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Original Paper
Using the Internet to Promote Health Behavior Change: A
Systematic Review and Meta-analysis of the Impact of Theoretical
Basis, Use of Behavior Change Techniques, and Mode of Delivery
on Efficacy
Thomas L Webb1; Judith Joseph2; Lucy Yardley2; Susan Michie3
1Department of Psychology, University of Sheffield, Western Bank, Sheffield, UK
2School of Psychology, Shackleton Building, University of Southampton, Highfield, Southampton, UK
3Research Department of Clinical, Educational & Health Psychology, University College London, 1-19 Torrington Place, London, UK
Corresponding Author:
Thomas L Webb
Department of Psychology
University of Sheffield
Western Bank, Sheffield, S10 2TN
UK
Phone: +44 114 222 6516
Fax: +44 114 276 6515
Email: t.webb@sheffield.ac.uk
Abstract
Background: The Internet is increasingly used as a medium for the delivery of interventions designed to promote health behavior
change. However, reviews of these interventions to date have not systematically identified intervention characteristics and linked
these to effectiveness.
Objectives: The present review sought to capitalize on recently published coding frames for assessing use of theory and behavior
change techniques to investigate which characteristics of Internet-based interventions best promote health behavior change. In
addition, we wanted to develop a novel coding scheme for assessing mode of delivery in Internet-based interventions and also
to link different modes to effect sizes.
Methods: We conducted a computerized search of the databases indexed by ISI Web of Knowledge (including BIOSIS Previews
and Medline) between 2000 and 2008. Studies were included if (1) the primary components of the intervention were delivered
via the Internet, (2) participants were randomly assigned to conditions, and (3) a measure of behavior related to health was taken
after the intervention.
Results: We found 85 studies that satisfied the inclusion criteria, providing a total sample size of 43,236 participants. On
average, interventions had a statistically small but significant effect on health-related behavior (d+= 0.16, 95% CI 0.09-0.23).
More extensive use of theory was associated with increases in effect size (P= .049), and, in particular, interventions based on
the theory of planned behavior tended to have substantial effects on behavior (d+= 0.36, 95% CI 0.15-0.56). Interventions that
incorporated more behavior change techniques also tended to have larger effects compared to interventions that incorporated
fewer techniques (P< .001). Finally, the effectiveness of Internet-based interventions was enhanced by the use of additional
methods of communicating with participants, especially the use of short message service (SMS), or text, messages.
Conclusions: The review provides a framework for the development of a science of Internet-based interventions, and our
findings provide a rationale for investing in more intensive theory-based interventions that incorporate multiple behavior change
techniques and modes of delivery.
(J Med Internet Res 2010;12(1):e4) doi:10.2196/jmir.1376
KEYWORDS
Internet; intervention; behavior change; meta-analysis; review
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Introduction
...without a scientific underpinning, the field [of Internet
interventions] may flounder [1] [LM Ritterband and DF Tate]
In June 2009 an estimated 25% of the world’s population had
access to the Internet, with estimates in Europe and North
America being considerably higher (50% and 74%, respectively)
[2]. Researchers in the field of health promotion have been quick
to capitalize on the exponential growth of the Internet, and over
the past decade, an increasing number of interventions designed
to promote changes in health behavior have been delivered via
the Internet [1,3]. For example, “Happy Ending” is a 54-week
Internet-based intervention designed to promote smoking
abstinence [4,5]. This intervention involves over 400 contact
emails that direct participants to a different webpage each day,
supplemented by interactive voice response (IVR) and short
message service (SMS), or text, monitoring and prompts. Other
Internet-based interventions, however, simply involve
embedding a short planning exercise within an online lifestyle
survey [6,7].
Quantitative reviews of Internet-based interventions report
positive–albeit highly variable and often small–effects on
behaviors such as physical activity, tobacco use, exercise, and
so on [8-12]. However, previous reviews have not systematically
coded the characteristics of each Internet-based intervention
and computed the effect size associated with each [1]. The
limited analyses of this kind that have been conducted suggest
that this approach may provide insight into the characteristics
of effective versus less effective interventions. For example,
Portnoy et al [8] coded whether the intervention included
information, motivation, or behavioral skills. The findings
suggested that the inclusion of motivational components (eg,
cost-benefit analyses) actually weakened the impact of the
interventions. Since the publication of the review by Portnoy
et al [8], a comprehensive taxonomy of behavior change
techniques has been published [13], along with a method for
assessing the extent to which behavioral interventions are
theory-based [14]; both these developments permit more
sophisticated coding of intervention content. Thus, the primary
aim of the present review was to use these new coding schemes
to identify the characteristics of effective Internet-based
interventions. A secondary aim was to develop a coding scheme
for the different modes by which Internet-based interventions
are delivered (eg, via scheduled access to an advisor or
automated feedback) and to link different modes of delivery to
effect size.
How Can the Characteristics of Internet-based
Interventions Be Conceptualized?
Three intervention characteristics may influence the impact on
behavior [15-18]: (1) the theoretical basis of the intervention,
(2) the behavior change techniques used, and (3) the mode of
delivery.
Theoretical Basis and Use of Theory and Predictors
Theoretical basis refers to the theory or theories used to develop
the intervention. For example, in an effort to promote physical
activity, Spittaels et al [19] directed participants to a website
that presented a tailored message based on the theory of planned
behavior [20]. In contrast, Carr et al [21] used social cognitive
theory [22] to develop a physical activity intervention that could
be delivered via the Internet. Theory can inform interventions
in a number of different ways, from identifying theoretical
constructs to be targeted (eg, attitude, self-efficacy) or
mechanisms underlying particular behavior change techniques
(eg, vicarious learning in modeling), to selecting participants
most likely to benefit (eg, people with particularly negative
attitudes). Despite assertions that use of theory leads to more
effective interventions [23-27], there is debate over the
importance of theory [28,29], and at present it is unclear whether
and how use of theory influences intervention effectiveness,
particularly in relation to Internet-based interventions [1]. A
large review of HIV-prevention interventions reported that use
of theory was positively related to extent of behavior change
[30], but this finding was simply based on whether or not theory
was cited. Although this is an important step in the right
direction, it would be useful to know how different uses of
theory impact on the effectiveness of interventions and whether
more extensive use of theory leads to larger effects than less
extensive use. Michie and Prestwich [14] have developed a
reliable coding scheme to assess the different ways that
behavioral interventions employ theory; use of this coding
scheme permits the present review to investigate these important
questions.
Behavior Change Techniques
Behavior change techniques refer to the specific strategies used
in the intervention to promote behavior change. For example,
some interventions designed to promote smoking abstinence
prompt barrier identification and problem solving (eg, [31]),
whereas other interventions prompt participants to monitor their
behavior (eg, [32]). In order to identify techniques contributing
to effectiveness across interventions and to ensure that effective
interventions can be replicated, it is crucial that standardized
definitions of the techniques included in behavior change
interventions are used and linked to intervention effectiveness
[33]. With this in mind, the present review used the taxonomy
of behavior change techniques developed by Abraham and
Michie [13] to code the content of the interventions.
Mode of Delivery
The interventions in the present review were delivered via the
Internet. The effects of this primary mode of delivery can be
estimated by examining studies that compare similar materials
presented via the Internet versus other modes, such as print
[34,35]. Internet-based interventions can, however, differ
substantially in their specific mode of delivery. For example,
content can be delivered in a more or less interactive manner
[36,37]. Interventions may also employ supplementary delivery
modes (eg, SMS messaging, email, telephone, or
videoconferencing) that may influence effectiveness. To our
knowledge, no coding scheme exists for assessing the mode
with which Internet-based interventions are delivered. Existing
coding schemes developed for systematic reviews of
non-Internet interventions [38] are not suitable because they
focus on the physical manner in which participants received the
intervention (eg, one-to-one or group) and the nature of the
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person delivering the intervention (eg, health educator or trained
facilitator). Therefore, the present review developed a new
coding scheme for assessing mode of delivery in Internet-based
interventions and used it to understand how each mode
influences the effectiveness of the intervention.
The Present Review
The present review sought to investigate which characteristics
of Internet-based interventions were associated with
effectiveness. By so doing, we answer the important applied
and theoretical questions: Which theories should researchers
draw on in developing interventions? How can theory best be
used to inform Internet-based interventions? What behavior
change techniques are effective when employed via the Internet?
Is the mode by which the intervention is delivered important?
Method
Selection of Studies
Identification and Screening
In July 2008 we conducted a computerized search using ISI
Web of Knowledge, which covers a number of databases
including Web of Science conference proceedings (1900-),
BIOSIS Previews (1985-), and Medline (1950-). We used the
following search terms: Web-based, Internet, digital, online,
technolog*, computer, treatment, RCT, trial, intervention,
behavio* change. (The asterisk automatically truncates the term
such that, for example, technolog* will also find technology,
technologies, etc) Studies had to include one or more of the
search terms in the title. We also sent an email to the distribution
list of the European Health Psychology Society to request
unpublished research. There were three inclusion criteria for
the meta-analysis. First, the primary components of the
intervention must have been delivered via the Internet (not
including CD-ROMs, SMS messaging, or other computer
applications). Second, the studies must have involved random
assignment of participants to a treatment group that received
an Internet-based intervention and a comparison group that
received either a control intervention or no intervention. Finally,
a measure of behavior related to health must have been taken
after the intervention. We did not include studies that only
measured symptoms (eg, anxiety, depression), cognitions (eg,
attitudes, intentions), outcomes presumed to be the consequence
of behavioral changes (eg, weight loss, blood glucose levels),
or behaviors unrelated to health (eg, use of literature services).
Eligibility and Inclusion
Figure 1 shows the flow of information through the different
phases of the review. We assessed 549 full-text articles for
eligibility. Of these, 140 studies (26%) were rejected because
the study did not include a measure of behavior related to health
(eg, [39]), 97 studies (18%) were rejected because the primary
components of the intervention were not delivered via the
Internet (eg, [40]), 88 studies (16%) were rejected because they
did not report intervention effects (typically, these were reviews
or protocol descriptions, eg, [41]), 84 studies (15%) were
rejected because they did not include a control group (eg, [42]),
20 studies (4%) were rejected because computers were used
only to tailor information that was presented in a
non-computerized format (eg, [43]), 17 studies (3%) were
rejected because they reported additional effects of an
intervention already included in the review (eg, [44]), 8 studies
(1%) were rejected because intervention effects were reported
in a manner that did not permit computation of an effect size
(eg, [45]). For these studies, it was decided not to estimate effect
sizes based on the significance levels reported because all the
effects for which full information was not available were
reported as non-significant. Assuming zero difference (d =
0.00) for these effects could systematically underestimate effect
sizes associated with particular intervention characteristics.
