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# Behavioural nudges increase COVID-19 vaccinations

## Abstract

Enhancing vaccine uptake is a critical public health challenge1. Overcoming vaccine hesitancy2,3 and failure to follow through on vaccination intentions3 requires effective communication strategies3,4. Here we present two sequential randomized controlled trials to test the effect of behavioural interventions on the uptake of COVID-19 vaccines. We designed text-based reminders that make vaccination salient and easy, and delivered them to participants drawn from a healthcare system one day (first randomized controlled trial) (n = 93,354 participants; clinicaltrials number NCT04800965) and eight days (second randomized controlled trial) (n = 67,092 individuals; clinicaltrials number NCT04801524) after they received a notification of vaccine eligibility. The first reminder boosted appointment and vaccination rates within the healthcare system by 6.07 (84%) and 3.57 (26%) percentage points, respectively; the second reminder increased those outcomes by 1.65 and 1.06 percentage points, respectively. The first reminder had a greater effect when it was designed to make participants feel ownership of the vaccine dose. However, we found no evidence that combining the first reminder with a video-based information intervention designed to address vaccine hesitancy heightened its effect. We performed online studies (n = 3,181 participants) to examine vaccination intentions, which revealed patterns that diverged from those of the first randomized controlled trial; this underscores the importance of pilot-testing interventions in the field. Our findings inform the design of behavioural nudges for promoting health decisions5, and highlight the value of making vaccination easy and inducing feelings of ownership over vaccines.

## Main

Vaccines have been crucial for eradicating or controlling several deadly infectious diseases1. However, mobilizing people to get vaccines remains a challenge. Low or delayed vaccination uptake continues to threaten global health, and can lead to outbreaks of vaccine-preventable diseases6. Developing evidence-based communication strategies to enhance voluntary vaccine uptake is therefore critical4. Previous work suggests two major approaches to increasing vaccinations3. The first aims to boost vaccine uptake intentions among those who are uncertain about vaccination. Given that changing intentions is insufficient7, the second approach involves helping people to follow through on their vaccination intentions and overcome sources of friction, such as forgetfulness8, hassle costs9 and procrastination10,11.

These approaches could help to increase vaccination rates in the context of the current COVID-19 pandemic12, which has had unprecedented costs13. Despite the growing availability of COVID-19 vaccines, 30% of US adults were still either unwilling or uncertain about getting the COVID-19 vaccine in late June 2021, and the hesitancy rate was similarly high in several other countries that had vaccines available14. Barriers to action may further lower vaccination rates among those who intend to get inoculated.

Nudges, defined as interventions that alter ‘people’s behavior in a predictable way without forbidding any options or significantly changing economic incentives’15, could improve the uptake of COVID-19 vaccines16. Low-cost behavioural interventions such as these have been effectively applied to other health-related decisions5, such as healthy eating17, exercising18 and influenza vaccinations19,20,21. To maximize vaccine uptake, it is critical to understand how to best design behavioural interventions to boost intentions to get vaccinated, remove barriers to following through on good intentions or both3.

Here we report data from two sequential large-scale randomized controlled trials (RCTs) that investigate whether nudging people to get vaccinated, using reminders that are carefully designed to reduce barriers to following through, can improve the uptake of COVID-19 vaccines. Reminders are a popular nudge22 and have proven effective across policy-relevant domains8,20,23,24. We further examine the benefits of combining our reminders with additional interventions, including (1) behaviourally informed messaging designed to amplify individuals’ desire to get vaccinated and (2) a traditional information-provision intervention aimed at correcting the misconceptions that drive vaccine hesitancy25,26. Testing the effects of interventions on actual uptake of COVID-19 vaccines extends previous work that has studied hypothetical interventions27,28.

## Promoting vaccine uptake

We conducted two preregistered RCTs at University of California, Los Angeles (UCLA) Health (‘Data availability’ in Methods). Participants in these RCTs were drawn from the UCLA Health primary and speciality care attributed patient list. Starting from 29 January 2021, once patients became eligible for the COVID-19 vaccine, UCLA Health sent them an initial invitation to schedule their vaccination appointment. On the first weekday after the initial invitation (hereafter, the ‘first reminder date’), we enrolled eligible patients (hereafter, ‘participants’﻿) in the first RCT. On the first weekday after the eighth day following the initial invitation (hereafter, the ‘second reminder date’), we enrolled participants eligible for the second RCT into it. The timeline and eligibility criteria are provided in ‘Enrolment and eligibility for RCTs’ in Methods; Fig. 1 shows the timeline, eligibility and randomization of the two RCTs.

In both RCTs, we randomized whether participants received text-message-based reminders or not. All reminders shared two elements that were intended to address two barriers to action. First, all reminders made vaccination top of mind to curb forgetfulness and prompt people to adopt the target behaviour8, 22. Second, all reminders sought to reduce inconvenience as a potential source of friction22 by including a link to the appointment-scheduling website and allowing participants to easily book their appointment immediately.

Our primary outcome was whether participants scheduled their first-dose appointment at UCLA Health within six days of receiving a text reminder. Our secondary outcome was whether participants obtained the first dose at UCLA Health within four weeks of the reminder; the reasoning behind these time windows is given in ‘Outcome measures for RCTs’ in Methods.

We focus our data reporting on participants who were enrolled in the RCTs by 23 February 2021, as specified in our preregistration. All exclusion criteria and analyses were preregistered (‘Enrolment and eligibility for RCTs’ in Methods, Supplementary Information sections 1.1 and 1.3).

### First-reminder RCT

On the first reminder date, we randomly assigned participants enrolled in the first RCT at a 4:1 ratio to the ‘follow-through reminder’ arm, in which they received a text reminder at 15:00 h that encouraged them to schedule a vaccination appointment, or to the ‘holdout’ arm, in which they did not get a reminder.

