Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Acquir Immune Defic Syndr. Author manuscript; available in PMC 2008 July 1.
Published in final edited form as:
PMCID: PMC2442469

Effects of a behavioral intervention on antiretroviral medication adherence among people living with HIV: The Healthy Living Project randomized controlled study

Mallory O. Johnson, Ph.D.,1 Edwin Charlebois, Ph.D.,1 Stephen F. Morin, Ph.D.,1 Robert H. Remien, Ph.D.,2 Margaret A. Chesney, Ph.D.,1 and The NIMH Healthy Living Project Team1,2,3,4,5



To examine the effect of a 15-session, individually delivered cognitive behavioral intervention on antiretroviral (ART) medication adherence.


A multisite, two-group, randomized controlled trial.


204 HIV-infected participants with self-reported ART adherence < 85% out of 3,818 screened were randomized into the trial. Potential participants were recruited for the main trial based on sexual risk criteria in Los Angeles, Milwaukee, New York, and San Francisco.


The primary outcome of the intervention was a reduction in HIV transmission risk behaviors. Fifteen 90-minute individually delivered sessions divided into three modules: Stress, Coping, and Adjustment; Safer Behaviors; and Health Behaviors, including an emphasis on ART adherence. Controls received no intervention until trial completion. Both groups completed follow-up assessments at 5, 10, 15, 20, and 25 months after randomization.

Main Outcome Measure

Self-reported ART adherence as measured by 3 day computerized assessment.


A significance difference in rates of reported adherence was observed between intervention and control participants at months 5 and 15, corresponding to the assessments following Stress, Coping and Adjustment module (5 month time point) and after the Health Behaviors module (15 month time point). The relative improvements among the intervention group compared to the control group dissipated at follow up.


Cognitive behavioral intervention programs may effectively improve ART adherence, but the effects of intervention may be short-lived.

Keywords: Antiretroviral therapy, adherence, compliance, RCT

The importance of adherence to antiretroviral therapy (ART) for HIV infection is well-documented [1, 2] and studies delineating the predictors of nonadherence are abundant [3]. Rigorously conducted randomized controlled intervention studies targeting ART adherence are relatively few and, as a result, there is a limited understanding about what types of interventions are effective in improving adherence to ART. Recent meta-analyses of studies to date have noted that, among these few studies, interventions that address multiple obstacles to adherence and those that target individuals with noted adherence problems showed the most promising results [4-6]

The purpose of the current analysis is to determine the effect of a multi-module individual counseling intervention on rates of ART adherence among participants in the Healthy Living Project clinical trial. The intervention is based on Social Action Theory (SAT), in which behaviors such as medication adherence and risky sexual activity are framed as the result of three interactive domains: (1) the environmental context, including sociodemographic variables; (2) responses to internal affective states such as depression and anxiety; and (3) the self regulation capacities of the individual, including outcome expectances and self efficacy for protective behaviors[7]. Three modules of 5 sessions each addressed these domains throughout each session organized around the following topics: Stress, Coping, and Adjustment (Module 1); Safer Behaviors, with a focus on HIV transmission risk (Module 2); and Health Behaviors, including provider relationships and medication adherence (Module 3). SAT constructs were imbedded within all intervention sessions.

The study was designed as a multisite, two-group, randomized controlled trial to test the effect of a 15-session, individually delivered cognitive behavioral intervention. The intervention was primarily developed for persons living with HIV with sexual risk of transmitting HIV to others and recruitment selected such individuals. In this article, we report effects of the intervention on ART adherence for those trial participants whose baseline ART adherence rates indicated problematic adherence. This approach was taken to ascertain whether the intervention can help increase adherence among those for whom an increase is indicated.


This study was conducted in four U.S. cities: Los Angeles, CA, Milwaukee, WI, New York, NY, and San Francisco, CA. Details of the baseline methods [8, 9] and intervention[10] have been published elsewhere. The study protocol, and assessment measures are available on the study Web site ( The institutional review boards at each of the participating institutions approved all study procedures. Voluntary, written informed consent was obtained from all participants.

