Abstract
Background
Patient-facing digital technologies may reduce barriers to and alleviate the burden on genetics services. However, no work has synthesised the evidence for patient-facing digital interventions for genomics/genetics education and empowerment, or to facilitate service engagement more broadly. It is also unclear which groups have been engaged by digital interventions.
Aim
This systematic review explores which existing patient-facing digital technologies have been used for genomics/genetics education and empowerment, or to facilitate service engagement, and for whom and for which purposes the interventions have been developed.
Methods
The review adhered to the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. Eight databases were searched for literature. Information was extracted into an Excel sheet and analysed in a narrative manner. Quality assessments were conducted using the Mixed Methods Appraisal Tool.
Results
Twenty-four studies were included, of which 21 were moderate or high quality. The majority (88%) were conducted in the United States of America or within a clinical setting (79%). More than half (63%) of the interventions were web-based tools, and almost all focussed on educating users (92%). There were promising results regarding educating patients and their families and facilitating engagement with genetics services. Few of the studies focussed on empowering patients or were community-based.
Conclusion
Digital interventions may be used to deliver information about genetics concepts and conditions, and positively impact service engagement. However, there is insufficient evidence related to empowering patients and engaging underserved communities or consanguineous couples. Future work should focus on co-developing content with end users and incorporating interactive features.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
Introduction
It has been estimated that 5.3% of new-borns will have genetic disorders (Verma & Puri 2015), with 30,000 children in the United Kingdom (UK) alone receiving a diagnosis every year (Gene People 2020; Genetic Alliance 2021). These can occur through genetic variants, which are heritable changes to the DNA sequence and may involve one or multiple genes (Richards et al. 2015). However, genetic disorders do not affect all populations equally. Research has shown a greater prevalence of genetic disorders in developing countries and non-Western populations due to higher numbers of consanguineous marriages (Posch et al. 2012; Verma & Puri 2015). This pattern is also observed in developed countries. For example, in the UK, the Born in Bradford cohort study with 12,453 women found that rates of consanguinity were significantly higher in Pakistani women, compared to their white British counterparts (37.5% vs. 0.0% for first-cousin marriage) (Bhopal et al. 2014). Genetic counselling with screening for genetic variants is essential to help identify possible genetic conditions, especially after marriage and before conception (Verma & Puri 2015). Additionally, given that some metabolic disorders require treatment with medication or lifestyle changes, newborn screening is essential. Improving understanding about genetic inheritance can empower affected families and reduce unexpected, affected births. However, significant unmet need for culturally sensitive genetic information and services leaves many families poorly supported.
General barriers to genetic counselling and related screening services have been reported in the literature. The stigmatisation of genetic disorders might deter individuals from accessing services due to potential discrimination, issues with seeking insurance, and blaming individuals and population groups (de Vries et al. 2020; Williams et al. 2010). Additionally, perceived religious and cultural barriers might affect engagement in communities where consanguineous marriages are practised (Alkuraya 2013). Genetic service-related barriers are also evident and were explored in a scoping review by Raspa et al. (2021). The scoping review identified key issues from the United States of America (USA) and abroad, including the limited number of genetic specialists, appointment waiting times, delivery by non-genetic providers, and reimbursements and obtaining a licence, concluding that health information technologies may help to overcome these barriers.
Digital technologies may help to educate, empower, and engage populations in genetic services, overcoming barriers related to stigmatisation and reducing the burden on genetic counselling services. Engagement can be patient-driven, whereby people may seek information online (Shepherd 2010) or on specific apps (Gasteiger et al. 2022; Talwar et al. 2019). Patients already send emails to genetics services, requesting information on things like access to testing and specialist advice regarding treatment (Shepherd 2010). Additionally, services may use technologies to distribute accurate and reliable information or to facilitate engagement with their services. For example, our systematic review of 22 patient-facing genetics/genomics apps uncovered that some apps allowed genetic testing kits to be purchased, with results and further information provided directly through the app (Gasteiger et al. 2022). There is also the possibility to provide genetic counselling via video teleconferencing (Gorrie et al. 2021) or over the phone (Tutty et al. 2019), and results could be provided through a patient portal linked to the electronic health record (Korngiebel et al. 2018).
While there is an abundance of literature exploring the use of technologies for genetics/genomics education and counselling, no work to date has synthesised the evidence for patient-facing digital interventions for genomics/genetics education and empowerment, or to facilitate service engagement more broadly. Related work has included a rapid systematic review to explore the benefits and disadvantages of telegenetics (genetic counselling via videoconferencing) (Gorrie et al. 2021) and a commentary on how digital tools can advance quality and equity in genomic medicine (Bombard & Hayeems 2020). We also conducted a systematic review of 22 UK patient-facing genetics/genomics apps, finding that there is a need for an accessible, culturally sensitive, and evidence-based app to improve genetic literacy within specific communities (Gasteiger et al. 2022). It is also unclear which groups and communities have been engaged by digital interventions and for which purposes.
Research questions
Two research questions guided the review: 1) What existing patient-facing digital technologies have been used for genomics/genetics education and empowerment or to facilitate service engagement? 2) For whom (patient groups, populations, or communities) and for which purposes have the interventions been developed?
Methods
We conducted a systematic review, which is reported in accordance with the updated Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines (Page et al. 2021). The protocol was registered in the international prospective register of systematic reviews in August 2022 (PROSPERO: CRD42022348127).
Search and screening process
Databases and keywords
Peer-reviewed literature was retrieved from PubMed, SCOPUS, Embase, Web of Science, CINAHL, PsycINFO, IEEE Xplore and the ACM Digital Library in June 2022. The supplementary file presents the specific search keywords and syntax for three database searches. Each keyword was separated by Boolean operators ‘AND’ and ‘OR.’ No limits were placed on the date of publication. However, where possible, limits were placed on the language (English) and species (human).
Inclusion and exclusion criteria
Qualitative, quantitative, and mixed-methods empirical studies published in English were included. The research had to report on a digital intervention for patients, families, the public, or communities used for genetics/genomics empowerment, education, or to facilitate service engagement.
Technical papers, non-empirical studies, theses, dissertations, conference proceedings, and grey literature were excluded. Research studies focussing on healthcare professionals (e.g., genetics counsellors, nurses, or doctors) were excluded unless information on patients or the public could be separated. Papers focussing on the development of interventions were also excluded, unless there was an evaluation component.
