The pooled SD served as the denominator in the ES calculation in between-subjects designs, and the SD of the paired comparisons served as the denominator in within-subjects designs. Most studies provided only proportional differences in overall adherence outcome levels between treatment and control groups, but several provided mean adherence levels and included SDs. We gave ESs a positive sign if the intervention improved ART adherence relative to the standard and a negative sign if adherence declined.18,20 ESs were calculated on the measures provided at the first follow-up after the intervention, a procedure that reduced methodologic variance across the compared studies. When a study offered 2 or more postintervention assessments of adherence, the first was used for primary analyses, and the second was also used in an exploratory analysis to evaluate how ESs might decay over time. When more than 1 assessment of ART adherence was provided for any given outcome, we averaged available ESs across measures.
Analyses of Effect Sizes
Analyses to examine weighted mean ESs across the sample were performed with fixed-effects and random-effects assumptions; analyses to examine whether features of the studies explained variability in the ESs followed fixed-effects assumptions.18,21 These models provide weighted mean ESs and effect modification by study characteristics. For cases in which study features significantly explained ES variability but were incorrectly specified, models incorporated random effects assumptions to assess whether the patterns identified in the fixed-effects model remained viable. Moreover, for the purpose of illustration, Table 1 also provides the equivalent OR22 for each ES or weighted mean ES [OR = exponential (ES × 1.81)]. Such an OR indicates the proportion of study participants in the intervention conditions whose medication adherence improved versus those in the control conditions. Finally, we examined the post hoc power of ESs using GPOWER software23 based on study ES estimates and reported sample size. Power has been traditionally understood as the sensitivity of an experiment, or it is the conditional probability of rejecting the null hypothesis (in this case, the hypothesis that the intervention, in reality, had no effect on adherence) when it is in fact false, essentially avoiding a type II error.24 Post hoc power or observed power is a function of the observed ES and its P value (see Lenth25 and Levine and Ensom26), and thus should not be used to draw conclusions regarding studies being "over-" or "underpowered."
Description of Studies
Table 1 shows the general sample and methodologic characteristics of the 24 separate intervention outcome studies, which produced a total of 25 postintervention ART adherence outcomes. Thirteen (52%) used a randomized comparison group, and 2 (8%) used a nonrandomized comparison group, whereas 10 (40%) of the interventions used a within-group design. Fourteen (56%) of the studies reported single follow-up assessments, and 11 (44%) reported more than 1 follow-up assessment; as noted, analyses focused on the first available follow-up assessments.
In terms of data reported in each of the reviewed studies, none of the studies included reports of adverse events, only 6 (25%) reported the proportion of patients on certain antiretroviral (ARV) medications (eg, protease inhibitors [PIs], nonnucleoside reverse transcriptase inhibitors [NNRTIs]), 7 (29%) made specific reference to their sample's length of time on ARVs, and 12 (50%) reported the number of pills characteristic of their sample's ARV regimen. As indicated in Table 1, the primary outcome measure was typically self-reported adherence, although 4 studies (17%) used MEMS caps and 17 studies (71%) descriptively or statistically supplemented their outcomes with indices of HIV viral load and/or CD4 cell counts.
The most frequently reported intervention features were reminder systems and some degree of counseling support, although the intensity and provider of that support varied. Nine (36%) of the 25 intervention studies reviewed explicitly stated that they used some form of reminder device or strategy (see McPherson-Baker et al27, Powell-Cope et al28, and Rigsby et al29), such as electronic reminders, pillboxes, stickers, and telephone reminders. Similarly, 18 (72%) of the 25 studies reviewed provided some level of counseling support by providers (see Margolin et al30) or specialized support staff (see Tuldra et al31) or some type of feedback regarding disease progression (see Haubrich et al32), whereas other interventions involved enlisting several sources of support (see McPherson-Baker et al27). Three studies included directly observed therapy in a prison setting or by visiting nurses as part of their intervention (see Kirkland et al33). Of the 21 studies that reported the duration of intervention exposure, the average length of intervention was 20 weeks, ranging from 4 to 48 weeks.
