Validating E-cigarette Dependence Scales Based on Dynamic Patterns of Vaping Behaviors

2021 ◽  
Author(s):  
Anne Buu ◽  
Zhanrui Cai ◽  
Runze Li ◽  
Su-Wei Wong ◽  
Hsien-Chang Lin ◽  
...  
Keyword(s):  
Current Knowledge ◽  
Ecological Validity ◽  
Secondary Analysis ◽  
Empirical Support ◽  
Cigarette Consumption ◽  
Linear Regression Models ◽  
Absolute Deviation ◽  
Dynamic Patterns ◽  
Cigarette Dependence ◽  
Dependence Measures

Abstract Introduction Existing e-cigarette dependence scales are mainly validated based on retrospective overall consumption or perception. Further, given that the majority of adult e-cigarette users also use combustible cigarettes, it is important to determine whether e-cigarette dependence scales capture the product-specific dependence. This study fills in the current knowledge gaps by validating e-cigarette dependence scales using novel indices of dynamic patterns of e-cigarette use behaviors and examining the association between dynamic patterns of smoking and e-cigarette dependence among dual users. Methods Secondary analysis was conducted on the 2-week ecological momentary assessment data from 116 dual users. The Smoothly Clipped Absolute Deviation penalty (SCAD) was adopted to select important indices for dynamic patterns of consumption or craving and estimate their associations with e-cigarette dependence scales. Results The fitted linear regression models support the hypothesis that higher e-cigarette dependence is associated with higher levels of e-cigarette consumption and craving as well as lower instability of e-cigarette consumption. Controlling for dynamic patterns of vaping, dual users with lower e-cigarette dependence tend to report higher day-to-day dramatic changes in combustible cigarette consumption but not higher average levels of smoking. Conclusions We found that more stable use patterns are related to higher levels of dependence, which has been demonstrated in combustible cigarettes and we have now illustrated in e-cigarettes. Furthermore, the e-cigarette dependence scales may capture the product-specific average consumption but not product-specific instability of consumption. Implications This study provides empirical support for three e-cigarette dependence measures: PS-ECDI, e-FTCD, and e-WISDM, based on dynamic patterns of e-cigarette consumption and craving revealed by EMA data that have great ecological validity. This is the first study that introduces novel indices of dynamic patterns and demonstrates their potential applications in vaping research.

Modeling COVID-19 Growing Trends to Reveal the Differences in the Effectiveness of Non-Pharmaceutical Interventions among Countries in the World (Preprint)

2020 ◽  
Author(s):  
You Chen ◽  
Yubo Feng ◽  
Chao Yan ◽  
Xinmeng Zhang ◽  
Cheng Gao
Keyword(s):  
Secondary Analysis ◽  
Reproduction Rate ◽  
Cumulative Number ◽  
Linear Regression Models ◽  
Growth Data ◽  
Dynamic Changes ◽  
The World ◽  
Four Levels ◽  
Almost All ◽  
Pharmaceutical Interventions

BACKGROUND Adopting non-pharmaceutical interventions (NPIs) can affect COVID-19 growing trends, decrease the number of infected cases, and thus reduce mortality and healthcare demand. Almost all countries in the world have adopted non-pharmaceutical interventions (NPIs) to control the spread rate of COVID-19; however, it is unclear what are differences in the effectiveness of NPIs among these countries. OBJECTIVE We hypothesize that COVID-19 case growth data reveals the efficacy of NPIs. In this study, we conduct a secondary analysis of COVID-19 case growth data to compare the differences in the effectiveness of NPIs among 16 representative countries in the world. METHODS This study leverages publicly available data to learn patterns of dynamic changes in the reproduction rate for sixteen countries covering Asia, Europe, North America, South America, Australia, and Africa. Furthermore, we model the relationships between the cumulative number of cases and the dynamic reproduction rate to characterize the effectiveness of the NPIs. We learn four levels of NPIs according to their effects in the control of COVID-19 growth and categorize the 16 countries into the corresponding groups. RESULTS The dynamic changes of the reproduction rate are learned via linear regression models for all of the studied countries, with the average adjusted R-squared at 0.96 and the 95% confidence interval as [0.94 0.98]. China, South Korea, Argentina, and Australia are at the first level of NPIs, which are the most effective. Japan and Egypt are at the second level of NPIs, and Italy, Germany, France, Netherlands, and Spain, are at the third level. The US and UK have the most inefficient NPIs, and they are at the fourth level of NPIs. CONCLUSIONS COVID-19 case growth data provides evidence to demonstrate the effectiveness of the NPIs. Understanding the differences in the efficacy of the NPIs among countries in the world can give guidance for emergent public health events. CLINICALTRIAL NA

