Neither more recent meta-analyses nor more impactful meta-analyses examined significantly more intervention content dimensions (r values = −0.04 and −0.07, P values = 0.78 and 0.61, respectively). Meta-analyses sampling more studies examined significantly more intervention dimensions (r = 0.33, P < 0.05). Figure 1 plots the number of intervention content dimensions reported as a function of date of database construction. Regardless of date, intervention content was unlikely to be considered in meta-analyses with small samples of studies. Of those including fewer than 36 studies, 51% evaluated no intervention content dimensions; of those with more than 35 studies, all but one evaluated at least one aspect of intervention content. Figure 1 represents the 12 (20%) most highly cited meta-analyses with solid circles; of these, only 2 (8%) examined more than 10 content dimensions and 4 (33%) examined none. Of the 37 meta-analyses that considered at least one dimension of intervention content, 23 (62%) found that at least one related significantly to risk reduction. Meta-analyses that examined more information content dimensions and that included larger samples of studies were more likely to find significant moderation patterns (r values = 0.47 and 0.45, respectively, P values < 0.001). In 1 larger meta-analysis, the authors reported lack of detail in source reports as an impediment to examining intervention content.62 Meta-analyses that found at least one information content dimension relating significantly to risk reduction had much larger samples of studies than did those that examined content but found no such patterns (mean values = 77.13 and 21.61, respectively, t = 2.75, P < 0.01), a pattern that remained intact when the meta-analysis with the largest sample was omitted. As for specific intervention content dimensions, there was insufficient overlap in coding across meta-analyses to conduct a quantitative assessment of the relationship between intervention content dimension and intervention effectiveness. (When reports do not code for a particular type of intervention content, it is unknown whether that type of intervention content was not present in the original studies or present but not coded.) Qualitatively, there were multiple meta-analyses that found that teaching skills (including behavioral, communication, and psychological skills) and motivational enhancement (eg, attitudinal arguments, motivational interviewing) often were associated with greater risk reduction behaviors (Table 1).
Role of Information Content in HIV Risk Reduction
Numerous meta-analyses have evaluated the efficacy of psychological, social, and behavioral interventions to reduce risk of acquiring or transmitting HIV (Table 1), and they have been appearing with increasing frequency over time. Although logically all intervention trials that these meta-analyses reviewed used some form of communication content, numerous meta-analyses reported examining no information content dimensions whatsoever and temporal trends suggest no tendency for content dimensions to appear with greater regularity (Fig. 1). Most meta-analyses examined at least one content dimension; meta-analyses with larger samples of studies were both more likely to consider information content and to find individual content dimensions related significantly to the magnitude of risk reduction that the trials gauged. A qualitative inspection of the significant dimensions (Table 1) suggests that behavioral interventions that incorporated skill development or motivational enhancement in some fashion had greater success than did those that excluded skill-related dimensions.
Although many of the meta-analyses in this sample have had considerable scholarly impact, the fact that the impact was unrelated to the number of information content dimensions examined suggests that consumers of meta-analyses on HIV risk reduction commonly cite the metaanalyses' results for purposes other than comparing ICs. Of note, very recent meta-analyses have had less chance to accrue citations relative to older meta-analyses. As examples, total citations were inversely correlated with date of publication (r = −0.32, P = 0.015) and no meta-analysis with a database compiled after 2009 was among the top 20% most impactful meta-analyses in this sample (Fig. 1). Therefore, we also examined whether scholarly impact in meta-analyses published before 2010 was related to using more intervention content dimensions; it was not, r = −0.25, P = 0.13. Instead, consumers of meta-analyses often have an explicit goal to assert that interventions have been shown to be efficacious in reducing risk for HIV in relation to some target population. Indeed, nearly all meta-analyses in this literature report statistically significant reductions in risk behavior and other relevant outcomes for those who participate in interventions as prior meta-reviews have shown.5,6 Relatedly, some meta-analyses examined methodologically diverse HIV risk reduction interventions but limited their samples in other respects, such as to particular nations or particular risk groups (Table 1). Without reports about the nature of the messages and activities in the intervention curricula, it is unknown the degree to which the interventions grouped by sample are similar in content. Finally, some meta-analyses limited their samples to trials that investigated a particular form of intervention content, such as motivational interviewing16 or eroticization of condoms.59 Doing so can be useful in documenting that a particular intervention content area has been linked to risk reduction, at least in terms of particular samples or locales, but the strategy assumes that no other type of content is responsible for the outcomes. A more powerful analytic design is to take into account all the intervention content dimensions simultaneously. Consumers of meta-analysis should be encouraged to take advantage of moderator trends in meta-analyses, which show how effect sizes vary based on coded features, such as intervention content.
Limitations of the Meta-Review
Limitations of our investigation stem from the sample of meta-analyses we examined and, in turn, from the trials they sampled. First and most obviously, our methods and results examined the numbers of dimensions of information content that the meta-analyses reported to investigate. It is entirely possible that meta-analysts coded dimensions that then did not appear in the final report; note that we were forced to estimate the number of coded information content dimensions in some cases.
