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ObjectiveThis study examines adolescents’ willingness to use the internet and other forms of technology for health promotion purposes (i.e., “eHealth promotion” willingness) and determines if a relationship exists between adolescents’ behavioral risks and their eHealth promotion willingness.MethodsA total of 332 adolescents provided data at a routine medical check-up, including assessments of technology access, eHealth promotion willingness, and multiple behavioral risk factors for child- and adult-onset disease (body mass index, physical activity, smoking, sun protection, depression).ResultsThe level of access to technology among the sample was high, with moderate willingness to engage in eHealth promotion. After adjusting for adolescents’ access to technology, the presence of multiple behavioral risk factors was positively associated with willingness to use technology for health promotion purposes (β =.12, p =.03).ConclusionsAdolescents with both single and multiple behavioral risk factors are in need of health promotion to prevent the onset of disease later in life. eHealth appears to be an acceptable and promising intervention approach with this population.
Leading causes of death in the United States include cardiovascular disease, cancer, and diabetes (Mokdad, Marks, Stroup, & Gerberding, 2004). Though biology and heredity are known to contribute significantly to these outcomes, behavioral factors play key roles in disease burden as well. Recent reports by the Centers for Disease Control and Prevention (Mokdad et al., 2004) and the World Health Organization (Ezzati, Lopez, Rodgers, Vander, & Murray, 2002) reaffirm the health consequences of behaviors such as tobacco use, poor diet, physical inactivity, and other lifestyle choices—concluding that almost 50% of all deaths may be attributable to a relatively small number of behavioral risks (Mokdad et al., 2004). These and other findings underscore the importance of behavioral risk factor surveillance, prevention, and reduction to improve population health.
Although the majority of the aforementioned chronic conditions and adverse outcomes manifest during adulthood, childhood (and adolescence in particular) plays an important role in the onset and maintenance of many actual causes of death (Tercyak & Tyc, 2006; Williams, Holmbeck, & Greenley, 2002; Windle et al., 2004). For example, 54% of high school students have tried cigarette smoking and 23% smoke at least monthly (Eaton et al., 2006). Experimenting with smoking, even a few puffs, can eventually lead to smoking on a daily basis, often within a few years (Gilpin, Choi, Berry, & Pierce, 1999). With respect to diet, only 20% of adolescents have sufficient (health beneficial) fruit and vegetable intake, and only 16% drink milk regularly (Eaton et al., 2006). Data for physical activity levels are also worrisome, as only 36% of adolescents meet current recommendations for aerobic activity (Eaton et al., 2006). Together, these and other factors contribute to the nearly 16% of adolescents who are at risk for becoming overweight, and the 13% already obese (Eaton et al., 2006). Although sun protection is effective in preventing skin cancer, only 9–18% of adolescents adequately protect their skin from the sun's harmful rays (Eaton et al., 2006). Reversing this trend is critical, especially since childhood is a sensitive developmental period in skin carcinogenesis (Mancini, 2004).
While the prevalence of several adolescent behavioral risk factors has decreased over the past 15 years (e.g., tobacco and substance use, sexual risk behaviors), many persist. Research suggests that when adolescents transition into young adulthood, their preexisting health risks (e.g., poor diet, physical inactivity, obesity, substance use) track over time (Baranowski et al., 1997; Cullen et al., 1999) and may become exacerbated; conversely, access to health care decreases for a majority of individuals (Harris, Gordon-Larsen, Chantala, & Udry, 2006). Several explanations exist as to why adolescent behavioral risk factors persist, including changes in adolescents’ social roles (e.g., leaving home, changing schools and peer groups, obtaining employment) which support their self-care and well-being (Baranowski et al., 1997; Windle et al., 2004). Also important to consider is the psychosocial stress that may accompany these changes, further accelerating adolescents’ behavioral risk profiles (Monroe, Slavich, Torres, & Gotlib, 2007; Park, 2003). Adolescents with pronounced depressive symptoms are more likely to smoke (Lloyd-Richardson, Papandonatos, Kazura, Stanton, & Niaura, 2002; Moolchan, Ernst, & Henningfield, 2000) and use other illicit substances (Simantov, Schoen, & Klein, 2000), consume a less healthful diet (Fulkerson, Sherwood, Perry, Neumark-Sztainer, & Story, 2004), be physically inactive (Anton et al., 2006), and to become obese (Goodman & Whitaker, 2002). If depressive symptoms remain unaddressed, the likelihood that adolescents will experience negative social and health outcomes in adulthood is increased (Fombonne, Wostear, Cooper, Harrington, & Rutter, 2001; Rao et al., 1995).
