In an age of behaviour change researchers in the field and experts in digital media and the entertainment industry have capitalised on the sophistication and innovation of today’s digital media technology to deliver interventions to improve the population’s health. Can more collaboration between these disciplines be a step forward in providing more effective digital interventions to change health behaviour?

Advances in digital and interactive technology has revolutionised and increased the impact of health promotion worldwide. Researchers in the field have taken advantage of this new technology which has led to a growing number of interventions promoting health behaviour change. However, until a recent systematic review, there was little known about which specific components of interventions have the most effect on behaviour. In a meta-analysis of various health internet interventions (85 studies, 43,236 participants), Webb, Joseph, Yardley & Michie (2010) used coding frames (taxonomy of behaviour change techniques) to assess the use of theory and behaviour change techniques to investigate which characteristics of the internet based interventions best promoted behaviour change. They also developed a new coding system to assess modes of delivery (such as video, sms, social networking) and linked this to how different modes produced different effects.

What were the main findings?
They reported that on average interventions had small yet statistically significantly positive effects on health related behaviour. In particular, studies using more extensive theory to underpin their intervention showed an increase in effect size compared to studies that did not. Also studies using more behaviour change techniques (BCTs) tended to have larger effects than studies using less. In regards to mode of delivery, supplementary modes (additional ways of interacting with people) tended to produce the largest effect on behaviour change, especially text messages. In regards to BCTs, the one’s that led to the greatest changes in behaviour were stress-management and communication skills training, however these techniques appeared in a small number of studies so it is difficult to generalize. Other BCTs such as modeling and providing normative information about other’s behaviour also had significant positive effects on behaviour.

Overall, Webb et al., (2010) found considerable variation in the effects of interventions with some having a large effect and others a small or negative effect. This highlights the importance of identifying which components of the intervention effect behaviour, and the size of this effect. However, another recent review that specifically looked at the area of adolescent obesity prevention and treatment showed that the majority of studies are failing to achieve this. Nguyen, Korman & Baur (2010) reviewed 24 studies and found that interventions involving parents led to positive outcomes as ‘parental variables relating to family and life satisfaction were the strongest mediators of adolescent weight loss’ (Nguyen et al., 2010:312). They suggest that further research is needed to see how different levels of parental involvement affects and sustains adolescent behaviour to prevent obesity. Unfortunately, the authors found it difficult to draw many other findings in light of the poor quality of the majority of studies. They recommend that future research should ensure high quality studies, especially relating to the prevention of obesity, studies delivered using innovative methods such as social networking platforms, studies involving parents, studies isolating intervention components, and studies assessing programme participation and ‘the relationship between intervention dose and outcomes’ (Nygen, 2010:312).

An earlier study also looking into online adolescent obesity prevention and treatment, gathered data and conducted a formative evaluation involving a systematic review of the literature, questionnaires and meetings with a panel of experts. The findings indicated some critical implications for future research in this area, including the importance of; I) integrating interventions into young people’s everyday lives; II) being culturally competent; III) collaboration with the entertainment industry, who are experts in developing fun and engaging ‘ehealth’ applications for young people (Ahern, Phalen, Le, Goldman, 2007). It seems a shame that few of these recommendations have been taken on board so far in recent interventions in this area and perhaps this is a reflection of the lack of collaboration between disciplines.

Why are digital media experts and the entertainment industry important?
Serious Games is now a fast growing area with virtual environments applied to preventive health in areas such as childhood obesity, sexual health, and helping parents to communicate better with their children (see the Serious Games Institute). In addition, existing social networking platforms may have a potential for effective and wide reaching health promotion. A recent study found that the advanced social networking site ‘Second Life’ may increase people’s confidence in their ability to exercise in the real world. Johnston (2011) allocated participants to either a virtual weight-loss programme where they could interact with one another in virtual training club or participants were allocated to group sessions in a traditional fitness club. While there were no differences in weight loss, the virtual group reported more healthy eating behaviour and confidence in their ability to carry out exercise even when barriers such as bad weather arose. However, until the final paper is published, it is not clear how participants were allocated to groups and how much the study was relying on self-reports compared to objective measures.

Nevertheless, this study demonstrates how researchers can start to test the ability of existing digital media platforms to provide innovative and effective interventions related to health behaviour change. Other products such as Nintendo Wii, Microsoft Kinect, Dance Dance Revolution further demonstrate the expertise of the entertainment industry in creating highly innovative and engaging virtual environments that are being used by both adults and young people worldwide. However, there is a lack of research evaluating the efficacy of such technology in the promotion of health. This may in part reflect the lifecycle of IT and the lifecycle of research. In addressing the challenge of adolescent obesity prevention, researchers may be able to harness the highly interactive, ubiquitous, state of the art technology that adolescents are engaging with today. However, sound psychological theory should not become secondary to the technology. It is important that interventions are underpinned by strong evidence to ensure their effectiveness and ability to produce long term behaviour change. Constant advances in digital media technology mean that the technology we are using today will not last forever but we hope that the theories (allowing for modifications) that underpin them will.

Conclusion
Webb et al., (2010) conducted their research under the premise that three factors of an intervention are associated with the effect on behaviour: theoretical basis; behaviour change techniques employed; and mode of delivery. Thus, if we agree with the authors then it seems logical that digital health behaviour change interventions utilise the expertise of health psychologists, public health researchers, and other health professionals who specialise in behaviour change to inform the first two characteristics of the intervention while experts in digital media and the entertainment industry should be used to deliver them in a highly engaging and innovative way.

Key reading(s):
Webb, T. L., Joseph, J., Yardley, L., & Michie, S. (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. Journal of Medical Internet Research, 12

References:
Ahern, D.K.; Phalen, J.M.; Le, L.X.; Goldman, R. (2007) Childhood Obesity Prevention and Reduction: Role of eHealth, Exploring the role that technology can play in reducing the incidence and prevalence of childhood obesity, Health e-Technologies Initiative.