Effects of Interventions on Adherence
ES calculations for each study are shown graphically in Figure 2. On average, treatment participants significantly improved adherence relative to the comparison; the confidence intervals for the fixed-effects and random-effects weighted means did not include 0 (fixed effects: M d = 0.25, 95% confidence interval [CI]: 0.18 to 0.33; random effects: M d = 0.35, 95% CI: 0.20 to 0.51). Yet, adherence success levels varied considerably across the studies (range: −1.19 to 1.45) as gauged by the significant homogeneity value [Q(25) = 72.49; P < 0.001]. Thus, the interventions were not equally effective. Post hoc power for the ESs ranged from 0.05 to 0.99 (M = 0.50, SD = 0.33).
We proceeded to examine whether study characteristics could account for this variability. As Table 2 indicates, demographic variables, such as gender composition of study participants, did not account for significant variability in ESs. Similarly, ES variability had no statistically significant relation to whether adherence change was examined in comparison to a control or relative to baseline. Features of the interventions were also assessed relative to the effects of the interventions, specifically in terms of whether or not the study intervention had an articulated theoretic basis and the coded intensity of the intervention. Neither the articulation of a theoretic basis for the intervention evaluated nor the level of intensity of the intervention related to ES. We explored potential effects of length of exposure to an intervention and measurement strategy on outcomes. Intervention duration did not significantly relate to adherence outcomes. Similarly, we did not find a significant difference in adherence outcomes between studies using MEMS to assess for adherence outcomes compared with those using self-report. Thus, neither study design, sample demographics, clear articulation of a theoretic basis, level of intervention intensity, duration of intervention exposure, nor measurement strategy related to ESs.
We next assessed the extent to which intervention effects differed on the basis of the preintervention or baseline adherence of study participants. Specifically, we evaluated the effects of interventions that targeted patients with known or anticipated adherence problems through recruitment strategies in comparison to studies that did not select participants on the basis of adherence problems at baseline. As Table 2 indicates, ESs significantly differed between these types of studies (QR = 15.75; P < 0.001). Studies targeting those with known or anticipated adherence problems exhibited significantly larger intervention effects (d = 0.62) than those studies that did not target poor adherers (d = 0.19). Thus, on average, interventions that recruited participants with known or anticipated adherence problems demonstrated, according to conventions (see Cohen24) a medium effect on adherence, whereas those with open recruitment or enrollment demonstrated a small ES. Indeed, within the studies that targeted poor adherers, the assumption of homogeneity was not violated [QW(9) = 15.01; P = 0.06] but was significant for those studies that did not target patients with known or anticipated problems with adherence at baseline [QW(15) = 41.73; P < 0.001]. Thus, the medium average ES seemed to be an adequate description of intervention effects when studies sought to intervene with individuals who had known or anticipated problems with adherence. The small average ES for studies that did not specifically enroll or target those with baseline adherence problems may not be a reliable or accurate point descriptor, because there was still significant variability in ESs among these studies. Nonetheless, a model that evaluated this study feature and that incorporated random-effect assumptions again concluded that baseline adherence problems were significantly linked to adherence ESs. Moreover, this model was correctly identified [Q(22) = 31.92; P = 0.08].
Finally, we assessed whether the effects of interventions on ART adherence changed with time. As Table 2 shows, there was no significant tendency for intervention efficacy to change with time, where the number of weeks between onset of intervention and assessment of adherence varied between 0 and 26. When all available ESs (see Table 1) were included, with the number of weeks ranging between 0 and 48, there was still no pattern for intervention effects to decay (β = −0.03; P = 0.81).