scholarly journals The climate dependence of the terrestrial carbon cycle; including parameter and structural uncertainties

2012 ◽  
Vol 9 (10) ◽  
pp. 13439-13496 ◽  
Author(s):  
M. J. Smith ◽  
M. C. Vanderwel ◽  
V. Lyutsarev ◽  
S. Emmott ◽  
D. W. Purves
Keyword(s):  
Carbon Cycle ◽  
Empirical Data ◽  
Current Knowledge ◽  
Empirical Support ◽  
Data Sets ◽  
Earth System ◽  
Model Formulation ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Cycle ◽  
Component Processes

Abstract. The feedback between climate and the terrestrial carbon cycle will be a key determinant of the dynamics of the Earth System over the coming decades and centuries. However Earth System Model projections of the terrestrial carbon-balance vary widely over these timescales. This is largely due to differences in their carbon cycle models. A major goal in biogeosciences is therefore to improve understanding of the terrestrial carbon cycle to enable better constrained projections. Essential to achieving this goal will be assessing the empirical support for alternative models of component processes, identifying key uncertainties and inconsistencies, and ultimately identifying the models that are most consistent with empirical evidence. To begin meeting these requirements we data-constrained all parameters of all component processes within a global terrestrial carbon model. Our goals were to assess the climate dependencies obtained for different component processes when all parameters have been inferred from empirical data, assess whether these were consistent with current knowledge and understanding, assess the importance of different data sets and the model structure for inferring those dependencies, assess the predictive accuracy of the model, and to identify a methodology by which alternative component models could be compared within the same framework in future. Although formulated as differential equations describing carbon fluxes through plant and soil pools, the model was fitted assuming the carbon pools were in states of dynamic equilibrium (input rates equal output rates). Thus, the parameterised model is of the equilibrium terrestrial carbon cycle. All but 2 of the 12 component processes to the model were inferred to have strong climate dependencies although it was not possible to data-constrain all parameters indicating some potentially redundant details. Similar climate dependencies were obtained for most processes whether inferred individually from their corresponding data sets or using the full terrestrial carbon model and all available data sets, indicating a strong overall consistency in the information provided by different data sets under the assumed model formulation. A notable exception was plant mortality, in which qualitatively different climate dependencies were inferred depending on the model formulation and data sets used, highlighting this component as the major structural uncertainty in the model. All but two component processes predicted empirical data better than a null model in which no climate dependency was assumed. Equilibrium plant carbon was predicted especially well (explaining around 70% of the variation in the withheld evaluation data). We discuss the advantages of our approach in relation to advancing our understanding of the carbon cycle and enabling Earth System Models make better constrained projections.

scholarly journals Machine Learning for Mortality Prediction in Pediatric Myocarditis

2021 ◽  
Vol 9 ◽  
Author(s):  
Fu-Sheng Chou ◽  
Laxmi V. Ghimire
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Regression Model ◽  
Current Knowledge ◽  
External Validation ◽  
Model Development ◽  
Kidney Injury ◽  
High Specificity ◽  
Mortality Prediction ◽  
Linear Regression Models