Finally, 5 studies (1%) were rejected because participants were
not randomly allocated to conditions (eg, [46]), and 4 studies
(1%) were rejected because the manuscripts were not written
in English (eg, [47]). In total, 85 reports of Internet-based
interventions met the inclusion criteria for the review.
Multimedia Appendix 1 presents the characteristics and effect
sizes associated with each intervention.
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Figure 1. Flow of information through the different phases of the review (adapted from [48])
Calculation of Effect Sizes for the Effect of
Internet-Based Interventions on Health-Related
Behavior
The effect size for post-intervention behavior differences
between the conditions was calculated in line with Cochrane
recommendations [49]. Specifically, the longest follow-up was
selected wherever possible. For example, where Brendryen et
al [5] followed up smokers at 3, 6, and 12 months, the 12-month
data was included in the review. Where studies examined more
than one behavior (such as Williamson et al’s study [50] of
weight loss behaviors in which exercise, overeating, and
avoidance of fattening foods were measured), the effect sizes
within the study associated with different behaviors were
subjected to meta-analysis in their own right prior to inclusion
in the main dataset. This procedure captures the richness of the
data and does not prioritize one outcome over another (eg,
effects on dietary outcomes and effects on physical activity are
considered equally important), while also maintaining the
independence of samples that is central to the validity of
meta-analysis [51]. Intention-to-treat analyses were used
wherever possible. Following Portnoy et al [8], where studies
employed more than one comparison condition, we selected the
most passive comparison condition for ease of interpretation.
For a detailed discussion of considerations relating to choice of
comparison conditions, see Danahar and Seeley [53].
Coding of Intervention Characteristics
Use of Theory and Predictors
The coding scheme developed by Michie and Prestwich [14]
was used to code how theory and predictors (constructs that are
not explicitly linked to a theory by the authors but are targeted
for intervention because they predict behavior) were used in the
design of the interventions. Items 1 through 6 of the coding
scheme identify whether theory or predictors are mentioned and
whether they are used to select recipients for the intervention,
to select or develop intervention techniques, or to tailor
intervention techniques to participants. Items 7 through 11
examine whether intervention techniques are explicitly linked
to theory-relevant constructs or predictors and, conversely,
whether theory-relevant constructs or predictors are linked to
intervention techniques. Items 12 through 17 were not evaluated
in the present review because they do not pertain to use of theory
in developing the intervention. These items focus on
methodological issues (randomization and measurement quality)
and whether theory was refined on the basis of outcomes. Where
the theoretical basis of the experimental intervention was
identical to that of the comparison intervention (eg, [34]), the
intervention was coded as not having a theoretical basis that
could explain differences in effect size between the conditions.
In addition to considering each use of theory separately, we also
summed items 1 through 11 to create an overall “use of theory”
score that could be used to evaluate whether more extensive
use of theory leads to larger effects than less extensive use. In
a slight change to the published recommendations, item 8 (“At
least one, but not all, intervention techniques are explicitly
linked to at least one theory-relevant construct/predictor”) was
coded as “yes” if item 7 (“All intervention techniques are
explicitly linked to at least one theory-relevant
construct/predictor”) was coded as “yes.” Similarly, item 11
(“At least one, but not all, theory-relevant constructs/predictors
are explicitly linked to at least one intervention technique”) was
coded as “yes” if item 10 (“All theory-relevant
constructs/predictors are explicitly linked to at least one
intervention technique”) was coded as “yes.” This ensured that
when we created the ”use of theory” score, reports that linked,
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for example, all theoretical constructs with intervention
techniques, were also credited as linking some theoretical
constructs with intervention techniques.
Theoretical Basis
Interventions were coded as having a particular theoretical basis
only if the theory was used to develop the intervention
techniques (item 5 of the coding scheme of Michie and
Prestwich [14]) rather than theory being simply mentioned (item
1).
Behavior Change Techniques
The behavior change techniques used in the interventions were
coded using an augmented 40-item version [52] of the 26-item
taxonomy developed by Abraham and Michie [13] (see Table
2for a list of techniques). Where the behavior change techniques
used by the experimental intervention were the same as those
in the comparison intervention (eg, [35]), the experimental
intervention was coded as not using any behavior change
techniques.
Mode of Delivery
Mode of delivery was coded using a novel coding scheme
developed by the present authors. For convenience, we divided
mode of delivery into (i) automated functions, (ii)
communicative functions, and (iii) use of supplementary modes.
Each category included a list of delivery modes, and we marked
whether or not each intervention used that mode. Automated
functions included: (a) the use of an enriched information
environment (eg, supplementary content and links, testimonials,
videos, or games), (b) automated tailored feedback based on
individual progress monitoring (eg, comparison to norms or
goals, reinforcing messages, or coping messages), and (c)
automated follow-up messages (eg, reminders, tips, newsletters,
encouragement). Communicative functions included: (d) access
to an advisor to request advice (eg, ”ask the expert” facility,
expert-led discussion board, or chat sessions), (e) scheduled
contact with advisor (eg, emails), and (f) peer-to-peer access
(eg, buddy systems, peer-to-peer discussions boards, forums,
or live chat). Finally, use of supplementary modes included the
use of (g) email, (h) telephone, (i) Short Messaging Service
(SMS), (j) CD-ROM, or (k) videoconferencing.
The features of intervention delivery that we coded were, to a
large extent, constrained by the features that authors typically
report and that can be easily and objectively verified (eg,
whether text messages were used). The list is not intended to
be exhaustive, and we recognize that there are other features
that may be important but that are not routinely reported or used
or that are hard to measure, for example, navigational format
(eg, the extent to which users are “tunnelled” to particular
information vs given free choice [54]), entertainment value (eg,
use of quizzes, stories, graphics), appearance (eg, color, layout,
screen size [18]), and credibility (eg, the extent to which the
website cites sources, credentials). As Internet-based
interventions become more common and standards of reporting
improve, it should be relatively easy to integrate these additional
delivery features into the present coding scheme.
Meta-analytic Strategy
We used Hedges gas the primary estimate of effect size for
each intervention. Hedges gis the difference between the two
means (for experimental and control conditions, respectively)
divided by the pooled standard deviation. Computations were
undertaken using Comprehensive Meta-Analysis Version 2
(Biostat, Englewood, NJ, USA) [55] with the exception of
meta-regression computations for which we used the weighted
least squares regression command in SPSS 15 for Windows
(SPSS Inc, Chicago, IL, USA).Weighted average effect sizes
(d+) were based on a random effects model because studies were
likely to be “different from one another in ways too complex
to capture by a few simple study characteristics” [56]. Effect
sizes were interpreted using Cohen’s [57] guidelines. According
to Cohen, d+= .20 should be considered a “small” effect size,
d+= .50 is a “medium” effect size, whereas d+= .80 is a “large”
effect size. The homogeneity Q statistic [58] was used to
evaluate variability across effect sizes from the primary studies.
When Q is statistically significant it indicates that the effect
sizes are heterogeneous. For the meta-regressions, ß is beta
weight or coefficient assigned to the predictor;t (and the
associatedP -value) tests whether the beta weight is significantly
different from zero.
Results
Effect of Internet-based Interventions on
Health-related Behavior
The weighted average effect size across all interventions was
d+= 0.16 with a 95% confidence interval from 0.09 to 0.23 based
on 85 studies (k = 85) and a total of 43,236 participants (see
Table 1). This means that the Internet-based interventions had,
on average, a small effect on health behavior according to
Cohen’s criteria [57]. While these qualitative indices are useful
for interpreting the findings of systematic reviews, however,
statistical effectiveness is not necessarily the same as clinical
effectiveness. For example, a relatively small effect of an
Internet-based intervention on smoking abstinence could have
substantial clinical significance [59]. On the other hand, an
Internet-based intervention that produces a reliable change in
fat intake has the potential to benefit a larger proportion of the
population than an intervention targeted at smokers. Given that
much of the cost associated with Internet-based interventions
is likely to be incurred at the design and development stage
rather than in delivering individual treatments, small effects
with the potential to have an impact on large numbers of people
may thus be significant for patient or population health.
We also calculated effect sizes separately for commonly targeted
behaviors (see Table 1). Small, but significant, effects on
behavior were observed for Internet-based interventions that
targeted only physical activity (d+= 0.24, k = 20, 95% CI
0.09-0.38), dietary behavior (d+= 0.20, k = 10, 95% CI
0.02-0.37), or alcohol consumption (d+= 0.14, k = 9, 95% CI
0.00-0.27). Interventions that targeted smoking abstinence
tended to have slightly smaller effects on behavior that did not
reach statistical significance (d+= 0.07, k = 12, 95% CI -0.04
to 0.18). Finally, we calculated effect sizes separately for
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interventions that targeted multiple behaviors (eg, Williamson
et al’s intervention [50] targeted physical activity and dietary
behavior) and those that targeted a single behavior. Interventions
that targeted multiple behaviors tended to have slightly smaller
effects on behavior (d+= 0.12, k = 10, 95% CI 0.08-0.17) than
did interventions that targeted a single behavior (d+= 0.17, k =
75, 95% CI 0.09-0.24), although both effects were statistically
significant.
Table 1. Weighted effect sizes (d+) for behavior change as a function of Internet-based interventions by behavior type
d+c
95% CI
Q b
k a
Behavior
0.24e
0.09-0.38
128.76c
20 Physical activity
0.20f
0.02-0.37
30.82b
10 Dietary behavior
0.14d
0.00-0.27
47.45c
9 Alcohol consumption
0.07-0.04 to 0.18
45.46b
12 Smoking abstinence
0.12f
0.08-0.177.9010 Interventions targeting multiple behaviors
0.17f
0.09-0.24
879.81c
75 Interventions targeting a single behavior
0.16f
0.09-0.23
896.67c
85 All studies
ak = the number of interventions included in the estimate of effect size
bQ = homogeneity for the subgroup of interventions
cd+ = weighted average effect size
dP < .05
eP < .01
fP < .001
Intervention Characteristics
Across all interventions, the homogeneity Q statistic was highly
significant (Q = 896.67, P < .001), which indicates considerable
variability across effect sizes from the primary studies. To
examine the impact of intervention characteristics on effect size,
we computed the weighted average effect size for behavior
change as a function of the theoretical basis of the interventions,
the different ways that the interventions used theory, the
behavior change techniques, and the mode of delivery. The
findings from these analyses are shown in Table 2. Multimedia
Appendix 2 shows the characteristics of each intervention.
