- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT06229444
Predict + Protect Study: Exploring the Effectiveness of a Predictive Health Education Intervention on the Adoption of Protective Behaviors Related to ILI
August 27, 2024 updated by: Evidation Health
Predict + Protect: A Randomized Controlled Trial Exploring the Effectiveness of a Predictive Health Education Intervention on the Adoption of Protective Behaviors Related to Influenza-like Illness (ILI)
The goal of this prospective, digital randomized controlled trial is to evaluate the effectiveness of a predictive ILI detection algorithm and associated alerts during influenza season for adults living in the contigent United States.
The main study objectives are to assess the effectiveness of predictive ILI detection algorithm and associated alerts on protective behaviors related to ILI and assess the accuracy of a predictive ILI detection algorithm using participant self-reported ILI symptoms and diagnosis.
Study Overview
Status
Active, not recruiting
Study Type
Interventional
Enrollment (Estimated)
15000
Phase
- Not Applicable
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Locations
-
-
California
-
San Mateo, California, United States, 94402
- Evidation Health
-
-
Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Yes
Description
Inclusion Criteria:
- Member of the Evidation platform
- 18 years or older
- Lives in the U.S.
- Currently owns and uses a consumer wearable activity tracker (Apple Watch, Garmin, or Fitbit) linked to their Evidation account
- Meets data density requirements for wearable data: Steps and heart rate data present for 15% of the last 60 days (or no fewer than 2 total days for Evidation accounts less than 60 days old)
Exclusion Criteria:
- Does not have an Evidation account
- Not 18 years or older
- Does not live in the U.S.
- Does not have an activity tracker linked to their Evidation account
- Enrolled in an Evidation supported ILI monitoring and engagement program, or clinical study (e.g., FluSmart)
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
- Primary Purpose: Prevention
- Allocation: Randomized
- Interventional Model: Factorial Assignment
- Masking: Single
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
|
Experimental: Proactive ILI content & Predictions
Participants will receive predictive alerts, reactive content after reporting symptoms or receiving an asymptomatic prediction, and ILI-related health educational content
|
Participants receive ILI-related education, feedback, and opportunities to self-monitor ILI symptoms, in addition they also receive alerts about potential ILI illness, and reactive and personalized content about protective health behaviors.
|
|
Experimental: No Proactive ILI content & Predictions
Participants will receive predictive alerts and reactive content after reporting symptoms or receiving an asymptomatic prediction, but will not receive proactive ILI content
|
Participants receive alerts about potential ILI illness, and reactive and personalized content about protective health behaviors.
|
|
Experimental: Proactive ILI content & No Predictions
Participants will not receive predictive alerts or reactive content after reporting symptoms but will receive proactive ILI content
|
Participants receive ILI-related education, feedback, and opportunities to self-monitor ILI symptoms.
|
|
Experimental: No Proactive ILI content & No Predictions
Participants will not receive predictive alerts or reactive content after reporting symptoms or proactive ILI content
|
Participants will not receive predictive alerts or reactive content after reporting symptoms or proactive IILI-related health educational content
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
The primary objective of this study is to assess the effectiveness of a predictive ILI detection algorithm and associated alerts on ILI-related health and behavioral outcomes
Time Frame: Through study completion, approximately 10 months
|
The difference between the predictive alert and the no predictive alert groups in the proportion of cohort members who performed any target health behavior 1-4 (i.e.
performed at least one of: reduced spread, tested, sought medical attention, or was treatment adherent)
|
Through study completion, approximately 10 months
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
The secondary objective is to assess the accuracy of an ILI detection algorithm using self-reported symptoms and ILI diagnosis
Time Frame: Through study completion, approximately 10 months
|
Evaluate algorithm performance (against labels from self-reported ILI symptoms and/or self-reported positive diagnosis) overall and per model deployed.
