DOI: 10.5061/dryad.vmcvdncsk
title: Worn region size of shoe outsole impacts human slips: Testing a mechanistic model

description: Shoe outsole tread wear has been shown to increase slip risk by reducing
 the tread’s ability to channel fluid away from the shoe-floor interface.
 This study establishes a connection between geometric features of the worn
 region size and slipping. A mechanistic pathway that describes the
 relationship between the worn region size and slip risk is assessed.
 Specifically, it is hypothesized that an increased worn region size leads
 to an increase in under-shoe fluid pressure, which reduces friction, and
 subsequently increases slipping. The worn region size, fluid pressure, and
 slip outcome were recorded for 57 participants, who were exposed to an
 unexpected slip condition. Shoes were collected from each participant and
 the available coefficient of friction (ACOF) was measured using a
 tribometer. A greater shoe worn region size was associated with increased
 slip occurrence. Specifically, a 1 mm increase in the
 characteristic length of the worn region (geometric mean of its width and
 length) was associated with an increase in slip risk of ~10%. Fluid
 pressure and ACOF results supported the mechanistic model: an increase in
 worn region size correlated with an increase in peak fluid pressure; peak
 fluid pressures negatively correlated with ACOF; and increased ACOF
 correlated with decreased slip risk. This finding supports the use of worn
 region size as a metric to assess the risk of slipping.
description: Detailed information on
 this study is available in [1]. Row structure: Each row represents a separate
 participant Column Structure: Column A (“Worn Region Size
 [mm^2]”) represents the product of the worn region length and width in
 mm<sup>2</sup> [1]. Column B (“ACOF”) represents the
 shoe average available coefficient of friction from the five slip testing
 trials [1]. Column C (“Slip Distance [m]”)
 represents the slip distance after exposure to the slippery contaminant.
 This variable was not reported in [1]. The methodological details for
 calculating this value is reported in [2]. Column D (“Peak Slip Speed
 [m/s]”) represents the peak slipping speed after exposure to the slippery
 contaminant as reported in [1]. Column E (“Age”) represents the
 age of the shoe as reported by the participant. The value “0” represents
 less than 6 months old, “6” represents between 6 and 12 months old, and
 “12” indicates more than 12 months. Column F (“Slip outcome based on
 slip distance”) represents a binary outcome variable to determine whether
 the participant experienced a slip. A value of 1 means a slip (Slip
 Distance [m] &gt; 0.03) and a value of 0 means no slip [2].
 Column G (“Slip outcome
 based on peak slip speed”) represents a binary outcome variable to
 determine whether the participant experienced a slip. A value of 1 means a
 slip (Peak Slip Speed [m/s]&gt; 0.2) and a value of 0 means no slip
 [1]. Column H
 (“Peak fluid pressure [kPa]”) represents the peak fluid pressure
 throughout the slip and across the fluid pressure sensors [1].
 Column I (“Slip Resistant
 Shoe”) represents a binary variable to indicate whether the shoe was
 considered to be slip-resistant (1) or no slip-resistant (0) as described
 in [1]. Column J
 (“Excluded from analyses?”) indicates whether a variable was excluded from
 some of the analyses. “Exclude from pressure” indicates that the
 participant was excluded from all analyses involving fluid pressure data
 because their foot did not slide over the fluid pressure sensors.
 "Exclude from ACOF" indicates that the participant was excluded
 from analyses involving ACOF because of missing data. "none"
 indicates that none of the data was excluded from the analyses.
 [1] Sundaram, V.H., Hemler, S.L.,
 Chanda, A., Haight, J.M., Redfern, M.S. and Beschorner, K.E., 2020. Worn
 region size of shoe outsole impacts human slips: Testing a mechanistic
 model. <i>Journal of Biomechanics</i>,
 p.109797. [2] Iraqi, A., Cham, R., Redfern, M.S.
 and Beschorner, K.E., 2018. Coefficient of friction testing parameters
 influence the prediction of human slips. <i>Applied
 ergonomics</i>, <i>70</i>,
 pp.118-126.