Ignite Creation Date:
2025-12-24 @ 9:24 PM
Ignite Modification Date:
2025-12-28 @ 9:56 AM
Study NCT ID:
NCT02200432
Status:
COMPLETED
Last Update Posted:
2015-07-09
First Post:
2014-07-15
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Patient Experience Recommender System for Persuasive Communication Tailoring
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D016540', 'term': 'Smoking Cessation'}], 'ancestors': [{'id': 'D015438', 'term': 'Health Behavior'}, {'id': 'D001519', 'term': 'Behavior'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'HEALTH_SERVICES_RESEARCH', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 972}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2014-05'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2015-07', 'completionDateStruct': {'date': '2015-06', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2015-07-07', 'studyFirstSubmitDate': '2014-07-15', 'studyFirstSubmitQcDate': '2014-07-23', 'lastUpdatePostDateStruct': {'date': '2015-07-09', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2014-07-25', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2015-03', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'message influence', 'timeFrame': 'up to five months post data collection', 'description': 'To evaluate the success of PERSPeCT in motivating smokers, we will conduct a pilot randomized trial. We hypothesize that the messages delivered by PERSPeCT will be more influential in encouraging a quit attempt, as compared with messages selected to be delivered by our current rule-based computer tailored messaging system.'}]}, 'oversightModule': {'oversightHasDmc': False}, 'conditionsModule': {'keywords': ['Smoking Cessation', 'Persuasive Communication Tailoring', 'Health Behavior Change', 'Patient decision support'], 'conditions': ['Smoking Cessation']}, 'referencesModule': {'references': [{'pmid': '32338619', 'type': 'DERIVED', 'citation': 'Faro JM, Nagawa CS, Allison JA, Lemon SC, Mazor KM, Houston TK, Sadasivam RS. Comparison of a Collective Intelligence Tailored Messaging System on Smoking Cessation Between African American and White People Who Smoke: Quasi-Experimental Design. JMIR Mhealth Uhealth. 2020 Apr 27;8(4):e18064. doi: 10.2196/18064.'}, {'pmid': '27826134', 'type': 'DERIVED', 'citation': 'Sadasivam RS, Borglund EM, Adams R, Marlin BM, Houston TK. Impact of a Collective Intelligence Tailored Messaging System on Smoking Cessation: The Perspect Randomized Experiment. J Med Internet Res. 2016 Nov 8;18(11):e285. doi: 10.2196/jmir.6465.'}], 'seeAlsoLinks': [{'url': 'http://www.decide2quit.org', 'label': 'Online smoking cessation intervention'}]}, 'descriptionModule': {'briefSummary': 'The purpose of this study is to maximize patient perspective and effectively support lifestyle choices, investigators will develop the "Patient Experience Recommender System for Persuasive Communication Tailoring." PERSPeCT is a computer system that will assess adult smokers\' perspective, to understand the patient\'s preferences for smoking cessation health messages, and provide personalized, persuasive health communication that is useful to the individual patient in making positive health behavior changes such as smoking cessation.', 'detailedDescription': 'To maximize patient perspective and effectively support lifestyle choices, we will develop the "Patient Experience Recommender System for Persuasive Communication Tailoring." PERSPeCT is an adaptive computer system that will assess a patient\'s individual perspective, understand the patient\'s preferences for health messages, and provide personalized, persuasive health communication relevant to the individual patient.\n\nInvestigators propose to overcome key weaknesses in existing top-down expert-driven health communication interventions by applying advanced machine learning algorithms to adaptively recommend messages based on the "collective intelligence" of thousands of patients. This work will leverage a paradigm-shifting "Web 2.0" approach to adaptive personalization with the potential for broad impact on the field of computer tailored health communication (CTHC).\n\nUsing knowledge from scientific experts, current CTHC interventions collect baseline patient "profiles" and then use expert-written, rule-based systems to target messages to subsets of patients. These market segmentation interventions show some promise in helping certain patients reach lifestyle goals. Although theoretically sound, rule-based systems may not account for socio-cultural concepts that have intrinsic importance to the targeted population, thus limiting their relevance. Further, the rules do not adapt to patient feedback.\n\nOutside healthcare, companies like Google, Amazon, Netflix and Pandora have made extensive use of adaptive recommendation systems to provide content with enhanced personal relevance. These systems use machine learning algorithms to derive personalized recommendations from a variety of data sources including preference feedback collected from individual users.