Reduction of Pain and Fatigue after Use of Over-The- Counter Socks Embedded With Haptic Vibrotactile Trigger Technology: Results from the Invigor Study
Bibliographic record
Abstract
Introduction: The prevalence of pain, pain-related diseases, and fatigue related issues are so vast that they are the leading reasons patients visit their primary care provider. Over 100 million people are estimated to live with chronic or recurrent pain and fatigue is estimated to affect more than 50% of the population of older adults. Conventional pharmacological treatments targeting the symptoms of pain and fatigue have been associated with dangerous adverse effects. Clinicians are continuously trying to identify effective, alternative treatment strategies to address pain and fatigue, especially those that are non-invasive and non-pharmacologic with limited side effect profiles. It is proposed that humans have a widely distributed and perhaps unique neural network or “neuromatrix” that contributes to the multidimensional experience of pain. This neuromatrix is genetically determined and influenced by multiple factors, of which sensory (nociceptive) input is only one. Researchers have shown that these pathways and areas of the brain that are associated with the neuromatrix can change in response to external stimuli. Understanding this complex pain neuromatrix may assist in identifying alternative approaches that reduce pain severity and interference and improve patient outcomes. There are various types of nerve fibers responsible for sensation and pain. A-β nerve fibers transmit information from Pacinian and Meissner corpuscles, which convey vibratory/sensory perception from the skin. According to the gate control theory of pain hypothesized decades ago by Melzack and Wall, vibration can stimulate inhibitory interneurons in the spinal cord that in turn act to reduce the amount of pain signal transmitted by A-δ and C transmitting pain fibers. The application of vibration has long been trialed for its analgesic effects. When you get a text or a call on your mobile phone, the vibration you feel is a form of haptic feedback. An enhanced technique known as haptic vibrotactile trigger technology (VTT) is designed to target the nociceptive pathways and theorized to disrupt the neuromatrix of pain. The technology is non-pharmacological and non-invasive, and has been incorporated into topical patches, wearable clothing, and other routes of delivery. The purpose of this IRB-approved, minimal risk observational study was to evaluate and compare patients’ experiences, perceptions and response for those who received haptic vibrotactile trigger technology (VTT) embedded non-pharmacologic, non-invasive, overthe-counter wearable device in the form of socks (Superneuro Haptic Vibrotactile Trigger Technology (VTT) Enhanced Socks; Srysty Holding Co, Toronto, Canada) versus those who did not. Methods: Baseline, 7- and 14-day data were recorded in 90 subjects who presented with pain and/or fatigue related issues or associated symptoms. The ‘active’ treatment group (TG) was comprised of eighty-five (85) adult subjects (61 females and 24 males) with a mean age of 54.8 years; there were five (5) adult subjects (3 females and 2 males) in the ‘inactive’ control group (CG). The study evaluated changes in overall pain severity, pain interference, and fatigue severity via validated scales including the BPI (Brief Pain Inventory) and the BFI (Brief Fatigue Inventory) as well as changes in the use of prescription and OTC medications, patient satisfaction, energy levels, and any side effects reported while using the VTT Enhanced socks. Future analysis will compare the outcomes reported here with a larger control as well as the addition of a crossover treatment group.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".