Evaluation of an online knowledge translation intervention to promote cancer risk reduction behaviours: findings from a randomized controlled trial

In Canada, an estimated one-third to one-half of all cancers are preventable through lifestyle modification such as smoking cessation, increasing physical activity, healthy eating, and reducing alcohol intake [1, 2]. Despite this, few Canadians engage in behaviours that are in line with evidence-based cancer prevention guidelines; 20% of Canadian adults smoke [3], 85% do not meet physical activity guidelines [4], 77% eat less than five servings of fruits and vegetables per day [5], and 20% of men and 8% of women consume more than two alcoholic drinks per day [6].

Although people generally understand that they should eat better, exercise more, drink less and not smoke, there is a lack of awareness of the link between these lifestyle behaviours and cancer risk. In a survey of Canadian adults, while 90% were aware of the link between smoking and cancer, knowledge was much lower for the link between cancer risk and diet (52%), alcohol intake (33%), being overweight (31%) and physical inactivity (28%) [7]. Similar results have been found in other countries. In a national survey of US adults, only 44% believed that individual behaviours contributed substantially to the risk of developing cancer [8], and a UK survey found only a small proportion of adults were aware that poor diet (32%), physical inactivity (22%) and frequent alcohol intake (33%) contributed to cancer risk [9]. While the acquisition of knowledge on cancer prevention is only one component of the process of behaviour change and reducing cancer risk, an effective and scalable knowledge translation (KT) strategy that can increase public knowledge of evidence-based cancer prevention recommendations may be an important step in this pathway.

Increasingly, many people turn to the internet and social media as a source of health information [10‐13]. A study of online searchers using Google AdWords found that over an 11-month period there were over 117 million unique searches in Canada alone related to cancer prevention (physical activity/exercise, healthy eating, weight loss and quitting smoking) [11]. Unfortunately, much of the health information available online is not based on scientific evidence [14, 15]. Members of the public may not have the knowledge, skills or time to sift through and identify credible messages [16‐18] and may be acting on recommendations which are unlikely to improve health. A National Cancer Institute survey found that almost half of Americans reported seeking out information about cancer online; of these, 58% of reported concerns about the quality of information [19], 48% found the search to require a lot of effort and 41% found it to be frustrating. Importantly, those with negative experiences with the process of searching for cancer information online were more likely to have inaccurate knowledge and beliefs about what can be done to prevent cancer [19].

The McMaster Optimal Aging Portal (the Portal) was launched in English in 2014, and French in 2017, as an online repository of evidence-based information to increase public access to trustworthy health information related to healthy aging [20]. Content (blog posts, evidence summaries and web-resource ratings) are developed and maintained through a team at McMaster University, and aim to provide easy-to-read, ‘bottom line’ messages appropriate for all audiences, with or without previous medical or scientific knowledge and training (see previous publications for a description of Portal development and content [21‐24]). In addition to being a source for accessible and trustworthy cancer prevention information for Canadian adults, it has potential to be particularly helpful for underserved populations and those in rural and remote locations who experience barriers to accessing health information through a healthcare provider, for example.

Evidence from recent systematic reviews suggests that websites and social media have the potential to improve health behaviours, self-efficacy [25] and health outcomes [26], including those related to cancer prevention [27]. For example, access to credible and reliable web information is associated with compliance to evidence-based recommendations for colorectal cancer screening [28]. However, it is not known if access to high-quality information about cancer prevention results in behaviour changes such as smoking cessation, increased physical activity, healthy eating, and reduced alcohol consumption. The purpose of this study is to understand if and how KT strategies used to disseminate information about cancer prevention through the Portal impact knowledge, intentions and health behaviours. A secondary aim was to compare outcomes in rural Canadians to those who live in urban areas.

Using a sequential explanatory mixed-methods design [29], we evaluated the Portal’s existing KT strategies to disseminate research on cancer prevention – specifically related to smoking, physical activity, healthy eating, and alcohol intake – to study participants. A two-arm randomized controlled trial (RCT) was conducted, followed by a qualitative process study to explore the findings from the RCT in greater depth. This approach, rather than a simple RCT, was selected to allow for a deeper analysis of not only the quantitative outcomes of interest, but also to gain greater understanding of the KT process. Ethical approval was provided by the Hamilton Integrated Research Ethics board (ID: 3285) and all participants provided written, informed consent.

