Virtually optimized insoles for offloading the diabetic foot: A randomized crossover study
Virtually optimized insoles for offloading the diabetic foot: A randomized crossover study
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DOI:
10.1016/j.jbiomech.2017.06.028
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发表时间:
2017-07-26
影响因子:
2.4
通讯作者:
Cavanagh, P. R.
中科院分区:
文献类型:
--
作者:
Telfer, S.;Woodburn, J.;Cavanagh, P. R.
Integration of objective biomechanical measures of foot function into the design process for insoles has been shown to provide enhanced plantar tissue protection for individuals at-risk of plantar ulceration. The use of virtual simulations utilizing numerical modeling techniques offers a potential approach to further optimize these devices. In a patient population at-risk of foot ulceration, we aimed to compare the pressure offloading performance of insoles that were optimized via numerical simulation techniques against shape-based devices. Twenty participants with diabetes and at-risk feet were enrolled in this study. Three pairs of personalized insoles: one based on shape data and subsequently manufactured via direct milling; and two were based on a design derived from shape, pressure, and ultrasound data which underwent a finite element analysis-based virtual optimization procedure. For the latter set of insole designs, one pair was manufactured via direct milling, and a second pair was manufactured through 3D printing. The offloading performance of the insoles was analyzed for forefoot regions identified as having elevated plantar pressures. In 88% of the regions of interest, the use of virtually optimized insoles resulted in lower peak plantar pressures compared to the shape-based devices. Overall, the virtually optimized insoles significantly reduced peak pressures by a mean of 41.3 kPa (p < 0.001, 95% CI [31.1, 51.5]) for milled and 40.5 kPa (p < 0.001, 95% CI [26.4, 54.5]) for printed devices compared to shape-based insoles. The integration of virtual optimization into the insole design process resulted in improved offloading performance compared to standard, shape-based devices. (C) 2017 Elsevier Ltd. All rights reserved.