Predict + Protect Study: Exploring the Effectiveness of a Predictive Health Education Intervention on the Adoption of Protective Behaviors Related to ILI

COVID-19 Clinical Trial

Official title:

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)

Clinical Trial Details — Status: Enrolling by invitation

Administrative data

• NCT number: NCT06229444
• Other study ID #: Predict + Protect Study
• Status: Enrolling by invitation
• Phase: N/A
• Start date: February 12, 2024
• Est. completion date: December 2024

Study information

• Verified date: February 2024
• Source: Evidation Health
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

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.

Recruitment information / eligibility

• Status: Enrolling by invitation
• Enrollment: 15000
• Est. completion date: December 2024
• Est. primary completion date: June 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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)

Related Conditions & MeSH terms

• COVID-19
• Influenza
• Influenza A
• Influenza B
• Influenza, Human
• Respiratory Syncytial Virus (RSV)

Intervention

Behavioral:
• ILI Predictive Alerts, Reactive Content, and Proactive 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.
• ILI Predictive Alerts, Reactive Content
Participants receive alerts about potential ILI illness, and reactive and personalized content about protective health behaviors.
• Proactive Content
Participants receive ILI-related education, feedback, and opportunities to self-monitor ILI symptoms.
• No Intervention
Participants will not receive predictive alerts or reactive content after reporting symptoms or proactive IILI-related health educational content

Locations

Country	Name	City	State
United States	Evidation Health	San Mateo	California

Sponsors (2)

Lead Sponsor	Collaborator
Evidation Health	Biomedical Advanced Research and Development Authority

Country where clinical trial is conducted

United States, 

References & Publications (18)

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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.

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. — View Citation

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. — View Citation

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. — View Citation

Rosenstock, I. M. (2000). Health Belief Model. In A. E. Kazdin (Ed.), Encyclopedia of psychology (Vol. 4, pp. 78-80). Oxford University Press.

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

* Note: There are 18 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	The exploratory objective is to assess differences in effectiveness between the four groups on ILI-related health and behavioral outcomes	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
Primary	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	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	The secondary objective is to assess the accuracy of an ILI detection algorithm using self-reported symptoms and ILI diagnosis	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

External 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, Office of Public Health Data, Surveillance, and Technology. 2023. RESP-NET Interactive Dashboard.
•   Centers for Disease Control and Prevention. COVID Data Tracker.