Identification of modified dynamic gait index cutoff scores for assessing fall risk in people with Parkinson disease, stroke and multiple sclerosis

Torchio, Alessandro; Corrini, Chiara; Anastasi, Denise; Parelli, Riccardo; Meotti, Matteo; Spedicato, Albino; Groppo, Elisabetta; d’Arma, Alessia; Grosso, Cristina; Montesano, Angelo; Cattaneo, Davide; Gervasoni, Elisa

doi:10.1016/j.gaitpost.2021.09.201

Cited by 12 publications

(11 citation statements)

References 31 publications

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“…The median 6MWT was 316 [IQR = 182], 332 [IQR = 194] and 372 [IQR = 152] m, and the median mDGI score was 40 [IQR = 21], 46 [IQR = 29] and 50 [IQR = 14], respectively. Following the cut-off values defined by Torchio et al 16 , 39 of the 95 participants (41%) had a low/minimal fall risk (mDGI score > 49), while 17 (18%) had a high fall risk (mDGI score 29). The number (percentage) of individuals with small/minimal fall risk was 18 (35%) for MS, 17 (59%) for PD, and 4 (27%) for the ST group.…”

Section: Resultsmentioning

confidence: 99%

“…The mDGI has been extensively validated in subjects with mobility impairments 14 , 15 and in different pathological cohorts such as stroke (ST), vestibular dysfunction, multiple sclerosis (MS), traumatic brain injuries, and Parkinson’s disease (PD) 12 , 13 . Recently, Torchio et al provided cut-off values to identify PwND with no or minimal risk of falls (mDGI score ) and PwND with high risk of falls (mDGI score ) 16 . The minimal detectable change in mDGI is 5 points in PwND 14 , 15 .…”

Section: Introductionmentioning

confidence: 99%

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Machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors

Liuzzi

Carpinella

Anastasi

et al. 2023

Sci Rep

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Poor dynamic balance and impaired gait adaptation to different contexts are hallmarks of people with neurological disorders (PwND), leading to difficulties in daily life and increased fall risk. Frequent assessment of dynamic balance and gait adaptability is therefore essential for monitoring the evolution of these impairments and/or the long-term effects of rehabilitation. The modified dynamic gait index (mDGI) is a validated clinical test specifically devoted to evaluating gait facets in clinical settings under a physiotherapist’s supervision. The need of a clinical environment, consequently, limits the number of assessments. Wearable sensors are increasingly used to measure balance and locomotion in real-world contexts and may permit an increase in monitoring frequency. This study aims to provide a preliminary test of this opportunity by using nested cross-validated machine learning regressors to predict the mDGI scores of 95 PwND via inertial signals collected from short steady-state walking bouts derived from the 6-minute walk test. Four different models were compared, one for each pathology (multiple sclerosis, Parkinson’s disease, and stroke) and one for the pooled multipathological cohort. Model explanations were computed on the best-performing solution; the model trained on the multipathological cohort yielded a median (interquartile range) absolute test error of 3.58 (5.38) points. In total, 76% of the predictions were within the mDGI’s minimal detectable change of 5 points. These results confirm that steady-state walking measurements provide information about dynamic balance and gait adaptability and can help clinicians identify important features to improve upon during rehabilitation. Future developments will include training of the method using short steady-state walking bouts in real-world settings, analysing the feasibility of this solution to intensify performance monitoring, providing prompt detection of worsening/improvements, and complementing clinical assessments.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors

Liuzzi

Carpinella

Anastasi

et al. 2023

Sci Rep

Self Cite

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“…Dynamic balance and gait were assessed with the Dynamic Gait Index, which is an eight-item scale scored on an ordinal scale with a total score of 24 points. It is a recommended scale in the subacute stroke population, as well as in the in-patient rehabilitation of PwMS with an EDSS up to 5.5 [ 28 , 29 , 30 , 31 ]. It was shown that the test can discriminate well between fallers and nonfallers in PwMS [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

Neurorehabilitation including Virtual-Reality-Based Balance Therapy: Factors Associated with Training Response

Wiskerke,

Kool,

Hilfiker

et al. 2024

Brain Sciences

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Background: Virtual reality (VR) therapy is increasingly used and has shown encouraging effects. Yet, it is unknown which patients respond best to VR-based balance therapy as part of neurorehabilitation. Methods: Data from 30 persons with stroke and 51 persons with multiple sclerosis who performed three to four weeks of VR-based balance therapy during in-patient rehabilitation were analysed. Participants were divided into responders and nonresponders based on achievement of the minimal clinically important difference in functional balance post intervention. Measures of balance, trunk function, mobility, gait, motivation, and exergame parameters were compared between groups. Results: Post intervention, all clinical measurements significantly improved (p < 0.05; effect size: 0.45–0.59). Participants that achieved the minimal clinically important difference in functional balance (n = 49; 60%) had significantly lower preintervention functional and dynamic balance (median(IQR): 39(27–46) versus 45(37–50); p = 0.02 and 11(6–15) versus 16(11–18); p = 0.03). They spent less time on higher difficulty exercises (11(8–17) versus 14.5(10–12); p = 0.03) and demonstrated increased motivation over time compared with nonresponders (1(−1–5) versus −2(−7–3); p = 0.03). Conclusion: Lower baseline balance ability, spending more time on adequately challenging exercises, and increased motivation potentially influence response to therapy. These factors can support the personalisation of VR-based balance therapy.

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“…Therefore, measuring balance is clearly of the utmost importance. Balance measurement allows identifying patients with an increased falling risk (e.g., [ 5 ]). In addition, measuring balance allows for checking whether the patient is responding well to balance treatments such as therapeutic exercise.…”

Section: Introductionmentioning

confidence: 99%

Differential Item Functioning of the Mini-BESTest Balance Measure: A Rasch Analysis Study

Caronni

Picardi

Scarano

et al. 2023

IJERPH

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The Mini-Balance Evaluation Systems Test (Mini-BESTest), a 14-item scale, has high content validity for balance assessment. This study further examines the construct validity of the Mini-BESTest with an emphasis on its measurement invariance. The Mini-BESTest was administered to 292 neurological patients in two sessions (before and after rehabilitation) and evaluated with the Rasch analysis (Many-Facet Rating Scale Model: persons, items, sessions). Categories’ order and fit to the model were assessed. Next, maps, dimensionality, and differential item functioning (DIF) were examined for construct validity evaluation. DIF was inspected for several clinically important variables, including session, diagnosis, and assistive devices. Mini-BESTest items had ordered categories and fitted the Rasch model. The item map did not flag severe construct underrepresentation. The dimensionality analysis showed that another variable extraneous to balance affected the score of a few items. However, this multidimensionality had only a modest impact on measures. Session did not cause DIF. DIF for assistive devices affected six items and caused a severe measurement artefact. The measurement artefact caused by DIF for diagnosis was negligible. The Mini-BESTest returns interval measures with robust construct validity and measurement invariance. However, caution should be used when comparing Mini-BESTest measures obtained with and without assistive devices.

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Identification of modified dynamic gait index cutoff scores for assessing fall risk in people with Parkinson disease, stroke and multiple sclerosis

Cited by 12 publications

References 31 publications

Machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors

Machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors

Neurorehabilitation including Virtual-Reality-Based Balance Therapy: Factors Associated with Training Response

Differential Item Functioning of the Mini-BESTest Balance Measure: A Rasch Analysis Study

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