Research on adolescent health behavior mostly focuses on single risk factors and relatively less attention is paid to simultaneously examining multiple behavioral risks. In actuality, adolescents’ health risk behaviors cooccur and are often comorbid (DuRant, Smith, Kreiter, & Krowchuk, 1999; Lowry, Kann, Collins, & Kolbe, 1996; Pate, Heath, Dowda, & Trost, 1996). In one of the most comprehensive reviews written on this subject to date (Guilamo-Ramos, Litardo, & Jaccard, 2005), researchers examined the overall strength of association among adolescent risk behaviors. Behaviors of interest included deviance (e.g., delinquency), cigarette, alcohol, and other substance use, and sexual activity (e.g., intercourse, condom use). The association between most behaviors was r =.35, and age- and cohort-related effects were present (younger adolescents, and the results of studies conducted in prior generations, displayed greater associations among adolescent risk behaviors). An earlier review reached similar conclusions (Fahs et al., 1999), recognizing the comorbidity of adolescents risks and the need to intervene.
Problem Behavior Theory (Jessor & Jessor, 1977), and its extension to adolescent health compromise (Donovan, Jessor, & Costa, 1991), predict that progress in preventing and controlling our nation's chronic disease risks would be enhanced by simultaneously attending to the behaviors that underlie them, and their cooccurrences. It has been suggested that multiple behavioral risk factors (such as tobacco use, poor diet, physical inactivity, and alcohol use) could be targeted simultaneously to prevent and control cancer and other chronic diseases (Orleans, 2004). A recent analysis of data from adolescents provides some estimates of risk factor comorbidity, but those data are difficult to interpret because of definitional criteria adopted. For example, the study suggests that 91% met recommendations for not smoking because they had not smoked more than 100 cigarettes in their lifetimes. In adolescents tobacco research, this definitional threshold is high and could miss key subgroups with less well-formed smoking habits. The study also found that 79% of adolescents met recommendations for healthy weight, and that fewer met recommendations for a healthy diet (64%) and physical activity (59%) (Pronk et al., 2004). Together, about one-third of adolescents were adherent to all healthful recommendations (4) and about two-thirds were adherent to some recommendations (3); depression had a negative impact on adherence. Like adolescents, adult primary care patients are unlikely to be screened for multiple behavioral risks, though >50% have such risk factors present (Coups, Gaba, & Orleans, 2004).
Given the current state of the problem and the modest benefits of existing adolescent health promotion interventions (Prochaska & Sallis, 2004; Tercyak & Tyc, 2006), more innovative ways to reach adolescents and impact their health behaviors are needed. Health informatics may afford such a breakthrough, especially when combined with other forms of health promotion, health education, and counseling strategies (Patrick et al., 2001). Health informatics is a multidisciplinary field merging information and computer sciences with health care—it deals with the resources, devices, and methods required to optimize the acquisition, storage, retrieval, and use of information in health and biomedicine. Health informatics tools include not only computers but also clinical guidelines, formal medical terminologies, and information and communication systems; it is a relatively new concept in public health and health care supported by electronic processes and communications (Strecher, Greenwood, Wang, & Dumont, 1999). The internet is one of the most common modalities of delivering health informatics interventions, with many differences in the depth of health material covered on the web (Skinner, 2002). A recent review of internet-based interventions for smoking behavior suggests that both smoking prevention and cessation are promising areas of work, but that greater methodological rigor and population specificity are needed to more fully understand their impact (Walters, Wright, & Shegog, 2006). Among adolescents and young adults, formative research suggests web-based smoking intervention is appealing due to accessibility, interactivity, trust, sustainability, and stimulation (Parlove, Cowdery, & Hoerauf, 2004); these features may subsequently increase intervention usage and satisfaction and assist in behavioral goal setting (Escoffery, McCormick, & Bateman, 2004). In an adolescent smoking prevention study, Shegog and colleagues (Shegog et al., 2005) reported a favorable impact of computer tailoring on middle school youths’ cognitive risks for smoking. The program, which assessed children's cognitive determinants of smoking and then provided intervention feedback based upon the child's responses, significantly changed several outcomes (i.e., smoking intentions, positive attitudes about smoking, self-efficacy expectations, and knowledge of the negative consequences of smoking). Other efforts to use the internet and electronic multimedia to change adolescent health behavior have also been reported as promising, including interactive computer programs developed for counseling youth about alcohol use (Bersamin, Paschall, Fearnow-Kenney, & Wyrick, 2007), physical activity (Marks et al., 2006), nutrition (Kypri & McAnally, 2005; Long & Stevens, 2004), and chronic illness management (Cadario et al., 2007; Joseph et al., 2007).
