InfluenzaNCT06229444

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

A study testing ILI Predictive Alerts, Reactive Content, and Proactive Content, ILI Predictive Alerts, Reactive Content and Proactive Content in people with Influenza and Influenza A, run by Evidation Health at 1 site in United States.

Completed

Finished. The study has ended and is not enrolling. Results may be posted on the official record.

Official record

Search all trials

Trial summary

Who is it for?: Ages 18 years and olderHealthy volunteers can take part
Conditions the study is for: Influenza, Influenza A, Influenza B
What is being tested?: ILI Predictive Alerts, Reactive Content, and Proactive Content, ILI Predictive Alerts, Reactive Content and Proactive Content
Type of treatment: Lifestyle or therapy programme
Could I receive a placebo?: No, everyone receives an active treatment
How many people: 17,043 participants
Where: 1 siteUnited States
Study dates: 12 Feb 2024to 6 Sep 2024
Phase: Interventional
Are there results?: None posted in the registry

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

Ages 18 years and older
All sexes
Healthy volunteers accepted
Adult, Older adult

Who may be able to join

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)

Who cannot join

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)

Trial locations and contacts

1 site in 1 country.

United States1 site

Evidation Health
San Mateo, California, 94402

Other trials in United States

About this study

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)

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.

Conditions: Influenza, Influenza A, Influenza B, COVID-19, Respiratory Syncytial Virus (RSV)
Being tested: ILI Predictive Alerts, Reactive Content, and Proactive Content, ILI Predictive Alerts, Reactive Content, Proactive Content, No Intervention
Keywords: Flu, Influenza, RSV, COVID, Wearable
Medical subject headings: Influenza, Human, COVID-19

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

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

Study groups in NCT06229444
Study group	Role	What they receive
Proactive ILI content & Predictions	Experimental	Participants will receive predictive alerts, reactive content after reporting symptoms or receiving an asymptomatic prediction, and ILI-related health educational content Receives: Behavioral: ILI Predictive Alerts, Reactive Content, and Proactive Content
No Proactive ILI content & Predictions	Experimental	Participants will receive predictive alerts and reactive content after reporting symptoms or receiving an asymptomatic prediction, but will not receive proactive ILI content Receives: Behavioral: ILI Predictive Alerts, Reactive Content
Proactive ILI content & No Predictions	Experimental	Participants will not receive predictive alerts or reactive content after reporting symptoms but will receive proactive ILI content Receives: Behavioral: Proactive Content
No Proactive ILI content & No Predictions	Experimental	Participants will not receive predictive alerts or reactive content after reporting symptoms or proactive ILI content Receives: Behavioral: No Intervention

What is being measured

The primary outcome is the main question the study is designed to answer.

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)
Measured: Through study completion, approximately 10 months

Secondary outcomes (1)

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
Measured: Through study completion, approximately 10 months

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

12 Feb 20246 Sep 2024

First posted29 Jan 2024
Study start12 Feb 2024
Primary completion5 Aug 2024
Study completion6 Sep 2024

Resources

Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
Publications (18)Papers the sponsor has linked to this study.
Other trials for InfluenzaStudies of the same condition, open and under way.
What the trial phases meanHow a treatment moves from first tests in people to approval.

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design

Study type: Interventional
Phase: Phase N/A
Primary purpose: Prevention
Allocation: Randomised
Intervention model: Factorial
Masking: Single blind
Who is masked: Participant
Enrolment: 17,043 participants (actual)

IDs, dates and publications

ClinicalTrials.gov ID: NCT06229444
Sponsor's protocol ID: Predict + Protect Study
Status last verified: Sep 2024
First submitted: 19 Jan 2024
Last updated: 19 Sep 2024
Results posted: No
Data monitoring committee: No
FDA-regulated drug: No
FDA-regulated device: No

Publications

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.
PubMed 36890264
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.
PubMed 29206909
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.
PubMed 35759279
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.
PubMed 35552722
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.
PubMed 33506230
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.
PubMed 36951890
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.
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.
PubMed 35480626
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.
PubMed 36963907
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.
PubMed 35898953
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.
PubMed 33431259
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.
PubMed 36165548
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.
PubMed 37747766
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.
PubMed 24275499

Want to know more?

The sponsor has not listed a contact. The official record may have more details.

Talk to your doctor first and take this page with you. Only the study team can confirm whether you can join.

Questions to ask the study team

What is the study trying to find out?
What would I need to do, and how often would I visit?
Could I receive a placebo instead of the treatment?
What are the possible risks and benefits for me?
Will it cost me anything? Are travel or time paid for?
Can I keep seeing my own doctor during the study?
What happens when the study ends?

