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Balance Control and Recovery in Diabetes Peripheral Neuropathy

Balance Control and Recovery in Diabetes Peripheral Neuropathy

Status
Recruiting
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT06544876
Acronym
DPN
Enrollment
60
Registered
2024-08-09
Start date
2023-09-12
Completion date
2028-08-31
Last updated
2024-08-09

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Diabetic Peripheral Neuropathy, Diabetic Peripheral Neuropathy Type 2, Diabetic Peripheral Neuropathy Type 2 - Uncontrolled, Healthy Aging

Keywords

diabetic peripheral neuropathy, balance, sensation, aging, older adult, foot, sit-to-stand, falls, falling

Brief summary

In this study the effects of diabetic peripheral neuropathy will be assessed on balance control, balance recovery, and muscle electrical activity in adults over 50 years. Aim 1: Determine muscle activity and balance control during a sit-to-stand in adults age above 50 with and without diabetic peripheral neuropathy. Aim 2: Assess local balance recovery and latency responses to lateral surface perturbation during quiet standing.

Detailed description

Diabetic peripheral neuropathy (DPN) is a common condition affecting patients with diabetes. The prevalence of DPN increases with age and the duration of having diabetes. Approximately 30% of patients with diabetes have peripheral neuropathy globally, and 4.5 million Americans have DPN. DPN typically affects more distal peripheral nerve branches, resulting in sensory loss. DPN causes axonal damage and leads to a loss of muscle strength. These degenerative effects significantly contribute to fall risks and feelings of instability. Falls most commonly occur during transitional tasks such as the sit-to-stand (STS) and stand-to-sit (StandTS). The overall objective of this study to assess the effects of DPN on balance control and muscle activity during transitional tasks (STS and StandTS) and during lateral perturbation while standing. Study procedures: 1. Measures of body weight, height, and limb diameter and measuring including leg length, knee width, elbow width, wrist width, and hand thickness. 2. Measures of sensation of the big toe and heel area for both legs. 3. Surface sensors will be placed on the leg muscles using non-allergic double adhesive tape. 4. Participants will sit down and stand up on a chair with adjustable height. 5. Then, participants will be asked to stand on a treadmill holding a ruler. The treadmill will slightly move left and right, and the muscle activity and balance control will be evaluated. 6. Finally, muscle strength of the legs' muscles will be collected.

Interventions

BEHAVIORALsit-to-stand

test for balance during movement from sitting to standing

BEHAVIORALstand to sit

test for balance during movement from standing to sitting

BEHAVIORALstanding perturbation

test for balance recovery following perturbation

OTHERMRI of sciatic nerve

measure of peripheral nerve diameter

Sponsors

Lisa Griffin
Lead SponsorOTHER

Study design

Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
BASIC_SCIENCE
Masking
NONE

Eligibility

Sex/Gender
ALL
Age
50 Years to 99 Years
Healthy volunteers
Yes

Inclusion criteria

* Type II diabetes with peripheral neuropathy

Exclusion criteria

* Foot ulcer * Partial amputation * Have experience of Stroke * Painful neuropathy * Inability to stand or walk independently

Design outcomes

Primary

MeasureTime frameDescription
Local dynamic stability using Motek and Vicon systemFirst session (immediately after intervention)Local dynamic stability will be assessed using the Motek treadmill and Vicon cameras. The Motek treadmill will provide the left and right perturbation, and the Vicon system will collect the kinematic data. Then, the MATLAB code will calculate local dynamic stability to identify impairment in balance recovery.
Center of pressure using force plateFirst session (immediately after intervention)Center-of-pressure sway will be assessed between groups. As participants sit down or stand up from a chair on the force plates, the ground reaction force will be collected. Then, using a mathematical approach, an ellipse will be fitted to the data to calculate the sway area. A higher sway area indicates an impairment during balance control.
Center of mass using Vicon cameras.First session (immediately after intervention)Center-of-mass sway volume will be assessed as the participant will walk in front of a high-speed camera, which will be recorded using the retroreflective markers. Then, a mathematical approach will be used to fit an ellipsoid to the data samples for each group. Higher sway volume means impairment in balance.
Joint moment using Nexus softwareFirst session (immediately after intervention)Joint moments will be assessed between groups using Vicon and force plates. This variable will be obtained using Nexus software, which combines both the inputs from Vicon and force plates.

Secondary

MeasureTime frameDescription
Muscle amplitude using root mean squareFirst session (immediately after intervention)Muscle amplitude will be collected using Delsys Tringo wireless surface electromyography (EMG). The EMG electrodes will be attached with double adhesive tape. Then, EMG amplitudes will be assessed using the root mean square technique in MATLAB software. Higher amplitude represents higher muscle activity.
Muscle onset time using electromyographyFirst session (immediately after intervention)The muscle onset time will be assessed using MATLAB code. An abrupt change in the EMG trace is defined as a muscle onset time. Any change in EMG onset time represents impairment in neuromuscular junctions or muscle and nerve electrical conduction.
Muscle co-activation index using EMGFirst session (immediately after intervention)The coactivation index will be assessed between two muscles in the same participant using MATLAB syntax. An increase in muscle coactivation represents an increase in active joint stiffness. This increase in active joint stiffness reduces the resultant joint moment, leading to impaired smoothness of movement and reducing the ability to perform daily activities.
Muscle energy frequency using EMG dataFirst session (immediately after intervention)Wavelet transform can identify the contribution of different muscle fibers (large or small, fast or slow twitch fibers) in the same task. Assessment of this method indicates what fiber type is affected by DPN.

Countries

United States

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026