Instruction manual usage: A comparison of younger people, older people and people with cognitive disabilities

This paper was presented at the DESRIST 2010 conference in Swizerland, 4-5 June 2010. Copyright © 2010, Springer Berlin/Heidelberg. The final version of this article can be viewed at the link below.When people are faced with new products for the first time or require assistance using features, the instruction manual is a key information source and therefore the design of instruction manuals is as important as the design of the product itself. There are often situations where the design embedded in the product is not sufficient to express its usage to the user. In addition, users differ significantly from each other in terms of their needs, expectations and capabilities. The main question is “are instruction manuals accessible enough and do they consider a variety of user groups?” This paper investigates the differences between three user groups (i.e. younger people, older people and people with cognitive disabilities) regarding their approach to understanding of instruction manuals. An experimental study was carried out testing thirty volunteer participants from the aforementioned user groups, using two digital products from two different market segments and their instruction manuals.The Turkish Board of Higher Educatio

Markers of myocardial damage predict mortality in patients with aortic stenosis

BACKGROUND: Cardiovascular magnetic resonance (CMR) is increasingly used for risk stratification in aortic stenosis (AS). However, the relative prognostic power of CMR markers and their respective thresholds remains undefined. OBJECTIVES: Using machine learning, the study aimed to identify prognostically important CMR markers in AS and their thresholds of mortality. METHODS: Patients with severe AS undergoing AVR (n = 440, derivation; n = 359, validation cohort) were prospectively enrolled across 13 international sites (median 3.8 years’ follow-up). CMR was performed shortly before surgical or transcatheter AVR. A random survival forest model was built using 29 variables (13 CMR) with post-AVR death as the outcome. RESULTS: There were 52 deaths in the derivation cohort and 51 deaths in the validation cohort. The 4 most predictive CMR markers were extracellular volume fraction, late gadolinium enhancement, indexed left ventricular end-diastolic volume (LVEDVi), and right ventricular ejection fraction. Across the whole cohort and in asymptomatic patients, risk-adjusted predicted mortality increased strongly once extracellular volume fraction exceeded 27%, while late gadolinium enhancement >2% showed persistent high risk. Increased mortality was also observed with both large (LVEDVi >80 mL/m(2)) and small (LVEDVi ≤55 mL/m(2)) ventricles, and with high (>80%) and low (≤50%) right ventricular ejection fraction. The predictability was improved when these 4 markers were added to clinical factors (3-year C-index: 0.778 vs 0.739). The prognostic thresholds and risk stratification by CMR variables were reproduced in the validation cohort. CONCLUSIONS: Machine learning identified myocardial fibrosis and biventricular remodeling markers as the top predictors of survival in AS and highlighted their nonlinear association with mortality. These markers may have potential in optimizing the decision of AVR