Testing a Music Listening mHealth Intervention for Stress Reduction in Early Recovery (CalmiFy II)

NCT07088237 | Not Yet Recruiting DMC

• 2 other identifiers: 20691R61AA031474
• Study Type: interventional
• Target: 30
• Locations: 0 countries
• Sites: N/A

Brief Summary

The overarching goal of this study is to develop and examine the feasibility of a music-listening intervention that can be deployed in "real time" to regulate emotions and reduce momentary stress among young adults within the first 12 months of recovery from alcohol use disorder. The investigators design the study with two phases to address three aims: Phase I includes the first two aims. For Aim 1, the investigators will conduct formative research with a sample of young adults who have are within 12 months of recovery (N = 30) to identify features of music selections that are most effective in reducing momentary stress in real-world, ambulatory settings. For Aim 2, the investigtors will focus on developing mobile health technology that uses passive sensing and machine learning to automatically predict moments of heightened stress in real-time and suggest specific musical selections when stress is detected. During Phase II (Aim 3), the investigators will test the feasibility of a novel music-listening intervention among a second unique sample of young adults who are within 12 months of recovery from AUD (N = 30). This protocol refers only to Phase II of the larger study.

Conditions

Alcohol Use Disorder (AUD)

Keywords

recovery; alcohol use disorder; stress; music listening

Outcome Measures

Primary Outcomes (16)

• Skin Conductance Response (SCR) Rate

  Reported as SCRs/minute and refers to duration-weighted rate of SCR across all clean segments of the electrodermal activity (EDA) data collected via the EmbracePlus wearable device.

  Assessed during the 14-day ambulatory assessment phase

• Skin Conductance Response (SCR) Amplitude

  Calculated from the period of the beginning / onset of the SCR to the peak value within the SCR (μS / μmho) across all clean segments of the electrodermal activity (EDA) data collected via the EmbracePlus wearable device. It is essentially a delta function from SCR onset to the SCR peak as determined by the change in the tonic EDA.

  Assessed during the 14-day ambulatory assessment phase

• Skin Conductance Response (SCR) Rise Time

  Reported as number of seconds (secs) and refers to the time taken from SCR onset to reach peak amplitude within the SCR across all clean segments of the electrodermal activity (EDA) data collected via the EmbracePlus wearable device.

  Assessed during the 14-day ambulatory assessment phase

• Skin Conductance Response (SCR) Decay Time

  Time (microseconds) taken by the SCR to drop from the apex to the minimum of the peak SCR across all clean segments of the electodermal activity (EDA) data collected via the EmbracePlus wearable device.

  Assessed during the 14-day ambulatory assessment phase

• Skin Conductance Response (SCR) Width

  The width of the SCR peak from onset to recovery duration measured in seconds (secs). Encompasses both rise and decay phases across all clean segments of the electrodermal activity (EDA) data collected via the EmbracePlus wearable device.

  Assessed during the 14-day ambulatory assessment phase

• Skin Conductance Response (SCR) Area Under the Curve (AUC)

  The baseline-corrected area under the phasic curve per SCR measured as µS·s across all clean segments of the electrodermal activity (EDA) data collected via the EmbracePlus wearable device.

  Assessed during the 14-day ambulatory assessment phase

• MeanNN

  A time-domain feature of the HRV data that refers to the mean RR interval. measured in milliseconds (ms) collected via the EmbracePlus wearable device.

  Assessed during the 14-day ambulatory assessment phase

• SDNN

  A time-domain feature of the HRV data that refers to the standard deviation of all the NN intervals for each 5 minute segment of a 24 hour HRV recording. measured in milliseconds (ms) collected via the EmbracePlus wearable device.

  Assessed during the 14-day ambulatory assessment phase

• RMSSD

  A time-domain feature of the HRV data that refers to the root mean square of successive RR interval differences. measured in milliseconds (ms) collected via the EmbracePlus wearable device.

  Assessed during the 14-day ambulatory assessment phase

• pNN50

  A time-domain feature of the HRV data that refers to the percentage of successive RR intervals that differ by more than 50 milliseconds. measured as a percentage (%); collected via the EmbracePlus wearable device.

  Assessed during the 14-day ambulatory assessment phase

• pNN20

  A time-domain feature of the HRV data that refers to the percentage of successive RR intervals that differ by more than 20 milliseconds. measured as a percentage (%); collected via the EmbracePlus wearable device.

  Assessed during the 14-day ambulatory assessment phase

• CVNN

  A time-domain feature of the HRV data that refers to the ratio of the SDNN/MeanNN to represent normalized HRV; collected via the EmbracePlus wearable device

  Assessed during the 14-day ambulatory assessment phase

• SD1

  A nonlinear Poincaré measure of short-term HRV that measures short-term HRV in milliseconds (ms) and correlates with baroreflex sensitivity (BRS), which is the change in IBI duration per unit change in BP, and HF power. The RMSSD is identical to the non- linear metric SD1, which reflects short-term HRV. SD1 predicts diastolic BP, HR Max - HR Min, RMSSD, pNN50, SDNN, and power in the Low Frequency (LF) and High Frequency (HF) bands, and total power during 5 minute recordings. Collected via the EmbracePlus wearable device.

  Assessed during the 14-day ambulatory assessment phase

• SD2

  A nonlinear Poincaré measure of long-term HRV that measures the standard deviation of each point from the y = x + average R-R interval. The SD2 provides a measure of short- and long-term HRV in milliseconds (ms) and correlates with Low Frequency (LF) power. Collected via the EmbracePlus wearable device.