Background: Pediatric myocarditis is a rare disease. The etiologies are multiple. Mortality associated with the disease is 5–8%. Prognostic factors were identified with the use of national hospitalization databases. Applying these identified risk factors for mortality prediction has not been reported.Methods: We used the Kids' Inpatient Database for this project. We manually curated fourteen variables as predictors of mortality based on the current knowledge of the disease, and compared performance of mortality prediction between linear regression models and a machine learning (ML) model. For ML, the random forest algorithm was chosen because of the categorical nature of the variables. Based on variable importance scores, a reduced model was also developed for comparison.Results: We identified 4,144 patients from the database for randomization into the primary (for model development) and testing (for external validation) datasets. We found that the conventional logistic regression model had low sensitivity (~50%) despite high specificity (>95%) or overall accuracy. On the other hand, the ML model struck a good balance between sensitivity (89.9%) and specificity (85.8%). The reduced ML model with top five variables (mechanical ventilation, cardiac arrest, ECMO, acute kidney injury, ventricular fibrillation) were sufficient to approximate the prediction performance of the full model.Conclusions: The ML algorithm performs superiorly when compared to the linear regression model for mortality prediction in pediatric myocarditis in this retrospective dataset. Prospective studies are warranted to further validate the applicability of our model in clinical settings.

scholarly journals EXTENDING GAMING DEMAND: SUBJECTIVE EXPERIENCES OF COGNITIVE, EMOTIONAL, PHYSICAL, AND SOCIAL VIDEOGAMING REQUIREMENTS

2020 ◽  
Author(s):  
Nicholas David Bowman ◽  
Jaime Banks
Keyword(s):  
Video Game ◽  
Secondary Analysis ◽  
Empirical Support ◽  
Emotional States ◽  
Subjective Experiences ◽  
End User ◽  
Making Sense ◽  
The Social ◽  
Emotional Demands ◽  
Social Responding

Videogames directly involve players as co-creators of on-screen events, and this interactivity is assumed to be a core source of their attraction as a successful entertainment medium. Although interactivity is an inherent property of the videogame, it is variably perceived by the end user—for some users, perceived as a more demanding process, taxing their already-limited attentional resources. At least four such demands have been explicated in extant literature: cognitive (making sense of game logics/tasks), emotional (affective responses to game events/outcomes), physical (managing controller inputs and interfaces), and social (responding to human/nonhuman in-game others). Past work has reported empirical support of these concepts through validation of closed-ended survey metrics (e.g., Video Game Demand Scale). The current study challenges and extends the demand concept through an analysis of players’ own language when describing videogame demands in short essays about gaming experiences—critical given that people may experience a phenomenon in ways not accounted for in deductive data approaches. A secondary analysis of qualitative data made freely available by VGDS authors revealed both convergence with and divergence from prior work. Comporting with VGDS, cognitive demands are mostly experienced by players as ludic concerns and physical demands are mostly experienced in relation to handheld controller perceptions. Diverging from VGDS, players’ emotional demands represented both basic and complex emotional states, and social demands manifest different depending on whether or not the social “other” is human or non-human: humans are considered demanding on interpersonal terms, whereas non-humans are considered demanding as personified evocative objects.

Predicting Return To Work After Workplace Injury: A Review of Current Literature

2005 ◽  
Vol 11 (2) ◽  
pp. 84-102
Author(s):  
Richard Hilton
Keyword(s):  
Return To Work ◽  
Living Arrangements ◽  
Current Knowledge ◽  
Secondary Analysis ◽  
Demographic Variables ◽  
Predictor Variables ◽  
Workplace Injury ◽  
Research Focus ◽  
Ongoing Research ◽  
Current Knowledge Base

Eighty-four predictor variables were identified from thirty-four studies that researched return to work after workplace injury. The six most studied variables were then critically reviewed. The variables were age, sex, living arrangements, employment maintenance, delay to rehabilitation, and employment type. Based on the number of statistical findings, and on review of the articles, age, employment maintenance, and delay to rehabilitation demonstrated strong relationships with return to work. The variables of sex, living arrangements and employment type did not demonstrate such relationships. While this research brought together the current knowledge base the inability to quantitatively analyse previous results was a major limitation. It was recommended that ongoing research in this area ensures that analysis and publication of results provides information that would allow such secondary analysis in the future. It was also recommended that the current research focus on demographic variables be shifted to more prospective intervention based research.

Associations of caregiver-oncologist discordance in prognostic understanding with caregiver-reported therapeutic alliance and anxiety.

