Criterios de selección y utilización de medios

One of the key objectives of this thesis is to understand the effect of different design features of a fully customised insole on plantar pressures in people with diabetes and neuropathy. To develop this understanding, it is necessary to quantify pressure offloading across a range of different insole designs and from these data, identify the best performing insole design. However, this approach requires plantar pressure measurement which is both precise and repeatable. Repeated measurements of physiological/biomechanical variables, such as plantar pressures, are associated with some variability. This variability can arise from variability in an individual’s capacity to repeat a given task as well as from errors/variability in the measurement processor, the circumstances under which the measurements take place (de Vet et al., 2006). Therefore, to be able to interpret correctly the results of subsequent chapters which examine the effects of different insole designs, it is necessary to have a clear understanding of the level of reproducibility of plantar pressure measurement in people with diabetes and peripheral neuropathy.

There are two main approaches to quantifying reproducibility referred to as agreement and reliability (de Vet et al., 2006). Agreement and reliability parameters focus on two different questions:

1. ‘‘How good is the agreement between repeated measurements?’’ This concerns the measurement error and assesses exactly how close the scores for repeated measurements are.

2. ‘‘How reliable is the measurement?’’ which characterises how well patients can be distinguished from each other despite measurement errors.

97 Reliability is the extent to which measurements can differentiate between patients, despite any measurement errors that may influence measured values. In contrast, agreement defines how close repeated measurements are to one another and is quantified in the actual unit of the measurement (de Vet et al., 2006).

The calculation of the Standard Error of Measurement (SEM) is based on the measurement from a single subject taken an infinite number of times. In this scenario, each of the individual measurements would be slightly different because of measurement error. However, the distribution of these measurements can be described by a normal distribution and will be observed to cluster around a ‘true’ mean value with a variability characterised by a within-subject standard deviation. The more reliable the measurement response, the less variability and so the smaller the within-subject standard deviation (Bruton, 2000). The SEM is calculated by averaging the spread of measurements for each individual across the whole group. This calculation takes into consideration the possibility that some of the observed change may be due to random measurement error. Therefore, it can be used to define the difference needed between separate measures on a subject for the difference in the measures to be considered real (Weir, 2005).

The Intraclass Correlation Coefficient (ICC) is used to quantify reliability by relating the measurement error to the variability between individuals in the population under study. It is defined as the ratio between the variability from true differences in the measured variable between individuals and the total variability, which is the sum of the true variability and the measurement error. Therefore, the ICC does not just reflect the measurement error but also the characteristics of the sample chosen. Consequently, the results have to be interpreted regarding the sample used. For example, it would be inappropriate to calculate the ICC from measurements on a group of healthy individuals (which is common in the literature because it is generally easier) and then apply the results to a particular patient group (Baker, 2013). An early paper suggested that values of the ICC as low as 0.6 should be regarded as indicating ‘substantial’ agreement and over 0.8 as ‘almost perfect’(Landis&Koch, 1977). More recent reports are less generous suggesting that ‘for many clinical measurements, reliability should exceed 0.9 to ensure reasonable validity’ (Portney, 2009).

98 Reproducibility of in-shoe plantar pressure collection has been previously studied by some authors who have demonstrated high agreement and reliability (Ramanathan et al., 2010, Sawacha, 2013, de Castro et al., 2014). These studies have used the most popular in-shoe devices, Novel Pedar, Walkinsense and F-Scan, with the Novel Pedar system regarded as having the best reproducibility. Amongst the studies testing Novel Pedar reproducibility, all reported good results in healthy subjects while wearing standard shoes with no insole inside (Murphy et al., 2005, Putti et al., 2007, Ramanathan et al., 2010). However, these findings may not extrapolate to patients with diabetes and neuropathy who often use complex contoured insoles. This patient group are likely to have impairments in balance associated with their neuropathy (Allet et al., 2008) which may affect cadence and foot biomechanics leading to inconsistent gait patterns (Allet et al., 2008).

Another limitation of previous studies investigating the reproducibility of plantar pressure data is that they have tended to focus on flat insoles (Putti et al., 2007, Ramanathan et al., 2010). It is therefore not clear whether similar levels of repeatability would be observed with contoured insoles, especially if worn by people with diabetes and neuropathy. Accordingly, the aim of this study was to develop a precise understanding of the agreement and reliability of pressure data collected from customised insoles in people with diabetes and peripheral neuropathy.