Biomechanical characteristics of peripheral diabetic neuropathy: A systematic review and meta-analysis of findings from the gait cycle, muscle activity and dynamic barefoot plantar pressure

Fernando, Malindu E.; Crowther, Robert G.; Lazzarini, Peter A; Sangla, Kunwarjit S.; Cunningham, Myles P.; Büttner, Petra; Golledge, Jonathan

doi:10.1016/j.clinbiomech.2013.08.004

Cited by 189 publications

(148 citation statements)

References 44 publications

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“…A typical gait study will assess somewhere between 20 and 100 individuals, divided into subgroups, 2 recent reviews of gait literature regarding diabetes found average study sizes of n = 48 43 and n = 52. 44 These study populations are subdivided most commonly into people with and without diabetes (with or without additional complications), giving an average group size within the studies reported of n = 21-25. 43,44 Within the United Kingdom there are in excess of 3 million patients diagnosed with diabetes; 1,2 therefore a typical gait study assesses a sample <0.00003% of the population, a sample level that is common among studies that require voluntary human participation and interaction.…”

Section: Participant Populations In Typical Studiesmentioning

confidence: 99%

Benefits, Challenges, and Potential Utility of a Gait Database for Diabetes Patients

Brown

Boulton

Bowling

et al. 2016

J Diabetes Sci Technol

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Gait analysis is a useful tool in understanding movement impairments, which impact on patient well-being. The use of gait analysis in patients with diabetes has led to improvements in health care including the treatment and prevention of ulceration and development of targeted exercise interventions. The current convention when analyzing gait is to address specific complications of diabetes, controlling for potential influencing conditions within a study sample to understand the effects of the few specific complications chosen for analysis. Databases allow for the storage of data in a structured format, allowing easy access to large quantities of data in a consistent, comparable manner. A database of gait analyses of patients with diabetes has the potential to include far greater sample sizes for statistical analyses, allowing multiple influencing factors to be assessed simultaneously, and relationships identified between multiple influencing factors. However, a database of this type would encounter ethical and methodological challenges in its implementation, which are discussed. This article introduces some of the potential benefits, challenges, and utility of a gait database for diabetes patients. We highlight that, whereas the creation of a database within this clinical population would be a complex process both ethically and practically, huge potential benefits could be gained, overcoming some of the limitations faced by traditional isolated gait analysis studies.

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Section: Participant Populations In Typical Studiesmentioning

confidence: 99%

Benefits, Challenges, and Potential Utility of a Gait Database for Diabetes Patients

Brown

Boulton

Bowling

et al. 2016

J Diabetes Sci Technol

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“…4,5 In general, neuropathy is determined by demography factors, while the development of DFU is mainly related to trauma, neuropathy and deformity. 6,7 The main complications of DM in India are neuropathy (13%-78%), microvascular complications (16%-53%) and DFU (7.3% -24%). 8,9 Foot infections are the most common problems in persons with diabetes.…”

Section: Introductionmentioning

confidence: 99%

“…Infections in patients with diabetes are difficult to treat because these individuals have impaired microvascular circulation, which limits the access of phagocytic cells to the infected area and results in a poor concentration of antibiotics in the infected tissues. [3][4][5][6][7] Except for chronic osteomyelitis, infections in patients with diabetes are caused by the same microorganisms that can infect the extremities of persons without diabetes. Gas gangrene is conspicuous because of its low incidence in patients with diabetes, but deep-skin and soft-tissue infections, which are due to gas-producing organisms, frequently occur in patients with these infections.…”

Section: Introductionmentioning

confidence: 99%

A study on the prevalence of risk factors and presence of diabetic foot ulcers in T2DM patients in K. R. Hospital, Mysuru

Chandrashekar

Muralidhar

2017

Int Surg J

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Background: India is one of the top ten diabetes mellitus (DM) countries. Thus, the aim of this study was to survey the prevalence of DFU risk factors and DFU prevalence among type 2 diabetes mellitus (T2DM) patients.Methods: An epidemiological study was conducted on an outpatient basis in K. R. hospital, Mysuru. All T2DM participants were ≥ 18 years were included. Demographic and foot care behavior were assessed using minimum data sheet (MDS). Meanwhile, presence of risk factors was evaluated for neuropathy and presence of angiopathy was evaluated with ankle brachial index (ABI) by using a hand-held doppler both dorsal and posterior tibial.Results: At the end of study, 249 T2DM participants were enrolled. The prevalence of DFU risk factors was 55.4% (95% CI: 53.7% - 57.0%), and prevalence of DFU was 12% (95% CI: 10.3% - 13.6%).Conclusions: Even though the prevalence of DFU is high, identification associated factors for presence of risk and DFU has not been integrated into national guideline. Thus, preventive strategies should be introduced at early stage to prevent presence of risk and DFU.

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“…A modelagem computacional é usada para investigar o papel individual dos músculos na geração de momentos articulares e potência durante a marcha (19), e poderia ser útil para elucidar possíveis mudanças na distribuição das forças musculares que culminam com o típico padrão de marcha descrito de pacientes diabéticos (20). Além disso, os estudos computacionais nos permitem elucidar se as mudanças da força muscular surgem nos diabéticos antes do início da neuropatia, ou apenas ocorrem na presença de neuropatia.…”

Section: Contextualização Do Problema De Estudounclassified

Papel da atividade muscular no padrão de marcha de diabéticos neuropatas: um estudo por modelagem computacional

Gomes¹

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Biomechanical characteristics of peripheral diabetic neuropathy: A systematic review and meta-analysis of findings from the gait cycle, muscle activity and dynamic barefoot plantar pressure

Cited by 189 publications

References 44 publications

Benefits, Challenges, and Potential Utility of a Gait Database for Diabetes Patients

Benefits, Challenges, and Potential Utility of a Gait Database for Diabetes Patients

A study on the prevalence of risk factors and presence of diabetic foot ulcers in T2DM patients in K. R. Hospital, Mysuru

Papel da atividade muscular no padrão de marcha de diabéticos neuropatas: um estudo por modelagem computacional

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