eHealth Engagement Scale


Published on

A description of the development of a scale for measuring consumer engagement with health content on websites and other digital media. We define engagement as the process of involving users in health content in ways that motivate and lead to health behavior change. Significant predictive validity with behavioral intentions and readiness to act was found in nine health content areas. The eHealth Engagement Scale may prove to be an important mediator of user retention of information, intentions to change, and ultimately efforts to undertake and achieve behavior change.

Published in: Health & Medicine, Technology
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide
  • Examples of these tools include ones that provide access to and use of health information, support health behavior change and disease prevention, allow individuals to monitor and manage their own health, online communities, various types of decision-making aids, methods to monitor and manage diseases and chronic conditions, and electronic and personal health records
  • Cummins, CO, Prochaska, JO, Driskell, MM, Evers, KE, Wright, JA, Prochaska, JM, & Velicer, WF. (2003). Development of Review Criteria to Evaluate Health Beha Evers, KE, Cummins, CO, Prochaska, JO & Prochaska, JM. Online Health Behavior and Disease Management Programs: Are We Ready for Them? Are They Ready for Us? J Med Internet Res 2005; 7(3):e27 [doi:10.2196/jmir.7.3.e27].vior Change Websites. Journal of Health Psychology; 8: 55-62.  
  • Can be influenced by a number of variables including: the information architecture and usability of a web site, the structure and format of the content itself,various user characteristics and motivations that are brought to the task.
  • Participants were recruited by telephone and e-mail solicitation to achieve an even distribution across the 10 Health and Human Service regions in the United States. Study eligibility was determined by a respondent answering “yes” to the following questions: 1. Have you ever used the Internet?2. In the past 12 months, have you used the Internet to send an e-mail message?3. Are you between the ages of 18–65? A stratified sampling strategy was also employed with the following parameters: A 60:40 ratio of women to men to reflect documented gender differences in health information-seeking behaviors10–15 percent African American10–15 percent Hispanic/Latino3–5 percent Asian or Pacific Islander10 percent high school, some high school, or GED30 percent some college30 percent college graduate30 percent college graduate plus additional training (i.e., more college, advanced degrees, certifications, continuing education)
  •  The content for each health topic was presented in three screen shots that presented content specific to that health area from three of nine health information components developed in our earlier work. These components included:  Understanding the problem Importance of preventing the problem Risk factors: what makes people more at risk for the problem Assessment of personal risk Overcoming barriers Motivators Strategies for preventing the problem Community Resources/LocatorGuidelines/Standards/ Recommendations The three information components that were addressed in each content area were determined in a formative study by a card-sorting task done by 81 participants not included in this study. These participants ranked the 3 “most useful” and 3 “least useful” information components for each health content area using actual expert advice for each one. Participants then rank-ordered the ‘most useful” cards and were questioned to assess their appropriateness, acceptability, applicability, and engagement properties (Note: this also served as a pre-test of these questions for inclusion in the current study).  At the bottom of each screen, participants were asked to respond to two 5-point Likert scale statements before proceeding to the next component (RQ 1 and 2 in Figure 1). These statements were constructed to assess the perceived usefulness of the information and the confidence or self-efficacy one had to address the individual health topic, as shown in the following example from the high blood pressure screening topic: 1. This page provided important information about preventing high blood pressure.(1–Strongly Agree to 5–Strongly Disagree).2. I feel more confident I can prevent high blood pressure.(1–Strongly Agree to 5–Strongly Disagree). This sequence of presentations of each component with the two Likert items was repeated for the other two health topics. After completing each of the three health content areas, the participants completed two questionnaires. The first one assessed the appropriateness, applicability, motivation, and intentions to change or engage in health behaviors relevant to the set of content components displayed for that health topic. The second questionnaire was the eHealth Engagement Scale in which participants rated each of the 12 descriptors on a 5-point Likert scale (1=Strongly Agree, 5=Strongly Disagree) for each health content area. After completing these screens, participants were offered the opportunity to provide open-ended comments about that health topic. A final set of questions about their demographic characteristics and Internet usage and connectivity were asked after the third set of questionnaires was completed. When these questions were answered, the participants received a confirmation page thanking them for their time.