Study Population

Between April 2000 and January 2002, HIV-infected individuals in the four study cities were recruited from community agencies and medical clinics for a baseline interview. The assessment was used to screen participants for eligibility in the randomized intervention trial. Potential participants were required to be at least 18 years of age, provide written informed consent and medical documentation of their HIV infection, be free of severe neuropsychological impairment or psychosis, and not be currently involved in another behavioral intervention study related to HIV. Severe neuropsychological impairment and psychosis were assessed on a case-by-case basis by interviewers in consultation with senior project personnel, including the clinical supervisor at the involved institution. Participants were eligible if they met criteria for sexual transmission risk described elsewhere [11].

Design and Procedures

Interviews were conducted in private settings in research offices, community-based organizations, and clinics using laptop computers.[8, 9] Procedures involved a combination of audio-computer-assisted self-interviewing (ACASI) and computer-assisted personal interviewing (CAPI) using the Questionnaire Development System (Nova Research Company, Bethesda, MD, U.S.A.). ACASI has been shown as an effective method of decreasing social desirability bias and thereby enhancing veracity of self-report of sensitive behaviors, including sexual and substance use risk acts.[12, 13] Participants received $50 for completing the baseline interview.


Trial eligibility criteria were programmed into the computerized assessment interview. Individuals determined to be eligible were asked to participate, then randomized. Simple randomization was implemented using computer-generated random numbers stored in a randomization table on a server computer housed at the Los Angeles study site. Details of the randomization procedures are described elsewhere [11].

Intervention Condition

The Healthy Living Project experimental intervention was designed on the basis of qualitative studies [10, 14]. It consisted of fifteen 90-minute individual counseling sessions grouped into three modules, each consisting of five sessions. Module 1 (Stress, Coping, and Adjustment), addressing quality of life, psychological coping, and achieving positive affect and supportive social relationships was delivered prior to the 5-month time point. Module 2 (Safer Behaviors), addressing self-regulatory issues, such as avoiding sexual and drug-related risk of HIV transmission or acquisition of additional sexually transmitted diseases, as well as disclosure of HIV status to potential partners, was delivered between the 5- and 10-month time points. Module 3 (Health Behaviors), addressing accessing health services, medication adherence, and active participation in medical care decision-making, was delivered between the 10- and 15-month time points. Table 2 provides an outline of session content in each of the three modules. Intervention sessions followed a standard structure and set of activities, but were individually tailored to participants’ specific life contexts, stressors, and goals. Participants received $10, $15 and $20 for attending each session of Modules 1, 2, and 3, respectively. Participants in the control condition received no active psychosocial interventions during the 25 months of the trial.

Table 2
Intervention Content

Facilitators were trained centrally in cognitive-behavioral intervention strategies and were “certified” if supervisors’ observations and quality assurance ratings indicated skilled implementation. All intervention sessions were audiotaped and 10% were rated at a central site to ensure replication with fidelity.

Follow-up assessment interviews were scheduled every 5 months for both the intervention and control groups. Participants received $30 for completing each assessment interview at 5, 10, 15, and 20 months, and $60 for the 25-month interview.


Medication Adherence

Recent self-reported antiretroviral medication adherence was assessed over a three-day period using an adherence survey developed for use in AIDS Clinical Trials [15]. Respondents indicated how many antiretroviral pills they had skipped during each of the previous three days. This measure has been used widely with diverse samples and the short term recall period has been associated with long term clinical outcomes. Adherence was assessed only for those ART medications that were reported in the adherence section of the interview. For the present study, we calculated percent adherence based on number of pills taken divided by the number of pills respondents reported being expected to take. For the current analyses, respondents were classified as “Low Adherers” if they reported less than 85% ART adherence at baseline, consistent with current literature on minimum levels of adherence (ranging between 80-95%) to achieve HIV viral suppression and clinical benefit [16, 17].

Statistical analysis

Mean percent adherence for ART for each member of the subsample was calculated at each time point that the participant indicated being on ARTs, averaging individual percent adherence to each drug. Categorical variables were compared (control versus intervention) using the Chi-Square Test or Fisher’s Exact Test where data not meet required assumptions for Chi-Square Test. Means for the control versus intervention for continuous variables were compared using the Wilcoxon Two-Sample Test. Analysis was performed using the SAS Statistical Analysis System, version 9 (SAS Institute Inc., Cary, NC.).


