Do kinematic gait parameters help to discriminate between fallers and non-fallers with Parkinson’s disease?

Delval, Arnaud; Betrouni, Nacim; Tard, Céline; Devos, David; Dujardin, Kathy; Defebvre, Luc; Labidi, Jordan; Moreau, Caroline

doi:10.1016/j.clinph.2020.11.027

Cited by 12 publications

(8 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The combination of clinical and gait outcomes better classified fallers and non-fallers among people with PD than clinical-only or gait-only combinations. Current findings are consistent with recent studies which demonstrated that the incorporation of objective measures of gait (quantified with wearable inertial sensors [6] or a motion capture system [14]) to standard clinical variables enhanced the classification of fallers and non-fallers in PD. Of note, the study by Vitorio and colleagues [6] assessed participants in the OFF levodopa state and had different gait measures in the top performing models: gait double support and turn duration variability.…”

Section: Discussionsupporting

confidence: 90%

“…Therefore, fall risk assessment in PD may be enhanced by the inclusion of objective measures of gait. Recent studies demonstrate that the addition of objective measures of gait to clinical variables improved the classification of fallers and non-fallers in PD [ 6 , 14 ]. However, these studies have major limitations: the one by Delval et al used an expensive motion capture system; Vitorio et al, despite applying low-cost wearable inertial sensors, assessed people with PD while OFF their medication (which limits ecological validity of findings).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Combination of Clinical and Gait Measures to Classify Fallers and Non-Fallers in Parkinson’s Disease

Araújo

Smaili

Morris

et al. 2023

Sensors

View full text Add to dashboard Cite

Although the multifactorial nature of falls in Parkinson’s disease (PD) is well described, optimal assessment for the identification of fallers remains unclear. Thus, we aimed to identify clinical and objective gait measures that best discriminate fallers from non-fallers in PD, with suggestions of optimal cutoff scores. METHODS: Individuals with mild-to-moderate PD were classified as fallers (n = 31) or non-fallers (n = 96) based on the previous 12 months’ falls. Clinical measures (demographic, motor, cognitive and patient-reported outcomes) were assessed with standard scales/tests, and gait parameters were derived from wearable inertial sensors (Mobility Lab v2); participants walked overground, at a self-selected speed, for 2 min under single and dual-task walking conditions (maximum forward digit span). Receiver operating characteristic curve analysis identified measures (separately and in combination) that best discriminate fallers from non-fallers; we calculated the area under the curve (AUC) and identified optimal cutoff scores (i.e., point closest-to-(0,1) corner). RESULTS: Single gait and clinical measures that best classified fallers were foot strike angle (AUC = 0.728; cutoff = 14.07°) and the Falls Efficacy Scale International (FES-I; AUC = 0.716, cutoff = 25.5), respectively. Combinations of clinical + gait measures had higher AUCs than combinations of clinical-only or gait-only measures. The best performing combination included the FES-I score, New Freezing of Gait Questionnaire score, foot strike angle and trunk transverse range of motion (AUC = 0.85). CONCLUSION: Multiple clinical and gait aspects must be considered for the classification of fallers and non-fallers in PD.

show abstract

Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Combination of Clinical and Gait Measures to Classify Fallers and Non-Fallers in Parkinson’s Disease

Araújo

Smaili

Morris

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…This has already been taken up for some deficits. For example, it is feasible to distinguish between static [48] and dynamic balance [19], and it is widely accepted that stride time variability is much more closely associated with dynamic imbalance than other gait parameters [49,50]. For bradykinesia, a new framework has recently been proposed that distinguishes between bradykinesia, hypokinesia, sequence effect, hesitations/halts, akinesia, and oligokinesia in the context of reduced movement [51], making the feature much more amenable to detection with new technology.…”

Section: Proposed Next Steps In Detection Quantification and Follow-u...mentioning

confidence: 99%

Identifying Subtle Motor Deficits Before Parkinson’s Disease is Diagnosed: What to Look for?

Maetzler,

Mirelman,

Pilotto

et al. 2024

JPD

View full text Add to dashboard Cite

Motor deficits typical of Parkinson’s disease (PD), such as gait and balance disturbances, tremor, reduced arm swing and finger movement, and voice and breathing changes, are believed to manifest several years prior to clinical diagnosis. Here we describe the evidence for the presence and progression of motor deficits in this pre-diagnostic phase in order to provide suggestions for the design of future observational studies for an effective, quantitatively oriented investigation. On the one hand, these future studies must detect these motor deficits in as large (potentially, population-based) cohorts as possible with high sensitivity and specificity. On the other hand, they must describe the progression of these motor deficits in the pre-diagnostic phase as accurately as possible, to support the testing of the effect of pharmacological and non-pharmacological interventions. Digital technologies and artificial intelligence can substantially accelerate this process.

show abstract

“…This method could be used isolated or combined with clinical tests to identify the progression of postural instability in PwPD. Recent studies have shown that models combining clinical and inertial sensor outcomes showed higher discriminative ability in classifying fallers and non-fallers among PwPD than clinical-only or mobility-only models ( 31 , 32 ).…”

Section: Introductionmentioning

confidence: 99%

A non-expensive bidimensional kinematic balance assessment can detect early postural instability in people with Parkinson’s disease

Santos,

d'Alencar,

Helene

et al. 2023

Front. Neurol.

View full text Add to dashboard Cite

BackgroundPostural instability is a debilitating cardinal symptom of Parkinson’s disease (PD). Its onset marks a pivotal milestone in PD when balance impairment results in disability in many activities of daily living. Early detection of postural instability by non-expensive tools that can be widely used in clinical practice is a key factor in the prevention of falls in widespread population and their negative consequences.ObjectiveThis study aimed to investigate the effectiveness of a two-dimensional balance assessment to identify the decline in postural control associated with PD progression.MethodsThis study recruited 55 people with PD, of which 37 were men. Eleven participants were in stage I, twenty-three in stage II, and twenty-one in stage III. According to the Hoehn and Yahr (H&Y) rating scale, three clinical balance tests (Timed Up and Go test, Balance Evaluation Systems Test, and Push and Release test) were carried out in addition to a static stance test recorded by a two-dimensional movement analysis software. Based on kinematic variables generated by the software, a Postural Instability Index (PII) was created, allowing a comparison between its results and those obtained by clinical tests.ResultsThere were differences between sociodemographic variables directly related to PD evolution. Although all tests were correlated with H&Y stages, only the PII was able to differentiate the first three stages of disease evolution (H&Y I and II: p = 0.03; H&Y I and III: p = 0.00001; H&Y II and III: p = 0.02). Other clinical tests were able to differentiate only people in the moderate PD stage (H&Y III).ConclusionBased on the PII index, it was possible to differentiate the postural control decline among the first three stages of PD evolution. This study offers a promising possibility of a low-cost, early identification of subtle changes in postural control in people with PD in clinical practice.

show abstract

Do kinematic gait parameters help to discriminate between fallers and non-fallers with Parkinson’s disease?

Cited by 12 publications

References 34 publications

Combination of Clinical and Gait Measures to Classify Fallers and Non-Fallers in Parkinson’s Disease

Combination of Clinical and Gait Measures to Classify Fallers and Non-Fallers in Parkinson’s Disease

Identifying Subtle Motor Deficits Before Parkinson’s Disease is Diagnosed: What to Look for?

A non-expensive bidimensional kinematic balance assessment can detect early postural instability in people with Parkinson’s disease

Contact Info

Product

Resources

About