The meaning of frailty according to Dutch older frail and non-frail persons

Frailty is a term often used by researchers and clinicians to describe a state in which older persons are at risk for adverse outcomes such as falls, disability, institutionalization and mortality. However, no study so far examined what frailty means to older persons. Therefore the aim of this study was to describe the meaning that older community-dwelling persons attach to frailty. Twenty-five semi-structured interviews were conducted. The interviews were analyzed using the grounded theory method. Frailty was described as being in poor health, having walking difficulties, feeling down, being anxious, having few social contacts and not being able to do the things one likes to do. Men described in more detail the physical dimension whereas women elaborated in more depth on the psychological and social component. Existing definitions of frailty should be adjusted to better reflect the meaning of frailty for older persons.     

The effects of measurement error on the MAX EWMAMS control chart

Recently, some researchers suggested using a single chart to monitor both location and scale parameters for a process simultaneously, in order to resolve some difficulties in control chart interpretation arising from the traditional approach. This study focuses on the Maximum Exponentially Weighted Moving Average and Mean Squared deviation (MAX EWMAMS) control chart in the presence of measurement error. An important issue in using this chart is that measurement error adversely affects the performance of the chart. In this study, we investigate the effects of measurement error on the performance of the MAX EWMAMS chart by calculating and comparing the average time to signal (ATS) associated with both the in-control and out-of-control states.     

Adaptive c-chart with estimated parameter

In the design of control charts, it is usually assumed that process parameters are known. However, in many practical applications the values of these parameters are unknown and should be estimated using historical in-control process observations. In this study, the performance of adaptive c-chart with estimated parameter is evaluated. It is demonstrated that by increasing the size and the number of samples in estimating the process parameter, the performance of the chart converges to that of the known parameter case. Finally the best phase I sampling scenarios are presented to make the chart with the estimated parameter perform as well as the chart with the known parameter.