The Health Belief Model (HBM) (Rosenstock, 1974) is a psychological theory of health behaviour change that posits that individuals are more likely to change their health behaviour if they feel they are personally susceptible to a health risk, that the health risk is severe and if they believe there are more benefits than barriers to engage in the behaviour or preventative behaviour (Michie et al., 2017). According to this model, cues to action trigger behaviour change.
In fertility awareness, the HBM helps us understand that individuals need to feel susceptible to fertility problems/infertility to change their behaviour/engage in preventative action (e.g. starting to try to become pregnant, freezing their eggs, etc.). There must be a benefit to taking action and changing their behaviour (e.g. being able to have children in the future). As such, through an HBM lens, to be most effective, fertility awareness interventions should target individuals’ sense of susceptibility to the risk of fertility problems along with the benefits and barriers of postponing childbearing (Glanz et al., 2015). Such interventions can include risk assessment and personalized advice.
One example is The Fertility Assessment and Counselling Clinic in Denmark (FAC; Hvidman et al., 2015), that includes an individualised assessment of one’s risk of fertility problems. Researchers from the FAC clinic have used the HBM to explain the mechanisms of attending FAC clinic in that it serves as a ‘cue to action’ wherein women and men make choices such as pursuing fertility treatment or ending a relationship with a partner who is not ready to have children after attending the FAC clinic (Sylvest et al., 2018; Koert et al., 2020). Another example is described below with partnered women who want children.
These three theoretical models, as with many other health behaviour theories, are based on the premise that an individual weighs the potential risks and benefits of changing a behaviour, considers how others will respond, and calculates the likelihood of success of that change (Ryan et al., 2014). The choice of which model to apply depends on the target population (see point 1). For example, adolescents hardly perceive themselves as susceptible to infertility, as has been largely demonstrated before with STIs (Samkange-Zeeb et al., 2011). However, the fear of being unable to conceive is an essential motivator for adults with a child wish. Hence, interventions with adolescents could be based on SCT, and interventions for people with a child wish can formulate hypotheses informed by the TPB.
The choice of theoretical model also depends on formulating the ‘why’ and ‘how’ questions and answers (Noar, 2004). For example, the ‘why’ question could be ‘Why do partnered women who want to have children delay motherhood when the risk of not achieving a live birth increases with age?’. If we take the HBM as rationale, the answer would include the following: they perceive themselves as not susceptible to infertility; they consider that the consequences of not achieving a live birth through spontaneous conception can be easily overcome (e.g. willingness to undergoing fertility treatment, adopting a child/children, or choosing to stay childless); they believe that trying to conceive earlier does not increase the probability of pregnancy; they believe that the benefits of conceiving earlier do not outweigh the advantages of delaying motherhood; they do not have triggers around them to motivate action (e.g. partner or peer pressure); and they do not believe they have enough self-efficacy to take action. Pedro et al. (2021) tested these hypotheses in a sample of partnered women. They observed that those who perceived themselves as at risk of being infertile were more willing to anticipate childbearing than those who did not perceive themselves at risk. Still, this willingness depended on perceiving infertility as a significant threat and the willingness to undergo MAR treatments. This study suggests that interventions with this population should target these mediators and include information about infertility and fertility treatments. The ‘how’ question for this problem could be ‘How can a fertility education programme prevent infertility in partnered women who want to have children?’. Formulating this question will allow us to identify the relationships between targeted constructs and the behavioural outcome, and identify the analyses needed to test if the interventions lead to change (Teixeira et al., 2020).
It is worth mentioning that adaptations or complementary models can be used to develop a fertility education tool, and even merging two theories that suit the population and the ‘why’ and ‘how’ questions can be appropriate for prevention (Noar, 2004). As mentioned above, health behaviour theories focus on personal attitudes and beliefs as inherent to individual behaviour change. One of the main critiques directed at these theories is that they do not consider cultural appropriateness (Tan and Cho, 2019) or environmental conditions (Noar, 2004).
Besides individual factors, the social, economic, cultural, historical, and political variables of a particular setting affect how individuals of a given generation perceive their fertility or reproductive health. Hence, a needs assessment must be conducted before developing any intervention or programme. Having the input from the target population is vital to better understand self-efficacy, triggers, and motivation (Noar, 2004). The benefits of involving the target population as co-creators of knowledge have been acknowledged for some years (Wittink and Oosterhaven, 2018).