  Assessed during the 14-day ambulatory assessment phase

• SD1/SD2

  A nonlinear Poincaré measure that captures the ratio of short- to long-term HRV and is calculated as the ratio of SD1/SD2. SD1/SD2 measures the unpredictability of the RR time series and and is correlated with the LF/HF ratio. Collected via the EmbracePlus wearable device.

  Assessed during the 14-day ambulatory assessment phase

• Music Listening History

  Music listening history will be collected via Spotify by requesting a complete streaming history record for each participant during the study period.

  Assessed during the 14-day ambulatory assessment phase

Secondary Outcomes (3)

• Satisfaction with Study

  Within 7 days of study completion.

• Time-line followback (TLFB) measure of alcohol use

  Within 7 days of study completion.

• Recollections of stressful events

  Within 7 days of study completion.

Study Arms (2)

Stress Feedback

ACTIVE COMPARATOR

This arm includes only the stress feedback component. The stress feedback draws on a skills-based model of emotion regulation that emphasizes the ability to identify and label emotions, followed by either actively modifying negative emotions or accepting negative emotions when necessary. Participants will receive a prompt to identify their current emotion, followed by questions regarding their current context.

Behavioral: Stress Feedback

Music Listening + Stress Feedback

EXPERIMENTAL

This arm includes both the stress feedback component and the music listening component. The music listening component is an adaptive playlist that is updated as changes in the user's stress level are detected. To provide personalized music recommendations, we use a supervised learning approach to design an algorithm, referred to as music feature prediction, which predicts optimal values of music features (e.g., energy, valence, instrumentalness, acousticness) that are hypothesized to result in reducing stress. These feature values, referred to as effective music features, are then used to generate a personalized music playlist.

Behavioral: Stress Feedback; Behavioral: Music Listening

Interventions

Stress Feedback BEHAVIORAL

The stress feedback draws on a skills-based model of emotion regulation that emphasizes the ability to identify and label emotions, followed by either actively modifying negative emotions or accepting negative emotions when necessary. Participants will receive a prompt to identify their current emotion, followed by questions regarding their current context.

Music Listening + Stress Feedback; Stress Feedback

Music Listening BEHAVIORAL

For the music recommendation component, our system suggests music that is tailored to the individual and the specific context. Because we will use machine learning to predict optimal music features based on physiological, contextual, and musical data, the music items will be naturally suggested based on current emotion and level of intensity as well as the current context and problem type. The music recommendation component is an adaptive playlist that is updated as changes in the user's stress level are detected. To provide personalized music recommendations, we use a supervised learning approach to design an algorithm, referred to as music feature prediction, which predicts optimal values of music features (e.g., energy, valence, instrumentalness, acousticness) that are hypothesized to result in reducing stress. These feature values, referred to as effective music features, are then used to generate a personalized music playlist.

Music Listening + Stress Feedback

Eligibility Criteria

• Age: 18 Years - 35 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64)

You may qualify if:

• Subject can and has signed an Institutional Review Board (IRB) approved informed consent form (ICF).
• Age ≥18 and ≤35 years.
• In early-stage recovery for alcohol use (within 12 months)
• Own a smartphone with a data plan
• Not experiencing symptoms of severe depression
• Not experiencing thoughts of suicide
• Meets the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) diagnostic criteria for alcohol use disorder (AUD)
• Not currently taking medication treatment for opioid use disorder (OUD)
• Able to speak and read English

You may not qualify if:

• Currently experiencing symptoms of severe depression
• Currently experiencing thoughts of suicide
• Currently taking medication treatment for opioid use disorder (OUD)
• Are unable to provide voluntary informed consent.
• Cannot read or speak English.

Sponsors & Collaborators

• Washington State University: lead
• National Institute on Alcohol Abuse and Alcoholism (NIAAA): collaborator

MeSH Terms

Conditions

Alcoholism

Condition Hierarchy (Ancestors)

Alcohol-Related Disorders; Substance-Related Disorders; Chemically-Induced Disorders; Mental Disorders

Study Officials

• Michael J Cleveland, Ph.D.

  Washington State University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Michael J Cleveland, Ph.D.

CONTACT

509-335-3816; michael.cleveland@wsu.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: DOUBLE
• Who Masked: PARTICIPANT, INVESTIGATOR
• Purpose: TREATMENT
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: A micro-randomization process will be used to determine when a participant receives the music-listening intervention. Each participant's day will be divided into six, 2-hour blocks for delivery of the music listening intervention. The start time for each day will vary depending on when they began wearing the sensor device and will be fixed once the day begins. Thus, each day comprises 12 hours (720 minutes), within which each minute is a candidate for an available decision point. Machine learning algorithms will use real-time physiological signals from the sensor device to classify minutes as probably stressed or probably not stressed. Minutes, stratified by stress-classification and time of day, will be randomly allocated (micro- randomized) to deploy either the (1) stress feed- back component alone, or (2) the stress feedback component plus the music listening recommendation.

Study Record Dates

• First Submitted: July 18, 2025
• First Posted: July 28, 2025
• Study Start (Estimated): December 1, 2026
• Primary Completion (Estimated): March 1, 2028
• Study Completion (Estimated): March 1, 2028
• Last Updated: May 5, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, ANALYTIC CODE
• Time Frame: Sharable scientific aggregate data generated from this project will be made available as soon as possible, and no later than 3 years past the end of the funding period. The duration of preservation and sharing of the data will be a minimum of 5 years after the funding period.
• Access Criteria: Individuals from the scientific community will be able to access the IPD and supporting information. Data will be discoverable online through standard web search of the study-level metadata as well as the persistent pointer from the DOI to the dataset