  • Median completion time = 23:42
  • A series of factor analyses were performed using the SYSTAT RAMONA module. Our attempt to conduct a confirmatory factor analysis of the original factor structure of the engagement tool as developed for advertising purposes was not very successful. The overall fit of the model was poor, c2(50, N = 682) = 689.08, p < .001; RMSEA = .136 (.127, .145). This was primarily due to smaller correlations of the Suspenseful item with the rest of the Involving factor indicators, as well as moderate correlations of Involving indicators with indicators of other factors - specifically between the Clever and Hip, Cool item (r = .626) and between the Thought-provoking and Convincing item (r = .625). The moderate to high correlations among factors (.383 – .795) also suggested that these factors were not as distinctive in this context as suggested by the previous commercial work. Consequently, when we deleted the Suspenseful, Thought-Provoking and Clever items from the Involving factor, the fit of the overall model improved (see Figure 2). This modified four-factor model had a reasonable overall fit, c2(23, N = 682) = 120.63, p < .001; RMSEA = .079 (.065, .093). By deleting these three items from the Involving factor, the Cronbach’s alpha for this factor modestly decreased from .878 to .853. We also performed an exploratory factor analysis on the current data using the Maximum Likelihood extraction method with an Oblimin rotation and Kaiser Normalization using SPSS. This resulted in a two-factor solution where Factor 1 resembled the original Involving factor and included Absorbing, Attention-grabbing, Surprising, Thought-provoking, Convincing, Balanced, Believable, and Not Dull. The second factor consisted of Surprising, Suspenseful, Clever, Hip/Cool, reflecting a dimension of “Stimulating.” However, both factors contained overlapping items for which loadings are greater than .5 (Absorbing, Attention-Grabbing, Clever and Not Dull).  
  • the modified 4-factor structure proved to provide the best fit with the data, c2(46, N = 682) = 183.498, p < .001; RMSEA = .066 (.056, .076). Both the Involving and Credible factors significantly predicted the Outcome factor (the three items above used as indicators). The four factors together accounted for approximately 56% of the outcome variance.
  • Such influences on user engagement may include how websites use different types of information architecture design, the value of tailoring and targeting content, scheduling of homework tasks and the tracking of progress, roles of media and interactivity, structure and value of community and social components (e.g., Web forums, peer ratings of content), and impact of email and/or mobile phone features
  • eHealth Engagement Scale

    1. 1. The Assessment of User Engagement with eHealth Content: The eHealth Engagement Scale1<br />R. Craig Lefebvre, PhD, Yuri Tada, PhD, Sandra W. Hilfiker, MA & Cynthia Baur, PhD<br />Health Communication, Marketing and Media Conference<br />12 August 2009<br />1Accepted for publication in the Journal of Computer-Mediated Communication<br />
    2. 2. eHealth Tools<br /> Enable consumers, patients, and informal caregivers to gather information, make healthcare decisions, communicate with healthcare providers, manage chronic disease, and engage in other health-related activities. <br />
    3. 3. The 5 As: Behavior Change Benchmarks<br />Ask, Advise, Assess, provide Anticipatory Guidance and Arrange Follow-Up 1.<br />An assessment of 273 health websites that targeted seven health behaviors 2.<br />Results: 25% used three or more of the 5 As; very low (&lt;20%) participation rates among the general population<br />Implications: Need more research to improve (1) participation rates and use of eHealth internet sites, and (2) the quality of the eHealth interventions to promote changes in health behaviors.<br />1. Cummins et al. (2003). Development of Review Criteria to Evaluate Health Behavior Change Websites. Journal of Health Psychology; 8: 55-62. <br /> 2. Evers et al (2005). Online Health Behavior and Disease Management Programs: Are We Ready for Them? Are They Ready for Us? J Med Internet Res; 7(3):e27 [doi:10.2196/jmir.7.3.e27].<br />
