Importance Of Design Features Research Articles

Developing an mHealth app for post-stroke upper limb rehabilitation: Feedback from US and Ethiopian rehabilitation clinicians.

Stroke is the leading cause of adult disability worldwide, with 70 percent of survivors exhibiting residual impairments of the upper limb that require frequent in-person visits to rehabilitation clinic over several months. This study explored rehabilitation clinician's preferences for design features to be included in an mHealth-enabled app for post-stroke upper limb rehabilitation. Data were collected via online survey, sampling participants from Ethiopia (n = 69) and the United States (n = 75). Survey results indicated that Ethiopian and US rehabilitation clinicians have different opinions about the importance of design features that should be included in a stroke tele-rehabilitation system which are likely due to differences in culture, the availability of human and physical resources, and how the field of rehabilitation is organized and managed. Our results, thus, indicate that mHealth technologies must be tailored to the geographical and cultural context of the end users.

The use of statistical tools in field testing of putative effects of genetically modified plants on nontarget organisms

To fulfill existing guidelines, applicants that aim to place their genetically modified (GM) insect-resistant crop plants on the market are required to provide data from field experiments that address the potential impacts of the GM plants on nontarget organisms (NTO's). Such data may be based on varied experimental designs. The recent EFSA guidance document for environmental risk assessment (2010) does not provide clear and structured suggestions that address the statistics of field trials on effects on NTO's. This review examines existing practices in GM plant field testing such as the way of randomization, replication, and pseudoreplication. Emphasis is placed on the importance of design features used for the field trials in which effects on NTO's are assessed. The importance of statistical power and the positive and negative aspects of various statistical models are discussed. Equivalence and difference testing are compared, and the importance of checking the distribution of experimental data is stressed to decide on the selection of the proper statistical model. While for continuous data (e.g., pH and temperature) classical statistical approaches – for example, analysis of variance (ANOVA) – are appropriate, for discontinuous data (counts) only generalized linear models (GLM) are shown to be efficient. There is no golden rule as to which statistical test is the most appropriate for any experimental situation. In particular, in experiments in which block designs are used and covariates play a role GLMs should be used. Generic advice is offered that will help in both the setting up of field testing and the interpretation and data analysis of the data obtained in this testing. The combination of decision trees and a checklist for field trials, which are provided, will help in the interpretation of the statistical analyses of field trials and to assess whether such analyses were correctly applied.We offer generic advice to risk assessors and applicants that will help in both the setting up of field testing and the interpretation and data analysis of the data obtained in field testing.