This study computed a mean effect size for 88 studies that provided computer-tailored feedback based on individual assessments using computer, print, or telephone communication channels. We also examined moderators that were hypothesized to influence the effects of tailored interventions. A significant effect size (g
= 0.17) was found for tailored interventions averaged across four health behaviors. The overall effect for tailored interventions represents a small to medium-size effect for population-based interventions (Rossi, 2003
) (where g
= 0.15, 0.20, and 0.25 for small, medium and large effects) and a 36% increase (OR
= 1.36) over the control conditions to which the interventions were compared. In addition, significant effects were found for each of the behaviors examined individually. It appears that systematic differences in tailoring methods is an unlikely explanation for the range of effect sizes across behaviors since the same research groups conducted interventions for each behavior and many tailoring techniques were shared across groups. Other possibilities are base rates and differences in the nature of the behaviors. Population rates of mammography (the lowest effect size reported) are the highest (>66%) compared to the other behaviors, which may produce a ceiling effect. Each behavior also presents a unique set of barriers to adherence and it is difficult to make conclusions regarding the relative difficulty of changing distinct behaviors.
These data show that computer-tailored interventions would have clinically significant impact on rates of behavioral risk factors. First, in terms of smoking cessation, the average point prevalence abstinence was 20% at final follow-up versus 14% in the comparison group, a clinically significant absolute increase of 6% in quit rates, and a rate comparable to that observed with 4-8 individual in-person counseling sessions (Fiore, 2008
). Second, for physical activity, 43% of participants receiving computer-tailored communications were adherent to physical activity recommendations (World Health Organization, 2002
) at follow-up versus only 34% in the comparison groups. With up to 40% of people in industrialized countries not engaging in any regular physical activity (Bauman, et al., 2009
), increasing rates of physical activity by the rate produced by these interventions would have an important impact on health outcomes. Third, since an estimated 27% of people eat five or more fruits or vegetables per day (Centers for Disease Control and Prevention, 2007
), increasing this rate by the effect size found in this study for fruit and vegetable intake (OR
= 1.36) would increase the absolute rate of fruit and vegetable consumption to 37%, a meaningful change that is highly recommended to prevent and control obesity and multiple chronic diseases (Centers for Disease Control and Prevention, 2009
). Finally, for receipt of least bi-annual mammography screening, computer-tailored interventions resulted in 56% adherence versus 50% in control groups, an important difference given that a secular trend reflecting a reduction of 4% in mammography screening rates existed during the period in which most of these studies were conducted (Breen, et al., 2007
In terms of moderators of effect size, dynamically-tailored interventions outperformed statically-tailored interventions, especially upon examination of longitudinal effects. The larger effect size for dynamically tailored interventions could be explained by increased number of overall contacts that dynamic tailoring necessitates, and indeed static tailoring with more than one contact showed similar effects (g
= .20) compared to dynamic tailoring (g
= .19). When examined longitudinally, however, greater intervention effects for dynamic tailoring as compared to static tailoring with multiple contacts were seen in three of four outcome time point categories (1-3, 4-6, and 13-24 months) and only dynamic tailoring remained significant at long-term follow up, an important finding in terms of intervention maintenance. These results suggest that more than just providing additional contact, updating feedback to reflect a person’s changes may increase information relevance and depth of processing (Petty and Elster, 1981
). The addition of systematic qualitative data as a complement to studies of dynamically tailored interventions would help to clarify the processes involved in this observed effect.
No significant differences were found by communication channel (print, computer, or automated phone). While conclusions cannot be drawn regarding automated phone intervention delivery with only three studies, the lack of difference between print and computer terminal-based feedback channels suggests that both channels can be effective means of health communication.
It also appears that intervening on up to three multiple behaviors at the same time does not negatively impact behavioral outcomes, with suggestion of a trend for larger effects as number of behaviors increased from one to three. Individual studies also support the feasibility of multiple behavior interventions (Vandelanotte, et al., 2008
). The effectiveness of multiple behavior change could reflect an underlying general health orientation that influences engagement in behaviors (Noar, et al., 2008
, Prochaska, et al., 2008
). Common change patterns have also been found across behaviors for both decisional balance (Hall and Rossi, 2008
) and self-efficacy (Grembowski, et al., 1993
) constructs, suggesting that similar principles can be applied to changing distinct behaviors.
In terms of study design characteristics, a nominally significant (p
= .10) trend was found suggesting that including only participants not engaging in a behavior may mitigate intervention effects. It was also predicted that reactive recruitment would result in larger effect sizes under the assumption that participants responding to ads and actively volunteering would be more ready to change. This hypothesis was not upheld, possibly because studies using reactive strategies made efforts to recruit people who were less ready to change (Hageman, et al., 2005
, Prochaska, et al., 1993
). Results did not favor non-U.S based studies as previously found by Noar. Whereas their sample of studies conducted outside the U.S. used shorter follow up time points, which likely explained this difference, our samples were similar in that non-U.S. studies followed up at 8.9 months on average and U.S. studies did so at 9.3 months. Study quality was also not related to effect size, which may be due to restricted range for the measure given a standard deviation of 1.6 on the 22-point scale. Differences in effect size were not found for mean age, percentage of minority participants, or gender likely attributable to efforts in most studies to randomize by demographic characteristics.
Study Strengths and Limitations
This study has a number of strengths that enhance its contribution to the study of computer-tailored interventions. First, it is the most representative and current review of studies that employed computer tailoring. We included studies using three different communication channels and searched multiple databases drawing from over 20 years of research. While publication bias has been cited as a problem in meta-analysis, it is likely minimal given that almost all intervention studies were large-scale funded projects. The fail-safe N suggests that a large number of studies would be needed to lower the average effect size to clinical nonsignificance. Second, this study distinguishes tailoring methodology from communication channels as has not been done previously. Third, we employed well-established meta-analytic techniques for effect size estimation and moderator analysis. Finally, we conducted novel moderator analyses not covered in previous reviews, examining effect sizes across outcome time points, intervention channels, multiple behaviors, design characteristics, and study quality.
On the other hand, methodological considerations necessarily limit the conclusions able to be drawn from the present work. First, given that effects decrease after intervention completion, using the final assessment from each study may underestimate the potential of tailored-interventions. If participant contact can be maintained, intervention effects may be higher than found here. Second, this study reported primary analyses of computer-tailored interventions and more work is needed to examine the relationship between effect size and additional intervention variables. We are currently preparing an analysis that focuses on the utility of tailoring options such as tailoring using specific constructs, use of theory, depth of tailoring, cultural tailoring, etc. (Rimer and Kreuter, 2006