Measurement of Osteoarthritic Patient Pain Through Electrodermal Activity Signals

Not Applicable

Not yet recruiting

• Conditions: Osteo Arthritis Knee

• Registration Number: NCT06701461
• Lead Sponsor: Dartmouth-Hitchcock Medical Center

Brief Summary

This pilot study aims to investigate the viability of using a smartwatch-based electrodermal activity (EDA) sensor to capture enough EDA signal to quantitatively assess pain in osteoarthritis subjects and test the feasibility of its methods and procedures for later use in subsequent larger-scale studies.

Detailed Description

Chronic pain, a disease in its own right, afflicts one in three adults in the US and poses an enormous economic burden ($560-$635 billion annually), more than heart disease, cancer, and diabetes. To treat pain, doctors often prescribe opioids to suffering patients. Paradoxically, prescription opioid abuse has become a national epidemic, costing $500 billion annually in medical, economic, social, and criminal ramifications. However, the development of effective treatment for chronic pain is hampered by the lack of a reliable biomarker that can quantify the level of pain and detect any attenuation after treatment. This is reflected in the failed statistical significance in many clinical trials of drugs for managing chronic pain (e.g., ONO-2952 and Ibodutant) or a large enrollment number being required to reveal significant but small effects (e.g., 1,798 enrollments for the trial on Renzapride).

Dysfunction of the autonomic nervous system (ANS) has been linked with many chronic pain conditions. The ANS is the primary pathway in brain-gut communication and manifests the body's emotional and psychological states. This makes it particularly relevant to pain, which has a strong emotional component. The ANS includes the sympathetic (SNS) and parasympathetic nervous systems (PNS), and chronic pain conditions reportedly correlate with an unchecked predominance of SNS activity and desensitized PNS. Thus, the PNS and SNS are promising targets for developing sensitive and robust biomarkers for chronic pain.

The investigators will leverage the EmbracePlus smartwatch for the non-invasive quantification of both SNS and PNS activities with time- and frequency-domain analysis of EDA. In this proposed pilot study, the investigators aim to establish whether this biomarker for quantifying pain levels shows promise for osteoarthritis patients when detected through a smartwatch. This is intended to be preliminary work to support a grant application for a more extensive study. In this work, the investigators will collect EDA measurements across up to 15 subjects (2/3 with symptomatic osteoarthritis (Kellgren-Lawrence grade \>= 3) and 1/3 control). Each participant's baseline response will first be measured using a thermal grill (a research device commonly used to induce a painful stimulus without injury). Participants will also report their results using a VAS. Then, Participants will be put through three OARSI standardized functional tests: the 30-second chair test, the 40m fast-paced walk, and the stair climb test. During these tests, subjects will receive a handheld clicker to mark moments of their sharpest pain. The results of each test will then be analyzed through a set of time- and frequency-domain analyses of the recorded bio-signals to extract key parameters and measure how well EDA signal detection captured both sharp and dull pain in subjects.

If effective, this method can be particularly useful. Existing commercial wearable sensors can collect patient data for a week at a time. This would allow for the collection of in-vivo and continuous patient pain data, which could greatly enhance the understanding of patient pain both pre- and post-treatment.

Study Timeline

• Study First Submitted: 2024-11-15
• Study First Submitted QC: 2024-11-19
• Study First Posted: 2024-11-22
• Status Verified: 2025-04
• Study Start: 2025-04
• Last Update Submitted: 2025-04-04
• Last Update Posted: 2025-04-08
• Primary Completion: 2025-05
• Study Completion: 2025-07

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 15

Inclusion Criteria

• Kellgren-Lawrence Grade >= 3

Exclusion Criteria

• Inflammatory arthropathy (e.g., rheumatoid arthritis), BMI >=35

Control subjects:

Exclusion Criteria:

• Complaints of lower extremity joint pain
• Known diagnosis of knee osteoarthpathy
• Prior history of knee surgery, knee injections, or injury to knee joints (e.g., meniscus tears, ligamentous injuries), BMI >=35

Both groups, exclusion:

• Subjects with chronic heart problems, including, but not limited to, chronic hypertension, heart palpitations, a weak or irregular heartbeat, or a previous heart attack,

• Subjects taking the following drugs within 12 hours of the experiment or during the experiment: caffeine, alcohol, psychoactive drugs, nicotine or marijuana, other recreational drugs, and medicine of any kind that is not normally taken daily. Such medications include;

  • NSAIDs
  • Acetaminophen
  • Steroidal anti-inflammatory agents
  • Bronchodilators
  • Appetite suppressants
  • Lipase inhibitors

• Women who are currently pregnant

• Subjects with Raynaud's syndrome

• Subjects with any of the following conditions: active skin lesions where EDA sensors are, vertigo or dizziness, and anyone with postural orthostatic tachycardia syndrome (POTS), peripheral neuropathy, seizure disorders, methicillin-resistant staphylococcus aureus (MRSA), impaired circulation, medical implants, open skin lesions, chronic eczema on hands where EDA and electrical stimulator's electrodes are attached, diabetes, and epilepsy.