Second, the sample of studies relied on a prior meta-review focused on behavioral interventions to reduce risk of HIV. It is possible that this review missed meta-analyses. More consequently, the trends we have identified in this meta-review may not well describe research in other HIV-relevant domains, such as strategies to increase adherence to ART. At least 4 meta-analyses have addressed such trials: Three meta-analyses identified no message content dimensions related to enhanced adherence.64–66 These 3 meta-analyses thus seem quite similar to those reviewed in the HIV risk reduction sample. The fourth meta-analysis, which is discussed at length below, used a formal taxonomy to categorize content with some success, which is discussed at length below.
Third, meta-analyses in our sample used information on content dimensions reported in publications, rather than coded directly from treatment manuals, intervention curricula, and other records of the literal content as used in the intervention. No meta-analysis in our sample mentioned having contacted authors of trials to learn more exact information about the content delivered in their interventions, although they routinely reported obtaining needed statistical information. Authors of meta-analyses often comment about the lack of information available on critical issues. One ART adherence meta-analysis remarked that the details of the interventions in question were so commonly underreported that it was not possible to examine whether the content of the intervention related to its success.64 The consequence may be that most past meta-analyses have not fully captured the extent to which elements of the communicated content are responsible for behavior change. Thus, our conclusions about which intervention content dimensions are associated with greater risk reduction should be considered preliminary.
A final limitation we will note relates to the methodological quality both of the meta-analyses in our sample and of the quality of the studies they sampled. In theory, the clearest knowledge gains should emerge from the highest quality studies on a phenomenon7; yet, methodological quality varies widely, and meta-analyses inconsistently take quality-related factors into account in their results and in their conclusions about the trends in studies.7,67 It is possible that our conclusions about the role of intervention content in the HIV risk reduction literature would change if methodological quality is better controlled.
Formalizing BCTs in HIV Prevention Trials
We believe that both future trials and meta-analyses of trials can profit by focusing much more specifically on potential BCTs underlying risk behavior change. Fortunately, in the last decade, scholars have begun to develop a shared and standardized method of classifying intervention content in the form of taxonomies of BCTs. These taxonomies serve many purposes, including as an aid to systematically describe the communication content of interventions for both research and practice.1,68 These taxonomies begin to hint at a virtual periodic table of the elements that go into making communication-based interventions successful: (1) They offer unique labels for each BCT, with clear unambiguous definitions. (2) They present a hierarchical structure based on the degree of connectedness between techniques. (3) They help to develop knowledge of boundary conditions for the impact of BCTs, which helps to inform theorizing on how best to improve future interventions and promote health and helps to make translation efforts more efficient in moving BCTs into relevant communities. (4) Because interventions are usually complex and composed of interacting components (eg, BCTs and modes of delivery), they allow one to investigate those interactions with greater precision,69 as investigators have shown with behavioral interventions for physical activity and obesity.70,71
Formally speaking, a BCT is an observable and replicable intervention component designed to change behavior. It is the smallest component compatible with retaining the postulated active ingredients—ie, the proposed mechanisms of change. It can be used alone or in combination with other BCTs.68,72 BCTs are well-specified, distinct nonoverlapping descriptors: They may or may not be successful at changing behavior in specific situations, and their theoretical mechanism may or may not be understood. BCTs may be a valuable tool for evidence synthesis because they may be key moderators in determining which type of communication content is more often successful and in uncovering the mechanisms of behavior change that have been fruitful in the past.
The first taxonomy of BCTs was developed to provide a method for reliably specifying intervention content in systematic reviewing of complex interventions.1 Subsequent taxonomies have been developed to address specific behavioral domains: physical activity and healthy eating,73 smoking cessation,74 and alcohol consumption.75 To develop shared language across behavioral domains with international consensus, Michie et al2 built on this work to develop BCT Taxonomy, version 1, labeled BCTTv1. This extensive cross-domain taxonomy comprises 93 BCTs with clear labels, definitions, and examples, organized into 16 groupings to facilitate its application. For clarity, in the remainder of this article, we refer to investigators' ad hoc efforts to operationalize active communication dimensions as “intervention content,” and we reserve the term “BCT” for instantiations that adopt a formal taxonomy.
The principal strengths of adopting a BCT approach are its precision in stating the specific element(s) of a message in a fashion that enables greater precision in research, which in turn should translate into improved success when trials' outcomes are generalized to the community. As we saw, past meta-analyses that have examined the ability of interventions to reduce HIV risk or to improve drug adherence have lacked this precision, making their findings less consistent and less parallel. These observations strongly suggest that fully incorporating BCT taxonomies would, in the long run, improve both scholarship and application related to HIV prevention and care. To this point, a pioneering ART meta-analysis3,76 explicitly operationalized BCTs by obtaining details of the intervention content used in the past ART adherence trials from the original researchers, who helped to systematically code the content of both treatment and control conditions. The numbers of BCTs explained appreciable amounts of variation in effects for both conditions (eg, tailored medication schedules, planning coping responses).
