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J Adv Nurs. Author manuscript; available in PMC 2011 Sep 1.
Published in final edited form as:
PMCID: PMC2923750

Implementation of Antiretroviral Therapy Adherence Interventions: A Realist Synthesis of Evidence

Jennifer Leeman, DrPHl, MDiv, Yun Kyung Chang, PhD, RN, EunJeong Lee, MPH, RN, Corrine I. Voils, PhD, and Margarete Sandelowski, PhD, RN



This paper is a report of a synthesis of evidence on implementation of interventions to improve adherence to antiretroviral therapy.


Evidence on efficacy must be supplemented with evidence on how interventions were implemented in practice and on how that implementation varied across populations and settings.

Data Sources

Sixty-one reports were reviewed of studies conducted in the United States of America in the period 2001 to December 2008. Fifty-two reports were included in the final analysis: 37 reporting the effects of interventions and 15 reporting intervention feasibility, acceptability, or fidelity.

Review Methods

An adaptation of Pawson’s realist synthesis method was used, whereby a provisional explanatory model and associated list of propositions are developed from an initial review of literature. This model is successively refined to the point at which it best explains empirical findings from the reports reviewed.


The final explanatory model suggests that individuals with HIV will be more likely to enrol in interventions that protect their confidentiality, to attend when scheduling is responsive to their needs, and both to attend and continue with an intervention when they develop a strong, one-to-one relationship with the intervener. Participants who have limited prior experience with antiretroviral therapy will be more likely to continue with an intervention than those who are more experienced. Dropout rates are likely to be higher when interventions are integrated into existing delivery systems than when offered as stand-alone interventions.


The explanatory model developed in this study is intended to provide guidance to clinicians and researchers on the points in the implementation chain that require strengthening.

Keywords: literature review, implementation, Antiretroviral Therapy, Adherence, Interventions, Realist Synthesis of Evidence, Nursing, Pawson’s method


Antiretroviral therapy (ART) has dramatically improved the health and longevity of people with HIV infection, but problems with adherence have prevented many from realizing the full benefits of treatment. Researchers have tested different approaches to improving adherence to ART, and have systematically reviewed the findings of those studies. However, the findings from these reviews exemplify the limitations of reviews of behavioral change interventions. Amico et al. (2006) reported that the effects of the interventions they reviewed varied considerably across studies. Such reviews provide only limited evidence to explain variation across studies and virtually no evidence on implementation. In their review of ART adherence interventions Simoni et al. (2006, p. S34) concluded that there was “a paucity of data to guide the implementation of adherence interventions in clinical settings.”

Efforts to increase the use of evidence in practice draw heavily on findings from systematic reviews of the literature, such as those conducted for the Cochrane Collaboration. Typically, systematic reviews summarize the evidence concerning the effect of an intervention on a defined set of outcomes. Although evidence on effectiveness is critical, evidence is needed also about how interventions were implemented in practice and how implementation varied across populations and settings (Brownson et al., 2009; Rychetnik et al., 2004). This is particularly important for behavioral change interventions, which typically require multiple interactions between intervener(s) and participant(s) over an extended period of time. This interaction can be viewed as an “implementation chain” that is only as strong as its weakest link (Pawson, 2006, p. 95). With enhanced understanding of the factors that affect each link, the overall chain can be strengthened.

When reviewing research reports, reviewers synthesize findings across interventions they have rendered comparable for the purpose of the review (Sandelowski et al., 2007). In rendering interventions comparable, reviewers simplify them, systematically divorcing evidence about an intervention’s effectiveness from information about the context in which it was delivered and how it was implemented in practice (Asthana & Halliday, 2006). As a result, reviewers typically conclude that the intervention works sometimes, but provide little information on how implementation may vary across different contexts and approaches (Briss et al., 2004). Indeed, it is this very information that may determine when the intervention will work.



The aim of the study was to systematically review and synthesize evidence on implementation of interventions to improve adherence to antiretroviral therapy among adults with HIV.


