Antiretroviral therapy (ART) is the standard of care for HIV-infected patients with laboratory or physical indicators of immunosuppression . ART has dramatically reduced AIDS-related mortality and morbidity [2–4], but its success depends on the patient's strict adherence to prescribed regimens [5,6]. Many studies have documented the relationship between adherence to ART and virologic, immunologic, and clinical outcomes, including progression to AIDS, occurrence of opportunistic infections, and mortality [7–13]. Close to perfect adherence to current ART regimens may be needed to achieve lasting viral suppression . However, mean adherence rates to ART range from 50 to 80% and only a minority (20–40%) achieve > 90% adherence when measured by electronic monitoring [6–9], which is widely considered the most accurate method of adherence measurement [14–17].
Effective interventions to improve and sustain optimal adherence are crucial for people living with HIV and for the public health. Currently, there is little evidence supporting specific approaches for improving ART adherence [18,19]. In a comprehensive review of adherence to ART interventions, Simoni et al.  examined intervention studies and reviews published through January 2003 and abstracts reporting interim findings from ongoing US National Institutes of Health-funded adherence intervention projects. Ten studies included control or comparison groups, but only seven included random assignment of participants; of these, only four incorporated a follow-up assessment period. Three of these four studies had encouraging findings, but all had numerous methodological problems [20–22], and all were individual focused. In sum, there is a paucity of tested and demonstrably effective interventions to improve adherence to ART.
The SMART (Sharing Medical Adherence Responsibilities Together) Couples Study is the first couple-based ART adherence intervention to be tested in a randomized controlled trial. A couple-based intervention was chose since it is known that social support can enhance treatment adherence [23–27] and family-oriented and couple-based interventions have demonstrated effectiveness in improving health outcomes in diseases such as chronic mental illness [28–30], drug addiction [31,32], diabetes mellitus , cancer [34,35], and long-term disabilities . The intervention with HIV-serodiscordant couples was designed to improve medication adherence among HIV-seropositive patients by fostering active support from their HIV-negative partners and also to address the sexual transmission concerns within the dyad (the latter is not the focus of this report). In addition to partner support, other cognitive–behavioral strategies were also employed, based on Ewart's social action theory , which emphasized how social context, in addition to individual cognitive and emotional factors, can influence health-care behaviors. This trial was designed to test the efficacy of the intervention in improving medication adherence among patients with poor adherence in a clinical setting.
This randomized controlled trial was conducted between August, 2000 and January, 2004 at two HIV/AIDS outpatient treatment clinics of St Luke's Roosevelt Hospital Center (SLRHC) in New York City. Recruitment, however, was citywide. Clinic providers were informed about the study, and recruitment flyers were posted in hospital outpatient HIV/AIDS treatment clinics, private medical practices, and community-based organizations. Potential participants called the study office and completed a 10 min telephone prescreening interview to assess basic eligibility. Participants were eligible if they were an HIV-serodiscordant couple (self-report) with a relationship duration of 6 months or more, and both partners were English-speaking adults (> 18 years of age). The HIV-seropositive partner needed to be in primary care and taking ART for at least 1 month. Couples meeting these criteria were scheduled for an in-person main screening appointment.
The couple's relationship status was confirmed at the main screening by independently asking each partner when and how they met, whether they considered themselves to be in a ‘committed’ relationship, and whether they expected to be in this relationship for at least another year. If the couple was eligible and interested in participation, each partner provided informed consent, the antiretroviral regimen of the HIV-seropositive partner was reviewed, and a Medication Event Monitoring System cap (MEMS cap; AARDEX, Zurich, Switzerland) was provided for electronic monitoring of ART adherence. Participants received $20 per partner for this appointment.
Couples returned 2 weeks later and were eligible for the intervention trial if < 80% of prescribed doses were taken within specified time windows during the 2-week MEMS observation period (see below). Patients with adherence < 80% were told that they were ineligible for the study (but not specifically why) and were referred to their health-care providers for routine follow-up. Each partner of eligible couples was separately administered the baseline interview prior to randomization.