Of 3,818 individuals screened for the intervention study, 1,072 were eligible and 936 (87%) of those eligible agreed to participate and were randomly assigned to the intervention or control arm of the trial. 633 (67.6%) of the randomized participants were on antiretroviral therapy at baseline and were therefore suitable for the current analyses. 204 (32.2% of those on ART) reported less than 85% adherence at baseline and were therefore categorized as Low Adherers and are the focus of the current analyses. Viral load assay data were available for 81% and CD4 assay values for 89% of participants at baseline. In support of the dichotomization of low and high adherence, the baseline viral load of each group was examined among those with laboratory assay data. Those categorized as high adherers were more likely to have viral load levels at less than 400copies, as determined by laboratory assay at baseline as compared to low adherers (60.7% of high adherers v. 38.2% of low adherers, p<.0001), supporting our use of 85% adherence as a cutoff.

Sample Characteristics

At baseline, the mean age of the 204 participants was 40.0 years (SD = 6.3 years). Most participants were male (77.9%) and 52% identified as gay. 56.2% percent of participants were African American, 31.0% were White, 6.9% were Hispanic, and 5.9% identified as another race or ethnicity. 54.9 % had education greater than high school. Mean self-reported CD4 count was 403 (SD = 284) and mean self-reported ART medication adherence at baseline was 60.8% for the prior 3 days. 107 and 97 participants were randomized to the intervention and control conditions, respectively. Table 1 presents participants’ demographic characteristics, health status, and adherence by randomization group.

Table 1
Baseline characteristics of study participants

Table 1 illustrates sample characteristics by randomization assignment. Of note, the baseline adherence reports remain comparable for the two arms. With the exception of nonsignificant trends towards greater reports of daily alcohol drinking and more ARV pills per day in the lagged group, the two arms were comparable along all other dimensions explored.

Retention and Completion Rates

Of the 204 participants included in the current analyses, assessment completion rates ranged from a high of 90.0% at 5 months to 76.5% at 25 months, with no differences noted by randomization status (see Table 1). Mean adherence at baseline for those missing one or more assessment (n=63) was 66.7%, which did not differ by randomization group but was higher than those who missed no assessment sessions (58.1%, p<.05). Of the 107 randomized to the intervention condition, 8.4% completed no sessions, 9.3% completed between 1-5 sessions, 7.5% completed 6-10 sessions, and 74.8% completed 11 or more sessions, with 72.9% of those randomized to the immediate condition completing all 15 sessions.

Intervention Effect on Adherence

Figure 1 depicts the trial outcome for ART adherence for participants with low adherence at trial entry. Following randomization, between 14 and 22 participants at each time point in each condition reported no longer being on ART, and were thus not included in the analysis for that time point (See Table 1). Overall, 101 participants reported at one or more of the five follow-up time point that they were not taking ART. This did not differ by randomization status.

Figure 1
Mean adherence over time for intervention and control conditions

At the five month assessment point, immediately following delivery of the Stress, Coping, and Adjustment intervention module to those in the immediate intervention arm, there was approximately 13% greater increase in ART adherence among participants still on ART in the intervention versus control group (p<.01). At 10 months, following the Safer Behaviors module, there was no difference between arms in adherence. At 15 months, immediately following the Health Behaviors Module, there was approximately 10% greater adherence among those randomized to immediate intervention (p<.05). No significant differences were observed at months 20 and 25.


The Healthy Living Project intervention was successful in improving ART adherence among participants with lower initial ART adherence; however the effect was only present at two of five time points, dissipating over time. At the 5 and 15 month assessments, both the intervention and control groups reported substantial increases in their adherence rates with the intervention group reporting a relative 10-13% improvement over the control group.

That both intervention and control groups increased their adherence to some degree irrespective of intervention receipt is not surprising given the design of the study. First, because for some analyses we selected for analysis only the minority of participants who reported low adherence prior to randomization, regression toward the mean would partially explain both groups’ increase in reported adherence. Likewise, the effects of repeated assessments and monitoring of adherence may have had an effect of increasing adherence through raising awareness of adherence in both groups. Further, a number of participants reported discontinuing ART during the trial and therefore did not have adherence data at follow-up. Participants in both conditions who stopped ART had average lower adherence at baseline than those who continued ART, which contributed to the increase in average adherence among both groups. However, that the patterns of discontinuation of ART did not differ by randomization condition and that the intervention group increased significantly over the control group supports the effect of the intervention over and above the influence of regression toward the mean and assessment effects.