We nested a 2 × 2 factorial design within the follow-through reminder arm to test whether reminders become more effective when combined with another behaviourally informed intervention to motivate action and/or with an information intervention that aims at shifting vaccination intentions.

The first factor varied whether the reminder attempted to further amplify people’s desire to act by inducing feelings of psychological ownership over the vaccine29,30. Reminders containing the ownership intervention (designated ‘ownership reminder’ and ‘ownership reminder with video’) indicated the vaccine had ‘just been made available for you’ and encouraged participants to ‘claim your dose’. We used online experiments to confirm that such language would make participants feel more strongly that the vaccine was already theirs (ordinary least squares (OLS) regressions, B = 0.376, s.e. = 0.084, P < 0.001, n = 1,987; B = 0.389, s.e. = 0.116, P < 0.001, n = 1,168) (Supplementary Tables 26, 36). Previous research has shown that similar language—such as ‘the flu vaccine is reserved for you’—increased uptake of influenza vaccinations20; psychological ownership could be one of the mechanisms at play.

The second factor manipulated whether the reminder contained a link to a 2-min video that provided information on COVID-19 and vaccine effectiveness, with the goal of correcting common misconceptions and boosting vaccination intentions. The video intervention was used in the ‘basic reminder with video’ and ‘ownership reminder with video’ sub-arms. We based the video on a literature review of vaccine hesitancy3,31,32 and our January 2021 survey of residents of California (USA) (n = 515) (‘Vaccination intention survey’ in Methods), which allowed us to identify common misconceptions about COVID-19 and authorized vaccines. A similar video intervention was used in previous work to increase influenza vaccinations20.

Our analysis includes 93,354 participants (43.3% male, 53.5% white (excluding Hispanic or Latino) (all racial demographic data use self-reported terms), average age = 72.8, s.d. = 10.3). Study arms were well-balanced on demographic characteristics (Extended Data Table 1). All reported effect sizes come from OLS regressions (or, precisely, a linear probability model33, given our binary outcome measures) with heteroscedasticity-robust standard errors that control for participant gender, age, race, ethnicity, preferred language, social vulnerability index, COVID-19 risk score and fixed effects of initial invitation dates. The results are robust to removing control variables, using logistic regressions and conducting intent-to-treat analyses with all participants enrolled in our RCTs by 23 February 2021 (Supplementary Information section 1.5).

In the holdout arm, 7.20% of participants made the first-dose appointment within six days of the first reminder date, and 13.89% received the first dose at UCLA Health within four weeks (Fig. 2). Our OLS regressions estimate that receiving a text reminder boosted appointment rates within six days by 6.07 percentage points and vaccination rates within four weeks by 3.57 percentage points (Extended Data Table 2), amounting to a relative increase of 84.33% and 25.71%, respectively. All reminder types outperformed the holdout arm (Extended Data Table 2). The top-performing reminder type contained the ownership language, and boosted appointment and vaccination rates at UCLA Health by 6.83 (94.84%) and 4.12 (29.63%) percentage points, respectively, relative to the holdout arm.

The gap between the follow-through reminder and holdout arms in vaccinations at UCLA Health persisted for eight weeks (Fig. 3), which suggests that reminders increased the number of vaccinated participants for as long as we observed (rather than only accelerating vaccinations). Notably, even if the holdout arm eventually caught up after the two months we observed, accelerating vaccination still benefits society34.

Within the follow-through reminder arm, adding the ownership language to the reminder further increased appointment and vaccination rates at UCLA Health by 1.51 and 1.09 percentage points, respectively (Extended Data Table 2), compared to the 12.58% appointment rates and 17.01% vaccination rates among people who received a reminder without such language. By contrast, we found no evidence that inviting participants to watch the video improved either outcome variable, relative to reminders without a video (Extended Data Table 2).

The average effect of a reminder held for both participants who received the influenza shot in either of the two recent seasons (n = 46,757) and those who did not (n = 46,597) (Fig. 4) but was larger among the former than the latter group, by 4.4 percentage points for appointments (OLS regression, B = 0.044, s.e. = 0.004, P < 0.001 for the interaction) and 2.3 percentage points for vaccinations at UCLA Health (OLS regression, B = 0.023, s.e. = 0.006, P < 0.001 for the interaction) (Supplementary Table 6).

Because our sample consists of predominantly elderly and white participants, we confirmed (Fig. 4) that the effects of follow-through reminders and ownership language largely held for racial and ethnic minorities as defined in Fig. 4 (n = 29,784) and participants under 65 years old (n = 9,279). Notably, the average effects of follow-through reminders on both appointments and vaccinations were comparable across white (n = 49,909), Hispanic (n = 10,624), Black (n = 5,109) and Asian (n = 7,553) participants (Extended Data Table 2). Identifying solutions to improving vaccine uptake among racial and ethnic minority groups is critical, as these groups have been disproportionately hurt by the COVID-19 pandemic35 and tend to experience increased vaccine hesitancy36.

### Second-reminder RCT

Participants who did not schedule their vaccine appointment a few days after the first reminder may have forgotten about it, been procrastinating or been more hesitant than those who got vaccinated. We conducted the second RCT to study the effect of sending these participants a second text reminder. On the second reminder date, we randomized eligible participants at a 6:1 ratio to receive another text message at 15:00 h that reminded them of vaccine availability and providing easy access to the scheduling website (the follow-through reminder arm) or to not receive the text message (the holdout arm).

To harness other psychological principles to motivate people to act, we randomized participants within the follow-through reminder arm to receive one of six messages that leveraged additional behavioural insights (‘Design of the second-reminder RCT’ in Methods). Following the preregistration, we present only the average effect of all text reminders combined relative to the holdout arm.

Our analysis includes 67,092 participants (43.5% male, 52.6% white (excluding Hispanic or Latino), average age = 73.7, s.d. = 10.0). Study arms were well-balanced on demographic characteristics (Extended Data Table 3).