Nguyen, B., Kornman.K.P., Baur. L.A., (2010) A Review of electronic interventions for preventions and treatment of overweight and obesity in young people, Obesity reviews, 12, 298-314

Johnston (2011) cited in Pappas, S., (2011) Health App Downloads Soar, But Do They Work? http://www.livescience.com/13328-health-smartphone-apps-weight-loss-quit-smoking.html

Webb, T. L., Joseph, J., Yardley, L., & Michie, S. (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. Journal of Medical Internet Research, 12

1. “The medium is the message.”

This is perhaps one of the best known of McLuhan’s sayings, one that catapulted him to fame after the publication of his book “Understanding Media: The Extensions of Man”. What McLuhan essentially means here is that the “content” of a message is the least important thing to analyze, even though it is the most apparent. When thinking of the Internet, something that McLuhan conceptualized as “the global village,” we should look at how messages are transmitted in order to gauge how they affect our perceptions and behaviors.

2. “Anyone who tries to make a distinction between education and entertainment doesn’t know the first thing about either.”

This is an especially salient quote when we are talking about the Web as it exists today. While online schools are still in their infancy, the learning potential of the Internet is already being exploited through educational games for children, OpenCourseWare initiatives, and more.

3. “As technology advances, it reverses the characteristics of every situation again and again. The age of automation is going to be the age of ‘do it yourself.”

Can any one phrase apply to the Internet of the 21st century better than “do it yourself?” McLuhan suggested in many of his books that media could be grouped into two types—”hot” and “cold,” one engaging different faculties and the other being more passive. McLuhan believed that we would cycle through the different types, one dominating the other, and he thought we were heading toward a highly interactive form of media, a very “do-it-yourself” type.

4. “In this electronic age we see ourselves being translated more and more into the form of information, moving toward the technological extension of consciousness.”

While this is a very vaguely worded assertion, and many people thought that, during the time that McLuhan was cranking out his ideas, he was stark raving mad. However, considering the recent publication of the popular general science book, “The Information: A Theory, A History, A Flood” by writer James Gleick, much of what McLuhan prophesized, especially this sentence, is only now becoming true.

Of course, McLuhan wrote about media very conceptually, and the way in which he approaches his theories are often convoluted and difficult to follow. But McLuhan spent a long career teaching composition and rhetoric at the university level, and, as such, he perhaps values more than anyone framing ideas into persuasive arguments.

For those of us who are interested in education and technology from a research perspective, McLuhan’s ideas can be easily applied in the classroom, especially one that is increasingly media-saturated. According to a New York Times article, Generation Y spends about 8 hours a day consuming media, which represents an unprecedented period of time. As such, talking about any form of media, but especially the Internet, will be relevant to all students in the 21st century.

This perhaps explains why McLuhan’s work has seen a resurgence in popularity, especially among younger academics and professionals. As noted in a Wired Magazine article:

“… in recent years, the explosion of new media – particularly the Web – has caused new anxieties. Or to put a more McLuhanesque spin on it, the advent of new digital media has brought the conditions of the old technologies into sharper relief, and made us suddenly conscious of our media environment. In the confusion of the digital revolution, McLuhan is relevant again.”

Author Bio:
This guest post is contributed by Leslie Johnson, who writes about health, green living, parenting related articles at masters in health administration.

Methods and Strategies

It is clear from the article that nine out of 17 studies reviewed utilized either targeted (i.e. specified audience) or tailored (i.e. personal) communication.  Nine interventions utilized support facilities (e.g. discussion boards, peer support or professional support), five utilized interactive content, some utilized use of reminders and incentives, and a few interventions were embedded in a social context (e.g. implemented in school).

All in all, it is clear that we have only begun to utilize the inherent possbilities in this rich medium called the Internet. As seen from the results, only six different strategies were used in the studies that were reviewed. Although a lot of variation can be found within each strategy and many strategies can be combined, we have yet to utilize the medium to its full extent with regard to designing health and behaviour change interventions.

Theory and Empirical Data

It appears that a wide variety of theories were applied to design the interventions. The basis for the selection of strategies was social learning theory, self-regulation theory, social norms, the transtheoretical model, information-motivation-behavioural skills model, and different theories of social support.

As Crutzen et al. (2011) point out, these theories are descriptive in nature where the aim is to describe behaviours or behaviour change. None of these theories are actually prescriptive. That is, inform the intervention designer exactly about how to design the intervention in order to induce behaviour change.

Of course, it is not difficult to imagine and design e.g. exercises, tasks, games, etc. that are based on these theories and that intend on inducing behaviour change, but it is not given that it will work in practice.

Furthermore, none of the interventions applied any specific theories of exposure facilitation or theories of dissemination and implementation which makes it even more difficult to study and improve exposure in future interventions.

Effectiveness of Strategies

It seems that support strategies are rarely used by participants. This effect may reflect that adolescents and young adults are rarely confronted with negative consequences of the target behaviours which Internet-interventions are designed for, resulting in low involvement and low motivation to comply.

Providing interactive content may thus be a great way of enforcing involvement in one’s behaviour change process. The results from the article showed that providing content in a more interactive way resulted in higher exposure.

Asking and answering questions, interactive quizzes and games, listening to audio, watching videos, etc. breaks the monotony of reading and requires a different kind of cognitive processing of content which may result in greater involvement. 

Also, the use of reminders seems to increase exposure. Text messages, email reminders, proactive IVRs (interactive voice response), etc. may increase exposure by “pulling in” users and making interventions more attractive. However, the use of incentives to make people use the intervention may be feasible for studies, but would increase costs extremely in real-life.

Thus, finding new ways of providing incentives is important. It could for instance be to apply gaming principles to make interventions self-rewarding, but it may also be a matter of finding an appropriate business model and commercialize interventions.

Conclusion

The authors of the article concluded that there seems to be a few strategies that can be used to facilitate exposure more successfully. However, they were not able to link specific strategies to effectiveness. So far, I think the Internet is a rich medium which we believe has not been fully utilized in Internet-delivered interventions and that research on Internet-delivered interventions has yet to improve beyond conducting mere studies of program effectiveness. More experimental evidence is needed, more optimization and process research is needed, and exposure/dissemination/implementation research that is linked to effectiveness data.