Given that adolescents are facile in using computers and other forms of technology to access information services, this is a promising mode of intervention delivery. Indeed, medical providers are becoming increasingly likely to communicate with their patients electronically to discuss a range of health issues (Beckjord et al., 2007), often resulting in improved patient satisfaction (Liederman, Lee, Baquero, & Seites, 2005). The extension and application of these approaches in the area of health promotion has been termed “eHealth promotion” by Skinner and colleagues, referring to “web-based health education and behavior change applications” (Skinner, Maley, & Norman, 2006). With computer technology (i.e., personal computing hardware and software) and the internet (i.e., world wide web) as leading sources of information for adolescents (Greenfield & Yan, 2006), and access to these sources available at many schools and local libraries (Sun et al., 2005), these are important resources for providing health information to young people (Borzekowski & Rickert, 2001; Gray, Klein, Noyce, Sesselberg, & Cantrill, 2005a; Hansen, Derry, Resnick, & Richardson, 2003).
Previous research has indicated that technologies such as the internet or compact discs have been associated with greater perceived confidentiality (Gray, Klein, Noyce, Sesselberg, & Cantrill, 2005b), as well as greater accessibility to health information and reduced social stigma (Skinner, Biscope, Poland, & Goldberg, 2003). Given the breath and depth of information available on the internet and through other digital media sources, coupled with the anonymity and accessibility of these technologies, our study was interested in determining if adolescents would be willing to receive information using technologies for health promotion purposes. A recent study among young college students reaffirms the importance of assessing how end-users of health informatics might find it an acceptable and attractive option to promote wellness and health behavior change (Atkinson, 2007). For example, that eHealth intervention developers remain mindful of: (a) the relative advantages of eHealth innovations for the target population over other (existing) means of intervention, (b) the fact that such innovations be simple and easy to use, and (c) that eHealth interventions might need to be experimented with by the users prior to full-scale adoption. Attending to these facets could help to conserve valuable resources during intervention development, and lead to more novel, impactful eHealth-mediated behavior changes.
We were also interested in determining if the presence of multiple behavioral risk factors (i.e., objective indicators of need for intervention vis-à-vis high body mass index (BMI), physical inactivity, lifetime cigarette smoking, insufficient sun protection, and depression) would be associated with adolescents’ willingness to engage in eHealth promotion. These interests were driven, in large part, by value-expectancy health and behavior theories (Eccles & Wigfield, 2002; Weinstein, 1988), and the Theory of Planned Behavior (TPB) (Ajzen, 2001) in particular, which posit an association between individual motivation and behavior change. In the TPB, individuals behave according to their intentions to perform an action, and also their perceptions of control over their actions (factors that may make it more or less difficult for them to behave in a particular way). In our study, willingness to engage in eHealth promotion served as a proxy measure of adolescents’ TPB-related intentions to act. In this case, their likelihood of using technology for health promotion purposes. Also under the TPB, the study's measurement of objective indicators of need for intervention are akin to factors that might affect perceptions of control. For example, the presence of multiple behavioral risk factors could facilitate adolescent's own beliefs, judgments, and motivations about acting in a health-promoting manner; those who are overweight, sedentary, smoke cigarettes, do not protect themselves from the sun, and/or feel down recognize such attributes within themselves as being harmful and in need of change. The TPB has been used as a guide to developing eHealth and behavior change interventions for adolescents (Ezendam, Oenema, van de Looij-Jansen, & Brug, 2007; Hutchinson, Jemmott, Jemmott, Braverman, & Fong, 2003; Sheehan et al., 1996; Tsorbatzoudis, 2005) and is promising in this respect.
Toward these ends, we hypothesized that willingness to engage in eHealth promotion would be positively associated with the presence of multiple (concurrent) behavioral risk factors. This type of information can inform the development of both tailored and targeted multiple behavior change interventions that maximize reach and effectiveness for adolescents.
