A Post-Hoc Qualitative Analysis Of Real Time Heads-Up Pollen Counting Versus Traditional Microscopic Counting In The Environmental Exposure Unit (EEU)

2014 ◽  
Vol 133 (2) ◽  
pp. AB18 ◽  
Author(s):  
Lisa Steacy ◽  
Terry Walker ◽  
Barnaby Hobsbawn ◽  
Jenny Thiele ◽  
Anne K. Ellis

Author(s):  
Vincent Berardi ◽  
John Bellettiere ◽  
Benjamin Nguyen ◽  
Neil E Klepeis ◽  
Suzanne C Hughes ◽  
...  

Abstract Few studies have examined the relative effectiveness of reinforcing versus aversive consequences at changing behavior in real-world environments. Real-time sensing devices makes it easier to investigate such questions, offering the potential to improve both intervention outcomes and theory. This research aims to describe the development of a real-time, operant theory-based secondhand smoke (SHS) intervention and compare the efficacy of aversive versus aversive plus reinforcement contingency systems. Indoor air particle monitors were placed in the households of 253 smokers for approximately three months. Participants were assigned to a measurement-only control group (N = 129) or one of the following groups: 1.) aversive only (AO, N = 71), with aversive audio/visual consequences triggered by the detection of elevated air particle measurements, or 2.) aversive plus reinforcement (AP, N = 53), with reinforcing consequences contingent on the absence of SHS added to the AO intervention. Residualized change ANCOVA analysis compared particle concentrations over time and across groups. Post-hoc pairwise comparisons were also performed. After controlling for Baseline, Post-Baseline daily particle counts (F = 6.42, p = 0.002), % of time >15,000 counts (F = 7.72, p < 0.001), and daily particle events (F = 4.04, p = 0.02) significantly differed by study group. Nearly all control versus AO/AP pair-wise comparisons were statistically significant. No significant differences were found for AO versus AP groups. The aversive feedback system reduced SHS, but adding reinforcing consequences did not further improve outcomes. The complexity of real-world environments requires the nuances of these two contingency systems continue to be explored, with this study demonstrating that real-time sensing technology can serve as a platform for such research.



2017 ◽  
Vol 139 (2) ◽  
pp. AB159
Author(s):  
Anne K. Ellis ◽  
Lisa M. Steacy ◽  
Alkis Togias ◽  
Charles P. Andrews ◽  
Daniel A. Ramirez ◽  
...  


1997 ◽  
Vol 79 (6) ◽  
pp. 533-540 ◽  
Author(s):  
James Halliday Day ◽  
Maureen Phyllis Briscoe ◽  
April Welsh ◽  
Jeffrey Norman Smith ◽  
Adrian Clark ◽  
...  


1992 ◽  
Vol 14 (3) ◽  
pp. 195-209 ◽  
Author(s):  
William P. Stewart ◽  
R. Bruce Hull


2009 ◽  
Vol 30 (3) ◽  
pp. 270-276 ◽  
Author(s):  
James H. Day ◽  
Maureen P. Briscoe ◽  
Jodan D. Ratz ◽  
Anne K. Ellis ◽  
Ruji Yao ◽  
...  


Systems ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 7
Author(s):  
John Turner ◽  
Dave Snowden ◽  
Nigel Thurlow

The substrate-independence theory utilizes sensemaking techniques to provide cognitively based scaffolds that guide and structure learning. Scaffolds are cognitive abstractions of constraints that relate to information within a system. The substrate-independence theory concentrates on the flow of information as the underlying property of the host system. The substrate-independence theory views social systems as complex adaptive systems capable of repurposing their structure to combat external threats by utilizing constructors and substrates. Constructor theory is used to identify potential construction tasks, the legitimate input and output states that are possible, to map the desired change in the substrate’s attributes. Construction tasks can be mapped in advance for ordered and known environments. Construction tasks may also be mapped in either real-time or post hoc for unordered and complex environments using current sensemaking techniques. Mapping of the construction tasks in real-time becomes part of the landscape, and scaffolds are implemented to aid in achieving the desired state or move to a more manageable environment (e.g., from complex to complicated).



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