In the case of fertility education, this means trying to involve people with different lived experiences including those who are not currently planning pregnancy but who might want children in the future, those who have previously faced or currently face fertility problems, and people who know that they will need fertility treatment to conceive, such as same-sex couples. It may also be useful to involve caregivers or primary attachment figures. For example, it has been demonstrated that sexual health interventions in schools that include the training of parents and peer facilitators are more effective than those who target students only (Poobalan et al., 2009). This might be useful for interventions trying to include fertility education and infertility prevention in sexual education curricula. Or, when considering an intervention to decrease decision uncertainty in potential donors, it might be advantageous to consult not only previous donors but also their spouses and children. Previous calls have been made for giving preference to eliciting qualitative or participatory research rather than sitting representatives of all groups in panel meetings in the case of children and adolescents (Larsson et al., 2018). This advice obviously applies to children of donors, but researchers should also consider separating groups by treatment outcome.
When recruiting co-creators, considering diversity is important to ensure the views of people of different ethnicities and socioeconomic backgrounds are represented. Some populations are recognised as understudied including men (Martins et al., 2016), single mothers by choice (Volgsten and Schmidt, 2021), and the and lesbian, gay, bisexual, transgender, intersex, queer/questioning (LGBTQ+) population (Kirubarajan et al., 2021). The gap in sexual health education for LGBTQ+ people is particularly evident and is increasingly documented (Keuroghlian et al., 2017), and a recent call has been made for inclusiveness in fertility education to reach greater equity of care (Mertes et al., 2023).
Involving end users (a small sample of people that meet all eligibility criteria of the target population) in the development of educational resources not only adds valuable insights to the content, design, and understandability but also helps to set priorities on the main questions and interactions with the public (Movsisyan et al., 2020). Hence, incorporating the users’ perspectives means that several rounds of ‘pretesting’ may be needed before a resource is ready to be launched. With the tool designed and at an alpha version, researchers can incorporate additions and recommendations for improvement from end users and ask for feedback on comprehension, attractiveness, acceptance, believability, motivation, and preliminary indications of effectiveness (Movsisyan et al., 2020). Experimental research designs can be used to execute these tasks, such as in-depth or semi-structured interviews, focus groups, and intercept surveys.
Besides representatives from the target population, it is essential that other stakeholders and implementers are consulted (Moore et al., 2019; O’Cathain et al., 2019) including health and education professionals who will be using the educational resource in their interactions with the target populations (Vaisson et al., 2021). Iterative consultation cycles are warranted to ensure that multiple perspectives are sought, and publications or reports should describe how feedback from stakeholders was incorporated (Skivington et al., 2021).
Reconsidering an example of the previous section, a needs assessment with partnered women with a child wish might reveal that participants need to increase their susceptibility to infertility and want to be informed, but that this information should be preferably transmitted through the interactions they have with reproductive health professionals, and that direct information should be given preferably at an earlier age. Trusted sources of information include gynaecologists, general practitioners and nurses in family planning (Khurana and Bleakley, 2015). The consultation with primary health care practitioners could, for example, reveal that in that particular region women in their early thirties regularly attend a family planning consult every year. Consequently, the focus of the intervention would shift from a direct information delivery to a randomised controlled trial where partnered women with a child wish at a primary health care centre/hospital would be randomized to receive evidence-based information on fertility and infertility from their family planning consultation doctors, personalizing information according to the desire for childbearing and the planned timing. In comparison with the ones who would follow the regular routine (control group), we would expect women in the intervention group to be more informed, use better strategies for trying to conceive with increased chances of spontaneous conception, and know when to seek specialized help with more timely referal to a fertility specialist.
While content is the most important part of any tool, how it is presented is crucial for effective engagement. After gathering a comprehensive understanding of the target population, selecting an appropriate theoretical framework that supports the establishment of hypotheses, and involving co-creators in the development of the resource, the team should have a good understanding of what will constitute effective engagement from the target audience. However, identifying the characteristics that enhance clarity and aid the communication of messages and materials to the public is of utmost importance. Several peer-reviewed guidelines exist that can help make resources as effective as possible. These evidence-based recommendations can be general to all interventions and specific to the chosen tool. An example of helpful guidelines is the Centers for Disease Control and Prevention (CDC) Clear Communication Index (Baur and Prue, 2014), which contains a list of evidence-based criteria for developing health information, including content, language and design. Another valuable checklist if the content is digital is the Health Literacy Online Strategies Checklist (Office of Disease Prevention and Health Promotion, 2016), with similar points, including the importance of using positive communication. Several governmental institutions have also produced recommendations that can be followed, such as the Office of Disease Prevention and Health Promotion (Hou, 2012) on writing and designing websites or the National Institutes of Health (National Institutes of Health, 2015) on how to make communication clear.
