Performance-based outcome measures are associated with cadence variability during community ambulation among individuals with a transtibial amputation

Abstract

Background: In the United States, Medicare Functional Classification Level (K-level) guidelines require demonstration of cadence variability to justify higher-level prosthetic componentry prescription; however, clinical assessment of cadence variability is subjective. Currently, no clinical outcome measures are associated with cadence variability during community ambulation. Objectives: Evaluate whether physical performance, i.e. 10-meter Walk Test (10mWT)-based walking speeds, L-Test, and Figure-of-8 Walk Test scores, is associated with community-based cadence variability among individuals with a transtibial amputation. Study design: Cross-sectional. Methods: Forty-nine participants, aged 18–85 years, with a unilateral transtibial amputation were included. Linear regression models were conducted to determine whether physical performance was associated with cadence variability (a unitless calculation from FitBit® OneTM minute-by-minute step counts), while controlling for sex, age, and time since amputation ( p ⩽ .013). Results: Beyond covariates, self-selected gait speed explained the greatest amount of variance in cadence variability (19.2%, p < .001). Other outcome measures explained smaller, but significant, amounts of the variance (11.1–17.1%, p = .001–.008). For each 0.1 m/s-increase in self-selected and fast gait speeds, or each 1-s decrease in L-Test and F8WT time, community-based cadence variability increased by 1.76, 1.07, 0.39, and 0.79, respectively ( p < .013). Conclusions: In clinical settings, faster self-selected gait speed best predicted increased cadence variability during community ambulation. Clinical relevance The 10-meter Walk Test may be prioritized during prosthetic evaluations to provide objective self-selected walking speed data, which informs the assessment of cadence variability potential outside of clinical settings.

Reported Outcome Measures in Studies of Real-World Ambulation in People with a Lower Limb Amputation: A Scoping Review

“…One study investigated the effect of various prosthetic accommodation durations [ 35 ]. Additionally, 28 studies investigated the correlation between real-world measurements and other variables, such as performance-based measures [ 28 , 33 , 34 , 37 , 38 , 47 , 50 , 53 , 56 , 61 ], self-reported outcomes [ 4 , 26 , 33 , 35 , 42 , 49 , 61 , 67 ], K-level [ 45 , 48 , 58 ], measures of gait quality [ 64 ], fall and injury incidence [ 30 ], aerobic capacity [ 28 ], demographic factors [ 26 , 61 ], limb fluid volume [ 43 ], clinical scores [ 62 ], comorbidities [ 47 ], stride-to-stride fluctuations [ 53 ], step variability [ 56 ], and prosthetists’ perceptions of participants’ prosthetic use [ 41 ]. Eight studies performed comparisons between groups, based on the etiology of amputation [ 26 ], level of amputation [ 51 ], K-level [ 46 , 52 , 57 ], surgery treatments [ 55 , 66 ], and chronic physical condition [ 60 ].…”

“…Some studies additionally included the time or frequency spent sedentary, i.e., inactivity [ 21 , 28 , 45 , 47 , 81 , 101 ]. Four articles demonstrated cadence distribution, by visualizing cadence per walking bouts categorized according to duration and number of bouts [ 86 ], or by quantifying the cadence variability [ 34 , 69 , 108 ]. Five articles reported the most intensive walking activity, by reporting the maximum or peak values of cadence averaged for a certain time-frame, such as the average cadence of the most intensive 60 min, 30 min, or 1 min [ 28 , 51 , 61 , 108 , 114 ].…”

“…Parameters related to activity intensity and fitness other than cadence, were walking speed [ 70 , 105 ], heart rate [ 63 , 67 ], or the acceleration of body movements in m/s 2 or g (=9.81 m/s 2 ) units [ 62 , 63 , 67 , 119 ]. Two articles reported the cadence variability scale parameter, which was a calculation of the distribution spread of cadence variability over the duration of the observation period [ 34 , 108 ].…”

“…This is not surprising, as wearable technologies enable monitoring a person’s natural behavior and allow for observation over time, or assessment to the effect of an intervention. Although there exists a large battery of performance-based tests that can detect changes in physical functions and capacity [ 121 ], real-world measurements have revealed that capacity is not necessarily the same as performance [ 34 , 37 ]. Studies have shown that half of older community dwelling adults classified by clinic-based tests as high functional capacity, exhibit low functional level behavior in the community [ 48 ].…”

“…ActiGraph (ActiGraph, Pensacola, USA) [29,31,32,39,41,43,45,47,53,70,75,[80][81][82][83]92,105,109], FitBit (FitBit, San Francisco, USA) [34,40,52,76,108,117], and activPAL (PAL Technologies, Glasgow, Scotland) [26,36,37,68,91,103,104].…”