Tailored interventions to promote mammography screening: A meta-analytic review

Abstract

Objective-To evaluate the effectiveness of tailored interventions, designed to reach one specific person based on her unique characteristics, for promoting mammography use.Method-This systematic review used meta-analytic techniques to aggregate the effect size of 28 studies published from 1997 through 2005. Potential study-level moderators of outcomes (sample, intervention, and methodological characteristics) were also examined.Results-A small but significant aggregate odds ratio effect size of 1.42 indicated that women exposed to tailored interventions were significantly more likely to get a mammogram (p < 0.001).The type of population recruited and participants' pre-intervention level of mammography adherence did not significantly influence this effect. Tailored interventions that used the Health Belief Model and included a physician recommendation produced the strongest effects. Interventions delivered in person, by telephone, or in print were similarly effective. Finally, defining adherence as a single recent mammogram as opposed to regular or repeated mammograms yielded higher effect sizes. Tailored interventions, particularly those that employ the Health Belief Model and use a physician recommendation, are effective in promoting mammography screening. Future investigations should strive to use more standardized definitions of tailoring and assessments of mammography outcomes. Conclusion-

How do we Optimize Message Matching Interventions? Identifying Matching Thresholds, and Simultaneously Matching to Multiple Characteristics

“…Specifically, after accounting for the main effects of either OPTIMIZING MESSAGE MATCHING 32 characteristics, the two matching effects had effects sizes of R 2 = 0.012 (for promotion focus) and R 2 = .010 (for interdependent self-construal). When taken together, the benefits of matching messages to both characteristics simultaneously was around R 2 = .022; that is, twice the magnitude of matching to either characteristic alone.To put these effects into perspective, we note that the average message matching effects reported in meta-analyses are usually between R 2 = .001 and .010 (Gallagher & Updegraff, 2012;Krebs, Prochaska, & Rossi, 2010;Lustria et al, 2013;Noar et al, 2007;O'Keefe & Jensen, 2006;Sohl & Moyer, 2007). Comparatively, the singular matching effects we report are on the larger side of this range, and the additive effect of matching to our two characteristics was greater than twice the magnitude of typical matching effects in the literature.…”

Health Beliefs and Illness Perceptions as Related to Mammography Uptake in Randomly Selected Women in Greece

“…Champion et al (2008) demonstrated that women who had not had a mammogram within the last 18 months and did not plan to have one within 6 months, had higher mean barriers, lower mean benefits and lower mean self-efficacy scores compared to women who planned to have a mammogram within 6 months, or as recommended by their health care provider. Tailored interventions that employ the HBM and include several of the model's factors (e.g., barriers, benefits, and self-efficacy) have been found to be effective in promoting mammography screening (Sohl & Moyer, 2007). Thus, certain constructs of the HBM have shown high potential for designing effective interventions that promote mammography screening.…”

Results of breast and cervical cancer Health Promotion Model for older Turkish women

“…In addition to studies with similar findings reporting that interventions increased mammography screening (4,13), there are also contrary studies stating that interventions were not effective in increasing the rate of mammography use (28,29,32). A meta-analysis study reported that individual specific practice, particularly HBM (3.3 times) and doctor recommendations were effective in increasing mammography screening (33). It has been reported that the rates of cervical cancer screening might be increased with appropriate training through identifying barriers (10,24,(34)(35)(36).…”