We adapted Pawson’s realist synthesis method (Pawson, 2006; Pawson et al., 2005) for this study. Pawson (2006, p. 94) advanced realist synthesis as a method to “discover the typical weak points and major stumbling blocks in the implementation of the interventions under review.” Realist synthesis differs from conventional reviews in both its purpose and methods. Its goal is explanatory rather than summative. The methodology is well suited to carrying out syntheses to develop plausible explanatory models in areas where data are insufficient to identify and test relationships. The end-product of the synthesis is not a summary of the evidence in support of relationships (e.g., between an intervention and outcome), but rather a beginning theory to explain “what works for whom in what circumstances and in what respects” (Pawson, 2006, p. 74).


The steps (described below) required to carry out a realist synthesis parallel those used in conventional reviews, but are less linear and sequential. Rather than reviewing a focused literature addressing a predetermined set of research questions, the review itself—generated by a beginning set of questions—leads to more questions and, therefore, a diverse range of publications to answer them. Because its focus is explanatory, a realist synthesis explores answers to a broad range of questions about why, when, and how an intervention works. To narrow the review focus, realist synthesis begins with substantial reading and review to identify the factors with the greatest potential explanatory value. The factors identified are then integrated into a provisional model and series of propositions to guide the subsequent review.

The purpose of our review was to explain when, why, and how implementation of ART adherence interventions work well. To begin to focus the review, we read literature on the implementation of behavioral change interventions. This literature addresses theoretical and practical issues related to the components of interventions, the process of implementation, and the influence of context (e.g., Davidson et al., 2003; Hawe et al., 2004; Roen et al., 2006; Rychetnik et al., 2002). We also read literature on ART adherence and systematic reviews of ART intervention studies to identify factors specific to the context or implementation of ART adherence interventions. We identified the central steps in implementing a behavioral change intervention, and then developed a provisional model of the factors that may be important. The initial model was very basic and was intended to focus the literature search and initial data extraction, as displayed in Figure 1 and summarized here:

Figure 1
Provisional Explanatory Model of Implementation

To change behavior, an individual must enrol in the intervention, attend intervention sessions, and continue to participate over time; an intervener must deliver the intervention with some level of fidelity. These steps may be influenced by characteristics of the participant, intervener, intervention, and setting.

Search Methods

We searched for two types of publications related to HIV adherence interventions with adults: primary reports of intervention studies and of studies of the feasibility, acceptability, or fidelity of an intervention or programme. We searched PubMed, CINAHL, PsychINFO, Academic Search Premier, and Sociological Abstracts for the time period 2001 to December 2008 using the search terms HIV, antiretroviral treatment, and adherence. Because we were interested specifically in complex behavioral change interventions, we excluded studies that simply tested the effects of a device (e.g., electronic reminder), journaling, or medications (e.g., antidepressant) and involved minimal interaction between intervener and participant. Because implementation is specific to context, and national context plays a major role in how HIV/AIDS is understood and managed, our review was focused on findings from studies conducted only in the USA. (Further details of the search strategies are available from the last author on request.)

Search Outcomes

Forty-six primary reports of intervention studies were included in the initial review. Nine of these studies were excluded from the final analysis because they included no useable data on enrolment, attendance, or retention. Table 1 gives an overview of the 37 primary reports of intervention studies included in the final analysis. Fifteen reports of feasibility, acceptability, or fidelity studies also were included in the review (Table 2).

Table 1
Profile of Intervention Studies
Table 2
Profile of Feasibility/Acceptability/Fidelity Studies

Quality Appraisal

In realist synthesis, rather than appraising and excluding publications prior to review, the reviewer mines each publication for evidence that may contribute to fuller development of the explanatory model (Pawson, 2006). Instead of entire reports being evaluated against a priori standards for quality appraisal, each piece of evidence is appraised for its utility and relevance (Pawson, 2008). The value of evidence depends on whether it contributes to better understanding of the questions addressed in the review.

Data Abstraction and Synthesis

Drawing on our review of literature on behavioral change interventions and systematic reviews of ART adherence studies, we created a tool to cover a broad range of information and to allow the consolidation of data from across studies. Although Pawson (2006) has argued against such tools as too limiting for extracting information from diverse types of publications, our tool was flexible enough to account for this diversity. Moreover, using it did not preclude us from returning to the original papers whenever new questions requiring further exploration arose. Table 3 gives an overview of the information extracted on contextual factors that may affect implementation and the implementation process. This template was applied to the body of each report (i.e., introduction, methods, results, and discussion sections) by two members of the research team. Differences in coding were resolved by consensus with other team members.

