Association of Multiple Behavioral Risk Factors with Adolescents’
Willingness to Engage in eHealth Promotion
Kenneth P. Tercyak,1 PHD, Anisha A. Abraham,2 MD, MPH, Amanda L. Graham,1 PHD,
Lara D. Wilson,1 BA, and Leslie R. Walker,3 MD
1
Lombardi Comprehensive Cancer Center, 2Georgetown University Hospital, Georgetown University Medical
Center, Washington, DC, USA, and 3Children’s Hospital and Regional Medical Center, Seattle, WA, USA
Objective This 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. Methods A 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). Results The 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 (b ¼ .12,
p ¼ .03). Conclusions Adolescents 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.
Key words
adolescents; behavioral risk factors; disease prevention; eHealth; health promotion.
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
All correspondence concerning this article should be addressed to Kenneth P. Tercyak, PhD, Cancer Control Program,
Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 3300 Whitehaven Street, NW, Suite
4100, Washington, DC 20007-2401, USA. E-mail: [email protected]
Journal of Pediatric Psychology 34(5) pp. 457–469, 2009
doi:10.1093/jpepsy/jsn085
Advance Access publication August 22, 2008
Journal of Pediatric Psychology vol. 34 no. 5 ß The Author 2008. Published by Oxford University Press on behalf of the Society of Pediatric Psychology.
All rights reserved. For permissions, please e-mail: [email protected]
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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, GordonLarsen, 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 (LloydRichardson, 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 ¼ .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 wellformed 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
Adolescent eHealth Promotion
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
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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.
Method
Participants
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.
Data Collection
Participants completed a comprehensive battery of selfreport 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).
Measures
eHealth Promotion
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 ¼ 1/no ¼ 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,
Adolescent eHealth Promotion
access and socioeconomic status are positively correlated
(r ¼ .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 ¼ .35, p < .0001).
Body Mass Index
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 BMIfor-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 BMIfor-age data were entered by two different coders, checked
for reliability, and 100% verified.
Physical Activity
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 ¼ 0, 7
days ¼ 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).
Smoking
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] ¼ 0, yes
[ever smoker] ¼ 1) (Eaton et al., 2006). This level of
smoking is both meaningful and informative in a primary
prevention context (Thomas, Baker, & Lorenzetti, 2007).
Sun Protection
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 ¼ 1, always ¼ 5). The internal consistency of the sun protection measure was adequate
(Cronbach’s coefficient a ¼ .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).
Depression
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 ¼ 0, most of the time ¼ 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 a ¼ .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).
Multiple Risk Factor Index
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 ¼ no risk factors, 5 ¼ all 5
risk factors), resulting in an ordered categorical variable
applicable to the entire sample (0 ¼ no risk factors, 1 ¼ 1
risk factor, 2 ¼ 2 risk factors, 3 ¼ 3 risk factors, 4 ¼ 4 risk
factors, 5 ¼ 5 risk factors).
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Tercyak et al.
Table I. Participant Demographic and Behavioral Characteristics
(N ¼ 332)
Variable
N/n
Age
332
%
Table II. Bivariate Associations of Adolescent Demographic and
Behavioral Characteristics and eHealth Willingness
M (SD)
Behavioral risk
factor index
16.28 (2.15)
r
Gender
P
t*
eHealth
willingness
P
Male
99
30
Age
Female
233
70
Gender
0.38 .71
Race
3.35 .0009
Race
White
129
39
African American
146
44
Hispanic
Other
29
28
9
8
Median area household income
Median area
.20 .0004
r
P
t*
P
.07 .22
.18 .001
0.27 .79
1.39 .17
.06 .29
household income
Behavioral risk
.11 .04
factor index
65k (29k)
*df ¼ 330.
Body mass index
At risk for overweight
72
22
Overweight
44
13
Table III. Multivariate Model of eHealth Willingness
Physical activity*
Insufficient vigorous physical activity
136
42
Variable
Insufficient moderate physical activity
Lifetime cigarette smoking
226
134
69
40
Access
.04
.09
.50
.62
Insufficient sun protection
147
44
Behavioral risk factor index
.12
.06
2.13
.03
b
bSE
t
P
Depression
Mild
52
16
Moderate/severe
49
15
*Percentages do not sum to 100% as categories overlap.
Demographic Variables
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).
Statistical Analysis
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).
Results
Demographic and Behavioral Characteristics
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.
eHealth Promotion
An analysis of mean scores on the AeHPS suggests a
high level of access to technology among the sample
(M ¼ 3.74, SD ¼ 0.72, range ¼ 0–4), and moderate willingness (M ¼ 1.84, SD ¼ 1.12, range ¼ 0–3); access and willingness were unrelated (r ¼ .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.
Bivariate and Multivariate Associations with
eHealth Promotion Willingness
Demographic characteristics were examined in relationship
to the multiple risk factor index score and adolescents’
willingness to engage in eHealth promotion (Table II).
Adolescent eHealth Promotion
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, b ¼ .12, bse ¼ .06, t ¼ 2.13, p ¼ .03:
adolescents with greater objective risk were more willing to
engage in eHealth promotion (Table III).
Discussion
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 (BasenEngquist, 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
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464
Tercyak et al.
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, theorybased 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.
Acknowledgment
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.
Received January 24, 2008; revisions received July 13,
2008; accepted July 14, 2008
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