Screening and literature management
The Rayyan software (https://www.rayyan.ai/) was used to manage the literature. A two-step screening process was conducted, whereby two authors (NG and AV) first independently reviewed the abstract and titles of the literature for eligibility. The two authors then independently read the full-texts to determine eligibility. Disagreements on eligibility were resolved through discussion until a consensus was met. Duplicates were automatically detected and removed by Rayyan.
Analysis
A data extraction sheet was developed in Microsoft Excel, into which relevant data were extracted (see Box 1). Descriptive statistics were generated where applicable, such as on the research design, participant sample (e.g., number of participants and countries), focus areas, and technologies used. We then conducted a formal narrative synthesis by broadly grouping and describing the studies by the following characteristics: 1) Target population characteristics; 2) intervention type, focus, and content; 3) Type of outcome (e.g., education, empowerment, service engagement).
Box 1. Information extracted from eligible research studies.
• First author |
• Year of publication |
• Title of study |
• Research design and methods |
• Study objective |
• Focus (genomics/genetics) |
• Technology description |
• Technology setting (e.g., part of service or independent) |
• Setting; country |
• Sample (type/population, size, age, gender, ethnicity) |
• Outcome (education, knowledge, awareness, service engagement, understanding) |
• Barriers/facilitators to use, adoption, uptake |
Quality appraisals
The evidence was appraised using the Mixed Methods Appraisal Tool (MMAT) (Hong et al. 2018). The MMAT can be used for qualitative, quantitative, and mixed-methods research. It consists of two screening questions and five study design-related questions, each of which is scored 1 (yes) or 0 (no). Similar to other reviews (Mogharbel et al. 2021; Pluye et al. 2009), we categorised each study as low quality (≤ 40%, i.e., met 1–2 criteria), moderate quality (60%-80%, i.e., met 3–4 criteria), or high quality (100%, i.e., met all 5 criteria). Each study was independently assessed by two authors (NG and AV). Interrater reliability was calculated using Cohen's kappa (κ) on IBM SPSS (version 27).
Results
The database search yielded 1407 records, of which 54 duplicates were removed. Of the 1353 titles and abstracts screened, 1309 were excluded as they did not meet the inclusion criteria. Three of the 44 full-texts could not be retrieved, leaving 41 studies for full-text screening. Of these, 16 were not eligible due to the following reasons: not primary research (n = 7), not an intervention (n = 6), not a peer-reviewed journal article (n = 2), did not focus on genetics education, empowerment, or service engagement (n = 1), and does not focus on patients or the public (n = 1). Ultimately, 24 studies were included in the review. Figure 1 outlines the literature search and screening process.
Characteristics and quality of the included studies
The 24 included research papers were published from 2008 (O'Neill et al. 2008) to 2022 (Bangash et al. 2022; Bradbury et al. 2022; Christian et al. 2022). Fifteen of the studies were experimental, and nine were observational. Specifically, 11 were randomised controlled trials (Adam et al. 2018; Biesecker et al. 2018; Bowen et al. 2011; Bradbury et al. 2022; Cragun et al. 2020; Gornick et al. 2018; Hernan et al. 2020; Prado et al. 2018; Vogel et al. 2019; Wang et al. 2021; Wierstra et al. 2018), six were mixed methods studies (Bangash et al. 2022; Boudreault et al. 2017; Brown-Johnson et al. 2021; Schmidlen et al. 2019; Solomon et al. 2020; Williams et al. 2018), four were non-randomised quantitative studies (Christian et al. 2022; Conijn et al. 2020; Hardy et al. 2018; O'Neill et al. 2008), two were descriptive quantitative studies (Beaudoin et al. 2011; Nazareth et al. 2021) and one was qualitative (Suckiel et al. 2021). Table 1 and the supplementary file provide more information on the study designs, including the sample and data collection methods.
Quality appraisals
The MMAT (Hong et al. 2018) was used to assess the quality of the included studies. The MMAT considers five different study designs, with two pre-screening questions and five questions per study design used to assess quality. For example, this may consider whether randomisation was performed and whether groups are comparable at baseline for RCTs or whether components of mixed methods studies are effectively integrated to answer the research question.
There was almost perfect agreement between the two raters using the MMAT (95%; κ = 0.870 (95% CI, 0.768 to 0.972; p < 0.001). Of the 11 randomised controlled trials, seven were rated as moderate quality, three as high quality and one as low quality. Six did not blind outcome assessors to the intervention, and three did not have complete outcome data (≥ 80%). Five mixed methods studies were deemed as moderate in quality, and one of low quality. None of the mixed methods studies met all the criteria for qualitative and quantitative research, mostly due to lacking information on the analysis method for the quantitative data. Half of the non-randomised quantitative studies were considered moderate quality; one was considered low quality and one was considered high quality. Half did not have complete outcome data and did not appear to account for confounding variables (e.g., previous technology use, level of education and pre-existing knowledge of genetics). Both descriptive quantitative studies were considered of moderate quality as they were at-risk for nonresponse bias due to low response rates and significant differences between users and non-users of the digital intervention. The qualitative study was considered high quality.
Target population characteristics
Of the 24 studies included, 21 (88%) were conducted in the USA, two (8%) were conducted in Canada, and one (4%) took place in the Netherlands (see Table 1). The sample sizes ranged from 9 to 61,070 participants (mean 2892, median 128). The recruitment settings were predominantly from within a clinical environment (n = 19, 79%), with one (4%) accessed either online or via a clinical setting (Bangash et al. 2022), three (13%) were available freely online (Conijn et al. 2020; Schmidlen et al. 2019; Wierstra et al. 2018) and one study recruited participants from within a university setting (4%) (Hardy et al. 2018).
The population focus for the studies varied. Those recruited from within a clinical setting tended to focus on a specific condition. Eight (33%) focussed on cancer (Bowen et al. 2011; Bradbury et al. 2022; Cragun et al. 2020; Gornick et al. 2018; Nazareth et al. 2021; O'Neill et al. 2008; Solomon et al. 2020; Vogel et al. 2019). Less than 10% focussed on exome sequencing (n = 2, 8%) (Biesecker et al. 2018; Hernan et al. 2020), metabolic conditions (n = 1, 4%) (Beaudoin et al. 2011), hypertrophic cardiomyopathy (n = 1, 4%) (Christian et al. 2022), rheumatoid arthritis (n = 1, 4%) (Prado et al. 2018), epilepsy (n = 1, 4%) (Adam et al. 2018), paediatric neurologic, immunologic, or cardiac disorders (n = 1, 4%) (Suckiel et al. 2021), autism (n = 1, 4%) (Williams et al. 2018), or irritable bowel disease (n = 1, 4%) (Wierstra et al. 2018).