Table 3
Categories of Information Extracted from Primary Reports

Focusing the review

To begin refining the explanatory model, we conducted an initial review of data from all 61 studies to identify contextual factors repeatedly mentioned as facilitators or barriers at each step in the implementation process. We focused on: (a) authors’ speculations about barriers and facilitators to successful implementation; (b) authors’ speculations about the effects that characteristics of the intervention, participants, interveners, or setting had on implementation; (c) analysis of moderators, mediators, or other factors that influenced implementation or intervention outcomes; and (d) participant reports of satisfaction with or acceptability of the intervention.

Based on this review, we identified broad themes and created a list of propositions further to refine the explanatory model. We identified neither themes nor propositions related to interveners’ delivery of the intervention because of the limited amount of information in the literature reviewed. These are displayed in Table 4 and summarized here:

Table 4
Propositions further refining the explanatory model

Patients targeted by ART interventions tend to be those at greatest risk of not adhering, and these patients tend to have high levels of distrust, economic distress, and social instability. They have concerns about confidentiality and may resist interventions that risk disclosure of their HIV status or other personal information (i.e., in the case of interventions delivered to groups or where a healthcare worker goes to an individual’s neighborhood or worksite to deliver ART and observe them take it). Adherence may have lower priority than other pressing concerns in their lives. Thus, their participation in an intervention may be more likely if they are offered instrumental support with other life concerns and/or financial incentives. Due to the instability in their lives, patients targeted by ART interventions may have trouble keeping scheduled appointments and continuing with an intervention over time. Flexible scheduling approaches may further facilitate interveners’ and participants’ ability to maintain contact with each other. Interventions may be implemented more successfully when they involve fewer intervention contacts and shorter durations. Developing strong relationships between the intervener and participant can lead to greater retention over time.

Synthesizing evidence

In realist synthesis, reviewers synthesize the extracted evidence by applying it to the explanatory model and then iteratively refining the model to best explain the existing data. In cases where evidence does not fit the model, the reviewer looks for other factors that may have more explanatory value. The synthesis ends with a summary of how the explanatory model has been revised.

We created a data matrix that summarized findings from each primary report of an intervention relating to each proposition (Table 5). Each step in the implementation process was coded as the rate of participation/completion of that step in a study. The approach used to code findings within each category is described in greater detail below. We then created scatter plots to represent visually the proposed relationships (e.g., enrolment rate by delivery mode) and looked for evidence of associations. If the patterns depicted by the scatter plot appeared not to fit with the proposition, we looked for an alternative proposition. If the association generally fitted the proposition, we examined the few outliers to explore why they did not fit and developed alternative propositions as needed.

Table 5
Data matrix

We categorized interventions into four types, primarily based on delivery mode. Nineteen publications (51.4%) were reports of in-person, individual counseling/education interventions. Eight (21.6%) were reports of group counseling/education interventions, five of which also involved individual in-person or telephone-delivered contacts. Eight publications (21.6%) were reports of directly observed therapy [DOT] that involved giving participants their medications and observing them take it. Two (5.4%) were reports of telephone-delivered reminder/problem-solving interventions. In two cases, authors reported a comparison between two interventions and combined relevant data for the two interventions. In both cases, we used our judgment to determine which was the primary intervention and assigned it to that category.


Consistent with realist synthesis methodology, we looked at each step in the explanatory model independently, analyzing data from all publications that included data pertinent to that step.

Step 1. Patient Enrols

The first step in intervening involves getting people to enrol. We operationalized enrolment as the percentage of those approached who agreed to participate in the study. We included only studies in which investigators identified a target population and then attempted to enrol all eligible members. Typically, the target population was all patients enrolled in clinics who met initial target criteria. We identified the proportion of patients who were eligible based on an initial screening who then declined to participate or failed to attend any of the intervention visits (i.e., viewed as passive declination). Because we were particularly interested in the number who declined to participate based on characteristics of the intervention, we also included the proportion of patients who declined following randomization where this information was available. We excluded studies in which participants were recruited through voluntary strategies (e.g., posting fliers) because those being screened to enrol were not representative of the total eligible population. Sixteen studies (43.2%) met the criteria for data on enrolment.

