring. Results Monitoring of the three metabolic parameters was significantly greater at baseline than at follow-up (p<.001). Weight was the most frequently monitored parameter. Having a diagnosis of diabetes or dyslipidemia was significantly associated with greater monitoring rates. Although monitoring rates did not vary significantly by psychiatric diagnosis, patients without a psychiatric diagnosis were less likely to be monitored than those with schizophrenia. Compared with patients taking antipsychotics with the lowest metabolic risk, those taking high-risk antipsychotics were more likely to have weight monitored at baseline (adjusted odds ratio [AOR]=1.20), whereas patients prescribed medium-risk antipsychotics were more likely to be monitored at baseline for glucose (AOR=1.12) and LDL (AOR=1.11). Conclusions Efforts to improve monitoring of antipsychotics' metabolic side effects are needed and should be applied for all patients regardless of diagnosis.
Historically, animal numbers have most often been in the hundreds for experiments designed to estimate the dose reduction factor (DRF) of a radiation countermeasure treatment compared to a control treatment. Before 2010, researchers had to rely on previous experience, both from others and their own, to determine the number of animals needed for a DRF experiment. In 2010, a formal sample size formula was developed by Kodell et al. This theoretical work showed that sample sizes for realistic, yet hypothetical, DRF experiments could be less than a hundred animals and still have sufficient power to detect clinically meaningful DRF values. However, researchers have been slow to use the formula for their DRF experiments, whether from ignorance to its existence or hesitancy to depart from “tried and true” sample sizes. Here, we adapt the sample size formula to better fit usual DRF experiments, and, importantly, we provide real experimental evidence from two independent DRF experiments that sample sizes smaller than what have typically been used can still statistically detect clinically meaningful DRF values. In addition, we update a literature review of DRF experiments which can be used to inform future DRF experiments, provide answers to questions that researchers have asked when considering sample size calculations rather than solely relying on previous experience, whether their own or others', and, in the supplementary material, provide R code implementing the formula, along with several exercises to familiarize the user with the adapted formula.
Temporal discounting refers to the reduction in the present subjective value of an outcome as a function of the temporal distance to that outcome. Though a number of mathematical models have been proposed to describe this time/value relationship, this search has largely excluded insights from the literature on memory decay. This study examines the utility of memory decay models by comparing the fits of four of these models to fits from established temporal discounting models using past and future temporal discounting data. These results (1) suggest that a single model describes valuation of both future and past outcomes, (2) indicate the exponential-power model, from memory decay literature, is statistically superior in fitting discounting data from both past and future outcomes, and (3) support the advancing perspective of the psychological interconnectedness of the future and past.
Accidental exposure to ionizing radiation may lead to delayed effects of acute radiation exposure (DEARE) in many organ systems. Activated protein C (APC) is a known mitigator of the acute radiation syndrome. To examine the role of APC in DEARE, we used a transgenic mouse model with 2- to 3-fold increased plasma levels of APC (high in APC, APCHi). Male and female APCHi mice and wild-type littermates were exposed to 9.5 Gy γ-rays with their hind-legs (bone marrow) shielded from radiation to allow long-term survival. At 3 and 6 months after irradiation, cardiac function was measured with ultrasonography. At 3 months, radiation increased cardiac dimensions in APCHi males, while decreases were seen in wild-type females. At this early time point, APCHi mice of both sexes were more susceptible to radiation-induced changes in systolic function compared to wild-types. At 6 months, a decrease in systolic function was mainly seen in male mice of both genotypes. At 6 months, specimens of heart, small intestine and dorsal skin were collected for tissue analysis. Female APCHi mice showed the most severe radiation-induced deposition of cardiac collagens but were protected against a radiation-induced loss of microvascular density. Both male and female APCHi mice were protected against a radiation induced upregulation of toll-like receptor 4 in the heart, but this did not translate into a clear protection against immune cell infiltration. In the small intestine, the APCHi genotype had no effect on an increase in the number of myeloperoxidase positive cells (seen mostly in females) or an increase in the expression of T-cell marker CD2 (males). Lastly, both male and female APCHi mice were protected against radiation-induced epidermal thickening and increase in 3-nitrotyrosine positive keratinocytes. In conclusion, prolonged high levels of APC in a transgenic mouse model had little effects on indicators of DEARE in the heart, small intestine and skin, with some differential effects in male compared to female mice.