As noted above, past meta-analyses focused on behavioral interventions have not formally used BCT taxonomies. Therefore, caution is merited in generalizing our meta-review's conclusions about active intervention content dimensions to specific BCTs. One problem appears because research reports commonly use labels for intervention content that poorly match taxonomies. For example, in our meta-review, one theme was that skill provision helps reduce HIV risk. Yet, BCT taxonomy research2 has mapped several more precise BCTs related to skills, such as behavioral rehearsal or practice, skills in self-assessment, goal setting, and self-monitoring, and social support and persuasive skills. An additional difficulty is that BCT scholars have shown that authors of trials frequently err when describing the content of the intervention and control conditions.1,77,78 No meta-analysis reported more than 18 information content dimensions, but it is likely that many meta-analyses underreported content dimensions.
It is important to note that intervention content is also present in the control arm of most studies, which often employ a “standard of care” design as a control for the intervention. Only a few meta-analyses in the present review examined the intervention content of control arms (Table 1) and treated effects as repeated observations rather than focusing on between-condition comparisons.12,13,26,45 Indeed, in the ART meta-analysis mentioned, the amount of change exhibited in some control conditions exceeded that for some intervention arms in the meta-analysis of BCTs used in ART trials.3,76 Assessing content in both arms and examining change over time rather than between conditions, therefore, made possible much better explanation of intervention success. In the meta-review sample, meta-analyses examined effects using between-group comparisons (ie, treatment vs. control at a posttest), which implicitly assumes that the control condition is the same across studies. As Abraham et al79 detailed, meta-analyses could instead code the content in both arms and examine effects temporally (eg, posttest vs. pretest risk).
At least 5 factors present challenges to the adoption of BCT taxonomies. First, using BCTs requires thorough understanding of the taxonomy to assure reliable and valid coding. Coding BCTs present in interventions is a highly skilled task, requiring familiarity with labels and definitions and the ability to make a series of complex interpretative judgments. BCTTv1 contains 93 BCTs, making it a formidable challenge to learn and requiring an effective program of coder training. Two formats of training programs have been developed by the BCT taxonomy team, including 1-day workshops and distance group tutorials. Wood and colleagues' (unpublished) study of 161 trainees evaluated their skills at coding intervention descriptions into BCTs and found that the training significantly increased agreement of trainees with expert consensus about BCTs identified in the descriptions. Training is now available in an online open-access training course (see http://www.ucl.ac.uk/health-psychology/bcttaxonomy for details).
Second, because intervention efforts were conducted to find a useful intervention rather than to test theory with great precision, the result is that intervention components are often confounded and poorly reported.4 Important variables may be omitted, poorly measured, or lack sufficient variation. As an example, a recent meta-analysis35 focused on behavioral interventions for adolescents found that provision of motivational and behavioral skill components increased adolescents' condom use. Yet, because nearly every trial provided information to participants, there was insufficient variability to test whether not providing information undermines the success of the intervention, as at least one prevention model80 predicts. If researchers more often systematically and cleanly manipulated the dimensions posited to improve the targeted outcomes, evidence synthesis could proceed more efficiently. Similarly, BCT research has revealed that treatment manuals often call for using more BCTs than publication reports1—more than twice as many in at least one case.81 Given that behavioral interventions are often delivered with less than 50% fidelity to that explicitly defined by a treatment manual, one can see why there may be a tenuous linkage between the intervention content reported in publications and those delivered in implementation.77,78
Third, greater precision in the reports of the content of interventions delivered is clearly necessary for a science of behavior change to advance or for implementation efforts to succeed. A recent survey by McCleary et al82 of randomized controlled trials published in prominent medical journals revealed that efficacious components are reported more frequently in pharmacologic than in nonpharmacologic interventions. In effect, current reporting practices restrict the range of information available in publications to code as BCTs, leaving open the possibility that intervention content actually matters more to the success of interventions than past meta-analyses have been able to determine (Table 1). Fortunately, reporting standards are improving, as shown in at least 2 efforts. (1) the Workgroup for Intervention Development and Evaluation Research (WIDER)83 has developed a checklist to assess the quality of the reporting of interventions in systematic reviews, as Abraham et al79 describe in this issue. (2) The Template for Intervention Description and Replication (TIDieR)84 provides a checklist of the minimum information required to report interventions, including surgical, pharmacologic, psychotherapeutic, and behavioral interventions. Developed using consensus development methods with international participants from several disciplines, it proposes a minimum set of information: brief name, why (rationale), what materials, what procedure, who provided, how, where, when and how much, tailoring, changes, how well monitored, and how well delivered. These aspects are mainly procedures for delivery (often referred to as “mode” of delivery) rather than the content of the active ingredients. As time passes and scholars report their trials with greater precision, understanding the factors behind successful behavior change should improve accordingly.
