Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-17T07:31:43.130Z Has data issue: false hasContentIssue false

Digital and social media opportunities for dietary behaviour change

Published online by Cambridge University Press:  16 October 2014

Aileen F. McGloin*
Affiliation:
Safefood, Block B, Abbey Court, Lower Abbey St., Dublin 1, Ireland
Sara Eslami
Affiliation:
Safefood, Block B, Abbey Court, Lower Abbey St., Dublin 1, Ireland
*
*Corresponding author: A. F. McGloin, fax +35314480699, email amcgloin@safefood.eu
Rights & Permissions [Opens in a new window]

Abstract

The way that people communicate, consume media and seek and receive information is changing. Forty per cent of the world's population now has an internet connection, the average global social media penetration is 39 % and 1·5 billion people have internet access via mobile phone. This large-scale move in population use of digital, social and mobile media presents an unprecedented opportunity to connect with individuals on issues concerning health. The present paper aims to investigate these opportunities in relation to dietary behaviour change. Several aspects of the digital environment could support behaviour change efforts, including reach, engagement, research, segmentation, accessibility and potential to build credibility, trust, collaboration and advocacy. There are opportunities to influence behaviour online using similar techniques to traditional health promotion programmes; to positively affect health-related knowledge, skills and self-efficacy. The abundance of data on citizens’ digital behaviours, whether through search behaviour, global positioning system tracking, or via demographics and interests captured through social media profiles, offer exciting opportunities for effectively targeting relevant health messages. The digital environment presents great possibilities but also great challenges. Digital communication is uncontrolled, multi-way and co-created and concerns remain in relation to inequalities, privacy, misinformation and lack of evaluation. Although web-based, social-media-based and mobile-based studies tend to show positive results for dietary behaviour change, methodologies have yet to be developed that go beyond basic evaluation criteria and move towards true measures of behaviour change. Novel approaches are necessary both in the digital promotion of behaviour change and in its measurement.

Type
Conference on ‘Changing dietary behaviour: physiology through to practice’
Copyright
Copyright © The Authors 2014 

The way that people communicate with each other, consume media and seek and receive information has changed dramatically. Newspaper readership is falling( 1 ), radio listenership fragmenting( Reference Shaw, Picard and Spanik 2 ) and people time shift their television viewing, skipping advertising( 3 ). Forty per cent of the world's population now has an internet connection compared with <1 % in 1995. This number is set to reach 43 % (3 billion) by the end of 2014( 4 ). Average global social media penetration is 39 %, ranging from 82 % in Canada to 12 % in India( Reference Kemp 5 ). One and a half-billion people have relatively fast access to the internet from their mobile phone( Reference Kemp 5 ). This large-scale move in population use of digital, social and mobile media presents an unprecedented opportunity to connect with individuals on issues concerning health and behaviour change and to weave health information into the daily lives of citizens. The present paper aims to investigate this in relation to dietary behaviour change, drawing on examples from both nutrition and food safety. It includes an exploration of the scientific and grey literature and relevant websites, combined with the authors’ communications industry knowledge, to sign-post future trends. Statistics on digital media use are focused on the island of Ireland.

Digital media is difficult to define, partly because it is ever changing, but in its broadest sense it is content that can be transmitted over the internet or computer/phone networks. Communication performed using digital technology can be carried out using multiple methods. Social media, known as the ‘participative internet’( Reference Jones and Fox 6 ), is a major aspect of digital communication and encompasses a broad set of internet-based communications, tools and technologies that expand interactivity and collaborative content sharing.

There is evidence that health information seekers are using the internet widely. In 2012, 72 % of internet users said that they looked online for health information( Reference Fox and Duggan 7 ), and 29 % said that they looked for information on food safety or recalls( Reference Fox 8 ). In addition, 60 % of smartphone applications downloaded in 2012 were weight and exercise related( 9 ). From an island of Ireland context, in Northern Ireland 26 % of people have sought health information online( 10 ). In this context, aspects of digital communication such as search engine optimisation and search marketing (the purchase of key search terms by advertisers) are important.

In terms of proactive communication, audiences can be reached using digital advertising, blogs, email marketing, online public relations activity and a plethora of social media platform, including Facebook, Twitter, Google+, YouTube, Instagram and Pinterest among others. Within these methods a variety of media such as text-based, image-based, video and animation can be used. Although there are production and human resource costs associated with the development of all of these types of digital content, it is cheaper than traditional marketing methods. The content is ‘evergreen’, meaning that it can be reused easily, which adds to its cost-effectiveness.

Characteristics of digital communications and dietary behaviour change

Several aspects of the digital environment offer opportunity to support behaviour change efforts. For the purpose of the present paper, these are divided into reach, engagement, accessibility: potential to build credibility and trust, collaboration and advocacy and research.

Reach

Reach, the potential of exposure to health communication, is an important aspect of the promotion of behaviour change campaigns. On the island of Ireland 65 % of households in the Republic of Ireland (ROI) have a broadband connection( 11 ) and 80 % of households in Northern Ireland have access to the internet( 10 ). In Northern Ireland, nearly half of consumers own a smartphone( 10 ), whereas in the ROI, in 2012, smartphone penetration was 57 %( 12 ). Ninety-six per cent of young people in the ROI have a smartphone with 42 % spending 2–4 h/d on their phone. Ninety per cent check their phone as soon as they wake up and 72 % as the last thing before going to sleep( Reference O'Leary 13 ). The consumption of information via digital channels is pervasive and for many, omnipresent. In relation to social media usage, a survey by Ipsos MRBI showed that in 2014, in the ROI 62 % of adults aged over 15 years use Facebook, 30 % Twitter, 26 % LinkedIn, 20 % Google+ with smaller but growing numbers using Pinterest and Instagram( Reference Ipsos 14 ). According to Ipsos MRBI, 76 % of the population belongs to a social community of some kind (AF McGloin, personal communication). In Northern Ireland, more than half (53 %) of the consumers reported using social networking sites, on par with the UK average( 10 ).