Getting a second reminder increased participants’ likelihood of scheduling the first-dose appointment within six days by 1.65 percentage points (53.36%) and obtaining the first dose at UCLA Health within four weeks by 1.06 percentage points (17.23%), relative to the 3.10% appointment rates and 6.16% vaccination rates in the holdout arm (Extended Data Table 4). All reminder types boosted appointments and vaccinations (Extended Data Table 4). Although small, these effects are noteworthy, as they are documented within a more hesitant population (as participants in the second RCT had not scheduled an appointment after two notifications and had been eligible for COVID-19 vaccines in California for some time).

## Effect on vaccination anywhere

Because the text reminders made eligibility at UCLA Health salient and reduced barriers to appointment scheduling at UCLA Health, we have focused on appointments and vaccinations at UCLA Health as our outcome measures. We also investigated the effect of receiving a text reminder on whether participants received the first dose inside or outside UCLA Health (hereafter, ‘anywhere’) within four weeks of getting a reminder (Supplementary Information section 1.5).

For the first RCT, we find that reminders increased vaccinations anywhere by 2.1 percentage points, relative to a baseline of 31.85% in the holdout arm (OLS regression, B = 0.021, s.e. = 0.004, P < 0.001, n = 93,354) (Supplementary Table 22). In addition, adding (versus not) the ownership language increased vaccinations anywhere by an additional 0.9 percentage points (OLS regression, B = 0.009, s.e. = 0.003, P = 0.010 without multiple comparison adjustment and P = 0.020 with a Holm–Bonferroni correction37, n = 74,605) (Supplementary Table 22). The fact that the effect of receiving one reminder on vaccinations at any location could last one month is notable, considering that participants may have been exposed to numerous sources of communication about the vaccine during this period.

Receiving a second reminder increased vaccination rates anywhere by 1.0 percentage points two weeks after the second reminder date (OLS regression, B = 0.010, s.e. = 0.004, P = 0.008, n = 67,092) (Supplementary Table 24), relative to a baseline of 12.04% in the holdout arm. Although this effect was not statistically significant at four weeks (OLS regression, B = 0.007, s.e. = 0.004, P = 0.127, n = 67,092) (Supplementary Table 23), sending a second text reminder can still contribute to accelerating vaccinations and avoiding unnecessary infections. It is also worth noting that, had we designed the reminders to remove barriers to getting vaccinated at a broad set of locations (rather than focusing on UCLA Health), our reminders might have exhibited larger effects on vaccination anywhere.

## Vaccination intentions versus actual uptake

To inform policy, researchers often use surveys of intentions to evaluate the effectiveness of interventions aimed at encouraging vaccine uptake3,27,28. Given that intentions do not always reflect real behaviours7, we tested how the interventions deployed in our first RCT affected vaccination intentions and explored whether hypothetical responses would match actual behavioural responses.

We ran three preregistered experiments on Amazon’s Mechanical Turk and Prolific Academic: two concurrently to the first RCT in February 2021 and one as a replication in April 2021 (total n = 3,181). We randomized participants to receive one of the four reminders from the first RCT, asking about their intentions to get vaccinated using different questions on a seven-point scale (‘Procedures for online experiments’ in Methods). In contrast to the patterns observed in the first RCT, the video intervention resulted in a small—but statistically significant—increase in people’s self-reported interest in getting the vaccine; however, we found no evidence that adding ownership language increased vaccination intentions (Extended Data Table 5).

The discrepancy between laboratory and field data (Extended Data Table 6) is unlikely to be driven by differences in political attitudes between samples38, as the aforementioned findings about video and ownership interventions generally held both for those who self-identified as ‘Democrat’ and as ‘Republican’ online (Extended Data Table 5). One potential explanation for these discrepant findings is that, although we could require all online participants to watch the video, less than 21% of the participants in the first RCT opted to watch it (Supplementary Information section 1.3.4), possibly because of being too busy or active avoidance of information39. Another possibility is that COVID-19 vaccine intentions were harder to change outside of a controlled online experiment, where various sources of information compete for people’s attention. As for the lack of evidence that ownership language affected vaccination intentions, it could be that individuals did not anticipate the motivating power of such language in hypothetical settings. Whereas the differences in sample characteristics and measurement (Extended Data Table 6) do not allow us to pinpoint the drivers of the discrepancy between our online studies and the first RCT, these results suggest that hypothetical responses to behavioural nudges should be taken with caution.

## Discussion

Our research highlights that behavioural science insights can increase and speed up COVID-19 vaccinations at close-to-zero marginal cost. Text-based reminders designed to overcome barriers to scheduling can effectively encourage vaccinations across different demographic groups, with effects persisting for at least eight weeks. These effects are heightened when follow-through reminders leverage psychological ownership, making people feel that a dose of the vaccine belongs to them. However, we find no evidence that combining reminders with a video-based information intervention further increases vaccination, which suggests that more work is needed to uncover when information interventions can help to overcome vaccine hesitancy. Additional analyses of our RCT sample reveal that only about 10% of participants did not keep or show up for their first-dose appointment, and approximately 90% of participants who received the first dose at UCLA Health scheduled their second dose (Supplementary Information section 1.6). Thus, the biggest barrier to increasing COVID-19 vaccinations is getting participants to schedule the first-dose appointment.

Our research has implications for enhancing the uptake of life-saving vaccines in general, as it highlights the power of making vaccination easy and eliciting feelings of ownership over the vaccine. Although promoting vaccinations at scale requires a multifaceted approach, our findings suggest that behavioural nudges could be an important strategy to consider. If sent to all 263 million adults in the USA40, and assuming the same absolute effect size observed in our first RCT would hold for the 60% of US adults who did not immediately obtain the vaccine41, our follow-through reminders could result in 3.31–5.68 million extra people getting vaccinated within a month of the reminder. This estimated range is based on the average effect of receiving the first reminder on vaccination rates anywhere (that is, 60% × 263 million × 2.1 percentage points) versus at UCLA Health (60% × 263 million × 3.6 percentage points). Similarly, reminders with the ownership framing would motivate 1.42–1.74 million extra people to get vaccinated than reminders without such framing (that is, 60% × 263 million × 0.9 percentage points–60% × 263 million × 1.1 percentage points).

