Comparison of lower limb muscle strength between diabetic neuropathic and healthy subjects using OpenSim

Scarton, Alessandra; Jonkers, Ilse; Guiotto, Annamaria; Spolaor, Fabiola; Guarneri, G.; Avogaro, Angelo; Cobelli, Claudio; Sawacha, Z.

doi:10.1016/j.gaitpost.2017.07.117

Cited by 18 publications

(10 citation statements)

References 28 publications

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“…Our study results was in accordance with this association between DPN and muscle function in a relatively younger population. In another small observational study by Scarton et al, 10 ) compared with non-diabetic controls, patients with DPN had incrased ankle rigidity. However, this study did not include data on the general function of the lower extremities such as walking speed.…”

Section: Discussionmentioning

confidence: 96%

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Diabetic Peripheral Neuropathy as a Risk Factor for Sarcopenia

Song

Moon

et al. 2019

Ann Geriatr Med Res

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Background: Diabetic peripheral neuropathy (DPN) is prevalent in patients with type 2 diabetes, and its prevalence increases with age. A previous study demonstrated the association between DPN and muscle dysfunction; however, there are limited data on the association between DPN and sarcopenia. Methods: We enrolled patients with type 2 diabetes and measured hand grip strength (HGS), lean body mass using a bio-impedance analysis, and gait speed using a 4-m walking test. Sarcopenia was diagnosed according to the criteria from the Asian Working Group for Sarcopenia. We also performed various examinations of neuropathy, including both smalland large-fiber neuropathy. Results: Among 170 participants (mean age, 61.5±6.6 years), 24 (14.1%) were diagnosed with sarcopenia. The Michigan Neuropathy Screening Instrument Questionnaire (MNSI-Q) scores were higher in patients with sarcopenia than in those without sarcopenia (2.7±1.3 vs. 2.4±1.3; p=0.008). However, other neuropathy examination results were not significantly associated with sarcopenia. The MNSI-Q score was negatively associated with HGS, with an odds ratio (OR) of 1.367 (95% confidence interval [CI], 1.122-1.667) in predicting the presence of sarcopenia. After adjusting for sex, body mass index, and diabetes duration, the MN-SI-Q score was associated with the presence of sarcopenia (adjusted OR=1.310; 95% CI, 1.041-1.647). Conclusion: In this population with type 2 diabetes, patients with sarcopenia had higher neuropathy questionnaire scores than those without sarcopenia. Therefore, active screening for sarcopenia should be performed in subjects with DPN.

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Section: Discussionmentioning

confidence: 96%

“… 9 ) Another observational study confirmed this finding in 10 subjects with diabetes with a mean age of 57.2 years. 10 ) However, there are limited data on the association between sarcopenia and DPN in large populations; therefore, larger-scale studies are needed to evaluate the association between DPN and sarcopenia.…”

Section: Introductionmentioning

confidence: 99%

Diabetic Peripheral Neuropathy as a Risk Factor for Sarcopenia

Song

Moon

et al. 2019

Ann Geriatr Med Res

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“…Previous studies reported that patients with DPN showed higher overall dynamic plantar pressure especially below the forefoot compared to patients without DPN [7,8]. This high planter pressure could be due to foot pain [9,10], limited joint mobility [11] or muscle imbalance [12]. High plantar pressures produced during walking may cause an increased risk of falls [10,13].…”

Section: Methodsmentioning

confidence: 98%

Improving Planter Pressure Distribution Could Reduce the Risk of Fall in Patients with Diabetic Neuropathy