Of 671 individuals who responded to online recruitment, 557 eligible participants completed the baseline questionnaire and were randomized to the intervention (n = 278) or control group (n = 279) (Fig. 2). Retention was high, with 88.3 and 84.2% of participants completing end of study and follow-up questionnaires respectively. Participants who failed to complete the end of study questionnaire were eligible and invited to complete the follow-up. There were no differences in loss to follow-up between groups. Participants who did not complete the end of study questionnaire were more likely to have not previously used the Portal (68.3% vs. 51.5%, p = 0.01), have lower baseline self-rated health (3.6 vs. 3.9 on a 5-point Likert scale, p = 0.02), and have lower baseline physical activity (28.7 vs. 35.3 points, p = 0.03). Participants who did not complete the follow-up questionnaire were more likely to have never used the Portal (58.8 vs. 52.4, p = 0.02), and have lower baseline physical activity (26.4 vs. 36.0 points, p < 0.001). No other descriptive characteristics were associated with loss to follow-up.

There were no baseline differences in demographic characteristics between the intervention and control groups (Table 1). Participants were predominantly older (65.2 ± 8.0 years), retired (71.6%) female (80.3%), and well-educated (94.1% had post-secondary education, and one-third had a post-graduate degree). Despite 51.4% reporting at least one chronic condition, 71.1% rated their health as ‘excellent’ or ‘very good’. Half of participants had never used the Portal before, and one-quarter were regular users.

Based on our findings, dissemination of evidence-based information through the Portal results in small increases in knowledge of cancer prevention guidelines, and may have an impact on health behaviours, particularly in certain subgroups. Overall, we found a very small increase in the number of bouts per week of light-intensity physical activity in the intervention group at end of study, as well as a small reduction in servings per week of liquor intake at follow-up, however the small magnitude of these changes may have limited clinical significance.

Of note is the very high knowledge of cancer prevention guidelines at baseline, as well as generally positive health behaviours reported by participants, and high educational levels. Our intervention, based on the TPB, aimed to alter participants’ attitudes towards behaviour through increased knowledge and awareness of cancer risk-reduction behaviours. The likelihood of observing change was limited by the ceiling effect as a result of participants’ already high baseline knowledge.

Subgroup analyses suggest that those with lowest baseline self-rated health may experience a greater change total physical activity than those with moderate-high self-rated health at baseline, which most of our study participants were. These results replicate our team’s previous findings from the Move4Age study [47]. This study used a similar approach to deliver evidence-based information through the Portal related to physical activity and physical mobility to middle-aged and older adults. In the Move4Age study, both intervention and control group participants reported significant changes in physical activity that were maintained at the 3-month follow-up period, however there were no significant differences between groups. Interestingly, when analyses were restricted to those with low self-rated health, the intervention group reported a greater improvement in physical activity.

In both of our Portal studies to date, our study sample consisted of primarily well-educated, retired females, which is likely the result of our recruitment methods through our existing networks of Portal partners. This is consistent with findings from a systematic review of reviews, which found that the reach of interventions included in reviews of internet-delivered lifestyle behaviour change interventions was primarily limited to female, highly-educated, white individuals living in high-income countries [48]. One advantage of online compared to in-person interventions is the potential to reduce health inequities due to improved access and scalability. However, this advantage is not likely to be realized if those who may have the most to gain from an intervention (i.e., those with lower self-rated health, low socioeconomic status, rural or remote individuals with limited access to a healthcare provider) are unlikely to become engaged [49]. In an analysis from the Health Informational National Trends Survey, individuals who are older, male, or have lower education are least likely to engage in eHealth activities [50]. Further work is needed to understand how to best design, adapt and deliver interventions to underserved populations who may have the most to gain from an intervention such as the Portal.