The insights from this work could inform strategies to motivate health-related behaviours more broadly, such as scheduling preventive care tests or participating in health-related programs. To that end, the discrepancy observed between our RCTs and online studies highlights the value of pilot-testing interventions in the field before deploying them at scale. As policymakers, public health experts and organizations strive to develop communication strategies to promote health-related behaviours, we hope that the effective interventions documented in our research—and behavioural science more generally—can become part of their toolbox.

## Methods

For RCTs, we predetermined the end date of enrolment for analyses reported herein, but we could not predetermine sample size by the enrolment deadline owing to uncertainty about how many UCLA Health participants would satisfy inclusion and exclusion criteria. We preregistered data-analysis plans contingent on the actual sample size on the basis of power analysis. We used power analysis to predetermine sample sizes for online experiments. RCTs and online experiments were randomized, and investigators were blinded to allocation during experiments.

### Ethics approval

This research was deemed to comply with all relevant ethical regulations. The Institutional Review Board at the UCLA approved the protocols of our randomized controlled trials (reference number 21-000268) and determined that a waiver of informed consent was appropriate. All online experiments and the vaccination intention survey were conducted under approval of the Institutional Review Board at Carnegie Mellon University (reference number IRBSTUDY2015_00000482), and informed consent was obtained from all online study participants as part of the enrolment process.

### Setting for the RCTs

We conducted the RCTs in partnership with UCLA Health, a large integrated academic health system in California. Extended Data Table 7 provides a comparison of demographic characteristics and vaccination rates between our RCT sample, UCLA Health primary and specialty care attributed patient population, Los Angeles County and California.

### Enrolment and eligibility for RCTs

Starting from 19 January 2021, UCLA Health invited primary and speciality care attributed patients who were eligible for the COVID-19 vaccine at the time to get vaccinated. UCLA Health followed the national Advisory Committee on Immunization Practices as well as state and county guidelines to determine patient COVID-19 vaccine eligibility phasing. Considering the large volumes of eligible patients in each phase, UCLA Health developed a risk model that incorporates clinical and social risk to subprioritize within each phase. According to this model, UCLA Health sent invitations to eligible patients in batches over time to guarantee enough vaccine supply for invited patients. The size of the batch was decided daily on the basis of (1) available doses, (2) available appointment slots and (3) expected appointment rate. If UCLA Health identified a patient as having already obtained the vaccine inside or outside UCLA Health when it was their turn to be invited, the health system did not send the invitation to that patient.

On the first reminder date, patients were automatically enrolled into the first RCT and became participants if they (1) had a SMS-capable telephone number, (2) had not scheduled the first-dose COVID-19 vaccination appointment at UCLA Health and (3) had not obtained the first dose anywhere by the end of the day before the first reminder date, according to the latest California Immunization Registry (CAIR) records UCLA Health could access as well as UCLA Health’s internal records. The earliest first reminder date was 1 February 2021.

On the second reminder date, patients were automatically enrolled in the second RCT and became participants if they (1) had a SMS-capable telephone number, (2) had not scheduled the first-dose COVID-19 vaccination appointment at UCLA Health and (3) had not obtained the first dose anywhere by the end of the day before the second reminder date. The earliest second reminder date was 9 February 2021.

Figure 1 shows the timeline, eligibility and randomization of the two RCTs. For both RCTs, participants within each batch were randomized at the individual level to treatments according to the design detailed in ‘Design of the first-reminder RCT’ and ‘Design of the second-reminder RCT’. Enrolment was conducted by the UCLA Health Office of Population Health and Accountable Care. Random assignment to interventions was performed by UCLA Health statisticians blind to the hypotheses and interventions using a computerized random number generator.

### Design of the first-reminder RCT

We randomly assigned participants following simple randomized procedures at a 4:1 ratio to either the follow-through reminder arm, in which they received a reminder at 15:00 h on the first reminder date, or the holdout arm, in which they received no reminders. All reminders were designed to nudge individuals to schedule their vaccination appointments by (1) making vaccination top of mind to curb forgetfulness, and (2) providing the direct link to the scheduling website to reduce friction and increase convenience. The basic reminder read ‘UCLA Health: [participant’s first name], you can get the COVID-19 vaccine at UCLA Health. Make a vaccination appointment here: uclahealth.org/schedule.’

We nested a 2 × 2 factorial design within the follow-through reminder arm. The first factor was whether or not the reminder sought to enhance participants’ feelings of psychological ownership over the vaccine to amplify their desire to obtain their vaccine (ownership intervention). The ownership intervention added language to the reminder to make participants feel as if the vaccine was already theirs. The ownership reminder read ‘UCLA Health: [participant’s first name], a COVID-19 vaccine has just been made available to you at UCLA Health. Claim your dose today by making a vaccination appointment here: uclahealth.org/schedule.’

The second factor was whether or not the reminder linked to a video that was designed to shift vaccination intentions by providing information about COVID-19 and the authorized vaccines (video intervention). The video intervention was based on a survey of the vaccine hesitancy literature3,31,32,36 as well as a survey that we conducted in January 2021 with California residents (as described in ‘Vaccination intention survey’). The video (Supplementary Video 1) first highlighted the pandemic as a challenge, providing statistics on infections and ease of transmission. It then proposed the vaccine as an easy and safe solution, providing information about its effectiveness. The basic reminder with video read ‘UCLA Health: [participant’s first name], you can get the COVID-19 vaccine at UCLA Health. Please watch this important 2 min video: [link]. Make a vaccination appointment here: uclahealth.org/schedule.’