Khallaf¹,

Fayed²,

Hegazy³

2020

J Neurol Exp Neurosci

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Objective: This study aimed at assessing the effectiveness of a task specific training guided by visual biofeedback in improving plantar pressure distribution to decrease risk of fall in individuals with Diabetic Peripheral Neuropathy (DPN). Methods: Thirty-four patients with DPN were included in the study and were randomly assigned into two equal groups. The study group received task specific training and 3-Dimensional terminal biofeedback. The control group received traditional strength training. Three sessions of assessments (pre, post and follow-up after a month) were done for all participants. Training was three times per week for 10 weeks. Berg Balance Scale (BBS) and Functional Reach Test (FRT) were used to assess the dynamic and anticipatory balance in addition to the risk of fall. E-med pedography was used to quantify the Planter Pressure (PP) beneath the hind and forefoot during walking. Results: Significant differences were noted in the BBS and FRT indicating a reduced risk of fall in the study groups. PP has been significantly changed in the study group and between the groups. Post-hoc test showed non-significant differences between the second and third measurements of the BBS, FRT and PP within the study group indicating along lasting effect of the applied treatments (p < 0.05). Conclusions: Task specific training and biofeedback may reduce the risk of fall by improving planter pressure distribution in individuals with DPN.

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“…In order to verify the reliability of the WTNr model across the pathological population, the PD group envelopes peak percentage position within the gait cycle of the experimentally measured muscles and the model-derived ones (Figure 2A) were compared, as in Scarton et al (2017) and in Hicks et al (2015), as well as the PD ID-generated joint moments in OpenSim versus the CEINMS-estimated torques Comparison between simulated muscles. EMG-informed activations (in blue) derived by the associated EMG (see Table 1), and OpenSim derived ones for the same muscles (in green).…”

Section: Wtnr Model Reliability In Pdmentioning

confidence: 99%

“…EMGinformed modeling (Sartori et al 2016(Sartori et al , 2017, could provide a deeper understanding of the biomechanical mechanism underlying disease progression. This methodology has been successfully applied across a range of neuromuscular pathologies (Scarton et al 2017) and healthy individuals (Varotto et al 2017). In this context a subject specific neuromusculoskeletal model (NMSM) is created for each individual, driven by their own neural-excitation patterns provided by the EMG signals.…”

Section: Introductionmentioning

confidence: 99%

Electromyography-informed modeling for estimating muscle activation and force alterations in Parkinson’s disease

Romanato

Volpe²,

Guiotto

et al. 2021

Computer Methods in Biomechanics and Biomedical Engineering

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Electromyography (EMG)-driven neuromusculoskeletal modeling (NMSM) enables simulating the mechanical function of multiple muscle-tendon units as controlled by nervous system in the generation of complex movements. In the context of clinical assessment this may enable understanding biomechanical factor contributing to gait disorders such as one induced by Parkinson's disease (PD). In spite of the challenges in the development of patient-specific models, this preliminary study aimed at establishing a feasible and noninvasive experimental and modeling pipeline to be adopted in clinics to detect PD-induced gait alterations. Four different NMSM have been implemented for three healthy controls using CEINMS, an OpenSim-compatible toolbox. Models differed in the EMG-normalization methods used for calibration purposes (i.e. walking trial normalization and maximum voluntary contraction normalization) and in the set of experimental EMGs used for the musculotendon-unit mapping (i.e. 4 channels vs. 15 channels). Model accuracy assessment showed no statistically significant differences between the more complete model (non-clinically viable) and the proposed reduced one (clinically viable). The clinically viable reduced model was systematically applied on a dataset including ten PD's and thirteen healthy controls. Results showed significant differences in the neuromuscular control strategy of the PD group in term of muscle forces and joint torques. Indeed, PD patients displayed a significantly lower magnitude on force production and revealed a higher amount of force variability with the respect of the healthy controls. The estimated variables could become a measurable biomechanical outcome to assess and track both disease progression and its impact on gait in PD subjects.

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Comparison of lower limb muscle strength between diabetic neuropathic and healthy subjects using OpenSim

Cited by 18 publications

References 28 publications

Diabetic Peripheral Neuropathy as a Risk Factor for Sarcopenia

Diabetic Peripheral Neuropathy as a Risk Factor for Sarcopenia

Improving Planter Pressure Distribution Could Reduce the Risk of Fall in Patients with Diabetic Neuropathy

Electromyography-informed modeling for estimating muscle activation and force alterations in Parkinson’s disease

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