In terms of reach, the use of digital communications for behaviour change is often questioned from an equality perspective. Central Statistics Office figures indicate that older people and those in lower socioeconomic groups are somewhat underrepresented( 11 ) (S. Elsami, personal communication). Forty-six per cent of 60–74 year olds had used the internet in the past 3 months. In addition, 76 % of unemployed individuals had used the internet in the previous 3 months (v. 88 % of employed individuals). The Central Statistics Office data also show that there is a social gradient of social media usage. Eighty-eight per cent of those in the highest socioeconomic group belong to a social media community compared with 49 % in the lowest socioeconomic group, indicating a steep social gradient in social media use. This trend is also seen in the USA( Reference Gibbons, Fleisher and Slamon 15 ). However, with almost half of those in the lowest socioeconomic groups participating in social media, this still represents an important communication opportunity within this target group in the ROI.

Engagement

Online content that can be timely, interesting, up-to-date, interactive and even personalised could help ensure that people will stay engaged with messages promoting dietary change. Evidence, from early health promotion research, shows that engagement is important for participant retention( Reference Glasgow, Lichtenstein and Marcus 16 ). Engaging content has also been shown to enhance memory and knowledge retention( Reference Erk, Kiefer and Grothe 17 Reference Svirko and Mellanby 19 ). Narration, or the art of storytelling, is an ancient form of communication of knowledge( Reference Chapman, Raymond and Powell 20 ) and has been explored already as a tool for communicating food safety information( Reference Chapman, MacLaurin and Powell 21 ). Consumers are communicating online through images, infographics, video, animation and text and public health agencies must do likewise for messages to be heeded within this medium.

The interactive and transactional nature of the environment is also important( Reference Noar and Grant Harrington 22 ). Some authors have argued that interactivity is necessary to promote personal relevance and behaviour change( Reference Noar and Grant Harrington 22 , Reference Cassell, Jackson and Cheuvront 23 ). Increasingly, mobile applications that promote physical activity or healthy eating are using gamification techniques to engage users. This is essentially the discipline of using video game mechanics to incentivise an audience and encourage them to follow specific behaviours( Reference Zichermann and Linder 24 ). Gamification uses concepts such as desire for reward, status, achievement, self-expression or respect and is usually driven by competitiveness or sometimes altruism. Increasingly social sharing has become a feature of games, applications and behaviour change programmes. Behaviour changes studies have shown the importance of social connection as a promoter of behaviour change( Reference Luke and Harris 25 ). Features such as these have the potential to promote empowerment in decision-making and can play a major role in supporting individuals as they seek positive behaviour change. Digital media has been shown to provide significant positive effects on empowerment (self-efficacy and mastery scales)( Reference Samoocha, Bruinvels and Elbers 26 ).

Being accessible: the potential for building credibility and trust

Social media in particular offers organisations charged with changing dietary behaviour the opportunity to get closer to their key audiences. This can even be done in ‘real-time’ with the advent of ‘Tweet-ups’, ‘Tweet chats’ and Google+ ‘hangouts’. Aspects such as speed of response, particularly in emergency situations, allow promoters of behaviour change to simply provide good customer service. Since online communication is multi-way, there is the additional potential that citizens will pass on the information to friends and family. The endorsement of these trusted individuals, adds to the credibility of the organisation and of the message. These individuals are the ‘non-traditional’ digital partners.

Slovic( Reference Slovic 27 ) identified four factors that have been observed in establishing trust. These include caring and empathy, dedication and commitment, competence and expertise, and honesty and openness. Social media provides a path for organisations to demonstrate these characteristics through the provision of consistent, timely, relevant and useful information. Since interactions online are often openly available, transparency is a common feature.

Collaboration/stakeholder engagement and advocacy

Features such as being able to share information, follow organisations and monitor online conversation not only allow organisations to communicate with the public, but also with stakeholders. Through digital platforms public health agencies can identify and engage with like-minded organisations to help expand consumer reach, amplify dissemination of key messages, and act as important advocates for each other and for behaviour change. The ease with which such collaboration can take place is novel and the formation of symbiotic relationships is potentially powerful( Reference Chapman, Raymond and Powell 20 ). To our knowledge this aspect of digital communication has yet to be measured.

Research potential

The online environment offers important opportunities to monitor public opinion on a variety of issues. There is tremendous potential to better understand target audiences by simply ‘listening’ to online conversations. Published examples include attitudes to obesity among chat board users and studies of major food safety outbreaks( Reference De Brun, McCarthy and McKenzie 28 Reference Rutsaert, Pieniak and Regan 30 ). Dr Brun et al. showed stigmatisation in relation to obesity was pervasive and provided valuable insight on the social and psychological consequences of stigma( Reference De Brun, McCarthy and McKenzie 28 ). The Esherichia coli o102:h4 outbreak in Germany that was responsible for over 4000 illnesses and fifty deaths generated 450 000 tweets posted by 54 381 users( Reference Diaz and Steward 31 ). The Food Standards Agency in the UK now uses Twitter to try to predict outbreaks of norovirus by monitoring increases in volume of key words on social media channels( Reference Rutter 32 ). Public health agencies can use this method to monitor public reaction to health promotion campaigns and can adjust campaign material in real-time to improve effectiveness.

Audience segmentation is a central tenet of campaigns to change dietary behaviour. Subgroups are targeted according to demographic, geographic, physical/personal history, psychographic or behavioural characteristics. The greater the understanding a communicator has of the target market, the more likely the message will be relevant, resonate with the target audience and promote behaviour change. Owing to the level of data available within the digital environment, and the lower cost in acquiring that data, segmentation of messaging provides the opportunity for tailoring messaging and increasing effectiveness. A meta-analysis of eighty-eight interventions that used computer tailoring to address smoking, diet, mammogram and physical activity found it to be consistently successful( Reference Krebs, Prochaska and Rossi 33 ).

