A new classification of diabetic gait pattern based on cluster analysis of biomechanical data

Machine learning for lumbar and pelvis kinematics clustering

Clustering algorithms such as k-means and agglomerative hierarchical clustering (HCA) may provide a unique opportunity to analyze time-series kinematic data. Here we present an approach for determining number of clusters and which clustering algorithm to use on time-series lumbar and pelvis kinematic data. Cluster evaluation measures such as silhouette coefficient, elbow method, Dunn Index, and gap statistic were used to evaluate the quality of decision making. The result show that multiple clustering evaluation methods should be used to determine the ideal number of clusters and algorithm suitable for clustering time-series data for each dataset being analyzed.

Clinical gait analysis reveals altered walking patterns in critical Covid 19 survivors

BACKGROUND

Covid-19 has dramatically increased the number of admissions in intensive care units due to respiratory complications. In some cases, the arousal of neurological impairments, such as peripheral neuropathies, have been revealed. The purpose of this research was to characterize the gait pattern and muscle activity changes in Covid-19 survivors compared to physiological gait.

METHODS

Twelve post-Covid-19 participants admitted to intensive care units and twelve non-disabled controls were considered. Kinematics, kinetics and surface electromyographic data were collected for each participant during walking. Post Covid-19 participants were further divided into two sub-groups, according to the number of days spent in the intensive care units. Lower limb joint angles, moments and powers were extracted as well as the muscle activity of four muscles bilaterally, the spatial, temporal and spatiotemporal parameters of gait and the ground reaction forces. The extracted variables were compared through OneWay-ANOVA or Kruskal-Wallis tests where appropriate (p < 0.05).

FINDINGS

Overall, the considered parameters revealed statistically significant reduction in gait speed, cadence, range of motion in the sagittal plane, anteroposterior and vertical ground reaction forces between pathological and control participants. Larger alterations of the gait patterns were highlighted in the post-Covid-19 group hospitalized in intensive care units longer than 35 days, where a reduced muscle activity was observed on all the analyzed muscles.

INTERPRETATION

Results suggested that the severity of gait impairments in post-Covid-19 participants might be correlated with intensive care units-bedding period. Gait biomechanics assessment could be adopted in the clinical decision-making process to improve treatment protocols in post-Covid-19 survivors.

Identification of movement phenotypes from occupational gesture kinematics: Advancing individual ergonomic exposure classification and personalized training

The identification of personalized preventive strategies plays a major role in contrasting the occurrence of work-related musculoskeletal disorders. This requires the identification of distinct movement patterns within large samples and the attribution of a proper risk level to each identified movement phenotype. We assessed the feasibility of this approach by exploiting wearable inertial measurement units to estimate the whole-body kinematics of 43 healthy participants performing 18 reach-to-manipulate movements, which differed based on the object's position in the space and the type of manipulation required. Through unsupervised clustering, we identified multiple movement phenotypes graded by ergonomic performance. Furthermore, we determined which joints mostly contributed to instantiating the ergonomic differences across clusters, emphasizing the importance of monitoring this aspect during occupational gestures. Overall, our analysis suggests that movement phenotypes can be identified within occupational motor repertoires. Assigning individual performance to specific phenotypes has the potential to inform the development of more effective and tailored interventions.

EMG analysis across different tasks improves prevention screenings in diabetes: a cluster analysis approach

The aim of this work was twofold: on one side to determine the most suitable parameters of surface electromyography (sEMG) to classify diabetic subjects with and without neuropathy and discriminate them from healthy controls and second to assess the role of the task acquired in the classification process. For this purpose 30 subjects were examined (10 controls, 10 diabetics with and 10 without neuropathy) whilst walking and stair ascending and descending. The electrical activity of six muscles was recorded bilaterally through a 16-channel sEMG system synchronised with a stereophotogrammetric system: Rectus Femoris, Gluteus Medius, Tibialis Anterior, Peroneus Longus, Gastrocnemius Lateralis and Extensor Digitorum. Spatiotemporal parameters of gait and stair climbing and the following sEMG parameters were extracted: signal envelope, activity duration, timing of activation and deactivation. A hierarchical clustering algorithm was applied to the whole set of parameters with different distances and linkage methods. Results showed that only by applying the Ward agglomerative hierarchical clustering (Hamming distance) to the all set of parameters extracted from both tasks, 5 well-separated clusters were obtained: cluster 3 included only DS subjects, cluster 2 and 4 only controls and cluster 1 and 5 only DNS subjects. This method could be used for planning rehabilitation treatments.

Graphical abstract

Machine Learning-Based Diabetic Neuropathy and Previous Foot Ulceration Patients Detection Using Electromyography and Ground Reaction Forces during Gait