The relative difference attributed to the intervention was approximately 10-13% adherence at the assessment immediately after intervention delivery. This raises several important questions. First, is this difference clinically meaningful? There is evidence that, depending on specific regimen characteristics and baseline level of adherence, a 10% increase in mean adherence may be associated with as much as a halving of viral load [18] and a 20-30% decreased risk of progression to AIDS [18, 19], suggesting that the magnitude of the current effect is potentially clinically meaningful. Furthermore, the current understanding of the relationships between adherence and viral resistance suggests that the risk of the development of resistance varies by class of ART and that there is no single cutoff below which the risk of resistance clearly outweighs the potential drug benefit [20]. The pattern of observed intervention effects is likely affected by the adherence threshold cutoff selected in the current analyses. However, when the data were analyzed post hoc with a 95% adherence selection criterion, the pattern of results was similar with the exception that the difference at 15 months did not reach statistical significance.

Second, is the relative increase in the intervention group an effect of increased attention by trial staff? Because the design of the trial did not include an attention only control, we are unable to determine whether the increase in subject contact is the driving force of the change. However, the intervention addressed factors that are repeatedly linked to nonadherence in the literature, including mental health and coping, problem solving, substance use, provider relations, and social support. The limited size of the subsample precludes the analysis of complex interactions with such mediators and moderators of adherence. Therefore, while the exact mechanism of change cannot be ascertained through the current study, the features of the intervention are consonant with an improvement in medication adherence. Likewise, without a counterbalancing of module order, it is not possible to determine which module is associated with the greatest likelihood of improving adherence. For instance, the focus in Module 2 on serostatus disclosure, communication, and reducing substance use may have had an independent effect on adherence when not preceded by the stress and coping module. Similarly, although there appeared to be no overall difference by randomization status, it is possible that patterns of ART discontinuation may have influenced the average adherence rates across the two groups, contributing to the group differences.

The treatment effect was not maintained at follow-up time points, suggesting that changes in adherence behavior may be difficult to sustain following discontinuation of intervention. Future investigations may employ a design that includes booster sessions and/or ongoing intervention to see if effects can be maintained over time.

There are limitations of note in the current study, namely, reliance on self-reported data, use of a convenience sample, and the demanding structure of the intervention. The use of self-reported adherence data, while often robust with demonstrated validity [21] has been questioned in HIV research. Currently, there is no gold standard for measuring adherence to antiretroviral medications. Self-reported levels of adherence are suspected of being inflated because of recall, social desirability, and other biases. To minimize the biases inherent in self-reported data, we employed several techniques. First, we used a validated measure of adherence that has demonstrated meaningful relationships with important outcomes, such as viral load, in other studies. Second, we used ACASI interviewing for the adherence portion of the interview, thereby removing the interviewer’s presence and minimizing social desirability bias. Finally, we included instructions that contained recall cues, which asked the respondent to carefully think back through events in the past three days and the computer referred to each day by name. Such approaches to computerized adherence assessment have shown favorable effects in other studies [22, 23]. The use of a non-probability sample limits the degree to which causal inferences and generalizations can be made from these findings. The sample was selected based on HIV sexual transmission risk criteria and there may be associated limits to generalization to other samples and populations. However, given the similarity of our sample’s characteristics to that of surveillance data from the CDC, the sample appears to be demographically representative of HIV positive persons in the U.S.[24]. The current intervention of 15 individualized sessions is likely of limited feasibility in many service delivery settings. Evaluation of briefer, more adherence focused variations of the HLP intervention which target the stress and coping and adherence content may result in more accessible alternatives to the current intervention. Finally, although the randomized design of the study is a methodological strength, we did not measure and control for additional sources of adherence support that participants accessed outside of the trial which may influence the findings.

In conclusion, the Healthy Living Project intervention showed evidence of significant improvements in ART adherence among participants with low adherence at trial entry. Additional investigation is warranted to detail the applicability of the intervention and possible variations across settings and populations.


This study was funded by cooperative agreements between the National Institute of Mental Health and the University of California, Los Angeles (U10MH057615); HIV Center/Research Foundation for Mental Hygiene Inc./New York State Psychiatric Institute (U10MH057636); the Medical College of Wisconsin (U10MH057631); and the University of California, San Francisco (U10MH057616).