Data to support segmentation can be collected in a variety of ways. Individuals who register for behaviour change programmes or mobile applications may opt to provide a wealth of personalised information. Websites offering display advertising provide important sociodemographic user data to advertisers. Social channels are becoming more and more sophisticated in how audiences can be targeted. Facebook users can be targeted by location, gender, interests and device-type. Twitter provides similar targeting opportunities, but in addition account holders can be targeted by key words. For example, if a user is conversing around a dietary issue such as weight loss, an advertiser can serve that individual relevant health messaging or links to resources on weight loss. Different messaging and resources can be provided for different audiences, even on the same topic. Some harder to reach audiences, such as young men, could be targeted more easily in this way.

Digital media and behaviour change techniques

To examine which behaviour change techniques could be used to promote behaviour change in the digital environment the taxonomy of behaviour change techniques developed by Michie et al. was used( Reference Michie, Ashford and Sniehotta 34 ). Three UK study centres collaborated in applying an existing taxonomy to two systematic reviews of interventions to increase physical activity and healthy eating. The twenty-six behaviour change techniques were revised upward to include forty behaviour change methods. Table 1 shows how each of these behaviour change techniques could potentially be executed in a digital environment using methods, including digital advertising, provision of web content, social media, video conferencing, the use of mobile applications and gaming. All are considered feasible given the extensive reach, interactive nature and versatility of the digital environment. Many are already being used in digital-based interventions( Reference Webb, Joseph and Yardley 35 Reference Cugelman, Thelwall and Dawes 37 ).

Table 1. Taxonomy of behaviour change techniques adapted from Michie et al. (2011) and potential for digital application( Reference Michie, Ashford and Sniehotta 34 )

WC, website content; SM, social media; DA, digital advertising; MA, mobile application; G, gaming, VC, Video conference, e.g. Skype, Google+ hangout.

From a theoretical perspective, a number of behaviour change theories could inform efforts to change dietary behaviour online( Reference Cassell, Jackson and Cheuvront 23 , Reference Webb, Joseph and Yardley 35 ). Social cognitive theory has perhaps the most potential in this domain given that it centres on the idea that behaviour is influenced by interactions with others and with the environment( Reference Bandura and Cliffs 38 ). When individuals lack the confidence to make changes to their behaviour (low self-efficacy) they are less likely to engage in those behaviours. Addressing this step-by-step through role modelling, demonstration of behaviours, timely prompts and a variety of other techniques noted by Michie et al. ( Reference Michie, Ashford and Sniehotta 34 ), could promote increased knowledge and skills, self-efficacy and ultimately, behaviour change.

Digital media: application to food and health interventions

As with any other intervention type, digital media strategies to promote public health should be based on evidence that the approach will lead to behaviour change. Encouragingly, a limited but a growing body of research is beginning to make this possible, with positive results linked to theoretical frameworks( Reference Webb, Joseph and Yardley 35 ). Web-based, Mobile and social media interventions are explored below.

Web-based interventions

Web-based interventions can be defined as primarily self-guided intervention programmes, delivered through a website, aiming to create positive change and/or improve/enhance knowledge, awareness and understanding( Reference Barak, Klein and Proudfoot 39 ). These interventions can typically include real-time support, goal-setting tools, alarms, reminders, BMI calculators, food and exercise tracking and a platform for sharing ideas with friends/peers. Web-based interventions offer potential solutions to challenges posed by face-to-face interventions because of their low cost, high reach, anonymity, adaptability and scale-ability( Reference Bennett, Herring and Puleo 40 ).

A number of reviews and meta-analyses have examined the success and effectiveness of web-based interventions in relation to health behaviour change and have revealed mixed findings. In the present paper, only food behaviour/weight loss-related interventions are explored. A Cochrane meta-analysis of weight loss/maintenance trials by Weiland et al. ( Reference Wieland, Falzon and Sciamanna 41 ) revealed that web-based interventions resulted in greater weight loss when compared with control conditions but significantly less weight loss compared with face-to-face interventions. This is similar to findings from four other meta-analyses( Reference Wieland, Falzon and Sciamanna 41 Reference Grunenberg, Lin and Baumeister 44 ). Reed et al. ( Reference Reed, Schifferdecker and Rezaee 42 ) conducted a meta-analysis of eleven randomised controlled trials and reported additional weight loss when web-based interventions were used to supplement face-to-face interventions; however, substituting face-to-face interventions with web-based interventions resulted in significantly less weight loss. Other reviews of web-based interventions have demonstrated greater weight loss/maintenance with increased number of log-ins, self-monitoring occasions, chatroom attendances and web-posts( Reference Neve, Morgan and Jones 45 , Reference Manzoni, Pagnini and Corti 46 ). However, it is important to note that some reviews have concluded that a meta-analysis could not reliably detect the effectiveness of web-based interventions due to the heterogeneity of designs and the small number of comparable studies( Reference Neve, Morgan and Jones 45 Reference Arem and Irwin 47 ).

Surprisingly, only a few studies have looked at the cost effectiveness of web-based interventions. Krukowski et al.( Reference Krukowski, Tilford and Harvey-Berino 48 ) conducted a 6-month cost-effectiveness evaluation comparing in-person and web-only weight loss interventions and reported that while in-person interventions resulted in more weight loss (−8·0 (sd  6·1) kg v. −5·5 (sd 5·6) kg), incremental cost-effectiveness ratio was significantly better for web-only interventions.