Fourth, the samples of participants targeted in particular trials and meta-analyses typically differ widely; yet, currently there is no way to standardize these differences other than by using demographic labels. Reports ought to describe their samples in terms of their representativeness—the extent to which they are similar or different from the populations in the communities from which they are drawn—but representativeness is often not reported, and indeed, standards for the criteria by which to judge representativeness are debatable. Demographics, because they can be tied to census data, are a convenient way to label samples and populations. Unfortunately, they often only loosely describe health status or risk, which is ultimately most of concern in health interventions. Intervention studies drawing on convenience and self-selected samples, in particular, lack a means of judging generalizability. Even in studies limited to 1 demographically defined population may have great variation in other demographic characteristics. For example, meta-analyses focused on adolescents typically include both genders ranging from preadolescents to emergent adults; they include numerous races and ethnicities; they include samples from numerous communities. Invisible in the reports is also a sense of the comparative health status and health resources of the samples; some adolescents may live in places with greater or lesser access to health and preventive care; some may be at greater or lesser risk of HIV infection and other illnesses. Health-related research would benefit from a standard method to gauge key features of their samples—including both community factors and health status at a moment in time. Systematic reporting of sample characteristics could then be linked to studies of the effectiveness of BCT, enabling science to state with greater precision the effectiveness of BCTs on HIV prevention within specific populations.
Finally, disentangling confounded effects in meta-analyses rests on having sufficiently large samples of studies that vary in the information content presented. In our meta-review, the typical meta-analysis reviewed fewer than 30 studies, and relatively recent meta-analyses have shown no tendency to increase samples of studies; thus, meta-analyses seem to use increasingly restrictive selection criteria. Unless future meta-analyses broaden their selection criteria, knowledge about the particular BCTs that underlie intervention success will remain impoverished.
The avenues we have outlined in this meta-review for methodological improvements in specifying BCTs and analyzing and evaluating HIV-related interventions offer the potential for vastly greater returns on future investment in research into interventions to reduce risk for HIV and to improve HIV care. This strategy will aid not only meta-analyses of trials but also original research on these topics. BCT taxonomies offer considerable advantages for authors of reports on clinical trials because BCT nomenclature permits efficient compact labels for the BCTs employed in intervention and control arms. As more trials are described in such careful terms, the result should be ever-clearer conclusions about what content actively drives behavior change.
Logically, improved science results in improved translation of effective interventions into communities that need to reduce risk or improve HIV care outcomes. An awareness of which combination of BCTs are effective and which are ineffective for which populations and settings could have a profound impact on cost-effectiveness by focusing on effective intervention elements and eliminating ineffective content. Implementation science85 can thus also be enhanced by thoroughly incorporating BCT taxonomies into its methods.
As BCT taxonomies are applied to a wider range of populations, settings, and behaviors, adaptations of language, and possibly also of concepts, will be needed, and new BCTs will be identified. To preserve a shared methodology and avoid the fragmentation of the field, it will be important to build on BCTTv1 in a coordinated fashion. Michie et al are developing an international consortium to monitor and collate experiences, adaptations, and findings so that the next version of the BCT taxonomy can be developed and released. The emergence of this taxonomy will depend on judgment, balancing the needs for stability, and for accumulation of evidence using a shared method against the need for refinement and extension. A further advance will be the development of a complete ontology linking BCTs, modes of delivery, context (target population and setting), and type of behavior. Such an ontology would help the development and selection of time- and cost-efficient intervention strategies that maximize effectiveness. An international collaboration of behavioral and computer scientists is engaged in this work, which is in its early stages.
To date, BCT research has focused more on the qualitative dimensions present in a communication rather than the amount of BCT offered. The latter theme has emerged in some of the extant meta-analyses; some have shown dose–responses in relation to risk reduction. For example, a meta-analysis on risk reduction for adolescents35 isolated time per session of motivational training and condom use skills as having been particularly successful. The fact that it was dosage per session rather than total dosage strongly implies that risk reduction can be accomplished in relatively brief interventions and need not require multiple sessions, a finding consistent with at least 2 other reviews.5,8 This direction would seem profitable for future meta-analyses to pursue.
In focusing on BCTs in HIV-related interventions, we might have given the unintended impression that BCTs are the only ingredient in successful health promotion. To the contrary, relevant theories increasingly are specifying aspects of the milieu that are important for risk reduction,86–88 as Kaufman et al's89 review in this issue concluded. Consider a recent meta-analysis of behavioral interventions36 to reduce African-Americans' sexual risk for HIV; its analytic models focused on sample differences (eg, age, HIV serostatus) and intervention content dimensions (eg, skill provision, motivational training). Like nearly all past meta-analyses, it did not examine whether any aspect of the social milieu surrounding risk reduction in the interventions had any bearing on results. Yet, in theory, the milieu may have a substantial role, given that intervention participants must live for extended periods in environments that may contradict or even be hostile to the message in the intervention itself—or indeed, hostile toward the people addressed by the intervention—before the success of the intervention is finally gauged. Of note, there was heterogeneity in risk-related outcomes in the meta-analysis that could not be explained using the features of the interventions, studies, and samples. Reid et al90 reanalyzed this database, adding residential segregation and prejudice levels of Caucasians toward African-Americans, defined at the level of the US county. Both factors related to risk reduction success; trials were more efficacious in places with less segregation or higher liking, and this pattern was especially marked for more vulnerable samples, such as adolescents. A limitation of this reanalysis, noted by the team, was that it did not theorize about which BCTs might prove especially valuable in difficult environments for particular populations. Future meta-analyses should explore such possibilities in detail. Effective interventions targeted at populations living in challenging circumstances deserve special attention.