ART adherence interventions have the potential to provide support for HIV-positive individuals on this therapy. To the extent that such interventions are effective, they can help individuals to achieve and maintain levels of adherence that maximize the health benefits of ART and minimize the opportunity for the development of multidrug resistance (MDR).10,11 At the level of the HIV epidemic, effective ART adherence interventions may in fact contribute to reducing the likelihood of transmission of MDR virus by way of reducing the number of individuals who develop it. The impact of ART adherence interventions on individual and public health is necessarily limited by the efficacy of such interventions to enhance suboptimal adherence and maintain optimal adherence.
The current study sought to establish the extent to which such interventions have been efficacious to date and which features of interventions and intervention studies might be associated with improvements in adherence. We reviewed ART adherence intervention outcome studies published in peer-reviewed journals from 1996 through December 2004; 24 studies met our criteria for inclusion and comprised samples ranging from 6 to 435 patients. Typically, studies attempted to improve ART adherence to "optimal" levels (eg, 90%-95% adherence). This sample of studies reported 25 assessments of adherence behavior after intervention and an additional 13 assessments of more extended follow-up assessments, each of which was converted to the standardized mean ES. Meta-analyses of ESs revealed that, overall, interventions demonstrated an average ES of approximately 0.35, which is conventionally of "small" magnitude,24 yet there was substantial variability across these ESs. Several factors did not account for any variability in efficacy, including gender of sample, study design (within-group vs. between-group designs), advocacy of theory in constructing the intervention, level of intensity of the intervention, length of exposure to an intervention, and measurement strategy. Yet, when participants with poor or suspected poor adherence were recruited, intervention ESs were significantly larger than for samples that did not select for this variable (ES of 0.62 vs. 0.19, respectively).
Intervention Efficacy and Study or Sample Features
Gender and other kinds of demographic variables have not traditionally been associated with adherence (see the articles by Avants et al34 and Catz et al35); thus, it is not surprising that gender exerted no apparent influence on the overall efficacy of the interventions. Similarly, equivalence ofESs for studies with between-group or within-group designs is reasonable, because differences between interventions using more or less rigorous designs may rest more on issues surrounding internal validity than on ES per se. It was, however, somewhat surprising that theoretic bases and level of intensity of the interventions were not associated with ESs.
We coded interventions on the basis of the articulated theoretic model used in intervention design and development. Whereas some studies clearly stated that they were theoretically based, many failed to provide specific details about their interventions. It is likely that most if not all of the sample had a particular theoretic rationale for the design and implementation of their intervention. Specific theoretic articulation may have been more a function of presentational style or limits in the size or scope of presentation. Also, the "cafeteria-style" approach that characterizes many adherence interventions36 may make it particularly difficult to distill theunderlying theories of interventions or their relative contributions to intervention effects. Nonetheless, the clear articulation of the theoretic basis of an intervention, regardless of the intervention's effect on adherence, is essential to organize the literature coherently.
The coding of intervention intensity levels, which was almost uniformly medium or higher in the current sample ofstudies, posed similar difficulties. Our team coded intensityonthe basis of the articulated strategies used in an intervention. Despite excellent reliability, it is difficult to determine if a lower level of intensity intervention was in reality low or was low simply because of selective or restricted presentation of features of the intervention. We suspect that authors were generally brief in reporting intervention features and specific strategies in their presentation of outcome studies, which may have lowered the validity of this coded variable. It is unfortunate that most study reports failed to provide sufficient information about these critical aspects of their interventions. These details are critically important to the organization and strategic progression of the literature.
In terms of measurement strategy, whereas the current sample's intervention effects on adherence did not seem to depend on the manner in which adherence was assessed (self-report vs. MEMS cap use), it is likely that the current sample did not have a sufficient number of studies using MEMS to assess baseline and postadherence to produce a reliable comparison to the large number of studies using self-report. Previous research has found that self-reported adherence tends to be higher than comparative levels of adherence estimated from MEMS data,37,38 although there is support for both measurement strategies in terms of their correlations with CD4 cell counts, viral load indices, and blood concentrations ofARV medications.11,31,32,39-43 A convention of using multiplemeasures would be a valuable strategy,44 but the inclusionofaMEMS type assessment strategy, specifically, may not berealistic for studies and interventions that have limitedresources. The costs associated with MEMS, in terms offinances and participant burden, make it an inaccessible strategy for some populations. As such, a reasonable combination of assessment strategies might include, at minimum, self-report- and/or MEMS-generated levels of adherence and at least 1 measure of biologic outcome. Only with multiple measures can changes in adherence be attributed fully to actual changes in behavior and not to changes in self-report alone.