Reference

Crutzen, R., de Nooijer, J., Brouwer, W., Oenema, A., Brug, J. & de Vries, N. K. (2011). Strategies to factilitate exposure to Internet-delivered health behavior change interventions aimed at adolescents or young adults: A systematic review. Health Education & Behaviour, 38,  49-62.

First, approximately 70% of your body weight and size is determined by your genes which your body will attempt to maintain. This is not anything you can do much about. Second, the remaining 30% are largely determined by environmental factors that influence our eating behaviours outside our conscious awareness. The good news is that you can change the evironmental factors to work for us instead of against us. Here I show you 10 practical and scientifically proven ways of changing your environment so that you can eat more healthy without thinking about it.

1. Use tall and narrow (highball) glasses.

People tend to pour and drink a lot more juice, soda, liquors, etc. when using short and wide glasses. When people evaluate the volume of cylindrical shapes such as glasses, they tend to focus on height at the expense of width (i.e. the vertical-horizontal illusion). Consequently, we underestimate how much we think we pour onto our glasses. Short and wide glasses should only be used for drinking water. So, replace your shortest and widest glasses with taller and narrower and put them in front in your cupboard.

2. Use small serving aids.

Research has demonstrated how easily we are fooled by the size of serving aids. Put four spoons full of mashed potatoes on a large plate (e.g. 12 inches) and you will most likely underestimate the amount of food on that plate. Put the same amount of mashed potatoes on a small plate (e.g. 8 inches) and you will most likely overestimate the amount of food on that plate. This is the size-contrast illusion in practice. The same amount of food can seem a lot or little depending on the size of serving aids you use. The principle applies not only to plates, but all sorts of serving aids such as spoons, bowls, forks, etc. So, go back in to your cupboards and find the smallest serving aids you have and make them easily available for use.

3. Turn on the lights while eating.

Candlelit dinners and dimmed lights increases our consumption by increasing comfort and decreasing self-consciousness. The more we enjoy a meal,  the longer the meal lasts. And the more time you spend eating your meal, the more often you end up eating a second portion, an unplanned dessert or having an extra drink. Also, soft or dimmed lights makes us less self-conscious which makes it harder for us to monitor and inhibit our consumption.

4. Turn of music and remove noise.

Listening to music we enjoy increases comfort just like dimmed lights. The same is true for slow and soft music. We stay longer, we feel more comfortable, we become less self-conscious, and we become more likely to eat more. More research is needed on noises and loud and up-tempo music, but it appears this too can lead us to overeat because we hurry to clean our plates and ignore signals of satiety.

5. Always eat at a table with no disturbances.

Very often we find ourselves eating at sports events, in movie theaters, grab a hot dog on the go, etc.  One major problem with eating in such situations where there usually is no table, is that our attention is directed on something else than eating. Disturbances such as watching television, reading a book, talking on the phone, etc. while eating makes us less aware of how much we eat.

6. Always leave leftover food on the table until you are finished eating.

A group of researchers conducted a study in restaurants where some waiters cleaned their customers’ tables during the meal. Other waiters were instructed to let the leftover food stay on the table until their customers’ were completely finished with the meal. The result were that those customers who could see all the leftovers ate less. The results could be explained by the fact that those customers with leftovers on their tables did not have to rely on their memory – they could easily see how much they had eaten.

7. Consider how often you should eat with others.

Eating is very much a shared or social activity with friends and family. Friends and family are good for you, however, eating with others also increases our consumption. The duration of meals are extended and simply observing others can set consumption norms on what is eaten and how much. The effects can be dramatic. Meals eaten with only one other person present increases intake by appr. 30%. Consumption is incremental with increases in the number of people we eat with. Eat with 7 persons around the dining table and it is not unlikely that you will eat twice the amount of food that you would have eaten if you were to eat alone. Consider suggesting social activities with friends and family that do not necessarily involve eating.

8. Buy food in medium or normal sized packages and portions.

Both large- and small-sized packages and portions makes us eat more. Large-sized packages and portions increases consumption with about 20% for meal-related foods and about 30-45% for snacks. The more food we stockpile and have available, the more we eat. We also have a cultural tendency to “clean our plates” although we are far beyond the point of satiety. Ironically, small-sized packages and portions also make us eat more because we tend to think of small sizes as diet food(!). The problem is that the food that often comes in small packages and sizes is exactly the energy dense and unhealthy food we really should eat less of.

9. Organize and re-structure your food.

Cleverly designed research demonstrated how a bowl of assorted M&Ms can increase how many you eat. People who were given a bowl of M&Ms with 10 different colours ate about 40% more than people who were given only 7 different colours. Taste could not explain these findings as all M&Ms tasted exactly the same. The lesson to be learned here is that perceived variety suggests consumption norms. This means that you should try to separate and organize foods on your plate in clearly visible patterns, never buy assorted candy, avoid having more than two kinds of food on your plate at buffets and receptions, etc.

These practical tips mainly help you eating more healthy by eating lesser portions or less food that is bad for you. Try reversing these practical tips to eat more healthy foods. For example, if you want to eat more fruits and vegetables: cut them in small pieces, serve them in a large bowl, put the bowl easily available on the table in your living room and eat it in front of the TV. You can find references to the research mentioned in this post and read more about these practical tips in Brian Wansink’s (2004) great review of environmental factors that influence your food consumption.

Reference

Wansink, B. (2004). Environmental factors that increase the food intake and consumption volume of unknowing consumers. Annual Review of Nutrition, 24, 455-479.

1.      Sometimes all you have to do is just to ask.

We often underestimate the likelihood that the recipient will comply with our requests (Flynn & Lake, 2008). It is important to recognize this because it can potentially lead to productivity losses and prevent accomplishing your goals. Moreover, holding a correct impression of how many say “Yes!” may not only increase staff motivation, but by applying the simple principle of requesting what you want, you appear open and honest. If openness and honesty does not persuade people into doing whatever you want them to do, at least it does not create much resistance.