2019 ◽  
Vol 37 (31_suppl) ◽  
pp. 15-15
Author(s):  
Kah Poh Loh ◽  
Huiwen Xu ◽  
Ronald M. Epstein ◽  
Supriya Gupta Mohile ◽  
Holly Gwen Prigerson ◽  
...  
Keyword(s):  
Life Expectancy ◽  
Therapeutic Alliance ◽  
Secondary Analysis ◽  
Linear Regression Models ◽  
Patient Death ◽  
System A ◽  
Caregiver Anxiety ◽  
The Relationship ◽  
Clinical Trial Information

15 Background: Discordance in prognostic understanding between caregivers of adults with cancer and the patient’s oncologist is common. However, the relationship between caregiver-oncologist discordance and caregiver bereavement outcomes is unknown. We evaluated the associations of caregiver-oncologist discordance in beliefs about the patient’s curability and life expectancy with caregiver-reported therapeutic alliance and anxiety. Methods: This is a secondary analysis of a multicenter study that assessed the effect of a communication intervention among patients with advanced cancer and their caregivers. Prior to intervention exposure, caregivers and oncologists were asked about their belief in the patient’s chances for cure and living ≥2 years: 100%, about 90%, about 75%, about 50/50, about 25%, about 10%, and 0%. Discordance was defined as a difference by 2 response levels on each prognostic understanding item. Outcomes at 7 months after patient death included caregiver-reported therapeutic alliance [modified 5-item Human Connection (THC) scale] and anxiety (Generalized Anxiety Disorder-7). We used multivariable linear regression models to assess the independent associations of discordance with therapeutic alliance and anxiety. Results: We included 97 caregivers (mean age 63, range 22-83). Approximately 40% of caregiver-oncologist dyads had discordant beliefs about curability (caregivers were more optimistic in 100% of dyads) and 63% had discordant beliefs about life expectancy (caregivers were more optimistic in 94% of dyads). On multivariate analysis, discordance in beliefs about prognostic estimates was associated with lower THC score (b = -6.94, SE 3.17, p = 0.03). Discordance in beliefs about curability was associated with lower anxiety levels (b = -1.79, SE 0.90, p = 0.05). Conclusions: Caregiver-oncologist discordance may decrease caregiver-reported therapeutic alliance and anxiety, both of which may shape how caregivers interact with the healthcare system. A better understanding the role of caregivers’ prognostic understanding will guide interventions to improve caregiver-oncologist therapeutic alliance and caregiver anxiety. Clinical trial information: NCT01485627.

Enteral Docosahexaenoic Acid Reduces Analgesic Administration in Neonates Undergoing Cardiovascular Surgery

2016 ◽  
Vol 69 (2) ◽  
pp. 150-160 ◽  
Author(s):  
Mariela Bernabe-Garcia ◽  
Mardia López-Alarcon ◽  
Alfredo Salgado-Sosa ◽  
Raul Villegas-Silva ◽  
Jorge Maldonado-Hernandez ◽  
...  
Keyword(s):  
Docosahexaenoic Acid ◽  
Cumulative Dose ◽  
Neonatal Intensive Care ◽  
Cardiovascular Surgery ◽  
Antinociceptive Effect ◽  
Secondary Analysis ◽  
Control Group ◽  
Linear Regression Models ◽  
Analgesic Effects ◽  
Negative Side Effects

Background: Neonates undergoing surgery require analgesic medication to ameliorate acute pain. These medications produce negative side effects. Docosahexaenoic acid (DHA) has an antinociceptive effect in animals, but this has not been evaluated in human neonates. We evaluated the DHA effect on cumulative dose and duration of analgesics administered to neonates undergoing cardiovascular surgery. Methods: A secondary analysis was performed with data from a clinical trial, in which enteral DHA was administered perioperatively compared with sunflower oil (SO). Present study assessed the antinociceptive effect of DHA by measuring the cumulative dose and duration of analgesics administered during postoperative stay in a neonatal intensive care unit. Multivariate linear regression models were performed. Results: Seventeen neonates received DHA and 18 received SO in the control group. Compared with the control group, the DHA group received lower cumulative dose (14.6 ± 2.2 vs. 25.2 ± 4.8 μg/kg, p = 0.029) and shorter duration of buprenorphine (2 days (1-8) vs. 4.5 days (1-12); p = 0.053). After adjusting for confounders, the DHA group received significantly lesser buprenorphine (β = -27 μg/kg, p = 0.028; R2 model = 0.90) for shorter duration (β = -9 days, p = 0.003; R2 model = 0.94). No differences in fentanyl or ketorolac were detected. Conclusions: Buprenorphine administration was reduced in neonates who received DHA, suggesting that DHA likely has analgesic effects.