One study (4%) had a broad focus on all health conditions (Brown-Johnson et al. 2021) and another (Wang et al. 2021) focused on underserved communities, looking at vulnerable patient populations, including racial/ethnic minorities. Studies which recruited the public online focussed on breast health (n = 1, 4%) (Bowen et al. 2011), expanded carrier screening and mucopolysaccharidosis type III (n = 1, 4%) (Conijn et al. 2020), American Sign Language (ASL) speakers in the deaf community (n = 1, 4%) (Boudreault et al. 2017), familial hypercholesterolemia (n = 1, 4%) (Bangash et al. 2022), and general genetic health and literacy (n = 1, 4%) (Schmidlen et al. 2019). One (4%) study focused on university students specifically focused upon the Jewish community (Hardy et al. 2018).
Demographic information was reported in some studies. Five (21%) of the studies were for women only, and for those that specified the gender of participants, male participation ranged from 2% (1242/61,070) to 67% (29/43). Age was not recorded in three of the studies. Ethnicity and race were not reported in six studies. In the studies that reported ethnicity or race, participation by white/Caucasian individuals ranged from 14.3% (39/273) to 100% (9/9). Only three studies had 50% or more of participants identifying as non-white (e.g., Hispanic/Latinx, African American or Asian). Characteristics of the included studies are further detailed in Table S3 in the supplementary file.
Summary of the interventions
Of the 24 included papers, 15 (63%) interventions used a web-based tool. One also had a video component that included an animated educational video (Hardy et al. 2018). One was a multi-model precision health platform that used data-driven medicine to predict and prevent disease, with four associated components: health coaching, remote monitoring, pharmacogenomics, and genetic screening (Brown-Johnson et al. 2021). Three (13%) other interventions consisted of videos (Boudreault et al. 2017; Conijn et al. 2020; Hernan et al. 2020), three (13%) were mobile applications (apps) (Suckiel et al. 2021; Vogel et al. 2019; Williams et al. 2018), two (8%) were chatbots (Nazareth et al. 2021; Schmidlen et al. 2019), and one (4%) was a webinar (Christian et al. 2022). Most (n = 20, 83%) were used as part of a clinical service, which involved being recruited when attending for a planned consultation with a clinical team. This meant a patient may need to be referred by a clinician to access the platform, and/or the platform is used to enhance the clinical service, the goal being to assess the level of genetic literacy and the impact of the digital intervention compared to standard care. For the four (17%) platforms that are used independently from a clinical setting, these focussed on educating users on genetic health. Table S4 in the supplementary file provides a summary of the digital interventions.
Almost all the studies focussed on educating users through the digital interventions (n = 22, 92%) (Adam et al. 2018; Bangash et al. 2022; Beaudoin et al. 2011; Biesecker et al. 2018; Boudreault et al. 2017; Bowen et al. 2011; Bradbury et al. 2022; Christian et al. 2022; Conijn et al. 2020; Cragun et al. 2020; Gornick et al. 2018; Hardy et al. 2018; Hernan et al. 2020; Nazareth et al. 2021; O'Neill et al. 2008; Prado et al. 2018; Schmidlen et al. 2019; Solomon et al. 2020; Suckiel et al. 2021; Vogel et al. 2019; Wierstra et al. 2018; Williams et al. 2018). The majority (n = 17, 71%) also explored facilitating service engagement (Bangash et al. 2022; Beaudoin et al. 2011; Biesecker et al. 2018; Bowen et al. 2011; Brown-Johnson et al. 2021; Christian et al. 2022; Conijn et al. 2020; Cragun et al. 2020; Hardy et al. 2018; Nazareth et al. 2021; O'Neill et al. 2008; Schmidlen et al. 2019; Solomon et al. 2020; Suckiel et al. 2021; Vogel et al. 2019; Wang et al. 2021; Williams et al. 2018). Only two (8%) intended to empower users (Adam et al. 2018; Cragun et al. 2020). The specific impacts on outcomes are reported later in the paper. Table 2 summarises the characteristics of the interventions.
Barriers and facilitators to uptake
Some authors acknowledged potential barriers to uptake and explored differences in demographics between users and non-users. Users tended to be younger (O'Neill et al. 2008), more motivated to change health behaviours (O'Neill et al. 2008) and have a higher level of health literacy (Wang et al. 2021). Barriers included not being able to use a computer (Wang et al. 2021), having a lower grade reading age than grade 12 (Wierstra et al. 2018) and being confused as to how to use the tool (Brown-Johnson et al. 2021; Wang et al. 2021; Williams et al. 2018). Strategies to increase participation included providing equipment (e.g., devices, genetic tests, associated counselling) at no cost to participants (Brown-Johnson et al. 2021) or providing a free meal (Hardy et al. 2018).
Outcomes of the interventions
Education, knowledge and understanding
A majority (n = 19, 79%) of the studies reported on changes to knowledge or understanding of genetic concepts (Adam et al. 2018; Beaudoin et al. 2011; Biesecker et al. 2018; Boudreault et al. 2017; Bowen et al. 2011; Bradbury et al. 2022; Christian et al. 2022; Conijn et al. 2020; Cragun et al. 2020; Gornick et al. 2018; Hardy et al. 2018; Hernan et al. 2020; O'Neill et al. 2008; Prado et al. 2018; Schmidlen et al. 2019; Solomon et al. 2020; Vogel et al. 2019; Wierstra et al. 2018; Williams et al. 2018). Three other studies did not directly report on changes to knowledge or understanding but instead focussed on experiences with the educational content (Bangash et al. 2022; Nazareth et al. 2021; Suckiel et al. 2021). For example, one stated that 71.4% of 61,070 users completed the genetic testing education section of the clinical chatbot and reported high acceptability with the content (Nazareth et al. 2021). In the study by Bangash et al. (2022) 56% (5/9) of the participants found the information easy to find and 78% (7/9) found it very easy to understand. Responses to the intervention in Suckiel et al. (2021) were also positive, with Spanish-speaking participants valuing the opportunity to read the content in their preferred language.