Support for proposition that patients are more likely to enrol in interventions that protect confidentiality than in those that do not

We grouped interventions according to whether their mode of delivery presented a greater or lesser risk to participants’ confidentially. Those risking confidentiality included directly observed therapy (DOT) and group interventions. Individual counseling/teaching and telephone interventions posed less risk to confidentiality. Of the 16 intervention studies with data on enrolment, 10 were individual counseling/teaching interventions, 3 were DOT interventions, and 3 were group counseling/teaching interventions. Patterns of participation rates in the reports of intervention studies offer support for the proposition that more patients will decline to participate in interventions that risk confidentiality than in those that protect confidentiality (Figure 2). The four interventions with the highest refusal rates included two DOT interventions (both with 40% refusal rates) and two group interventions (53% and 40% refusal rates, respectively). Of the 10 interventions involving individual, in-person counseling/teaching, six had relatively low refusal rates (9–15%) and the remaining four had moderate rates (23%–33%). One group intervention (Koenig et al., 2008) had a low refusal rate (18%) and did not fit the pattern. We looked for an explanation and found that it targeted individuals starting their first ART regimen. We found no consistent pattern, however, when we tested the proposition that prior experience using ART would affect enrolment using data from the nine studies that had data on both enrolment and ART experience.

Figure 2
Percent Decline by Delivery Mode

Additional data supported the proposition that participants may resist participation in interventions that risk confidentiality (i.e., DOT and group interventions). In reports of intervention studies, authors reported that participants declined to participate because they were uncomfortable in groups (Simoni et al., 2007), and did not want visits at home or work if assigned to the DOT arm of the study (Garland et al., 2007). One of the acceptability/feasibility studies was specifically focused on the acceptability of DOT. Santos et al. (2006) asked HIV-positive patients if they would participate in a DOT programme in which a trained healthcare provider met with patients in the location of their choosing. Only 17% of 47 respondents preferred DOT to administering their own medications; 13% saw it as a nuisance and 18% as a burden. Commonly-cited concerns were loss of privacy and interference with family, work, and home life. Of those who preferred self-administration, 33% thought that DOT would expose them to scrutiny.

Support for proposition that patients are more likely to enrol in interventions that offer financial incentives or help with instrumental needs

Nine of the 16 intervention study reports included information on the financial incentives for participants. The total amount a participant could earn through study participation ranged from US$40 to $1,172. Instrumental support was a component of only one of the 16 studies. There was no pattern indicating a potential relationship between enrolment rates and financial incentives or instrumental support.

Step 2. Patient Attends

We operationalized attendance as the ratio of actual contacts to planned contacts between participants and interveners. Nineteen of the reports of intervention studies (51.4%) included useable information about attendance: 10 testing individual counseling/teaching interventions, three testing DOT, 4 testing group interventions, and two testing telephone interventions.

Support for proposition that attendance rates are greater when planned contacts are fewer

No patterns were identified to support the proposition that attendance would be better when there were fewer planned contacts.

Support for proposition that participants’ attendance rates are greater if scheduling is responsive to participant than if it is not

We categorized interventions as having flexible scheduling based on how readily participants could adjust the timing and/or location of the contact. DOT and telephone interventions were classified as more flexible, whereas group and individual teaching/counseling interventions with set times for sessions were classified as less flexible. Overall, patterns in the data supported the proposition that flexible scheduling explains greater rates of attendance (Figure 3). Most DOT interventions offer flexibility in the time of contacts and many offer flexibility in location; DOT interventions had the highest rates of attendance. The lowest attendance rate among DOT interventions was for the one that was not flexible and linked DOT to participants’ methadone appointments (Lucas et al., 2007). Groups are particularly difficult to schedule in a fashion that is responsive to all participants, and they also had low attendance rates. Contrary to the proposition, however, in-person, individual teaching/counseling interventions had consistently good attendance rates, even though participants had to attend at a set time for their appointments.