Mobile health

Mobile health (mHealth) involves disseminating public health and medical information through mobile computing and communication technologies such as mobile phones, personal digital assistants, tablets and portable media players( Reference Free, Phillips and Watson 49 ). mHealth provides a convenient and personal approach to health education and communication as well as the ability to share and monitor health conditions through text messages and mobile applications( Reference Patrick, Griswold and Raab 50 ). In addition to text-messages, mHealth research in data-tracking devices is growing. Weight management mHealth applications have become increasingly popular over the past few years. According to Kasbo et al. in 2012, 60 % of smartphone applications downloaded were weight and exercise related( 9 ). While none of the reviews identified examined mHealth applications on their own, two systematic reviews investigated the use of mobile phone applications among other mobile technology features( Reference Stephens and Allen 51 , Reference Woo, Chen and Ghanavati 52 ). Both reviews cautiously concluded that overall, mobile phone interventions had small but positive effects on weight loss behaviour change. It is important to note that the interventions included in these reviews were very heterogeneous in design, outcomes and measures (patient satisfaction v. weight loss) and results. Shaw et al. ( Reference Shaw and Bosworth 36 ) reviewed fourteen SMS-only interventions for obesity and found that eleven studies demonstrated statistically significant beneficial effects on weight, diet and exercise, whereas three studies did not show an effect on the above variables. One study that aimed to assess the effect of varying delivery mechanism of a weight-loss intervention conducted a three- armed parallel group trial and compared smartphone application, website and paper diary interventions in overweight and obese participants( Reference Carter, Burley and Cade 53 ). The authors found significantly higher weight loss, patient adherence, satisfaction and acceptability in the smartphone application group than web or paper-based interventions. In addition, drop-out rates were lowest among the smartphone application users.

When interpreting the findings of mHealth reviews, it is important to note some unique limitations of these studies, mainly the numerous applications available, the heterogeneity of their components and the frequent updates in application design and mobile phone devices that make it more difficult to conduct clinical trials of their effectiveness( 9 , Reference Patrick, Griswold and Raab 50 ). Another significant challenge for the implementation of these interventions is that while consumers are already using mobile applications to access health information, many clinicians and providers are not yet familiar with the countless possibilities of mHealth and how to incorporate it into practice.

Social media

Evidence about social media's impact on health knowledge, behaviour and outcomes show these tools to be effective in meeting individual and population health needs( Reference Chapman, Raymond and Powell 20 , Reference Webb, Joseph and Yardley 35 , Reference Korda and Itani 54 ). Most research addresses specific interventions and approaches, which vary widely in focus, target population, theoretical foundations, and mode of delivery, functionality and usability which makes it difficult to find out what works and how, and it complicates efforts to compare approaches.

A meta-analysis of interactive digital health communication applications by Murray et al. ( Reference Murray, Burns and See 55 ) showed that these applications improved users’ knowledge, social supports, health behaviours and clinical outcomes. More recently, Napolitano et al. ( Reference Napolitano, Hayes and Bennett 56 ) demonstrated the potential of an innovative weight loss intervention where participants were randomly assigned to either a Facebook, Facebook plus personalised text messaging and feedback, or a control group. Results showed that 8 weeks post-treatment, the Facebook plus group had significantly greater weight loss (−2·4 (sd 2·5) kg) than Facebook (−0·63 (sd 2·4) kg) and control (−0·24 (sd 2·6) kg). Woolley and Peterson( Reference Woolley and Peterson 57 ) reported that increases in physical activity of the participants were linked to their participation on the study Facebook page designed to motivate healthy attitudes and behaviours. Similarly, Rote( Reference Rote 58 ) conducted an 8-week randomised pre-post walking intervention of information resources coupled with a dedicated Facebook group and found that the Facebook group participants significantly increased their steps per d over those who received the information resources only. In another study, Mychasiuk and Benzies( Reference Mychasiuk and Benzies 59 ) demonstrated that Facebook can provide effective means of improving participant retention in a longitudinal intervention and reduce attrition rates significantly.

Social media can provide a channel for sharing personalised information, for social support and can facilitate a sense of connectedness among individuals. Tailored messaging, repurposing and applying multiple complementary delivery modes to reinforce key themes, can encourage users to engage with web-based applications as well as with other users and are among the most promising strategies( Reference Webb, Joseph and Yardley 35 ). Use of communicative functions, especially access to an advisor to request advice also tends to be effective in supporting behaviour change( Reference Fry and Neff 60 ). However, one of the challenges of social media behaviour change interventions is measuring meaningful engagement, i.e. actually engaging with content as intended. The lack of evidence guiding public health enterprises is also a major challenge, but in part results from the difficulty that exists in evaluating such complex campaigns that often use different and multiple aspects of social media. Many researchers adapt established, proven approaches used in traditional health promotion efforts( Reference Glasgow 61 , Reference O'Grady, Witteman and Bender 62 ). Selecting and applying appropriate metrics for evaluation present a challenge as most forms of social media are not designed with evaluation and assessment in mind, challenging evaluators to use basic analytics generated by the sites themselves.

Next steps for digital interventions

Web-based, mHealth and social media approaches have shown promising results when used in behaviour change interventions but currently the majority of the literature mainly supports their use in conjunction with traditional intervention methods. This hesitation may partly be due to the lack of consensus on features common to successful strategies, stemming from the wide variety of approaches, methods and outcomes addressed in various interventions. More emphasis is needed on the use of validated theoretical frameworks in the design and evaluation of digital interventions and development of evaluation metrics to guide health promotion initiatives. In addition, efforts should be made to facilitate health promotion and healthcare providers’ knowledge and trust in order to achieve successful implementation of these innovative behavioural interventions.

Future trends in digital communications

A paper published in early 2014 identified ten key future trends in digital marketing( Reference Burke-Garcia and Scally 63 ). These included buzz monitoring (social media monitoring) and social research, bringing the health care provider and health consumer closer, expanded traditional information dissemination models, non-traditional digital partners, social customer relations management (targeting), electronic health (e-health) and the release of personal health data, convergence of mobile and social for health, a changing role of the digital communicator, the advent of pure digital media campaigns and ‘big data’ i.e. a hyper abundance of data. In the time between the publication of Burke-Garcia and Scally's paper and the present paper those future trends have largely become current and most have already been discussed earlier.

Big data deserves further discussion and offers huge potential for understanding and better targeting key audiences. Currently, much discussion of big data is rooted in the potential for cost efficiencies within health care systems( Reference Groves, Kayyali and Knott 64 ). The focus is on health data, captured in electronic health records and other datasets, plus deep genotypic and phenotypic data, for example from genome sequencing. This approach has the potential to deliver great health benefits as well as cost savings. Digital giants such as Google are already investing in health-related big data for example in their X Baseline study that includes genetic and molecular information( Reference Barr 65 ). From a health perspective, behavioural and attitudinal data are rarely mentioned. Burke Garcia and Scally called for monitoring data to be overlaid with other research to bring a deeper understanding to the development of communications efforts( Reference Burke-Garcia and Scally 63 ). There is a certainly a need for more rigorous and consistent measurement when it comes to digital media. Those promoting behaviour change need to become better at interrogating this data and understanding how to use the results. Better understanding of the needs of consumers could help inform the development and delivery of services and campaigns.