Sixteen studies reported positive effects on knowledge, whereby the understanding of genetics information (e.g., genetic testing, genetic health conditions, recessive inheritance) improved due to the digital intervention (Adam et al. 2018; Beaudoin et al. 2011; Biesecker et al. 2018; Bowen et al. 2011; Bradbury et al. 2022; Christian et al. 2022; Conijn et al. 2020; Cragun et al. 2020; Gornick et al. 2018; Hardy et al. 2018; Prado et al. 2018; Schmidlen et al. 2019; Solomon et al. 2020; Vogel et al. 2019; Wierstra et al. 2018; Williams et al. 2018). This was measured through knowledge quizzes or self-reported changes by participants. For example, in one qualitative study, participants remarked that they could take their time with the chatbot, which helped them understand the information. Many participants also stated that they learned more using the chatbot than when speaking to a person in the clinic. Some studies also found the digital interventions equivalent to, or noninferior to genetic counsellors regarding knowledge outcomes. Biesecker et al. (2018) reported their web-based platform as noninferior immediately after the intervention, one month later and six months later. Adam et al. (2018) also noted that participants in the DECIDE intervention group and genetic counselling control groups had increased knowledge, with DECIDE appearing equivalent at conveying information.
Three studies specifically mentioned that knowledge could be shared with others, including family and friends. Boudreault et al. (2017) reported that participants were motivated to tell their family and friends about the educational video, while participants in the study by Solomon et al. (2020) wanted to share information with their family, friends and other patients with cancer. Lastly, participants thought the cascade chatbot in the study by Schmidlen et al. (2019) could help share genomic test results and answer their relative's questions as it knows “all of the relevant clinical details regarding the results.” This could be achieved through the Family Sharing Tool which deploys a link to share genetic test results with others via text, email or Facebook Messenger.
A few studies reported unexpected negative findings, whereby there were no significant differences in knowledge outcomes between those who did and those who did not use the digital intervention (Hernan et al. 2020; O'Neill et al. 2008). In one study, knowledge scores were significantly lower in parents in the intervention group who watched an educational video before a genetic counselling appointment, compared to the control group who did not watch the video (Hernan et al. 2020). Boudreault et al. (2017) also found that participants in their study did not fully comprehend the material conveyed, with participants highlighting the need for the messaging to be improved by reducing the amount of content and using simpler language.
Participants across other studies also suggested improving the content within the digital interventions. Recommendations included adding more information about how to respond to health problems experienced by children with specific disorders, genetic screening for children, treatment options, risk of disease, testing options for family, the frequency and prevalence of the variant, and an explanation for why testing negative would not remove all future disease risk (Bangash et al. 2022; Beaudoin et al. 2011; Biesecker et al. 2018; Schmidlen et al. 2019). Participants in one study also disagreed on the framing of the content, whereby some believed prognostic information should convey a sense of hope, while others desired more factual information (Solomon et al. 2020).
Service engagement
Seventeen studies (71%) reported on direct or expected impacts to service engagement or the delivery of clinical genetics healthcare (e.g., testing, diagnosis, and counselling) (Bangash et al. 2022; Beaudoin et al. 2011; Biesecker et al. 2018; Bowen et al. 2011; Brown-Johnson et al. 2021; Christian et al. 2022; Conijn et al. 2020; Cragun et al. 2020; Hardy et al. 2018; Nazareth et al. 2021; O'Neill et al. 2008; Schmidlen et al. 2019; Solomon et al. 2020; Suckiel et al. 2021; Vogel et al. 2019; Wang et al. 2021; Williams et al. 2018). Studies reported on direct impacts, evidenced by uptake of screening attendance and healthy behaviours (Bowen et al. 2011; Conijn et al. 2020; Hardy et al. 2018; Vogel et al. 2019), positive experiences of genetics services (Brown-Johnson et al. 2021; Christian et al. 2022; Suckiel et al. 2021; Williams et al. 2018) and positive attitudes toward genetics services (Conijn et al. 2020). For example, utilisation of cancer genetic counselling services (Vogel et al. 2019) and mammography screening (Bowen et al. 2011) improved, along with a 22% increase in breast self-exam behaviours (40% before the intervention and 62% after) (Bowen et al. 2011). Positive experiences of genetics services were reported by Brown-Johnson et al. (2021) whereby patients reported an enhanced sense of partnership and accountability with the healthcare team due to digital health. In another study, 88% of 82 participants agreed that the live and interactive webinar would be an acceptable replacement for a one-to-one appointment with a genetics counsellor (Christian et al. 2022). Conijn et al. (2020) also reported on positive attitudes, finding that participants who watched the educational video on expanded carrier screening had a more positive attitude toward preconception screening than those who read only the text description.
Across the literature, expected changes to service engagement could be characterised by attitudes toward or planning to engage (or not engage) with services (Beaudoin et al. 2011; Biesecker et al. 2018; Cragun et al. 2020; O'Neill et al. 2008) or expected changes to the delivery of care (Bangash et al. 2022; Schmidlen et al. 2019; Solomon et al. 2020). For example, in a study with 53 participants, 30.2% had discussed or were planning to discuss the health information from the Genetics Home Reference online resource with physicians (Beaudoin et al. 2011). 28.3% also reported that the information influenced or might influence their future health decisions, and 11.3% either contacted or planned to contact a local support group.
Some negative findings were also reported, whereby participants in the study by Solomon et al. (2020) wanted to be reassured that the interactive features within the digital health tool meant that they would be responded to by the healthcare team and not “lost in cyberspace someplace.” Additionally, parents in the study by Biesecker et al. (2018) who were educated with the web-based platform had stronger decisional conflict (uncertainty) about genetics testing compared to non-parents. Lastly, participants had no significant change in attitudes toward hereditary cancer genetic testing after using a web-based educational tool (Cragun et al. 2020).
Empowerment
Only two studies (8%) reported on empowerment (Adam et al. 2018; Cragun et al. 2020), defined by the extent to which an individual believes they have decisional control, cognitive control, behavioural control, hope and emotional regulation (McAllister et al. 2011). The interventions in both studies were for pre-test genetics counselling and used self-reported and subjective empowerment measures. Adam et al. (2018) used the validated and sensitive 24-item Genetic Counselling Outcome Scale (GCOS-24) scale, which measures patient-reported empowerment in clinical genetics services (McAllister et al. 2011). In contrast, Cragun et al. (2020) measured empowerment by asking participants about the extent to which they feel they can make an informed/empowered choice about genetic testing. Both studies reported improvements in empowerment. However, Adam et al. (2018) reported that the DECIDE online decision and educational tool only increased empowerment by less than 2%. Cragun et al. (2020) reported on greater impacts, whereby 74% (227 of 305) of participants felt empowered to make a decision after viewing the web-based educational tool, compared to only 29% before. This finding was statistically significant.
