Does the Presence of Cognitive Impairment Exacerbate the Risk of Falls in People with Peripheral Neuropathy? An Application of Body-Worn Inertial Sensors to Measure Gait Variability

Abstract

People with peripheral neuropathy (PN) are at risk of falling. Many people with PN have comorbid cognitive impairment, an independent risk factor of falls, which may further increase the risk of falling in people with PN. However, the negative synergic effect of those factors is yet to be reported. We investigated whether the presence of cognitive impairment exacerbates the risk of falls in people with PN by measuring gait variability during single-task walking and dual-task walking. Forty-four adults with PN were recruited. Based on the Montreal Cognitive Assessment (MoCA) scores, 19 and 25 subjects were cognitively impaired and intact, respectively. We measured coefficients of variation of gait speed, stride length, and stride time using validated body-worn sensors. During single-task walking, no between-group differences were observed (all p > 0.05). During dual-task walking, between-group differences were significant for gait variability for gait speed and stride length (51.4% and 71.1%, respectively; p = 0.014 and 0.011, respectively). MoCA scores were significantly correlated with gait variability for gait speed (r = 0.319, p = 0.035) and stride length (r = 0.367, p = 0.014) during dual-task walking. Our findings suggest that the presence of cognitive impairment exacerbates the risk of falls in people with PN.

Relationship between peripheral neuropathy and cognitive performance in the elderly population

“…[ 30 ] Gu et al indicates that patients with peripheral neuropathy and cognitive impairment carry a higher risk of falls compared to those with peripheral neuropathy but without cognitive impairment. [ 31 ] In a cross-sectional study including 101 participants, Tine et al discovers that a reduction in cognitive function adversely affects gait, despite the presence of peripheral neuropathy. [ 32 ] Per Lorraine et al, diabetes is associated with cognitive dysfunction, but the presence of peripheral microvascular disease does not add to cognitive decline.…”

Wearable Health Technology to Quantify the Functional Impact of Peripheral Neuropathy on Mobility in Parkinson’s Disease: A Systematic Review

“…The most commonly reported parameters computed from the filtered signals were spatiotemporal gait parameters: gait speed (m/s), stride and step length (m), stride and step time (sec), number of steps, double limb support time (%), and cadence (steps/min). Coefficient of variation (CV) of gait speed and stride length and time (%) was calculated in eight studies [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. Gait speed initiation, number of steps, and total distance required to reach steady-state walking were studied in four papers [ 34 , 35 , 37 , 38 ].…”

“…Spatiotemporal parameters were significantly different between PNP patients and healthy controls only in studies investigating gait under more challenging conditions. Kang et al [ 32 ] described a statistically significant difference between DPN and healthy participants in the coefficient of variation of gait speed and stride length during dual-task gait. De Bruin et al [ 41 ] found significant differences in speed, step length, and cadence when comparing DPN patients during dual-task walking on paved trajectories compared to single-task.…”

“…Both conditions have severe consequences on the peripheral nervous system and affect somatosensory function. In particular, diabetic peripheral neuropathy (DPN) affects up to half of the population with diabetes [ 32 ] and chemotherapy-induced PNP (CIPN) afflicts up to 40% of patients suffering from cancer [ 33 ]. As PNP is most probably a PD-associated symptom, we investigated the main PNP and PD motor characteristics to guide future studies using wearable technology to consider this phenotype in PD.…”

Hybrid Deep-Learning Framework Based on Gaussian Fusion of Multiple Spatiotemporal Networks for Walking Gait Phase Recognition

“…It is not surprising that there exist the developing trends of human motion analysis using IMUs, for instance, gait phase detection by statistical analysis or machine learning and exoskeleton decision-making control by postural stability metrics. For instance, Kang et al [13] used inertial information obtained from the IMU on the calf to study whether cognitive impairment increases the fall risk of patients. Gohar et al [14] used inertia information on the chest to realize ID identification of personnel.…”