To Burke-Garcia and Scally's ten future trends there are others worth adding. Gamification has already been mentioned. These techniques are widely used by organisations as diverse as the US army, airlines, retailers and fast food companies and promote loyalty and retention( Reference Zichermann and Linder 24 ). Many mobile applications are already using features such as leader boards, rewards for achievements and incentives for further achievement, along with a facility to share achievements socially. Recent research from Vodafone shows that 62 % of children aged between 4 and 11 years are now using internet devices such as smartphones and tablets( 66 ). According to the Health Behaviour of School Children Study 27·2 % of boys and 8·4 % of girls spend over 2 h/d using a computer or games console( Reference Currie, Zanotti and Morgan 67 ). Future generations will be extremely familiar with this approach and potentially motivated by it.

Equally, commercial companies are using a number of novel advertising methods that could easily be applied to dietary behaviour change. These include behavioural marketing, geo-marketing and adaptive marketing. Behavioural marketing is the potential to target an individual with key messages based on past online behaviour. For example, if a person were to conduct an online search for weight loss information, they could then be served a digital advertisement for a weight loss service. Geo-marketing is the potential to target marketing messages to an individual, based on their current location. This technology could enable real-time influence on decision-making around food via mobile technology and geographical information systems. Adaptive marketing is the ability to change; in real-time, elements of a behaviour change campaign based on consumer response via internet search or online comment, and could include geographical elements to ensure the campaign was delivered where there was most interest, or most need. At the current pace of change by the time the present paper is published it's likely that these future trends will be commonplace.

Challenges and barriers

Although the potentialities offered by digital health communication are vast, and in many cases still to be discovered, various challenges remain. A major challenge identified by many in relation to digital health communication relates to inequalities in access and usability( Reference Fesenmainer 68 , Reference Liang 69 ). The digital divide is a priority in relation to age, socioeconomic status and geographic area( Reference Liff, Shepard and Wajcman 70 Reference Livingstone and Helsper 73 ). In an island of Ireland context, this is certainly an issue but the level of access and participation is still relatively high in all socioeconomic groups and therefore digital methods cannot be excluded from efforts to reach these population groups. Equally, global trends showing increase in internet access, social media usage and smart phone ownership indicate that this situation is changing.

Protecting citizen's privacy is another challenge-facing digital health communicators. The use of technology, which allows individuals to take pictures, record audio and video, store clinical and laboratory data, radiological images and diagnostic reports and access to electronic health records, can become dangerous for the maintenance of confidentiality of data if devices are lost or compromised( Reference Bert, Giacometti and Gualano 74 ). The public is positively disposed to the use of their health data to benefit patients but governance and security issues must continue to be taken seriously( Reference Whitehead 75 ). In addition, ethical issues remain around the use of publicly available personal information in research( Reference Zimmer 76 ).

With digital discussions, misinformation and false assertions may be easily disseminated via social media and be widely believed( Reference Chung 77 ). In addition, such discussions may be more susceptive to social amplification of risk, in which risks assessed by technical experts as relatively minor, elicit strong public concerns that result in substantial impacts on society and economy( Reference Kasperson, Renn and Slovic 78 ). One study examined accuracy of search results for five common paediatric questions and showed that it was highly variable. The authors found that 39 % of the 500 sites searched gave correct information. Governmental sites gave uniformly accurate advice. News sites gave correct advice in 55 % of cases. No sponsored sites were encountered that gave the correct advice( Reference Scullard, Peacock and Davies 79 ). In terms of the public's ability to assess quality of information Sillence et al.( Reference Sillence, Briggs and Harris 80 ) showed that women seeking information on hormone replacement therapy could reject general websites as source of information but sometimes also rejected good quality information due to poor design. Therefore, the source and presentation of information is important as well as accuracy.

The ability to spread information rapidly online is a risk as well as a benefit. Reports to date suggest that many public health organisations, when present on social media, tend to use one-way messaging in the broadcast manner that is suitable for traditional media, but ignore the engagement element that is central to social media( Reference Neiger, Thackeray and Van Wagenen 81 ). In contrast, Post et al.( Reference Post, Taylor and Sanders 82 ) demonstrate the dramatic impact of interactive communication on social media in relation to health behaviour outcomes. While a democratic environment can empower individuals to engage and change behaviour, the user-generated content and interactivity that are hallmarks of the digital environment mean that public health bodies must enter into an uncontrolled situation to truly participate in digital communications. Reputations can be quickly made and destroyed, so reluctance is understandable. This can be mitigated through the development and implementation of comprehensive policies and processes and by employing adaptive communicators, with both dietary and technical knowledge, to engage the public.

Perhaps most fundamental for effective development of digital communication for behaviour change is the lack of comprehensive evaluation to date( Reference Burke-Garcia and Scally 63 ). According to Bert et al.( Reference Bert, Giacometti and Gualano 74 ), the development of applications for disease prevention and health promotion on digital platforms to date have been totally disconnected from the logic of monitoring and control of content both in terms of scientific validity and user understanding. Most intervention studies are classic, controlled experiments. Measurements tend to include website visits, ‘clicks’, and social engagements such as ‘likes’, ‘follows’, ‘comments’ and ‘shares’. Few have truly measured behaviour change and this remains a central dilemma. The development of effective, measurable digital communication for behaviour change currently represents the great uncontrolled experiment and approaches to its measurement present an enormous challenge.