In the ownership reminder with video sub-arm, the reminder contained both the ownership and video interventions and read: ‘UCLA Health: [participant’s first name], a COVID-19 vaccine has just been made available to you at UCLA Health. Please take 2 simple steps: 1. Watch this important 2 min video: [link]. 2. Claim your dose today by making a vaccination appointment here: uclahealth.org/schedule.’

In all sub-arms, participants whose preferred language was Spanish received the text reminder (and the video (Supplementary Video 2), in the relevant cases) in Spanish. Participants within the follow-through reminder arm were randomly assigned following simple randomization procedures to one of these four sub-arms with an equal probability.

### Design of the second-reminder RCT

Eight days after the initial notification, eligible participants were enrolled in the second RCT. They were randomized following simple randomization procedures at a 6:1 ratio to the follow-through reminder arm, in which another text reminder was sent at 15:00 h on the second reminder date, or the holdout arm with no reminders. Randomization was independent between the first and second RCTs (Supplementary Information section 1.1). Similar to the first RCT, all text reminders in the second RCT heightened the salience of vaccine availability (so as to combat forgetfulness) and provided the direct link to the appointment scheduling website (so as to increase convenience).

We nested a 2 × 3 factorial design within the follow-through reminder arm, in which we leveraged behavioural insights to motivate people to schedule a vaccination appointment via different messaging. The first factor varied whether the reminder emphasized prosocial (versus personal) benefits of getting vaccinated42,43. The second factor manipulated whether the reminder highlighted the exclusivity of having early access to the vaccine (early access framing), whether it framed the act of obtaining the vaccine as an opportunity to chart a new path forward (fresh start framing) or neither. The early access framing sought to leverage the principle of scarcity to increase vaccine demand44,45, as vaccination was still exclusive at the early stage of distribution (January–February 2021). The fresh start framing was inspired by previous work showing that people are motivated to take actions at new beginnings46,47. Here, we tested whether framing getting the vaccine as an opportunity to chart a new path forward for participants themselves or society could mobilize participants to get inoculated.

### Analyses and exclusion criteria of RCTs

All analyses and exclusion criteria follow the preregistrations. We focus on participants enrolled in either RCT by 23 February 2021. This sample consists of participants eligible to get vaccinated at UCLA Health from 19 January to 22 February 2021, including participants at or above 65 years old, participants with any transplant and high-risk participants with qualifying pre-existing conditions. We report results using data extracted on 25 May 2021. We excluded participants who were enrolled in the first (second) RCT but either scheduled a vaccination appointment at UCLA Health by 15:00 h on their corresponding first (second) reminder date or obtained a COVID-19 vaccine somewhere before their corresponding first (second) reminder date according to the latest appointment and vaccination records UCLA Health could access on 25 May 2021. These participants could not have been motivated to schedule or obtain the first dose by our text reminders; thus, excluding them allows us to more accurately estimate the effect of our interventions on participants who could benefit from receiving our interventions. We additionally excluded participants under 18 years old as we only applied for the permission of the Institutional Review Board to analyse data about adult participants. The proportion of participants who were excluded in the analysis stage did not statistically significantly differ across conditions (Supplementary Table 1), and our results are robust if we conduct intent-to-treat analyses involving all participants who were enrolled in the RCTs by 23 February 2021 (Supplementary Information section 1.5).

For the first RCT, our preregistered analysis about participants enrolled by 23 February 2021 aimed to investigate (1) the average effect of sending a follow-through reminder; (2) whether all reminder types would outperform the holdout arm; (3) the effect of adding the video intervention to the reminder; (4) the effect of adding the ownership intervention; and (5) whether the aforementioned effects would differ between participants who received versus did not receive the influenza vaccine in either of two recent influenza seasons.

For the second RCT, our preregistered analysis about participants enrolled by 23 February 2021 aimed to investigate (1) the average effect of sending a second follow-through reminder and (2) whether all reminder types outperformed the holdout arm. Because we were uncertain about how many people would be enrolled in the second RCT by 23 February 2021, we preregistered to not compare sub-arms to each other with this data. Supplementary Information sections 1.3 and 1.4 describe for the scope of analyses we plan to conduct once the full data collection has been completed about all participants ever enrolled in our RCTs from the beginning of the trials until UCLA Health stops sending out COVID-19 vaccine invitations.

### Procedures for online experiments

We ran two preregistered online experiments in February 2021, concurrently with the randomized controlled trials. In addition, we ran a preregistered replication experiment online in April 2021, when all US adults had become eligible to receive the vaccine.

In the February 2021 experiments, we instructed participants to imagine becoming eligible for the COVID-19 vaccine and receiving a text message from their healthcare provider encouraging them to get vaccinated. We randomly assigned participants to read one of the four reminders from the first RCT. Participants in the ‘video’ conditions were also instructed to watch the video. After reading the message, participants indicated their likelihood of scheduling a vaccination appointment: ‘How likely would you be to schedule a vaccination appointment after receiving this message from your healthcare provider?’ (1, not at all likely, to 7, very likely). They also rated the persuasiveness of the text message (1, not at all persuasive, to 7, very persuasive). To check whether the messages containing ownership language increased feelings of psychological ownership over the vaccine as we intended, we asked participants, ‘To what extent does the text message make you feel that the COVID-19 vaccine is already yours?’ (1, not at all, to 7, very much)30. To understand how the video may have changed viewers’ perceptions and beliefs, we measured participants’ beliefs about the prevalence of COVID-19, worry about spreading the virus, perceived vaccine effectiveness, anticipated regret for not getting the vaccine and trust in the vaccine (Supplementary Information section 2 for questions and results).

