Castro MA1,2, Fonseca P3, Paiotti F4, Pocinho M5, Carvalho D6,7, Vinha E7, Vilas-Boas JP3,8
1Coimbra Health School -IPC, Physiotherapy, Coimbra, Portugal, 2University of Coimbra, Centre for Mechanical Engineering, Materials and Processes (CEMMPRE), Coimbra, Portugal, 3University of Porto, LABIOMEP – Porto Biomechanics Laboratory, Porto, Portugal, 4Universidade de São Paulo, Escola de Educação Física e Esporte, São Paulo, Brazil, 5Health College of Coimbra -IPC, Coimbra, Portugal, 6University of Porto, Faculty of Medicine, Porto, Portugal, 7Centro Hospitalar de São João, Department of Endocrinology, Diabetes and Metabolism, Porto, Portugal, 8University of Porto, Faculty of Sport, Porto, Portugal
Background: Diabetes Mellitus is an epidemic metabolic and chronic disease that is responsible for great mortality and morbidity worldwide. Half the patients who have had diabetes for more than 20 years develop peripheral neuropathy, which is expected to lead to biomechanical disorders, particularly at the lower limbs. Gait assessment may be useful to detect diabetes impact on walking and lower limbs performance in an early stage.
Purpose: This study aims to characterize the gait of people with diabetes classified in categories 0 and 2 of the IWGDF (International Working Group on Diabetic Foot) Risk Classification System.
Methods: A cross-sectional design was used. One hundred and twenty full-body walking trials of ten volunteer patients (7 males and 3 females) with diabetes was analyzed. Patients were selected from the diabetes outpatient clinic based on the 2015 IWGDF Risk Classification System and distributed in two groups: no peripheral neuropathy and peripheral neuropathy with peripheral arterial disease and/or a foot deformity. Exclusion criteria, based on medical history, were the presence of any disturbance that might affect gait like an orthopedic, neurological or visual impairment or other, including current injury, pain, active ulceration or previous amputation.
A 11-camera Qualisys 3D motion capture system (Qualisys AB, Sweden), operating at a 200 Hz sampling frequency, was used to track the displacement of 77 retroreflective markers that comprised a full-body marker setup based in the IOR model. Ground reaction forces were measure by four Bertec (Bertec Corporation, USA) force platforms (2 platforms of 40x60 cm and 2 platforms of 60x90 cm) recording at a 2000 Hz sampling rate. The arrangement of force platforms allowed for the measurement of three consecutive steps. A starting line was established so that the participant had to perform 4 gait cycles before reaching the force platforms in order to stabilize gait velocity. No other constraints were placed over the participants, which were instructed to walk normally at their preferred speed. If more than one foot was contacting the force platform or if a clear targeting behaviour was perceived by the researchers, the trial was discarded and a new one was performed without notifying the participant.
Results: The group without peripheral neuropathy shows a faster gait with higher cadence, greater stride and step length and less double stance time and stride width. The group with peripheral neuropathy shows a trend to produce lower amplitude Ground Reaction force, and later in time, which could contribute for the differences observed in speed and cadence.
Gait spatiotemporal parameters, such as the stride width and length, the duration of the gait cycle and the double limb support phase, as well as the gait speed and the statures per second, can predict 58% the peripheral neuropathy.
Conclusion(s): This study highlights the biomechanics differences in gait of people with diabetes classified in different risk groups, and the importance of spatiotemporal gait parameters as predictors of the risk of peripheral neuropathy.
Implications: Gait spatiotemporal parameters easily assessed in clinical practice are valuable for an early identification of diabetic complication.
Keywords: Walking, biomechanics, diabetes
Funding acknowledgements: N/A
Purpose: This study aims to characterize the gait of people with diabetes classified in categories 0 and 2 of the IWGDF (International Working Group on Diabetic Foot) Risk Classification System.
Methods: A cross-sectional design was used. One hundred and twenty full-body walking trials of ten volunteer patients (7 males and 3 females) with diabetes was analyzed. Patients were selected from the diabetes outpatient clinic based on the 2015 IWGDF Risk Classification System and distributed in two groups: no peripheral neuropathy and peripheral neuropathy with peripheral arterial disease and/or a foot deformity. Exclusion criteria, based on medical history, were the presence of any disturbance that might affect gait like an orthopedic, neurological or visual impairment or other, including current injury, pain, active ulceration or previous amputation.
A 11-camera Qualisys 3D motion capture system (Qualisys AB, Sweden), operating at a 200 Hz sampling frequency, was used to track the displacement of 77 retroreflective markers that comprised a full-body marker setup based in the IOR model. Ground reaction forces were measure by four Bertec (Bertec Corporation, USA) force platforms (2 platforms of 40x60 cm and 2 platforms of 60x90 cm) recording at a 2000 Hz sampling rate. The arrangement of force platforms allowed for the measurement of three consecutive steps. A starting line was established so that the participant had to perform 4 gait cycles before reaching the force platforms in order to stabilize gait velocity. No other constraints were placed over the participants, which were instructed to walk normally at their preferred speed. If more than one foot was contacting the force platform or if a clear targeting behaviour was perceived by the researchers, the trial was discarded and a new one was performed without notifying the participant.
Results: The group without peripheral neuropathy shows a faster gait with higher cadence, greater stride and step length and less double stance time and stride width. The group with peripheral neuropathy shows a trend to produce lower amplitude Ground Reaction force, and later in time, which could contribute for the differences observed in speed and cadence.
Gait spatiotemporal parameters, such as the stride width and length, the duration of the gait cycle and the double limb support phase, as well as the gait speed and the statures per second, can predict 58% the peripheral neuropathy.
Conclusion(s): This study highlights the biomechanics differences in gait of people with diabetes classified in different risk groups, and the importance of spatiotemporal gait parameters as predictors of the risk of peripheral neuropathy.
Implications: Gait spatiotemporal parameters easily assessed in clinical practice are valuable for an early identification of diabetic complication.
Keywords: Walking, biomechanics, diabetes
Funding acknowledgements: N/A
Topic: Human movement analysis
Ethics approval required: Yes
Institution: São João Hospital and Porto Faculty of Medicine
Ethics committee: São João Hospital and Porto Faculty of Medicine
Ethics number: CES 213-16
All authors, affiliations and abstracts have been published as submitted.
