JMIR RESEARCH PROTOCOLS

Lopes et al

Proposal

Digital Gamification Tool (Let’s Control Flu) to Increase Vaccination
Coverage Rates: Proposal for Algorithm Development
Henrique Lopes1, PhD; Ricardo Baptista-Leite1,2, MD; Catarina Hermenegildo1, MA; Rifat Atun3, MD
1

NOVA Center for Global Health, NOVA Information Management School, Universidade Nova de Lisboa, Lisbon, Portugal

2

Department of International Health, Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University,
Maastricht, Netherlands
3

Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States

Corresponding Author:
Henrique Lopes, PhD
NOVA Center for Global Health
NOVA Information Management School
Universidade Nova de Lisboa
Campus de Campolide
Lisbon, 1070-312
Portugal
Phone: 351 962499020
Email: hlopes@novaims.unl.pt

Abstract
Background: Influenza represents a critical public health challenge, disproportionately affecting at-risk populations, including
older adults and those with chronic conditions, often compounded by socioeconomic factors. Innovative strategies, such as
gamification, are essential for augmenting risk communication and community engagement efforts to address this threat.
Objective: This study aims to introduce the “Let’s Control Flu” (LCF) tool, a gamified, interactive platform aimed at simulating
the impact of various public health policies (PHPs) on influenza vaccination coverage rates and health outcomes. The tool aligns
with the World Health Organization’s goal of achieving a 75% influenza vaccination rate by 2030, facilitating strategic
decision-making to enhance vaccination uptake.
Methods: The LCF tool integrates a selection of 13 PHPs from an initial set proposed in another study, targeting specificpopulation groups to evaluate 7 key health outcomes. A prioritization mechanism accounts for societal resistance and the synergistic
effects of PHPs, projecting the potential policy impacts from 2022 to 2031. This methodology enables users to assess how PHPs
could influence public health strategies within distinct target groups.
Results: The LCF project began in February 2021 and is scheduled to end in December 2024. The model creation phase andits application to the pilot country, Sweden, took place between May 2021 and May 2023, with subsequent application to other
European countries. The pilot phase demonstrated the tool’s potential, indicating a promising increase in the national influenza
vaccination coverage rate, with uniform improvements across all targeted demographic groups. These initial findings highlightthe tool’s capacity to model the effects of PHPs on improving vaccination rates and mitigating the health impact of influenza.
Conclusions: By incorporating gamification into the analysis of PHPs, the LCF tool offers an innovative and accessible approachto supporting health decision makers and patient advocacy groups. It enhances the comprehension of policy impacts, promoting
more effective influenza prevention and control strategies. This paper underscores the critical need for adaptable and engaging
tools in PHP planning and implementation.
International Registered Report Identifier (IRRID): RR1-10.2196/55613(JMIR Res Protoc 2024;13:e55613) doi: 10.2196/55613
KEYWORDS
influenza; gamification; public health policies; vaccination coverage rates; health promotion

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Introduction

circulating influenza viruses and the seasonal influenza
vaccination administered [13].

Influenza Epidemiology and Public Health Impact

Recognizing influenza as a considerable global public healthchallenge, the WHO has advocated for annual influenza
vaccination as the most effective measure to combat the
influenza. Priority is notably given to older population groups,
who face an increased risk of severe influenza disease. However,
the effectiveness of influenza vaccinations in these groups iscompromised by immune senescence, leading to diminishedantibody responses. In response, higher-dose influenza antigen
vaccines have been developed, enhancing the immune response
and proving to be more effective in preventing influenza
infections [14], hospitalizations, and reducing mortality rates[15] compared with standard-dose vaccines.

Influenza is an infectious respiratory disease caused by anairborne virus. There are 4 identified types of influenza viruses,with types A and B being responsible for most of the seasonal
influenza epidemics that occur annually [1,2]. The influenza
virus can cause mild to severe disease, with risk groups (older
people, pregnant women, young children, and individuals withchronic health conditions and autoimmune diseases) being more
susceptible to severe forms of the disease [1].
The World Health Organization (WHO) estimates that annual
influenza epidemics result in approximately 1 billion infections,
3 to 5 million cases of severe illness, and between 290 and
650,000 deaths [3]. The severity of influenza depends on
multiple factors, including the virulence of the virus strain andthe level of preexisting immunity in the population [1]. Influenza
is also responsible for the worsening of previous healthconditions in the individual and has consequences on different
domains of individual health [4], such as cardiovascular [5],neurological, renal, respiratory, and diabetic complications.
However, despite the large number of respiratory infections
worldwide, it is difficult to estimate the proportion of
hospitalizations attributable to influenza across countries orover time [6]. During influenza outbreaks, health systems
experience increased demand for services due to an influx ofpatients seeking medical care. This surge in patient demand canstrain health care resources, including hospital beds, medical
staff, and supplies [7]. As a result, the pressure on health systems
during influenza outbreaks has an impact on access to care forother patients who require medical attention for
non–influenza-related conditions, especially those at risk forcomplications [8].
Another study, published in 2023, which carried out ameta-analysis to improve understanding of the estimates of
hospitalizations associated with influenza, concluded thatseasonal influenza epidemics result in 3.2 million
hospitalizations per year globally [9]. A study that evaluated
10 influenza seasons (between 2008 and 2018) in Portugal toestimate the clinical and economic costs of influenza reported
that, on average, hospitalizations due to influenza were 11.6
cases per 100,000 inhabitants [10].