Similarly, studies did not consistently report certain information about the ART regimens for patients in their samples that may have influenced rates of adherence (eg, pill burden, doses per day, or the experience of acute adverse medication effects) and categorization of what would be considered optimal adherence (eg, type of ART medications prescribed). There is a growing recognition that the relation between adherence and health outcomes is complex and at times nonlinear,9 making the failure to describe a sample's ART regimen characteristics fully increasingly problematic. As a general standard, the types of ART medications prescribed, length of time on ART therapy, and number of pills and doses in a regimen should be provided in all treatment outcome studies.
Although the studies reviewed provided insufficient information to assess for the potential impact of ART regimen characteristics on adherence intervention outcomes, we were able to assess the impact of certain enrollment strategies. Studies that specifically targeted patients with known or anticipated problems with adherence had significantly larger effects on adherence at posttest than those that did not target such patients. We found no evidence that intervention effects decayed with time, suggesting that adherence effects were not an artifact of selecting initially low-adherent patients at baseline. Thus, interventions targeting those with poor adherence seemed to have a strong impact on adherence that held over time. As more studies of intervention outcomes become available with extended periods of follow-up, this tentative conclusion can be assessed further.
In contrast, studies that did not recruit participants with known or anticipated problems with ART adherence had a generally small effect on adherence, but the ESs underlying this mean effect lacked homogeneity. It is likely that other features of the intervention, such as the population studied or the study design itself, may explain these effects. The limited size of the current literature prohibits extensive exploration of such features. As more outcome studies of ART interventions targeting patients across the spectrum of adherence needs and experience with ART become available for review, future research should work toward identifying the underlying factors contributing to variability in effects.
We also evaluated the current studies in terms of their post hoc power to detect an intervention effect (see Table 1). For illustrative purposes, it may be more useful to consider the extent to which the current sample would have been sufficiently powered to detect the intervention effects we identified in our synthesis. Our general estimation of sample size requirements for the recommended 90% power of statistical tests to detect significant population ESs23 suggests that a large proportion of the studies in the current sample were underpowered. Assuming a population effect of 0.62, which is toward the higher end of the meta-analytic results in the current study, only 6 (25%) of the studies in the current sample would have had sample sizes large enough (approximately n = 92) for 90% power. Using the lowest average ES in the current study, d = 0.19, none of the studies would have the sample size to reach 90% power, which is arguably not cost-effective, given the paucity of effect and enormity of the sample (n = 952) required to reach 90% power. Depending on the study design, clinical relevance or significance is likely to be a better metric for desired ES of a given intervention. Thus, using our estimates for ESs, our results are similar to the conclusions of other reviews of the ART adherence intervention literature13,15; studies tend to be underpowered.
There are several limitations in the current study that qualify and provide context to our results and conclusions. Primarily, these results were limited by the fairly small sample of published ART adherence intervention outcome studies and the dependence on rater interpretation for the extrapolation of intervention characteristics. Only 24 separate studies published in peer-reviewed journals met criteria for inclusion. Studies that did not clearly articulate an intervention or report pre- and postintervention assessments of ART adherence were excluded. This criterion eliminated a number of studies that used directly observed therapy or MEMS caps as their only method of measuring adherence and provided no real baseline measurement. Focusing on published work also eliminated a number of potentially promising interventions. Initially, we had in fact included conference presentations and posters in our search, but we later eliminated them because reports provided so little information about the interventions. In addition, we also had some difficulty in ascertaining intervention components from published work; however, as a whole, the group of reports included here were thorough enough for our analyses. This problem, however, brings up the potential limitation of publication bias. Of the 25 separate time-one ES estimates, 9 (36%) were significant (seeFig. 2), which does not suggest publication bias (by conventional expectations, only 5% of the trials would yield a significant result). Nonetheless, the limited sample size and paucity of detailed information about the interventions in many of the included studies are worthy of note.