Participants were healthy adolescents between 11 and 21 years old who attended a routine general medical check-up at an adolescent medicine clinic housed within an academic medical center in Washington, DC. All potential participants had to be age-eligible, read and understand English, be free of any impairment that might compromise ability to provide valid informed consent/assent (e.g., mental retardation, pervasive developmental delay), and be in good general health (i.e., absence of major medical illness such as cancer or cardiovascular disease). The research protocol was reviewed and approved by the medical center's institutional review board and covered by a federal confidentiality certificate.
Study research assistants attended adolescent medicine clinics at predetermined times to identify potentially eligible participants. All parents or legal guardians of participants under 18 years old were first approached in the clinic waiting area, followed by approaching participants themselves; adolescents aged 18 years old and over were approached directly while awaiting their appointments. Determination of study eligibility status was facilitated by collaborating clinical staff review of scheduled appointments, including participant age. Written informed consent/assent was obtained from all parents and/or participants.
Participants completed a comprehensive battery of self-report and other study measures. Measures were administered in private and the procedure generally lasted 30 min or less. Nominal incentives (i.e., $5 gift cards to national media outlets) were used to acknowledge participants’ time and effort and the study's consent rate was 88% (332/378).
The Adolescent eHealth Promotion Scale (AeHPS) is a seven-item measure developed by the authors to assess adolescents’ access to and willingness to use technology for health promotion purposes (Wilson, Wine, Walker, & Tercyak, 2006). The first four AeHPS items inquire about computer, internet, e-mail, and technological compatibility access: (a) Do you have regular access to a computer at home, school, or work?, (b) Are one or more of these computers connected to the internet (world wide web)?, (c) Do you have a personal e-mail account?, and (d) Do you have access to a computer that can run software from a compact disk (CD), digital video disc (DVD), or other audio and video graphics storage device? These items are summed together to yield a score on the access scale. The remaining three items inquire about willingness to use technology (i.e., e-mail and multimedia software, portable electronic devices, the internet) for health promotion purposes and are summed to form the willingness scale score: (e) Would you be willing to receive and learn health information (information about weight management, exercise promotion, smoking prevention and reduction, skin protection, and stress management) if it were delivered to you by e-mail or multimedia presentation?; (f) Would you be willing to use a laptop computer, DVD player, or other portable electronic device to receive and learn health information?, and (g) Would you be willing to use the internet to connect to websites which present health information? All items on the AeHPS are presented in a forced choice format (yes = 1/no = 0), and higher scores reflect greater access and willingness to use eHealth promotion. In an earlier report (Wilson et al., 2006), the Kuder-Richardson-20 (KR-20) reliability of the AeHPS was 0.76 for access and 0.77 for willingness. In the present sample, KR-20 estimates were 0.74 for access and 0.69 for willingness. Validity was established by correlating the AeHPS's access scale score with an area-based measure of socioeconomic status (see below); as expected, access and socioeconomic status are positively correlated (r =.25, p <.0001). Willingness scale validity was established through its correlation with an independent item assessing the amount of time (hours/week) adolescents would spend in eHealth promotion; also as expected, adolescents’ eHealth promotion willingness and hours/week are positively correlated (r =.35, p <.0001).
Medical record abstraction was used to determine adolescents’ current metric height (cm) and weight (kg). Height, weight, age, and gender data were then used to compute BMI-for-age using a standard formula: weight (kg)/[height (m)]2, with BMI plotted on the CDC's gender-specific BMI-for-age growth charts to obtain a percentile ranking (Mei et al., 2002). Healthy adolescent weight corresponds to those at the 5–84th percentiles, at risk of overweight is the 85–94th percentiles, and overweight is at or above the 95th percentile (Krebs & Jacobson, 2003). All BMI-for-age data were entered by two different coders, checked for reliability, and 100% verified.
Two items from the CDC's Youth Risk Behavior Survey were used to determine the amounts of vigorous (at least 20 min of aerobic physical activity, resulting in sweating and hard breathing) and moderate physical activity (at least 30 min of anaerobic physical activity, not resulting in sweating and hard breathing) adolescents reportedly engaged in during the past 7 days (0 days = 0, 7 days = 7) (Eaton et al., 2006). According to the CDC, insufficient physical activity is defined as not participating in at least 20 min of vigorous physical activity on 3 or more of the past 7 days and not participating in at least 30 min of moderate physical activity on 5 or more of the past 7 days (Eaton et al., 2006).