Use of Theory and Predictors
Of the different uses of theory proposed by Michie and
Prestwich’s coding scheme [14], theory or predictors were most
commonly used to select or develop intervention techniques (k
= 37). Over 20% of the interventions, however, mentioned
theory (k = 30), linked at least one intervention technique to
theory (k = 19), linked at least one theory-relevant construct to
an intervention technique (k = 18), or mentioned a target
construct as a predictor of behavior (k = 18). Interventions that
used theory or predictors to select recipients for the intervention
tended to have the largest effects on behavior (d+= 0.33, k = 3,
95% CI 0.15-0.52) with most other uses of theory tending to
have smaller effects (Median d+= 0.19). Overall, meta-regression
indicated that increased use of theory had a significant positive
impact on effect sizes (ß = 0.22, t = 2.00, P = .049).
Interventions that made extensive use of theory tended to have
larger effects on behavior than did interventions that made less
extensive or no use of theory.
Theoretical Basis
Only three theories were used by three or more studies to
develop the intervention; social cognitive theory (SCT) [22],
the transtheoretical model (TTM) [60], and the theory of
reasoned action/planned behavior (TPB) [20,61]. Effect sizes
associated with interventions based on the TPB tended to have
larger effects on behavior (d+= 0.36, k = 9, 95% CI 0.15-0.56)
than did interventions based on the TTM (d+= 0.20, k = 12, 95%
CI 0.08-0.33) that, in turn, had larger effects than did
interventions based on SCT (d+= 0.15, k = 12, 95% CI
0.04-0.25).
Behavior Change Techniques
The most commonly used behavior change techniques (used by
30% or more of interventions) were providing information on
the consequences of behavior in general (k = 29), prompting
self-monitoring of behavior (k = 28), and identifying barriers
and/or problem solving (k = 26). The largest effects on behavior
were observed for interventions that provided stress management
(d+= 0.50, 95% CI 0.27-0.72) or general communication skills
training (d+= 0.49, 95% CI 0.25-0.73), although these were used
by relatively few interventions (k = 5 and 3, respectively).
Modeling, relapse prevention/coping planning, facilitating social
comparison, goal setting, action planning, and provision of
feedback on performance all had effects on behavior that
exceeded d+= 0.20 (Median d+= 0.28). Finally, a few strategies
had small and non-significant effects on behavior: use of
follow-up prompts, self-monitoring of behavioral outcome,
emotional control training, and provision of information about
others approval. Overall, meta-regression indicated that the
number of behavior change techniques employed had a
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significant positive impact on effect size (ß = 0.36,t = 3.48,P <
.001). Interventions that used more techniques tended to have
larger effects on behavior than did interventions that used fewer
techniques.
Mode of Delivery
Only one mode of delivery was used by 30% or more of
interventions–providing an enriched information environment
(k = 30). Over 20% of interventions, however, provided access
to an advisor to request advice (k = 23), used peer-to-peer access
(k = 20), used email in addition to the Internet-based intervention
(k = 19), or provided automated tailored feedback (k = 18). For
convenience of interpretation, effect sizes for modes of delivery
were divided into three subgroups: automated functions,
communicative functions, and use of supplementary modes. In
terms of automated functions, small, but significant, effects on
behavior were observed for interventions that provided
automated tailored feedback (d+= 0.18, k = 18, 95% CI
0.07-0.28) or an enriched information environment (d+= 0.15,
k = 30, 95% CI 0.07-0.23). Interventions that provided
automated follow-up messages tended not to have significant
effects on behavior (d+= 0.09, k = 14, 95% CI -0.01 to 0.19).
Of the communicative functions, interventions that provided
access to an advisor to request advice tended to have
small-to-medium effects on behavior (d+= 0.29, k = 23, 95%
CI 0.16-0.42), while smaller effects on behavior were observed
for interventions that provided scheduled contact with an advisor
(d+= 0.22, k = 13, 95% CI 0.09-0.36) or peer-to-peer access
(d+= 0.20, k = 20, 95% CI 0.09-0.21). Finally, use of additional
modes appeared to have distinct effects on behavior change
with Internet-based interventions that also used text messages
having large effects on behavior (d+= 0.81, k = 4, 95% CI
0.14-1.49), Internet-based interventions using the telephone
having small-to-medium effects (d+= 0.35, k = 7, 95% CI
0.09-0.61), and interventions using email as an additional mode
of delivery tending to have small effects on behavior (d+= 0.18,
k = 19, 95% CI 0.07-0.29).
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Table 2. Effect sizesaby theoretical basis, use of theory, behavior change techniques, and mode of delivery
d+d
95% CI
Qc
Kb
Theoretical Basis
0.36b
0.15-0.56
108.44h
9Theory of reasoned action/planned behavior (TPB) [20,61]
0.20g
0.08-0.33
68.99h
12Transtheoretical model (TTM) [60]
0.15g
0.04-0.2518.6212Social cognitive theory (SCT) [22]
2Elaboration likelihood model (ELM) [62]
1Extended parallel process model (EPPM) [63]
1Self-regulation theory (SRT) [64]
1Precaution adoption process model (PAPM) [65]
1Diffusion of innovations model (DIM) [66]
1Health belief model (HBM) [67,68]
1Social norms theory (SNT) [69]
Use of Theory
0.33h
0.15-0.522.8434. Theory/predictors used to select recipients for the intervention
0.23a
0.03-0.439.8569. Group of techniques are linked to a group of constructs/predictors
0.21h
0.13-0.29
191.40h
375. Theory/predictors used to select/develop intervention techniques
0.21g
0.11-0.31
60.07h
182. Targeted construct mentioned as predictor of behavior
0.21g
0.07-0.34
67.75h
116. Theory/predictors used to tailor intervention techniques to recipients
0.19h
0.11-0.28
161.33h
301. Theory/model of behavior mentioned
0.19g
0.09-0.29
93.65h
198. At least one of the intervention techniques is linked to theory
0.18a
0.05-0.32
57.13h
123. Intervention based on single theory
0.18-0.02to 0.37
47.70h
1010. All theory-relevant constructs are linked to intervention techniques
0.17g
0.07-0.27
70.63h
1811. At least one of the theory-relevant constructs is linked to an intervention
technique
27. All intervention techniques are linked to theory
Behavior Change Technique
0.50h
0.27-0.726.73535. Stress management
0.49h
0.25-0.734.38339. General communication skills training
0.35e
-0.01to 0.70
24.80h
521. Model/demonstrate the behavior
0.32h
0.17-0.47
38.31h
1434. Relapse prevention/coping planning
0.29a
0.04-0.553.25427. Facilitate social comparison
0.27h
0.16-0.38
126.24h
255. Goal setting (behavior)
0.25h
0.13-0.37
101.67h
187. Action planning
0.22g
0.09-0.34
77.38h
1919. Provide feedback on performance
0.20h
0.10-0.30
112.52h
268. Barrier identification/problem solving
0.13-0.282520. Provide instruction
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d+d
95% CI
Qc
Kb
0.20h
97.95h
0.20-0.17-0.575.45322. Teach to use prompts/cues
0.18g
0.07-0.28
94.32h
164. Provide normative information about others’behavior
0.18h
0.10-0.27
41.32h
1528. Plan social support/social change
0.18h
0.09-0.287.17713. Provide rewards for behavior
0.16h
0.07-0.24
80.81h
2816. Prompt self-monitoring of behavior
0.14h
0.06-0.21
114.14h
291. Provide information on the consequences in general
0.14g
0.04-0.24
47.57h
122. Provide information on the consequences for individual
0.13-0.10 to 0.35
39.35h
526. Use of follow up prompts
0.12-0.03 to 0.26
45.73h
1317. Prompt self-monitoring of behavioral outcome
0.11a
0.02-0.192.89312. Reinforcing effort toward behavior
0.09-0.03 to 0.22
35.39h
1136. Emotional control training
0.06-0.11 to 0.23
10.48a
53. Provide information about others’approval
26. Goal setting (outcome)
210. Prompt review of behavioral goals
214. Shaping
223. Environmental restructuring
225. Prompt practice
124. Agree behavioral contract
131. Fear Arousal
132. Prompt self-talk
137. Motivational interviewing
09. Set graded tasks
011. Prompt review of outcome goals
015. Prompting generalisation of behavior
018. Prompting focus on past success
029. Prompt identification as role model
030. Prompt anticipated regret
33. Prompt use of imagery
38. Time management
40. Provide non-specific social support
Mode of Delivery: Automated Functions
0.18g
0.07-0.28
83.75h
18b. Automated tailored feedback
0.15h
0.07-0.23
117.24h
30a. Enriched information environment
0.09-0.01 to 0.19
49.81h
14c. Automated follow-up messages
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d+d
95% CI
Qc
Kb
Mode of Delivery: Communicative Functions
0.29h
0.16-0.42
121.15h
23d. Access to advisor to request advice
0.22g
0.09-0.36
35.70h
13e. Scheduled contact with advisor
0.20h
0.09-0.21
88.21h
20f. Peer-to-peer access
Mode of Delivery: Additional Modes
0.81a
0.14-1.49
39.22h
4i. Text message (SMS)
0.35g
0.09-0.61
19.02g
7h. Telephone
0.18g
0.07-0.29
143.98h
19g. Email
1j. CD-ROM
1k. Videoconferencing
aEffect sizes are ordered within category by size of effect. Characteristics supported by less than three interventions were not examined in order to
ensure reliable evaluations of the impact of particular intervention characteristics on effect size.
bk = the number of interventions included in the estimate of effect size
cQ = homogeneity across the subgroup of interventions
dd+ = weighted average effect size
eRemoving Mikolajczak et al [70] from the evaluation of the effects of modeling on behavior change rendered the effect size significant (k = 4, Q =
13.84, 95% CI 0.14-0.84, d+ = 0.49,P = .006)
fP < .05
gP < .01
hP < .001
Discussion
Overall Findings
The primary aim of the present review was to relate the
characteristics of Internet-based interventions to their
effectiveness in promoting health behavior change. Like
previous reviews, the interventions tended to have variable
effects on behavior (ie, the homogeneity Q statistic was
significant), and the average effect on behavior was statistically
small. Thus, while some interventions had very large effects (d
> 1.00) on behavior (eg, [21,71,72]), others were found to have
small or even negative effects on behavior (eg, [73,74]). The
considerable variability in the effectiveness of Internet-based
interventions makes it important to systematically identify the
characteristics of effective interventions and to relate these to
effect size.