2.      More options promote indecisiveness.

Startup and small businesses often offer only few options and products. If successfully managed, the business will grow and attract new customers which open up the possibility for expanding the product portfolio. Most people usually consider having more choices to be a good thing. However, as research shows, and as many businesses often painfully have experienced, this is not always a good business idea. An abundance of choices most often overwhelms customers and leads to indecisiveness (i.e. fewer purchases).

3.      Be the first to throw out the anchor.

During negotiations, the first meetings or first few minutes in a meeting, the parties often dance around the table reluctant to be the first to present their offer. Is this the right strategy for achieving the highest bid? No. Research shows that the one that first puts the bid out on the table achieves superior outcomes (Galinsky & Mussweiler, 2001). Why? The first offer “anchors” the negotiation and the parties tend not to move that far away from the anchor. Remember, though, that the first offer should be realistic!

4.      Humor people.

Humor brings people closer together and helps establish relationships (Kurtzberg et al., 2009). Moreover, humor seems to make people put down their guard in negotiations by proposing less extreme offers. In business, the possibility for using humor is very limited and not all people find all jokes, cartoons, and other fun stuff equally humoring. So attempts at making people laugh and come in good mood should be made with caution, however, there is no doubt that interaction with users and business outcomes can become more effective when using humor.

5.      “I don’t mean to sound rude, but…”.

Have you ever found it difficult to say something and tried to get ahead of the situation by saying things like “I trust you will manage this situation, but don’t forget to…”? Well, guess what? Research shows that if you do say such things, you are going to be perceived as someone who doesn’t trust his co-workers or rude (see El-Alayli et al. 2008). In fact, it is better just to say things as they are: “Manage the situation and remember to …” or even better “I trust you will manage this situation.”

What are some of the practical implications of these principles?

First of all, if you want someone to participate in online projects, it can be more cost-effective just to ask people politely if they would like to join than spending a lot of time and effort finding clever ways to persuade them. No one really likes to be persuaded – they like to think it was a self-determined choice. Second, if you want users to start using or buying your online product, be selective about which products you wish to push. It is much easier for users to make a decision to buy or use products from a small sample than the entire range of your products. Third, as long as you are sensitive to what price people are willing to pay, you should not have to be afraid of displaying the price of your products. Most e-commerce sites already do this, however, a few do not. There is nothing that turns a buyer off more than seeing a price tag that is way beyond imagination at checkout. Do not quite know what people want to pay? Well, talk to them and find out what they are willing to pay – sometimes you will be happily surprised. Fourth, try using a bit of humor. Consider e.g. including a non-offensive and appropriate cartoon in your electronic meeting notice. Humor gives people something to talk about, loosens up the atmosphere, and brings people closer together. Fifth, avoid startups like “I don’t mean to sound rude, but…” when chatting with people regardless whether they are close and personal friends or professional relationships. It is very likely that you end up being perceived exactly the way you want to avoid being perceived. The same rule applies online as offline.

References

El-Alayli, A., Myers, C. J., Petersen, T. L., Lystad, A. L. (2008). “I don’t mean to sound arrogant, but…” The effects of using disclaimers on person perception. Personality and Social Psychology Bulletin, 34, 130-143.

Flynn, F. J., & Lake, V. K. B. (2008). If you need help, just ask: Underestimating compliance with direct requests for help. Journal of Personality and Social Psychology, 95, 128-143.

Galinsky, A. D., & Mussweiler, T. (2001). First offers as anchors: The role of perspective-taking and negotiator focus. Journal of Personality and Social Psychology, 81, 657-669.

Goldstein, N. J., Martin, S. J. & Cialdini, R. B. (2009). Yes! 50 Scientifically Proven Ways to Be Persuasive . New York: Free Press.

Kurtzberg, T. R., Naquin, C. E. & Belkin, L. Y. (2009). Humor as a relationship-building tool in online negotiations. International Journal of Conflict Management, 20, 377-397.

Even if strategies are immediately applied in practice, there are appropriate designs, such as a time series design (Chen et al., 2005; Murry, Stam, & Lastovicka, 1993), to test effectiveness of these strategies. In such a design, strategies are first applied separately. Subsequently, combinations of several strategies are applied. Intervention use is monitored during the whole period to determine which strategy or combination between them is most effective. Although this design is more sensitive to confounding effects (e.g. changing environment), strategies are tested in a less isolated way and this could give more insight into effectiveness regarding dissemination and exposure in real life.

If all methods described above are inapplicable due to limited resources, one could test the effectiveness of dissemination strategies by simply asking visitors where they came from and how they heard about the intervention (Gordon, Akers, Severson, Danaher, & Boles, 2006). Although this method is based on self-report and therefore less objective, it is easily applicable and less time consuming. Yet, this method remains a last resort (when all else fails).

Dissemination and exposure do not only depend on the intervention itself, however, but also on its users. There is evidence that the acquisition of skills to use a website may influence its adoption (Paswan & Ganesh, 2003). It has also been shown, however, that Internet self-efficacy is not a significant predictor of exposure (Steele, Mummery, & Dwyer, 2007). If familiarity with a website increases, then perceived usability influences loyalty to the website (Casaló, Flavián, & Guinalíu, 2008). This is in line with the principle of cognitive lock-in, accounting for users’ preference of better-known websites (Murray & Häubl, 2002). In terms of Internet-delivered interventions, this could be conducive to revisits of the intervention.

Implications for practice
Caution regarding the use of strategies to improve dissemination and exposure is recommended as long as evidence-based insight into effectiveness is scarce. Evidence-based insight into strategies which aim to improve dissemination and exposure, however, could also be gained by applying strategies in practice and investigating, for example, server registrations (objective) or visitors’ self-reporting (subjective). Applying strategies in practice can be seen as natural experiments to test effectiveness of these strategies. Although this research method is more common in fields where laboratory experiments are more difficult, such as sociology and economics (Angrist & Evans, 1998), there are also examples in the field of health promotion – e.g. with regard to the effect of a smoking ban (Sargent, Shepard, & Glantz, 2004).