### Sample for online experiments

We recruited participants from Amazon’s Mechanical Turk (MTurk) and Prolific Academic (Prolific) who had not received a COVID-19 vaccine or scheduled a first-dose vaccination appointment at the time of the study. To be assigned to treatment, participants had to first pass a Captcha and an attention check question. To be included in the analysis, participants had to complete our preregistered dependent variables and not report having technical problems with the video. Considering these criteria, our first February 2021 online experiment consists of 1,163 participants. Our second February 2021 online experiment consists of 840 participants recruited from Prolific who satisfied similar criteria as those in the first online experiment, except that we additionally required that they did not report having taken a similar survey on MTurk. In both experiments, we attempted to recruit a balanced sample of individuals who self-reported as Democrat or Republican to test the generalizability of our findings (Supplementary Information section 2.1 for recruitment detail). Participants received US$0.90 on MTurk and US$1.10 on Prolific for completing our 6-min survey. Across the two February online experiments, our sample consists of 2,003 participants (47.1% male, 71.8% white (excluding Hispanic or Latino), 51.8% Democrat, average age = 37.9, s.d. = 13.4).

For our April 2021 online experiment, we recruited participants on MTurk and Prolific using the same eligibility criteria as the second online experiment. Participants on MTurk received US$0.90 or US$1.00 (we boosted the pay to US$1.00 on the third day of data collection to attract more respondents) and those on Prolific received US$1.10 for completing our 6-min survey. Our sample consists of 1,178 participants (44.9% male, 71.6% white (excluding Hispanic or Latino), 40.8% Democrat, average age = 36.7, s.d. = 12.0).

### Vaccination intention survey

To design the video used in our first RCT, we ran a survey in January 2021 involving 515 residents of California recruited on MTurk and Prolific (49.3% male, 42.9% white (excluding Hispanic or Latino), 70.9% Democrat, average age = 33.9, s.d. = 12.7). Participants received US$1.00 on MTurk or US$1.20 on Prolific for completing our 9-min survey. We asked participants to consider the authorized vaccines (Pfizer and Moderna) when taking the survey. We elicited their vaccination intentions by asking ‘If one of the COVID-19 vaccines were available to you today, would you get the vaccine?’41. Participants chose one from four options: ‘Definitely would get the vaccine’, ‘Probably would get the vaccine’, ‘Probably would not get the vaccine’ and ‘Definitely would not get the vaccine’. We then elicited participants’ beliefs and perceptions about COVID-19 and the vaccines. Specifically, we measured beliefs about infection likelihood with and without the vaccine and the severity of COVID-19. We collected feelings of vulnerability to COVID-19, fear of infection, worry of transmitting COVID-19 to others, anticipated regret for not getting the vaccine and trust in the vaccine. We compared answers to these questions among people who reported that they definitely would get the vaccine versus those feeling more uncertain. Supplementary Information section 5 describes all variables and results.

### Methods of investigating intentions versus actual uptake

In Extended Data Table 6, we report statistics about the estimated effects of adding ownership language and a video-based information intervention to a reminder on vaccination intentions (based on online experiments) versus actual vaccine uptake (based on the first RCT). The statistics we report include the 95% confidence interval, the absolute value of Cohen’s d or h, and ηp2 of each estimated effect. To calculate Cohen’s h for the binary outcomes measured in the first RCT, we use 2 × arcsine √Pwith an intervention − 2 × arcsine √Pwithout an intervention48 in which √Pwith an intervention represents the percentage of participants who scheduled an appointment for (or obtained) the first dose at UCLA Health within six days (or within four weeks) of the first reminder date among those who received a text reminder containing a given intervention and √Pwithout an intervention represents the percentage among participants who received a text reminder without that intervention. To calculate ηp2 for the online experiments and the first RCT, we use ηp2 = F × dfnumerator/(F × dfnumerator + dfdenominator)49 in which the F value and numerator and denominator degrees of freedom came from the OLS regressions reported in Supplementary Tables 5, 39.

### Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this paper.

## Data availability

The two RCTs were pre-registered at clinicaltrials.gov (first-reminder RCT, https://clinicaltrials.gov/ct2/show/NCT04800965; second-reminder RCT, https://clinicaltrials.gov/ct2/show/NCT04801524). The three online experiments were preregistered at aspredicted.org (online experiment 1, https://aspredicted.org/blind.php?x=u2ng5c; online experiment 2, https://aspredicted.org/blind.php?x=ae3ci5; and online experiment 3, https://aspredicted.org/blind.php?x=7wf9er and https://aspredicted.org/blind.php?x=u82hy5). The data analysed in this Article about randomized controlled trials were provided by UCLA Health and contain protected health information. To protect participant privacy, we cannot publicly post individual-level data. Qualified researchers with a valuable research question and relevant approvals including ethical approval can request access to the de-identified data about these trials from the corresponding author. A formal contract will be signed and an independent data protection agency should oversee the sharing process to ensure the safety of the data. Data about our online experiments and vaccination intention survey are available at: https://osf.io/qn8hr/?view_only=cf7b2bc590054aee8c4a2bae99ef20c5.

## Code availability

The code to replicate the analyses and figures in the Article and its Supplementary Information is available at https://osf.io/qn8hr/?view_only=cf7b2bc590054aee8c4a2bae99ef20c5.

## References

1. 1.

Greenwood, B. The contribution of vaccination to global health: past, present and future. Phil. Trans. R. Soc. Lond. B 369, 20130433 (2014).

2. 2.

MacDonald, N. E. & the SAGE Working Group on Vaccine Hesitancy. Vaccine hesitancy: definition, scope and determinants. Vaccine 33, 4161–4164 (2015).

3. 3.

Brewer, N. T., Chapman, G. B., Rothman, A. J., Leask, J. & Kempe, A. Increasing vaccination: putting psychological science into action. Psychol. Sci. Public Interest 18, 149–207 (2017).

4. 4.

Jarrett, C., Wilson, R., O’Leary, M., Eckersberger, E. & Larson, H. J. Strategies for addressing vaccine hesitancy – a systematic review. Vaccine 33, 4180–4190 (2015).