The NIMH Healthy Living Trial Group

Research Steering Committee (site principal investigators and NIMH staff collaborators)

Margaret A. Chesney, Ph.D.1, Anke A. Ehrhardt, Ph.D.2, Jeffrey A. Kelly, Ph.D.3, Willo Pequegnat, Ph.D.4, Mary Jane Rotheram-Borus, Ph.D.5

Collaborating Scientists, Co-Principal Investigators, and Investigators

Abdelmonem A. Afifi, Ph.D.5, Eric G. Benotsch, Ph.D.3, Michael J. Brondino, Ph.D.3, Sheryl L. Catz, Ph.D.3, Edwin D. Charlebois, Ph.D., M.P.H.1, William G. Cumberland, Ph.D.5, Don C. DesJarlais, Ph.D. 6, Naihua Duan, Ph.D. 5, Theresa M. Exner, Ph.D. 2, Rise B. Goldstein, Ph.D., M.P.H.5, Cheryl Gore-Felton, Ph.D.3, A. Elizabeth Hirky, Ph.D.2, Mallory O. Johnson, Ph.D.1, Robert M. Kertzner, M.D. 2, Sheri B. Kirshenbaum, Ph.D.,2 Lauren E. Kittel, Psy.D. 2, Robert Klitzman, M.D. 2, Martha Lee, Ph.D5., Bruce Levin, Ph.D. 2, Marguerita Lightfoot, Ph.D.5, Stephen F. Morin, Ph.D.1, Steven D. Pinkerton, Ph.D.3, Robert H. Remien, Ph.D.2, Fen Rhodes, Ph.D. 5, Juwon Song, Ph.D.5, Wayne T. Steward, PhD, M.P.H.1 , Susan Tross, Ph.D.2, Lance S. Weinhardt, Ph.D.3, Robert Weiss, Ph.D. 5, Hannah Wolfe, Ph.D. 7, Rachel Wolfe, Ph.D. 7, F. Lennie Wong, Ph.D.5

Data Management and Analytic Support

Philip Batterham, M.A.5, W. Scott Comulada, M.S.5, Tyson Rogers, M.A.5, Yu Zhao, M.S.5

Site Project Coordinators

Jackie Correale, MPH2, Kristin Hackl, M.S.W3, Daniel Hong, M.A.5, Karen Huchting, B.A.5, Joanne D. Mickalian, M.A.1, Margaret Peterson, M.S.W.3


Christopher M. Gordon, Ph.D.4, Dianne Rausch, Ph.D.4, Ellen Stover, Ph.D.4

  1. University of California, San Francisco
  2. New York State Psychiatric Institute/Columbia University, New York
  3. Medical College of Wisconsin, Milwaukee
  4. National Institute of Mental Health, Bethesda, Maryland
  5. University of California, Los Angeles
  6. Beth Israel Medical Center, New York
  7. St. Luke’s-Roosevelt Medical Center, New York