Vaccination for Influenza Prevention
Vaccination against influenza began in the early 1940s, marking
a considerable advancement in public health with theintroduction of trivalent vaccines [11]. This approach evolved,
and in 2012, quadrivalent vaccines were introduced [11],offering broader protection against the influenza virus. The
WHO is currently deliberating on the possibility of returning
to trivalent vaccines [12], showcasing the ongoing evolution
and reassessment of strategies to combat influenza. The
effectiveness of the influenza vaccine, which varies between
40% and 60% in the general population, depends on various
factors, including patient characteristics such as age andunderlying health conditions, as well as the match between thehttps://www.researchprotocols.org/2024/1/e55613

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This targeted approach to vaccination aligns with the WHO’s
Global Influenza Strategy 2019 to 2030 [16], which recommends
a goal of increasing vaccination coverage rates (VCRs) for
influenza to 75% by 2030, a goal also echoed by the European
Union (EU) in a 2009 Council Recommendation [17]. This 75%
target applies especially to older adults, extending as well toother high-risk groups, including persons with chronic
conditions and health workers, advocating for comprehensive
coverage to mitigate the impact of influenza. Despite concerted
efforts, reaching this target has been challenging; as of 2018,
influenza vaccination coverage among people aged ≥65 years
was <50% in most EU countries, averaging only 39% acrossthese nations and indicating that no country had yet met the
75% coverage target [18].
VCRs are crucially impacted by social inequalities, including
financial, cultural, and linguistic barriers [19,20]. Evidence froma broad range of studies highlights this correlation, pointing out
a concerning lack of data on VCRs among specific susceptiblegroups in various countries. The situation is further complicatedby the interaction between multimorbidity and social exclusion,
creating a vicious cycle where social inequality exacerbates
health disparities, thereby deepening social vulnerabilities[21-23]. This interconnection between social determinants andhealth outcomes underscores the necessity of addressing thesechallenges holistically to enhance VCRs and strengthen the
global response to influenza.
In this context, health management strategies implemented byhealth and governance authorities, alongside other healthdecision makers, are pivotal. Public health policies (PHPs) playa key role in managing potential risks by preempting andmitigating their impacts. Vaccination-related PHPs exemplify
this approach effectively; by fostering immunity within thecommunity, they not only reduce the likelihood of diseasetransmission but also ensure that any infections are less severe.
This diminishes the demand for hospitalization and decreases
mortality risk. Such policies, therefore, are not merely preventive
measures but also crucial interventions that address the root
causes of health inequities, aiming to break the cycle ofmultimorbidity and social exclusion by ensuring equitable accessto vaccination and health care services.

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Gamification Strategies in Public HealthIn the realm of scenario planning and policy development,
traditional methodologies have steadily evolved to incorporate
advanced technological capabilities, facilitating a more
integrated and dynamic approach to learning and assessment.
The advent of gamification, defined as the integration of gamedesign elements in nongaming contexts to bolster engagement[24], represents a substantial leap forward in this evolution.

Lopes et algamification strategies to ensure their effectiveness andrelevance in the use of gamification in the health sector [27].
Adopting gamification in PHP decision-making introduces acomplex landscape of ethical implications that warrant carefulconsideration. Despite its potential to enhance public healthinitiatives by promoting behavior change and increasing
engagement being considerable, it also raises concerns regarding
data privacy, and informed consent.

At its core, gamification uses game design elements innongaming scenarios to augment engagement levels. As
delineated in the study by Brangier and Marache-Francisco [25],the gamification design process is anchored in the principles ofhuman-computer interaction, underscoring the importance ofuser-centric design and interaction. This approach togamification has gained traction across various sectors, including
education, health care, marketing, and human resources,
evidencing its versatility and widespread applicability.

Data privacy emerges as a critical concern, as gamification
strategies often rely on collecting and analyzing personal healthinformation to personalize interventions and track progress. The
ethical management of this data is paramount. According to thestudy by Mittelstadt et al [28], ensuring data protection andprivacy in digital health interventions requires robust encryption
methods and transparent data handling policies. Without
stringent safeguards, there is a risk of unauthorized access tosensitive information, potentially leading to misuse.

However, a substantial fraction of gamification initiatives
stumble, primarily due to flawed design strategies. This
underscores the criticality of a well-articulated design process,attuned to the nuances of user engagement and interaction
dynamics. Moreover, the aspect of measurement emerges as apivotal consideration in gamification, advocating for a dynamic
approach toward redesigning gamified experiences throughreal-time feedback and user data analytics. However, theadoption of metrics as a quantification tool remains
inconsistently applied across gamification frameworks,
highlighting an area ripe for further exploration andstandardization [24].

Informed consent is another cornerstone of ethical considerations
in gamification. Participants must be fully aware of how theirdata will be used, the nature of the gamified intervention, andits potential risks and benefits. As Thaler and Sunstein [29]argue in “Nudge,” while gamification can guide behavior in
beneficial ways, it must not manipulate or coerce participants.
Ensuring that consent is informed and voluntary preservesautonomy and respects individual decision-making.