5. 5.

Patel, M. S., Volpp, K. G. & Asch, D. A. Nudge units to improve the delivery of health care. N. Engl. J. Med. 378, 214–216 (2018).

6. 6.

Jansen, V. A. A. et al. Measles outbreaks in a population with declining vaccine uptake. Science 301, 804 (2003).

7. 7.

Sheeran, P. Intention–behavior relations: a conceptual and empirical review. Eur. Rev. Soc. Psychol. 12, 1–36 (2002).

8. 8.

Karlan, D., McConnell, M., Mullainathan, S. & Zinman, J. Getting to the top of mind: how reminders increase saving. Manage. Sci. 62, 3393–3411 (2016).

9. 9.

Bhargava, S. & Manoli, D. Psychological frictions and the incomplete take-up of social benefits: evidence from an IRS field experiment. Am. Econ. Rev. 105, 3489–3529 (2015).

10. 10.

Shu, S. B. & Gneezy, A. Procrastination of enjoyable experiences. J. Mark. Res. 47, 933–944 (2010).

11. 11.

Rogers, T., Milkman, K. L., John, L. K. & Norton, M. I. Beyond good intentions: prompting people to make plans improves follow-through on important tasks. Behav. Sci. 1, 33–41 (2015).

12. 12.

Wood, S. & Schulman, K. Beyond politics–promoting COVID-19 vaccination in the United States. N. Engl. J. Med. 384, e23 (2021).

13. 13.

Cutler, D. M. & Summers, L. H. The COVID-19 pandemic and the \$16 trillion virus. J. Am. Med. Assoc. 324, 1495–1496 (2020).

14. 14.

Shelburne, P. U.S. lags behind global counterparts in driving down vaccine hesitancy, https://morningconsult.com/global-vaccine-tracking (accessed on 3 July 2021).

15. 15.

Thaler, R. H. & Sunstein, C. NUDGE: Improving Decisions About Health, Wealth, and Happiness, vol. 47 (Penguin, 2009).

16. 16.

Volpp, K. G., Loewenstein, G. & Buttenheim, A. M. Behaviorally informed strategies for a national COVID-19 vaccine promotion program. J. Am. Med. Assoc. 325, 125–126 (2021).

17. 17.

Wisdom, J., Downs, J. S. & Loewenstein, G. Promoting healthy choices: information versus convenience. Am. Econ. J. Appl. Econ. 2, 164–178 (2010).

18. 18.

Milkman, K. L. et al. Megastudies improve the impact of applied behavioral science. Nature (in the press).

19. 19.

Chapman, G. B., Li, M., Colby, H. & Yoon, H. Opting in vs opting out of influenza vaccination. J. Am. Med. Assoc. 304, 43–44 (2010).

20. 20.

Milkman, K. L. et al. A megastudy of text-based nudges encouraging patients to get vaccinated at an upcoming doctor’s appointment. Proc. Natl Acad. Sci. USA 118, e2101165118 (2021).

21. 21.

Milkman, K. L., Beshears, J., Choi, J. J., Laibson, D. & Madrian, B. C. Using implementation intentions prompts to enhance influenza vaccination rates. Proc. Natl Acad. Sci. USA 108, 10415–10420 (2011).

22. 22.

Sunstein, C. R. in The Handbook of Privacy Studies (eds Sloot, B. & Groot, A.) 173–180 (Amsterdam Univ. Press, 2018).

23. 23.

Fishbane, A., Ouss, A. & Shah, A. K. Behavioral nudges reduce failure to appear for court. Science 370, eabb6591 (2020).

24. 24.

Dale, A. & Strauss, A. Don’t forget to vote: text message reminders as a mobilization tool. Am. J. Pol. Sci. 53, 787–804 (2009).

25. 25.

Benartzi, S. et al. Should governments invest more in nudging? Psychol. Sci. 28, 1041–1055 (2017).

26. 26.

Loewenstein, G. & Chater, N. Putting nudges in perspective. Behav. Public Policy 1, 26–53 (2017).

27. 27.

Pink, S. L., Chu, J., Druckman, J., Rand, D. G. & Willer, R. Elite party cues increase vaccination intentions among Republicans. Proc. Natl Acad. Sci. USA 118, e2106559118 (2021).

28. 28.

Serra-Garcia, M. & Szech, N. Choice architecture and incentives increase COVID-19 vaccine intentions and test demand. SSRN Electron J. CESifo Working Paper No. 9003, https://ssrn.com/abstract=3827616 (2021).

29. 29.

Pierce, J. L., Kostova, T. & Dirks, K. T. Toward a theory of psychological ownership in organizations. Acad. Manage. Rev. 26, 298–310 (2001).

30. 30.

Shu, S. B. & Peck, J. Psychological ownership and affective reaction: emotional attachment process variables and the endowment effect. J. Consum. Psychol. 21, 439–452 (2011).

31. 31.

Kreps, S. et al. Factors associated with US adults’ likelihood of accepting COVID-19 vaccination. JAMA Netw. Open 3, e2025594 (2020).

32. 32.

Lazarus, J. V. et al. A global survey of potential acceptance of a COVID-19 vaccine. Nat. Med. 27, 225–228 (2021).

33. 33.

Gomila, R. Logistic or linear? Estimating causal effects of experimental treatments on binary outcomes using regression analysis. J. Exp. Psychol. Gen. 150, 700–709 (2021).

34. 34.

Castillo, J. C. et al. Market design to accelerate COVID-19 vaccine supply. Science 371, 1107–1109 (2021).

35. 35.

Price-Haywood, E. G., Burton, J., Fort, D. & Seoane, L. Hospitalization and mortality among black patients and white patients with COVID-19. N. Engl. J. Med. 382, 2534–2543 (2020).

36. 36.