The study protocol was reviewed and approved by the Institutional Review Boards of the New York State Psychiatric Institute and St Luke's-Roosevelt Hospital Center, and all participants provided written consent. An independent Performance and Safety Monitoring Board, consisting of physicians, statisticians, consumers, ethicists, and other scientists, monitored and assessed unblinded participant safety outcomes throughout the study. No serious adverse events were identified.
Randomization and study protocol
Couples were randomly assigned to one of two conditions: the brief intervention, a four-session couple-focused adherence program, or the control condition, usual care through the medical provider of the HIV-seropositive partner. A randomization table was constructed from a random numbers list and stratified by couple type. Randomization was conducted by the study's project director while assessors and all other personnel (except for intervention facilitators) were blind to study arm assignment throughout the trial. Couples in both conditions returned for an assessment 8 weeks after baseline (2 weeks after the intervention), and the HIV-seropositive partners alone completed follow-up assessments at week 20 (3 months after the intervention) and week 32 (6 months after the intervention).
Data collection and outcome assessment
For the pre-baseline screening and all subsequent assessments, the MEMS cap was fixed to the bottle containing the ART component with the most complex dosage regimen (in terms of the number of doses and pills per day). At baseline, participants were instructed to use the MEMS cap continuously until the week 8 assessment, at which point study staff collected the cap. For the week 20 and week 32 assessments, staff reissued MEMS caps to participants 2 weeks prior to the assessment and instructed them to use the cap for 2 weeks and return it at the time of the assessment.
Audio computer-assisted self-interviewing (ACASI) and computer-assisted personal interviewing (CAPI) were used to assess demographic variables, number of medical appointments attended, and potential psychosocial mediators and moderators of medication adherence. Participants received $25 per partner for the baseline assessment, $30 per partner for the week 8 assessment, and $40 for the week 20 and week 32 assessments.
The primary measure of adherence was the MEMS cap, which consisted of a medication bottle cap with a microchip that recorded the date and time of each bottle opening. MEMS data were downloaded into computer software to calculate adherence summary scores for the percentage of prescribed doses taken (without regard to timing) and the percentage of prescribed doses taken within specified time windows (e.g., for twice-a-day regimens, intended dosage times were set 12 h apart, with ±2 h windows around each intended dosage time). The adherence summary scores were adjusted through participant self-reports of errors in MEMS use (e.g., number of occasions in which doses were dispensed from a container other than the MEMS bottle, or the MEMS bottle was opened without removing a dose). This strategy has been validated in other studies and has been shown to strengthen the relationship between electronically monitored adherence and virologic treatment outcomes .
HIV-seropositive participants provided a 12 ml. blood sample at baseline and at week 8 for viral load and CD4 cell count assays. Viral HIV RNA was measured using the Amplicor HIV-1 Monitor Kit (Roche Molecular Systems, Branchburg, New Jersey, USA), which provided ultrasensitive analysis (detection limit 50 copies/ml). The absolute CD4 cell count was determined through flow cytometry. If a participant did not provide a blood sample, his/her medical chart was examined for clinical outcomes within appropriate time intervals (for baseline assessment, from 2 months prior to and 1 month after the assessment; for week 8, up to 3 months after the assessment). If a participant did not provide a blood sample and medical chart data could not be accessed, self-reported biomarkers were used.
Grounded in Ewart's social action theory , the brief intervention aimed to improve patients’ adherence to HIV/AIDS medical care regimens by fostering the support of their partners; in addition the intervention sought to help couples to address issues of sex and intimacy. The intervention was individually administered to each couple by a nurse practitioner through four 45–60 min sessions held over 5 weeks. The session content included structured discussions and instruction, as well as specific problem-solving and couple-communication exercises. Key components included education about the importance of adherence to avoid viral resistance and maintain health, identifying patterns of non-adherence, developing communication and problem-solving strategies to overcome adherence barriers, optimizing partner support, and building confidence in the couple for achieving and maintaining improved adherence. Participants were reimbursed $20 per partner for each intervention session.