1. Abraham C, Michie S. A taxonomy of behavior change techniques used in interventions. Health Psychol. 2008;27:379–387.
2. Michie S, Richardson M, Johnston M, et al.. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med. 2013;46:81–95.
3. de Bruin M, Viechtbauer W, Hospers HJ, et al.. Standard care quality determines treatment outcomes in control groups of HAART-adherence intervention studies: implications for the interpretation and comparison of intervention effects. Health Psychol. 2009;28:668–674.
4. Johnson BT, Michie S. Towards healthy theorising about health behaviours in the maze of messy reality: a reaction to Peters, de Bruin, and Crutzen. Health Psychol Rev. 2014; [epub ahead of print].
5. Johnson BT, Scott-Sheldon LAJ, Carey MP. Meta-synthesis of health behavior change meta-analyses
. Am J Public Health. 2010;100:2193–2198.
6. Noar SM. Behavioral interventions to reduce HIV-related sexual risk behavior: review and synthesis of meta-analytic evidence. AIDS Behav. 2008;12:335–353.
7. Johnson BT, Low RE, MacDonald HV. Panning for the gold in health research: incorporating studies' methodological quality in meta-analysis. Psychol Health. 2014; [epub ahead of print].
8. Eaton LA, Huedo-Medina TB, Kalichman SC, et al.. Meta-analysis of single-session behavioral interventions for STI/HIV prevention: implications for bundling multiple prevention packages. Am J Public Health. 2012;102;e34–e44.
9. Lyles CM, Kay LS, Crepaz N, et al.. Best-evidence interventions: findings from a systematic review of HIV behavioral interventions for US populations at high risk, 2000–2004. Am J Public Health. 2007;97:133–143.
10. Smoak ND, Scott-Sheldon LAJ, Johnson BT, et al.. Sexual risk reduction interventions do not inadvertently increase the overall frequency of sexual behavior: a meta-analysis of 174 studies with 116,735 participants. J Acquir Immune Defic Syndr. 2006;41:374–384.
11. Tan JY, Huedo-Medina TB, Warren MR, et al.. A meta-analysis of the efficacy of HIV/AIDS prevention interventions in Asia, 1995–2009. Soc Sci Med. 2012;75:676–687.
12. Albarracín D, McNatt PS, Klein CT, et al.. Persuasive communications to change actions: an analysis of behavioral and cognitive impact in HIV prevention. Health Psychol. 2003;22:166–177.
13. Albarracín D, Gillette JC, Earl AN, et al.. 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;131:856–897.
14. Albarracín J, Albarracín D, Durantini M. Effects of HIV-prevention interventions for samples with higher and lower percents of Latinos and Latin Americans: a meta-analysis of change in condom use and knowledge. AIDS Behav. 2008;12:521–543.
15. Berg RC, Ross MW, Tikkanen R. The effectiveness of MI4MSM: how useful is motivational interviewing as an HIV risk prevention program for men who have sex with men? A systematic review. AIDS Educ Prev. 2011;23:533–549.
16. Burke BL, Arkowitz H, Menchola M. The efficacy of motivational interviewing: a meta-analysis of controlled clinical trials. J Consult Clin Psychol. 2003;71:843–861.
17. Carvalho FT, Concalves TR, Faria ER, et al.. Behavioral intervention to promote condom use among women living with HIV (Review). Cochrane Libr. 2012;1–34.
18. Chin HB, Sipe TA, Elder R, et al.. The effectiveness of group-based comprehensive risk-reduction and abstinence education interventions to prevent or reduce the risk of adolescent pregnancy, human immunodeficiency virus, and sexually transmitted infections: two systematic reviews for the Guide to Community Preventive Services. Am J Prev Med. 2012;42:272–294.
19. Copenhaver MM, Johnson BT, Lee IC, et al.. Behavioral HIV risk reduction
among people who inject drugs: meta-analytic evidence of efficacy. J Subst Treat. 2006;31:163–171.
20. Crepaz N, Lyles CM, Wolitski RJ, et al.. Do prevention interventions reduce HIV risk behaviours among people living with HIV? A meta-analytic review of controlled trials. AIDS. 2006;20:143–157.
21. Crepaz N, Horn AK, Rama SM, et al.. The efficacy of behavioral interventions in reducing HIV risk sex behaviors and incident sexually transmitted disease in black and Hispanic sexually transmitted disease clinic patients in the United States: a meta-analytic review. Sex Transm Dis. 2007;34:319–332.
