[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100599726":3},{"organization":4,"armGroups":7,"interventions":20,"overallOfficials":30,"centralContacts":34,"locations":25,"responsibleParty":39,"collaborators":41,"id":45,"slug":46,"hasResults":47,"nctId":48,"briefTitle":49,"officialTitle":50,"acronym":51,"eligibilityCriteria":52,"healthyVolunteers":47,"sex":53,"minAge":54,"maxAge":55,"enrollmentInfo":56,"targetDuration":25,"studyType":59,"phases":60,"briefSummary":62,"conditions":63,"keywords":65,"overallStatus":70,"whyStopped":25,"lastUpdateSubmitDate":71,"lastUpdatePostDateStruct":72,"startDateStruct":75,"completionDateStruct":77,"leadSponsor":79,"locationsCount":25},{"fullName":5,"class":6},"Washington State University","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"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.",[13],"Behavioral: Stress Feedback",{"label":15,"type":16,"description":17,"interventionNames":18},"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.",[13,19],"Behavioral: Music Listening",[21,26],{"type":22,"name":9,"description":23,"armGroupLabels":24,"otherNames":25},"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.",[15,9],null,{"type":22,"name":27,"description":28,"armGroupLabels":29,"otherNames":25},"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.",[15],[31],{"name":32,"affiliation":5,"role":33},"Michael J Cleveland, Ph.D.","PRINCIPAL_INVESTIGATOR",[35],{"name":32,"role":36,"phone":37,"phoneExt":25,"email":38},"CONTACT","509-335-3816","michael.cleveland@wsu.edu",{"type":40,"investigatorFullName":25,"investigatorTitle":25,"investigatorAffiliation":25,"oldNameTitle":25,"oldOrganization":25},"SPONSOR",[42],{"name":43,"class":44},"National Institute on Alcohol Abuse and Alcoholism (NIAAA)","NIH","100599726","testing-a-music-listening-mhealth-intervention-for-stress-reduction-in-early-recovery-calmify-ii-100599726",false,"NCT07088237","Testing a Music Listening mHealth Intervention for Stress Reduction in Early Recovery (CalmiFy II)","Testing a Music Listening mHealth Intervention for Stress Reduction in Early Recovery","CalmiFy II","Inclusion Criteria:\n\n* Subject can and has signed an Institutional Review Board (IRB) approved informed consent form (ICF).\n* Age ≥18 and ≤35 years.\n* In early-stage recovery for alcohol use (within 12 months)\n* Own a smartphone with a data plan\n* Not experiencing symptoms of severe depression\n* Not experiencing thoughts of suicide\n* Meets the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) diagnostic criteria for alcohol use disorder (AUD)\n* Not currently taking medication treatment for opioid use disorder (OUD)\n* Able to speak and read English\n\nExclusion Criteria:\n\n* Currently experiencing symptoms of severe depression\n* Currently experiencing thoughts of suicide\n* Currently taking medication treatment for opioid use disorder (OUD)\n* Are unable to provide voluntary informed consent.\n* Cannot read or speak English.","ALL","18 Years","35 Years",{"count":57,"type":58},30,"ESTIMATED","INTERVENTIONAL",[61],"NA","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.",[64],"Alcohol Use Disorder (AUD)",[66,67,68,69],"recovery","alcohol use disorder","stress","music listening","NOT_YET_RECRUITING","2026-04-29",{"date":73,"type":74},"2026-05-05","ACTUAL",{"date":76,"type":58},"2026-12-01",{"date":78,"type":58},"2028-03-01",{"name":5,"class":6}]