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

NCT ID: NCT07088237

Last Updated: 2025-07-28

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 30 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2026-12-01
• Study Completion Date: 2028-03-01

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. We design the study with two phases to address three aims: Phase I includes the first two aims. For Aim 1, we 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, we 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), we 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.

Detailed Description

The goal of this study is to 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 12 months of the detoxification from, or reducing, their alcohol use. For this phase, we will conduct a randomized controlled trial of a music-listening intervention that was developed in Phase I of the larger study. The music-listening intervention consists of a personalized Spotify playlist that is deployed when participants are experiencing increased levels of physiological signals of stress, which are detected automatically via a wearable sensor device (Empatica EmbracePlus wristband). This study is a pilot feasibility clinical trial that will be delivered via a micro-randomized trial (MRT) design that randomizes the intervention components in real-time. As a pilot study, primary outcomes include quantitative and qualitative data that measure feasibility and acceptability of the just-in-time intervention.

Participants will be recruited either from regional mental health agencies or from ongoing or completed studies within our college, and will provide data to assess feasibility of the developed mHealth music intervention in individuals in early stages of recovery from alcohol use disorder.

The music listening intervention will be deployed via a micro-randomized process that randomizes the intervention components each time the intervention may be delivered. The participants will be asked to wear the sensor device for 14 days, which will be paired with our music intervention app on a smartphone (hereafter referred to as the "CalmiFy" app). Participants will also be required to listen to music on their smart phone only through the Spotify app, using a Spotify premium account created specifically for the study. When randomized to receive the music listening intervention (a personalized Spotify playlist), participants will report their current context and problem type. Based on these responses, our system then suggests music that is tailored to the individual and the specific context. Participants will receive a notification when the intervention (i.e., Spotify playlist) is deployed. They will have the option of accepting it, delaying it for 5 minutes, or cancelling it altogether (i.e., they are not available).

Participants will be surveyed on days in which the music intervention was delivered to assess efficacy of the intervention by asking if the music selection was helpful (or if it actually increased stress) and whether they opt to continue to use the app for the following days. Within 7 days of completion of the 14-day trial, structured qualitative interviews will be scheduled with each participant to better understand their device usage and its efficacy. Guided by the unified theory of acceptance and use of technology (UTUAT), the interview will ask questions about acceptability, barriers and facilitators of using the app in their daily life, the degree to which continuous monitoring affected behavior, and interest in using the app in the future. The interview will include questions to better understand the detected stress events including validity of the automatic detection and context surrounding the event. Participants will also be asked to return the Empatica EmbracePlus sensor device at the time of the interview.

Study Procedures

Online pre-screening. Interested potential participants will first be directed to a secure online screening questionnaire that assesses basic inclusion criteria (age between 18 - 35 years, early-stage recovery, and own a smartphone with a data plan). The pre-screening survey will be administered via REDCap. The pre-screening survey will include preferred contact information, including mobile phone, text, and email address. Interested individuals who pass the initial screening, will be contacted by study staff in order to schedule an in-person visit where the subject will be asked a series of more detailed screening questions to determine their eligibility for the study. Those who do not meet the pre-screening criteria will be informed that they are not eligible for the study.

B2. Informed consent and In-person screening. Those who endorse the pre-screening criteria will take part in an in-person study entry interview where they will provide written informed consent and complete additional measures assessing eligibility. Informed consent will be assembled in writing for each participant to read and take home if they wish. Research coordinators will walk through the informed consent packet with the individual before they begin participation. The consent form will be signed electronically via REDCap, and the participant may take the paper copy home with them.

Prior to initiating the informed consent process, potential participants will also be asked to provide breath samples to determine their blood alcohol content (BAC). Participants whose BAC level is greater than 0.00, but less than 0.05, will be asked to either remain in the clinic until breath results indicate otherwise, or given the option of rescheduling their visit. Participants whose BAC results indicate impairment (> 0.05) will be given the option of either waiting in the clinic until BAC < 0.5 or, alternately, research staff will offer to schedule a ride-share company (e.g. Uber) to drive them to their home.

After reviewing the informed consent document with each participant, the research staff will administer the in-person screening survey. This survey will include the following components: 1) the Patient Health Questionnaire (PHQ-9); 2) the Ask Suicide-Screening Questions (ASQ) tool; and 3) the Alcohol Symptom Checklist (ASC).

The nine-item version of the Patient Health Questionnaire (PHQ-9), will be administered to ensure the absence of depressive symptoms. We will exclude individuals who indicate they are experiencing severe depressive symptoms, operationalized by a score of 20 or higher on the PHQ-9.