Algorithm performance will be assessed across a variety of dimensions including ROC AUC, sensitivity, specificity, PPV, and NPV
|
Through study completion, approximately 10 months
|
Other Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
The exploratory objective is to assess differences in effectiveness between the four groups on ILI-related health and behavioral outcomes
Time Frame: Through study completion, approximately 10 months
|
The difference between all groups in the proportion of cohort members who performed any target health behavior 1-4 (i.e.
performed at least one of: reduced spread, tested, sought medical attention, or was treatment adherent)
|
Through study completion, approximately 10 months
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Sponsor
Collaborators
Investigators
- Principal Investigator: Ernesto H.N. Ramirez, PhD, Evidation
Publications and helpful links
The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.
General Publications
- McCambridge J, Witton J, Elbourne DR. Systematic review of the Hawthorne effect: new concepts are needed to study research participation effects. J Clin Epidemiol. 2014 Mar;67(3):267-77. doi: 10.1016/j.jclinepi.2013.08.015. Epub 2013 Nov 22.
- Tokars JI, Olsen SJ, Reed C. Seasonal Incidence of Symptomatic Influenza in the United States. Clin Infect Dis. 2018 May 2;66(10):1511-1518. doi: 10.1093/cid/cix1060.
- Wiemken TL, Khan F, Puzniak L, Yang W, Simmering J, Polgreen P, Nguyen JL, Jodar L, McLaughlin JM. Seasonal trends in COVID-19 cases, hospitalizations, and mortality in the United States and Europe. Sci Rep. 2023 Mar 8;13(1):3886. doi: 10.1038/s41598-023-31057-1.
- Temple DS, Hegarty-Craver M, Furberg RD, Preble EA, Bergstrom E, Gardener Z, Dayananda P, Taylor L, Lemm NM, Papargyris L, McClain MT, Nicholson BP, Bowie A, Miggs M, Petzold E, Woods CW, Chiu C, Gilchrist KH. Wearable Sensor-Based Detection of Influenza in Presymptomatic and Asymptomatic Individuals. J Infect Dis. 2023 Apr 12;227(7):864-872. doi: 10.1093/infdis/jiac262.
- Mezlini A, Shapiro A, Daza EJ, Caddigan E, Ramirez E, Althoff T, Foschini L. Estimating the Burden of Influenza-like Illness on Daily Activity at the Population Scale Using Commercial Wearable Sensors. JAMA Netw Open. 2022 May 2;5(5):e2211958. doi: 10.1001/jamanetworkopen.2022.11958.
- Shapiro A, Marinsek N, Clay I, Bradshaw B, Ramirez E, Min J, Trister A, Wang Y, Althoff T, Foschini L. Characterizing COVID-19 and Influenza Illnesses in the Real World via Person-Generated Health Data. Patterns (N Y). 2020 Dec 13;2(1):100188. doi: 10.1016/j.patter.2020.100188. eCollection 2021 Jan 8.
- Hunter V, Shapiro A, Chawla D, Drawnel F, Ramirez E, Phillips E, Tadesse-Bell S, Foschini L, Ukachukwu V. Characterization of Influenza-Like Illness Burden Using Commercial Wearable Sensor Data and Patient-Reported Outcomes: Mixed Methods Cohort Study. J Med Internet Res. 2023 Mar 23;25:e41050. doi: 10.2196/41050.
- Mayer C, Tyler J, Fang Y, Flora C, Frank E, Tewari M, Choi SW, Sen S, Forger DB. Consumer-grade wearables identify changes in multiple physiological systems during COVID-19 disease progression. Cell Rep Med. 2022 Apr 19;3(4):100601. doi: 10.1016/j.xcrm.2022.100601. eCollection 2022 Apr 19.
- Nestor B, Hunter J, Kainkaryam R, Drysdale E, Inglis JB, Shapiro A, Nagaraj S, Ghassemi M, Foschini L, Goldenberg A. Machine learning COVID-19 detection from wearables. Lancet Digit Health. 2023 Apr;5(4):e182-e184. doi: 10.1016/S2589-7500(23)00045-6. No abstract available.
