Working…
COVID-19 is an emerging, rapidly evolving situation.
Get the latest public health information from CDC: https://www.coronavirus.gov.

Get the latest research information from NIH: https://www.nih.gov/coronavirus.
ClinicalTrials.gov
ClinicalTrials.gov Menu

Voice Changes During ECT (VAPRE)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04420793
Recruitment Status : Recruiting
First Posted : June 9, 2020
Last Update Posted : June 9, 2020
Sponsor:
Information provided by (Responsible Party):
Sean Christensen, Medical University of South Carolina

Tracking Information
First Submitted Date March 10, 2020
First Posted Date June 9, 2020
Last Update Posted Date June 9, 2020
Actual Study Start Date November 7, 2019
Estimated Primary Completion Date June 30, 2021   (Final data collection date for primary outcome measure)
Current Primary Outcome Measures
 (submitted: June 3, 2020)
  • Acoustic feature: zero crossing rate [ Time Frame: Throughout a course of electroconvulsive therapy (ECT) which may last between 2 and 7 weeks. ]
    crossings per second
  • Acoustic feature: energy and entropy [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    decibels
  • Acoustic feature: spectral centroid, spectral spread, spectral entropy, spectral flux, spectral rolloff [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    hertz
  • Acoustic feature: Mel-Frequency Cepstral Coefficients, Chroma Vectors, and Chroma Deviation [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    unitless
  • Linguistic features: question ratio, filler ratio, number ratio, type token ratio [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    unitless ratio
  • Linguistic features: verb frequency, noun frequency, pronoun frequency, adverb frequency, adjective frequency, particle frequency, conjunction frequency, pronoun frequency [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    percentage
  • Linguistic features: standardized word entropy [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    decibels/log(total word count)
  • Linguistic features: Brunets index [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    W (lexical richness)
  • Linguistic features: Honores statistic [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    R (lexical richness)
  • Linguistic features: rate of speech [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    words per minute
  • Meta-features: fatigue [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Machine learning approach to evaluate binary outcome: fatigued or awake
  • Meta-features: audio quality [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Machine learning approach to evaluate binary outcome: bad or good
  • Meta-features: sentiment [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Machine learning approach to evaluate binary outcome: sad or happy
  • Meta-features: stress [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Machine learning approach to evaluate binary outcome: stressed or not stressed
  • Meta-features: gender [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Machine learning approach to evaluate binary outcome: male or female
  • Meta-features: accent [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Machine learning approach to evaluate a categorical outcome of accent region: england, indian, australian, etc.
  • Meta-features: length [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    seconds
  • Meta-features: age [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Machine learning approach to evaluate estimated decade-age: 10s, 20s, 30s, 40s, 50s, 60s, 70s, 80s, 90s, etc.
  • Comparing the voice feature(s) with greatest statistically significant change to Patient Health Questionnaire (PHQ)-9 scores [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    The voice feature(s) found to have changed most significantly will be compared to Patient Health Questionnaire-9 scores which approach a total score that is less than 8, indicative of reduced depressive symptoms throughout Electroconvulsive Therapy
  • Acoustic Feature Specific Changes within and across sessions [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Generalized mixed linear model will be used to evaluate which acoustic features change with P value threshold of <0.05
  • Linguistic Feature Specific Changes within and across sessions [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Generalized mixed linear model will be used to evaluate which linguistic features change with P value threshold of <0.05
  • Meta-Feature Specific Changes within and across sessions [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Generalized mixed linear model will be used to evaluate which meta features change with P value threshold of <0.05
Original Primary Outcome Measures Same as current
Change History No Changes Posted
Current Secondary Outcome Measures
 (submitted: June 3, 2020)
  • Acoustic Feature Specific Changes between sessions [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Acoustic voice features will be evaluated using continuous averages using one sample t-tests and we will be testing the difference over time to a null hypothesis of 0 (for no change) in the one sample t-test.
  • Linguistic Feature Specific Changes between sessions [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Linguistic voice features will be evaluated using continuous averages using one sample t-tests and we will be testing the difference over time to a null hypothesis of 0 (for no change) in the one sample t-test.
  • Meta-Feature Specific Changes between sessions [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Meta-voice features will be evaluated using continuous averages using one sample t-tests and we will be testing the difference over time to a null hypothesis of 0 (for no change) in the one sample t-test.
  • Patient Chart Review Data: Age [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    years
  • Patient Chart Review Data: inpatient/outpatient status [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    binary: inpatient or outpatient
  • Patient Chart Review Data: gender [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    male, female, unspecified
  • Patient Chart Review Data: race [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    african american, caucasian, hispanic, asian american, etc.
  • Patient Chart Review Data: Psychiatric diagnosis for ECT [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Diagnosis indicated for receiving ECT
  • Patient Chart Review Data: PHQ-9 score at each session [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Total (0-27) on 9 question scale
  • Patient Chart Review Data: Psychiatric hospitalizations [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Number
  • Patient Chart Review Data: Past Psychiatric Medication Trials [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Number
  • Patient Chart Review Data: Current Psychiatric Medication Trials [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Number
  • Patient Chart Review Data: Classes of Current Psychiatric Medications [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Categorical: Sedative, Selective Serotonin Reuptake Inhibitor, etc.
  • Patient Chart Review Data: Suicidal Ideation at ECT consult [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Binary: yes/no
  • Patient Chart Review Data: Past Suicide Attempts [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Number
  • Patient Chart Review Data: Psychiatric Review of Systems [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Descriptive, categorical
  • Patient Chart Review Data: Family Psychiatric History [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Categorical regarding psychiatric diagnoses of family members
  • Patient Chart Review Data: Number of non-psychiatric medical diagnoses [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Number
  • Patient Chart Review Data: Past non-psychiatric medication trials [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Number
  • Patient Chart Review Data: Current non-psychiatric Medications [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Number
  • Patient Chart Review Data: Tobacco use history [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Binary (yes/no)
  • Patient Chart Review Data: Tobacco use pack year [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    pack-year (total years smoked*average packs per day)
  • Patient Chart Review Data: Prior ECT treatment [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Binary (yes/no)
  • Patient Chart Review Data: total # of prior ECT treatments for response in the past [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Numerical
  • Patient Chart Review Data: Prior transcranial magnetic stimulation (TMS) treatment [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Binary (yes/no)
  • Patient Chart Review Data: Prior response to ECT or TMS [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Categorical
  • Patient Chart Review Data against voice features [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    We will evaluate voice features with statistically significant changes against respective participant chart review data using a general linear model with a clustering component for repeated measures which may also be applied to contrast statements evaluating overall change over time (from baseline to end).
  • Generating ROC curves: stress [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Clinically determined stress level (mood) will be compared to meta-feature extractions of stressed vs. not stressed from voice recordings will be used to calculate an area under the receiver operating curve (AUOC)
  • Generating ROC curves: fatigue [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Clinically determined fatigue (motor) will be compared to meta-feature extractions of fatigued vs. awake from voice recordings will be used to calculate an area under the receiver operating curve (AUOC)
  • Generating ROC curves: sentiment [ Time Frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks. ]
    Clinically determined sentiment (affect and mood) will be compared to meta-feature extractions of happy vs. sad from voice recordings will be used to calculate an area under the receiver operating curve (AUOC)
Original Secondary Outcome Measures Same as current
Current Other Pre-specified Outcome Measures Not Provided
Original Other Pre-specified Outcome Measures Not Provided
 