22. Crepaz N, Marshall KJ, Aupont LW, et al.. The efficacy of HIV/STI behavioral interventions for African American females in the United States: a meta-analysis. Am J Public Health. 2009;99:2069–2078.
23. Cross JE, Saunders CM, Bartelli D. The effectiveness of educational and needle exchange programs: a meta-analysis of HIV prevention strategies for injecting drug users. Qual Quantity. 1998;32:165–180.
24. Darbes L, Crepaz N, Lyles C, et al.. The efficacy of behavioral interventions in reducing HIV risk behaviors and incident sexually transmitted diseases in heterosexual African Americans. AIDS. 2008;22:1177–1194.
25. Denison JA, O'Reilly KR. HIV voluntary counseling and testing and behavioral risk reduction in developing countries: A meta-analysis, 1990-2005. AIDS and Behavior 2008;12(3):363–373.
26. Durantini MR, Albarracín D, Earl A, et al.. Conceptualizing the influence of social agents of change: a meta-analysis of HIV prevention interventions for different groups. Psychol Bull. 2006;132:212–248.
27. Earl A, Albarracín D. Nature, decay, and spiraling of the effects of fear-inducing arguments and HIV counseling and testing: a meta-analysis of the short- and long-term outcomes of HIV-prevention interventions. Health Psychol. 2007;26:496–506.
28. Healton CG, Messeri P. The effect of video interventions on improving knowledge and treatment compliance in the sexually transmitted disease clinic setting: lesson for HIV health education. Sex Transm Dis. 1993;20:70–76.
29. Henny KD, Crepaz N, Lyles CM, et al.. Efficacy of HIV/STI behavioral interventions for heterosexual African American men in the United States: a meta-analysis. AIDS Behav. 2012;16:1092–1114.
30. Herbst JH, Sherba RT, Crepaz N, et al.. A meta-analytic review of HIV behavioral interventions for reducing sexual risk behavior of men who have sex with men. J Acquir Immune Defic Syndr. 2005;39:228–241.
31. Herbst JH, Beeker C, Mathew A, et al.. The effectiveness of individual-, group-, and community-level HIV behavioral risk-reduction interventions for adult men who have sex with men: a systematic review. Am J Prev Med. 2007;32(suppl 4):S38–S67.
32. Huedo-Medina TB, Boynton MH, Warren MR, et al.. Efficacy of HIV prevention interventions in Latin American and Caribbean Nations, 1995–2008: a meta-analysis. AIDS Behav. 2010;14:1237–1251.
33. Johnson BT, Carey MP, Marsh KL, et al.. Interventions to reduce sexual risk for the human immunodeficiency virus in adolescents, 1985–2000: a research synthesis. Arch Pediatr Adolesc Med. 2003;157:381–388.
34. Johnson BT, Carey MP, Chaudoir SR, et al.. Sexual risk reduction for persons living with HIV: research synthesis of randomized controlled trials, 1993 to 2004. J Acquir Immune Defic Syndr. 2006;41:642–650.
35. Johnson BT, Scott-Sheldon LAJ, Huedo-Medina TB, et al.. Interventions to reduce sexual risk for human immunodeficiency virus in adolescents: a meta-analysis of trials, 1985-2008. Arch Pediatr Adolesc Med. 2011;165:77–84.
36. Johnson BT, Scott-Sheldon LAJ, Smoak ND, et al.. Behavioral interventions for African Americans to reduce sexual risk of HIV: a meta-analysis of randomized controlled trials. J Acquir Immune Defic Syndr. 2009;51:492–501.
37. Johnson WD, Hedges LV, Ramirez G, et al.. HIV prevention research for men who have sex with men: a systematic review and meta-analysis. J Acquir Immune Defic Syndr. 2002;30:S118–S129.
38. Johnson WD, Hedges LV, Diaz RM. Interventions to modify sexual risk behaviors for preventing HIV infection in men who have sex with men. Cochrane Database Syst Rev. 2003;CD001230.
39. Johnson WD, Holtgrave DR, McClellan WM, et al.. HIV intervention research for men who have sex with men: a 7-year update. AIDS Educ Prev. 2005;17:568–589.
40. Johnson WD, Diaz RM, Flanders WD, et al.. Behavioral interventions to reduce risk for sexual transmission of HIV among men who have sex with men. Cochrane Database Syst Rev. 2008;3.
41. Kalichman SC, Carey MP, Johnson BT. Prevention of sexually transmitted HIV infection: a meta-analytic review of the behavioral outcome literature. Ann Behav Med. 1996;18:6–15.
42. Kaufman ZA, Spencer TS, Ross DA. Effectiveness of sport-based HIV prevention interventions: a systematic review of the evidence. AIDS Behav. 2013;17:987–1001.
43. Kennedy CE, Medley AM, Sweat MD, et al.. Behavioural interventions for HIV positive prevention in developing countries: a systematic review and meta-analysis. Bull World Health Organ. 2010;88:615–623.