Discussion
This systematic review synthesised evidence from 24 studies on patient-facing digital interventions for genomics/genetics education, empowerment, and service facilitation. There were promising results regarding educating patients and their families and facilitating engagement with genetics services. However, little literature explored empowerment, with only one study using a validated measure (Adam et al. 2018).
The review also explored for whom and for which purposes the interventions had been developed, finding that the majority of the interventions were delivered in the USA and within clinical environments, while few engaged underserved populations. In fact, only one study focussed on racial/ethnic minorities (Wang et al. 2021), ASL speakers in the deaf community (Boudreault et al. 2017) and students within a Jewish community (Hardy et al. 2018), respectively. This gap in research highlights a need for further engagement with underserved communities that may be at higher risk of genetic disorders or consanguinity. Although rates of consanguinity are decreasing, an estimated 8.5% of children worldwide have consanguineous parents (Shawky et al. 2013) and the practice is still prevalent in parts of the Middle-East, North Africa, and West Asia (Acharya & Sahoo 2021; Bittles 2001; Heidari et al. 2014). In the UK, it was estimated that 59.3% of Pakistani women were married to first or second cousins (Bhopal et al. 2014). However, none of the studies were conducted in these countries or specifically targeted genetic screening or counselling in consanguineous couples.
Additionally, patients often needed to be referred to the intervention by a clinician (e.g., genetics counsellor). This may result in inequitable access, given that there are many barriers to engagement with genetic healthcare services, including a lack of knowledge of genetic services or personal risk (Delikurt et al. 2015; Geer et al. 2001; Willis et al. 2017); education levels (Willis et al. 2017); cost and logistics (Geer et al. 2001; Willis et al. 2017); lack of decision support (Willis et al. 2017) and language and a lack of bilingual providers (Augusto et al. 2019; Kinney et al. 2010). Some groups may also experience stigmatisation or discrimination due to their genetic disorders (Williams et al. 2010). A combination of these factors and potential stigmatisation might create barriers to engagement with genetics services, resulting in groups of individuals who remain underserved and could have been overlooked in the research. This highlights an opportunity for more community-based work and targeted engagement to support equitable access by underserved communities.
Many considerations for technology development were also highlighted within the review. For example, there were only three apps, and more than half of the interventions were web-based, with few having interactive features. It was unsurprising that not many apps had been evaluated, given that our review of 22 patient-facing genetics apps available in the UK identified that none had been formally trialled or tested (Gasteiger et al. 2022). However, it was interesting to note that there was a trend toward more interactive emerging technologies in recent years, with literature published in the last three years reporting on chatbots accessible via smartphones and computers. A key benefit of digital technology is the possibility for interactivity, defined by an element of responsiveness, exchange of information and varying user control (Walther et al. 2005). Interactive features may include personalisation and tailoring, feedback and monitoring, prompts and cues and goal-setting, which are also commonly used behaviour change techniques in digital interventions (Dugas et al. 2020; Taj et al. 2019). Emerging technologies like virtual reality, augmented reality and video games can also foster interactivity through immersion, presence and customisation, while helping make messaging around healthy behaviours more relatable (Hudgens et al.).
Clearly, the literature was dominated by quantitative research (71% of the studies), and although there were six mixed methods studies, there was only one qualitative study. The qualitative work in these studies was important in reporting potential improvements to the content and use of the interventions. For example, some studies reported on language preferences, whereby some participants did not understand the content conveyed (Boudreault et al. 2017), and other users may prefer factual information while some prefer hopeful language (Solomon et al. 2020). The qualitative data also uncovered that some people might want to share the information with family, friends or other patients (Boudreault et al. 2017; Schmidlen et al. 2019; Solomon et al. 2020). This highlights a need for user-friendly language and technical features that enable content to be shared. Further, it supports the need for more qualitative research and co-developed content with participation from future end users to ensure that it meets their expectations and needs, and is inclusive for more under-served communities.
Recommendations for future research and technology development
This review has highlighted a need for further research that specifically engages underserved groups and communities at-risk of genetic disorders or consanguinity. It is crucial to understand whether and how digital interventions may reach groups who already experience barriers to engaging with genetic services. Additionally, more exploration of patients' experiences with and perceptions of the digital interventions is needed, to understand whether the content is acceptable. Lastly, underlying these research opportunities is a focus on individual and community-based empowerment, which puts patients at the centre of healthcare, and, by definition, means that patients will have increased autonomy over health-related decision-making and actions (Health promotion glossary 1998). Validated patient-reported outcome measures like the GCOS-24 scale which assesses empowerment in genetics counselling and testing services (McAllister et al. 2011) should be employed.
The main implication for the design of future genetics/genomics digital interventions is the inclusion of qualitative co-design or participatory design methods with future end users. Including qualitative methods such as interviews, focus groups, or talk-aloud protocols is vital to understanding user needs, experiences and any usability or acceptability issues early in the design of an intervention. This can allow for an iterative design process during each phase of development and evaluation. Additionally, as explained previously, the incorporation of interactive features is recommended in future interventions through established behaviour change techniques (e.g., tailoring and personalisation, feedback and monitoring, goal setting and prompts/cues). Lastly, content should be simple and not too detailed, addressing a group's average reading age. For example, the average reading age level in the UK is nine years, with 16.4% of adults in England having very poor literacy skills (National Literacy Trust 2023). Producing content at lower reading ages is imperative to ensure it is easy to understand.
Being able to share information with others would also be beneficial. For example, this may include being able to download some of the content (e.g., videos) for private sharing. Other technologies may also link in with email or social media sites, whereby users can directly share content (or the app) with friends and family through their email or sites like Facebook Messenger and Whatsapp.
Strengths and limitations
A key strength was the inclusion of different study designs, with the results from the mixed methods and qualitative studies providing a richer understanding of patient experiences and perceptions of the digital interventions. The review also builds on previous work, which has typically focussed on one technology (e.g., videoconference in Gorrie et al. (2021)). By taking a broader approach to digital interventions, the review has highlighted how different technologies have been employed and simultaneously identified potential applications for new emerging technologies (e.g., chatbots and precision health) in genetics healthcare. Lastly, the quality appraisals were conducted in a rigorous manner, with almost perfect agreement between the two researchers.