\n\nWithin the scope of this Patient-Centered Outcomes Research Institute (PCORI) pilot, investigators will address the challenges of adapting machine learning recommender systems to CTHC in the specific context of patient decision support for smoking cessation. Investigators have chosen this domain because smoking is a major preventable cause of death, and because we have an existing database of 1,000 persuasive messages developed in a current federal grant (R01 CA129091). Specific study aims are to:\n\nAim 1: Collect Explicit Feedback data in order to train PERSPeCT Recruit 700 smokers using multiple, complimentary strategies, and using a web interface, ask smokers to provide (a) Perspectives on smoking and quitting and socio-cultural context information and (b) Ratings of the influential aspect of smoking cessation messages.\n\nAim 2: Design, Implement and Validate a customized recommendation framework This will involve (a) developing and implementing a machine learning recommender system that integrates patient profiles, message metadata, web site views and influence ratings, and (b)training the model and validating its predictive performance.\n\nAim 3: Conduct a pilot randomized trial (n = 120 smokers) of PERSPeCT. Investigators hypothesize that the PERSPeCT system will (H1) Select messages of increasing influence as smokers provide more message ratings and (H2) Select messages with better influence than a rule-based CTHC system when smokers provide a sufficient number of ratings CTHC systems support patient decisions about behaviors, lifestyles, and choices. PERSPeCT addresses areas of interest for PCORI, namely: 1) Identifying, testing, and/or evaluating methods that can be used to assess the patient perspective when researching behaviors, lifestyles, and choices within the patient\'s control; and 2) Developing, refining, testing, and/or evaluating patient-centered approaches, including decision support tools. The study team is uniquely positioned to accomplish these ambitious aims within the scope of this PCORI pilot because investigators will utilize an existing database of persuasive messages from a previous study, two years of data on the effectiveness of these messages and a trans-disciplinary team with expertise in health communication, web systems engineering, and machine learning recommender systems.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Adult smokers, 18 years of age or older with Internet access\n* Pregnant women.\n* English speakers able to obtain consent\n\nExclusion Criteria:Prisoners\n\n* Adult unable to consent\n* Infants, Children, Teenagers (those under the age of 18 years old)'}, 'identificationModule': {'nctId': 'NCT02200432', 'acronym': 'PERSPECT', 'briefTitle': 'Patient Experience Recommender System for Persuasive Communication Tailoring', 'organization': {'class': 'OTHER', 'fullName': 'University of Massachusetts, Worcester'}, 'officialTitle': 'PERSPECT: Patient Experience Recommender System for Persuasive Communication Tailoring', 'orgStudyIdInfo': {'id': '14762'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'NO_INTERVENTION', 'label': 'Control', 'description': "The study's current rule-based CTHC system is embedded within the Decide2Quit.org web service. Control smokers will receive the current Decide2Quit.org system, including informational web pages, an interactive quit plan, plus pushed email messages. The messages will be selected using the current rule-based CTHC system. The CTHC selects messages based on decision rules (e.g: readiness to quit, gender) using information from a smoker's baseline profile. Participants will receive one message per day for 30 days"}, {'type': 'EXPERIMENTAL', 'label': 'Intervention', 'description': 'The PERSPeCT intervention smokers will receive all components of the Decide2Quit.org web service, but persuasive email messages will be selected by the PERSPeCT recommender system developed in Aim 2. PERSPeCT will use data (see Figure 1) to predict messages that would be most influential to the participant. Intervention smokers will receive one PERSPeCT-generated message per day for 30 days. With each message rating, the PERSPeCT system will further adapt to patient preferences.', 'interventionNames': ['Other: PERSPeCT Recommender System']}], 'interventions': [{'name': 'PERSPeCT Recommender System', 'type': 'OTHER', 'description': 'PERSPeCT will use data to predict messages that would be most influential to the participant in health behavioral change.\n\n"Patient Experience Recommender System for Persuasive Communication Tailoring." PERSPeCT is an adaptive computer system that will assess a patient\'s individual perspective, understand the patient\'s preferences for health messages, and provide personalized, persuasive health communication relevant to the individual patient.', 'armGroupLabels': ['Intervention']}]}, 'contactsLocationsModule': {'locations': [{'zip': '01655', 'city': 'Worcester', 'state': 'Massachusetts', 'country': 'United States', 'facility': 'The University of Massachusetts Medical School', 'geoPoint': {'lat': 42.26259, 'lon': -71.80229}}], 'overallOfficials': [{'name': 'Thomas K Houston, MD, MPH', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'UMMS'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University of Massachusetts, Worcester', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Division Chief, Health Informatics and Implementation Science', 'investigatorFullName': 'Thomas Houston', 'investigatorAffiliation': 'University of Massachusetts, Worcester'}}}}