Use of Theory
Interventions differed substantially in their use of theory, but
more extensive use of theory was associated with larger effect
sizes. This finding is consistent with assertions that interventions
can benefit from using behavior change theory [23-27] and
extends the evidence base to interventions delivered on the
Internet. Three theories–social cognitive theory (SCT) [22], the
transtheoretical model (TTM) [60], and the theory of reasoned
action/planned behavior (TPB) [20,61]–were used much more
frequently than others. However, only the use of the TPB to
inform intervention design led to substantially larger effects
than were observed across all interventions. Effect sizes were
small-to-medium, comparable to those reported in reviews of
non-Internet interventions that used the TPB to develop the
intervention [75], and were not simply the consequence of TPB
interventions targeting a different set of behaviors. (Interventions
based on the TPB targeted a similar range of health-related
behaviors to those based on the TTM or SCT.) The observed
effectiveness of the TPB in promoting health behavior change
stands in contrast to recent assertions that the TPB is primarily
a predictive model rather than a model of behavior change that
can inform interventions (eg, [76]). However, the heterogeneity
of effects across findings means that the findings should be
treated with caution and should provide an empirical basis for
experimental studies that can demonstrate cause and effect
[77,78]. Such studies are also important because Michie and
Prestwich’s coding of use of theory [14] used in the present
review is, necessarily, based on what is reported in the
manuscripts; it is of course possible that manuscripts can report
having used theory without actually having done so (and vice
versa).
Behavior Change Techniques
The finding that interventions that incorporated more behavior
change techniques tended to have larger effects than
interventions that incorporated fewer techniques justified the
investment in relatively elaborate interventions. This finding
may be a consequence of different techniques targeting different
aspects of the behavior change process [18], and future research
might usefully consider how particular combinations of
techniques might be especially effective in promoting behavior
change [33]. However, there is also evidence that very simple
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interventions can prove effective in some contexts (eg, providing
instruction for influencing online food purchases [79] and if-then
planning for promoting dental flossing [7]), and issues of cost
versus benefit should always be a consideration in designing
interventions to promote health behavior change [80]. Tate et
al [81] provide a useful discussion of cost versus effectiveness
in relation to Internet-based interventions, and we echo their
call for future research to collect cost-effectiveness data.
The two behavior change techniques that were associated with
the greatest changes in behavior were stress management and
general communication skills training. It is interesting that both
techniques influence behavior change indirectly via mechanisms
such as facilitating problem-solving, promoting self-efficacy
[82], or diminishing the impact of stressors that may prevent
behavior change [83]. However, relatively few interventions
employed these techniques, so the findings should be treated
with caution and form the basis for future research.Given the
effectiveness of stress management training, it is perhaps
surprising that emotional control training was less effective in
promoting behavior change. Of the 11 interventions (45%) that
incorporated emotional control training, 5 reported negative
effect sizes on behavior [31,32,84,85]. Authors reported that in
many of these interventions they simply included “strategies to
manage mood [85]”or “information on ... dealing with
relationships and feelings [31].” In contrast, stress management
training tended to be more intensive. For example, the
intervention reported by Hänggi [86] incorporated 4 stress
management modules that were based on cognitive behavioral
principles. Again, these differences might form a useful basis
for future empirical investigation.
Two other findings in relation to behavior change techniques
warrant comment. First, it was notable that providing
information about others’ approval (subjective or injunctive
norms) seemed to be less effective than providing normative
information about others’behavior (descriptive norms, d+= 0.06
and 0.18, respectively). This finding supports the distinction
between the two types of normative influence [87] and research
that shows that descriptive norms can exert a more powerful
effect on behavior and decision making than injunctive norms
(eg, [88,89]). Second, effect sizes associated with modeling,
while substantial overall, were also highly variable rendering
the overall estimate of effectiveness non-significant. Modeling
is usually used to boost self-efficacy [22], and the present
interventions tended to incorporate embedded videos
demonstrating the focal behavior within the online intervention
(eg, [70,90,91]). The variability in effect sizes in the present
review was primarily caused by Mikolajczak et al’s
“Queermasters” intervention [70], which reported a negative
effect on uptake of HIV testing at the three month follow up (d
= -0.23). The authors attributed this finding to the relatively
short follow-up, which may not have given participants
opportunity to act on their newly formed positive intentions.
Removing Mikolajczak et al from the evaluation of the effects
of modeling on behavior change rendered the effect size
significant (k = 4, Q = 13.84, 95% CI = 0.14-0.84, d+= 0.49, P
= .006).
Mode of Delivery
The present review developed a novel coding scheme for the
mode by which Internet-based interventions are delivered.
Dividing mode of delivery into automated functions,
communicative functions, and use of supplementary modes
proved informative, with distinct effects being identified within
each category. Text messages were highly effective and used
in several ways: to promote interaction with the intervention
[4,5], send motivational messages (eg, reminders of the benefits
of exercise [37]), challenge dysfunctional beliefs [71], or provide
a cue to action [35]. Use of communicative functions, especially
access to an advisor to request advice, also tended to be
effective. It may be that, although the Internet provides a suitable
medium for delivering interventions, personal contact via email
[92], online [93,94], or text message [95] helps to support
behavior change.
Conclusion
The present review is, to our knowledge, the first to
systematically code the characteristics of Internet-based
interventions designed to promote behavior change and to link
these characteristics to effect size. The strengths of the review
are the systematic, meta-analytic approach, the use of established
coding frames where possible, and the large number of different
interventions that focus on a range of different behaviors. The
findings suggest that the effectiveness of Internet-based
interventions is associated with more extensive use of theory
(in particular the use of the theory of planned behavior),
inclusion of more behavior change techniques, and use of
additional methods of interacting with participants (especially
text messages). The review provides a framework for research
that can contribute to a science of Internet-based interventions
[1] and our findings provide a rationale for investing in more
intensive theory-based interventions that incorporate multiple
behavior change techniques and modes of delivery. However,
the heterogeneity of effects across findings and the relatively
small number of interventions associated with some
characteristics means that the findings should be treated with
caution and provide an empirical basis for experimental studies
that can demonstrate cause and effect.
Acknowledgments
This review was inspired by a workshop on Internet-based behaviour change interventions in addiction sponsored by the Society
for the Study of Addiction. The authors would like to thank Hongmei Han, Marney White, and Donald Williamson for providing
additional information concerning their research. We also thank Craig Whittington for statistical assistance and Robert West for
helpful comments on earlier drafts of this manuscript. This review was funded in part by an ESRC grant (RES-149-25-1069)
awarded to LY and SM. This grant funds the Southampton “LifeGuide” node of the National Centre for e-Social Science
(www.lifeguideonline.org) and supported JJ.
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Multimedia Appendix
Effect Sizes for Interventions Included in the Meta-Analysis
[PDF file (Adobe PDF),60 KB - jmir_v12i1e4_app1.pdf ]
Multimedia Appendix
Intervention Characteristics for Interventions Included in the Meta-Analysis
[PDF file (Adobe PDF),68 KB - jmir_v12i1e4_app2.pdf ]
References
1. Ritterband LM, Tate DF. The science of internet interventions. Introduction. Ann Behav Med 2009 Aug;38(1):1-3. [Medline:
19816750] [doi: 10.1007/s12160-009-9132-5]
2. Internet World Stats. World Internet Users and Population Stats URL: http://www.internetworldstats.com/stats.htm.Accessed:
2009-10-06.(Archived by WebCite® at) [WebCite Cache ID 5kK6ed847]
3. Murray E, Khadjesari Z, White IR, Kalaitzaki E, Godfrey C, McCambridge J, et al. Methodological challenges in online
trials. J Med Internet Res 2009;11(2):e9 [FREE Full text] [Medline: 19403465] [doi: 10.2196/jmir.1052]
4. Brendryen H, Kraft P. Happy ending: a randomized controlled trial of a digital multi-media smoking cessation intervention.
Addiction 2008 Mar;103(3):478-84; discussion 485-6. [Medline: 18269367] [doi: 10.1111/j.1360-0443.2007.02119.x]
5. Brendryen H, Drozd F, Kraft P. A digital smoking cessation program delivered through internet and cell phone without
nicotine replacement (happy ending): randomized controlled trial. J Med Internet Res 2008;10(5):e51 [FREE Full text]
[Medline: 19087949] [doi: 10.2196/jmir.1005]
6. Skår S, Sniehotta FF, Molloy GJ, Prestwich A, Araújo-Soares V. Do brief online planning interventions increase physical
activity amongst university students? A randomised controlled trial. Psychol Health. In press .
7. Sniehotta FF, Araújo Soares V, Dombrowski SU. Randomized controlled trial of a one-minute intervention changing oral
self-care behavior. J Dent Res 2007 Jul;86(7):641-645. [Medline: 17586711] [doi: 10.1177/154405910708600711]
8. Portnoy DB, Lori AJ, Sheldon S, Johnson BT, Carey MP. Computer-delivered interventions for health promotion and
behavioral risk reduction a meta-analysis of 75 randomized controlled trials,1988–2007. In: Prev Med Jul 2008, Vol.
