Hypnosis-Based Machine Learning Biomarker Study
Purpose
This study seeks to contribute to the growing body of literature on hypnosis by providing robust, data-driven insights into the physiological mechanisms underlying trance states. The integration of electroencephalogram (EEG) and other wearable-derived physiological data will offer a comprehensive assessment of the changes that occur during a standardized hypnosis protocol: the Harvard Group Scale of Hypnotic Susceptibility (HGSHS:A). The results of this study are intended to facilitate derivation and validation of an Artificial Intelligence/Machine Learning (AI/ML)-based monitor that quantifies a patient's instantaneous emotional/arousal state along the spectrum that spans anxiety through states of calmness and trance. Future investigations will explore the ability of using such an interactive virtual system as a component of a closed-loop adaptive device to create optimal states of non-pharmacological sedation using personalized audiovisual content to allay anxiety and discomfort during medical procedures, such as percutaneous biopsies.
Conditions
- Disorder; Trance
- Anxiety
Eligibility
- Eligible Ages
- Between 18 Years and 65 Years
- Eligible Sex
- All
- Accepts Healthy Volunteers
- Yes
Inclusion Criteria
- Written informed consent obtained from participant and ability and willingness for participant to comply with the requirements of the study. - Adults of all genders, ages 18-65 - Healthy volunteers - English-speaking
Exclusion Criteria
- Participating currently in experimental drug trials. - Recent (<1 year) or current history of substance use disorder. - Diabetes T1 or T2, major cardiovascular or respiratory diseases, major neurological diseases, or limited mobility - Presence of a condition or abnormality that in the opinion of the investigator would compromise the safety of the patient or the quality of the data. - Adults that cannot consent. - Chronic use of psychoactive medications. - Chronic use of antiepileptic medications. - Active substance use disorder. - Participants reporting significant phobias or anxiety disorders triggered by imagery or situations involving insects (specifically flies), enclosed spaces or elevators (claustrophobia), or heights (acrophobia).
Study Design
- Phase
- N/A
- Study Type
- Interventional
- Allocation
- N/A
- Intervention Model
- Single Group Assignment
- Intervention Model Description
- This project will facilitate within-subjects and between-subjects comparisons in a limited number of subjects exposed to a standardized hypnotic susceptibility protocol -- the Harvard Group Scale of Hypnotic Susceptibility (HGSHS:A). This is a widely used test to assess an individual's susceptibility to hypnotic suggestion. The State Trait Anxiety Inventory (STAI) will be perform immediately prior to and following the hypnotic protocol. Together, these reliable and valid data will serve as the gold standards to facilitate training of a machine-learning model that will attempt to use physiological data (EEG and wearable-derived) and high-resolution audiovisual recordings of subject/hypnotist responses to develop a patient monitoring tool that measures emotional/arousal states spanning the spectrum from anxiety through calm.
- Primary Purpose
- Supportive Care
- Masking
- None (Open Label)
Arm Groups
| Arm | Description | Assigned Intervention |
|---|---|---|
|
Experimental Guided Hypnosis |
All participants will undergo a single, standardized hypnotic susceptibility protocol using the Harvard Group Scale of Hypnotic Susceptibility: Form A (HGSHS:A). |
|
Recruiting Locations
More Details
- Status
- Recruiting
- Sponsor
- Icahn School of Medicine at Mount Sinai
Detailed Description
This is an interventional study that will acquire data to characterize the time course of physiological biomarkers and audiovisual observations of depth of trance before, during and upon emergence from a standardized hypnotic susceptibility protocol. The subjects will also complete the State Trait Anxiety Inventory (STAI) immediately before and after the hypnosis protocol. The differences in the biomarker signals among subjects of different degrees of hypnotic susceptibility and different pre-post levels of state anxiety will facilitate between- and within-subjects comparisons that will be supplemented by computer vision analysis of subject responses. The full data set will be used to facilitate derivation and validation of a novel machine-learning monitoring tool to measure instantaneous emotional/arousal levels along a spectrum that spans anxiety through calmness and trance.
