Ignite Creation Date:
2025-12-25 @ 2:38 AM
Ignite Modification Date:
2025-12-26 @ 1:16 AM
Study NCT ID:
NCT06566534
Status:
COMPLETED
Last Update Posted:
2025-01-16
First Post:
2024-08-20
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Personalized Nudging to Increase Influenza Vaccinations
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D007251', 'term': 'Influenza, Human'}, {'id': 'D015438', 'term': 'Health Behavior'}], 'ancestors': [{'id': 'D012141', 'term': 'Respiratory Tract Infections'}, {'id': 'D007239', 'term': 'Infections'}, {'id': 'D009976', 'term': 'Orthomyxoviridae Infections'}, {'id': 'D012327', 'term': 'RNA Virus Infections'}, {'id': 'D014777', 'term': 'Virus Diseases'}, {'id': 'D012140', 'term': 'Respiratory Tract Diseases'}, {'id': 'D001519', 'term': 'Behavior'}]}}, 'documentSection': {'largeDocumentModule': {'largeDocs': [{'date': '2025-01-13', 'size': 147313, 'label': 'Statistical Analysis Plan', 'hasIcf': False, 'hasSap': True, 'filename': 'SAP_001.pdf', 'typeAbbrev': 'SAP', 'uploadDate': '2025-01-13T15:23', 'hasProtocol': False}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'NONE', 'maskingDescription': 'Although patients will not be explicitly informed of which arm they were randomized to, they will be aware of the messages they receive. The care provider will be provided an information sheet describing the intervention, but they will not be explicitly told which patients are enrolled or their randomized arm assignment.'}, 'primaryPurpose': 'PREVENTION', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 77482}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2024-09-09', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-01', 'completionDateStruct': {'date': '2024-12-31', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2025-01-13', 'studyFirstSubmitDate': '2024-08-20', 'studyFirstSubmitQcDate': '2024-08-20', 'lastUpdatePostDateStruct': {'date': '2025-01-16', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-08-22', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-12-31', 'type': 'ACTUAL'}}, 'outcomesModule': {'otherOutcomes': [{'measure': 'Number of Patients With Flu Shot Receipt On or Before December 31, 2024', 'timeFrame': 'Up to 4 months after randomization', 'description': 'Received a flu shot on or before December 31, 2024'}, {'measure': 'Number of Patients with Flu Shot Receipt Between Enrollment Date and First Eligible Appointment', 'timeFrame': '4 Days', 'description': 'Patients received the flu shot at or before their first eligible appointment'}, {'measure': 'Number of Patients with Flu Diagnosis (encounter diagnosis or flu test)', 'timeFrame': 'Up to 8 months after randomization', 'description': 'Patients received a flu diagnosis via encounter diagnosis or flu test between enrollment and April 30, 2025.'}, {'measure': 'Number of Patients with Flu-related Complications', 'timeFrame': 'Up to 11 months after randomization', 'description': 'Patients experienced flu-related complications before as defined by relevant diagnosis, hospitalization, or death, between enrollment and July 31, 2025 as recorded in the electronic health record'}], 'primaryOutcomes': [{'measure': 'Number of Patients With Flu Shot Receipt Between Enrollment Date and Target Appointment Date', 'timeFrame': 'Between the enrollment date and target appointment date (at least 4 days and up to 4 months)', 'description': 'Our field experiment will be conducted with Geisinger Health patients via SMS messages sent prior to their first flu shot-eligible appointment during the study period, referred to as the "target appointment." The key dependent variable is whether patients receive a flu shot at or before their target appointment (as recorded in their electronic health records).\n\nIf patients cancel or do not show up for their target appointment after they have been randomized to a treatment and then schedule a new appointment during the study period, their new flu-shot eligible appointment becomes the target appointment and the outcome window extends from three days prior to the original appointment through the date of the appointment.\n\nPatients who miss their target appointment and do not reschedule within the study period will still be included in the analysis. Their outcome window is from three days prior to the original appointment through the date of the original canceled appointment.