Limitations include that the reviewed papers were limited to the English language. This means that research with underserved ethnic communities might have been missed if it had been published in another language. Additionally, the studies may have been prone to selection and response bias, given that the samples were limited to people who had access to technology and the digital literacy skills to use it. Lastly, digital interventions emerge quickly, so newer interventions may not be represented by the published papers.
Conclusions
Evidence suggests that digital interventions may be used to deliver information about genetics concepts and conditions, and positively impact service engagement. There is less evidence for empowering patients and engaging underserved communities or consanguineous couples. Future work should focus on co-developing appropriate content with future end users and continue to incorporate interactive features.
References
Acharya S, Sahoo H (2021) Consanguineous Marriages in India: Prevalence and Determinants. J Health Manag 23(4):631–648. https://doi.org/10.1177/09720634211050458
Adam S, Birch PH, Coe RR, Bansback N, Jones AL, Connolly MB, Demos MK, Toyota EB, Farrer MJ, Friedman JM (2018) Assessing an Interactive Online Tool to Support Parents’ Genomic Testing Decisions. J Genet Couns. https://doi.org/10.1007/s10897-018-0281-1
Alkuraya F (2013) Impact of new genomic tools on the practice of clinical genetics in consanguineous populations: the Saudi experience. Clin Genet 84(3):203–208. https://doi.org/10.1111/cge.12131
Augusto B, Kasting ML, Couch FJ, Lindor NM, Vadaparampil ST (2019) Current Approaches to Cancer Genetic Counseling Services for Spanish-Speaking Patients. J Immigr Minor Health 21(2):434–437. https://doi.org/10.1007/s10903-018-0772-z
Bangash H, Makkawy A, Gundelach JH, Miller AA, Jacobson KA, Kullo IJ (2022) Web-Based Tool (FH Family Share) to Increase Uptake of Cascade Testing for Familial Hypercholesterolemia: Development and Evaluation. JMIR Hum Factors 9(1):e32568. https://doi.org/10.2196/32568
Beaudoin DE, Longo N, Logan RA, Jones JP, Mitchell JA (2011) Using information prescriptions to refer patients with metabolic conditions to the Genetics Home Reference website. J Med Libr Assoc 99(1):70–76. https://doi.org/10.3163/1536-5050.99.1.012
Bhopal R, Petherick E, Wright J, Small N (2014) Potential social, economic and general health benefits of consanguineous marriage: results from the Born in Bradford cohort study. Eur J Public Health 24(5):862–869. https://doi.org/10.1093/eurpub/ckt166
Biesecker BB, Lewis KL, Umstead KL, Johnston JJ, Turbitt E, Fishler KP, Patton JH, Miller IM, Heidlebaugh AR, Biesecker LG (2018) Web Platform vs In-Person Genetic Counselor for Return of Carrier Results From Exome Sequencing: A Randomized Clinical Trial. JAMA Intern Med 178(3):338–346. https://doi.org/10.1001/jamainternmed.2017.8049
Bittles A (2001) Consanguinity and its relevance to clinical genetics. Clin Genet 60(2):89–98. https://doi.org/10.1034/j.1399-0004.2001.600201.x
Bombard Y, Hayeems RZ (2020) How digital tools can advance quality and equity in genomic medicine. Nat Rev Genet 21(9):505–506. https://doi.org/10.1038/s41576-020-0260-x
Boudreault P, Wolfson A, Berman B, Venne VL, Sinsheimer JS, Palmer C (2017) Bilingual Cancer Genetic Education Modules for the Deaf Community: Development and Evaluation of the Online Video Material. J Genet Couns 27(2):457–469. https://doi.org/10.1007/s10897-017-0188-2
Bowen DJ, Robbins R, Bush N, Meischke H, Ludwig A, Wooldridge J (2011) Effects of a Web-based intervention on women’s breast health behaviors. Transl Behav Med 1(1):155–164. https://doi.org/10.1007/s13142-011-0028-0
Bradbury AR, Lee JW, Gaieski JB, Li S, Gareen IF, Flaherty KT, Herman BA, Domchek SM, DeMichele AM, Maxwell KN, Onitilo AA, Virani S, Park S, Faller BA, Grant SC, Ramaekers RC, Behrens RJ, Nambudiri GS, Carlos RC, Wagner LI (2022) A randomized study of genetic education versus usual care in tumor profiling for advanced cancer in the ECOG-ACRIN Cancer Research Group (EAQ152). Cancer 128(7):1381–1391. https://doi.org/10.1002/cncr.34063
Brown-Johnson CG, Safaeinili N, Baratta J, Palaniappan L, Mahoney M, Rosas LG, Winget M (2021) Implementation outcomes of Humanwide: integrated precision health in team-based family practice primary care. BMC Fam Pract 22(1):28. https://doi.org/10.1186/s12875-021-01373-4
Christian S, Tagoe J, Delday L, Bernier FP, Kohut R, Ferrier R (2022) IMPACT webinars: Improving Patient Access to genetic Counselling and Testing using webinars—the Alberta experience with hypertrophic cardiomyopathy. J Commun Genet 13(1):81–89. https://doi.org/10.1007/s12687-021-00564-x
Conijn T, Nijmeijer SCM, Lakeman P, Henneman L, Wijburg FA, Haverman L (2020) Preconception expanded carrier screening: Impact of information presented by text or video on genetic knowledge and attitudes. J Genet Couns 30(2):457–469. https://doi.org/10.1002/jgc4.1332
Cragun D, Weidner A, Tezak A, Zuniga B, Wiesner GL, Pal T (2020) A Web-Based Tool to Automate Portions of Pretest Genetic Counseling for Inherited Cancer. J Natl Compr Canc Netw 18(7):841–847. https://doi.org/10.6004/jnccn.2020.7546
de Vries J, Landouré G, Wonkam A (2020) Stigma in African genomics research: Gendered blame, polygamy, ancestry and disease causal beliefs impact on the risk of harm. Soc Sci Med 258:113091. https://doi.org/10.1016/j.socscimed.2020.113091
Delikurt T, Williamson GR, Anastasiadou V, Skirton H (2015) A systematic review of factors that act as barriers to patient referral to genetic services. Eur J Hum Genet 23(6):739–745. https://doi.org/10.1038/ejhg.2014.180
