Haptic Vibrotactile Trigger Technology: Disrupting the Neuromatrix to Reduce Pain Severity and Interference: Results from the HARMONI Study
Bibliographic record
Abstract
The prevalence of pain and pain-related diseases are so vast that they are the leading causes of disability and disease burden across the globe. Over 100 million people are estimated to live with chronic or recurrent pain in the United States and it is the most common reason patients consult primary care clinicians. Conventional pharmacological treatments for pain have been associated with dangerous adverse effects. Identifying effective, alternative treatment strategies, including those that are non-invasive and non-pharmacologic and that have reduced and limited side effect profiles, will provide options that may be preferable in how clinicians traditionally treat pain. Understanding the pain neuromatrix may assist in identifying these alternative approaches that reduce pain severity and interference, and that improve patient outcomes. The neuromatrix of pain is a network of neuronal pathways and circuits responding to sensory (nociceptive) stimulation. Researchers have shown that these pathways and areas of the brain that are associated with the neuromatrix can change in response to external stimuli. Haptic vibrotactile trigger technology (VTT) is designed to target the nociceptive pathways and theorized to disrupt the neuromatrix of pain. The technology has been incorporated into non-invasive, non-pharmacological topical patches and other routes of delivery. The purpose of this IRB-approved, minimal risk study was to evaluate patients’ experiences and/or perceptions and patient response for those who received a haptic vibrotactile trigger technology (VTT) embedded non-pharmacologic, non-invasive, over-the-counter pain patch (FREEDOM Super Patch with VTT; Srysty Holding Co, Toronto, Canada). Methods: Baseline, 7- and 14-day data were recorded in one hundred forty-eight (148) adult subjects (96 females and 52 males) with a mean age of 53 years who presented with mild, moderate and even severe musculoskeletal, arthritic and neurological pain. The study evaluated changes in overall severity and interference scores via a validated scale (Brief Pain Inventory (BPI)), changes in the use of prescription and OTC medications, patient satisfaction, and any side effects reported while using the patch. Future analyses will compare the outcomes reported here with non-active control and crossover treatment groups. Results: The results showed statistically significant decreases in mean BPI severity and interference scores after using the VTT embedded pain patch. After 14 days, the vast majority of patients reported “less” or “a lot less” usage of oral medications and were very/extremely satisfied with the patch. Results also showed statistically significant and positive outcomes in all measured Quality of Life (QoL) components with improvements in general activity, mood, relations with other people, sleep, normal work, walking ability, and enjoyment of life. Conclusions: Study results indicate that this non-pharmacologic, non-invasive, haptic vibrotactile trigger technology (VTT) embedded topical patch reduces pain severity and interference scores and may reduce the use of concurrent medications, including prescribed anti-inflammatory and other oral medication for adult patients with arthritic, neuropathic, and musculoskeletal pain. Results reported suggest that the non-pharmacological topical pain patch has incredible potential to be added to the current arsenal of noninvasive and nonpharmacological pain therapies.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".