Diabetic neuropathy (DN) is one of the prevalent forms of neuropathy that involves alterations in biomechanical changes in the human gait. Diabetic foot ulceration (DFU) is one of the pervasive types of complications that arise due to DN. In the literature, for the last 50 years, researchers have been trying to observe the biomechanical changes due to DN and DFU by studying muscle electromyography (EMG) and ground reaction forces (GRF). However, the literature is contradictory. In such a scenario, we propose using Machine learning techniques to identify DN and DFU patients by using EMG and GRF data. We collected a dataset from the literature which involves three patient groups: Control (n = 6), DN (n = 6), and previous history of DFU (n = 9) and collected three lower limb muscles EMG (tibialis anterior (TA), vastus lateralis (VL), gastrocnemius lateralis (GL)), and three GRF components (GRFx, GRFy, and GRFz). Raw EMG and GRF signals were preprocessed, and different feature extraction techniques were applied to extract the best features from the signals. The extracted feature list was ranked using four different feature ranking techniques, and highly correlated features were removed. In this study, we considered different combinations of muscles and GRF components to find the best performing feature list for the identification of DN and DFU. We trained eight different conventional ML models: Discriminant analysis classifier (DAC), Ensemble classification model (ECM), Kernel classification model (KCM), k-nearest neighbor model (KNN), Linear classification model (LCM), Naive Bayes classifier (NBC), Support vector machine classifier (SVM), and Binary decision classification tree (BDC), to find the best-performing algorithm and optimized that model. We trained the optimized the ML algorithm for different combinations of muscles and GRF component features, and the performance matrix was evaluated. Our study found the KNN algorithm performed well in identifying DN and DFU, and we optimized it before training. We found the best accuracy of 96.18% for EMG analysis using the top 22 features from the chi-square feature ranking technique for features from GL and VL muscles combined. In the GRF analysis, the model showed 98.68% accuracy using the top 7 features from the Feature selection using neighborhood component analysis for the feature combinations from the GRFx-GRFz signal. In conclusion, our study has shown a potential solution for ML application in DN and DFU patient identification using EMG and GRF parameters. With careful signal preprocessing with strategic feature extraction from the biomechanical parameters, optimization of the ML model can provide a potential solution in the diagnosis and stratification of DN and DFU patients from the EMG and GRF signals.

Gait Parameters and Peripheral Neuropathy in Patients With Diabetes: A Meta-Analysis

OBJECTIVE

To investigate the relationship between diabetic peripheral neuropathy (DPN) and gait abnormality in diabetic patients.

METHODS

Related research concerning the gait of diabetic patients with and without DPN was collected and analyzed by searching PubMed, Embase, and Web of Science. Statistical analysis was performed by using RevMan 5.3 software.

RESULTS

Twenty-one studies were included in this meta-analysis, consisting of 499 diabetic neuropathy patients and 467 diabetes controls without neuropathy. Meta-analysis results showed lower gait velocity, shorter stride length, longer stride time, longer stance time, and higher maximum knee extension moment in the DPN group, compared with their counterparts.

CONCLUSION

Among diabetic patients, those complicated with DPN possess lower gait velocity, shorter stride length, longer stride time, longer stance time, and higher maximum knee extension moment.

Gut Microbiota and Complications of Type-2 Diabetes

The gut microbiota has been linked to the emergence of obesity, metabolic syndrome and the onset of type 2 diabetes through decreased glucose tolerance and insulin resistance. Uncontrolled diabetes can lead to serious health consequences such as impaired kidney function, blindness, stroke, myocardial infarction and lower limb amputation. Despite a variety of treatments currently available, cases of diabetes and resulting complications are on the rise. One promising new approach to diabetes focuses on modulating the gut microbiota with probiotics, prebiotics, synbiotics and fecal microbial transplantation. Differences in gut microbiota composition have been observed in preclinical animal models as well as patients with type 2 diabetes and complications such as diabetic nephropathy, diabetic retinopathy, diabetic neuropathy, cerebrovascular disease, coronary heart disease and peripheral artery disease compared to healthy controls. Severity of gut microbiota dysbiosis was associated with disease severity and restoration with probiotic administration in animal models and human patients has been associated with improvement of symptoms and disease progression. Characterizing the gut microbiota dysbiosis in different diseases and determining a causal relationship between the gut microbiota and disease can be beneficial in formulating therapeutic interventions for type 2 diabetes and associated complications. In this review, we present the most important findings regarding the role of the gut microbiota in type 2 diabetes and chronic complications as well as their underlying mechanisms.

The effect of induced joint restriction on plantar pressure during gait - a pilot study

BACKGROUND

The aim of this study was to analyse the effect of induced lower limb joint restriction on plantar pressures during gait. Focusing on restricting a single joint, without the effect of other co-morbidities, would provide better understanding as to the resultant plantar loadings during gait, which would be especially beneficial in patients requiring offloading procedures.

RESEARCH QUESTION

Does induced lower limb joint restriction affect plantar pressure distribution during gait?

METHODS

A prospective, quasi-experimental study was conducted, recruiting ten healthy, adult participants who were instructed to walk barefoot over a Tekscan HR Mat™. This procedure was repeated after separately inducing restriction of the hip, knee and ankle joints. Mean peak plantar pressure (MPP) and pressure-time integral (PTI) data were analysed to compare between unrestricted and restricted data.

RESULTS

Significant plantar pressure changes were observed in the heel and first metatarsal regions. Rearfoot PTIs were increased with restriction of the contralateral hip (left p <0.001) (right p =0.02) and knee joints (left p =0.01) (right p =0.04). Both MPPs (left p =0.01; right p =0.01) and PTIs (left p =0.004; right p =0.03) were increased in the first metatarsal when restricting the hip joint of the same limb. MPP was decreased in the left first metatarsal with induced knee (left p =0.01; right p =0.04) and ankle (left and right p <0.001) joint restriction. Finally, MPP was decreased in the right first metatarsal with knee (left and right p =0.01) and ankle (left p =0.04; right p =0.01) joint restriction.

SIGNIFICANCE

Limited joint mobility may have a direct effect on plantar pressure, particularly with restriction in the hip and knee joints, hence careful attention should be given especially in patients with conditions involving plantar loadings. Results in this study also show that PTI changes during gait should be equally evaluated clinically along with peak plantar pressure analysis.

