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

Sponsor
Washington State University
Study ID
NCT07088237
Status
Not Yet Recruiting

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Conditions

  • Alcohol Use Disorder (AUD)

Eligibility Criteria

Sex
ALL
Age
18 Years - 35 Years
Healthy Volunteers
Not accepted

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 — 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.

Study Details

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.

Key Dates

First listed
Jul 28, 2025
Start date
Dec 1, 2026
Status verified
Apr 2026
Primary completion
Mar 1, 2028
Completion
Mar 1, 2028

Study Design

Enrollment
30 participants (estimated)
Allocation
RANDOMIZED
Intervention model
SINGLE_GROUP
Primary purpose
TREATMENT

Arms

  • Active Comparator: 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.
  • Experimental: 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.

Primary Outcome Measure

Skin Conductance Response (SCR) Rate [ Time Frame: Assessed during the 14-day ambulatory assessment phase ]

Central Contacts

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