Another limitation in the general literature is the insufficient reporting of ART regimen and certain sample characteristics. Features of an ART regimen can be complex, sometimes with numerous prescribed medications requiring complicated dosing schedules and dietary restrictions. Although such complexities in ART regimens can make adherence difficult, the adherence literature cautions that the development of simpler ART regimens does not necessarily guarantee increased long-term adherence. A number of studies suggest that decreasing complexity of regimens may strengthen adherence (see Altice et al45); however, adherence rates are actually quite similar across different diseases and medical regimens of markedly differing complexity (see Holzemer etal46 and Horne47). In fact, some studies have found that reducing regimen complexity may not be a pivotal determinant of adherence to ART therapy (see Gao et al48 and Singh et al49). As ART regimens become simpler (eg, involving fewer pills per day), the consequences of missing a single ART dose may actually become more medically severe. As studies provide more detail about the regimen complexity of their samples, the impact of complexity on adherence behaviors can be further investigated.
Similarly, variability in operationalizing adherence continues to characterize the adherence outcome literature36 and makes cross-study comparisons of actual effects on adherence difficult. For instance, there was great variability in how percentages of adherence levels were reported, with some studies reporting the cutoff values at 80% and others at 90% or 95%. Such variability makes the estimation of ceiling effects across studies difficult to estimate.
Recommendations for Future Research
One possible avenue of exploration for future research is the identification of a "continuum" of ART adherence needs. It is currently unclear as to whether or not the needs of the treatment- naive patient are similar to those of patients with an extended ART history. Nor is it necessarily reasonable to assume that the needs of patients "failing" to adhere to ART are similar to those who are working to maintain "optimal" or even prefect adherence. It is arguable that, similar to other areas of health behavior (see Bellg50), there are separate needs and processes that establish a novel health behavior and those that maintain an achieved health behavior. In the case of ART adherence, it is also likely that the needs of a particular individual vary along these dimensions over time. Thus, recognizing these potential differences, interventions that attempt to intervene with groups of patients scattered across a continuum of adherence needs may need to be particularly sensitive to the possibility that a given intervention may be differentially effective for these subgroups. Similarly, the operationalization of outcomes likely differs among subgroups depending on whether success is defined as no change in adherence in adherence (maintenance) or as an increase in adherence from a low pretest level. The clear identification of adherence needs for groups with diverse ART adherence histories and practices, development of interventions with components specifically targeted to meeting those needs, and use of appropriate evaluation strategies are exciting areas for further scientific and practical exploration.
Given the results of the current study, it is reassuring that ART adherence interventions seem to be moderately successful in improving adherence in groups of patients with known or anticipated adherence problems. Interventions targeting individuals that span across a continuum of ART adherence needs (eg, treatment-naive to long-term maintainers) demonstrated low ESs on average but were also quite diverse in their effects. Future research can help to explore the potential reasons for such variability in effects of these interventions by assessing differential intervention needs between groups of patients who are treatment naive, those who are experiencing problems with adherence, and those who are maintaining optimal adherence. Ultimately, interventions that offer a compendium of resources and strategies for patients with diverse and changing ART adherence needs are likely hold the most promise.
Special thanks to Stephanie Macoul, Brian Marini, I-Fen Tu, and Megan O'Grady for their assistance with the qualitative coding in this study.
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Keywords:© 2006 Lippincott Williams & Wilkins, Inc.
antiretroviral therapy/highly active antiretroviral therapy; HIV/AIDS; adherence intervention; research synthesis; meta-analysis