It is well known that increasing knowledge alone is often inadequate to change behaviours to a sufficient degree that improvement in long-term health outcomes will be realized [51]. Internet-delivered interventions are often based primarily around provision of educational materials to support behaviour change in an electronic format. Recent reviews have found that incorporating additional evidence-based behaviour change techniques is important to maximize the effect of these interventions, with the number of behaviour change techniques correlated with intervention effect size [30]. Individual-tailoring, goal setting and action planning, self-monitoring, feedback, social support and social comparison, and modelling are associated with increased effectiveness of eHealth or mHealth interventions [52‐54]. For example, a recent study that evaluated the effect of ‘MyPlan1.0’, a physical activity intervention for recently retired adults in Belgium, found that those who completed three online ‘modules’ which included tailored feedback that targeted intention to change for motivation, action planning and self-monitoring resulted in an increase in walking and leisure-time vigorous physical activity after one-month compared to a control group [55]. For those who design online health resources such as the Portal, it is challenging to find ways to efficiently incorporate individualization and tailoring while maintaining broad reach and generalizability. Tailoring may be accomplished in a variety of ways, either manually by a researcher (human tailoring) or expert system (computer tailoring) using developed algorithms [56]. Tailoring can range from quite simple (i.e., using the individual’s first name in materials, using a baseline assessment only) to highly complex (i.e., dynamic tailoring where ongoing monitoring or feedback informs tailoring throughout an intervention) [57]. A recent systematic review of tailored eHealth interventions targeting weight loss found a wide range of tailoring methods utilized across studies, including theoretical basis, when, how often, and how tailoring was conducted, and what variables or factors tailoring was based on [58]. Overall, the authors found that tailoring was more effective in supporting weight loss than generic interventions or wait-list controls [58]. However, in order to enhance the impact of these tools and resources, a better understanding of the components necessary for eliciting behaviour change, and specific mechanisms of tailoring that are most effective, is needed.

Another important aspect to consider when interpreting results from this and other online interventions is the actual ‘dose’ of intervention received. A previous review of 83 web-based health interventions found that only half of participants engage with the interventions in the way they were designed [59]. User engagement data collected here via Google analytics estimate that less than one-third of participants engaged with intervention content through email alerts on any given week. Our qualitative data helps to explain the apparent discrepancies between the high self-reported email use (95% reported using email during the intervention period) and low weekly usage (ranging from 6.8 to 83.1%). Participants reported one of the benefits of receiving intervention content by email was that they were able to quickly and easily self-select the content that was most interesting and relevant to them. So, while 95% reported using email, they did not do so on a weekly basis. This is not surprising given the range of topics covered related to cancer prevention. Only three individuals in our study self-identified as smokers at baseline, thus we would not expect the majority of participants to be interested in engaging in content about smoking cessation. Tailoring of intervention content by baseline behaviours, or self-reported interest (as described above) may be one way to maximize intervention engagement in future studies. Further rigorous research is needed to understand which methods of tailoring are most effective at promoting engagement, and thus having the greatest likelihood of eliciting behaviour change.

Although we were not able to track intervention group engagement with social media posts, based on self-reported usage, engagement with Twitter and Facebook content appears very low. This finding has implications for the design of future interventions in this population of older adults. Previous studies have found a positive impact of social media interventions on health behaviours such as smoking cessation [60], and weight loss [61], however these studies have been primarily conducted in younger adults whose usage patterns and preferences may be different from the older adults included in our study.

The use of the internet and social media to disseminate information and promote behaviour change, particularly with respect to cancer prevention and treatment is growing rapidly; however evaluation of such real-world tools and websites on actual behaviour change is lacking [62]. Here we present the second in a series of studies undertaken to evaluate the impact of the McMaster Optimal Aging Portal, a freely-accessible online repository of evidence-based information on knowledge, intentions and health behaviours of middle-aged and older adults. Dissemination of evidence-based cancer prevention information through the Portal appears to improve knowledge of risk-reduction strategies, and may have a small impact on self-reported health behaviours in particular groups. There is a need to understand how best to engage with these groups, such as those with lower perceived self-rated health, who may stand to benefit most from an intervention delivered through the Portal. Next steps are to understand how tailoring of interventions and KT strategies may help to encourage more meaningful changes in health behaviours and ultimately long-term health outcomes of Portal users, and to assess whether enhanced tailoring results in greater improvements in lifestyle behaviours, as well as knowledge and attitudes in more high-needs groups.