The application of gamification in educational contexts,
particularly for fostering sustainable behaviors, stands out as apromising approach. Despite existing knowledge gaps, empiricalevidence attests to the efficacy of gamification in elevating
motivation, engagement, and satisfaction among learners. Thisenhanced learning environment, in turn, cultivates a fertile
ground for the dissemination of sustainability principles andother educational content, demonstrating the transformative
potential of gamification as a methodological tool in learning
scenarios [26].
These advancements have rendered the simultaneousquantitative and qualitative modeling of policies affecting
various facets of the health system and wider society not only
feasible but also widely accessible. This confluence oftechnology and methodology paves the way for an enrichedunderstanding and assessment of policies through sophisticatedsimulations that blend numerical data with narrative contexts,
thereby facilitating a holistic view of potential outcomes andimpacts.
Despite this growing interest in the integration of game-likeelements into learning scenarios, especially within the healthsector, focused design frameworks for health education throughgamification remain underdeveloped. This area presents a unique
opportunity for pioneering health-specific frameworks thatemphasize prototyping, experimentation, measurement, andcontinuous iteration. Such frameworks are crucial for refining

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Gamification can oversimplify complex policy issues ifinterpreted verbatim without critical thinking. Especially if used
as a game of health policy–related challenges and not a way of
rehearsing ideas, there is a risk of reducing nuanced issues tooverly simplistic solutions. This oversimplification can lead tomisinterpretations and misapplications of policy measures,
potentially undermining the effectiveness of interventions anddisregarding the intricacies of health outcomes. In addition,
given that gamification strategies often use rewards, incentives,and competition to drive behavior, its use in health-related
contexts may impact individuals’ autonomy anddecision-making if it is not framed in critical thinking.
Therefore, in the development of this tool, under nocircumstances does the individual receive any form of retribution
for use or performance.

The “Let’s Control Flu” Tool
In the intricate balance between ethical considerations and the
exploratory potential of gamification, the “Let’s Control Flu”
(LCF) tool emerges as a digital, interactive tool designed tosupport policy decision-making specific to influenza. The LCF
tool aims to help enhance VCRs and fulfill the objectives
outlined in the WHO’s Global Influenza Strategy 2019 to 2030.
Rooted in the qualitative framework suggested in the study by
Kassianos et al [30], this project seeks to align VCR
achievements with the benchmarks established by the WHO
and the Council of Europe. The LCF tool demystifies thecreation of epidemiological scenarios, eliminating the need forusers to possess in-depth knowledge of epidemiologicalmodeling techniques or to engage with complex data sets, suchas extensive statistical series. This user-friendly approachfacilitates a more accessible and informed decision-making
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Lopes et alprocess for policy makers striving to combat influenza throughincreased vaccination uptake.

Methods

The LCF tool innovatively applies gamification principles toassist stakeholders in understanding the nuances of different
PHPs regarding influenza vaccination. It achieves this byproviding a simulated environment where users can experimentwith various policy scenarios, thus offering insights into thepotential impact on VCRs before their implementation. This
simulation enables stakeholders to grasp the intricacies,
trade-offs, and consequences of their policy decisions, fostering
a deeper understanding and facilitating more informed choices
about the PHPs to be adopted and executed. The engaging andinteractive presentation of information within the LCF tool
simplifies the comprehension of policy impacts, encouraging
decisions backed by data-driven insights.

Overview

The limitations sector of this paper acknowledges the constraints
and challenges faced by the LCF tool. Despite its innovative
approach and initial success, the tool’s effectiveness andapplicability are subject to ongoing evaluation and refinement.
Originally piloted in Sweden, the LCF tool’s application has
proven successful, marking an important first step in its practicaluse. Following this initial implementation, efforts are underway
to broaden the tool’s application across 3 additional European
countries. This expansion is aimed at enhancing the tool’s
robustness and increasing its relevance across different
geographic contexts, thereby contributing valuable insights to
the global effort to improve influenza vaccination coveragethrough informed policy making.

The development of the tool was underpinned by an integrated
epidemiological model, encapsulating the burden of influenza
disease alongside the predictive impacts of selected PHPs on
targeted populations. Initial steps in tool development involved
a critical reduction in the number of policies underconsideration: on the basis of the foundational work by
Kassianos et al [30], the scope was narrowed from 42 to 13
policies, spanning across the 5 pillars identified in their research(Multimedia Appendix 1). This refinement was necessitated by
the current state of knowledge, which does not allow for a
discrete modeling of the effects attributable to each of the 42
PHPs initially proposed. Consequently, a strategy was used tomerge closely related PHPs into coherent bundles, maintaining
the original pillar-based categorization to ensure methodologicalorthogonality.
To ascertain the robustness and reliability of the model, we used
a rigorous selection criterion for the scientific literature included
in our analysis. Detailed in Textboxes 1 and 2, the inclusionand exclusion criteria were designed to encompass only themost pertinent and high-quality studies. In adherence to the
PRISMA (Preferred Reporting Items for Systematic Reviews
and Meta-Analyses) guidelines, we executed a systematic andcomprehensive literature review. The PRISMA flowchart(Figure 1) delineates our methodical approach to the selectionof publications, underscoring the scrutiny at each phase of article
identification, screening, and inclusion. It is pertinent to notethe exclusion of data from 2021 and 2022 to mitigate theconfounding effects of the COVID-19 pandemic on the influenza
landscape [31].