Classification of Diabetic Walking for Senior Citizens and Personal Home Training System Using Single RGB Camera through Machine Learning

Senior citizens have increased plasma glucose and a higher risk of diabetes-related complications than young people. However, it is difficult to diagnose and manage elderly diabetics because there is no clear symptom according to current diagnostic criteria. They also dislike the invasive blood sample test. This study aimed to classify a difference in gait and physical fitness characteristics between senior citizens with and without diabetes for a non-invasive method and propose a machine-learning-based personal home-training system for training abnormal gait motions by oneself. We used a dataset for classification with 200 over 65-year-old elders who walked a flat and straight 15 m route in 3 different walking speed conditions using an inertial measurement unit and physical fitness test. Then, questionnaires for participants were included to identify life patterns. Through results, it was found that there were abnormalities in gait and physical fitness characteristics related to balance ability and walking speed. Using a single RGB camera, the developed training system for improving abnormalities enabled us to correct the exercise posture and speed in real-time. It was discussed that there are risks and errors in the training system based on human pose estimation for future works.

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

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.

A Wearable Gait Analysis System Used in Type 2 Diabetes Mellitus Patients: A Case-Control Study

BACKGROUND

Previous studies have shown that the gait of patients with type-2 diabetes mellitus is abnormal compared with the healthy group. Currently, a three-dimensional motion analyzer system is commonly used for gait analysis. However, it is challenging to collect data and use in clinical study due to extensive experimental conditions and high price. In this study, we used a wearable gait analysis system (Gaitboter) to investigate the spatial and temporal parameters, and kinematic data of gait in diabetic patients, especially those with peripheral neuropathy. The aim of the study is to evaluate the wearable gait analysis system in diabetic study.

MATERIALS AND METHODS

We conducted a case-control study to analyze the gait of type 2 diabetes mellitus. Gaitboter was used to detect and collect gait data in the ward of Beijing Chao-yang Hospital, Capital Medical University from June 2018 to October 2018. We collected the gait data of participants (N= 146; 73 patients with type 2 diabetes, 16 with peripheral neuropathy and 57 without peripheral neuropathy, and 73 matched controls). The gait data (stance phase, swing phase, double-foot stance phase, single-foot stance phase, walking cadence, stride length, walking speed, off-ground angle, landing angle, maximum swing angle, minimum swing angle, and foot progression angle) in diabetic patients were recorded and compared with controls. SPSS 22.0 statistical software was used to analyzed the gait parameter data.

RESULTS

We found that the landing angle and the maximum swing angle of diabetes patients with or without peripheral neuropathy were significantly less than those of the control group (P < 0.05). The walking speed of diabetes patients with peripheral neuropathy is significantly less than those of the control group (P < 0.05).

CONCLUSION

This study confirms that the wearable gait analysis system (Gaitboter) is an ideal system to identify abnormal gait in patients with type 2 diabetes and provides a new device and method for diabetes-related gait research.

The Association of Diabetes With Knee Pain Locations, Pain While Walking, and Walking Speed: Data From the Osteoarthritis Initiative

OBJECTIVE

Osteoarthritis (OA) and diabetes mellitus (DM) often coexist and can result in negative outcomes. DM can affect pain and walking speed in people with knee OA; however, the impact of DM on OA is understudied. The purpose of this study was to investigate the association between diabetes and knee pain locations, pain severity while walking, and walking speed in people with knee OA.

METHODS

A cross-sectional analysis was used. Data from 1790 individuals from the Osteoarthritis Initiative (mean [SD] age = 69 [8.7] years) with knee pain were included and grouped into knee OA and diabetes (n = 236) or knee OA only (n = 1554). Knee pain locations were categorized as no pain, localized pain, regional pain, or diffuse pain. Knee pain during a 20-m walk test was categorized as no pain, mild, moderate, or severe knee pain. Walking speed was measured using the 20-m walk test. Multinomial and linear regression analyses were performed.

RESULTS

Diabetes was associated with regional knee pain (odds ratio [OR] = 1.77; 95% CI = 1.01-3.11). Diabetes was associated only with moderate (OR = 1.78; 95% CI = 1.02-3.10) or severe (OR = 2.52; 95% CI = 1.01-6.28) pain while walking. Diabetes was associated with decreased walking speed (B = -0.064; 95% CI = -0.09 to -0.03).

CONCLUSIONS

Diabetes was associated with regional knee pain but not with localized or diffuse knee pain and was associated with moderate to severe knee pain while walking and slower walking speed in people with knee OA.

IMPACT

Clinicians can use a knee pain map for examining knee pain locations for people with diabetes and knee OA. Knee pain during walking and walking speed should be screened for people with knee OA and diabetes because of the influence of diabetes on these parameters in this population.

LAY SUMMARY

Diabetes might be associated with specific knee pain locations, pain during activities such as walking, and reduced walking speed in people with knee OA.

Clustering classification of diabetic walking abnormalities: a new approach taking into account intralimb coordination patterns

BACKGROUND

It is well recognized that diabetes and peripheral neuropathy have a detrimental effect on gait. However, there are large variations in the results of studies addressing this aspect due to the heterogeneity of diabetic population in relation to presence and severity of diabetes complications. The aim of this study is to adopt an unsupervised classification technique to better elucidate the gait changes throughout the entire spectrum of diabetes and neuropathy.

