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.
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
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.
Single group
Stress Feedback
BehavioralThe 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
BehavioralFor 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.
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.
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.
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.
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.
Satisfaction with Study
Participants will be asked to participate in an interview at study completion that asks about their experience, including if the study interfered with their daily life, if they experienced any problems during the study, and overall satisfaction with the study.
Time-line followback (TLFB) measure of alcohol use
Participants will be asked about recent alcohol use using a TLFB calendar method at the end-of-study interview.
Recollections of stressful events
Participants will use a TLFB calendar method to provide recollections of stressful events that occurred during the study.
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Washington State University
Lead sponsor
National Institute on Alcohol Abuse and Alcoholism (NIAAA)
Collaborator
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