Textbox 1. Inclusion criteria for scientific works included in the model.
Inclusion criteria•

Only pre-post intervention or controlled studies were included, enabling the effects of the policy to be effectively assessed independently of any
background (and potentially not described) policies already in place.

•Studies were included whenever they contained data on one or more of the populations of interest for the model (children, health professionals;
pregnant women; older adults, including adults aged ≥65 years and adults aged between 50 and 64 years; and high-risk patients).

•

Studies reporting seasonal vaccination uptake were included, as opposed to pandemic influenza vaccination. This restriction was placed to ensurecomparability of studies used to inform the model (ie, avoid pooling data for seasonal and pandemic influenza, as the coverage may not becomparable).

Textbox 2. Exclusion criteria for scientific works included in the model.
Exclusion criteria•

Reviews were not included.

•

Articles in any other language than English.

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Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart of systematic literature review process. TLR:
targeted literature review.

The interventions delineated in the included studies were
meticulously aligned with the PHP categories identified in the
study by Kassianos et al [30]. To ensure the validity andaccuracy of the model construction, a National Advisory Board(NAB) comprising 4 eminent epidemiologists and public healthexperts from the Swedish health system was constituted. The
board’s formation was predicated on their prior contributions
to scientific literature and health authority reports over the last
decade, ensuring their expertise was both relevant and current.
The NAB was instrumental in the validation process,meticulously verifying the model-generated values andendorsing the results. This process was crucial, particularly in
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instances where the scientific literature did not provide
ecologically valid data to substantiate the selection of proxies
necessary for the tool’s development.

Public Health Policies
The set of 13 PHPs incorporated into the LCF model maintained
the original categorization into 5 pillars proposed by Kassianos
et al [30], each targeting different aspects of public health toenhance vaccination rates and mitigate the health impacts ofinfluenza.

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Pillar 1: Health Authority Accountability andStrengthening the Influenza Program
VCRs targets set at national and regional levels forrecommended population: To boost influenza vaccination rates[32], policies set targets at national and regional levels for
specific population groups [33]. In Sweden, eg, this policy was
implemented nationwide. Strategies programs [34-36] include
educating individuals about vaccine benefits, improving vaccine
accessibility, and offering incentives for vaccination, all aimed
at increasing vaccine uptake.
Funding of influenza vaccinations for all recommended groups:
To enhance influenza vaccine access, reduce the burden of
disease, and prevent influenza-related deaths, immunization
programs [37-41], intervention campaigns, and strategies[32,42-50] can be created. In addition, funds may be allocatedto cover the cost of influenza shots for vulnerable groups, suchas older adults, children, health care workers, and individuals
with underlying health conditions. By doing so, public healthofficials improve vaccine accessibility and safeguard those witha higher risk of flu complications.
Nationwide regular monitoring of patient VCR at vaccinationsite and at health care professional (HCP) level by healthauthorities: Regular tracking of vaccine uptake in designatedhealth facilities such as clinics, hospitals, and pharmacies [51,52]
allows for monitoring immunization program effectiveness andidentifying areas of improvement. By monitoring immunization
coverage rates, public health authorities can use data to enhance
programs and achieve desired health outcomes for the
population.
HCP VCR as part of performance criteria in hospitals: Boosting
vaccine uptake among health care workers and minimizing
disease transmission through vaccination. This policy mandates
that health worker immunization coverage rates be used as aperformance criterion in health care facilities, including hospitals
and primary care settings [53]. It encourages health care workers
to get vaccinated, prioritizing their well-being and minimizing
disease spread among patients, thereby fostering a safer andhealthier environment for all.
Sustainable procurement system to ensure appropriate vaccine
supply: Ensuring consistent and reliable availability of necessary
vaccines for all individuals in need [54], irrespective of theirlocation or financial circumstances. This policy aims to establish
a procurement system that effectively manages vaccine
acquisition, storage, and distribution to health units, considering
cost, quality, and sustainability factors.

Pillar 2: Facilitated Access to Vaccination
Access to multiple vaccination settings: Enhancing vaccine
access and reducing barriers to vaccination ensures that
individuals can receive vaccines in various locations, including
hospitals [48,53,55-57], clinics [36,58], pharmacies [39,59-62],
schools [43,52,63-70], and other settings [46,71]. By doing this,public health authorities increase convenience and improve thechances of individuals getting vaccinated.
Call-to-action communications to target groups by multiple
stakeholders: This policy targets influenza-related stakeholders,
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Lopes et al
including public health agencies, health professionals, schools,
and community organizations. Various methods such as email[69,70,72-74], SMS text messages [75-81], phone calls, healthapps, and others [49,82] are used to remind target groups about
the importance of vaccination. The objective is to boost vaccine
uptake by reminding individuals in target groups ofvaccination’s significance and encouraging them to get
vaccinated [83-86].
HCP pop-up notification or SMS text message to population tovaccinate eligible patients: Health care providers use pop-up
notifications or SMS text messages to remind eligible patientsof the importance of vaccination and encourage them to get
vaccinated [84,85,87-89], thus increasing vaccine uptake.