METHODS

Sixty subjects were assessed and classified into four groups using a fuzzy logic model: 13 controls (55 ± 7years), 18 diabetics subjects without neuropathy (59 ± 6 years, 11 ± 7 diabetes years), 7 with mild neuropathy (56 ± 4years, 19 ± 7 diabetes years), and 22 with moderate to severe neuropathy (57 ± 5 years, 14 ± 8 diabetes years). Data were gathered by six infrared cameras at 100 Hz regarding lower limb joint kinematics (angles and angular velocities) and the relative phase for the hip-ankle, hip-knee, and knee-ankle were calculated. The K-means clustering algorithm was adopted to classify subjects considering the whole kinematics time series. A one-way ANOVA test was used to compare both clinical and kinematics parameters across clusters.

RESULTS

Only the classification based on the intralimb coordination variables succeeded in defining 5 well separated clusters with the following clinical characteristics: controls were grouped mainly in Cluster 2, diabetics in Cluster 4, and neuropathic subjects in Cluster 5 (which included various degrees of severity). Hip-ankle coordination in Clusters 4 and 5 were significantly different (p < 0.05) with respect to Cluster 2, mainly in the stance phase. During the swing phase, differences were observed in the ankle-knee coordination (p < 0.05) across clusters.

CONCLUSION

Classification based on intralimb coordination patterns succeeded in efficiently categorize gait alterations in diabetic subjects. It can be speculated that variables extracted from sagittal plane kinematics might be adopted as a support to clinical decision making in diabetes.

Exploring the Application of Pattern Recognition and Machine Learning for Identifying Movement Phenotypes During Deep Squat and Hurdle Step Movements

Background

Movement screens are increasingly used in sport and rehabilitation to evaluate movement competency. However, common screens are often evaluated using subjective visual detection of a priori prescribed discrete movement features (e.g., spine angle at maximum squat depth) and may not account for whole-body movement coordination, or associations between different discrete features.

Objective

To apply pattern recognition and machine learning techniques to identify whole-body movement pattern phenotypes during the performance of exemplar functional movement screening tasks; the deep squat and hurdle step. Additionally, we also aimed to compare how discrete kinematic measures, commonly used to score movement competency, differed between emergent groups identified via pattern recognition and machine learning.

Methods

Principal component analysis (PCA) was applied to 3-dimensional (3D) trajectory data from participant's deep squat (DS) and hurdle step performance, identifying emerging features that describe orthogonal modes of inter-trial variance in the data. A gaussian mixture model (GMM) was fit and used to cluster the principal component scores as an unsupervised machine learning approach to identify emergent movement phenotypes. Between group features were analyzed using a one-way ANOVA to determine if the objective classifications were significantly different from one another.

Results

Three clusters (i.e., phenotypes) emerged for the DS and right hurdle step (RHS) and 4 phenotypes emerged for the left hurdle step (LHS). Selected discrete points commonly used to score DS and hurdle step movements were different between emergent groups. In regard to the select discrete kinematic measures, 4 out of 5, 7 out of 7 and 4 out of 7, demonstrated a main effect (p < 0.05) between phenotypes for the DS, RHS, and LHS respectively.

Conclusion

Findings support that whole-body movement analysis, pattern recognition and machine learning techniques can objectively identify movement behavior phenotypes without the need to a priori prescribe movement features. However, we also highlight important considerations that can influence outcomes when using machine learning for this purpose.

Effect of an educational booklet for prevention and treatment of foot musculoskeletal dysfunctions in people with diabetic neuropathy: the FOotCAre (FOCA) trial II, a study protocol of a randomized controlled trial

Background

This study is a part of a series of two clinical trials. We consider diabetic polyneuropathy (DPN), a common chronic and progressive complication of diabetes mellitus that has several impacts on individuals’ foot health and quality of life. Based on the current trends of self-monitoring and self-care, providing a tool with foot-related exercises and educational care may help patients to avoid or reduce the musculoskeletal complications resulting from DPN, improving autonomous performance in daily living tasks. The aim of this trial is to evaluate the effects of an educational booklet for foot care and foot muscle strengthening on DPN symptoms and severity, clinical outcomes, and gait biomechanics in patients with DPN.

Methods/design

The FOotCAre (FOCA) trial II study has been designed as a single-blind, two-parallel-arm randomized controlled trial. It will include 48 patients with DPN who will be randomly allocated to a control (recommended foot care by international consensus with no foot exercises) group or an intervention (foot-related exercises using an educational booklet three times/week at home for 8 weeks) group. Participants from both groups will be assessed at baseline, after 8 weeks, and at 16 weeks for follow-up. The primary outcomes are the DPN symptoms and severity, and the secondary outcomes are foot–ankle kinematics, gait kinetics, plantar pressure distribution during gait, tactile and vibratory sensitivities, foot strength, functional balance, and foot health and functionality.

Discussion

The booklet is a management tool that allows users to be autonomous in their treatment by choosing how and where to perform the exercises. This allows the patients to perform the exercises regularly as a continuous habit for foot care and health, which is an important element in the management of the diabetic foot. As the booklet focuses on specific foot–ankle exercises, we expect that it will improve the clinical aspects of DPN and produce beneficial biomechanical changes during gait, becoming a powerful self-management tool that can be easily implemented to improve the performance of daily living tasks.

Trial registration

ClinicalTrials.gov, NCT04008745. Registered on 2 July 2019.