Conclusions

As early as 1998, Cassell et al.( Reference Cassell, Jackson and Cheuvront 23 ) dubbed the internet ‘the hybrid communications channel with the persuasive properties of interpersonal communication and the reach of mass media communication’. While this remains the case, the massive and ongoing growth in the use of digital and mobile technology by citizens has heralded a new era in communication for behaviour change. Developments in social and mobile media, in particular, have meant that the online environment offers key features such as reach, accessibility and potential for engagement, research and collaboration that make it rich ground for promoting behaviour change. The digital environment presents great possibilities but great challenges in this domain.

Digital communication is by its nature uncontrolled, multi-way and co-created and concerns remain in relation to inequalities, privacy, misinformation and lack of thorough evaluation. Thus while studies tend to show positive results for behaviour change, their applicability to the real-life situation is unknown. Methodologies have yet to be developed that go beyond basic evaluation criteria and move towards true measures of behaviour change. The ‘new media’ is no longer new and novel approaches need to be developed both in the promotion of behaviour change and in its measurement. This will involve new thinking, new skills and particularly new collaborations involving experts in nutrition, behaviour change, and communication and information technologies.

Financial Support

None.

Conflicts of Interest

None.

Authorship

A. M. directed, researched and drafted the manuscript. S. E. researched and drafted sections of the manuscript.

Supplementary material

To view supplementary material for this article, please visit http://dx.doi.org/10.1017/S0029665114001505.