In addition to the PHQ-9, determination of imminent risk of suicide risk among potential subjects will be assessed using the Ask Suicide-Screening Questions (ASQ) tool developed by the National Institute of Mental Health (NIMH). The ASQ tool is a set of four brief suicide screening questions that takes less than 5 minutes to administer. If a subject answers "No" to the four questions, screening is complete for that subject and no intervention is necessary. If a subject answers "Yes" to any of the four questions, or refuses to answer, they will be considered a positive screen and an additional assessment will be administered to determine potential risk vs. imminent risk. If imminent risk is identified the clinical staff will be alerted immediately and the subject will be kept in sight. If potential risk is identified, the clinic staff will be notified and will administer a brief suicide safety assessment.

The 11-item Alcohol Symptom Checklist is a self-report questionnaire that asks patients whether they have experienced each of the 11 Alcohol Use Disorder (AUD) criteria within the past year. Each of the 11 items on the Alcohol Symptom Checklist maps onto one the 11 criteria for AUD as currently defined by the Diagnostic and statistical manual of mental disorders, 5th edition, published by the American Psychiatric Association. Patients indicate whether each AUD criterion was present or absent within the past year and Alcohol Symptom Checklist scores reflect AUD criteria counts that range from 0-11. Endorsing 2-3 criteria, 4-5 criteria, or 6-11 criteria is consistent with DSM-5 definitions for mild, moderate, or severe AUD, respectively. Participants who endorse at least 2 criteria will be eligible for the study

Baseline Survey and Orientation Session. Participants who meet full eligibility criteria will be directed immediately to the online baseline survey, which will be completed on a laptop computer in the private office using REDCap software. The survey is estimated to take 20 minutes to complete. After completing the baseline survey, the participants will be provided with a detailed explanation of the study procedures for each study component, focusing on instructions for wearing the sensor device, installing the CalmiFy app on their smartphone, and phone survey components. Training for the wearable wristband will include information about how to wear and remove the wristband, and how to care for the wristband.

The participants will also receive instructions about using the Spotify app during the orientation session. For participants with an existing Spotify account, this training will emphasize switching to the research Spotify account, rather than their personal account during the study period. As part of this process, participants with an existing Spotify account will be asked to transfer up to 5 of their Spotify playlists from their personal Spotify accounts to the research study Spotify account. To accomplish this, participants will follow these steps: 1) open their personal Spotify account; 2) open the desired playlist; 3) right-click on the playlist and select 'Invite Collaborators'; 4) share the link with the research study Spotify account; 5) log into the research study Spotify account and use the shared link to access the playlist; 6) save the playlist by creating a new personal copy that is not collaborative. The study coordinator will then assist the participant create a new playlist that is composed of the five songs indicated in the baseline survey (Survey item: "Please list below five (5) songs that you would use to calm down in a stressful situation").

For participants who do not have a personal Spotify account, the training will focus first on downloading the Spotify app to their mobile phone, followed by general instructions about using the Spotify app. The study coordinator will then assist the participant create a new playlist that is composed of the five songs indicated in the baseline survey (Survey item: "Please list below five (5) songs that you would use to calm down in a stressful situation").

Lastly, the study coordinator will ask each participant if they would like to schedule the brief music therapy session (conducted online via Zoom) at this time. If so, participants will be provided with a list of available dates and times from which they may confirm the session. Participants will also have the option of scheduling the brief music therapy session at a later time and such individuals will be informed that they will receive an email message within 24 hours that contains information about the sessions and an online link for scheduling. Each participant will be assigned a unique study ID number that will be used to link data from each of the study components to specific individuals.

Brief Music Therapy Session. Within 7 days of the baseline assessment, each participant will meet with a music therapist for a 30-minute training session via Zoom. The therapist will begin the session by assessing the client's stress levels and identifying any physical or emotional symptoms associated with stress. This information will be recorded and used to determine the appropriate music to use during the session. Based on the assessment, the therapist will select 2-3 songs that match the client's emotional and physiological state during the time of the session. After listening to each song with the participant, the therapist will discuss the musical elements of the song and encourage the participant to use the selected songs in their playlist for the next phase of the study. Additionally, in discussion with the participant, the music therapist will identify and note types of music genres or specific songs that may be associated with or triggering of the participant's alcohol use or misuse. Information about the stress and emotional levels of the participant, selected songs for stress regulation and a list of songs/type of music to be avoided for each participant, will be used to inform (feed) the development of the machine-learning algorithm for automated musical selection.

Pilot Feasibility Test. Following the orientation and music therapy sessions, the participants will be asked to wear the sensor device during waking hours for 14 consecutive days, which will be paired with our music intervention app (CalmiFy) on a smartphone.

Music-Listening Intervention. The music-listening intervention draws on input from the participant's personality profile, music listening habits and frequency and intensity of stress occurrence to initiate a skills-based model of emotion regulation that emphasizes the ability to first identify and label emotions, followed by either actively modifying negative emotions or accepting negative emotions when necessary.