See locations

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Study at a glance

From the registry record, last updated by the sponsor on 19 Sep 2024. Recruitment status is as the sponsor reported it: confirm with the study team.

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)
ClinicalTrials.gov ID: NCT06229444
Acronym / sponsor ID: Predict + Protect Study
Conditions: Influenza, Influenza A, Influenza B, COVID-19, Respiratory Syncytial Virus (RSV)
Keywords: Flu, Influenza, RSV, COVID, Wearable
Study type: Interventional
Recruitment status: Completed
Enrolment: 17,043 participants (actual)
Lead sponsor: Evidation HealthIndustry (pharma, biotech, devices)
Collaborators: Biomedical Advanced Research and Development Authority
Study start: 12 Feb 2024 (actual)
Primary completion: 5 Aug 2024 (actual)
Study completion: 6 Sep 2024 (actual)
First posted: 29 Jan 2024 (actual)
Results: Not posted in the source record

Clinical summary

The research question as the sponsor registered it. This describes what the study plans to test, not what it found.

Study design

Primary purpose: Prevention
Allocation: Randomised
Intervention model: Factorial
Masking: Single blind (participant)
Number of arms: 4

Arm	Type	Receives
Proactive ILI content & Predictions	Experimental	Behavioral: ILI Predictive Alerts, Reactive Content, and Proactive ContentParticipants will receive predictive alerts, reactive content after reporting symptoms or receiving an asymptomatic prediction, and ILI-related health educational content
No Proactive ILI content & Predictions	Experimental	Behavioral: ILI Predictive Alerts, Reactive ContentParticipants will receive predictive alerts and reactive content after reporting symptoms or receiving an asymptomatic prediction, but will not receive proactive ILI content
Proactive ILI content & No Predictions	Experimental	Behavioral: Proactive ContentParticipants will not receive predictive alerts or reactive content after reporting symptoms but will receive proactive ILI content
No Proactive ILI content & No Predictions	Experimental	Behavioral: No InterventionParticipants will not receive predictive alerts or reactive content after reporting symptoms or proactive ILI content

Who may qualify

Ages: 18 years and older
Sex: All
Healthy volunteers: Accepted
Age groups: Adult, Older adult

Key inclusion criteria (5 listed)

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)

Key exclusion criteria (5 listed)

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)

Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.

Interventions

Intervention	Type	Arms
ILI Predictive Alerts, Reactive Content, and Proactive ContentParticipants 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.	Behavioural	Proactive ILI content & Predictions
ILI Predictive Alerts, Reactive ContentParticipants receive alerts about potential ILI illness, and reactive and personalized content about protective health behaviors.	Behavioural	No Proactive ILI content & Predictions
Proactive ContentParticipants receive ILI-related education, feedback, and opportunities to self-monitor ILI symptoms.	Behavioural	Proactive ILI content & No Predictions
No InterventionParticipants will not receive predictive alerts or reactive content after reporting symptoms or proactive IILI-related health educational content	Behavioural	No Proactive ILI content & No Predictions

Primary outcome

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)

1 secondary outcome

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

Results and safety

The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.

Research links

Official record on ClinicalTrials.govLast updated by the sponsor 19 Sep 2024.
Background: PubMed 36890264Wiemken 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.
Background: PubMed 29206909Tokars 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.
Background: PubMed 35759279Temple 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.
Background: PubMed 35552722Mezlini 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.
Background: PubMed 33506230Shapiro 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.
Background: PubMed 36951890Hunter 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.
BackgroundMerrill 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.
Background: PubMed 35480626Mayer 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.
Background: PubMed 36963907Nestor 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.
BackgroundRosenstock, I. M. (2000). Health Belief Model. In A. E. Kazdin (Ed.), Encyclopedia of psychology (Vol. 4, pp. 78-80). Oxford University Press.
Background: PubMed 35898953Zewdie 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.
Background: PubMed 33431259Mercadante 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.
Centers for Disease Control and Prevention. COVID Data Tracker.Link provided by the sponsor.

Related trials for Influenza

Enrolling by invitationObservational study
Long-term Observation of Hemagglutinin and Neuraminidase Inhibition Antibody Titers After Influenza Challenge
Post Influenza
RecruitingObservational study
Screening for LID Clinical Studies Unit Healthy Volunteer Protocols
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RecruitingPhase 2
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Referral and contact

AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.

Ernesto H.N. Ramirez, PhD principal investigator

Evidation

1 site in 1 country: see every site and its status.

Questions about this trial

What is this clinical trial studying?

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 5 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: finished, as of the sponsor's last update on 19 Sep 2024. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.

How can a doctor refer a patient or contact the research team?

The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

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). It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT06229444, which also lists 18 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Sep 2024.