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['vaccination', 'health promotion', 'health behavior', 'reminder systems', 'behavioral economics', 'behavioral risk assessment', 'machine learning', 'personalization', 'nudges'], 'conditions': ['Influenza, Human']}, 'referencesModule': {'references': [{'pmid': '33926993', 'type': 'BACKGROUND', 'citation': "Milkman KL, Patel MS, Gandhi L, Graci HN, Gromet DM, Ho H, Kay JS, Lee TW, Akinola M, Beshears J, Bogard JE, Buttenheim A, Chabris CF, Chapman GB, Choi JJ, Dai H, Fox CR, Goren A, Hilchey MD, Hmurovic J, John LK, Karlan D, Kim M, Laibson D, Lamberton C, Madrian BC, Meyer MN, Modanu M, Nam J, Rogers T, Rondina R, Saccardo S, Shermohammed M, Soman D, Sparks J, Warren C, Weber M, Berman R, Evans CN, Snider CK, Tsukayama E, Van den Bulte C, Volpp KG, Duckworth AL. A megastudy of text-based nudges encouraging patients to get vaccinated at an upcoming doctor's appointment. Proc Natl Acad Sci U S A. 2021 May 18;118(20):e2101165118. doi: 10.1073/pnas.2101165118."}, {'pmid': '36195982', 'type': 'BACKGROUND', 'citation': 'Patel MS, Milkman KL, Gandhi L, Graci HN, Gromet D, Ho H, Kay JS, Lee TW, Rothschild J, Akinola M, Beshears J, Bogard JE, Buttenheim A, Chabris C, Chapman GB, Choi JJ, Dai H, Fox CR, Goren A, Hilchey MD, Hmurovic J, John LK, Karlan D, Kim M, Laibson D, Lamberton C, Madrian BC, Meyer MN, Modanu M, Nam J, Rogers T, Rondina R, Saccardo S, Shermohammed M, Soman D, Sparks J, Warren C, Weber M, Berman R, Evans CN, Lee SH, Snider CK, Tsukayama E, Van den Bulte C, Volpp KG, Duckworth AL. A Randomized Trial of Behavioral Nudges Delivered Through Text Messages to Increase Influenza Vaccination Among Patients With an Upcoming Primary Care Visit. Am J Health Promot. 2023 Mar;37(3):324-332. doi: 10.1177/08901171221131021. Epub 2022 Oct 4.'}], 'seeAlsoLinks': [{'url': 'https://www.cdc.gov/flu-burden/php/about/index.html?CDC_AAref_Val=https://www.cdc.gov/flu/about/burden/index.html', 'label': 'Centers for Disease Control and Prevention. (2020). Disease Burden of Influenza.'}]}, 'descriptionModule': {'briefSummary': 'The purpose of this study is to prospectively test whether personalized, message-based nudges can increase flu vaccination compared with nudges that are not personalized or no nudges.', 'detailedDescription': 'On average, 8% of the US population gets sick from influenza each flu season. Since 2010, the annual disease burden of influenza in the U.S. has included 9-41 million illnesses, 140,000-710,000 hospitalizations, and 12,000-52,000 deaths. The Centers for Disease Control and Prevention (CDC) recommends flu vaccination to everyone aged 6 months and older, with rare exceptions; almost anyone can benefit from the vaccine, which can reduce illnesses, missed work, hospitalizations, and death.\n\nSuccessful efforts to get patients vaccinated against influenza have included text message reminders timed to precede upcoming flu shot-eligible appointments by up to 3 days. For example, the Roybal-funded flu shot megastudy conducted with Penn Medicine and Geisinger patients assessed the effectiveness of numerous types of messages in increasing vaccination, relative to standard communications by the respective health systems; an average 2.1 percentage point absolute increase (or 5% relative increase) in flu shots occurred due to the messages. Similarly, follow-up analysis of the megastudy using machine learning revealed that interventions differed in relative effectiveness across groups of patients as a function of overlapping covariates (e.g., age, sex, insurance type, and comorbidities). This analysis found that nudges optimally targeted to subgroups who responded most strongly to those nudges in the megastudy would have resulted in up to three times the increases in vaccination observed when simply randomly assigning patients to messages.\n\nThe present study aims to prospectively test the efficacy of a patient-facing, message-based nudge via short message service (SMS) texts that is predicted by this retrospective machine learning algorithm to be most effective for them (Personalized Nudge) relative to Passive Control (no messages), Active Control (simple reminder message), and Best Nudge (best performing message from the 2020 megastudy). Patients will be randomized to one of these four arms.