47(1):3-16. [Medline: 18403003]
9. Shahab L, McEwen A. Online support for smoking cessation: a systematic review of the literature. Addiction 2009
Nov;104(11):1792-1804. [Medline: 19832783] [doi: 10.1111/j.1360-0443.2009.02710.x]
10. Vandelanotte C, Spathonis KM, Eakin EG, Owen N. Website-delivered physical activity interventions a review of the
literature. Am J Prev Med 2007 Jul;33(1):54-64. [Medline: 17572313] [doi: 10.1016/j.amepre.2007.02.041]
11. Walters ST, Wright JoA, Shegog R. A review of computer and Internet-based interventions for smoking behavior. Addict
Behav 2006 Feb;31(2):264-277. [Medline: 15950392] [doi: 10.1016/j.addbeh.2005.05.002]
12. Wantland DJ, Portillo CJ, Holzemer WL, Slaughter R, McGhee EM. The effectiveness of Web-based vs. non-Web-based
interventions: a meta-analysis of behavioral change outcomes. J Med Internet Res 2004 Nov 10;6(4):e40 [FREE Full text]
[Medline: 15631964] [doi: 10.2196/jmir.6.4.e40]
13. Abraham C, Michie S. A taxonomy of behavior change techniques used in interventions. Health Psychol 2008
May;27(3):379-387. [Medline: 18624603] [doi: 10.1037/0278-6133.27.3.379]
14. Michie S, Prestwich A. Are interventions theory-based? Development of a theory coding scheme. Health Psychol 2010
Jan;29(1):1-8. [Medline: 20063930] [doi: 10.1037/a0016939]
15. Bootzin R. Behavior Modification and Therapy: An Introduction. Cambridge, MA: Winthrop; 1975.
16. Kanfer F, Goldstein A. Helping People Change: A Textbook of Methods. New York, NY: Pergamon Press; 1986.
17. Hardeman W, Griffin S, Johnston M, Kinmonth AL, Wareham NJ. Interventions to prevent weight gain: a systematic review
of psychological models and behaviour change methods. Int J Obes Relat Metab Disord 2000 Feb;24(2):131-143. [Medline:
10702762] [doi: 10.1038/sj.ijo.0801100]
18. Ritterband LM, Thorndike FP, Cox DJ, Kovatchev BP, Gonder-Frederick LA. A behavior change model for internet
interventions. Ann Behav Med 2009 Aug;38(1):18-27. [Medline: 19802647] [doi: 10.1007/s12160-009-9133-4]
19. Spittaels H, De Bourdeaudhuij I, Brug J, Vandelanotte C. Effectiveness of an online computer-tailored physical activity
intervention in a real-life setting. Health Educ Res 2007 Jun;22(3):385-396 [FREE Full text] [Medline: 16971674] [doi:
10.1093/her/cyl096]
20. Ajzen I. The Theory of Planned Behavior. Organ Behav Hum Decis Process 1991;50(2):179-211. [doi:
10.1016/0749-5978(91)90020-T]
21. Carr LJ, Bartee RT, Dorozynski C, Broomfield JF, Smith ML, Smith DT. Internet-delivered behavior change program
increases physical activity and improves cardiometabolic disease risk factors in sedentary adults: results of a randomized
controlled trial. Prev Med 2008 May;46(5):431-438. [Medline: 18207228] [doi: 10.1016/j.ypmed.2007.12.005]
22. Bandura A. Human agency in social cognitive theory. Am Psychol 1989 Sep;44(9):1175-1184. [Medline: 2782727] [doi:
10.1037/0003-066X.44.9.1175]
J Med Internet Res 2010 | vol. 12 | iss. 1 | e4 | p.12http://www.jmir.org/2010/1/e4/ (page number not for citation purposes)
Webb et alJOURNAL OF MEDICAL INTERNET RESEARCH
XSL
•
FO
RenderX
23. Griffin S, Kinmonth AL, Skinner C, Kelly J. Educational and Psychosocial Interventions for Adults with Diabetes. London,
United Kingdom: British Diabetic Association; 1999.
24. Marteau T, Dieppe P, Foy R, Kinmonth A, Schneiderman N. Behavioural medicine: changing our behaviour. BMJ 2006
Feb 25;332(7539):437-438 [FREE Full text] [Medline: 16497737] [doi: 10.1136/bmj.332.7539.437]
25. Michie S, Sheeran P, Rothman A. Advancing the science of behaviour change. Invited editorial. Psychol Health
2007;22(3):249-253. [doi: 10.1080/14768320701233582]
26. Painter JE, Borba CPC, Hynes M, Mays D, Glanz K. The use of theory in health behavior research from 2000 to 2005: a
systematic review. Ann Behav Med 2008 Jun;35(3):358-362. [Medline: 18633685] [doi: 10.1007/s12160-008-9042-y]
27. Rothman AJ. "Is there nothing more practical than a good theory?": Why innovations and advances in health behavior
change will arise if interventions are used to test and refine theory. Int J Behav Nutr Phys Act 2004 Jul 27;1(1):11 [FREE
Full text] [Medline: 15279674] [doi: 10.1186/1479-5868-1-11]
28. Jeffery RW. How can Health Behavior Theory be made more useful for intervention research? Int J Behav Nutr Phys Act
2004 Jul 23;1(1):10 [FREE Full text] [Medline: 15272938] [doi: 10.1186/1479-5868-1-10]
29. Oxman AD, Fretheim A, Flottorp S. The OFF theory of research utilization. J Clin Epidemiol 2005 Feb;58(2):113-6;
discussion 117-20. [Medline: 15680741] [doi: 10.1016/j.jclinepi.2004.10.002]
30. Albarracín D, Gillette JC, Earl AN, Glasman LR, Durantini MR, Ho M. A test of major assumptions about behavior change:
a comprehensive look at the effects of passive and active HIV-prevention interventions since the beginning of the epidemic.
Psychol Bull 2005 Nov;131(6):856-897 [FREE Full text] [Medline: 16351327] [doi: 10.1037/0033-2909.131.6.856]
31. Patten CA, Croghan IT, Meis TM, Decker PA, Pingree S, Colligan RC, et al. Randomized clinical trial of an Internet-based
versus brief office intervention for adolescent smoking cessation. Patient Educ Couns 2006 Dec;64(1-3):249-258. [Medline:
16616449] [doi: 10.1016/j.pec.2006.03.001]
32. Muñoz RF, Lenert LL, Delucchi K, Stoddard J, Perez JE, Penilla C, et al. Toward evidence-based Internet interventions:
A Spanish/English Web site for international smoking cessation trials. Nicotine Tob Res 2006 Feb;8(1):77-87. [Medline:
16497602] [doi: 10.1080/14622200500431940]
33. Michie S, Abraham C, Whittington C, McAteer J, Gupta S. Effective techniques in healthy eating and physical activity
interventions: a meta-regression. Health Psychol 2009 Nov;28(6):690-701. [Medline: 19916637] [doi: 10.1037/a0016136]
34. Marks JT, Campbell MK, Ward DS, Ribisl KM, Wildemuth BM, Symons MJ. A comparison of Web and print media for
physical activity promotion among adolescent girls. J Adolesc Health 2006 Jul;39(1):96-104. [Medline: 16781967] [doi:
10.1016/j.jadohealth.2005.11.002]
35. Moore MJ, Soderquist J, Werch C. Feasibility and efficacy of a binge drinking prevention intervention for college students
delivered via the Internet versus postal mail. J Am Coll Health 2005;54(1):38-44. [Medline: 16050327] [doi:
10.3200/JACH.54.1.38-44]
36. Eysenbach G. Medicine 2.0: social networking, collaboration, participation, apomediation, and openness. J Med Internet
Res 2008;10(3):e22 [FREE Full text] [Medline: 18725354] [doi: 10.2196/jmir.1030]
37. Hurling R, Fairley BW, Dias MB. Internet-based exercise intervention systems: Are more interactive designs better? Psychol
Health 2006;21(6):757-772. [doi: 10.1080/14768320600603257]
38. Webb TL, Sheeran P. Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental
evidence. Psychol Bull 2006 Mar;132(2):249-268. [Medline: 16536643] [doi: 10.1037/0033-2909.132.2.249]
39. Christensen H, Griffiths KM, Korten A. Web-based cognitive behavior therapy: analysis of site usage and changes in
depression and anxiety scores. J Med Internet Res 2002 Feb 15;4(1):e3 [FREE Full text] [Medline: 11956035] [doi:
10.2196/jmir.4.1.e3]
40. Couper MP, Peytchev A, Strecher VJ, Rothert K, Anderson J. Following up nonrespondents to an online weight management
intervention: randomized trial comparing mail versus telephone. J Med Internet Res 2007;9(2):e16 [FREE Full text]
[Medline: 17567564] [doi: 10.2196/jmir.9.2.e16]
41. Murray E, McCambridge J, Khadjesari Z, White IR, Thompson SG, Godfrey C, et al. The DYD-RCT protocol: an on-line
randomised controlled trial of an interactive computer-based intervention compared with a standard information website
to reduce alcohol consumption among hazardous drinkers. BMC Public Health 2007;7(1):306 [FREE Full text] [Medline:
17963483] [doi: 10.1186/1471-2458-7-306]
42. Linke S, Murray E, Butler C, Wallace P. Internet-based interactive health intervention for the promotion of sensible drinking:
patterns of use and potential impact on members of the general public. J Med Internet Res 2007;9(2):e10 [FREE Full text]
[Medline: 17513281] [doi: 10.2196/jmir.9.2.e10]
43. Dijkstra A, De Vries H, Roijackers J. Long-term effectiveness of computer-generated tailored feedback in smoking cessation.