Regardless of the chosen way to deliver information, literacy is a factor to consider when developing a fertility education tool and should be evaluated before public release. Highly literate individuals can apply their skills both in situations that require content knowledge and new content (Nutbeam et al., 2018) and are more likely to use social media platforms as a source of health-related information than people with low literacy (Kim and Xie, 2017). This discrepancy might be explained by the fact that the readability level of online health information exceeds the recommended sixth-grade level (Kim and Xie, 2017). Even when we incorporate the users’ perspective in the development stage and consider diversity, social desirability and agreement, bias must be considered in participatory design (Arcia et al., 2016). This is because volunteers are often biased by familiarity with the health issue and culture (Ospina-Pinillos et al., 2018), and recruitment is frequently carried out through patient associations. These biases may lead to an overestimation of users’ literacy levels. Hence, assessing readability, and understanding and testing the tool on people with limited literacy skills is essential to ensure that the content is accessible and easy to understand and can prevent dropout or attrition rates.
Literacy can be evaluated both for printed and digital materials. The Suitability of Assessment Materials scale (Doak et al., 1996) assesses both readability and comprehension of printed materials, including dimensions such as content (e.g., purpose and scope), literacy demand (e.g. reading level), graphics (e.g. relevance of illustrations), layout and typography (e.g. subheadings use), learning, stimulation and motivation (e.g. self-efficacy), and cultural appropriateness (e.g. cultural images). It is the most cited method for assessing the accessibility of patient materials beyond reading level (Ryan et al., 2014). When considering an online tool, the eHealth Literacy Scale (Norman and Skinner, 2006) is the most used screening tool to measure knowledge, comfort, and perceived skills at finding, evaluating, and applying e-health information to health problems (Kim and Xie, 2017), providing important clues on users’ comfort and skill in using information technology for health information.
Other factors, besides absence of bias and readability, found to be relevant before launching a health education tool are accessibility, usefulness, comprehensiveness, credibility, and interactivity (Kim and Xie, 2017). These components are essential to all target populations. Interactive tools, for example, were found to be more effective than static contents not only in adolescents but also in older adults, and people with low socio-economic status (Kim and Xie, 2017), and medical health professionals (Car et al., 2019).
Design is also a very important aspect of educational resources and often neglected because researchers are unfamiliar with it. There are existing guidelines that consider how learning is facilitated, and engagement can be boosted through illustrations and charts, target audience familiarity with characters when using videos, and the speed of audio. The US Department of Health and Human Services web design and usability guidelines include strength of evidence for each recommendation (U.S. Department of Health and Human Services & U.S. General Services Administration, 2006). If the educational resource consists mainly of videos (for example, when producing a national campaign), it is worth using specific video guidelines (e.g. Brame, 2016). When considering developing a decision aid for people facing fertility treatment and its options, the research team should use the quality criteria from the International Patient Decision Aid Standards (IPDAS, Elwyn et al., 2006; Volk et al., 2013).
The design of a resource will influence engagement with it. In the context of fertility education, engagement refers to a desire and capability to actively participate by interacting with the designed resource or tool to optimise reproductive decision-making. It is the responsibility of the research team to devise educational resources that motivates users to take action (Hou, 2012). Engagement is critical in individual digital change behaviour interventions, where attrition is high, almost half of the material provided is not accessed, and interventions are evaluated by participants as too time-demanding (Car et al., 2019). Testing a beta version or pilot testing of any resource will help reduce these risks. When conducting a pilot study, both quantitative and qualitative research can make significant contributions and have different advantages (Creswell, 2015), and in most cases, the use of mixed methods will maximise the benefits. Having participants test the resource will show that engagement goes far beyond their ability to use technology, which often does not correlate with behaviour change (Michie et al., 2017). Pilot results will help establish the minimum engagement required for the desired change in behaviour for a particular educational resource, as it has been shown that change points vary according to intervention types (Michie et al., 2017).
The need for developing and deploying fertility education has emerged in this century as profound changes in the transition to parenthood and family configurations have occurred. This paper provides guidance for developing educational tools to increase fertility awareness and literacy, and facilitate decision-making by individuals. Key messages include:
EliranRecruitment of end-users as co-creators should consider racial, ethnic, and socioeconomic disparities to ensure inclusiveness and equity. Understudied populations such as men, single mothers by choice, and the LGBTQ+ population should be included in the development of generic or specific interventions to address existing gaps in fertility knowledge and education.
M.V.M., E.K., R.S., and E.M. designed the study. M.V.M. drafted most part of the manuscript, with contributions from E.K., R.S., M.M-R., E.M., K.H., and J.H. All authors made significant contributions critically revising the manuscript and approved the final version for submission.
J.H. has received consultancy fees from Gedeon Richter, Haleon, and Natural Cycles. J.H. has also received payment for talks and travel support from Bayer, Merck, Gedeon Richter and Cook IVF. J.H. also receives author royalties for the book ‘Your Fertile Years’. The other authors have no conflicts of interest to declare.
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