\n\nOf the 19 original messages from the megastudy, 12 can be carried out at Geisinger in 2024; the other 7 are either no longer relevant (e.g., those that refer to an ongoing coronavirus pandemic) or cannot be carried out due to a technical limitation (e.g., Geisinger is unable to send pictures, so nudges with pictures are excluded). A treatment assignment tree based on the algorithm described above will be applied to this subset of nudges to generate rules for assigning patients to personalized messages based on observed covariates.\n\nThe last patients will be enrolled on December 28th for appointments scheduled on December 31st. At least 90,000 patients are expected to be enrolled.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Age 18+\n* Has not received the 2024 flu vaccine according to the Geisinger electronic health record (EHR) prior to randomization\n* Has a non-acute, flu-shot eligible, in-person Geisinger appointment scheduled with enough time to be randomized\n* Has a Geisinger primary care provider\n\nExclusion Criteria:\n\n* Cannot be contacted by SMS (e.g., due to insufficient/missing contact information in the EHR or because they opted out)\n* Appointment type or department not approved for outreach by Geisinger leadership at the time of outreach\n* Has an allergy to flu vaccines according to any EHR allergy table known to the study team\n* Has a health maintenance modifier indicating they are permanently discontinued from receiving a seasonal flu shot\n* Shares a phone number with someone who has already been enrolled'}, 'identificationModule': {'nctId': 'NCT06566534', 'briefTitle': 'Personalized Nudging to Increase Influenza Vaccinations', 'organization': {'class': 'OTHER', 'fullName': 'Geisinger Clinic'}, 'officialTitle': 'A Prospective Randomized Trial of Personalized Nudges to Increase Influenza Vaccinations', 'orgStudyIdInfo': {'id': '2024-0561'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'NO_INTERVENTION', 'label': 'Passive Control', 'description': 'Patients randomized to this arm will receive no special communications, beyond what Geisinger sends out as standard practice.'}, {'type': 'EXPERIMENTAL', 'label': 'Active Control', 'description': 'Patients will receive a simple message encouraging them to get a flu shot at their appointment.', 'interventionNames': ['Behavioral: Reminder']}, {'type': 'EXPERIMENTAL', 'label': 'Best Nudge', 'description': 'Patients will receive the nudge found to be numerically most effective in the megastudy, including language that a flu vaccine is "reserved" for them at their upcoming appointment.', 'interventionNames': ['Behavioral: Reminder']}, {'type': 'EXPERIMENTAL', 'label': 'Personalized Nudge', 'description': 'Patients will receive the nudge predicted to be most effective for them on the basis of the machine learning-derived treatment assignment trees.', 'interventionNames': ['Behavioral: Reminder']}], 'interventions': [{'name': 'Reminder', 'type': 'BEHAVIORAL', 'description': 'Flu shot messages via SMS', 'armGroupLabels': ['Active Control', 'Best Nudge', 'Personalized Nudge']}]}, 'contactsLocationsModule': {'locations': [{'zip': '17822', 'city': 'Danville', 'state': 'Pennsylvania', 'country': 'United States', 'facility': 'Geisinger Clinic', 'geoPoint': {'lat': 40.96342, 'lon': -76.61273}}], 'overallOfficials': [{'name': 'Christopher F Chabris, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Geisinger Clinic'}]}, 'ipdSharingStatementModule': {'infoTypes': ['STUDY_PROTOCOL', 'SAP', 'ANALYTIC_CODE'], 'timeFrame': "By the paper's online publication date. Data will remain available for as long as the Open Science Framework hosts it.", 'ipdSharing': 'YES', 'description': 'Data with no personally identifiable information will be made available to other researchers on the Open Science Framework for transparency. This will include the essential data and code needed to replicate the analysis that yielded reported findings.', 'accessCriteria': 'The data on the Open Science Framework will be open to anyone requesting that information.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Geisinger Clinic', 'class': 'OTHER'}, 'collaborators': [{'name': 'Massachusetts Institute of Technology', 'class': 'OTHER'}, {'name': 'University of Michigan', 'class': 'OTHER'}, {'name': 'Abdul Latif Jameel Poverty Action Lab', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Christopher F Chabris, PhD', 'investigatorAffiliation': 'Geisinger Clinic'}}}}