Dugas, M., Gao, G., & Agarwal, R. (2020). Unpacking mHealth interventions: A systematic review of behavior change techniques used in randomized controlled trials assessing mHealth effectiveness. Digit Health, 6(2055207620905411). https://doi.org/10.1177/2055207620905411
Gasteiger N, Vercell A, Davies A, Dowding D, Khan N, Davies A (2022) Patient-facing genetic and genomic mobile apps in the UK: a systematic review of content, functionality, and quality. J Community Genet 13(2):171–182. https://doi.org/10.1007/s12687-022-00579-y
Geer KP, Ropka ME, Cohn WF, Jones SM, Miesfeldt S (2001) Factors influencing patients’ decisions to decline cancer genetic counseling services. J Genet Couns 10(1):25–40. https://doi.org/10.1023/a:1009451213035
Gene People. (2020). https://genepeople.org.uk/about-us/
Genetic Alliance. (2021). Genetic Disorders UK. https://geneticalliance.org.uk/member/genetic-disorders-uk/
Gornick MC, Kurian AW, An LC, Fagerlin A, Jagsi R, Katz SJ, Hawley ST (2018) Knowledge regarding and patterns of genetic testing in patients newly diagnosed with breast cancer participating in the iCanDecide trial. Cancer 124(20):4000–4009. https://doi.org/10.1002/cncr.31731
Gorrie A, Gold J, Cameron C, Krause M, Kincaid H (2021) Benefits and limitations of telegenetics: A literature review. J Genet Couns 30:924–937
Hardy MW, Kener HJ, Grinzaid KA (2018) Implementation of a Carrier Screening Program in a High-Risk Undergraduate Student Population Using Digital Marketing, Online Education, and Telehealth. Public Health Genomics 21(1–2):67–76. https://doi.org/10.1159/000493971
Heidari F, Dastgiri S, Akbari R, Khamnian Z, Khanlarzadeh E, Baradaran M, Jabbary-Fam S, Badrazar S, Heidarzadeh M, Tajaddini N (2014) Prevalence and Risk Factors of Consanguineous Marriage. Eur J Gen Med 11(4):248–255. https://doi.org/10.15197/sabad.1.11.81
Hernan R, Cho MT, Wilson AL, Ahimaz P, Au C, Berger SM, Guzman E, Primiano M, Shaw JE, Ross M, Tabanfar L, Chilton I, Griffin E, Ratner C, Anyane-Yeboa K, Iglesias A, Pisani L, Roohi J, Duong J, Martinez J, Appelbaum P, Klitzman R, Ottman R, Chung WK, Wynn J (2020) Impact of patient education videos on genetic counseling outcomes after exome sequencing. Patient Educ Couns 103(1):127–135. https://doi.org/10.1016/j.pec.2019.08.018
Hong Q, FÀBregues S, Bartlett G, Boardman F, Cargo M, Dagenais P, Gagnon M-P, Griffiths F, Nicolau B, O’Cathain A, Rousseau M-C, Vedel I, Pluye P (2018) The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers. Educ Inf 34(4):285–291
Hudgens, L. G., Ramble, T. M., & Ahn, S. J. Digital Interactive Media and Health Behavior Change. In The International Encyclopedia of Media Psychology (pp. 1–8). https://doi.org/10.1002/9781119011071.iemp0279
Kinney AY, Gammon A, Coxworth J, Simonsen SE, Arce-Laretta M (2010) Exploring attitudes, beliefs, and communication preferences of Latino community members regarding BRCA1/2 mutation testing and preventive strategies. Genet Med 12(2):105–115. https://doi.org/10.1097/GIM.0b013e3181c9af2d
Korngiebel DM, West KM, Burke W (2018) Clinician-Stakeholders’ Perspectives on Using Patient Portals to Return Lynch Syndrome Screening Results. J Genet Couns 27(2):349–357. https://doi.org/10.1007/s10897-017-0179-3
McAllister M, Wood AM, Dunn G, Shiloh S, Todd C (2011) The Genetic Counseling Outcome Scale: a new patient-reported outcome measure for clinical genetics services. Clin Genet 79(5):413–424. https://doi.org/10.1111/j.1399-0004.2011.01636.x
Mogharbel A, Dowding D, Ainsworth J (2021) Physicians’ Use of the Computerized Physician Order Entry System for Medication Prescribing: Systematic Review. JMIR Med Inform 9(3):e22923. https://doi.org/10.2196/22923
National Literacy Trust. (2023). Adult literacy. https://literacytrust.org.uk/parents-and-families/adult-literacy/
Nazareth S, Hayward L, Simmons E, Snir M, Hatchell KE, Rojahn S, Slotnick RN, Nussbaum RL (2021) Hereditary Cancer Risk Using a Genetic Chatbot Before Routine Care Visits. Obstet Gynecol 138(6):860–870. https://doi.org/10.1097/aog.0000000000004596
O’Neill SC, White DB, Sanderson SC, Lipkus IM, Bepler G, Bastian LA, McBride CM (2008) The feasibility of online genetic testing for lung cancer susceptibility: uptake of a web-based protocol and decision outcomes. Genet Med 10(2):121–130. https://doi.org/10.1097/GIM.0b013e31815f8e06
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:n71. https://doi.org/10.1136/bmj.n71
Pluye P, Gagnon M, Griffiths F, Johnson-Lafleur J (2009) A scoring system for appraising mixed methods research, and concomitantly appraising qualitative, quantitative and mixed methods primary studies in Mixed Studies Reviews. Int J Nurs Stud 46(4):529–546
Posch A, Springer S, Langer M, Blaicher W, Streubel B, Schmid M (2012) Prenatal genetic counseling and consanguinity. Prenat Diagn 32(12):1133–1138. https://doi.org/10.1002/pd.3971
Prado MG, Iversen MD, Yu Z, Miller Kroouze R, Triedman NA, Kalia SS, Lu B, Green RC, Karlson EW, Sparks JA (2018) Effectiveness of a Web-Based Personalized Rheumatoid Arthritis Risk Tool With or Without a Health Educator for Knowledge of Rheumatoid Arthritis Risk Factors. Arthritis Care Res (hoboken) 70(10):1421–1430. https://doi.org/10.1002/acr.23510
Raspa M, Moultrie R, Toth D, Haque SN (2021) Barriers and Facilitators to Genetic Service Delivery Models: Scoping Review. Interact J Med Res 10(1):e23523. https://doi.org/10.2196/23523
Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody W, Hegde M, Lyon E, Spector E, Voelkerding K, Rehm H, Laboratory Quality Assurance ACMG, Committee. (2015) Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 17(5):405–424. https://doi.org/10.1038/gim.2015.30