Lifetime cigarette smoking status was assessed by a single item from the CDC's Youth Risk Behavior Survey to determine if the adolescent had ever tried or experimented with smoking, even a few puffs (no [never smoker] = 0, yes [ever smoker] = 1) (Eaton et al., 2006). This level of smoking is both meaningful and informative in a primary prevention context (Thomas, Baker, & Lorenzetti, 2007).
Adolescents’ adherence to common sun protection recommendations (e.g., applying sunscreen, wearing protective clothing, sun avoidance) was assessed with an eight-item measure (Gritz et al., 2003). Each item is rated on a 5 point Likert scale of how often the sun protection behavior is performed (never = 1, always = 5). The internal consistency of the sun protection measure was adequate (Cronbach's coefficient α =.78). We defined sufficient adherence to sun protection as never or rarely following four or fewer recommendations and insufficient adherence (i.e., nonadherence) to sun protection as never or rarely following five or more recommendations (Eaton et al., 2006; Gritz et al., 2003).
We used the Center for Epidemiologic Studies-Depression Scale (CES-D) (Radloff, 1977) to measure the presence of adolescent depressive symptoms. All 20 items on the CES-D are rated along a 4 point Likert scale to indicate how frequently in the past week each symptom occurred (rarely or none of the time = 0, most of the time = 3); scores range from 0 to 60 and higher scores indicate a greater degree of depressive symptoms. In our study sample, the internal consistency of the CES-D was adequate (Cronbach's coefficient α =.90). This measure is widely used in adolescent health and behavior research (Schimmer, Tsao, & Knapp, 1977); adolescent CES-D scores of 0–15 are considered minimal, 16–23 are mild, and those at or above 24 are moderate/severe (Rushton, Forcier, & Schectman, 2002).
Modeled after prior research in adolescent health psychology (Soldz & Cui, 2001; Tercyak, Donze, Prahlad, Mosher, & Shad, 2006b), a categorical risk factor index was created based upon a combination of the five adolescent behavioral risk factors of interest. Each risk factor was dichotomized along the lines suggested by commonly accepted standards of practice in adolescent health promotion and disease prevention (Richmond, Freed, Clark, & Cabana, 2006). The risk factors were: (a) BMI-for-age (healthy weight, at risk for overweight/overweight), (b) physical activity (sufficient vigorous and/or moderate physical activity, insufficient vigorous and moderate physical activity), (c) lifetime smoking (never smoking, ever smoking), (d) sun protection (sufficient adherence to recommendations, insufficient adherence to recommendations), and (e) depression (minimal or mild depressive symptoms, moderate/severe depressive symptoms). Adolescent participants received a score of 1 for each risk factor present, and a score of 0 to indicate risk factor absence. Individual risk factor scores were added together (0 = no risk factors, 5 = all 5 risk factors), resulting in an ordered categorical variable applicable to the entire sample (0 = no risk factors, 1 = 1 risk factor, 2 = 2 risk factors, 3 = 3 risk factors, 4 = 4 risk factors, 5 = 5 risk factors).
Age, gender, and race were reported; primary residential address information was used to create an area-based socioeconomic measure (via conversion of zip code to median household income based on US census data) (Krieger et al., 2003; Tercyak, Donze, Prahlad, Mosher, & Shad, 2006a).
Independent variables (Table I) were examined for their univariate properties and then in bivariate fashion to determine associations with the dependent variable (eHealth willingness); we also examined risk factor prevalence, and bivariate associations of adolescent demographic characteristics and the risk factor index score to further our understanding of these associations (Table II). Independent variables with significant (p <.05) associations were then retained in a multivariate linear regression model to represent the relationship among these variables as observed in our data (Table III).
The study sample's demographic and behavioral characteristics are presented in Table I. The majority of adolescent participants were female, African American or White, and lived in predominantly middle class income areas.
Also shown in Table I, 13% of the sample met criteria for being classified as overweight, 42–69% engaged in insufficient physical activity, 40% had ever smoked, 44% were insufficiently protected from the sun, and 15% met screening criteria for depression. When combined, the multiple risk factor index ranged in value from 0 to 5. The middle (median) and most common (modal) values were two risk factors (33%); 28% of the sample had only one risk factor, 19% had three risk factors, <10% of the sample had four or all five risk factors, and 14% did not have any risk factors.