44. LaCroix JM, Pellowski JA, Lennon CA, et al.. Behavioural interventions to reduce sexual risk for HIV in heterosexual couples: a meta-analysis. Sex Transm Infect. 2013;89:620–627.
45. Lennon CA, Huedo-Medina TB, Gerwien DP, et al.. A role for depression in sexual risk reduction for women? A meta-analysis of HIV prevention trials with depression outcomes. Soc Sci Med. 2012;75:688–698.
46. Levin KD. Preventing Sexually Transmitted HIV Infection in Adolescents: Predicting Condom Use Behaviors and Reducing Risk (PhD). New York, NY: Syracuse University; 2002.
47. Logan T, Cole J, Leukefeld C. Women, sex, and HIV: social and contextual factors, meta-analysis of published interventions, and implications for practice and research. Psychol Bull. 2002;128:851–885.
48. Meader N, Li R, Des Jarlais DC, et al.. Psychosocial interventions for reducing injection and sexual risk behaviour for preventing HIV in drug users. Cochrane Database Syst Rev. 2010;CD007192.
49. Medley A, Kennedy C, O'Reilly K, et al.. Effectiveness of peer education interventions for HIV prevention in developing countries: a systematic review and meta-analysis. AIDS Educ Prev. 2009;21:181–206.
50. Michielsen K, Chersich MF, Luchters S, et al.. Effectiveness of HIV prevention for youth in sub-Saharan Africa: systematic review and meta-analysis of randomized and nonrandomized trials. AIDS. 2010;24:1193–1202.
51. Mize SJ, Robinson BE, Bockting WO, et al.. Meta-analysis of the effectiveness of HIV prevention interventions for women. AIDS Care. 2002;14:163–180.
52. Mullen PD, Ramirez G, Strouse D, et al.. Meta-analysis of the effects of behavioral HIV prevention interventions on the sexual risk behavior of sexually experienced adolescents in controlled studies in the United States. J Acquir Immune Defic Syndr. 2002;30:S94–S105.
53. Neumann MS, Johnson WD, Semaan S, et al.. Review and meta-analysis of HIV prevention intervention research for heterosexual adult populations in the United States. J Acquir Immune Defic Syndr. 2002;30(suppl 1):S106–S117.
54. Noar SM, Carlyle K, Cole C. Why communication is crucial: meta-analysis of the relationship between safer sexual communication and condom use. J Health Commun. 2006;11:365–390.
55. Ojo O, Verbeek JH, Rasanen K, et al.. Interventions to reduce risky sexual behaviour for preventing HIV infection in workers in occupational settings. Cochrane Database Syst Rev. 2011;CD005274.
56. Ota E, Wariki WM, Mori R, et al.. Behavioral interventions to reduce the transmission of HIV infection among sex workers and their clients in high-income countries. Cochrane Database Syst Rev. 2011;CD006045.
57. Prendergast ML, Urada D, Podus D. Meta-analysis of HIV risk-reduction interventions within drug abuse treatment programs. J Consult Clin Psychol. 2001;69:389–405.
58. Scott-Sheldon LAJ, Fielder RL, Carey MP. Sexual risk reduction interventions for patients attending sexually transmitted disease clinics in the United States: a meta-analytic review, 1986 to early 2009. Ann Behav Med. 2010;40:191–204.
59. Scott-Sheldon LAJ, Johnson BT. Eroticizing Creates safer sex: a research synthesis. J Prim Prev. 2006;27:619–640.
60. Semaan S, Des Jarlais DC, Sogolow E, et al.. A meta-analysis of the effect of HIV prevention interventions on the sex behaviors of drug users in the United States. J Acquir Immune Defic Syndr. 2002;30(suppl 1):S73–S93.
61. Wariki WM, Ota E, Mori R, et al.. Behavioral interventions to reduce the transmission of HIV infection among sex workers and their clients in low- and middle-income countries. Cochrane Database Syst Rev. 2012;2:CD005272.
62. Weinhardt LS, Carey MP, Johnson BT, et al.. Effects of HIV counseling and testing on sexual risk behavior: a meta-analytic review of published research, 1985–1997. Am J Public Health. 1999;89:1397–1405.
63. Fonner VA, Denison J, Kennedy CE, O'Reilly K, Sweat M. Voluntary counseling and testing (VCT) for changing HIV-related risk behavior in developing countries. Cochrane Database Syst Rev. 2012;9:CD001224.
64. Amico KR, Harman JJ, Johnson BT. Efficacy of antiretroviral therapy adherence interventions: a research synthesis of trials, 1996 to 2004. J Acquir Immune Defic Syndr. 2006;41:285–297.
65. Simoni JM, Pearson CR, Pantalone DW, et al.. Efficacy of interventions in improving highly active antiretroviral therapy adherence and HIV-1 RNA viral load - a meta-analytic review of randomized controlled trials. J Acquir Immune Defic Syndr. 2006;43:S23–S35.
66. Finitsis DJ, Pellowski JA, Johnson BT. Text message intervention designs to promote adherence to antiretroviral therapy (ART): a meta-analysis of randomized controlled trials. PloS One. 2014;9:e88166.