The intervention consists of two components: 1) stress feedback that asks the participant to identify name their current emotion and level of intensity, using a 2-D grid of emotional valence and intensity; and 2) a personalized music-listening component. The stress feedback component will be delivered via a smartphone app developed for the proposed study called CalmiFy to determine the subjects' current emotional state and level of intensity.

The personalized music listening component is a Spotify playlist that is developed through a machine-learning algorithm. The machine-learning algorithm uses physiological readings from the Empatica EmbracePlus device, user's baseline survey information, and the user's music profile data from Spotify to determine optimal values of the music features (e.g., tempo, danceability, etc.) to create a personalized music playlist. The personalized music-listening recommendation will also incorporate information obtained from a brief music therapy training session, based on the iso principle, administered during the baseline assessment.

Micro-Randomization Process. The music-listening intervention will be deployed via a micro-randomized trial (MRT) design. The 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 feedback component alone, or (2) the stress feedback component plus the music listening recommendation. The probability of a minute being randomized to deliver the music-listening intervention or not will be balanced according to whether the current episode is classified as probably stressed or probably not stressed. Within a 2-hour block, this process is limited to one randomization occasion. Moments will be randomized to receive the music listening intervention or not in a 1:1 manner, and factors to ensure this 1:1 process will be included in the algorithm, including historical data of what has already been triggered that day. Other conditions include time since last intervention (at least 60 minutes), good quality data from the sensor device, not driving, phone battery at least 10\% and not engaged in physical activity. Participants will receive a notification when the intervention is sent to participants. They will have the option of accepting it, delaying it for 5 minutes, or cancelling it altogether (i.e. not available).

Intervention Deployment. As soon as a stress event is detected, users will be prompted to initiate a report that will determine the personalized intervention. Participants will be asked to first identify their current emotion and level of intensity. If participants report a positive emotion, they will be asked to confirm the absence of stress and the app will close. If a negative emotion is reported, participants will report their current context (e.g., interpersonal conflict, inconvenienced, unsafe surroundings). If the moment is randomized to receive the music listening component, our system will suggest music that is tailored to the individual and the specific context. Participants will be surveyed on days in which the music intervention was delivered with a 3-item questionnaire to assess the subjective view of the efficacy of interventions by asking whether they found the music selection helpful in managing and reducing stress, if the music selection was helpful and whether they opt in to continue to use the device for the following days. The survey will be administered through our current data collection infrastructure, called CalmiFy, which includes a front-end app along with back-send server and databases for collecting self-reported survey data. Each phone survey is expected to require only 1-2 minutes to complete.

Structured Qualitative Interview. Within 7 days of completion of the 14-day pilot study, structured qualitative interviews will be scheduled with each participant to better understand their device usage and its efficacy in stress management. The interview will last about 30 minutes and will include questions about usability, acceptability, barriers and facilitators of using the app in their daily life, the degree to which continuous monitoring affected behavior, and interest in using the app in the future. The interview will include questions to better understand the detected stress events, including validity of the automatic detection and the context surrounding the event. The interview session will also include administration of a timeline-follow back (TLFB) measure of recent alcohol use. In this procedure, participants will first be presented with a chart of the U.S. Standard Drink definition and then asked to indicate the number of drinks consumed on each calendar day across the 14-day assessment period. The interviews will be audio recorded for later transcription. Participants will also be asked to return the Empatica EmbracePlus sensor device at the time of the interview.

Statistical Methods

Data collected in the proposed study will comprise three types: 1) physiological data obtained from continuous monitoring via the EmbracePlus wearable device; 2) self-reported quantitative data obtained from baseline surveys and music intervention responses; and 3) qualitative data obtained from structured interviews at the conclusion of the pilot feasibility study. To account for missing data in the quantitative components, we will use full information maximum likelihood estimation (FIML) approaches,4 which have been shown to result in unbiased parameter estimates under many missing data situations in the context of longitudinal data, including under some violations of assumptions, which will also be assessed using Little's MCAR test.

Self-Reported Data. Prior to main analyses, we will conduct preliminary data screening of the self-reported quantitative data. Descriptive statistics and preliminary Pearson correlation analyses will be conducted to determine the univariate relations among all variables. Attrition analyses will be conducted on study variables and sociodemographic characteristics to determine significant differences between groups. Univariate and multivariate assumptions will also be assessed. Data will be screened for outliers and missing data and analysis decisions adjusted accordingly and as needed. This comprehensive screening will ensure accurate analysis in the later steps of the analysis plan.