Study protocol for a randomized controlled trial on the effect of the Diabetic Foot Guidance System (SOPeD) for the prevention and treatment of foot musculoskeletal dysfunctions in people with diabetic neuropathy: the FOotCAre (FOCA) trial I

BACKGROUND

This study is part of a series of two clinical trials. Taking into account the various musculoskeletal alterations of the foot and ankle in people with diabetic peripheral neuropathy (DPN) and the need for self-care to avoid more serious dysfunctions and complications, a self-manageable exercise protocol that focuses on strengthening the foot muscles is presented as a potentially effective preventive method for foot and gait complications. The aim of this trial is to investigate the effect of a customized rehabilitation technology, the Diabetic Foot Guidance System (SOPeD), on DPN status, functional outcomes and gait biomechanics in people with DPN.

METHODS/DESIGN

Footcare (FOCA) trial I is a randomized, controlled and parallel two-arm trial with blind assessment. A total of 62 patients with DPN will be allocated into either a control group (recommended foot care by international consensus with no foot exercises) or an intervention group (who will perform exercises through SOPeD at home three times a week for 12 weeks). The exercise program will be customized throughout its course by a perceived effort scale reported by the participant after completion of each exercise. The participants will be assessed at three different times (baseline, completion at 12 weeks, and follow-up at 24 weeks) for all outcomes. The primary outcomes will be DPN symptoms and severity classification. The secondary outcomes will be foot-ankle kinematics and kinetic and plantar pressure distribution during gait, tactile and vibration sensitivities, foot health and functionality, foot strength, and functional balance.

DISCUSSION

As there is no evidence about the efficacy of rehabilitation technology in reducing DPN symptoms and severity or improving biomechanical, clinical, and functional outcomes for people with DPN, this research can contribute substantially to clarifying the therapeutic merits of software interventions. We hope that the use of our application for people with DPN complications will reduce or attenuate the deficits caused by DPN. This rehabilitation technology is freely available, and we intend to introduce it into the public health system in Brazil after demonstrating its effectiveness.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT04011267. Registered on 8 July 2019.

The beneficial effects of probiotic administration on wound healing and metabolic status in patients with diabetic foot ulcer: A randomized, double-blind, placebo-controlled trial

BACKGROUND

This study was conducted to evaluate the effects of probiotic supplementation on wound healing and metabolic status in subjects with diabetic foot ulcer (DFU).

METHODS

This randomized, double-blind, placebo-controlled trial was conducted among 60 subjects (aged 40-85 years old) with grade 3 diabetic foot ulcer. Individuals were randomly divided into 2 groups (30 subjects each group) to receive either probiotic or placebo daily for 12 weeks.

RESULTS

After the 12-week intervention, compared with the placebo, probiotic supplementation led to significant reductions in ulcer length (-1.3 ± 0.9 vs. -0.8 ± 0.7 cm, P = .01), width (-1.1 ± 0.7 vs. -0.7 ± 0.7 cm, P = .02), and depth (-0.5 ± 0.3 vs. -0.3 ± 0.3 cm, P = .02). Furthermore, significant reductions in fasting plasma glucose (-29.6 ± 30.3 vs. -5.8 ± 39.8 mg/dL, P = .01), serum insulin concentrations (-4.3 ± 7.9 vs. +0.4 ± 8.5 μIU/mL, P = .03), and haemoglobin A1c (-0.6 ± 0.5 vs. -0.2 ± 0.4%, P = .003) and a significant rise in the quantitative insulin sensitivity check index (+0.01 ± 0.01 vs. -0.01 ± 0.02, P = .003) were seen following supplementation of probiotic compared with the placebo. Additionally, compared with the placebo, probiotic supplementation resulted in significant decreases in serum total cholesterol (-4.8 ± 16.1 vs. +7.0 ± 27.1 mg/dL, P = .04), high-sensitivity C-reactive protein (-9.0 ± 14.7 vs. -1.7 ± 8.6 mg/L, P = .02), plasma malondialdehyde (-0.8 ± 0.8 vs. -0.2 ± 0.8 μmol/L, P = .001), and significant increases in plasma nitric oxide (+6.2 ± 8.2 vs. +0.8 ± 8.0 μmol/L, P = .01) and total antioxidant capacity concentrations (+179.3 ± 97.2 vs. -85.1 ± 203.4 mmol/L, P < .001).

CONCLUSIONS

Overall, probiotic supplementation for 12 weeks among subjects with diabetic foot ulcer had beneficial effects on ulcer size, glycaemic control, total cholesterol, high-sensitivity C-reactive protein, plasma nitric oxide, total antioxidant capacity, and malondialdehyde levels.

Muscle force distribution of the lower limbs during walking in diabetic individuals with and without polyneuropathy

BACKGROUND

Muscle force estimation could advance the comprehension of the neuromuscular strategies that diabetic patients adopt to preserve walking ability, which guarantees their independence as they deal with their neural and muscular impairments due to diabetes and neuropathy. In this study, the lower limb's muscle force distribution during gait was estimated and compared in diabetic patients with and without polyneuropathy.

METHODS

Thirty individuals were evaluated in a cross-sectional study, equally divided among controls (CG) and diabetic patients with (DNG) and without (DG) polyneuropathy. The acquired ground reaction forces and kinematic data were used as input variables for a scaled musculoskeletal model in the OpenSim software. The maximum isometric force of the ankle extensors and flexors was reduced in the model of DNG by 30% and 20%, respectively. The muscle force was calculated using static optimization, and peak forces were compared among groups (flexors and extensors of hip, knee, and ankle; ankle evertors; and hip abductors) using MANOVAs, followed by univariate ANOVAs and Newman-Keuls post-hoc tests (p < 0.05).