Figure 3
Attendance Rates by Intervention Type

Evaluating the new proposition that attendance rates are greater when the intervener and patient develop a strong relationship than when they do not

Because the data did not fully fit the proposition, we looked to additional data from the reports to explore possible alternative propositions for the high attendance rates at teaching/counseling interventions. We found evidence that the strength of the relationship between the intervener and participant may also affect attendance. In several studies where researchers interviewed or surveyed participants and interveners about their perceptions of ART adherence interventions, the most dominant theme was the importance of participants’ of relationships with interveners. In an acceptability study, Bontempi et al. (2004) conducted focus groups with participants in two HIV programmes, and found that all participants spoke about the importance of the adherence programme nurses who gave them social support, instilled trust, and provided positive reinforcement. Participants in DOT interventions also noted the relationship with the intervener as one of the most valuable aspects of the intervention (Garland et al., 2007; Ma et al., 2008; Visnegarwala et al., 2006; Wohl et al., 2004). In the two studies in which providers of adherence support were interviewed, they described the importance of spending time and serving as liaisons between clients and other healthcare providers (Bontempi et al., 2004; Shelton et al., 2006).

Based on these findings, we decided to test the proposition that attendance rates are greater when the intervener and participant develop a strong relationship. A strong relationship was operationalized as individual, in-person contacts with an intervener (i.e., individual teaching/counseling and DOT interventions). Group and telephone interventions were classified as developing less strong relationships between participants and interveners. Although telephone interventions can develop strong relationships between participants and interveners, both telephone interventions included in the review involved brief, scripted calls rather than more in-depth counseling. Testing the relationship between attendance and strength of relationship resulted in the same breakdown as depicted in Figure 3, which shows support for an association between strength of the relationship and attendance. Further support for this proposition is provided by one study in which investigators compared a group to an individual, in-person intervention and found that attendance was higher for the individual intervention (Purcell et al., 2007).

Step 3. Intervener Delivers

The reports of ART adherence interventions gave little information on how interventions were delivered, and therefore we were unable to develop any propositions. Although a few reports offered descriptions of the process used to monitor fidelity, only five gave information on fidelity, and the authors four of these reports discussed fidelity only in general terms (Dilorio et al., 2008; Golin et al., 2006; Johnson et al., 2007; & Purcell et al., 2007). For example, Dilorio et al. stated that, “overall,” nurses were adherent to scripts and used appropriate motivational interviewing skills. Thrasher et al. (2006) provided additional data on the integrity of delivery for a subset of 47 patients in the Golin et al. intervention study. Using a well-established coding system, they found that, despite extensive training and supervision, interveners met only two of five quality benchmarks in less than half the sessions (18.7% and 43.7%, respectively), and the other three benchmarks in 62.5%, 85.4%, and 100% of sessions, respectively. Detail also was provided on the areas where interveners needed to improve, such as using more reflective statements and open-ended questions.

Step 4. Participant Continues

We operationalized whether participants continue with an intervention as the proportion not dropping out prior to the end of the intervention. We also included data on dropouts prior to completion of study data collection if they were the only data available. Twenty-seven publications (73%) had useable data on dropout rates.

Support for proposition that participants are less likely to drop out of interventions with shorter durations

No overall patterns were found between the duration of an intervention and participant dropout rates (Figure 4.).

Figure 4
Dropout Rates by Intervention Duration

Support for proposition that retention rates are greater when the intervener and participant develop a strong relationship than when they do not

We operationalized strength of relationship as described above; relationships are stronger with in-person, individual contacts. The pattern of dropout rates in the primary reports of intervention studies provides support for the proposition that strong relationships are associated with greater retention over time (Figure 5). The group interventions all had moderately high dropout rates. The majority of DOT and individual teaching/counseling interventions had relatively low dropout rates, although two individual teaching/counseling (Harwell et al., 2003; Levin et al., 2006; Smith et al., 2003) and three DOT interventions had dropout rates greater than 40% (Harwell et al., 2003; Lucas et al., 2007; Lucas et al., 2006). We examined the data from each of these publications for alternative explanations for the high dropout rates. Three of the interventions integrated adherence into an existing care delivery process rather than designing it as a stand-alone intervention. Lucas et al. (2007; 2006) integrated DOT into care delivery at a methadone maintenance clinic. Levin et al. (2007) integrated an individual teaching/counseling intervention into care delivery at an HIV/AIDS clinic. The Harwell et al. (2003) study enrolled only 11 participants and, therefore, its findings may not be representative. The authors of the report of the study with the highest dropout rate (64%) (Smith et al., 2003) speculated that the intervention delivery site may have been a barrier; this was a large medical center, and many participants lived in surrounding rural communities several miles away.