- Merrill MA, Safranchik E, Kolbeinsson A, Gade P, Ramirez E, Schmidt L, Foshchini L, Althoff T. Homekit2020: A benchmark for time series classification on a large mobile sensing dataset with laboratory tested ground truth of influenza infections. Proceedings of Machine Learning Research LEAVE UNSET:1-22, 2023.
- Rosenstock, I. M. (2000). Health Belief Model. In A. E. Kazdin (Ed.), Encyclopedia of psychology (Vol. 4, pp. 78-80). Oxford University Press.
- Zewdie A, Mose A, Sahle T, Bedewi J, Gashu M, Kebede N, Yimer A. The health belief model's ability to predict COVID-19 preventive behavior: A systematic review. SAGE Open Med. 2022 Jul 22;10:20503121221113668. doi: 10.1177/20503121221113668. eCollection 2022.
- Mercadante AR, Law AV. Will they, or Won't they? Examining patients' vaccine intention for flu and COVID-19 using the Health Belief Model. Res Social Adm Pharm. 2021 Sep;17(9):1596-1605. doi: 10.1016/j.sapharm.2020.12.012. Epub 2020 Dec 30.
- Gutierrez F, Wolfe J. Using the Health Belief Model to improve influenza vaccination rates. JAAPA. 2022 Oct 1;35(10):46-47. doi: 10.1097/01.JAA.0000873832.52485.65.
- Richardson KM, Jospe MR, Saleh AA, Clarke TN, Bedoya AR, Behrens N, Marano K, Cigan L, Liao Y, Scott ER, Guo JS, Aguinaga A, Schembre SM. Use of Biological Feedback as a Health Behavior Change Technique in Adults: Scoping Review. J Med Internet Res. 2023 Sep 25;25:e44359. doi: 10.2196/44359.
- LaFave SE, Granbom M, Cudjoe TKM, Gottsch A, Shorb G, Szanton SL. Attention control group activities and perceived benefit in a trial of a behavioral intervention for older adults. Res Nurs Health. 2019 Dec;42(6):476-482. doi: 10.1002/nur.21992. Epub 2019 Oct 24.
- Lee JL, Foschini L, Kumar S, Juusola J, Liska J, Mercer M, Tai C, Buzzetti R, Clement M, Cos X, Ji L, Kanumilli N, Kerr D, Montanya E, Muller-Wieland D, Ostenson CG, Skolnik N, Woo V, Burlet N, Greenberg M, Samson SI. Digital intervention increases influenza vaccination rates for people with diabetes in a decentralized randomized trial. NPJ Digit Med. 2021 Sep 17;4(1):138. doi: 10.1038/s41746-021-00508-2.
- Mansournia MA, Higgins JP, Sterne JA, Hernan MA. Biases in Randomized Trials: A Conversation Between Trialists and Epidemiologists. Epidemiology. 2017 Jan;28(1):54-59. doi: 10.1097/EDE.0000000000000564. Erratum In: Epidemiology. 2018 Sep;29(5):e49. doi: 10.1097/EDE.0000000000000846.
Helpful Links
- Centers for Disease Control and Prevention, National Center for Immunization and Respiratory Diseases (NCIRD). Past Seasons Estimated Influenza Disease Burden
- Centers for Disease Control and Prevention, National Center for Immunization and Respiratory Diseases (NCIRD). Past Seasons Estimated Influenza Disease Burden
- Centers for Disease Control and Prevention. COVID Data Tracker.
- Centers for Disease Control and Prevention, Office of Public Health Data, Surveillance, and Technology. 2023. RESP-NET Interactive Dashboard.
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Actual)
February 12, 2024
Primary Completion (Actual)
August 5, 2024
Study Completion (Estimated)
September 6, 2024
Study Registration Dates
First Submitted
January 19, 2024
First Submitted That Met QC Criteria
January 19, 2024
First Posted (Actual)
January 29, 2024
Study Record Updates
Last Update Posted (Actual)
August 29, 2024
Last Update Submitted That Met QC Criteria
August 27, 2024
Last Verified
August 1, 2024
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- Predict + Protect Study
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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