RESULTS

From the middle to late stance phase, DG showed a lower soleus muscle peak force compared to the CG (p=0.024) and the DNG showed lower forces in the gastrocnemius medialis compared to the DG (p=0.037). At the terminal swing phase, the semitendinosus and semimembranosus peak forces showed lower values in the DG compared to the CG and DNG. At the late stance, the DNG showed a higher peak force in the biceps short head, semimembranosus, and semitendinosus compared to the CG and DG.

CONCLUSION

Peak forces of ankle (flexors, extensors, and evertors), knee (flexors and extensors), and hip abductors distinguished DNG from DG, and both of those from CG. Both diabetic groups showed alterations in the force production of the ankle extensors with reductions in the forces of soleus (DG) and gastrocnemius medialis (DNG) seen in both diabetic groups, but only DNG showed an increase in the hamstrings (knee flexor) at push-off. A therapeutic approach focused on preserving the functionality of the knee muscles is a promising strategy, even if the ankle dorsiflexors and plantarflexors are included in the resistance training.

Effects of the lateral amplitude and regularity of upper body fluctuation on step time variability evaluated using return map analysis

The aim of this study was to evaluate the effects of the lateral amplitude and regularity of upper body fluctuation on step time variability. Return map analysis was used to clarify the relationship between step time variability and a history of falling. Eleven healthy, community-dwelling older adults and twelve younger adults participated in the study. All of the subjects walked 25 m at a comfortable speed. Trunk acceleration was measured using triaxial accelerometers attached to the third lumbar vertebrae (L3) and the seventh cervical vertebrae (C7). The normalized average magnitude of acceleration, the coefficient of determination ($R^2$) of the return map, and the step time variabilities, were calculated. Cluster analysis using the average fluctuation and the regularity of C7 fluctuation identified four walking patterns in the mediolateral (ML) direction. The participants with higher fluctuation and lower regularity showed significantly greater step time variability compared with the others. Additionally, elderly participants who had fallen in the past year had higher amplitude and a lower regularity of fluctuation during walking. In conclusion, by focusing on the time evolution of each step, it is possible to understand the cause of stride and/or step time variability that is associated with a risk of falls.

Efficacy measures associated to a plantar pressure based classification system in diabetic foot medicine

AIMS

The concept of 'classification' has, similar to many other diseases, been found to be fundamental in the field of diabetic medicine. In the current study, we aimed at determining efficacy measures of a recently published plantar pressure based classification system.

METHODS

Technical efficacy of the classification system was investigated by applying a high resolution, pixel-level analysis on the normalized plantar pressure pedobarographic fields of the original experimental dataset consisting of 97 patients with diabetes and 33 persons without diabetes. Clinical efficacy was assessed by considering the occurence of foot ulcers at the plantar aspect of the forefoot in this dataset. Classification efficacy was assessed by determining the classification recognition rate as well as its sensitivity and specificity using cross-validation subsets of the experimental dataset together with a novel cohort of 12 patients with diabetes.

RESULTS

Pixel-level comparison of the four groups associated to the classification system highlighted distinct regional differences. Retrospective analysis showed the occurence of eleven foot ulcers in the experimental dataset since their gait analysis. Eight out of the eleven ulcers developed in a region of the foot which had the highest forces. Overall classification recognition rate exceeded 90% for all cross-validation subsets. Sensitivity and specificity of the four groups associated to the classification system exceeded respectively the 0.7 and 0.8 level in all cross-validation subsets.

CONCLUSIONS

The results of the current study support the use of the novel plantar pressure based classification system in diabetic foot medicine. It may particularly serve in communication, diagnosis and clinical decision making.

GAIT ANALYSIS DRIVEN 2D FINITE ELEMENT MODEL OF THE NEUROPATHIC HINDFOOT

The diabetic foot is one of the most serious complications of diabetes mellitus and it can lead to foot ulcerations and amputations. Finite element analysis quantifies the loads developed in the different anatomical structures and describes how these affect foot tissue during foot–floor interaction. This approach for the diabetic subjects’ foot could provide valuable information in the process of plantar orthosis fabrication and fit. The purpose of this study was to develop two finite element models of the hindfoot, of healthy and diabetic neuropathic subjects. These models accounts for in vivo kinematics, kinetics, plantar pressure (PP) data and magnetic resonance images. These were acquired during gait analysis on 10 diabetic neuropathics and 10 healthy subjects. Validity of the models has been assessed through comparison between the peak PPs of simulated and experimental data: root mean square error (RMSE) in percentage of the experimental peak value was evaluated. Two different finite elements analysis were performed: subject-specific simulations in terms of both geometry and gait analysis, and by adopting the complete gait analysis dataset as boundary conditions. Model predicted plantar pressures were in good agreement with those experimentally measured. Best agreement was obtained in the subject-specific case (RMSE of 13%).

Gait characteristics in older adults with diabetes and impaired fasting glucose: The Rotterdam Study

AIMS

To investigate the association of diabetes mellitus and impaired fasting glucose with gait in the general middle-aged and elderly population.

METHODS

We performed a cross-sectional study on 3019 participants from the population-based Rotterdam Study (aged >45years, 54% women). The presence of diabetes mellitus and impaired fasting glucose was evaluated by measuring serum glucose levels and by documenting anti-diabetic treatment. Participants underwent gait analysis using an electronic walkway. Thirty gait variables were summarized into five independent gait domains for normal walking (Rhythm, Variability, Phases, Pace and Base of Support), one for turning (Turning) and one for walking heel to toe (Tandem), which were averaged into Global Gait. Linear regression analyses were performed to determine the association of diabetes, impaired fasting glucose and continuous glucose levels within the normal range with gait.