Health Educ Res 1998 Jun;13(2):207-214 [FREE Full text] [Medline: 10181019] [doi: 10.1093/her/13.2.207]
44. Strecher VJ, Shiffman S, West R. Moderators and mediators of a web-based computer-tailored smoking cessation program
among nicotine patch users. Nicotine Tob Res 2006 Dec;8 Suppl 1(1):S95-101. [Medline: 17491176] [doi:
10.1080/14622200601039444]
45. McConnon A, Kirk SFl, Cockroft JE, Harvey EL, Greenwood DC, Thomas JD, et al. The Internet for weight control in an
obese sample: results of a randomised controlled trial. BMC Health Serv Res 2007;7(1):206 [FREE Full text] [Medline:
18093289] [doi: 10.1186/1472-6963-7-206]
J Med Internet Res 2010 | vol. 12 | iss. 1 | e4 | p.13http://www.jmir.org/2010/1/e4/ (page number not for citation purposes)
Webb et alJOURNAL OF MEDICAL INTERNET RESEARCH
XSL
•
FO
RenderX
46. An LC, Perry CL, Lein EB, Klatt C, Farley DM, Bliss RL, et al. Strategies for increasing adherence to an online smoking
cessation intervention for college students. Nicotine Tob Res 2006 Dec;8 Suppl 1(1):S7-12. [Medline: 17491165] [doi:
10.1080/14622200601039881]
47. Hänggi Y. Kann web-basierte Stressprävention erfolgreich sein? Erfahrungen mit dem Online-Elterntraining gegen
Familienstress. Z Klin Psychol 2006;35(3):169-177. [doi: 10.1026/1616-3443.35.3.169]
48. Prisma Group, Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and
meta-analyses: the PRISMA statement (Chinese edition). Zhong Xi Yi Jie He Xue Bao 2009 Sep;7(9):889-896 [FREE Full
text] [Medline: 19747449] [doi: 10.3736/jcim20090918]
49. Higgins J, Green S. Cochrane handbook for systematic reviews of interventions Version 5.0.1. The Cochrane Collaboration
2008.
50. Williamson DA, Walden HM, White MA, York-Crowe E, Newton RL, Alfonso A, et al. Two-year internet-based randomized
controlled trial for weight loss in African-American girls. Obesity (Silver Spring) 2006 Jul;14(7):1231-1243. [Medline:
16899804] [doi: 10.1038/oby.2006.140]
51. Hunter JE, Schmidt EL. Methods of Meta-analysis: Correcting Error and Bias in Research Findings. Newbury Park, CA:
Sage; 1990.
52. Ashford S, Edmunds J, French DP. What are the most effective techniques for changing self-efficacy to promote physical
activity: a meta analysis. 2009 Sep Presented at: DHP Conference; September 2009; Aston, United Kingdom.
53. Danaher BG, Seeley JR. Methodological issues in research on web-based behavioral interventions. Ann Behav Med 2009
Aug;38(1):28-39. [Medline: 19806416] [doi: 10.1007/s12160-009-9129-0]
54. Danaher BG, McKay HG, Seeley JR. The information architecture of behavior change websites. J Med Internet Res
2005;7(2):e12 [FREE Full text] [Medline: 15914459] [doi: 10.2196/jmir.7.2.e12]
55. Borenstein M, Hedges L, Higgins J, Rothstein H. Comprehensive Meta-analysis Version 2. , Englewood, NJ: Biostat; 2005.
56. Cooper H, Hedges L. The Handbook of Research Synthesis. New York, NY: Russell Sage Foundation; 1994:526.
57. Cohen J. A power primer. Psychol Bull 1992 Jul;112(1):155-159. [Medline: 19565683] [doi: 10.1037/0033-2909.112.1.155]
58. Cochran WG. The combination of estimates from different experiments. Biometrics 1954;10(1):101-129. [doi:
10.2307/3001666]
59. West R. The clinical significance of "small" effects of smoking cessation treatments. Addiction 2007 Apr;102(4):506-509.
[Medline: 17362283] [doi: 10.1111/j.1360-0443.2007.01750.x]
60. Prochaska JO, DiClemente CC. The Transtheoretical Approach: Crossing the Traditional Boundaries of Therapy. Homewood,
IL: Dow Jones-Irwin; 1984.
61. Fishbein M, Ajzen I. Belief, Attitude, and Behavior. An Introduction to Theory and Research. Reading, MA: Addison-Wesley;
1975.
62. Petty RE, Cacioppo JT. The Elaboration Likelihood Model of persuasion. In: Berkowitz L, editor. Advances in Experimental
Social Psychology (vol. 19). New York, NY: Academic Press; 1986:123-205.
63. Witte K. Putting the fear back into fear appeals: the extended parallel process model. Commun Monogr 1992;59(4):329-349.
[doi: 10.1080/03637759209376276]
64. Kanfer FH. Helping People Change: A Textbook of Methods. 3rd edition. New York, NY: Pergamon Press; 1986.
65. Weinstein ND. The precaution adoption process. Health Psychol 1988;7(4):355-386. [Medline: 3049068] [doi:
10.1037/0278-6133.7.4.355]
66. Rogers EM. Diffusion of Innovations. Glencoe, IL: Free Press; 1962.
67. Rosenstock IM. Why people use health services. Milbank Mem Fund Q 1966 Jul;44(3):Suppl:94-Suppl127. [Medline:
5967464] [doi: 10.2307/3348967]
68. Becker MH. The health belief model and personal health behavior. Health Educ Monog 1974;2:324-473.
69. Perkins HW. The Social Norms Approach to Preventing School and College Age Substance Abuse: A Handbook for
Educators, Counsellors, and Clinicians. San Francisco, CA: Jossey-Bass; 2003.
70. Mikolajczak J, Van Breukelen G, Kok G, Hospers HJ. Evaluating the Effects of an Online HIV-Prevention Intervention to
Promote HIV-Testing Behavior Among Men Who Have Sex With Men (MSM) [dissertation]. Maastricht, Netherlands:
Maastricht University; 2008.
71. Hurling R, Catt M, Boni MDe, Fairley BW, Hurst T, Murray P, et al. Using internet and mobile phone technology to deliver
an automated physical activity program: randomized controlled trial. J Med Internet Res 2007;9(2):e7 [FREE Full text]
[Medline: 17478409] [doi: 10.2196/jmir.9.2.e7]
72. Kim C, Kang D. Utility of a Web-based intervention for individuals with type 2 diabetes: the impact on physical activity
levels and glycemic control. Comput Inform Nurs 2006;24(6):337-345. [Medline: 17108753] [doi:
10.1097/00024665-200611000-00008]
73. Frosch DL, Kaplan RM, Felitti VJ. A randomized controlled trial comparing internet and video to facilitate patient education
for men considering the prostate specific antigen test. J Gen Intern Med 2003 Oct;18(10):781-787 [FREE Full text] [Medline:
14521639] [doi: 10.1046/j.1525-1497.2003.20911.x]
J Med Internet Res 2010 | vol. 12 | iss. 1 | e4 | p.14http://www.jmir.org/2010/1/e4/ (page number not for citation purposes)
Webb et alJOURNAL OF MEDICAL INTERNET RESEARCH
XSL
•
FO
RenderX
74. Rodriguez HP, von Glahn T, Rogers WH, Chang H, Fanjiang G, Safran DG. Evaluating patients' experiences with individual
physicians: a randomized trial of mail, internet, and interactive voice response telephone administration of surveys. Med
Care 2006 Feb;44(2):167-174. [Medline: 16434916] [doi: 10.1097/01.mlr.0000196961.00933.8e]
75. Hardeman W, Johnston M, Johnston DW, Bonetti D, Wareham NJ, Kinmonth AL. Application of the Theory of Planned
Behaviour in behaviour change interventions: a systematic review. Psychol Health 2002;17:123-158.
76. Sniehotta FF. Towards a theory of intentional behaviour change: plans, planning, and self-regulation. Br J Health Psychol
2009 May;14(Pt 2):261-273. [Medline: 19102817] [doi: 10.1348/135910708X389042]
77. Sheeran P. Does changing cognitions cause health behaviour change? Psychol Health 2006;21:S137.
78. Sniehotta FF. An experimental test of the Theory of Planned Behaviour. Applied Psychology: Health and Well-Being
2009;1(2):257-270. [doi: 10.1111/j.1758-0854.2009.01013.x]
79. Huang A, Barzi F, Huxley R, Denyer G, Rohrlach B, Jayne K, et al. The effects on saturated fat purchases of providing
internet shoppers with purchase- specific dietary advice: a randomised trial. PLoS Clin Trials 2006;1(5):e22 [FREE Full
text] [Medline: 17013429] [doi: 10.1371/journal.pctr.0010022]
80. Gold BC, Burke S, Pintauro S, Buzzell P, Harvey-Berino J. Weight loss on the web: A pilot study comparing a structured
behavioral intervention to a commercial program. Obesity (Silver Spring) 2007 Jan;15(1):155-164. [Medline: 17228043]
[doi: 10.1038/oby.2007.520]
81. Tate DF, Finkelstein EA, Khavjou O, Gustafson A. Cost effectiveness of internet interventions: review and recommendations.
Ann Behav Med 2009 Aug;38(1):40-45 [FREE Full text] [Medline: 19834778] [doi: 10.1007/s12160-009-9131-6]
82. Monti PM, Rohsenow DJ, Swift RM, Gulliver SB, Colby SM, Mueller TI, et al. Naltrexone and cue exposure with coping
and communication skills training for alcoholics: treatment process and 1-year outcomes. Alcohol Clin Exp Res 2001
Nov;25(11):1634-1647. [Medline: 11707638] [doi: 10.1111/j.1530-0277.2001.tb02170.x]
83. Shiffman S. Coping with temptations to smoke. J Consult Clin Psychol 1984 Apr;52(2):261-267. [Medline: 6715652] [doi:
10.1037/0022-006X.52.2.261]
84. Jones M, Luce KH, Osborne MI, Taylor K, Cunning D, Doyle AC, et al. Randomized, controlled trial of an internet-facilitated
intervention for reducing binge eating and overweight in adolescents. Pediatrics 2008 Mar;121(3):453-462 [FREE Full
text] [Medline: 18310192] [doi: 10.1542/peds.2007-1173]
85. McKay HG, Danaher BG, Seeley JR, Lichtenstein E, Gau JM. Comparing two web-based smoking cessation programs:
randomized controlled trial. J Med Internet Res 2008;10(5):e40 [FREE Full text] [Medline: 19017582] [doi: 10.2196/jmir.993]
86. Hänggi Y. Web-based prevention: the online parental training in coping with stress in the family. 2004 Presented at:
European Society on Family Relations; September, 2004; Fribourg, Switzerland.
87. Reno RR, Cialdini RB, Kallgren CA. The transsituational influence of social norms. J Pers Soc Psychol 1993;64(1):104-112.
[doi: 10.1037/0022-3514.64.1.104]
88. Conner M, McMillan B. Interaction effects in the theory of planned behaviour: studying cannabis use. Br J Soc Psychol
1999 Jun;38 ( Pt 2)(2):195-222. [Medline: 10392450] [doi: 10.1348/014466699164121]
89. Poliakoff E, Webb TL. What factors predict scientists’intentions to participate in public engagement of science activities?