Implications for future research
To increase evidence-based insight into effectiveness of strategies regarding dissemination and exposure, future research should not be limited to experimental research in controlled settings, but also use alternatives such as a time series design. Such a time series design can also be used to test effectiveness of dissemination strategies for existing Internet-delivered interventions.

References:
Angrist, J., & Evans, W. (1998). Children and their parents’ labor supply: evidence from exogenous variation in family size. American Economic Review, 88, 450-477.

Casaló, L., Flavián, C., & Guinalíu, M. (2008). The role of perceived usability, reputation, satisfaction and consumer familiarity on the website loyalty formation process. Computers in Human Behavior, 24, 325-345.

Chen, J., Smith, B. J., Loveday, S., Bauman, A., Costello, M., Mackie, B., et al. (2005). Impact of a mass media campaign upon calls to the New South Wales Hep C helpline. Health Promotion Journal of Australia, 16, 11-14.

Crutzen, R., De Nooijer, J., Brouwer, W., Oenema, A., Brug, J., & De Vries, N. K. (submitted). Strategies to facilitate exposure to Internet-delivered health behaviour change interventions aimed at adolescents or young adults: a systematic review.

Gordon, J. S., Akers, L., Severson, H. H., Danaher, B. G., & Boles, S. M. (2006). Successful participant recruitment strategies for an online smokeless tobacco cessation program. Nicotine & Tobacco Research, 8, S35-S41.

Murray, K. B., & Häubl, G. (2002). The fiction of no friction: A user skills approach to cognitive lock-in. Advances in Consumer Research, 29, 8-10.

Murry, J. P., Stam, A., & Lastovicka, J. L. (1993). Evaluating an anti-drinking and driving advertising campaign with a sample survey and time series intervention analysis. Journal of the American Statistical Association, 88, 50-56.

Paswan, A. K., & Ganesh, G. (2003). Familiarity and interest: in a learning center service context. Journal of Services Marketing, 17, 393-419.

Sargent, R. P., Shepard, R. M., & Glantz, S. A. (2004). Reduced incidence of admissions for myocardial infarction associated with public smoking ban: before and after study. British Medical Journal, 328, 977-980.

Steele, R. M., Mummery, W. K., & Dwyer, T. (2007). Examination of program exposure across intervention delivery modes: face-to-face versus internet. International Journal of Behavioral Nutrition and Physical Activity, 4, 7.

The determination if an Internet-delivered intervention has been disseminated successfully does not only depend on your final target, but also on the denominator you choose to estimate reach. In a study by Graham et al. (2006), for example, Internet users were recruited based on use of the terms quit(ting) smoking or stop(ping) smoking in a major search engine. When a user clicked on a link to the intervention in the results of a search engine query, an intercept page appeared inviting them to participate in the study of Graham and colleagues. If they accepted, three questions were asked to determine preliminary eligibility. Reach estimates vary depending on the denominator selected: 2.7% of all Internet users seeking cessation information; 6.9% of those who demonstrated preliminary interest in the study; 13.7% of those who were eligible to participate; 21.1% of those eligible and recruited; and 51.3% of those consented.

This also raises an interesting statistical issue; since it is difficult to identify the population to which samples refer when there is no clear sampling method (i.e., everybody can access your intervention). Questions need to be asked to determine eligibility to participate. It remains unclear, however, whether missing data regarding eligibility is due to visitors’ perception of non-eligibility (e.g., parents visiting the intervention because they are interested in the subject or non-smokers accidentally hitting on a website aimed at smoking cessation) or actual drop-out of potential participants. Appropriate methods of analysis are needed to deal with the vast amount of missing data (Christensen, Griffiths, & Korten, 2002) and to determine the recruited sample size from your target group.

The difficulties regarding measurement of online reach are not limited to the field of health promotion. The Interactive Advertising Bureau Europe (IAB Europe) and the European Interactive Advertising Association (EIAA) have announced to develop a measurement standard for website reach. In this project, denominated as Measurement of Interactive Audience Project (MIA Project), several online measurement methods will be evaluated (Marketing Online, 2007).

Implications for practice
It is recommended to report the target group of your intervention, the number exposed to recruitment, the number who responded, the number eligible, and the number who actually participated to determine whether dissemination has been successful. These numbers should be compared with a final target which should be determined in advance and which depends on the intervention (e.g., its subject and target group). Despite this dependence on the intervention itself, one may look at other Internet-delivered interventions to get an idea about the order of magnitude of a suitable final target.

Implications for future research
Analyses regarding data from Internet-delivered interventions are not as straightforward as they may seem, since data can be missing for several reasons (e.g., visitors which do not perceive the intervention as being relevant to them once they logged on or visitors whose needs are fulfilled halfway the intervention). Although it is probably too conservative to consider all missing data as drop-outs, this is recommended as long as appropriate methods of analysis are lacking. Furthermore, due to the openness of Internet as a medium, not all visitors of your intervention are necessary members of your target group (e.g., parents, people who are generally interested in the topic of your intervention). These “participants” should be excluded from data analysis.

References:
Christensen, H., Griffiths, K. M., & Korten, A. (2002). Web-based cognitive behavior therapy: analysis of site usage and changes in depression and anxiety scores. Journal of Medical Internet Research, 4, e3.

Graham, A. L., Bock, B. C., Cobb, N. K., Niaura, R., & Abrams, D. B. (2006). Characteristics of smokers reached and recruited to an internet smoking cessation trial: a case of denominators. Nicotine & Tobacco Research, 8, S43-48.

Marketing Online. (2007). Europese standaard voor meten online bereik in de maak [European standard for measuring online reach in preparation]. Retrieved October 26, 2009, from http://www.marketingonline.nl/nieuws/ModuleItem52230.html.