References

1. The Irish Times (2014) Newspaper sales down across the daily and Sunday markets. The Irish Times, February 21st, 2014. Available at http://www.irishtimes.com/business/sectors/media-and-marketing/newspaper-sales-down-across-the-daily-and-sunday-markets-1.1699140 (accessed 2 August 2014)Google Scholar
2. Shaw, H, Picard, R & Spanik, HA. (2010) Irish Boradcasting Landscape: Economic and environmental review for the Broadcasting Authority of Ireland. http://www.bai.ie/wordpress/wp-content/uploads/20110323_StratEconAnlysRpt_vFINAL_AC.pdf Google Scholar
3. Nielsen Ireland (2013) Update on Time-shiften Viewing. http://www.agbnielsen.com/Uploads/Ireland/IrelandTSVUpdateApril2013.pdf Google Scholar
4. Internet Live Stats. Available at http://www.internetlivestats.com/internet-users (accessed 7 August 2014)Google Scholar
5. Kemp, S (2014) Social, Digital and Mobile Worldwide in 2014. Available at http://wearesocial.net/blog/2014/01/social-digital-mobile-worldwide-2014 (accessed 10 August 2014)Google Scholar
6. Jones, S & Fox, S (2009) Generations online in 2009. Pew Internet and American Life Project. http://www.pewinternet.org/files/old-media//Files/Reports/2009/PIP_Generations_2009.pdf (accessed 10 August 2014)Google Scholar
7. Fox, S & Duggan, M. (2013) Health Online. Available at http://www.pewinternet.org/2013/01/15/health-online-2013 (accessed 3 August 2014)Google Scholar
8. Fox, S. (2011) Health Topics. Available at http://www.pewinternet.org/2011/02/01/health-topics-2 (accessed 5 August 2014)Google Scholar
9. Verasoni. Mobile Health Applications: 2012 study. Available at http://verasoni.com/mobile-health-applications-2012-study (accessed 8 August 2014)Google Scholar
10. Ofcom (2013) The Communications Market Report: Northern Ireland. Available at http://stakeholders.ofcom.org.uk/market-data-research/market-data/communications-market-reports/cmr13/northern-ireland (accessed 30 May 14)Google Scholar
11. Central Statistics Office (2013) Information Society Statistics – Households. Available at http://www.cso.ie/en/releasesandpublications/er/isshh/informationsocietystatistics-households2013 (accessed 30 May 2014)Google Scholar
12. Google (2012) Our Mobile Plant. Understanding the Mobile Consumer Available at http://think.withgoogle.com/mobileplanet/en (accessed 2 July 2014)Google Scholar
13. O'Leary, S. (2013) The Irish Digital Consumer Report. Available at http://shaneoleary.me/blog/index.php/the-irish-digital-report-consumer-2013 (accessed 7 June 2014)Google Scholar
14. Ipsos, MORI (2014) Social Networking Quarterly. Available at http://www.ipsosmrbi.com/social-networking-may-2014.html (accessed 15 July 2014)Google Scholar
15. Gibbons, MC, Fleisher, L, Slamon, RE et al. (2011) Exploring the potential of Web 2·0 to address health disparities. J Health Commun 16, Suppl. 1, 7789.Google Scholar
16. Glasgow, RE, Lichtenstein, E & Marcus, AC (2003) Why don't we see more translation of health promotion research to practice? Rethinking the efficacy-to-effectiveness transition. Am J Public Health 93, 12611267.Google Scholar
17. Erk, S, Kiefer, M, Grothe, Jo et al. (2003) Emotional context modulates subsequent memory effect. NeuroImage 18, 439447.Google Scholar
18. Gray, JR, Braver, TS & Raichle, ME. (2002) Integration of emotion and cognition in the lateral prefrontal cortex. Proc Natl Acad Sci USA 99, 41154120.Google Scholar
19. Svirko, E & Mellanby, J (2008) Attitudes to e-learning, learning style and achievement in learning neuroanatomy by medical students. Med Teach 30, e219e27.CrossRefGoogle ScholarPubMed
20. Chapman, B, Raymond, B & Powell, D (2014) Potential of social media as a tool to combat foodborne illness. Perspect Public Health 134, 225230.Google Scholar
21. Chapman, B, MacLaurin, T & Powell, D (2011) Food safety infosheets: design and refinement of a narrative-based training intervention. Br Food J 113.Google Scholar
22. Noar, SM & Grant Harrington, N (2012) eHealth Applications: Promising Strategies for Behaviour Change. London: Routeledge.Google Scholar
23. Cassell, MM, Jackson, C & Cheuvront, B (1998) Health communication on the Internet: an effective channel for health behavior change? J Health Commun 3, 7179.Google Scholar
24. Zichermann, G & Linder, J (2010) Game-based Marketing: Inspire Customer Loyalty through Rewards, Challenges and Contests, 1st ed., USA: Wiley.Google Scholar
25. Luke, DA & Harris, JK (2010) Network analysis in public health: history, methods, and applications. Annu Rev Public Health 28, 6993.Google Scholar
26. Samoocha, D, Bruinvels, DJ, Elbers, NA et al. (2010) Effectiveness of web-based interventions on patient empowerment: a systematic review and meta-analysis. J Med Internet Res 12, e23.Google Scholar
27. Slovic, P (1999) Trust, emotion, sex, politics, and science: surveying the risk-assessment battlefield. Risk Anal 19, 689701.Google Scholar
28. De Brun, A, McCarthy, M, McKenzie, K et al. (2014) Weight stigma and narrative resistance evident in online discussions of obesity. Appetite 72, 7381.Google Scholar
29. Kuttschreuter, M, Rutsaert, P, Hilverda, F et al. (2014) Seeking information about food-related risks: the contribution of social media. Food Qual Pref 37, 1018.CrossRefGoogle Scholar
30. Rutsaert, P, Pieniak, Z, Regan, Á et al. (2014) Social media as a useful tool in food risk and benefit communication? A strategic orientation approach. Food Policy 46, 8493.Google Scholar
31. Diaz, E & Steward, AI (2012) Tracking twitter for epicemic intelligence. Web Science 2012, Proc 4th Annual ACM Web Science Conf; Illinois, USA.Google Scholar
32. Rutter, T. (2013) #Barf – how Twitter can Reduce the Spread of Norovirus. Available at http://www.theguardian.com/public-leaders-network/2013/dec/11/twitter-reduce-spread-norovirus (accessed 11 August 2014)Google Scholar
33. Krebs, P, Prochaska, JO & Rossi, JS (2010) A meta-analysis of computer-tailored interventions for health behavior change. Prev Med 51, 214221.Google Scholar
34. Michie, S, Ashford, S, Sniehotta, FF et al. (2011) A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: the CALO-RE taxonomy. Psychol Health 26, 14791498.Google Scholar
35. Webb, TL, Joseph, J, Yardley, L et al. (2010) Using the internet to promote health behaviour change: a systematic review and meta-analysis of the impact of theoretical basis, use of behaviour change techniques and mode of delivery on efficacy. J Med Internet Res 12 (1) e4.Google Scholar
36. Shaw, R & Bosworth, H (2012) Short message service (SMS) text messaging as an intervention medium for weight loss: a literature review. Health Inf J 18, 235250.Google Scholar
37. Cugelman, B, Thelwall, M & Dawes, P (2011) Online interventions for social marketing health behaviour change campaigns: a meta-analysis of psychological architectures and adherence factors. J Med Internet Rese 13.Google Scholar
38. Bandura, A (1997) Social Learning Theory [Cliffs, E, editor]. New York: Prentice-Hall.Google Scholar
39. Barak, A, Klein, B & Proudfoot, JG (2009) Defining internet-supported therapeutic interventions. Ann Behav Med 38, 417.Google Scholar
40. Bennett, GG, Herring, SJ, Puleo, E et al. (2010) Web-based weight loss in primary care: a randomized controlled trial. Obesity 18, 308313.Google Scholar
41. Wieland, LS, Falzon, L, Sciamanna, CN et al. (2012) Interactive computer-based interventions for weight loss or weight maintenance in overweight or obese people. Cochrane Database Syst Rev 8, CD007675.Google Scholar
42. Reed, VA, Schifferdecker, KE, Rezaee, ME et al. (2012) The effect of computers for weight loss: a systematic review and meta-analysis of randomised trials. J Gen Int Med 27, 99108.Google Scholar