Primary Outcomes: Physiological Data from Wearable Device. Preliminary steps will also assess the validity of the physiological data collected via the wearable sensor device, including EDA and HRV. We will use the recommended tools and procedure by the Empatica guidelines to remove artifacts and extract features of the EDA and HRV signals to be used in further analyses. The Empatica EmbracePlus computes the heart rate (HR) and the inter-beat intervals (IBI) from BVP (Blood Volume Pulse) signal. We will assess the validity of the IBI which provides heart rate variability (HRV).

The EDA peak detection analysis provides a set of features corresponding to each EDA peak. We will utilize the EDA-Explorer public scripts to detect the EDA peaks.6 Previous studies have shown peaks from the EDA signal correlate with emotional arousal in humans. Important EDA features include (1) EDA: the EDA value at Apex of the peak; (2) rise-time: time that takes the EDA peak to reach its maximum value; (3) max derivative; (4) amplitude (5) decay time: the time that takes the signal to drop from the Apex to the minimum of the peak; (6) SRC-width: the width of the peak (number of the samples); and (8) AUC: the area under the curve.

For heart rate signals, we will compute statistical features that are considered time-domain indices. These HRV measures are directly extracted from the IBI/RR interval signals. The RR interval is the interval between two successive heartbeats. We will measure mean of the RR interval (MRR), standard deviation of the RR interval (STDRR), root mean square successive difference of the RR intervals (RMSSD), coefficient of variance of the RR intervals (CVRR), mean of the heart rate (MHR), and standard deviation of the heart rate (SDHR). MRR, STDRR, RMSSD are features that represent the HRV, while, MHR and SDHR are features that are extracted from the heart rate.

Qualitative Data from Structured Interviews. The structured interviews will be audiorecorded for transcription and coding. Transcriptions will be verified by at least one team member. Systematic thematic analyses, a method for identifying, analyzing, and reporting patterns (themes) within data will be used to identify relevant themes from the interview data. Findings will be reported at the descriptive level, in which themes and illustrative quotes are provided. The resulting themes will be discussed among the research team to integrate results into development of potential adaptations to the proposed music-listening intervention.

Statistical Analyses . The pilot study proposed in this study is not designed to evaluate outcomes with the same rigor as traditional tests. Rather, the primary analyses of quantitative data from the pilot test will focus on descriptive statistics to provide preliminary validation of recruitment and retention rates, acceptability ratings, and mean levels of key constructs (e.g., average number of stress events, average frequency of music listening intervals). We will also compute the false positive rate by computing the proportion of detected events that resulted in participant reports of positive emotions. We will also estimate the validity of the automatic stress detection algorithm by comparing the number of reports triggered with data obtained in the qualitative interviews. This will allow us to compute the number of false positive/false negative events. Analysis of usage data from the music app will be used to determine acceptability and feasibility of the intervention. We will compute the mean amount of time spent on the app, the mean number of reports made on the app, and the average amount of time spent on each screen. Qualitative data from the interviews will be summarized using appropriate analysis techniques (e.g., thematic analysis).

We will use exploratory analyses to examine the effect of the music listening intervention on the secondary (proximal) outcome of whether stress occurs in the 2-hour window following micro-randomization. In these analyses, the independent variable is whether the music listening intervention is delivered or not delivered. The moderating (potential tailoring) variable is whether the participant is stressed or not stressed at the time of micro-randomization. Following established procedures, we will compute the minute-level outcome of whether the participant is probably stressed during each of the 120 minutes in the post-randomization window. We will then use log-linear regression to model the probability of being stressed. These analyses will control for potential confounding factors, such as age, biological sex, musical background and experience, and alcohol use history. Support for our hypotheses will be provided by significance of two interaction terms: stress episode type (probably vs. not probably stressed) and randomized treatment condition (stress component alone vs. stress component plus music listening component).

Conditions

Alcohol Use Disorder (AUD)

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: TREATMENT
• Blinding Strategy: DOUBLE

Study Groups

Stress Feedback

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.

Group Type: ACTIVE_COMPARATOR

Stress Feedback

Intervention Type: 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

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.

Group Type: EXPERIMENTAL

Stress Feedback

Intervention Type: 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

Intervention Type: 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.

Interventions

Stress Feedback

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.

Intervention Type: BEHAVIORAL

Music Listening

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.

Intervention Type: BEHAVIORAL

Eligibility Criteria

Inclusion Criteria

• 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

Exclusion Criteria

• 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.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 35 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

National Institute on Alcohol Abuse and Alcoholism (NIAAA)

NIH

Sponsor Role: collaborator

Washington State University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Michael J Cleveland, Ph.D.

Role: PRINCIPAL_INVESTIGATOR

Washington State University

Central Contacts

Michael J Cleveland, Ph.D.

Role: CONTACT

michael.cleveland@wsu.edu

509-335-3816

Other Identifiers

R61AA031474

Identifier Type: NIH

Identifier Source: secondary_id

View Link

20691

Identifier Type: -

Identifier Source: org_study_id