Sci Commun 2007;29(2):242-263. [doi: 10.1177/1075547007308009]
90. Mevissen F, Ruiter R, Meertens RM, Zimbile FR, Schaalma HP. Justify your love. Testing an online STI risk communication
intervention on condom use and STI testing. Psychol Health. Forthcoming .
91. Wade SL, Carey J, Wolfe CR. The efficacy of an online cognitive-behavioral family intervention in improving child behavior
and social competence following pediatric brain injury. Rehabil Psychol 2006;51(3):179-189. [doi:
10.1037/0090-5550.51.3.179]
92. Chan DS, Callahan CW, Hatch-Pigott VB, Lawless A, Proffitt HL, Manning NE, et al. Internet-based home monitoring
and education of children with asthma is comparable to ideal office-based care: results of a 1-year asthma in-home monitoring
trial. Pediatrics 2007 Mar;119(3):569-578 [FREE Full text] [Medline: 17332210] [doi: 10.1542/peds.2006-1884]
93. Glasgow RE, Boles SM, McKay HG, Feil EG, Barrera M. The D-Net diabetes self-management program: long-term
implementation, outcomes, and generalization results. Prev Med 2003 Apr;36(4):410-419. [Medline: 12649049] [doi:
10.1016/S0091-7435(02)00056-7]
94. Gold MR, Siegel JE, Russell LB, Weinstein MC. Cost-effectiveness in Health and Medicine. Oxford, United Kingdom:
Oxford University Press; 1996.
95. Rodgers A, Corbett T, Bramley D, Riddell T, Wills M, Lin R, et al. Do u smoke after txt? Results of a randomised trial of
smoking cessation using mobile phone text messaging. Tob Control 2005 Aug;14(4):255-261 [FREE Full text] [Medline:
16046689] [doi: 10.1136/tc.2005.011577]
96. An LC, Hennrikus DJ, Perry CL, Lein EB, Klatt C, Farley DM, et al. Feasibility of Internet health screening to recruit
college students to an online smoking cessation intervention. Nicotine Tob Res 2007 Jan;9 Suppl 1(1):S11-S18. [Medline:
17365722] [doi: 10.1080/14622200601083418]
97. Bersamin M, Paschall MJ, Fearnow-Kenney M, Wyrick D. Effectiveness of a Web-based alcohol-misuse and harm-prevention
course among high- and low-risk students. J Am Coll Health 2007;55(4):247-254. [Medline: 17319331] [doi:
10.3200/JACH.55.4.247-254]
J Med Internet Res 2010 | vol. 12 | iss. 1 | e4 | p.15http://www.jmir.org/2010/1/e4/ (page number not for citation purposes)
Webb et alJOURNAL OF MEDICAL INTERNET RESEARCH
XSL
•
FO
RenderX
98. Bosak K, Yates B, Pozehl B. An internet physical activity intervention to reduce coronary heart disease risk in the metabolic
syndrome population. Circulation 2007;116(9):319. [doi: 10.1161/CIRCULATIONAHA.107.186213]
99. Buller DB, Woodall WG, Zimmerman DE, Slater MD, Heimendinger J, Waters E, et al. Randomized trial on the 5 a day,
the Rio Grande Way Website, a web-based program to improve fruit and vegetable consumption in rural communities. J
Health Commun 2008;13(3):230-249. [Medline: 18569356] [doi: 10.1080/10810730801985285]
100. Celio AA, Winzelberg AJ, Wilfley DE, Eppstein-Herald D, Springer EA, Dev P, et al. Reducing risk factors for eating
disorders: comparison of an Internet- and a classroom-delivered psychoeducational program. J Consult Clin Psychol 2000
Aug;68(4):650-657. [Medline: 10965640] [doi: 10.1037/0022-006X.68.4.650]
101. Chiauzzi E, Green TC, Lord S, Thum C, Goldstein M. My student body: a high-risk drinking prevention web site for college
students. J Am Coll Health 2005;53(6):263-274 [FREE Full text] [Medline: 15900990] [doi: 10.3200/JACH.53.6.263-274]
102. Christakis DA, Zimmerman FJ, Rivara FP, Ebel B. Improving pediatric prevention via the internet: a randomized, controlled
trial. Pediatrics 2006 Sep;118(3):1157-1166 [FREE Full text] [Medline: 16951011] [doi: 10.1542/peds.2006-0209]
103. Christensen H, Leach LS, Barney L, Mackinnon AJ, Griffiths KM. The effect of web based depression interventions on
self reported help seeking: randomised controlled trial [ISRCTN77824516]. BMC Psychiatry 2006;6(1):13 [FREE Full
text] [Medline: 16595018] [doi: 10.1186/1471-244X-6-13]
104. Cintron A, Phillips R, Hamel MB. The effect of a web-based, patient-directed intervention on knowledge, discussion, and
completion of a health care proxy. J Palliat Med 2006 Dec;9(6):1320-1328. [Medline: 17187540] [doi:
10.1089/jpm.2006.9.1320]
105. Cook RF, Billings DW, Hersch RK, Back AS, Hendrickson A. A field test of a web-based workplace health promotion
program to improve dietary practices, reduce stress, and increase physical activity: randomized controlled trial. J Med
Internet Res 2007;9(2):e17 [FREE Full text] [Medline: 17581811] [doi: 10.2196/jmir.9.2.e17]
106. Cussler EC, Teixeira PJ, Going SB, Houtkooper LB, Metcalfe LL, Blew RM, et al. Maintenance of weight loss in overweight
middle-aged women through the Internet. Obesity (Silver Spring) 2008 May;16(5):1052-1060. [Medline: 18309301] [doi:
10.1038/oby.2008.19]
107. Etter J. Comparing the efficacy of two Internet-based, computer-tailored smoking cessation programs: a randomized trial.
J Med Internet Res 2005;7(1):e2 [FREE Full text] [Medline: 15829474] [doi: 10.2196/jmir.7.1.e2]
108. Fordis M, King JE, Ballantyne CM, Jones PH, Schneider KH, Spann SJ, et al. Comparison of the instructional efficacy of
Internet-based CME with live interactive CME workshops: a randomized controlled trial. JAMA 2005 Sep 7;294(9):1043-1051
[FREE Full text] [Medline: 16145024] [doi: 10.1001/jama.294.9.1043]
109. Gollings EK, Paxton SJ. Comparison of internet and face-to-face delivery of a group body image and disordered eating
intervention for women: a pilot study. Eat Disord 2006;14(1):1-15. [Medline: 16757445] [doi: 10.1080/10640260500403790]
110. Hager R, Hardy A, Aldana S. Evaluation of an Internet, stage-based physical activity intervention. Med Sci Sports Exerc
2001;33(5):S231. [doi: 10.1097/00005768-200105001-01301]
111. Heinicke BE, Paxton SJ, McLean SA, Wertheim EH. Internet-delivered targeted group intervention for body dissatisfaction
and disordered eating in adolescent girls: a randomized controlled trial. J Abnorm Child Psychol 2007 Jun;35(3):379-391.
[Medline: 17243014] [doi: 10.1007/s10802-006-9097-9]
112. Jacobi C, Morris L, Beckers C, Bronisch-Holtze J, Winter J, Winzelberg AJ, et al. Maintenance of internet-based prevention:
a randomized controlled trial. Int J Eat Disord 2007 Mar;40(2):114-119. [Medline: 17080447] [doi: 10.1002/eat.20344]
113. Jago R, Baranowski T, Baranowski JC, Thompson D, Cullen KW, Watson K, et al. Fit for Life Boy Scout badge: outcome
evaluation of a troop and Internet intervention. Prev Med 2006 Mar;42(3):181-187. [Medline: 16458955] [doi:
10.1016/j.ypmed.2005.12.010]
114. Japuntich SJ, Zehner ME, Smith SS, Jorenby DE, Valdez JA, Fiore MC, et al. Smoking cessation via the internet: a
randomized clinical trial of an internet intervention as adjuvant treatment in a smoking cessation intervention. Nicotine
Tob Res 2006 Dec;8 Suppl 1(1):S59-S67. [Medline: 17491172] [doi: 10.1080/14622200601047900]
115. Kosma M, Cardinal BJ, McCubbin JA. A pilot study of a web-based physical activity motivational program for adults with
physical disabilities. Disabil Rehabil 2005 Dec 15;27(23):1435-1442. [Medline: 16418058] [doi:
10.1080/09638280500242713]
116. Kypri K, McAnally HM. Randomized controlled trial of a web-based primary care intervention for multiple health risk
behaviors. Prev Med 2005;41(3-4):761-766. [Medline: 16120456] [doi: 10.1016/j.ypmed.2005.07.010]
117. Lin C, Wittevrongel L, Moore L, Beaty BL, Ross SE. An Internet-based patient-provider communication system: randomized
controlled trial. J Med Internet Res 2005;7(4):e47 [FREE Full text] [Medline: 16236699] [doi: 10.2196/jmir.7.4.e47]
118. Lorig KR, Ritter PL, Laurent DD, Plant K. Internet-based chronic disease self-management: a randomized trial. Med Care
2006 Nov;44(11):964-971. [Medline: 17063127] [doi: 10.1097/01.mlr.0000233678.80203.c1]
119. Marshall AL, Leslie ER, Bauman AE, Marcus BH, Owen N. Print versus website physical activity programs: a randomized
trial. Am J Prev Med 2003 Aug;25(2):88-94. [Medline: 12880874] [doi: 10.1016/S0749-3797(03)00111-9]
120. McKay HG, King D, Eakin EG, Seeley JR, Glasgow RE. The diabetes network internet-based physical activity intervention:
a randomized pilot study. Diabetes Care 2001 Aug;24(8):1328-1334 [FREE Full text] [Medline: 11473065] [doi:
10.2337/diacare.24.8.1328]
J Med Internet Res 2010 | vol. 12 | iss. 1 | e4 | p.16http://www.jmir.org/2010/1/e4/ (page number not for citation purposes)
Webb et alJOURNAL OF MEDICAL INTERNET RESEARCH
XSL
•
FO
RenderX
121. Meigs JB, Cagliero E, Dubey A, Murphy-Sheehy P, Gildesgame C, Chueh H, et al. A controlled trial of web-based diabetes
disease management: the MGH diabetes primary care improvement project. Diabetes Care 2003 Mar;26(3):750-757 [FREE
Full text] [Medline: 12610033] [doi: 10.2337/diacare.26.3.750]
122. Napolitano MA, Fotheringham M, Tate D, Sciamanna C, Leslie E, Owen N, et al. Evaluation of an internet-based physical
activity intervention: a preliminary investigation. Ann Behav Med 2003;25(2):92-99. [Medline: 12704010] [doi:
10.1207/S15324796ABM2502_04]
123. Nguyen HQ, Donesky-Cuenco D, Wolpin S, Reinke LF, Benditt JO, Paul SM, et al. Randomized controlled trial of an
internet-based versus face-to-face dyspnea self-management program for patients with chronic obstructive pulmonary
disease: pilot study. J Med Internet Res 2008;10(2):e9 [FREE Full text] [Medline: 18417444] [doi: 10.2196/jmir.990]
124. Oenema A, Brug J, Dijkstra A, de Weerdt I, de Vries H. Efficacy and use of an internet-delivered computer-tailored lifestyle
intervention, targeting saturated fat intake, physical activity and smoking cessation: a randomized controlled trial. Ann
Behav Med 2008 Apr;35(2):125-135. [Medline: 18363076] [doi: 10.1007/s12160-008-9023-1]
125. Paschall MJ, Bersamin M, Fearnow-Kenney M, Wyrick D, Currey D. Short-term evaluation of a web-based college alcohol
misuse and harm prevention course (College Alc). J Alcohol Drug Educ 2006;50:49-65.