An analysis of mean scores on the AeHPS suggests a high level of access to technology among the sample (M = 3.74, SD = 0.72, range = 0–4), and moderate willingness (M = 1.84, SD = 1.12, range = 0–3); access and willingness were unrelated (r =.00), suggesting these scales are independent. Less then 5% of the sample reported having no access to a computer at home, school, or elsewhere, and <20% reported no willingness to engage in any eHealth promotion activity.
Demographic characteristics were examined in relationship to the multiple risk factor index score and adolescents’ willingness to engage in eHealth promotion (Table II). Among continuous independent variables that were analyzed via Pearson product–moment correlations, age was positively related to the index and socioeconomics was negatively related to the index, suggesting that older adolescents and those from lower socioeconomic backgrounds were at greater objective risk; the multiple risk factor index score was significantly correlated with willingness. Among discrete independent variables analyzed via Student t-tests, participant gender was consistently unrelated to risk and willingness, and race (dichotomized as white, nonwhite) was significantly associated with objective risk (with white adolescents at greater risk).
After adjusting for the putative effect of technology access, linear regression modeling suggested that the presence of multiple behavioral risk factors was positively associated with willingness to use technology for health promotion purposes, β =.12, βse =.06, t = 2.13, p =.03: adolescents with greater objective risk were more willing to engage in eHealth promotion (Table III).
The primary purpose of this study was to examine adolescents’ willingness to engage in eHealth promotion as defined by Skinner et al. (2006), and to identify if a relationship exists between adolescents’ objective need for intervention (i.e., the presence of multiple behavioral risk factors) and their eHealth promotion willingness. With respect to adolescents’ willingness, we found it to be at a moderate level, and that their access to technology at home or school (which could enable them to take advantage of eHealth) was strong. That more than four-fifths of adolescents surveyed were willing to use e-mail and multimedia software, portable electronic devices, and the internet to engage with health promotion content is encouraging, speaking to the potential for eHealth to reach adolescents.
Interestingly, participants’ access to technology and their willingness to engage in eHealth promotion were unrelated. This may be due to access to additional technological devices capable of delivering eHealth and other content (e.g., cellular telephones, personal digital assistants, MP3 players, video media players) that were not specifically inquired about in our research, and/or the highly prevalent level of technology access among children in US society (Bremer, 2005; Montgomery, 2000). Prior work suggests adolescents use a wide variety of information technologies, including those that are highly interactive (Skinner et al., 2003). With increasingly interactive modes of electronic information access now available to adolescents, greater attention should be paid to both the quality and innovativeness of how eHealth is researched and developed. Additional work may be needed to more deeply characterize the range and types of technology access that most adolescents have, and to put more tools into place to better adapt existing technology to rapidly changing and innovative technological environments—these are all necessary elements of successful eHealth content delivery (Gray, Klein, Cantrill, & Noyce, 2002).
In addition to these issues, it will also be important to further assess the needs of more diverse groups of adolescents, including those from nonWestern cultures and for whom English is not their primary language. eHealth promotion may be a way to make health promotion messages more accessible and relevant to a wider audience, especially when it is not possible for a medical provider to do so. While this study did not specifically address racial, ethnic, or cultural dimensions of adolescents’ willingness to engage in eHealth promotion, a majority of the sample was female and nonwhite. Within this diverse group of adolescents, access was high and willingness was moderately strong. This underscores the importance of also ensuring the competence of the technology to facilitate communication—linguistically, culturally, and otherwise (Hutchinson et al., 2007; Norman & Skinner, 2006).
Our analysis of adolescents’ behavioral risk factors suggests they tend to cooccur and present multiply (rather than singly). This finding is consistent with prior research on adolescent problem behaviors (Basen-Engquist, Edmundson, & Parcel, 1996; Li, Stanton, & Yu, 2007; Sanchez et al., 2007). A large-scale behavioral epidemiologic survey conducted with over 2,000 middle school students in North Carolina revealed that among 16 behavioral risk factors assessed by the study, adolescents reported engaging in an average of four behaviors. The four most commonly reported risk behaviors were not wearing a helmet when riding a bicycle (75%), physical fighting (65%), carrying a knife or club (44%), and not wearing a helmet when in-line skating or skateboarding (41%) (DuRant et al., 1999). That study also highlighted the important role of early cigarette use as a potential predisposing factor to other health risk behaviors, as it was consistently and strongly associated with the others.