In HIV-specific clinics in New York City, from which the majority of the patients were recruited, standard clinical care provides attention to adherence-related issues from a multidisciplinary treatment team. Before a patient is prescribed first or new ART, the patient and medical provider have agreed that the patient is ready and able to start the new therapy. Dosing, common side effects, and the importance of adherence to the regimen as prescribed are discussed. Patients are instructed to contact the clinic to speak with either their medical provider or a nurse if they have difficulties with the regimen. Follow-up with the patient's medical provider usually occurs within 2–4 weeks after initiating a new regimen. When ongoing adherence problems are identified, a member of the treatment team assesses the patient to determine the underlying causes and how to address them. Patients with adherence problems are scheduled to see their medical provider monthly.
Intervention and assessment fidelity
Interviewers were trained and certified by the study team to perform data collection, and supervision was provided through weekly meetings and systematic reviews of interview audiotapes. The intervention facilitators received training and satisfactorily completed pilot sessions before meeting with trial participants. Curriculum fidelity was maintained through systematic reviews of session audiotapes and weekly supervision meetings with the study's principal investigator and project director.
Sample size was determined by power calculations designed to detect a 15% difference in mean adherence between the control and experimental groups at the week 8 assessment. This time point was deemed to be the primary test of intervention efficacy, and a 15% mean difference in the groups was considered an effect size that was clinically meaningful. The a priori primary tests of intervention effect examined group differences using MEMS data for proportion of prescribed doses taken and proportion of prescribed doses taken within specified time windows. These analyses, as well as adherence analyses at the week 20 and week 32 assessments, followed an intention-to-treat model with multiple imputations of missing data so that every randomized participant was included in analyses. Backward stepwise regression analyses were used to determine which variables were good predictors of follow-up adherence. These variables were the basis for imputation models that provided estimates for participants missing follow-up adherence data. After multiple imputs, 10 completed datasets were used to estimate the intervention effects. Rubin's formula  was used to calculate variance for the intervention effects. Secondary analyses were limited to those with follow-up data. Standard statistics (t-tests, regressions, chi-square tests) were used where appropriate.
Figure 1 shows recruitment and screening results. Ineligibility at prescreening was primarily because participants were not in a serodiscordant relationship or the HIV-seropositive partner was not taking ART. At the main screening, ineligibility was primarily because the relationship was of insufficient length, and at the final screening, ineligibility was because of adherence > 80% within specified time windows.
Retention for study assessment appointments was 91% at week 8, 86% at week 20, and 85% at week 32. Within the intervention arm, 73% of the couples completed the four intervention sessions, and 86% of the couples completed at least three intervention sessions.
A total of 215 HIV-serodiscordant couples were enrolled in the study. The sample included women and men and largely consisted of lower-income racial/ethnic minorities (Table 1). The HIV-seropositive partners primarily attended hospital-based HIV/AIDS outpatient treatment clinics. With one exception, there were no significant differences between the two study arms on sociodemographic variables; the one difference was that HIV-seropositive participants in the control arm reported a significantly higher annual income than those in the intervention arm (t = −2.163; P = 0.032).
Adherence was equivalent in the two groups at baseline. Intention-to-treat analyses showed significant group differences in adherence change from baseline to week 8 (2 weeks after the intervention) in terms of proportion of prescribed doses taken (change score b = −10.84; P = 0.021) and proportion of doses taken within specified time windows (change score b = −22.38; P < .001; see Table 2 for mean adherence at all assessment points). There were differences in adherence change between the groups at the week 20 assessment (3 months after the intervention), but only the proportion of doses taken within specified time windows was significant (change score b = −13.17; P = 0.028). There were no significant differences in adherence change between the two groups at the week 32 assessment (6 months after the intervention) on either adherence outcome. While not significant with intent-to-treat analysis, an analysis of ‘completers only’ showed a significant intervention effect at week 32 on the proportion of prescribed doses taken within specified time windows (t = 2.24; P = 0.026).
