Development of a predictive model for optimum insole stiffness

Abstract

Aim The main aim of this study was to develop a selection algorithm/statistical model that is predictive of the optimum insole stiffness on a patient-specific basis, for patients living with Type 1 or 2 diabetes mellitus and diabetic peripheral neuropathy/neuroischemia. Method A cohort of 65 participants (n=65) who fit within the inclusion criteria were recruited from the Diabetes and Endocrine foot clinic at outpatients, Mater Dei Hospital, Msida. Plantar pressure data was collected with the Pedar® in-shoe system (Novel. DE®, Munich, Germany). Relevant demographic and anthropometric data were also collected (ex; BMI and weight). All participants underwent a control test and eight experimental tests. In the control test, the participants walked a minimum number of 14 steps per foot at their preferred walking speed while wearing a standardized shoe type which was fitted with the Pedar® in-shoe sensor only. In the experimental tests, a similar procedure was carried out, with the exception that the participants had different bespoke polyurethane (BPU) foam simple insoles of various stiffnesses fitted within the shoes as well. The experimental tests were performed one at a time for the 8 different BPU foam stiffnesses (Shore A values; 2-40), for every participant. The peak plantar pressure (PPP) and pressure time integral (PTI) values of every participant were averaged and recorded for all the tests. The percentage change in PPP and PTI of every BPU foam was calculated in relation to the respective baseline condition of the heaviest loaded foot for every participant. This was performed for the entire foot and the 4 regions of interest (ROIs), being the toes, metatarsal heads, midfoot and heel. The BPU foams that achieved the most reduction in these parameters from the baseline condition of the heaviest loaded foot were identified for every participant and were denoted as “patient-specific optimum BPU foams”. These were identified separately for PPP and PTI reduction. The group-optimum BPU foams for PPP and PTI reduction were identified separately as well. The patient-specific optimum foam data was compared with the group-optimum foam data, and it was seen whether the insole stiffness could be generalised or not for the whole sample. If the insole stiffness could not be generalised with the group-optimum BPU foams, this indicated that a statistical model that was predictive of the optimum insole stiffness could possibly be created on the basis of the correlations present between the patient-specific optimum BPU foams and other possible optimum insole stiffness predictors (ex; BMI, weight, and baseline PPP/PTI). Possible predictors that could be indicative of the optimum insole stiffness were identified and fitted to the model. Possible predictors that were not predictive of the optimum insole stiffness were omitted from the model. A model was constructed on the basis of the predictors that showed to be of a good fit for predicting the optimum insole stiffness. Results Friedman’s analysis indicated that a significant difference in the percentage change in PPP and PTI (%) was present between the different BPU foams in the entire foot and the four regions of interest. Moreover, some foams were found to reduce these parameters, whilst others were found to increase them. The Paired-sample t-test showed that the group-optimum BPU foam for PPP reduction (BPU04) and PTI reduction (BPU02) could not be generalized for the whole sample population. The One-sample t-test indicated that a significant reduction in PPP and PTI was obtained by the patient-specific optimum BPU foams in the entire foot, metatarsal head, and heel regions and not in the midfoot and toes regions as this was close to zero. Nevertheless, when the reduction in PPP and PTI was averaged for the 4 ROIs, this resulted in an overall significant reduction in these parameters, hence the patient-specific optimum BPU foams of the entire foot could be generalised for the 4 ROIs by utilising a footbed of a uniform stiffness for the entire foot. This indicated that the model only required to predict a uniform insole stiffness for the entire foot. Finally, Multinomial logistic regression showed that a model that predicts the ideal BPU foam stiffness for the entire foot could be fitted only for the dependent variable “Patient-specific optimum BPU for PPP (entire foot)” using the predictors baseline PPP (kPa) and baseline PTI (kPa/cm2). These predictors were 47.4% representative of the patient-specific optimum BPU for PPP. Conclusion In conclusion, the findings of this study demonstrated that Podiatrists should be encouraged to use this predictive model for optimum insole stiffness as this would provide an indication of the ideal stiffness of a foot orthosis for this category of patients, on the basis of baseline PPP (entire foot) and baseline PTI (entire foot), and hence be able to effectively reduce these parameters in the tissues that are under stress.