Data entry workers perceptions and satisfaction response to the "stop and stretch" software program

Cumulative Trauma Disorder (CTD) is a collection of chronic musculoskeletal disorders caused by frequent, sustained, and repetitive movements, most notably by computer usage at the workplace. A computer based break reminder program (Stop and Stretch) has been developed and installed to prevent CTDs caused by prolonged computer usage at the workplace. We investigated users' opinions to the Stop and Stretch program at their work place. 19 computer users were recruited as the subjects of the study. We conducted a survey after all the subjects used the Stop and Stretch program for one month. Among the nineteen subjects, 52.5% or 10 noticed a difference of symptoms after using the program; 63.3% or 12 thought the program had positive effect on their productivity; 100% or all 19 thought the program was easy to follow; 100% or all 19 thought it was helpful; 94.7% or18 were satisfied with the program; and the same value would recommend the program to others. When grouped into those who had prior experience with using stretch and exercise as part of their work routine15 subjects had no prior experience; and 14 participants within that group were satisfied or very satisfied with the program; 93.3% or 14 would recommend it to co-workers; and over half of those 15 thought the program is easy to use. The study provided insight to the response to using "stretch break software" and provided indicators of satisfaction with the Stop and Stretch program and that the program had sufficient usability and acceptance within a workplace setting which might be applied in other work settings similar to these.     

Diagnostic tests for the distribution of random effects in multivariate mixed effects models

Abstract

Fourier methods are proposed for testing the distribution of random effects in classical and robust multivariate mixed effects models. The test statistics involve estimation of the characteristic function of random effects. Theoretical and computational issues are addressed while Monte Carlo results show that the new procedures compare favorably with other methods.     

An improved comorbidity summary score for measuring disease burden and predicting mortality with applications to two national cohorts

Abstract

Research involving administrative healthcare data to study patient outcomes requires the investigator to account for the patient’s disease burden in order to reduce the potential for biased results. Here we develop a comorbidity summary score based on variable importance measures derived from several statistical and machine learning methods and show it has superior predictive performance to the Elixhauser and Charlson indices when used to predict 1-year, 5-year, and 10-year mortality. We used two large Veterans Administration cohorts to develop and validate the summary score and compared predictive performance using the area under ROC curve (AUC) and the Brier score.     

Seasonal Adjustment of Time Series Using One-Sided Filters

This article presents a model-based signal extraction seasonal adjustment procedure to extract estimates of the independent unobserved seasonal and nonseasonal components from an observed time series. The decomposition yields a one-sided filter that is optimal for adjusting the most recent observation under the assumption of using only the past observed series. Some advantages of this procedure are that no forecasts are required for implementation and there are no problems of revision of estimates or questions of concurrent adjustment. Comparisons are made with existing procedures using two-sided filters.