In Susan Weinschenk’s (2009) book ”Neuro Web Design” she writes about how websites influence people’s choices unconsciously. Weinschenk shows how influence and persuasion is wired into our brains. This essentially explains why we find attractive people or dangerous car chases persuasive without even being aware of that. Many of the examples used center around the known principles from Cialdini’s (2007) seminal book “Influence”. The best is of course to read both Weinschenk’s and Cialdini’s books, however, if you have little time available, here is a summary of some of the main principles of persuasion:

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1. Active participation creates involvement. All social network sites require active participation by its members and they are also the fastest growing websites. I have no idea what the next big thing in technology is going to be, but the safest bet is on technology that is used to communicate and be social. There is nothing people enjoy more than uploading personal photos, sharing videos, expressing their opinions, etc. Someone once said a bit humorously: “Anyone who denies that they enjoy talking about themselves is either lying or lacking in self-understanding”. I would not agree with the statement literally, but I agree that we love to share our selves with others.

2. Low-ball tactics. If you would like people to share some information so you can learn more about the users of your shareware application, you may find it difficult to ask people to submit information online. But, the chances are that if you start by requesting a small donation to keep continuing to make the application, and then request for information, more people will be willing to share information. Being asked to share information when you have declined to donate money may seem like a minor thing to do. However, make sure the initial request seems reasonable and somewhat in proportion to your second request (the target). It would not be a good idea to begin by requesting a donation of €50 and then asking for information.

3. Foot-in-the-door tactic. This is more or less the opposite of low-ball tactics. You begin by making small requests and then increase the scope of your requests. A funny example of the principle in practice was illustrated on a Norwegian television show where an interviewer knocked on people’s doors and simply asked them about how they were living, next he asked for permission to come in and see how they were living, and so it went that he even got to look into people’s bedside drawer a place you would normally not let many people in.

4. Social validation. Learning to know what others think is important. Sometimes we are introduced to products or services we do not know much about. How to decide if this is interesting or good for you? Well, have a look at what others have done or think about it. iTunes’ Genius function is a good example of social proof. Genius recommends music albums and songs based on what music you have on your iPod or iPhone. And you can also see what other people that listen to the same music as you have bought. You can give ratings, write reviews, and read what others think. This can be quite effective, especially when an album or song is just a mouse click away.

5. Reciprocity. Giving someone a preview, demo or sample of your product will make that person feel indebted to you, and no one likes to feel indebted. How do most people resolve this uncomfortable situation? By giving back (or reciprocating). Notice that there is an asymmetrical power relation here too. The person who first performs a service also has the power to decide when he or she wants something in return (“I gave you a demo which ends right here. Now, if you want more, you will have to pay”).

6. Rewards. Rewarding people’s behaviour reinforces that particular behaviour. Simple learning principles like this can be very effective. However, they should be used with caution. For example, rewarding people on a variable-ratio schedule makes the rewards seem scarce and unpredictable, and ask anyone with a gambling or gaming problem and they will tell you that a game with a variable-ratio schedule is more addictive than any other game. Rewards can also switch the motivation for using a technology from intrinsically to extrinsically driven and we know that people often lose interest in what they were doing when this happens. Not sure how this motivational change can affect business? Here is a little thought experiment: When you start paying a child that enjoys making puzzles a small amount of money (e.g. €1) for each puzzle they complete for a short period of time. And then you withdraw the reward (money). If you compare the number of completed puzzles, do you think the child would complete more or fewer puzzles after the inducements compared to before?

We have covered six of the principles here and now. There are more, so stay tuned!

References

Cialdini, R. B. (2007). Influence: The psychology of persuasion. HarperCollins: New York.

Weinschenk, S. M. (2009). Neuro web design: What makes them click? New Riders: Berkeley, CA.

See also

www.whatmakesthemclick.blogspot.com

The digital environment (e.g. Internet, mobile phones, smart phones) that is now an integral part of our daily lives is becoming an increasingly important means of sustaining the health of people worldwide, whether by providing access to a wealth of information, by linking geographically dispersed communities of peers and professionals, or by supporting self-management of health and illness. The Internet is therefore rapidly becoming both a medium and a focus for health psychology research.

The Internet can be used as a source of naturally occurring observations and data, as in the qualitative study by Rodham, McCabe, and Blake (2009) of Internet communication between people with Complex Regional Pain Syndrome. It can also be used to give people personalized feedback about their health risks, as in the study of predictors of online diabetes risk test taking by van Koningsbruggen and Das (2009). Perhaps most significantly, it provides a cost-effective means of making automated behavior change interventions widely available, such as the stress and alcohol reduction programmes deployed in two papers in a recent issue of Psychology & Health (Crutzen et al., 2009; Fridici, Lohaus, & Glaß, 2009).

A meta-analysis of 75 randomized controlled trials has provided support for the effectiveness of digital interventions (Internet, mobile phones, etc.) in the health promotion area (Portnoy, Scott-Sheldon, Johnson & Carey, 2008). But how and why can digital interventions be effective in promoting sustained behaviour change? And can different digital tools serve different purposes?

In Eysenbach’s (2001) definition of the ‘10 E’s’ integral to e-health, three of the key ingredients are encouragement of a new relationship with the patient which focuses on empowering and educating them. Digital interventions can provide new forms of access to self-care, video and audio delivery for those with reading difficulties and anonymous social support for those who are unable or unwilling to consult health professionals in person. While there is concern that the ‘digital divide’ could limit the extent to which those from more socioeconomically deprived backgrounds may benefit from digital interventions (Murray, Burns, See, Lai, & Nazareth, 2005), this problem may diminish as the Internet grows more ubiquitous; for example, young people already routinely use the Internet daily, with minimal differences in access due to socioeconomic background.