By design, all the patients randomized had a baseline adherence level of < 80% of prescribed doses taken within specified time windows. A more clinically meaningful outcome may be the percentage of study participants who achieved higher levels of adherence following the intervention. Figure 2 shows significant group differences at the primary outcome time-point (week 8) in the percentage of participants who took > 80%, > 90%, and > 95% of doses, and Fig. 3 shows significant group differences in the percentage of participants who took > 80%, > 90%, and > 95% of doses within specified time windows. The proportion of participants achieving these three adherence levels was significantly higher in the treatment group than in the control group for both adherence outcomes. Differences between the two groups were then examined on the > 90% criterion (the level most often reported in the literature) for the week 20 and week 32 assessments. In all cases, the intervention group had a higher proportion of participants achieving > 90% of prescribed doses taken and prescribed doses taken within specified time windows (data not shown). However, the only difference that reached significance was for prescribed doses taken within specified time windows at week 20 (12% versus 2%; P = 0.016).
There were no differences in primary outcomes based on gender, ethnicity, income, relationship length, or whether the HIV-positive participant was part of a same-sex versus an opposite-sex couple. In the intervention group, there were no changes in the number of medical appointments attended throughout the study period. Among controls, there was no change in this variable from baseline to week 8 or week 20; however, there was a significant decrease in number of medical appointments attended from baseline to week 32 (P = 0.016).
At baseline, mean viral load was 15 865 copies/ml for the intervention group and 11 276 copies/ml for the control group; at week 8 the means increased to 16 015 and 23 195 copies/ml, respectively. Although viral load in the control group increased by an average of 12 000 copies/ml and stayed relatively stable in the intervention group, the difference was not significant and remained so after log transformation. Improvement in adherence, however, was marginally associated with a reduction in viral load in the full sample (r = −0.138; P = 0.077). CD4 cell counts fell slightly for both groups.
This is one of very few randomized controlled studies to demonstrate effectiveness in improving ART adherence, especially with the use of electronic monitoring and intention-to-treat analyses, and it is the first published study of a couple-focused intervention for ART adherence. We have been able to identify only three published randomized controlled trials that demonstrated intervention effects on ART adherence at follow-up assessments, and in each study the adherence outcome was assessed by self-report and intention-to-treat analyses were not conducted despite significant attrition in some instances [21,22,40].
All participants had suboptimal adherence levels at baseline as defined in the study eligibility criteria. In analyses of the proportion of prescribed doses taken (regardless of dosage timing), the intervention group had a mean adherence level 2 weeks post-intervention (76%) that was greater than the control group (60%) and was in the upper range of adherence rates reported in studies of general clinic populations (50–80%) [6,8,9]. Also, 47% of the intervention group demonstrated adherence greater than 90% post-intervention, which was nearly double the percentage in the control group (25%) and is higher than percentages found in general clinic populations (20–40%). The > 15% difference in mean adherence between the two study arms at 2 weeks after the intervention is clinically meaningful because recent studies have shown that a 10% difference in adherence to ART reduces the risk of progressing to AIDS and AIDS mortality by 20% [12,38]. The dissipation of intervention effects over time was not surprising given the absence of booster sessions in the study design. Overall, these findings are very encouraging, given the difficulty of altering behavior in a treatment-experienced population with established low adherence levels and the paucity of tested interventions with positive adherence outcomes in any population.
A specific goal of our intervention was to get patients to take their medication within specified time windows. In analyses of prescribed doses taken within specified time windows, the mean level of adherence in the intervention group showed significant improvement following the intervention (from 43% to 58%), compared with a decline in mean adherence found in the control arm. High levels of adherence to regular dosage-timing intervals were found in a minority of intervention group participants (approximately 15–30% depending on the criterion used), compared with almost none of the control group (< 5%). Although few studies have examined the importance of dosage timing, there is evidence that it may play an important role in virologic outcome [41–43], and clinicians encourage patients to take medications at regular intervals to ensure optimal therapeutic coverage.