Figure 5
Dropout Rates by Delivery Mode

Evaluation of new proposition that retention rates are lower when ART interventions are integrated into existing delivery systems rather than delivered as stand-alone interventions

Based on the findings on an association between strong relationships and retention, we evaluated a new proposition that integrating interventions into existing delivery systems would be associated with lower retention rates than would stand-alone interventions (Figure 6). Stand-alone interventions were those that created an intervention involving separately-scheduled appointments whose primary purpose was to increase ART adherence. For DOT interventions, these typically involved a community worker meeting with the participant to deliver ART and observe its use. For individual, telephone, and group teaching/counseling interventions, stand-alone interventions typically involved a series of protocol-driven sessions whose primary purpose was to increase ART adherence. All four of the interventions that integrated delivery into existing care systems had high rates of dropout. As noted above, two integrated DOT into care delivery at a methadone maintenance clinic and the third integrated an individual teaching/counseling intervention into care delivery at an HIV/AIDS clinic (Levin et al., 2006; Lucas et al., 2007; Lucas et al., 2006). In the fourth study, researchers trained primary care providers to deliver a brief teaching/counseling intervention as part of participants’ regular clinic appointments. Overall, the findings suggest that interventions integrated into an existing delivery system may be associated with higher dropout rates than stand-alone interventions.

Figure 6
Dropout Rates by Whether Stand Alone or Integrated

Evaluation of new proposition that retention rates are higher when participants are less ART-experienced

An expectation of the fact that retention rates are higher when participants are less ART-experienced might be that individuals who had not had previous failures with ART would be more likely to continue with an intervention over time. We evaluated the data in support of this proposition and found that dropout rates tended to be lower when a high proportion of participants (>60%) were ART-naïve or had just one prior regimen, as compared to a high proportion who had experienced prior failures with ART (Figure 7). The nine studies that had a high proportion of experienced ART participants (>60%) had dropout rates ranging from 10 to 57%, with four having dropout rates greater than 40%. The eight studies that had a high proportion of naïve or inexperienced ART participants had dropout rates ranging from 13 to 38%, with only one having a dropout rate higher than 28%.

Figure 7
Dropout Rates Based on Antiretroviral Therapy (ART) Experience Level

Revised Explanatory Model

Based on our review of evidence, we revised our initial explanatory model to that shown in Figure 8 and summarized as follows:

Figure 8
Revised Explanatory Model

Participants will be more likely to enrol in interventions that protect their confidentiality, to attend when scheduling is responsive to their needs, and to both attend and continue with an intervention when they develop a strong, one-to-one relationship with the intervener. Participants who have limited prior experience with ART will be more likely to continue with an intervention than those who are more experienced. Dropout rates are likely to be higher when interventions are integrated into existing delivery systems than when offered as stand-alone interventions.


Prior reviews of ART adherence interventions have reported that interventions, on the whole, had a positive effect on levels of adherence (Amico et al., 2006; Rueda, et al 2006; Simoni et al., 2006). Data on effectiveness does not tell the whole story, however, as interventions are only effective for the people who participate in them. To improve outcomes across affected populations, evidence also is needed on how to increase enrolment and participation in interventions over time. In response to this need, a growing number of scholars have advocated greater attention to evidence on the process of implementing interventions in practice (Green & Glasgow, 2006; Glasgow & Emmons, 2007). In this review, we systematically reviewed ART adherence interventions for the purpose of synthesizing evidence on implementation. Our final model is intended to identify factors that may help to explain strong and weak links in the process of implementing ART adherence interventions. For example, DOT interventions are weak at the point of enrolment. However, when they are scheduled to be responsive to participants’ needs and are delivered by an individual committed to adherence, they have high rates of attendance and good rates of retention. Therefore, people implementing a DOT intervention should be aware that, although it has strengths, they may need to offer an alternative as a large proportion of their patients may decline to participate.

The final model also points to areas in need of further research. We found that integrating an intervention into an existing delivery system may be associated with higher dropout rates. Stand-alone interventions typically have dedicated staff and resources. Dropout rates may be higher when interventions are integrated into existing systems because staff must balance time and resources across multiple other priorities. This presents a challenge to the broadly-recognized need to embed interventions within existing systems to ensure their adoption and maintenance over time (Allotey et al., 2008). Further research might be conducted to explore the relative strengths of stand-alone versus integrated interventions, taking into account the potential differential effects on organizational versus patient maintenance of the intervention over time. Further research could also be conducted to explore ways to improve retention of patients in intervention programs when they are integrated into existing delivery systems.