RESULTS

Diabetes mellitus was associated with worse Global Gait (Z-score difference -0.19, 95% confidence interval (CI) -0.30; -0.07), worse Pace (-0.20, 95% CI -0.30; -0.10) and worse Tandem (-0.21, 95% CI -0.33; -0.09), after adjusting for age, sex, height and weight. The association with Tandem remained significant after additional adjustment for cardiovascular risk factors. Impaired fasting glucose and continuous glucose levels within the normal range were not associated with any of the gait domains.

CONCLUSION

In our population-based study diabetes mellitus was associated with worse Global Gait, which was mostly reflected in Pace and Tandem. These associations were partly driven by other cardiovascular risk factors, emphasizing the importance of optimal control of cardiovascular risk factor profiles in patients with diabetes.

Alterations in the lower limb joint moments precede the peripheral neuropathy diagnosis in diabetes patients

BACKGROUND

Changes in gait patterns in individuals with diabetes and neuropathy are still inconclusive. Our aim was to identify differences in the net intralimb moments distribution and lower limb kinematics during gait in different stage of diabetes.

SUBJECTS AND METHODS

This was an observational cross-sectional study that assessed 38 adults: a control group (n=12), a group with diabetes (n=12), and a group with diabetic neuropathy (n=14). The flexor and extensor joint moment peaks and kinematics of ankle, knee, and hip angles were compared among groups (by analysis of variance).

RESULTS

At initial contact, both diabetes groups present more hip flexion and smaller hip extensor moment. During late midstance, hip extension decreases, and flexion moment increases in both diabetes groups. For the same diabetes groups, during push off, the hip is more flexed, and the hip extensor moment decreases. Only for the diabetes group without neuropathy is the knee markedly more flexed, and the extensor moment is higher than in the other groups. At push off, the ankle is less extended in both diabetes groups, but the ankle extensor moment is significantly smaller only in neuropathic subjects.

CONCLUSIONS

The biomechanical modifications on the gait appeared to be a continuous process that was already revealed in patients without neuropathy. The use of the hip joint as a mechanism of forward progression of the body, instead of using the ankle, was more evident and consistent for the patients with diabetic neuropathy. The knee seems to have a major role in those with diabetes without neuropathy who presented higher extensor moments to support the body during early stance.

Elevated plantar pressure in diabetic patients and its relationship with their gait features

PURPOSE

High plantar pressure is a major risk factor for diabetic foot ulcers. The relationship between plantar pressure and foot mobility has been investigated in some studies. However, when the foot is in motion, foot mobility is only a small feature of the gait. Therefore, we investigated relationship between high plantar pressure and gait and also studied the motion of the trunk. In addition, we investigated the relationship between gait and patient characteristics to identify patients at high-risk of developing diabetic foot ulcers.

METHODS

The relationships between elevated plantar pressure, gait features, and patient characteristics were analyzed. Plantar pressure distribution in the stance phase was divided on the four plantar segments. Elevated plantar pressure was defined as being more than the mean plus one standard deviation of the corresponding segment in non-diabetic subjects. Plantar pressure distribution was measured by an F-scan system, and gait features were measured using wireless motion sensors attached to the sacrum and feet. Patient characteristics were obtained from medical records or by interview.

RESULTS

Small roll and yaw motions of the body and yaw motion of the foot during the mid-stance phase were related to the elevated plantar pressure in 57 diabetic patients. Furthermore, these gait features were related to sensory neuropathy, diabetes duration, patient weight, toe-gap force, and ankle range of motion.

CONCLUSION

Given our findings, it may be possible to prevent diabetic foot ulcers by increasing foot motion during the mid-stance phase. Passive exercise aimed at expanding ankle range of motion in patients with sensory neuropathy or long-standing diabetes may assist in achieving this.

Classification of forefoot plantar pressure distribution in persons with diabetes: a novel perspective for the mechanical management of diabetic foot?

Background

The aim of this study was to identify groups of subjects with similar patterns of forefoot loading and verify if specific groups of patients with diabetes could be isolated from non-diabetics.

Methodology/Principal Findings

Ninety-seven patients with diabetes and 33 control participants between 45 and 70 years were prospectively recruited in two Belgian Diabetic Foot Clinics. Barefoot plantar pressure measurements were recorded and subsequently analysed using a semi-automatic total mapping technique. Kmeans cluster analysis was applied on relative regional impulses of six forefoot segments in order to pursue a classification for the control group separately, the diabetic group separately and both groups together. Cluster analysis led to identification of three distinct groups when considering only the control group. For the diabetic group, and the computation considering both groups together, four distinct groups were isolated. Compared to the cluster analysis of the control group an additional forefoot loading pattern was identified. This group comprised diabetic feet only. The relevance of the reported clusters was supported by ANOVA statistics indicating significant differences between different regions of interest and different clusters.

Conclusion/s Significance

There seems to emerge a new era in diabetic foot medicine which embraces the classification of diabetic patients according to their biomechanical profile. Classification of the plantar pressure distribution has the potential to provide a means to determine mechanical interventions for the prevention and/or treatment of the diabetic foot.