Pillar 3: HCP Accountability and Engagement
Regular HCP education and training: This policy ensures HCPs,
including doctors, nurses, and other professionals, receive
ongoing education and updates on new technologies, medical
advancements, and changes in vaccination practices[33,34,48,65,77,87-94]. Its objective is to equip HCPs with thenecessary knowledge and skills to deliver safe, effective, andhigh-quality care to patients.
Fair and specific HCP compensation per vaccination:
Establishing a system where health professionals receive faircompensation for administering vaccines [95]. This aims to
incentivize HCPs to administer vaccines and ensure they areadequately rewarded for their time and efforts. Fair
compensation not only supports HCPs but also encourages their
dedication to protecting public health.
Mandatory HCP vaccination: Mandating or strongly
recommending that health care workers receive specific vaccines
as part of their job [35,36,53,96-106]. This policy aims tosafeguard public health by minimizing the transmission ofcommunicable diseases in health care settings such as clinics
or hospitals, particularly to patients with weakened immune
systems. By requiring or strongly recommending vaccinationfor HCPs, the policy aims to prevent them from being carriersof diseases that could be transmitted to their patients.

Pillar 4: Awareness of the Burden and Severity of the
DiseaseCoordinated multistakeholder awareness and communication
campaigns: This policy promotes immunization throughcollaboration and coordination among various stakeholders,
including government agencies, hospitals and other healthorganizations [33,34,46,47,52,57,68-71,77,83,86-89,91,97,105,
107-110], health professionals [32,41,42,58,65,72,111,112],
community organizations, schools, and universities [43,113],
as well as the media [105,114]. Its goal is to deliver a cohesive
message about the importance of immunization, reaching a wide
audience with information on the benefits of immunization andhow to access it. By addressing concerns, dispellingmisinformation, and creating a supportive environment[115,116], the policy aims to enhance vaccine uptake.

Pillar 5: Belief in Influenza Vaccination Benefits
Positive media coverage of vaccines: The policy promotes a
positive approach to vaccine promotion, highlighting the benefits
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Lopes et alof vaccination and countering negative or misleading
information. It aims to foster a vaccine-friendly environmentby showcasing the safety, efficacy, and importance of vaccines
through various media channels, such as news articles, public
service announcements, and other media content [41]. The
policy’s purpose is to boost vaccine uptake by offering accurate
information, debunking myths, and empowering the public tomake informed decisions about vaccination.

4.

Model Design

When policy effects were extrapolated from the literature, theinherent challenges associated with integrating qualitative
findings directly into quantitative models led to the necessityof generating temporary proxies to bridge the gap between
qualitative insights and the quantitative demands of the model.
This process was initiated with comprehensive discussions
within the scientific coordination team of the project, after which
the proposed proxies underwent a rigorous phase of deliberationand validation by the NAB, ensuring that the proxies accuratelyreflect the intended policy effects. The final step in this process
involved the validation of the proxies by the project’s externalscientific adviser, who evaluated their suitability and alignment
with the model’s objectives and the underlying empiricalevidence. This layered approach to proxy generation andvalidation ensured the model’s integrity and the reliability of
its projections, accommodating instances where direct
quantitative data had been lacking.

The model categorizes the population into 6 age groups—12 to
14, 15 to 34, 35 to 49, 50 to 64, and >65 years—and identifies
5 key target groups: children (aged 0-14 years), older adults(aged >65 years), health professionals, pregnant women, andhigh-risk patients (aged 15-64 years with ≥1 chronic condition).
This segmentation acknowledges the critical challenge ofchronic undervaccination in high-risk patients, which poses
substantial barriers to achieving optimal health outcomes andprotection against influenza and its complications [117].
To assess the impact of PHPs on these target groups, the model
simulates 7 health outcomes over a 10-year horizon (2022-2031),
including influenza infections averted, hospitalizations averted,
influenza-related general practitioner visits averted, workdays
lost (productivity impact), influenza-related deaths averted,
hospitalizations averted due to cardiovascular complications,and deaths averted due to cardiovascular complications. These
outcomes are evaluated both across the total population andwithin each target group, providing a comprehensive overview
of PHP effectiveness in mitigating influenza-related morbidity
and mortality.
In the development of the epidemiological model, weights wereassigned to each policy across different target groups andspecified time frames using a multifaceted approach. Thismethodology enabled a rigorous, evidence-based evaluation of
PHPs’ impact on VCRs, accounting for the variability andcomplexity of policy effects across different demographic andrisk groups. It encompassed the following:
1.

2.

3.

Policy effects derived from the literature: weights were
calculated as weighted averages for each policy and target
group based on a comprehensive review of the existing
literature. This approach ensures that the model’s
parameters are grounded in empirical evidence, reflecting
the documented effects of PHPs on VCRs.
Imputation from similar policies or different target groups:
in instances of incomplete data for a specific policy withina target group, weights were imputed based on the relative
effects of the most analogous policies available.
Alternatively, effects documented for the same policy indifferent target groups were adjusted and applied,facilitating a coherent extrapolation of policy impacts.
Projected increases for subsequent years: for policy effects
lacking data beyond the first year, a conservative projection
was applied, assuming a 10% increase in the second year
and a 20% increase for the third year onward. This
assumption is based on the initial year’s effect, providing
a structured approach to model the temporal dynamics ofpolicy impacts.