Another key problem is the finding that high attrition rates seem to pose a potential short-coming of digital health interventions (see for example Matano, Koopman, Wanat, Winzelberg, Whitsell, Westrup, Futa, Clayton, Mussman & Taylor, 2007). Clearly, we need research into how digital interventions can best be designed to hold the interest of the user. While the main motivation for initial use is the expected utility of use, continued use is probably heavily influenced by experienced utility (which in turn fuels expected future utility). This may be increased by offering the client relevant, individually tailored material and feedback, which has been shown to increase program use and engagement (see e.g. Strecher, McClure, Alexander, Chakraborty, Nair, Konkel, Greene, Couper, Carlier, Wiese, Little, Pomerleau & Pomerleau, 2008). Specifically, individuals must be able to gauge their progress against some frame of reference, which might include their own change plan (compared with their own prior history), the behavioral progress of others who are in a similar situation to themselves, or a regimen from a trustworthy source (for overview see Kraft, Drozd & Olsen, 2009). In other words, efforts to change are likely to be successful when individuals receive timely monitoring and feedback on their progress (see e.g. Brendryen, Drozd & Kraft, 2008). With such feedback, individuals can be motivated by their own achievements. They can modify their strategies and gauge the proximity of their goals. For example, Internet weight loss programs are likely to be most effective when they require participants to keep regular records of their food intake and physical activity, and the program provides feedback on their performance (Tate, Jackovny & Wing, 2003).

Additionally, more elaborate interventions can be more effective. For example, a review of 15 Internet-delivered interventions to promote increased physical activity (Vandelanotte, Spathoris, Eakin, & Owen, 2007) found that interventions were more successful if they required participants to engage more with the intervention (using techniques such as email, weekly modules, or online coaching or chat sessions). Generally, increased interactivity seems to improve the emotional quality of an intervention (i.e. how it makes you feel) (Norman, 2003). Hence, the extent that interactivity can increase user involvement must be considered a critical characteristic of a digital intervention.

It is pretty obvious that we are just at the start of the development of digital health care. Rapid technological change means that this field is already posing new questions and challenges for digital interventions and research. For example, many future interventions can be expected to be delivered through mobile phones, which will offer new opportunities for tracking people’s activities and health status and offering timely advice. At the same time, a more focused and tailored approach to intervention design may be needed that is compatible with the restrictions of interacting via a smaller interface. Given the exciting scope for innovation and the demand for technological solutions to health problems, it is certain that research into digital interventions is set to grow substantially in the foreseeable future.

References

Brendryen, H., Drozd, F. & Kraft, P. (2008). A digital smoking cessation program delivered through Internet and cell phone without nicotine replacement (Happy Ending): Randomized controlled trial. Journal of Medical Internet Research, 10, e51.

Collins, L. M., Murphy, S. A., & Strecher, V. (2007). The Multiphase Optimization Strategy (MOST) and the Sequential Multiple Assignment Randomized Trial (SMART): new methods for more potent eHealth Interventions. American Journal of Preventive Medicine, 32, S112-S118.

Crutzen, R., de Nooijer, D., Brouwer, W., Oenema, A., Brug, J., & de Vries, N. (2009). Effectiveness of online word of mouth on exposure to an Internet-delivered intervention. Psychology and Health, 24, 651-661.

Eysenbach, G. (2002). Issues in evaluating health websites in an Internet-based randomized controlled trial. Journal of Medical Internet Research, 4, e17.

Fridici, M., Lohaus, A., & Glaß, C. (2009). Effects of incentives in web-based prevention for adolescents: Results of an exploratory field study. Psychology and Health, 24, 663-675.

Kraft, P., Drozd, F. & Olsen, E. (2009). ePsychology: Designing theory-based health promotion interventions. CAIS, 24, 399-426.,

Matano, R., Koopman, C., Wanat, S., Winzelberg, A., Whitsell, S., Westrup, D., Futa, K., Clayton, J. Mussman, L. & Taylor, C. (2007). A pilot study of an interactive web site in the workplace for reducing alcohol consumption. Journal of Substance Abuse Treatment, 1, 71-80.

Murray, E., Burns, J., See, S. T., Lai, R., & Nazareth, I. (2005). Interactive health communication applications for people with chronic disease. Cochrane Database of Systematic Reviews, Issue 4, Art. No.: CD004274.

Norman, D. A. (2003). Emotional design: Why we love (or hate) everyday things. New York: Basic Books.

Portnoy, D. B., Scott-Sheldon, L. A. J., Johnson, B. T. & Carey, M. P. (2008). Computer-delivered interventions for health promotion and behavioural risk reduction: A meta-analysis of 75 randomized controlled trials, 1988 – 2007. Preventive Medicine, 47, 3-16.

Rodham, K., McCabe, C., & Blake, D. (2009). Seeking support: an interpretative phenomenological analysis of an Internet message board for people with Complex Regional Pain Syndrome. Psychology and Health, 24, 619-634.

Strecher VJ, McClure JB, Alexander GL, Chakraborty B, Nair VN, Konkel JM, Greene SM, Collins LM, Carlier CC, Wiese CJ, Little RJ, Pomerleau CS, Pomerleau OF. (2008). Web-based smoking-cessation programs: results of a randomized trial. American Journal of Preventive Medicine, 5, 373-81.

Tate, D. F., Jackovny, E. H., & Wing, R. R. (2003). Effects of Internet behavioral counseling on weight loss in adults at risk for type 2 diabetes. Journal of the American Medical Association, 289, 1833-1836.

van Koningsbruggen, G. M. & Das, E. (2009). Don’t derogate this message! Self-affirmation promotes online type 2 diabetes risk test taking. Psychology and Health, 24, 635-649.

Vandelanotte, C., Spathoris, K. M., Eakin, E. G., & Owen, N. (2007). Website-delivered physical activity interventions. American Journal of Preventive Medicine, 33, 54-64.

A full version of this article can be found here:

Kraft, P. & Yardley, L. (2009). Current issues and new directions in psychology and health: What is the future of digital interventions for health behaviour change? Psychology & Health, 24, 615–618.

Evidence from efficacy trials indicates that exposure rates to Internet-delivered interventions are low (De Nooijer, Oenema, Kloek, Brug, De Vries, & De Vries, 2005), and they may be even lower when these interventions are implemented in real life rather than in a research setting (Evers, Cummins, Prochaska, & Prochaska, 2005). Exposure of individuals to the intervention content, through use of the intervention, is necessary since attention is a prerequisite to establish desired behaviour change (McGuire, 1985). Therefore, it remains important to assess exposure to Internet-delivered interventions.