67. Protogerou C, Johnson BT. Factors underlying the success of behavioral HIV-prevention interventions for adolescents: A meta-review
. AIDS and Behavior. 2014; [epub ahead of print; June 6, 2014].
68. Michie S, Abraham C, Eccles MP, et al.. Strengthening evaluation and implementation by specifying components of behaviour change interventions: a study protocol. Implement Sci. 2011;6:10.
69. Michie S, Johnson BT, Johnston M. Advancing cumulative evidence on behaviour change techniques and interventions: a comment on Peters, de Bruin, and Crutzen (2013). Health Psychol Rev. 2014; [epub ahead of print; May 2, 2014].
70. Dombrowski SU, Sniehotta FF, Avenell A, et al.. Identifying active ingredients in complex behavioural interventions for obese adults with obesity-related co-morbidities or additional risk factors for co-morbidities: a systematic review. Health Psychol Rev. 2012;6:7–32.
71. Michie S, Abraham C, Whittington C, et al.. Effective techniques in healthy eating and physical activity interventions: a meta-regression. Health Psychol. 2009;28:690–701.
72. Michie S, Johnston M. (2013). Behavior change techniques. In Turner J. R. (Ed.) Encyclopedia of Behavioral Medicine (pp. 182-187). Springer New York.
73. Michie S, Ashford S, Sniehotta FF, et al.. A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: the CALO-RE taxonomy. Psychol Health. 2011;26:1479–1498.
74. Michie S, Hyder N, Walia A, et al.. Development of a taxonomy of behaviour change techniques used in individual behavioural support for smoking cessation. Addict Behav. 2011;36:315–319.
75. Michie S, Whittington C, Hamoudi Z, et al.. Identification of behaviour change techniques to reduce excessive alcohol consumption. Addiction. 2012;107:1431–1440.
76. de Bruin M, Viechtbauer W, Schaalma HP, et al.. Standard care impacts intervention effects in HAART adherence RCTs: a meta-analysis. Arch Intern Med. 2010;170:240–250.
77. Lorencatto F, West R, Christopherson C, et al.. Assessing fidelity of delivery of smoking cessation behavioural support in practice. Implement Sci. 2013;8:40.
78. Hardeman W, Michie S, Fanshawe T, et al.. Fidelity of delivery of a physical activity intervention: predictors and consequences. Psychol Health. 2008;23:11–24.
79. Abraham C, Johnson BT, de Bruin M, et al.. Enhancing reporting of behavior change intervention evaluations. J Acquir Immune Defic Syndr. 2014;66(suppl 3):S293–S299.
80. Fisher JD, Fisher WA. Changing AIDS-risk behavior. Psychol Bull. 1992;111:455–474.
81. Lorencatto F, West R, Bruguera C, et al.. A method for assessing fidelity of delivery of telephone behavioral support for smoking cessation. J Consult Clin Psychol. 2014;82:482–491.
82. McCleary N, Duncan EM, Stewart F, et al.. Active ingredients are reported more often for pharmacologic than non-pharmacologic interventions: an illustrative review of reporting practices in titles and abstracts. Trials. 2013;14:146.
83. Albrecht L, Archibald M, Arseneau D, et al.. Development of a checklist to assess the quality of reporting of knowledge translation interventions using the Workgroup for Intervention Development and Evaluation Research (WIDER) recommendations. Implement Sci. 2013;8:52.
84. Hoffmann TC, Glasziou PP, Milne R, et al.. Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. BMJ. 2014;348:g1687.
85. Padian NS, Holmes CB, McCoy SI, et al.. Implementation science for the US President's Emergency Plan for AIDS Relief (PEPFAR). J Acquir Immune Defic Syndr. 2011;56:199–203.
86. Johnson BT, Redding CA, DiClemente RJ, et al.. A network-individual-resource model for HIV prevention. AIDS Behav. 2010;14(suppl 2):204–221.
87. Latkin C, Weeks MR, Glasman L, et al.. A dynamic social systems model for considering structural factors in HIV prevention and detection. AIDS Behav. 2010;14:222–238.
88. Albarracín D, Tannenbaum MB, Glasman LR, et al.. Modeling structural, dyadic, and individual factors: the inclusion and exclusion model of HIV related behavior. AIDS Behav. 2010;14:239–249.
89. Kaufman MR, Cornish F, Zimmerman RS, et al.. Health behavior change models for HIV prevention and AIDS care: practical recommendations for a multi-level approach. J Acquir Immune Defic Syndr. 2014;66(suppl 3):S250–S258.
90. Reid AE, Dovidio JF, Ballester E, et al.. HIV prevention interventions to reduce sexual risk for African Americans: the influence of community-level stigma and psychological processes. Soc Sci Med. 2014;103:118–125.
Keywords:© 2014 by Lippincott Williams & Wilkins
communication strategies; intervention content; HIV risk reduction; HIV care and AIDS care; meta-review; meta-analyses