Influenza, Human
Conditions
Keywords
Influenza vaccination, behavioral science interventions, vaccination promotion
Brief summary
This research aims to identify which behavioral science strategies are most effective at increasing flu vaccination rates overall and based on patients' individual characteristics. Past behavioral science interventions have shown promise in increasing flu vaccinations. For example, successful interventions have encouraged people to make concrete plans for when they will get a flu vaccination, sent automated calls or text messages reminding patients to get a flu vaccination , or provided financial incentives for getting vaccinated. Although these results are promising, these studies have been conducted in isolation on different populations, which makes it difficult to compare their interventions' effectiveness or to have enough power to reliably detect differing responses to interventions based on individual characteristics. This research will simultaneously test 22 different SMS interventions to increase flu vaccinations compared to a holdout control condition in a mega-study and apply machine learning to identify which interventions work best for whom. The interventions are designed by behavioral science experts from the Behavior Change for Good Initiative (BCFG), Penn Medicine Nudge Unit (PMNU), and Geisinger Behavioral Insights Team (BIT). Customers of a large retail pharmacy who received a flu shot from the pharmacy last year and receive SMS notifications will be included in this study. We expect this to include approximately 1.2 million participants. The specific aims of this research are to identify (1) which behavioral science strategies effectively increase flu vaccination rates overall, and (2) which strategies are most effective for different subgroups (e.g., based on age, gender, race).
Interventions
BEHAVIORALFlu shot text messages
Participants will receive text messages per descriptions listed in the arms.
Sponsors
University of Pennsylvania
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
DOUBLE (Caregiver, Outcomes Assessor)
Masking description
As treated participants will receive text messages, there is no scope for blinding. Care providers will not be made aware of subjects' participation in the study, or assigned treatment arms. The study team will only receive data on subjects' assigned arms and outcomes at the end of the study.
Intervention model description
Participants will be randomly assigned to the different arms.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
1. Have agreed to receive SMS messages from the pharmacy 2. Received a flu shot from the pharmacy in the 2019-2020 flu season, as documented in their pharmacy records.
Exclusion criteria
\-
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Whether participants receive a flu shot vaccination at our pharmacy partner by December 31, 2020 | 98 days | The primary outcome measure is whether participants receive a flu shot at our retail pharmacy partner by December 31, 2020 (as recorded in their pharmacy records) after receiving the SMS intervention on September 25, 2020. Participants who receive a flu shot before September 25, 2020 when they received the SMS intervention will be excluded from the analyses. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Whether participants receive a flu shot vaccination at our pharmacy partner by October 31, 2020 | 37 days | The secondary outcome measure is whether participants receive a flu shot at our retail pharmacy partner by October 31, 2020 (as recorded in their pharmacy records) after receiving the SMS intervention on September 25, 2020. Participants who receive a flu shot before they receive the SMS intervention on September 25, 2020 will be excluded from the analyses. |
Countries
United States
Outcome results
None listed