43. Kodama, S, Saito, K, Tanaka, S et al. (2012) Effect of Web-based lifestyle modification on weight control: a meta-analysis. Int J Obes (Lond) 36, 675685.CrossRefGoogle ScholarPubMed
44. Grunenberg, E, Lin, J & Baumeister, H (2013) Effectiveness of web-based psychological interventions for weight loss-a systematic review. Die Rehabil 52, 182187.Google Scholar
45. Neve, M, Morgan, PJ, Jones, PR et al. (2010) Effectiveness of web-based interventions in achieving weight loss and weight loss maintenance in overweight and obese adults: a systematic review with meta-analysis. Obes Rev 11, 306321.Google Scholar
46. Manzoni, GM, Pagnini, F, Corti, S et al. (2011) Internet-based behavioral interventions for obesity: an updated systematic review. Clin Pract Epidemiol Ment Health 7, 1928.Google Scholar
47. Arem, H & Irwin, M (2011) A review of web-based weight loss interventions in adults. Obes Rev 12, e236e243.Google Scholar
48. Krukowski, RA, Tilford, JM, Harvey-Berino, J et al. (2012) Comparing behavioural weight loss modalities: incremental cost-effectiveness of an internet-based versus an in-person condition. Obes 19, 16291635.Google Scholar
49. Free, C, Phillips, G, Watson, L et al. (2013) The effectiveness of mobile health technologies to improve health care service delivery processes: a systematic review and meta analysis. PLoS Med 10 (1), e1001363.Google Scholar
50. Patrick, K, Griswold, WG, Raab, F et al. (2008) Health and the mobile phone. Am J Prev Med 35, 177181.Google Scholar
51. Stephens, J & Allen, J (2013) Mobile phone interventions to increase physical activity and reduce weight: a systematic review. J Cardiovasc Nurs 28, 320329.Google Scholar
52. Woo, J, Chen, J, Ghanavati, V et al. (2013) Effectiveness of cellular phone based interventions for weight loss in overweight and obese adults: a systematic review. Ortho Musc Syst 3, 141.Google Scholar
53. Carter, MC, Burley, VJ & Cade, JE (2013) Development of ‘My Meal Mate’ – A smartphone intervention for weight loss. Nutr Bull 38, 8084.Google Scholar
54. Korda, H & Itani, Z (2013) Harnessing social media for health promotion and behavior change. Health Promot Pract 14, 1523.CrossRefGoogle ScholarPubMed
55. Murray, E, Burns, J, See, TS et al. (2005) Interactive health communication applications for people with chronic disease. Cochrane Database Syst Rev CD004274.Google Scholar
56. Napolitano, MA, Hayes, S, Bennett, GG et al. (2013) Using Facebook and text messaging to deliver a weight loss program to college students. Obesity (Silver Spring) 21, 2531.Google Scholar
57. Woolley, P & Peterson, M (2012) Efficacy of a health-related facebook social network site on health-seeking behaviors. Soc Mar Q 18, 2939.Google Scholar
58. Rote, A. (2013) Examining the efficacy of a Facebook mediated intervention to increase steps per day in college freshmen: University of Wisconsin Milwaukee.Google Scholar
59. Mychasiuk, R & Benzies, K (2012) Facebook: an effective tool for participant retention in longitudinal research. Child Care Health Dev 38, 753756.CrossRefGoogle ScholarPubMed
60. Fry, JP & Neff, RA (2009) Periodic prompts and reminders in health promotion and health behavior interventions: systematic review. J Med Internet Res 11, e16.Google Scholar
61. Glasgow, RE (2007) eHealth evaluation and dissemination research. Am J Prev Med 32, Suppl. 5, S119S26.Google Scholar
62. O'Grady, L, Witteman, H, Bender, JL et al. (2009) Measuring the impact of a moving target: towards a dynamic framework for evaluating collaborative adaptive interactive technologies. J Med Internet Res 11.Google Scholar
63. Burke-Garcia, A & Scally, G (2014) Trending now: future directions in digital media for the public health sector. J Public Health 26 January, 1–8.CrossRefGoogle ScholarPubMed
64. Groves, P, Kayyali, B, Knott, D et al. (2013) The Big-data Revolution in US Health Care: Accelerating Value and Innovation. Available at http://www.mckinsey.com/insights/health_systems_and_services/the_big-data_revolution_in_us_health_care (accessed 13 August 2014)Google Scholar
65. Barr, A. (2014) Google's new Moonshot Project: The Human Body. Available at http://online.wsj.com/articles/google-to-collect-data-to-define-healthy-human-1406246214 (accessed 13 August 2014)Google Scholar
66. Vodafone. (2013) Help is at Hand: Vodafone and Moshi Monsters team up to teach kids ‘Digital Facts of Life’, Available at http://blog.vodafone.co.uk/2013/11/18/help-hand-vodafone-moshi-monsters-team-teach-kids-digital-facts-life (accessed 11 August 2014)Google Scholar
67. Currie, C, Zanotti, C, Morgan, A et al. (2012) Social determinants of health and well-being among young people. Health behaviour in school aged children (HBSC) study: International report from the 2009/2010 survey. Copenhagen.Google Scholar
68. Fesenmainer, J. (2003) Executive Summary: Measuring Outcomes of Digital Divide Investment to Community Technology Centers. Available at http://www.communitydevelopment.uiuc.edu/PDFs/ExecutiveSummary.pdf (accessed 13 August 2014)Google Scholar
69. Liang, TH (2014) Matrix analysis of the digital divide in eHealth services using awareness, want, and adoption gap. J Med Internet Res 14, e11.Google Scholar
70. Liff, S, Shepard, A, Wajcman, J et al. (2004) An evolving gender divide? OII Internet Issue, Brief No 2.Google Scholar
71. Aqili, SV & Isfandyari Moghaddam, A (2008) Bridging the digital divite: the role of librarians and information professionals in the third millennium. Electron Libr 26, 226237.Google Scholar
72. Roy, S & Ghose, S (2006) Internet adoption as a two-stage transition: converting internet non-users to internet users and to online buyers. Int J Mark Res 48, 321349.Google Scholar
73. Livingstone, S & Helsper, E (2010) Balancing opportunities and risks in teenagers’ use of the internet: the role of online skills and internet self-efficacy. New Media Soc 12, 309329.Google Scholar
74. Bert, F, Giacometti, M, Gualano, M et al. (2014) Smartphones and health promotion: a review of the evidence. J Med Syst 38, 111.Google Scholar
75. Whitehead, S (2014) 360 of Health Data: Harnessing Big Data for Better Health. Available at http://www.abpi.org.uk/our-work/library/medical-disease/Pages/360%CB%9A-of-Health-Data-harnessing-big-data-for-better-health.aspx (accessed 14 August 2014).Google Scholar
76. Zimmer, M (2012) Research Ethics in the 2·0 Era: New Challenges for Researchers and IRBS. Available at http://www.slideshare.net/michaelzimmer/research-ethics-in-the-20-era-new-challenges-for-researchers-and-irbs (accessed 14 August 2014).Google Scholar
77. Chung, IJ (2011) Social amplification of risk in the internet environment. Risk Anal 31, 18831896.Google Scholar
78. Kasperson, RE, Renn, O, Slovic, P et al. (1998) The social amplification of risk: a conceptual framework. Risk Anal 8, 177187.Google Scholar
79. Scullard, P, Peacock, C & Davies, P (2010) Googling children's health: reliability of medical advice on the internet. Arch Dis Child 95, 580582.Google Scholar
80. Sillence, E, Briggs, P, Harris, PR et al. (2007) How do patients evaluate and make use of online health information? Soc Sci Med 64, 18531862.Google Scholar
81. Neiger, BL, Thackeray, R, Van Wagenen, SA et al. (2012) Use of social media in health promotion: purposes, key performance indicators, and evaluation metrics. Health Promot Pract 13, 159164.Google Scholar
82. Post, SD, Taylor, SC, Sanders, AE et al. (2013) If you build (and moderate) it, they will come: the Smokefree Women Facebook page. J Natl Cancer Inst Monogr 2013, 206208.Google Scholar
Figure 0

Table 1. Taxonomy of behaviour change techniques adapted from Michie et al. (2011) and potential for digital application(34)

Supplementary material: File

McGloin and Eslami Supplementary Material

Supplementary Material

Download McGloin and Eslami Supplementary Material(File)
File 17.5 KB