scholarly journals When the Nature of ‘Nature’ is Inconsistent: Evaluating the Natural Environment in Attention Restoration Theory

2020 ◽  
Author(s):  
Naseem Dillman-Hasso
Keyword(s):  
Randomized Controlled Trials ◽  
Cognitive Processes ◽  
Natural Environment ◽  
Attentional Control ◽  
Controlled Trials ◽  
Attention Restoration Theory ◽  
Attention Restoration ◽  
Randomized Controlled ◽  
Art Research

The Attention Restoration Theory (ART; Kaplan & Kaplan, 1989) postulates that exposure to nature can help improve cognitive processes, specifically attentional control. These benefits are hypothesized to help with concentration and focus. However, there is tremendous variability in the definitions and manipulations of nature in research on ART. This complicates extrapolation from the results and makes it harder to see if nature itself is the restorative component or rather some other facet. This review evaluates randomized controlled trials studying the ART from 2013-2018 and catalogues differences in how nature was operationalized across studies. The paper presents suggestions for more methodologically consistent ART research, including direct replications, and an updated scale for measuring the restorativeness of an environment. This preprint is an unpublished senior thesis.

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani
Keyword(s):  
Randomized Controlled Trials ◽  
Discrete Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Controlled Trials ◽  
Missing Covariates ◽  
Indicator Method ◽  
Imputation Methods ◽  
Randomized Controlled ◽  
Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

Interventions for suicide and self-injury: A meta-analysis of randomized controlled trials across nearly 50 years of research.

2020 ◽  
Vol 146 (12) ◽  
pp. 1117-1145
Author(s):  
Kathryn R. Fox ◽  
Xieyining Huang ◽  
Eleonora M. Guzmán ◽  
Kensie M. Funsch ◽  
Christine B. Cha ◽  
...  
Keyword(s):  
Randomized Controlled Trials ◽  
Meta Analysis ◽  
Controlled Trials ◽  
Randomized Controlled ◽  
Self Injury

Randomized Controlled Trials in the Context of Behavioral Medicine

2009 ◽  
Author(s):  
Jennifer L. Steel ◽  
Leigh A. Gemmell ◽  
David A. Geller ◽  
Michael Spring ◽  
Jonathan Grady ◽  
...  
Keyword(s):  
Behavioral Medicine ◽  
Randomized Controlled Trials ◽  
Controlled Trials ◽  
Randomized Controlled
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