Whole-Day Gait Monitoring in Patients with Alzheimer’s Disease: A Relationship between Attention and Gait Cycle

Abstract

Background: Gait impairment in patients with Alzheimer’s disease (AD) and its relationship with cognitive function has been described, but reports of gait analysis in AD in daily living are limited.Objective: To investigate whether gait pattern of patients with AD in daily living is associated with cognitive function.Methods: Gait was recorded in 24 patients with AD and 9 healthy controls (HC) for 24 hours by using a portable gait rhythmogram. Mean gait cycle and gait acceleration were compared between the AD and HC groups. For the AD group, these gait metrics were assessed for correlations with cognitive function, as determined by the Mini Mental State Examination and Wechsler Memory Scale-Revised (WMS-R).Results: Although both gait parameters were not different between the patients with AD and HC, gait cycle in patients with AD was positively correlated with attention/concentration scores on the WMS-R (r = 0.578), and not with memory function. Patients with AD with attention scores as high as HC displayed a longer gait cycle than both HC (p = 0.048) and patients with AD with lower attention scores (p = 0.011). The patients with AD with lower attention scores showed a similar gait cycle with HC (p = 0.994).Conclusion: Patients with AD with impaired attentional function walk with faster gait cycle comparable to HC in daily living walking, which was unexpected based on previous gait analysis in clinical settings. This result probably reflects diminished consciousness to either the environment or instability of gait in the patients with AD with impaired attention.

Human Gait Analysis in Neurodegenerative Diseases: A Review

“…AD patients show hyperkinesia, apraxia, and abnormalities in walking and trunk movements. Gait disturbances reported in early AD include slower gait with shorter stride length, lower cadence (longer stride time/gait cycle) and greater stride-to-stride variability [24]. PD is the second most common neurodegenerative disease after AD.…”

“…Patients, in fact, could perform well because of the awareness of being observed. This has led to the necessity of developing systems for long-term gait monitoring, in particular in free-living or home environments in order to reduce contextual factors and obtain more objective results with respect to short-distance gait analysis [24], [72].…”

Inertial wearables as pragmatic tools in dementia

“…Additionally, 7-day wearable data has contributed to the identification of individuals at risk of falls compared to lab-based assessment [25] or even predicted falls in 6 months [62]. Yet, compared to other cohorts, most free-living gait studies conducted in people with dementia have been small (n=24, 20 and 10) [63][64][65], focusing on the feasibility of remote monitoring. Twenty-four hour habitual use of a single wearable (waist mounted device) unexpectedly showed a faster gait cycle for those with Alzheimer's disease compared to healthy controls [63].…”

“…Yet, compared to other cohorts, most free-living gait studies conducted in people with dementia have been small (n=24, 20 and 10) [63][64][65], focusing on the feasibility of remote monitoring. Twenty-four hour habitual use of a single wearable (waist mounted device) unexpectedly showed a faster gait cycle for those with Alzheimer's disease compared to healthy controls [63]. The authors hypothesised that these differences possibly reflect reduced Alzheimer's disease consciousness to either the environment or instability of gait.…”

A Proposal for New Algorithm that Defines Gait-Induced Acceleration and Gait Cycle in Daily Parkinsonian Gait Disorders

“…This system consists of two components: the first component is a wearable device with an acceleration sensor, and the second one is an automatic gait detection algorithm. Gait-induced accelerations are deduced from limb and trunk movements using a mathematical algorithm known as the "pattern-matching method" [41][42][43][44][45][46][47][48][49][50][51][52][53][54]. Thus, the PGR can quantitatively identify various movement-induced accelerations and gait-induced accelerations, respectively.…”