In our study, post-intervention adherence differences between the two study arms can be attributed to both improvement in the treatment group and adherence decline in the control group. While it is widely acknowledged that adherence levels decline with time [8,44], we believe the results observed are also a consequence, at least in part, of our adherence measurement procedures. The introduction of adherence monitoring (pre-baseline) may have increased adherence early in the study. Since participants used the MEMS caps continually for 8 weeks, we hypothesize that the mean level of adherence for the control group regressed, over time, to a level of adherence that was likely to be the truer baseline mean of our sample. Readministering the MEMS caps 2 weeks prior to the week 20 and week 32 assessments was likely to have boosted adherence again, but more modestly since it was less novel to the participants at those times. While we cannot prove this hypothesis, the effects of MEMS adherence measurement on medication-taking behavior needs further consideration and investigation by researchers in this field.
The lack of robust findings for viral load outcomes is understandable since most participants were not starting new regimens at the point of study entry, had a history of multiple drug regimens, and were screened for poor adherence. Therefore, they were likely to have had significant antiretroviral resistance already. Recent studies point to the complexities in the relationship between adherence and biologic outcomes . Data show that the number of HIV drug-resistance mutations can increase with higher levels of adherence in the absence of complete viral suppression, and that particular resistance patterns are more likely at the highest levels of adherence in some drug classes. Potential resistance in patients with high levels of adherence on partially suppressive regimens may, therefore, explain why we saw a significant difference in adherence but only a trend for difference in viral load.
While the relationship between adherence and drug resistance is complex, better adherence continues to be strongly associated with reduced likelihood of AIDS illness progression and death, and even patients with drug-resistant virus and detectable viremia continue to derive clinical benefit from ART [10–12,46–48]. Furthermore, more suppressive regimens are more likely to align higher levels of adherence with a reduced risk of antiretroviral resistance. With that said, it is also important to note that our study employed mixed methods of biomarker collection (i.e., blood specimens, chart abstraction, self-report), which likely weakened our ability to assess a true relationship between adherence change and traditional biomarkers.
This study was limited by the self-selective nature of the participants. Although providers referred many participants, and all participants had low levels of adherence, the mode of study recruitment selected for motivated patients with motivated HIV-serodiscordant partners. Participants also received financial compensation for attending intervention and assessment appointments. Serodiscordant couples are additionally a restricted population, although with some refinement this couple-focused intervention could prove to be effective with a broader range of dyads.
This brief, theoretically based, dyad-focused behavioral intervention was effective in improving medication adherence among ethnically diverse and relatively poor patients in an urban clinic setting. Outcomes of this trial suggest that patients should be encouraged to bring relationship partners with them to medical appointments and that clinicians should try to include patients’ partners in discussions of treatment decisions and adherence. This approach does not incur significant cost and is, therefore, feasible in resource-poor settings. Involving a significant other in the enhancement of support for adherence to ART can benefit the individual and the public health.
We thank Robert Kertzner for his contributions to the study design, Olivia Copeland, Daniel Krieger, Anna Smith, and Robert Warford for their assistance with the conduct of this study, and David Bangsberg, Alan Berkman, and Margaret Chesney for their comments on early manuscript drafts. We are grateful to Victoria Sharp and the clinical staff at St. Luke's-Roosevelt Hospital Center for their collaboration on this project and to the men and women who participated in this study.
Sponsorship: This study was funded by the National Institute of Mental Health (R01 MH61173), with additional support from the Columbia–Rockefeller Center for AIDS Research (NIAID; P30 AI42848) towards the processing of biological assays.
1. Department of Health and Human Services, Panel on Clinical Practices for Treatment of HIV Infection. Guidelines for the Use of Antiretroviral Agents in HIV-1-infected Adults and Adolescents
. Rockville, MD: US Department of Health and Human Services; 2003.
2. Mocroft A, Ledergerber B, Katlama C, Kirk O, Reiss P, d’Arminio Monforte A, et al
. Decline in the AIDS and death rates in the EuroSIDA study: an observational study. Lancet 2003; 362:22–29.
3. Palella FJ, Delaney KM, Moorman AC, Loveless MO, Fuhrer J, Satten GA, et al
. Declining morbidity and mortality among patients with advanced HIV infection. N Engl J Med 1998; 338:853–860.