126. Pike KJ, Rabius V, McAlister A, Geiger A. American Cancer Society's QuitLink: randomized trial of Internet assistance.
Nicotine Tob Res 2007 Mar;9(3):415-420. [Medline: 17365773] [doi: 10.1080/14622200701188877]
127. Prestwich A. The Use of Implementation Intentions in Promoting Goal Attainment [dissertation]. Leeds, United Kingdon:
University of Leeds; 2003.
128. Prochaska JO, Butterworth S, Redding CA, Burden V, Perrin N, Leo M, et al. Initial efficacy of MI, TTM tailoring and
HRI's with multiple behaviors for employee health promotion. Prev Med 2008 Mar;46(3):226-231. [Medline: 18155287]
[doi: 10.1016/j.ypmed.2007.11.007]
129. Riper H, Kramer J, Smit F, Conijn B, Schippers G, Cuijpers P. Web-based self-help for problem drinkers: a pragmatic
randomized trial. Addiction 2008 Feb;103(2):218-227. [Medline: 18199300] [doi: 10.1111/j.1360-0443.2007.02063.x]
130. Ritterband LM, Cox DJ, Walker LS, Kovatchev B, McKnight L, Patel K, et al. An Internet intervention as adjunctive
therapy for pediatric encopresis. J Consult Clin Psychol 2003 Oct;71(5):910-917. [Medline: 14516239] [doi:
10.1037/0022-006X.71.5.910]
131. Roberto AJ, Zimmerman RS, Carlyle KE, Abner EL. A computer-based approach to preventing pregnancy, STD, and HIV
in rural adolescents. J Health Commun 2007;12(1):53-76. [Medline: 17365349] [doi: 10.1080/10810730601096622]
132. Ross SE, Moore LA, Earnest MA, Wittevrongel L, Lin C. Providing a web-based online medical record with electronic
communication capabilities to patients with congestive heart failure: randomized trial. J Med Internet Res 2004 May
14;6(2):e12 [FREE Full text] [Medline: 15249261] [doi: 10.2196/jmir.6.2.e12]
133. Saitz R, Palfai TP, Freedner N, Winter MR, Macdonald A, Lu J, et al. Screening and brief intervention online for college
students: the ihealth study. Alcohol Alcohol 2007;42(1):28-36 [FREE Full text] [Medline: 17130139] [doi:
10.1093/alcalc/agl092]
134. Shimazu A, Kawakami N, Irimajiri H, Sakamoto M, Amano S. Effects of web-based psychoeducation on self-efficacy,
problem solving behavior, stress responses and job satisfaction among workers: a controlled clinical trial. J Occup Health
2005 Sep;47(5):405-413 [FREE Full text] [Medline: 16230834] [doi: 10.1539/joh.47.405]
135. Spittaels H, De Bourdeaudhuij I, Vandelanotte C. Evaluation of a website-delivered computer-tailored intervention for
increasing physical activity in the general population. Prev Med 2007 Mar;44(3):209-217. [Medline: 17197015] [doi:
10.1016/j.ypmed.2006.11.010]
136. Steele R, Mummery WK, Dwyer T. Using the Internet to promote physical activity: a randomized trial of intervention
delivery modes. J Phys Act Health 2007 Jul;4(3):245-260. [Medline: 17846455]
137. Strecher VJ, Shiffman S, West R. Randomized controlled trial of a web-based computer-tailored smoking cessation program
as a supplement to nicotine patch therapy. Addiction 2005 May;100(5):682-688. [Medline: 15847626] [doi:
10.1111/j.1360-0443.2005.01093.x]
138. Strecher VJ, McClure JB, Alexander GL, Chakraborty B, Nair VN, Konkel JM, et al. Web-based smoking-cessation
programs: results of a randomized trial. Am J Prev Med 2008 May;34(5):373-381 [FREE Full text] [Medline: 18407003]
[doi: 10.1016/j.amepre.2007.12.024]
139. Swartz LHG, Noell JW, Schroeder SW, Ary DV. A randomised control study of a fully automated internet based smoking
cessation programme. Tob Control 2006 Feb;15(1):7-12 [FREE Full text] [Medline: 16436397] [doi: 10.1136/tc.2003.006189]
140. Thombs DL, Olds RS, Osborn CJ, Casseday S, Glavin K, Berkowitz AD. Outcomes of a technology-based social norms
intervention to deter alcohol use in freshman residence halls. J Am Coll Health 2007;55(6):325-332. [Medline: 17517544]
[doi: 10.3200/JACH.55.6.325-332]
141. Tomnay JE, Pitts MK, Kuo TC, Fairley CK. Does the Internet assist clients to carry out contact tracing? A randomized
controlled trial using web-based information. Int J STD AIDS 2006 Jun;17(6):391-394. [Medline: 16734961] [doi:
10.1258/095646206777323391]
142. van den Berg MH, Ronday HK, Peeters AJ, le Cessie S, van der Giesen FJ, Breedveld FC, et al. Using internet technology
to deliver a home-based physical activity intervention for patients with rheumatoid arthritis: A randomized controlled trial.
Arthritis Rheum 2006 Dec 15;55(6):935-945 [FREE Full text] [Medline: 17139640] [doi: 10.1002/art.22339]
J Med Internet Res 2010 | vol. 12 | iss. 1 | e4 | p.17http://www.jmir.org/2010/1/e4/ (page number not for citation purposes)
Webb et alJOURNAL OF MEDICAL INTERNET RESEARCH
XSL
•
FO
RenderX
143. Vinokur AD, Merion RM, Couper MP, Jones EG, Dong Y. Educational web-based intervention for high school students
to increase knowledge and promote positive attitudes toward organ donation. Health Educ Behav 2006 Dec;33(6):773-786.
[Medline: 16923836] [doi: 10.1177/1090198106288596]
144. Walters ST, Vader AM, Harris TR. A controlled trial of web-based feedback for heavy drinking college students. Prev Sci
2007 Mar;8(1):83-88. [Medline: 17136461] [doi: 10.1007/s11121-006-0059-9]
145. White MA, Martin PD, Newton RL, Walden HM, York-Crowe EE, Gordon ST, et al. Mediators of weight loss in a
family-based intervention presented over the internet. Obes Res 2004 Jul;12(7):1050-1059. [Medline: 15292468] [doi:
10.1038/oby.2004.132]
146. Winett RA, Anderson ES, Wojcik JR, Winett SG, Bowden T. Guide to health: nutrition and physical activity outcomes of
a group-randomized trial of an Internet-based intervention in churches. Ann Behav Med 2007 Jun;33(3):251-261. [Medline:
17600452]
147. Zabinski MF, Pung MA, Wilfley DE, Eppstein DL, Winzelberg AJ, Celio A, et al. Reducing risk factors for eating disorders:
targeting at-risk women with a computerized psychoeducational program. Int J Eat Disord 2001 May;29(4):401-408.
[Medline: 11285577] [doi: 10.1002/eat.1036]
148. Zabinski MF, Wilfley DE, Calfas KJ, Winzelberg AJ, Taylor CB. An interactive psychoeducational intervention for women
at risk of developing an eating disorder. J Consult Clin Psychol 2004 Oct;72(5):914-919. [Medline: 15482051] [doi:
10.1037/0022-006X.72.5.914]
Abbreviations
DIM: diffusion of innovations model
ELM: elaboration likelihood model
EPPM: extended parallel process model
HBM: health belief model
PAPM: precaution adoption process model
SCT: social cognitive theory
SMS: short message service
SNT: social norms theory
SRT: self-regulation theory
TPB: theory of reasoned action/planned behavior
TTM: transtheoretical model
Edited by G Eysenbach; submitted 09.10.09; peer-reviewed by P Kraft, R Botelho; comments to author 30.10.09; revised version
received 12.11.09; accepted 16.11.09; published 07.02.10
Please cite as:
Webb TL, Joseph J, Yardley L, Michie S
Using the Internet to Promote Health Behavior Change: A Systematic Review and Meta-analysis of the Impact of Theoretical Basis,
Use of Behavior Change Techniques, and Mode of Delivery on Efficacy
J Med Internet Res 2010;12(1):e4
URL: http://www.jmir.org/2010/1/e4/
doi:10.2196/jmir.1376
PMID:
© Thomas L Webb, Judith Joseph, Lucy Yardley, Susan Michie. Originally published in the Journal of Medical Internet Research
(http://www.jmir.org), 07.02.2010. This is an open-access article distributed under the terms of the Creative Commons Attribution
License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any
medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete
bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information
must be included.
J Med Internet Res 2010 | vol. 12 | iss. 1 | e4 | p.18http://www.jmir.org/2010/1/e4/ (page number not for citation purposes)
Webb et alJOURNAL OF MEDICAL INTERNET RESEARCH
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