Ruiz, J. B. & Bell, R. A. Predictors of intention to vaccinate against COVID-19: Results of a nationwide survey. Vaccine 39, 1080–1086 (2021).

37. 37.

Holm, S. A simple sequentially rejective multiple test procedure. Scand. Stat. Theory Appl. 6, 65–70 (1979).

38. 38.

Fridman, A., Gershon, R. & Gneezy, A. COVID-19 and vaccine hesitancy: a longitudinal study. PLoS ONE https://doi.org/10.1371/journal.pone.0250123 (2021).

39. 39.

Golman, R., Hagmann, D. & Loewenstein, G. Information avoidance. J. Econ. Lit. 55, 96–135 (2017).

40. 40.

US Census Bureau. American Community Survey (ACS), https://www.census.gov/programs-surveys/acs (accessed on 25 May 2021) (2020).

41. 41.

Funk, C. & Tyson, A. Intent to get a COVID-19 vaccine rises to 60% as confidence in research and development process increases. https://www.pewresearch.org/science/2020/12/03/intent-to-get-a-covid-19-vaccine-rises-to-60-as-confidence-in-research-and-development-process-increases/ (2020).

42. 42.

Li, M., Taylor, E. G., Atkins, K. E., Chapman, G. B. & Galvani, A. P. Stimulating influenza vaccination via prosocial motives. PLoS ONE 11, e0159780 (2016).

43. 43.

Grant, A. M. & Hofmann, D. A. It’s not all about me: motivating hand hygiene among health care professionals by focusing on patients. Psychol. Sci. 22, 1494–1499 (2011).

44. 44.

Goldstein, N. J., Martin, S. J. & Cialdini, R. B. Yes!: 50 Scientifically Proven Ways to Be Persuasive (Free, 2008).

45. 45.

Imas, A. & Madarasz, K. Mimetic dominance and the economics of exclusion: private goods in public context. SSRN Electron J. https://doi.org/10.2139/ssrn.3630697 (2020).

46. 46.

Dai, H., Milkman, K. L. & Riis, J. The fresh start effect: temporal landmarks motivate aspirational behavior. Manage. Sci. 60, 2563–2582 (2014).

47. 47.

Dai, H., Milkman, K. L. & Riis, J. Put your imperfections behind you: temporal landmarks spur goal initiation when they signal new beginnings. Psychol. Sci. 26, 1927–1936 (2015).

48. 48.

Cohen, J. Statistical Power Analysis for the Behavioral Sciences (2nd ed.) (Lawrence Erlbaum Associates,1988).

49. 49.

Ben-Shachar, M., Lüdecke, D. & Makowski, D. Effect size: estimation of effect size indices and standardized parameters. J. Open Source Softw. 5, 2815 (2020).

## Acknowledgements

Funding support for this research was provided by UCLA Health, Anderson School of Management, Anderson Behavioral Lab and Carnegie Mellon University. We thank UCLA Health, particularly L. Ho, J. Ileto, A. Machalinski, and T. Tieu, for making this research possible; S. Bhargava, K. Brabaw, G. Chapman, N. Goldstein, K. Haggag, I. Hurst, C. Nyungen, K. Milkman, M. Serra-Garcia and K. Shonk for feedback on the paper; and J. Cervantez, M. Lanyon and S. Permut for assistance with this research.

## Author information

Authors

### Contributions

D.M.C., H.D. and S.S. conceptualized the project; H.D., H.M., S.P., M.S. and S.S. curated the data; H.D., S.S. and S.V. undertook formal analyses; D.M.C. and M.A.H. acquired funding; D.M.C., H.D., S.S., M.A.H., N.R., and L.R. performed the investigations; D.M.C., H.D., S.S. and M.A.H. designed the methodology; D.M.C., H.D. and S.S. administrated the project; D.M.C., H.D. and S.S. supervised the work; H.D., S.S. and S.V. validated the findings; H.D. and S.S. visualized the data; H.D. and S.S. wrote the original draft, and D.M.C., H.D., S.S., M.A.H., H.M., S.P., M.S., L.R., N.R., and S.V. contributed to the review and editing of the paper.

### Corresponding author

Correspondence to Daniel M. Croymans.

## Ethics declarations

### Competing interests

The authors declare no competing interests. The authors did not receive financial or non-financial benefits from UCLA Health or speaking/consulting fees related to any of the interventions presented here.

Peer review information Nature thanks the anonymous reviewers for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## Supplementary information

### Supplementary Information

This file contains Supplementary text, Supplementary Methods, Supplementary Figures 1 - 5, Supplementary Tables 1 – 41 and Supplementary References.

### Video Addressing Vaccine Hesitancy (English Version)

Supplementary Video 1 . This is the English version of the video we developed to address COVID-19 vaccine hesitancy. It was used in the first RCT (presented to patients who did not indicate a preference for Spanish) and online experiments. We based this video on a literature review of vaccine hesitancy and our January 2021 survey of California residents (N=515; 'Vaccination Intention Survey' in Methods), which allowed us to identify common misconceptions about COVID-19 and authorized vaccines.

### Video Addressing Vaccine Hesitancy (Spanish Version)

Supplementary Video 2 . This is the Spanish version of the video we developed to address COVID-19 vaccine hesitancy. It was used in the first RCT (presented to patients who indicated a preference for Spanish). We based this video on a literature review of vaccine hesitancy and our January 2021 survey of California residents (N=515; `Vaccination Intention Survey' in Methods), which allowed us to identify common misconceptions about COVID-19 and authorized vaccines. This video is identical to Supplementary Video 1 except that the voice and text here are in Spanish.

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Reprints and Permissions

Dai, H., Saccardo, S., Han, M.A. et al. Behavioural nudges increase COVID-19 vaccinations. Nature 597, 404–409 (2021). https://doi.org/10.1038/s41586-021-03843-2

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• ### Text-message nudges encourage COVID vaccination

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