There are several measures to assess exposure to Internet-delivered interventions, such as frequency and duration of visits, but there is no gold standard. Each exposure measure relates to a different aspect of exposure (Danaher, Boles, Akers, Gordon, & Severson, 2006). One can visit an intervention very frequent, for example, but only for a short period of time. Duration of visits, on the other hand, does not necessarily give a clear picture of participants’ online activity, since one does not know to how much information participants are possibly exposed. Therefore, number of visited web pages would be more appropriate to assess online activity. All such exposure measures can be tracked objectively and are, in contrast to self-reported exposure measures, independent of participants’ memory, interpretation, or social desirability.

Linking exposure measures to variables at the individual level

To fruitfully utilise exposure measures it has to be possible to link them to variables at the individual level, to be able to compare subgroups that differ on socio-demographic, psycho-social, or behavioural measures. Moreover, exposure measures on the individual level can also be linked to outcome measures of an intervention. By doing so, it is possible to study potentially mediating effects of objectively tracked exposure on interventions’ outcome measures. The latter, however, is no common practice to date (Crutzen, De Nooijer, Brouwer, Oenema, Brug, & De Vries, submitted). For example, recent studies (An et al., 2006; Chen & Yeh, 2006; Escoffery, McCormick, & Bateman, 2004) reported a limited number of exposure measures and did not relate them to the intervention’s outcome measures, making it impossible to study potentially mediating effects of exposure.

Practical implications

There are no known technical barriers to track exposure measures of Internet-delivered interventions. It is important, however, to realize this from the start of an intervention development process and to involve technical staff during this initial phase (Crutzen, De Nooijer, Brouwer, Oenema, Brug, & De Vries, e-pub ahead of print). Furthermore, dependent on the laws in certain countries or states, it may be necessary to take additional steps. In a recent Dutch project, for example, it was necessary to register the project at the Dutch Data Protection Authority, which supervises the fair and lawful use and security of personal data (Crutzen, De Nooijer, Candel, & De Vries, 2008). If these legal issues are covered, we recommend to track as many exposure measures as possible since there is no gold standard. Furthermore, having exposure measures available is also useful during process evaluation of Internet-delivered interventions, as has been shown in other studies (Barak & Fisher, 2003; Lou, Zhao, Gao, & Shah, 2006; Patten et al., 2007; Roberto, Zimmerman, Carlyle, & Abner, 2007). These exposure measures provide detailed insight into where participants either leave the intervention website or have come to a standstill. This information can be used to adapt interventions to users’ needs and therewith increase exposure rates and probability of behaviour change.

References:

An, L. C., Perry, C. L., Lein, E. B., Klatt, C., Farley, D. M., Bliss, R. L., et al. (2006). Strategies for increasing adherence to an online smoking cessation intervention for college students. Nicotine & Tobacco Research, 8 (S1), S7-S12.

Barak, A., & Fisher, W. A. (2003). Experience with an Internet-based, theoretically grounded educational resource for the promotion of sexual and reproductive health. Sexual and Relationship Therapy, 18, 293-308.

Chen, H.-H., & Yeh, M.-L. (2006). Developing and evaluating a smoking cessation program combined with an Internet-assisted instruction program for adolescents with smoking. Patient Education and Counseling, 61, 411-418.

Crutzen, R., De Nooijer, J., Brouwer, W., Oenema, A., Brug, J., & De Vries, N. K. (e-pub ahead of print). A conceptual framework for understanding and improving adolescents’ exposure to Internet-delivered interventions. Health Promotion International.

Crutzen, R., De Nooijer, J., Brouwer, W., Oenema, A., Brug, J., & De Vries, N. K. (submitted). How to facilitate exposure to Internet-delivered health behavior change interventions aimed at adolescents or young adults? A systematic review.

Crutzen, R., De Nooijer, J., Candel, M. J. J. M., & De Vries, N. K. (2008). Adolescents who intend to change multiple health behaviours choose greater exposure to an Internet-delivered intervention. Journal of Health Psychology, 13, 906-911.

Danaher, B. G., Boles, S. M., Akers, L., Gordon, J. S., & Severson, H. H. (2006). Defining participant exposure measures in web-based health behavior change programs. Journal of Medical Internet Research, 8, e15.

De Nooijer, J., Oenema, A., Kloek, G., Brug, J., De Vries, H., & De Vries, N. K. (2005). Bevordering van Gezond Gedrag via Internet: Nu en in de Toekomst [Promotion of Healthy Behavior through the Internet: Now and in the Future]. Maastricht: Maastricht University.

Escoffery, C., McCormick, L., & Bateman, K. (2004). Development and process evaluation of a web-based smoking cessation program for college smokers: innovative tool for education Patient Education and Counseling, 53, 217-225.

Evers, K. E., Cummins, C. O., Prochaska, J. O., & Prochaska, J. M. (2005). Online health behavior and disease management programs: are we ready for them? Are they ready for us? Journal of Medical Internet Research, 7, e27.

Lou, C. H., Zhao, Q., Gao, E. S., & Shah, I. H. (2006). Can the Internet be used effectively to provide sex education to young people in China? Journal of Adolescent Health, 39, 720-728.

McGuire, W. J. (1985). Attitudes and attitude change. In M. Lindsay & E. Aronson (Eds.), The Handbook of Social Psychology (pp. 233-346). New York: Random House.

Patten, C. A., Rock, E., Meis, T. M., Decker, P. A., Colligan, R. C., Pingree, S., et al. (2007). Frequency and type of use of a home-based, Internet intervention for adolescent smoking cessation. Journal of Adolescent Health, 41, 437-443.

Roberto, A. J., Zimmerman, R. S., Carlyle, K. E., & Abner, E. L. (2007). A computer-based approach to preventing pregnancy, STD, and HIV in rural adolescents. Journal of Health Communication, 12, 53-76.