Relationship between skin resistance level and static balance in type II diabetic subjects
Introduction
The inhibitory component of the skin against given electrical current is called the electrical skin resistance [1]. Skin resistance level (SRL) is related to skin conductance which changes in the presence of sweat, a fluid composed of water and ions. It is determined by passing a weak current through the measuring changes in electricity flow or by measuring the current generated by the body itself. It has been correlated with emotion, attention and stress [2].
Skin foot plantar area (SFPA) has many mechanoreceptors (Pacinian, Meissner, Merkel and Ruffini) is sensitive to joint pressure and tension. In addition: blood vessels, sweat gland activity and interstitial fluid exist under normal physiological conditions for SFPA. Loss of pressure sensitivity in SFPA, for instance in diabetic foot, etc., is related to risk of falling [3], [4], [5]. For evaluation of balance and risk of falling are used many dynamic and static balance tests. Static standing balance duration (SSBD) test is a commonly used for measurement of balance capabilities, and a significant predictor of falls [6].
Loss of sensation and autonomic problems can be seen in diabetic patients’ feet. In the corresponding patients; impairment of normal microcirculation and loss of sympathetic tonus give rise to arterio-venous shunts, edema and inflammation. Studies carried out on diabetic patients in the literature showed that the SFPA stiffened, impairment of the skin blood flow and sensation or absence in sweating affected the SSBD and played an important role for risk of falling [7], [8], [9], [10]. There may be various approaches determining changes of the SFPA affecting the SSBD. Therefore, it was thought that measurement of the SRL is important to find out changes of the SFPA which cause crucial conclusions. Although there are many parameters affecting the SSBD, how changes of the SFPA in diabetic subjects affect the SSBD has not been investigated yet. Many researchers have focused on evaluation of the relation between the skin response and diabetic neuropathy [11], [12], [13], [14]. To the best of authors’ knowledge, there has not been any research carried out on the relation between the SRL and SSBD in diabetic subjects. Therefore, the aim of the present study was to evaluate relationship between the SRL and SSBD in diabetic subjects.
Section snippets
Materials and methods
Sixty-eight healthy young voluntary students from the University of Dumlupinar, Physical Therapy and Rehabilitation School, in addition to 30 type II diabetic patients and 30 healthy non-diabetic subjects, in total 128 subjects, were participated to the study. All patients who had type II diabetes mellitus participated to the study from various clinics and hospitals in Kütahya, Turkey, from February to September 2005. Criteria for having diabetes mellitus included the following: treatment with
Results
The subjects’ demographic data are described in Table 1. No statistically significant differences between the diabetic and the healthy non-diabetic groups were found in demographic data. Age, body mass and body mass index values were observed statistically lower in the healthy young group than the others (P < 0.05) (Table 1). SSBDs were found to be statistically significant in all groups (P < 0.001) (Table 2). The SSBDs of diabetic and healthy non-diabetic groups were found to be lower than the
Discussion
Many researchers have focused on evaluation of the relation between the skin response and diabetic neuropathy [11], [12], [13], [14]. Specifically, Cimbiz et al. used healthy subjects to evaluate the relationship between the SRL and the SSBD [18]. In particular this study considered diabetic subjects to evaluate if there is a relation between the SRL and the SSBD.
Human beings keep their balance when nerve signals from three different systems are accurately sent to and processed by the brain.
Conflict of interest
There are no conflicts of interest.
References (26)
- et al.
Evaluation of balance and physical fitness in diabetic neuropathic patients
J. Diab. Complications
(2005) - et al.
Mild diabetic neuropathy affects ankle motor function
Clin. Biomech. (Bristol, Avon)
(2001) - et al.
Unipedal stance testing as an indicator of fall risk among older outpatients
Arch. Phys. Med. Rehabil.
(2000) Plantar soft tissue loading under the medial metatarsals in the standing diabetic foot
Med. Eng. Phys.
(2003)- et al.
Relationship between sympathetic skin response and power spectral analysis of heart rate variation in patients with type 2 diabetes
J. Diab. Complications
(2004) - et al.
Multivariate analysis of skin impedance data in long-term type 1 diabetic patients
Chemometr. Intel. Lab. Syst.
(1998) - et al.
Evaluation of physical fitness in patients with type 2 diabetes mellitus
Diab. Res. Clin. Pract.
(2003) - et al.
Standing balance and trunk position sense in impaired glucose tolerance (IGT)-related peripheral neuropathy
J. Neurol. Sci.
(2008) - et al.
Evaluation of diabetic neuropathy through the quantitation of cutaneous nerves
J. Neurol. Sci.
(2000) - et al.
Skin temperature in the neuropathic diabetic foot
J. Diab. Complications
(2001)
The basal electrical skin resistance of acupuncture points in normal subjects
Yonsei Med. J.
Factors associated with falls in older patients with diffuse polyneuropathy
J. Am. Geriatr. Soc.
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2013, Clinical BiomechanicsCitation Excerpt :The increase in the Young's modulus of the plantar soft tissue at the first metatarsal head was positively correlated with the visual ratio and the somatosensory ratio. There are many mechanoreceptors located in the plantar soft tissue, such as the Pacinian, Meissner, Merkel, and Ruffini, which are sensitive to joint pressure and tension (Gulbandilar et al., 2008). These somatosensory inputs are important for postural control.
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