Quantifying gait and postural control adds valuable information that aids in understanding neurological conditions where motor symptoms predominate and cause considerable functional impairment. Disease-specific clinical scales exist; however, they are often susceptible to subjectivity, and can lack sensitivity when identifying subtle gait and postural impairments in prodromal cohorts and longitudinally to document disease progression. Numerous devices are available to objectively quantify a range of measurement outcomes pertaining to gait and postural control; however, efforts are required to standardise and harmonise approaches that are specific to the neurological condition and clinical assessment. Tools are urgently needed that address a number of unmet needs in neurological practice. Namely, these include timely and accurate diagnosis; disease stratification; risk prediction; tracking disease progression; and decision making for intervention optimisation and maximising therapeutic response (such as medication selection, disease staging, and targeted support). Using some recent examples of research across a range of relevant neurological conditions—including Parkinson’s disease, ataxia, and dementia—we will illustrate evidence that supports progress against these unmet clinical needs. We summarise the novel ‘big data’ approaches that utilise data mining and machine learning techniques to improve disease classification and risk prediction, and conclude with recommendations for future direction.

Gait is complex, described by diverse characteristics underpinned by widespread central nervous system networks including motor and cognitive functions. Despite this, neural substrates of discrete gait characteristics are poorly understood, limiting understanding of gait impairment in ageing and disease. This structured review aims to map gait characteristics, defined from a pre-specified model reflecting independent gait domains, to brain imaging parameters in older adults. Fifty-two studies of 38,029 yielded were reviewed. Studies showed inconsistent approaches when mapping gait assessment to neural substrates, limiting conclusions. Gait impairments typically associated with brain deterioration, specifically grey matter atrophy and white matter integrity loss. Gait velocity, a global measure of gait control, was most frequently associated with these imaging markers within frontal and basal ganglia regions, and its decline predicted from white matter volume and integrity measurements. Fewer studies assessed additional gait measures or functional imaging parameters. Future studies mapping regional neuroanatomical and functional correlates of gait are needed, including those which take a multi-process network perspective to better understand mobility in health and disease.

Objective: We aimed to refine the hypothesis that dementia has a unique signature of gait impairment reflective of underlying pathology by considering two dementia subtypes, Alzheimer's disease (AD) and Lewy body disease (LBD), and exploring the role of cognition in disease‐specific gait impairments.Background: Accurately differentiating AD and LBD is important for treatment and disease management. Early evidence suggests gait could be a marker of dementia due to associations between discrete gait characteristics and cognitive domains.Updated Hypothesis: We hypothesize that AD and LBD have unique signatures of gait, reflecting disease‐specific cognitive profiles and underlying pathologies. An exploratory study included individuals with mild cognitive impairment or dementia due to LBD (n = 45) and AD (n = 36) and 29 older adult controls. An instrumented walkway quantified 16 gait characteristics reflecting five independent domains of locomotion (pace, rhythm, variability, asymmetry, and postural control). The LBD group demonstrated greater impairments in asymmetry and variability compared with AD; both groups were more impaired in pace and variability domains than controls. Executive dysfunction explained 11% of variance for gait variability in LBD, whereas global cognitive impairment explained 13.5% of variance in AD; therefore, gait impairments may reflect disease‐specific cognitive profiles. With a refined hypothesis that AD‐ and LBD‐specific signatures of gait reflect discrete pathologies, future studies must examine the relationship between a validated model of gait with neural networks, using recognized biomarkers and postmortem follow‐up.Major Challenges for Hypothesis: Differential diagnosis of AD and LBD used appropriate criteria and required consensus from an expert diagnostic panel to improve diagnostic accuracy. Future work should follow the framework set out in Parkinson's disease to establish unique signatures of gait as proxy measures of disease‐specific pathology; that is, use a validated gait model to explore the progressive relationship between gait, cognition, and pathology.Linkage to Other Major Theories: These exploratory findings support the theory of interacting cognitive‐motor networks, as the gait‐cognition relationship may reflect cognitive control over motor networks. Unique signatures of gait may reflect different temporal patterns of pathological burden in neural areas related to cognitive and motor function.

Distinguishing dementia subtypes can be difficult due to similarities in clinical presentation. There is increasing interest in discrete gait characteristics as markers to aid diagnostic algorithms in dementia. This structured review explores the dif

Laboratory-based gait assessments are indicative of clinical outcomes (e.g., disease identification). Real-world gait may be more sensitive to clinical outcomes, as impairments may be exaggerated in complex environments. This study aims to investigate how different environments (e.g., lab, real world) impact gait. Different walking bout lengths in the real world will be considered proxy measures of context. Data collected in different dementia disease subtypes will be analysed as disease-specific gait impairments are reported between these groups. Thirty-two people with cognitive impairment due to Alzheimer’s disease (AD), 28 due to dementia with Lewy bodies (DLB) and 25 controls were recruited. Participants wore a tri-axial accelerometer for six 10 m walks in lab settings, and continuously for seven days in the real world. Fourteen gait characteristics across five domains were measured (i.e., pace, variability, rhythm, asymmetry, postural control). In the lab, the DLB group showed greater step length variability (p = 0.008) compared to AD. Both subtypes demonstrated significant gait impairments (p < 0.01) compared to controls. In the real world, only very short walking bouts (<10 s) demonstrated different gait impairments between subtypes. The context where walking occurs impacts signatures of gait impairment in dementia subtypes. To develop real-world gait assessment as a clinical tool, algorithms and metrics must accommodate for changes in context.