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Integration of expert opinions: insights andrecommendations were solicited from the NAB to informthe weighting process, particularly in areas where empirical
data were sparse or ambiguous. This inclusion of expert
judgment ensures that the model remains adaptable andrelevant to current public health contexts, incorporating thenuanced understanding of seasoned professionals in thefield.

In scenarios where explicit data concerning the uncertainty ofparameters was unavailable, our approach entailed theadjustment of these parameters through the application ofsuitable statistical distributions, premised on an SE estimatedat 10% of the base-case value. This procedure was designed toembed a quantifiable measure of uncertainty into the model,
thereby ensuring a broader and more realistic spectrum ofpotential outcomes.
The model was also designed with a flexible, modular structure,making it easy to update with new scientific findings without
having to change its core algorithm. This flexibility is crucial
for continually improving the model, allowing regular updates
to its parameters and data. The modular design also supports
replacing preliminary estimates with actual, up-to-date
information, which improves the model’s accuracy andrelevance as new research becomes available.
Figure 2 showcases the compartmental model designed toelucidate the dynamics among vaccination strategies, influenza
transmission, and consequent health outcomes. This model
delineates the population into distinct compartments, primarily
focusing on the age-stratified cohort of noninfected individuals
who are initially unvaccinated. Upon the application of
vaccination policies, a transition of these individuals into thevaccinated cohort is anticipated, contingent upon theirnoninfection status, which predicates the absence of healthcomplications or influenza-induced mortality, thereby
facilitating a cyclic annual assessment. In the event of influenza
infection, the model accounts for resultant health implications,
including hospital admissions, general practitioner consultations,loss in work productivity, and cardiovascular complications,using a formulaic approach that integrates vaccine efficacy andage-specific infection rates. Identical principles extend to the
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unvaccinated population who contract the virus, after which themodel delineates 3 potential pathways postinfection: mortality
attributable to influenza, mortality from alternate causes, orLopes et al
recovery. The recovery probabilities are quantified through aformula that encapsulates influenza-associated mortality rates
across different age groups.

Figure 2. Graphic representation of the model adopted to estimate the effect of the 13 elected public health policies. VCR: vaccination coverage rate.

The Operational Functionality of the Tool
The design of this tool prioritized accessibility anduser-friendliness, targeting a wide audience that spans HCPs,
the public, and patient advocacy groups. By facilitating aninteractive platform, it enables users to easily simulate theimpact of selecting from the 13 distinct PHPs on various healthoutcomes within specific target groups, for any year between
2022 and 2031.
Upon the selection of policies, a prioritization mechanism isapplied where the weight of each subsequent policy decreases
fractionally to reflect its comparative impact on the overall
scenario. This methodology underscores the varying significanceof individual policies and their synergistic effects whencombined. This system also incorporates a realistic element ofsocietal resistance to change, adjusting the cumulative weightof selected PHPs to reflect this factor.
Furthermore, the tool bases its analysis on existing PHPs withina country, recognizing prior efforts and their varying degrees
of success. The simulation outcomes represent the scenario of
maximum policy efficacy, acknowledging that complete
population compliance is an ideal rather than a constant reality
due to people’s inherent resistance to change. The possibilityof achieving perfect policy implementation is speculated for
future contexts, suggesting that even in such ideal conditions,
there is potential for further enhancement. This could beachieved through the adoption of new communication andeducational strategies leveraging emerging technologies,
adapting to changing societal contexts, and more effectively
engaging target populations.

Ethical Considerations
The LCF project was initiated at the Catholic University ofPortugal; was subject to the code of ethics NR/R/1419/2015 at
the time; and was later transferred, together with the team, tohttps://www.researchprotocols.org/2024/1/e55613

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the NOVA-IMS Faculty of the Universidade Nova de Lisboa,
where it was subject to the supervision of its ethics committee,
in accordance with the Universidade Nova de Lisboa code of
ethics (approval 15464/2014) published on December 19, 2014.
Access to data was only granted to published and publicmaterial, and there was no access to personal data or any otherdata that could be used to identify someone.

Democratizing Access and Combating Misinformation
The development of this tool democratizes access to complex
epidemiological scenario modeling, allowing users without
advanced technical knowledge of PHPs and health impact
modeling to explore the potential effects of different healthpolicy scenarios. This capability not only streamlines thedecision-making process for health policy but also upholds theprinciple of technological transparency, ensuring that theunderlying mechanisms and assumptions of the tool are clear
and understandable.
The dedicated website [118] hosting the LCF tool serves as an
educational resource, offering scientifically accurate information
about influenza and vaccination in accessible terms. Thisapproach is instrumental in combating misinformation andreducing vaccine hesitancy, thereby supporting global efforts
to lessen the impact of seasonal and pandemic influenza [119].
In this context, it is also relevant to acknowledge the differences
between vaccine resistance and vaccine hesitancy [120,121],
where vaccine resistance often stems from deeply rooted
ideological, religious, or philosophical beliefs and, in contrast,
vaccine hesitancy is typically driven by fear and doubts,
frequently fueled by misinformation or distorted news.
Recognizing these differences is crucial for tailoring intervention
strategies effectively, as in contexts where vaccine resistanceis prevalent due to entrenched beliefs, efforts may need to focus
on long-term engagement and education; conversely, in areas
where hesitancy predominates, addressing misinformation and
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JMIR RESEARCH PROTOCOLS
building trust through transparent communication may prove
more effective. Understanding these nuances is essential for
optimizing the impact of our approach across diverse
populations, as applying this tool in different legal and culturalcontexts may face increased inertia from the public.