scholarly journals Effects of Exposure to Road, Railway, Airport and Recreational Noise on Blood Pressure and Hypertension

2021 ◽  
Vol 18 (17) ◽  
pp. 9145 ◽  
Author(s):  
Davide Petri ◽  
Gaetano Licitra ◽  
Maria Angela Vigotti ◽  
Luca Fredianelli
Keyword(s):  
Blood Pressure ◽  
Road Traffic ◽  
Current Knowledge ◽  
Cross Sectional Study ◽  
Linear Regression Models ◽  
Multiple Sources ◽  
Night Time ◽  
Noise Sources ◽  
Cross Sectional ◽  
The Impact

Noise is one of the most diffused environmental stressors affecting modern life. As such, the scientific community is committed to studying the main emission and transmission mechanisms aiming at reducing citizens’ exposure, but is also actively studying the effects that noise has on health. However, scientific literature lacks data on multiple sources of noise and cardiovascular outcomes. The present cross-sectional study aims to evaluate the impact that different types of noise source (road, railway, airport and recreational) in an urban context have on blood pressure variations and hypertension. 517 citizens of Pisa, Italy, were subjected to a structured questionnaire and five measures of blood pressure in one day. Participants were living in the same building for at least 5 years, were aged from 37 to 72 years old and were exposed to one or more noise sources among air traffic, road traffic, railway and recreational noise. Logistic and multivariate linear regression models have been applied in order to assess the association between exposures and health outcomes. The analyses showed that prevalence of high levels of diastolic blood pressure (DBP) is consistent with an increase of 5 dB (A) of night-time noise (β = 0.50 95% CI: 0.18–0.81). Furthermore, increased DBP is also positively associated with more noise sensitive subjects, older than 65 years old, without domestic noise protection, or who never close windows. Among the various noise sources, railway noise was found to be the most associated with DBP (β = 0.68; 95% CI: −1.36, 2.72). The obtained relation between DBP and night-time noise levels reinforces current knowledge.

scholarly journals Determination of Quantile Range of Optimal Hyperparameters Using Bayesian Estimation

2021 ◽  
Vol 47 (3) ◽  
pp. 988-998
Author(s):  
Ayoade I. Adewole ◽  
Olusoga A. Fasoranbaku
Keyword(s):  
Bayesian Estimation ◽  
Prior Distribution ◽  
Parameter Estimates ◽  
Distributed Data ◽  
Linear Regression Models ◽  
Absolute Deviation ◽  
Data Set ◽  
Empirical Bayesian ◽  
Bayesian Inferences ◽  
Bayesian Estimations

Bayesian estimations have the advantages of taking into account the uncertainty of all parameter estimates which allows virtually the use of vague priors. This study focused on determining the quantile range at which optimal hyperparameter of normally distributed data with vague information could be obtained in Bayesian estimation of linear regression models. A Monte Carlo simulation approach was used to generate a sample size of 200 data-set. Observation precisions and posterior precisions were estimated from the regression output to determine the posterior means estimate for each model to derive the new dependent variables. The variances were divided into 10 equal parts to obtain the hyperparameters of the prior distribution. Average absolute deviation for model selection was used to validate the adequacy of each model. The study revealed the optimal hyperparameters located at 5th and 7th deciles. The research simplified the process of selecting the hyperparameters of prior distribution from the data with vague information in empirical Bayesian inferences. Keywords: Optimal Hyperparameters; Quantile Ranges; Bayesian Estimation; Vague prior

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