• Participants who have a skin sensitivity to metals, have a pacemaker or defibrillator, or have recent head trauma within the past two weeks (even without loss of consciousness)

• Participants who cannot feel physical pain or have a history of self-harm

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: SINGLE_GROUP

Primary Outcome Measures

Name	Time	Method
:Difference in pain measurements as reported by the participant via a visual analogue scale versus smartwatch reported electrodermal activity sensors	40 minutes of test time, occurring all in one day.	As a pilot study, the goal is not hypothesis testing but rather examining the feasibility of this approach to measuring patient pain in osteoarthritic patients. Thus, the statistical analysis will be primarily descriptive. The baseline experiment is being done to see whether the smartwatch measurements are at all comparable to prior similar experiments using the thermal grill (Posada-Quintero 2016; Posada-Quintero 2021). For this reason, though not typical for a pilot study, participant responses to the baseline test will use similar analysis to prior electrodermal activity (EDA) based thermal grill studies to see if the smartwatch poses a viable EDA signal collection source. This primarily consists of repeated measures analysis of variance (ANOVA) comparing patient-reported visual analog scale (VAS) scores to EDA output.
Patient-reported pain during standardized Osteoarthritis Research Society International (OARSI) 30-second chair test	5 minutes	Patients will be asked to participate in the standardized OARSI 30-second chair test. During the OARSI functional tests, the timestamps of patient reports of pain will be recorded by the experimenter.
Electrodermal activity (EDA) signals during standardized Osteoarthritis Research Society International (OARSI) 30-second chair test	0 minutes. Concurrent with measurement of patient-reported pain.	A smartwatch will capture the patient's EDA signals during the 30-second chair test
Patient-reported pain during standardized Osteoarthritis Research Society International (OARSI) 40m fast-paced walk test	10 minutes	Patients will be asked to participate in the standardized OARSI 40m fast-paced walk test. During the OARSI functional tests, the timestamps of patient reports of pain will be recorded by the experimenter.
Electrodermal activity (EDA) signals during standardized Osteoarthritis Research Society International (OARSI) 40m fast-paced walk test	0 minutes. Concurrent with measurement of patient-reported pain.	A smartwatch will capture the patient's EDA signals during the 40m fast-paced walk test
Patient-reported pain during standardized Osteoarthritis Research Society International (OARSI) stair climb test	10 minutes	Patients will be asked to participate in the standardized OARSI stair climb test. During the OARSI functional tests, the timestamps of patient reports of pain will be recorded by the experimenter.
Electrodermal activity (EDA) signals during standardized Osteoarthritis Research Society International (OARSI) stair climb test	0 minutes. Concurrent with measurement of patient-reported pain.	A smartwatch will capture the patient's EDA signals during the stair climb test
Accuracy of predicting patient pain from electrodermal activity signal (EDA) during standardized Osteoarthritis Research Society International (OARSI) function tests	0 minutes. Done after all data has been collected.	We will treat the processed EDA signal as a binary classifier (i.e., prediction of pain or no pain at any given moment). The accuracy of these predictions will be calculated against the actual patient-reported pain.
Percentage of correctly identified pain events (Sensitivity) as measured by the smartwatch (True positive events)	0 minutes. Done after all data has been collected.	In these tests, we will treat the processed EDA signal as a binary classifier (i.e., prediction of pain or no pain at any given moment). The sensitivity of these predictions will be calculated against the actual patient-reported pain.
Percentage of correctly identified non-pain events (Specificity) as measured by the smartwatch (True negative non-events)	0 minutes. Done after all data has been collected.	In these tests, we will treat the processed EDA signal as a binary classifier (i.e., prediction of pain or no pain at any given moment). The specificity of these predictions will be calculated against the actual patient-reported pain.

Trial Locations

Locations (1)

Dartmouth-Hitchcock Medical Center; 🇺🇸 Lebanon, New Hampshire, United States