    4. 4. Engagement is turning on a prospect to a brand idea enhanced by the surrounding context(Advertising Research Foundation).<br />
    5. 5. “…engagement starts from the vantage point of the customer - either current or potential. By starting there and thinking about whether they are engaged, how can we engage them, and how can we help them achieve their desires, goals or needs, then the risk of not being invited in goes away because you become more relevant. “<br />
    6. 6. Engagement in eHealth<br /> The process of involving users in health content in ways that motivate and lead to health behavior change.<br />
    7. 7. Engagement and Advertising Effectiveness<br />
    8. 8. Can Advertising Measures of Engagement be Used for eHealth?<br />The variability of results achieved across numerous stimulus conditions (i.e., whether it is sensitive to variations in user perceptions of different materials). <br />The internal consistency of the scale. <br />Its underlying factor structure.<br />Its association with immediate outcomes (intentions and self-efficacy to change health behaviors after viewing content).<br />
    9. 9. Method<br /> prototype development.<br />Ninehealth topics: overweight/obesity, physical activity, preventing falls in the elderly, high blood pressure screening, influenza immunization, nutrition, tobacco cessation, colorectal cancer screening, and talking to children about drugs. <br />
    10. 10. Engagement Items<br /><ul><li>Absorbing
    11. 11. Attention-Grabbing
    12. 12. Stimulating
    13. 13. Surprising
    14. 14. Suspenseful
    15. 15. Thought-Provoking
    16. 16. Clever
    17. 17. Convincing
    18. 18. Balanced
    19. 19. Believable
    20. 20. (Not) Dull
    21. 21. Hip/Cool</li></li></ul><li>Method<br />Online and telephone recruitment of stratified sample (N=403)<br />Assigned to condition:<br />Seeking information about a health problem for themselves or someone they know.<br />Wanting to find out if they or someone they know has a health problem or reason to be concerned about a health problem.<br />Seeking information to prevent the onset of a health problem.<br />
    22. 22. Protocol<br />
    23. 23. Participant Characteristics<br />260/403 (65% completion rate)<br />30 removed – response bias (15), +3 SD completion time (14), double exposure (1)<br />Final sample = 230 (88%). Χ2 for demographic differences was n.s.<br />60:40 split of women (n = 138) to men (n = 92)<br />55% of the sample was between the ages of 30-49 years old<br />skewed towards the upper levels of household income (HHI) with 19% (44) reporting HHI &gt; $100,000 and 9% (21) reporting HHI &lt; $20,000 (Median = $40,001-$70,000).<br />
    24. 24. Involving <br />
    25. 25. Involving<br />
    26. 26. Credible<br />
    27. 27. Factor Structure<br />Three solutions: confirmatory, modified 4-factor, and a 2-factor.<br />Tested against predictive validity criteria:<br />The information made me feel more confident that I can do something.<br />The information made me feel more prepared to do something.<br />The information made me feel more prepared to do something in the next month.<br />
    28. 28. Path Model of Modified 4-Factor Solution to Explain Immediate Outcome Variables<br />
    29. 29. Implications<br />A measure of engagement developed for commercial television advertising viewing to predict advertising effectiveness can be used to assess user engagement with eHealth content.<br />A modified 9-item version of the original scale accounted for 56% of the variance in self-reported confidence and intentions to act across nine different health topics.<br />
    30. 30. Implications<br />This modified 4-factor model was subsequently used to analyze the engagement data for each of the nine health content areas separately. Our results (not shown here) found that the factor structure of the modified 4-factor solution was stable across all nine topic areas. <br />User engagement may prove to be an important mediator of user retention of information, intentions to change, and ultimately efforts to undertake and achieve behavior change. <br />