We found the realist synthesis approach useful, but noted several limitations to its use in synthesizing evidence on the implementation of ART adherence interventions. Realist synthesis is suited for reviews of literature where evidence is limited, but gaps in the evidence on implementation were at times too great to allow us to develop explanatory propositions. Many of the reports of intervention studies did not contain data on enrolment, attendance, and dropout rates. Only five publications included data on the fidelity of intervention delivery. These findings are consistent with those from other reviewers who have noted the limited amount of information on implementation included in reports on behavioral change interventions (Egan et al., 2009; Leeman et al., 2006). Inconsistency in whether intervention reports include implementation-relevant data, in what data are reported, and the manner of presentation all increase the potential for bias in our findings. Moreover, as the data reported were collected as part of a research study, the findings may not be generalizable to implementing an intervention in a practice setting. For example, we do not know whether participants declined to enrol because they did not want to be part of a research study rather than because they objected to the particular intervention.


Evidence on implementation is critical to policymakers, managers, and clinicians responsible for effecting change in practice. Synthesizing evidence to inform implementation of behavioral change interventions is possible only to the extent that researchers collect and report data on enrolment, attendance, fidelity, and retention. Intervention researchers can conduct their research in ways that yield more data on implementation, such as doing more process evaluations and designing studies to include diverse patient populations across multiple delivery settings and then collecting data on how implementation varied across contexts. Researchers also can revise their research reports to include more data and guidance on implementation. An intervention is only as strong as the weakest wink in its implementation chain. The explanatory model developed in this study is intended to begin to provide the evidence needed to strengthen each link in this implementation chain.


What is already known about this topic

  • Antiretroviral therapy has dramatically improved the health and extended the lives of people with HIV, but problems with adherence have prevented many from realizing the full benefits of treatment.
  • Findings from systematic reviews of antiretroviral therapy adherence intervention studies provide little information on the implementation of interventions or on how implementation may vary across different contexts and approaches.
  • Clinicians need this kind of evidence to guide the implementation of interventions in practice.

What this paper adds

  • An explanatory model showing the strong and weak links in the chain of implementation of antiretroviral therapy adherence interventions.
  • A step-by-step view of how realist synthesis can be used to synthesize evidence on behavioral change interventions.

Implications for practice

  • The explanatory model developed in this study provides guidance on how to strengthen implementation of antiretroviral therapy adherence interventions.
  • Efforts to synthesize evidence to inform implementation are possible only to the extent that researchers collect and report data on enrolment, attendance, fidelity, and retention.


The method study referred to here, ‘Integrating qualitative and quantitative research findings’, is funded by the National Institute of Nursing Research, National Institutes of Health (5R01NR004907, 3 June 2005–31 March 2010). This material is the result of work also supported with resources and the use of facilities at the Durham Veterans Affairs Medical Center. The views expressed in this paper are those of the authors and do not necessarily represent those of the Department of Veterans Affairs.


Conflict of interest: No conflict of interest has been declared by the authors.

Author Contributions: JL, CV & MS were responsible for the study conception and design

JL, YC & EL performed the data collection

JL, YC & EL performed the data analysis.

JL, YC, EL & CV were responsible for the drafting of the manuscript.

JL & CV made critical revisions to the paper for important intellectual content.

JC provided statistical expertise.

CV & MS obtained funding

YC & EL provided administrative, technical or material support.

MS supervised the study

Contributor Information

Jennifer Leeman, Research Associate Professor, School of Nursing, University of North Carolina at Chapel Hill USA.

Yun Kyung Chang, Social Research Specialist, School of Nursing, University of North Carolina at Chapel Hill USA.

EunJeong Lee, Doctoral Student, School of Nursing, University of North Carolina at Chapel Hill USA.

Corrine I. Voils, Research Health Science Specialist and Associate Professor, Health Services Research and Development Service, Durham Veterans Affairs Medical Center.

Margarete Sandelowski, Cary C. Boshamer Distinguished Professor, School of Nursing, University of North Carolina at Chapel Hill.


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