Results
The LCF project, initiated in February 2021, is projected toconclude in December 2024. The phase focused on model
creation and its implementation in the pilot country, Sweden,
occurred from May 2021 to May 2023.
The pilot phase of the project was concluded successfully andrevealed positive outcomes on the Swedish population. For
example, the use of the tool to simulate the comprehensive
application of all 13 PHPs across various target populations forthe year 2025 provided insightful projections, as the results
demonstrate a substantial potential increase in the national
influenza VCR, from 18.6% (1,940,964/1,042,283,400) to 28.4%(2,962,903/1,042,294,300). This comprehensive approachrevealed increases in VCRs across all specified target
populations: older adults experienced a substantial increase to75% (1,636,707/218,227,600) increase, as well as in high-riskpatients who also achieved 75% (825,076/110,010,100),
pregnant women’s VCR went up to 57.3% (61,894/10,804,700),
and health workers saw an increase to 53.1%(276,466/52,085,500). Children VCR went up to 0.8%
(15,907/179,057,900) rise, representing an increase of 50%
from the baseline value, knowing that high-risk children are
vaccinated. Moreover, the anticipated health benefits from thisfull-scale policy implementation are considerable, including
the avoidance of 21,935 influenza infections and 277
hospitalizations. In addition, there would be 5871 fewer
physician visits, a reduction of 19,829 workdays lost due toinfluenza, 38 lives saved from influenza-related deaths, 62
hospitalizations prevented due to cardiovascular complications,and ultimately, 8 fewer deaths from cardiovascular issues related
to influenza.

Discussion
Principal Findings
Gamification is an innovative approach that can play aconsiderable role in improving PHP decision-making. By using
gaming mechanics and design, gamification can make complex
information and issues more accessible and engaging, leading
to a greater understanding of public health challenges, namely
for health policy makers who come from outside the professional
health sphere and need support tools for their decision-making
that provide them with a decision that is best supported byscience. Gamification can also create opportunities for active
participation and collaboration, allowing individuals to actively
shape PHPs and decisions. In addition, gamification canfacilitate the collection of real-time data and feedback, providing
valuable insights into the impact of PHPs and informing
decision-making processes.
By combining play with the need for informed policy decisions,gamification has the potential to positively impact public healthhttps://www.researchprotocols.org/2024/1/e55613

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Lopes et al
and help ensure that policies are both effective and evidence
based and also helps to address social determinants of healthby engaging disadvantaged communities and increasing theiraccess to health information and resources. Through interactive
experiences, gamification can help to break down barriers andincrease health literacy, which can lead to better healthoutcomes. Furthermore, gamification can foster a sense ofcommunity and collaboration among individuals, which canhelp to build a shared understanding of the importance of publichealth and create a culture of health.

Limitations
In this paper, we delineate several principal limitations inherent
to the use of gamification strategies within the realm ofpredictive modeling. First, we must acknowledge the ethicaland methodological implications posed by gamification: thesemethodologies do not offer precise forecasts of future events
but rather serve as heuristic guides. They project potential
futures predicated on historical data, presuming all othervariables remain constant (ceteris paribus). This deterministic
nature inherently restricts the model’s ability to predict future
impacts with absolute certainty.
Second, the foundation of such modeling lies in data analysis,
which encounters substantial challenges regarding theavailability and quality of data. This is particularly pronounced
at the local level, where the procurement and qualification ofdata sources, often designed for disparate purposes withincomplex ecosystems, necessitate the generation of proxymeasures. Despite the rigorous and meticulous process of
producing and validating these proxies, they introduce inherent
risks and uncertainties into the modeling efforts.
Third, we must consider the impact of abrupt contextual shiftsthat can fundamentally disrupt the health care management
landscape, thereby breaching the fundamental principles ofmodeling. The COVID-19 pandemic serves as a striking
example, illustrating how populations initially compliant withnational vaccination strategies may suddenly exhibit resistancedue to widespread disinformation campaigns. This phenomenon
has led to a decline in VCRs for numerous vaccines, culminatingin the resurgence of diseases such as polio in locations such as
London and New York, a scenario deemed inconceivable adecade ago.
In addition, our model does not account for the influence ofother policies that may directly or indirectly affect healthoutcomes. These include frameworks for occupational health,such as specific recommendations and obligations within various
work contexts, as well as the management of health issues within
institutions focused on older adult care. Furthermore, the model
does not incorporate the integration of health issues into broader
economic and policy areas, commonly referred to as Health in
All Policies. Despite being endorsed by the EU since 2011,
Health in All Policies has seen limited practical implementation.
Such policy integration could have a considerable impact on
vaccination rates, not only for influenza but also for othervaccines administered throughout an individual’s life.
Consequently, our model does not capture this dynamic aspectof health management that extends beyond the specific scopeof influenza vaccination.
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Given these limitations, we advocate for the periodic
reassessment of the modeling framework, recommending anannual (or, ideally, biannual) review. This process is intended
to incorporate the latest bibliographic resources and replace
proxy measures with evidence-based values. Such a practice
not only ensures the model’s ongoing refinement but also
contributes to the accumulation of evidence necessary forevaluating health impacts in the context of vaccination policy
development or modification.