Schmidlen T, Schwartz M, DiLoreto K, Kirchner HL, Sturm AC (2019) Patient assessment of chatbots for the scalable delivery of genetic counseling. J Genet Couns 28(6):1166–1177. https://doi.org/10.1002/jgc4.1169
Shawky R, Elsayed S, Zaki M, Nour El-Din S, Kamal F (2013) Consanguinity and its relevance to clinical genetics. Egypt J Med Hum Genet 14(2):157–164. https://doi.org/10.1016/j.ejmhg.2013.01.002
Shepherd M (2010) Monogenic diabetes: information seeking and genetic testing access via e-mail. Int Diabetes Nurs 7(2):50–53. https://doi.org/10.1002/edn.157
Solomon, I. B., McGraw, S., Shen, J., Albayrak, A., Alterovitz, G., Davies, M., Fitz, C. D. V., Freedman, R. A., Lopez, L. N., Sholl, L. M., Allen, E. V., Mortimer, J., Fakih, M., Pal, S., Reckamp, K. L., Yuan, Y., & Gray, S. W. (2020). Engaging Patients in Precision Oncology: Development and Usability of a Web-Based Patient-Facing Genomic Sequencing Report. JCO Precision Oncol (4), 307–318. https://doi.org/10.1200/po.19.00195
Suckiel SA, Odgis JA, Gallagher KM, Rodriguez JE, Watnick D, Bertier G, Sebastin M, Yelton N, Maria E, Lopez J, Ramos M, Kelly N, Teitelman N, Beren F, Kaszemacher T, Davis K, Laguerre I, Richardson LD, Diaz GA, Pearson NM, Ellis SB, Stolte C, Robinson M, Kovatch P, Horowitz CR, Gelb BD, Greally JM, Bauman LJ, Zinberg RE, Abul-Husn NS, Wasserstein MP, Kenny EE (2021) GUÍA: a digital platform to facilitate result disclosure in genetic counseling. Genet Med 23(5):942–949. https://doi.org/10.1038/s41436-020-01063-z
Taj F, Klein M, van Halteren A (2019) Digital Health Behavior Change Technology: Bibliometric and Scoping Review of Two Decades of Research. JMIR Mhealth Uhealth 7(12):e13311
Talwar D, Yeh Y, Chen W-J, Chen L-S (2019) Characteristics and quality of genetics and genomics mobile apps: a systematic review. Eur J Hum Genet 27:833–840. https://doi.org/10.1038/s41431-019-0360-2
Tutty E, Petelin L, McKinley J, Young M-A, Meiser B, Rasmussen VM, Forbes Shepherd R, James PA, Forrest LE (2019) Evaluation of telephone genetic counselling to facilitate germline BRCA1/2 testing in women with high-grade serous ovarian cancer. Eur J Hum Genet 27(8):1186–1196. https://doi.org/10.1038/s41431-019-0390-9
Verma IC, Puri RD (2015) Global burden of genetic disease and the role of genetic screening. Semin Fetal Neonatal Med 20(5):354–363. https://doi.org/10.1016/j.siny.2015.07.002
Vogel RI, Niendorf K, Petzel S, Lee H, Teoh D, Blaes AH, Argenta P, Rivard C, Winterhoff B, Lee HY, Geller MA (2019) A patient-centered mobile health application to motivate use of genetic counseling among women with ovarian cancer: A pilot randomized controlled trial. Gynecol Oncol 153(1):100–107. https://doi.org/10.1016/j.ygyno.2019.01.019
Walther JB, Pingree S, Hawkins RP, Buller DB (2005) Attributes of interactive online health information systems. J Med Internet Res 7(3):e33–e33. https://doi.org/10.2196/jmir.7.3.e33
Wang C, Paasche-Orlow MK, Bowen DJ, Cabral H, Winter MR, Norkunas Cunningham T, Trevino-Talbot M, Toledo DM, Cortes DE, Campion M, Bickmore T (2021) Utility of a virtual counselor (VICKY) to collect family health histories among vulnerable patient populations: A randomized controlled trial. Patient Educ Couns 104(5):979–988. https://doi.org/10.1016/j.pec.2021.02.034
Wierstra K, Sutton R, Bal J, Ismond K, Dieleman L, Halloran B, Kroeker K, Fedorak R, Berga KA, Huang V (2018) Innovative Online Educational Portal Improves Disease-Specific Reproductive Knowledge Among Patients With Inflammatory Bowel Disease. Inflamm Bowel Dis 24(12):2483–2493. https://doi.org/10.1093/ibd/izy161
Williams J, Erwin C, Juhl A, Mengeling M, Bombard Y, Hayden M, Quaid K, Shoulson I, Taylor S, Paulsen J (2010) In their own words: reports of stigma and genetic discrimination by people at risk for Huntington disease in the International RESPOND-HD study. Am J Med Genet 153B(6):1150–1159. https://doi.org/10.1002/ajmg.b.31080
Williams JL, Rahm AK, Zallen DT, Stuckey H, Fultz K, Fan AL, Bonhag M, Feldman L, Segal MM, Williams MS (2018) Impact of a Patient-Facing Enhanced Genomic Results Report to Improve Understanding, Engagement, and Communication. J Genet Couns 27(2):358–369. https://doi.org/10.1007/s10897-017-0176-6
Willis AM, Smith SK, Meiser B, Ballinger ML, Thomas DM, Young MA (2017) Sociodemographic, psychosocial and clinical factors associated with uptake of genetic counselling for hereditary cancer: a systematic review. Clin Genet 92(2):121–133. https://doi.org/10.1111/cge.12868
World Health Organization. (1998). Health promotion glossary.
Funding
NG is funded by the National Institute for Health and Care Research Applied Research Collaboration Greater Manchester (NIHR ARC GM). AV is partially funded by the National Institute for Health and Care Research Applied Research Collaboration Greater Manchester (NIHR ARC GM) and supported by the NIHR Manchester Biomedical Research Centre. The views expressed in this publication are those of the authors and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.
Author information
Authors and Affiliations
Contributions
AD, NK and NG designed the review, with support from ACD, AV and DD. NG and AV located the literature, extracted and analysed the data and wrote the first draft of the manuscript. All authors participated in the interpretation of the study findings and the preparation of the manuscript. All authors approved the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Gasteiger, N., Vercell, A., Khan, N. et al. Digital interventions for genomics and genetics education, empowerment, and service engagement: A systematic review. J Community Genet 14, 227–240 (2023). https://doi.org/10.1007/s12687-023-00648-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12687-023-00648-w