1. Bangsberg DR, Perry S, Charlebois ED, Clark RA, Roberston M, Zolopa AR, Moss A. Non-adherence to highly active antiretroviral therapy predicts progression to AIDS. AIDS. 2001;15:1181–1183. [PubMed]
2. Chesney MA, Ickovics J, Hecht FM, Sikipa G, Rabkin J. Adherence: a necessity for successful HIV combination therapy. AIDS. 1999;13(Suppl A):S271–278. [PubMed]
3. Fogarty L, Roter D, Larson S, Burke J, Gillespie J, Levy R. Patient adherence to HIV medication regimens: a review of published and abstract reports. Patient Education & Counseling. 2002;46:93–108. Review. [PubMed]
4. Simoni JM, Frick PA, Pantalone DW, Turner BJ. Antiretroviral Adherence Interventions: A Review of Current Literature and Ongoing Studies. Topics in HIV Medicine. 2003;11:185–198. [PubMed]
5. Simoni JM, Pearson CR, Pantalone DW, Marks G, Crepaz N. Efficacy of Interventions in Improving Highly Active Antiretroviral Therapy Adherence and HIV-1 RNA Viral Load: A Meta-Analytic Review of Randomized Controlled Trials. Journal of AIDS and Human Retrovirology. In Press.
6. 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. [PubMed]
7. Ewart CK. Social action theory for a public health psychology. Am Psychol. 1991;46:931–946. [PubMed]
8. Johnson MO, Catz SL, Remien RH, Rotheram-Borus MJ, Morin SF, Charlebois ED, et al. Theory guided, empirically supported avenues for intervention on HIV medication nonadherence: Findings from the Healthy Living Project. AIDS Patient Care STDS. 2003;17:645–656. [PubMed]
9. Weinhardt LS, Kelly JA, Brondino MJ, Rotheram-Borus MJ, Kirshenbaum SB, Chesney MA, et al. HIV Transmission Risk Behavior Among Men and Women Living With HIV in Four U.S. Cities. Journal of AIDS and Human Retrovirology. 2004;36:1057–1066.
10. Gore-Felton C, Rotheram-Borus MJ, Kelly JA, Weinhardt LS, Catz SL, Chesney MA, et al. Healthy Living Project: Individually-Tailored Multidimensional Intervention for HIV-Infected Persons. AIDS Educ Prev. 2005;17:21–39. [PubMed]
11. HLP. Effects of a Behavioral Intervention to Reduce Risk of Transmission Among People Living With HIV: The Healthy Living Project Randomized Controlled Study. J Acquir Immune Defic Syndr. 2007;44:213–221. [PubMed]
12. Turner CF, Ku L, Rogers SM, Lindberg LD, Pleck JH, Sonenstein FL. Adolescent sexual behavior, drug use, and violence: Increased reporting with computer survey technology. Science. 1998;280:867–873. [PubMed]
13. Gribble JN, Miller HG, Rogers SM, Turner CF. Interview mode and measurement of sexual behaviors: Methodological issues. Journal of Sex Research. 1999;36:16–24.
14. Remien RH, Hirky AE, Johnson MO, Weinhardt LS, Whittier D, Minh-Le G. Adherence to Medication Treatment: A Qualitative Study of Facilitators and Barriers Among a Diverse Sample of HIV+ Men and Women in Four U.S. Cities. AIDS & Behavior. 2003;7:61–72. [PubMed]
15. Chesney MA, Ickovics JR, Chambers DB, Gifford AL, Neidig J, Zwickl B, Wu AW. Self-reported adherence to antiretroviral medications among participants in HIV clinical trials: the AACTG adherence instruments. Patient Care Committee & Adherence Working Group of the Outcomes Committee of the Adult AIDS Clinical Trials Group (AACTG) AIDS Care. 2000;12:255–266. [PubMed]
16. Paterson DL, Swindells S, Mohr J, Brester M, Vergis EN, Squier C, et al. Adherence to protease inhibitor therapy and outcomes in patients with HIV infection. Ann Intern Med. 2000;133:21–30. [PubMed]
17. Garcia de Olalla P, Knobel H, Carmona A, Guelar A, Lopez-Colomes JL, Cayla JA. Impact of adherence and highly active antiretroviral therapy on survival in HIV-infected patients. J Acquir Immune Defic Syndr. 2002;30:105–110. [PubMed]
18. Bangsberg DR, Hecht FM, Charlebois ED, Zolopa AR, Holodniy M, Sheiner L, et al. Adherence to protease inhibitors, HIV-1 viral load, and development of drug resistance in an indigent population. AIDS. 2000;14:357–366. [PubMed]
19. Hogg RS, Heath K, Bangsberg D, Yip B, Press N, O’Shaughnessy MV, Montaner JS. Intermittent use of triple-combination therapy is predictive of mortality at baseline and after 1 year of follow-up. AIDS. 2002;16:1051–1058. [PubMed]
20. Bangsberg DR, Acosta EP, Gupta R, Guzman D, Riley ED, Harrigan PR, et al. Adherence-resistance relationships for protease and non-nucleoside reverse transcriptase inhibitors explained by virological fitness. AIDS. 2006;20:223–231. [PubMed]
21. Simoni JM, Kurth AE, Pearson CR, Pantalone DW, Merrill JO, Frick PA. Self-report measures of antiretroviral therapy adherence: A review with recommendations for HIV research and clinical management. AIDS Behav. 2006;10:227–245. [PubMed]
22. Stewart KE. ICAAC. San Diego, CA: 1998. Patterns of self-reported adherence to ART in a prospective clinical cohort.
23. Bangsberg DR, Bronstone A, Hofmann R. A computer-based assessment detects regimen misunderstandings and nonadherence for patients on HIV antiretroviral therapy. AIDS Care. 2002;14:3–15. [PubMed]
24. CDC. HIV/AIDS surveillance report: Midyear 2001 edition. MMWR CDC Surveill Summ. 2001;13