In the present study, we observed a significant association between adolescents’ objective behavioral risk level/need for intervention and their willingness to engage in eHealth promotion. This is both an interesting and encouraging finding, particularly among adolescents. Those with the greatest level of health-related need are among the most challenged to sustain behavior change. This is evidenced by the relatively modest successes achieved-to-date by adolescent prevention programs for obesity (Summerbell et al., 2005), tobacco and other forms of substance use (Faggiano et al., 2005; Thomas & Perera, 2006; Thomas et al., 2007), and depression (Merry, McDowell, Hetrick, Bir, & Muller, 2004). It is also evidenced by the fact that, among those enrolled in behavioral intervention programs, attrition is often highest among those at greatest risk/in greatest need (Biglan et al., 1991; Hansen, Collins, Malotte, Johnson, & Fielding, 1985; Livingstone, McCaffrey, & Rennie, 2006; May et al., 2007). Our data suggest reasonably good correspondence between risk and initial willingness to engage in eHealth promotion—a point that could be capitalized upon by those designing and developing such interventions, particularly during the early stages of preparation for intervention and the behavior change process itself. The evidence base upon which to develop and implement multiple behavior change interventions continues to grow, though more in the adult than pediatric literatures (Goldstein, Whitlock, & DePue, 2004). Adolescent primary care is a logical environment in which to implement risk behavior screenings paired with multiple behavior change interventions, and this approach is consistent with existing practice guidelines (Richmond et al., 2006).
Of course, several challenges remain. These include the development and evaluation of theory-based multiple behavior change interventions that are effective for adolescents, knowledge about whether or not efforts to change multiple behaviors are more efficient than single interventions, and how to translate or integrate this work within the context of eHealth promotion (Driskell, Dyment, Mauriello, Castle, & Sherman, 2008; Hyman, Pavlik, Taylor, Goodrick, & Moye, 2007; Nigg, Allegrante, & Ory, 2002; Skinner et al., 2006). Perhaps the most relevant example of this type of thinking comes from obesity prevention, and the results of a randomized controlled trial to affect adolescent primary care patients’ diet, physical activity, and sedentary behaviors (Patrick et al., 2006). The intervention consisted of office-based, computer-assisted behavioral assessments with corresponding goal setting, as well as brief provider counseling and ongoing mail/telephone counseling. The study observed relative improvements in each of these behavioral risk factors, and suggested that a dose–response relationship existed between intervention intensity and magnitude of behavior change. The incorporation of interactive behavior change technologies into adolescent medicine has been heralded as a goal of adolescent medicine (Mackenzie, 2000), and this point is underscored by potentially high interest and motivation to engage in this form of technology among adolescents as demonstrated in our study.
The present work is not without its limitations, including the relatively narrow assessment of technology access and eHealth promotion willingness; additional research with the AeHPS is necessary to further demonstrate its utility and validity and relationship with health outcome. Also, the level of detail ascertained about each of the behavioral risk factors was consistent with a screening approach, but lacked more in-depth assessment of behavioral frequency and intensity that would be helpful to more appropriately execute and monitor the success of any behavior change intervention. Well-validated, theory-based measures (including measures derived from the TPB) with operational definitions for risk behavior may be useful complements for this purpose. And finally, our convenience sampling strategy and resulting sample limit the external validity of the results (i.e., predominantly female participants). Subsequent prospective work with a larger and more diverse sample of adolescents would allow for a more meaningful understanding of the cooccurrence of behavioral risk factors, and how these relate to eHealth promotion willingness across time.
Despite these limitations, the results suggest that both opportunities and needs exist for multiple behavior change and eHealth promotion among adolescents in a primary care setting. Given the magnitude of the public health problems at hand (Tercyak, 2008), and the promises of interactive behavior change technologies to prevent and control health problems during adolescence and adulthood (Mackenzie, 2000), breakthroughs in the ways in which we access and intervene with adolescents are emerging.
The authors would like to thank the participants in this research. Support for the study was provided by the National Cancer Institute at the National Institutes of Health (CA091831 to K.P.T.).
Conflicts of interest: None declared.