Conclusions
The field of work on increasing VCRs lends itself well togamification because (1) general trust in vaccines has been
deeply shaken by misinformation and fake news during the
COVID-19 pandemic, and it is possible to demonstrate theimmense advantages that good vaccine coverage provides; (2)in particular, influenza is one of the diseases with the highest
pandemic potential, and only through vaccination against thedisease will it be possible to mitigate its potential effects; and(3) the entire community, from policy makers, often
disconnected from health issues, to the most susceptible citizen,
can understand how and where the benefits of influenza
vaccination work.
The journey toward improving VCRs globally is not solely amedical challenge but a multifaceted endeavor that requires
addressing the underlying social determinants of health. Theintertwined relationship between social inequalities andmultimorbidity highlights a critical area for intervention. As
evidenced, these disparities in social, financial, cultural, andlinguistic domains not only hinder equitable access tovaccination but also exacerbate the vulnerability of marginalized
communities, thereby impeding the overall effectiveness ofinfluenza vaccination efforts. Therefore, it is imperative thatstrategies to enhance VCRs also focus on increasing literacy,
especially in health-related matters. Empowering individuals
with knowledge and understanding of the importance ofLopes et alvaccination can drive more informed decisions, leading to higher
uptake rates. In addition, concerted efforts to combat social
inequalities are paramount. By creating more inclusive healthsystems and policies that actively address the barriers faced by
disadvantaged groups, a way can be paved for a more equitable
distribution of health resources, including vaccines. Ultimately,enhancing health literacy and addressing social inequalities
stand as potential catalysts for boosting VCRs, thereby
strengthening the global response to influenza and enhancing
public health outcomes for all communities.
The design and implementation of the LCF tool have shownpromising results in facilitating a broad and user-friendly
approach to evaluating PHPs impacts on influenza VCRs andhealth outcomes. Its capacity to simulate the effects of various
PHPs, considering both individual significance and collectivesynergy, highlights the tool’s utility in strategic health planning.
Furthermore, the tool’s acknowledgment of societal resistanceand its foundation on existing PHPs underscore the complexityof health policy efficacy. By offering a platform for dynamic
scenario testing, the tool not only assesses current strategies but
also identifies opportunities for enhancement, reinforcing theimportance of continual innovation and adaptability in publichealth initiatives. This approach underscores the evolving natureof health strategies and the critical need for responsive,technology-driven solutions to meet public health challenges
effectively.
In our interpretation, we see potential for developing the tool
in several directions: (1) through its application in other legal
and cultural contexts; (2) giving it new capabilities, namely thepossibility of cost assessment; (3) adapting the tool to otherhuman vaccination contexts, namely those with low or very low
VCRs, as is generally the case with adult vaccination; and (4)developing the gamification capacity used in this context forother public health situations, namely in the prevention ofchronic diseases.

Acknowledgments
The authors would like to thank Hans Winberg, Matti Sällberg, Toomas Timpka, and Armin Spreco for their work and contributions
to the National Advisory Board for Sweden.
The “Let’s Control Flu” project received funding from Sanofi; however, the research was conducted independently of thesponsoring company.

Authors' Contributions
HL and RBL contributed to the conceptualization. HL and CH contributed to data curation, formal analysis, investigation,
resources, and software. RBL contributed to funding acquisition. HL contributed to methodology, project administration, andvalidation. HL and RA contributed to supervision. CH contributed to the visualization. HL, CH, RBL, and RA contributed towriting—original draft. HL, CH, RBL, and RA contributed to writing—review and editing.

Conflicts of Interest
None declared.

Multimedia Appendix 1
Public health policies of the “Let’s Control Flu” model, organized by Pillar, based on the study by Kassianos et al.
[DOCX File , 17 KB-Multimedia Appendix 1]

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Abbreviations
EU: European Union
HCP: health care professional
LCF: Let’s Control Flu
NAB: National Advisory Board
PHP: public health policy
PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
VCR: vaccination coverage rate
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Lopes et al

WHO: World Health Organization

Edited by S Ma; submitted 18.12.23; peer-reviewed by A McCallum, B Bunt; comments to author 07.03.24; revised version received
27.03.24; accepted 20.06.24; published 10.09.24
Please cite as:
Lopes H, Baptista-Leite R, Hermenegildo C, Atun R
Digital Gamification Tool (Let’s Control Flu) to Increase Vaccination Coverage Rates: Proposal for Algorithm Development
JMIR Res Protoc 2024;13:e55613
URL: https://www.researchprotocols.org/2024/1/e55613
doi: 10.2196/55613
PMID: 39255031©Henrique Lopes, Ricardo Baptista-Leite, Catarina Hermenegildo, Rifat Atun. Originally published in JMIR Research Protocols(https://www.researchprotocols.org), 10.09.2024. This is an open-access article distributed under the terms of the Creative
Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The
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