Descriptive Information
Brief Title Voice Changes During ECT
Official Title Voice Analysis in Patients Receiving Electroconvulsive Therapy
Brief Summary Depressed patients talk differently when they are depressed compared to when they are well. But it is hard to actually measure what the differences are. The study team will record voice samples from patients with mood disturbances, like depression, over the course of their receiving an electroconvulsive therapy (ECT) series. The study team will try and measure or quantify exactly what has changed in their speech and voice. The study team will choose ECT as it is one of the most effective and rapid treatment for depression. The study team will use a service provided by a company, NeuroLex, who has complex computer programs (artificial intelligence, AI) to analyze the voice samples.
Detailed Description Not Provided
Study Type Observational
Study Design Observational Model: Case-Only
Time Perspective: Prospective
Target Follow-Up Duration Not Provided
Biospecimen Not Provided
Sampling Method Non-Probability Sample
Study Population Patients undergoing electroconvulsive therapy (ECT) at the Medical University of South Carolina on an outpatient or inpatient basis.
Condition
  • Unipolar Depression
  • Bipolar Depression
  • Bipolar Disorder, Manic
Intervention Other: Questionnaire
3 voice recording tasks and 1 text entry task will be performed.
Study Groups/Cohorts ECT and Voice Recorded Group
This is an add-on study of voice samples to be gathered during ECT clinical treatments. The ONLY research procedures are four tasks on an online form, one text task and three voice recording tasks. These voice recordings will take place in a private room on the 5th floor of the Institute of Psychiatry on the same day of a patient's ECT treatment. The questionnaire will take less than 10 minutes.
Intervention: Other: Questionnaire
Publications * Not Provided

*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Recruitment Information
Recruitment Status Recruiting
Estimated Enrollment
 (submitted: June 3, 2020)
50
Original Estimated Enrollment Same as current
Estimated Study Completion Date June 30, 2021
Estimated Primary Completion Date June 30, 2021   (Final data collection date for primary outcome measure)
Eligibility Criteria

INCLUSION CRITERIA:

  • Any candidate for electroconvulsive therapy who is about to initiate their ECT course at Medical University of South Carolina (MUSC) for a clinically indicated diagnosis
  • Age 18 to 90 years old
  • Able to speak and understand English
  • Able to give consent to participate in the study

EXCLUSION CRITERIA:

  • Any medical condition that limits the ability to speak or speak clearly, for example a history of head and/or neck cancer, spinal cord injury affecting speech, amyotrophic lateral sclerosis, or those with absence of critical anatomical structures involved in speech.
  • Any voice characteristics that may limit the ability to speak English clearly including speech impediments or heavy accents (as evidenced by the pronunciation of the English language in such a non-standard way that research staff). If study staff have significant difficulty understanding the participant's responses in conversation, this may warrant exclusion.
  • Patients who are receiving ECT by involuntary order, by order of their guardian, or by a court order, as evidenced by patient report or brief chart review.
  • Patients who elect to not receive their full course of ECT at MUSC.
Sex/Gender
Sexes Eligible for Study: All
Ages 18 Years to 89 Years   (Adult, Older Adult)
Accepts Healthy Volunteers No
Contacts
Contact: Sean Christensen, MD 843-792-8013 christse@musc.edu
Listed Location Countries United States
Removed Location Countries  
 
Administrative Information
NCT Number NCT04420793
Other Study ID Numbers 00089198
Has Data Monitoring Committee No
U.S. FDA-regulated Product
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
IPD Sharing Statement
Plan to Share IPD: No
Responsible Party Sean Christensen, Medical University of South Carolina
Study Sponsor Medical University of South Carolina
Collaborators Not Provided
Investigators
Principal Investigator: Sean Christensen, MD Medical University of South Carolina
PRS Account Medical University of South Carolina
Verification Date June 2020