Ignite Creation Date:
2025-12-24 @ 6:43 PM
Ignite Modification Date:
2025-12-29 @ 4:10 AM
Study NCT ID:
NCT07052357
Status:
ENROLLING_BY_INVITATION
Last Update Posted:
2025-07-04
First Post:
2025-06-26
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Intern Health Study 2025
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D009043', 'term': 'Motor Activity'}], 'ancestors': [{'id': 'D001519', 'term': 'Behavior'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NA', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'PREVENTION', 'interventionModel': 'SINGLE_GROUP'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 1000}}, 'statusModule': {'overallStatus': 'ENROLLING_BY_INVITATION', 'startDateStruct': {'date': '2025-04-03', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-06', 'completionDateStruct': {'date': '2026-06-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-06-26', 'studyFirstSubmitDate': '2025-06-26', 'studyFirstSubmitQcDate': '2025-06-26', 'lastUpdatePostDateStruct': {'date': '2025-07-04', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-07-04', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-06-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Average daily mood', 'timeFrame': 'Daily, through study completion at the end of intern year (1 year)', 'description': 'Through the mobile app, participants enter a mood score (scale 1 - 10) every day of the study. 1 corresponds to lowest mood and 10 corresponds to highest mood.'}, {'measure': 'Average daily step count', 'timeFrame': 'Daily, through study completion at the end of intern year (1 year)', 'description': "Participant's daily step counts are recorded through a fitness tracker. High step counts are considered a positive outcome as it indicates more physical activity."}, {'measure': 'Average nightly sleep duration', 'timeFrame': 'Daily, through study completion at the end of intern year (1 year)', 'description': "Participant's nightly sleep duration (in minutes) is recorded through a fitness tracker. High sleep duration is considered a positive outcome."}, {'measure': 'Patient Health Questionnaire-9 (PHQ-9)', 'timeFrame': 'Quarterly (every 3 months for 1 year)', 'description': 'Prior to the start of the intervention and at quarterly intervals throughout internship year, all participants complete the Patient Health Questionnaire 9. High scores on the PHQ-9 correspond to a larger number of depressive symptoms.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Depression - Major Depressive Disorder', 'Mood', 'Sleep', 'Physical Activity']}, 'referencesModule': {'seeAlsoLinks': [{'url': 'https://www.srijan-sen-lab.com/intern-health-study', 'label': 'Sen Lab Website'}, {'url': 'https://www.internhealthstudy.org/', 'label': 'Study Participant Website'}]}, 'descriptionModule': {'briefSummary': 'The aim of this study is to evaluate the efficacy of using a reinforcement learning algorithm to determine the optimal content of a mobile health intervention (message delivered via smartphone) for improving the mood, physical activity, and sleep of medical interns.', 'detailedDescription': "Due to their high workloads, less sleep and physical activity and other stressors, medical interns suffer from depression at higher rates than the general population. The goal of this study is to evaluate the efficacy of a mobile health intervention intending to help prevent the degradation of health behaviors and the development of depression. The intervention sends mobile phone notifications which aim to help interns improve their mood, maintain physical activity, and obtain adequate sleep during their internship year. A reinforcement learning algorithm will use prior survey, daily mood, and wearable data to make three types of choices each day: 1) whether to send a message or not on a given day, and, if sending a message, 2) the therapeutic strategy (Behavioral Strategy, Cognitive Strategy, Mindfulness, Motivational Interviewing, Distanced Self-Talk), and 3) whether or not to include feedback (the intern's own data) in the message."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Medical intern during the 2025-2026 internship year\n* iPhone or Android phone user\n* Completed the Intern Health Study consent and baseline survey by June 20 prior to the start of intern year\n\nExclusion Criteria:\n\n* None'}, 'identificationModule': {'nctId': 'NCT07052357', 'acronym': '(IHS 2025)', 'briefTitle': 'Intern Health Study 2025', 'organization': {'class': 'OTHER', 'fullName': 'University of Michigan'}, 'officialTitle': 'Intern Health Study 2025', 'orgStudyIdInfo': {'id': 'HUM00033029.25'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Within-participant micro-randomization', 'description': "Each week a policy outcome is chosen at random with ⅓ mood, ⅓ activity, ⅓ sleep - this determines which category of message a participant will receive.\n\nEach day in the study, a reinforcement learning algorithm will determine 1) if a participant will receive a notification that day or no notification that day, 2) the therapeutic strategy employed by the notification (Behavioral Strategy, Cognitive Strategy, Mindfulness, Motivational Interviewing, Distanced Self-Talk), and 3) if personalized data feedback will be included.\n\nIf assigned to receive a notification, 1 core message set that meets the above criteria will be randomly selected from a pool of 358 core message sets. Each core message set will be comprised of 4 messages containing comparable content, however they will be tailored based on the participant's wearable (steps, sleep) or mood data for the specified time interval (7 days, 30 days, since the start of internship) as follows: 1) no data, 2) low, 3) moderate, or 4) high.", 'interventionNames': ['Behavioral: Intern Health Study behavioral change mobile notification']}], 'interventions': [{'name': 'Intern Health Study behavioral change mobile notification', 'type': 'BEHAVIORAL', 'description': "The study's mobile app will be used to deliver push notifications. The notifications appear on the participant's phone lock screen. The notifications include 3 categories: mood notifications, activity notifications, sleep notifications. Mood notifications aim to increase the participant's mood. Activity notifications aim to increase the participant's physical activity. Sleep notifications aim to increase the participant's sleep duration. All notifications are categorized as one of five therapeutic approaches: 1) CBT-Behavioral, 2) CBT-Cognitive, 3) Distanced Self-Talk, 4) Mindfulness, 5) Motivational Interviewing.", 'armGroupLabels': ['Within-participant micro-randomization']}]}, 'contactsLocationsModule': {'locations': [{'zip': '48375', 'city': 'Ann Arbor', 'state': 'Michigan', 'country': 'United States', 'facility': 'University of Michigan', 'geoPoint': {'lat': 42.27756, 'lon': -83.74088}}]}, 'ipdSharingStatementModule': {'url': 'https://www.openicpsr.org/openicpsr/project/129225/version/V1/view', 'timeFrame': 'Data will be made available 12 months after the end of the study It will be made available indefinitely after that date.', 'ipdSharing': 'YES', 'description': 'De-identified survey data (baseline survey, plus quarterly survey which contains the PHQ-9) will be shared via the ICPSR repository (https://www.openicpsr.org/openicpsr/project/129225/version/V1/view).', 'accessCriteria': 'Deidentified data will be publicly available via ICPSR https://www.openicpsr.org/openicpsr/project/129225/version/V1/view'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University of Michigan', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Frances and Kenneth Eisenberg Professor of Depression and Neurosciences', 'investigatorFullName': 'Srijan Sen', 'investigatorAffiliation': 'University of Michigan'}}}}