Association between physical activity levels and physiological factors underlying mobility in young, middle-aged and older individuals living in a city district

Maintaining adequate levels of physical activity is known to preserve health status and functional independence as individuals grow older. However, the relationship between determinants of physical activity (volume and intensity) and physiological factors underlying mobility (cardio-respiratory fitness, neuromuscular function and functional abilities) is still unclear. The aim of this study was to investigate the association between objectively quantified physical activity and a spectrum of physiological factors underlying mobility in young, middle-aged and older individuals living in a city district. Experiments were carried out on 24 young (28 ± 2 years), 24 middle-aged (48 ± 2 years) and 24 older (70 ± 3 years) gender-matched volunteers. Physical activity was monitored by a wearable activity monitor to quantify volume and intensity of overall physical activity and selected habitual activities over 24 hours. Ventilatory threshold was assessed during an incremental cycling test. Torque, muscle fiber conduction velocity and agonist-antagonist coactivation were measured during maximal voluntary contraction of knee extensors and flexors. Ground reaction forces were measured during sit-to-stand and counter-movement jump. K-means cluster analysis was used to classify the participants' physical activity levels based on parameters of volume and intensity. Two clusters of physical activity volume (i.e., high and low volume) and three clusters of physical activity intensity (i.e. high, medium and low intensity) were identified in all participants. Cardio-respiratory fitness was associated with volume of overall physical activity as well as lying, sitting, standing, walking and stair climbing. On the other hand, neuromuscular function and functional abilities showed a significant association with intensity of overall physical activity as well as postural transition, walking and stair climbing. As a practical application, the relative role played by volume and intensity of overall physical activity and selected habitual activities should be taken into account in the design of preventative training interventions to preserve mobility as individuals grow older.

Validation of plantar pressure measurements for a novel in-shoe plantar sensory replacement unit

An article by Ferber and coauthors in Journal of Diabetes Science and Technology reported on the ability of a novel in-shoe plantar sensory replacement unit (PSRU) to provide alert-based feedback derived from analyzing plantar pressure (PP) threshold measurements in real time. The study aimed at comparing the PSRU device to a gold standard pressure-sensing device (GS-PSD) to determine the correlation between concurrent measures of PP during walking. Data were collected simultaneously from 10 participants who walked overground with both devices. The variable of interest was the number of recorded data points greater than 32 mmHg for each of the PSRU sensors and corresponding average recordings from the GS-PSD. Authors concluded that the PSRU provides analogous data to the GS-PSD. However, several aspects of the study should be considered when interpreting their clinical relevance.

Fear of falling is prevalent in older adults with diabetes mellitus but is unrelated to level of neuropathy

BACKGROUND

Patients with diabetic peripheral neuropathy (DPN) demonstrate gait alterations compared with their nonneuropathic counterparts, which may place them at increased risk for falling. However, it is uncertain whether patients with DPN also have a greater fear of falling.

METHODS

A voluntary group of older adults with diabetes was asked to complete a validated fear of falling questionnaire (Falls Efficacy Scale International [FES-I]) and instructed to walk 20 m in their habitual shoes at their habitual speed. Spatiotemporal parameters of gait (eg, stride velocity and gait speed variability) were collected using a validated body-worn sensor technology. Balance during walking was also assessed using sacral motion in the mediolateral and anteroposterior directions. The level of DPN was quantified using vibration perception threshold from the great toe.

RESULTS

Thirty-four diabetic patients (mean ± SD: age, 67.6 ± 9.2 years; body mass index, 30.9 ± 5.7; hemoglobin A1c, 7.9% ± 2.3%) with varying levels of neuropathy (mean ± SD vibration perception threshold, 34.6 ± 22.9 V) were recruited. Most participants (28 of 34, 82%) demonstrated moderate to high concern about falling based on their FES-I score. Age (r = 0.6), hemoglobin A1c level (r = 0.39), number of steps required to reach steady-state walking (ie, gait initiation) (r = 0.4), and duration of double support (r = 0.44) were each positively correlated with neuropathy severity (P < .05). Participants with a greater fear of falling also walked with slower stride velocities and shorter stride lengths (r = -0.3 for both, P < .05). However, no correlation was observed between level of DPN and the participant's actual concern about falling.

CONCLUSIONS

Fear of falling is prevalent in older adults with diabetes mellitus but is unrelated to level of neuropathy.

Clustering and classification of regional peak plantar pressures of diabetic feet

High plantar pressures have been associated with foot ulceration in people with diabetes, who can experience loss of protective sensation due to peripheral neuropathy. Therefore, characterization of elevated plantar pressure distributions can provide a means of identifying diabetic patients at potential risk of foot ulceration. Plantar pressure distribution classification can also be used to determine suitable preventive interventions, such as the provision of an appropriately designed insole. In the past, emphasis has primarily been placed on the identification of individual focal areas of elevated pressure. The goal of this study was to utilize k-means clustering analysis to identify typical regional peak plantar pressure distributions in a group of 819 diabetic feet. The number of clusters was varied from 2 to 10 to examine the effect on the differentiation and classification of regional peak plantar pressure distributions. As the number of groups increased, so too did the specificity of their pressure distributions: starting with overall low or overall high peak pressure groups and extending to clusters exhibiting several focal peak pressures in different regions of the foot. However, as the number of clusters increased, the ability to accurately classify a given regional peak plantar pressure distribution decreased. The balance between these opposing constraints can be adjusted when assessing patients with feet that are potentially "at risk" or while prescribing footwear to reduce high regional pressures. This analysis provides an understanding of the variability of the regional peak plantar pressure distributions seen within the diabetic population and serves as a guide for the preemptive assessment and prevention of diabetic foot ulcers.