4. Sepkowitz K. AIDS: the first 20 years. N Engl J Med 2001; 344:1764–1772.
5. Chesney MA, Ickovics J, Hecht FM, Sikipa G, Rabkin J. Adherence: a necessity for successful HIV combination therapy. AIDS 1999; 13:S271–S278.
6. 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.
7. Arnsten JH, Demas PA, Farzadegan H, Grant RW, Gourevitch MN, Chang CJ, et al
. Antiretroviral therapy adherence and viral suppression in HIV-infected drug users: comparison of self-report and electronic monitoring. Clin Infect Dis 2001; 33:1417–1423.
8. Howard AA, Arnsten JH, Lo Y, Vlahov D, Rich JD, Schuman P, et al
. A prospective study of adherence and viral load in a large multi-center cohort of HIV-infected women. AIDS 2002; 16:2175–2182.
9. McNabb J, Ross JW, Abriola K, Turley C, Nightingale CH, Nicolau DP. Adherence to highly active antiretroviral therapy predicts outcome at an inner-city human immunodeficiency virus clinic. Clin Infect Dis 2001; 33:700–705.
10. Bangsberg DR, Perry S, Charlebois E, Clark RA, Roberston M, Zolopa AR, et al
. Non-adherence to highly active antiretroviral therapy predicts progression to AIDS. AIDS 2001; 15:1181–1183.
11. deOlalla P, Knobel H, Carmona A, Guelar A, Lopez-Colomes J, Cayla J. Impact of adherence on highly active antiretroviral therapy on survival in HIV-infected patients. J Acquir Immune Defic Syndr 2001; 30:105–110.
12. Hogg RS, Heath K, Bangsberg D, Yip B, Press N, O'Shaughnessy MV, et al
. Intermittent use of triple-combination therapy is predictive of mortality at baseline and after 1 year of follow-up. AIDS 2002; 16:1051–1058.
13. Wood E, Hogg RS, Yip B, Harrigan PR, O'Shaughnessy MV, Montaner JS. Effect of medication adherence on survival of HIV-infected adults who start highly active antiretroviral therapy when the CD4+ cell count is 0.200 to 0.350 × 10(9) cells/L. Ann Intern Med 2003; 139:810–816.
14. Bangsberg DR, Hecht FM, Charlebois E, Chesney M, Moss AR. Comparing objective methods of adherence assessment: electronic medication monitoring and unannounced pill count. AIDS Behav 2001; 5:275–281.
15. Kastrissios H, Suárez JR, Katzenstein D, Girard P, Sheiner LB, Blaschke TF. Characterizing patterns of drug-taking behavior with a multiple drug regimen in an AIDS clinical trial. AIDS 1998; 12:2295–2303.
16. Liu H, Golin CE, Miller LG, Hays RD, Beck CK, Sanandaji S, et al
. A comparison study of multiple measures of adherence to HIV protease inhibitors. Ann Intern Med 2001; 134:968–977.
17. Wagner GJ, Ghosh-Dastidar B. Electronic monitoring: adherence assessment or intervention? HIV Clin Trials 2002; 3:45–51.
18. 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 Educ Couns 2002; 46:93–108.
19. Simoni JM, Frick PA, Pantalone AB, Turner BJ. Antiretroviral adherence interventions: a review of current literature and ongoing studies. Top HIV Med 2003; 11:185–198.
20. Knobel H, Carmona A, Lopez JL, Gimeno JL, Saballs P, Gonzalez A, et al
. Adherence to very active antiretroviral treatment: impact of individualized assessment. Enferm Infecc Microbiol Clin 1999; 17:78–81.
21. Rigsby MO, Rosen MI, Beauvais JE, Cramer JA, Rainey PM, O'Malley SS, et al
. Cue-dose training with monetary reinforcement: pilot study of an antiretroviral adherence intervention. J Gen Intern Med 2000; 15:841–847.
22. Tuldra A, Fumaz CR, Ferrer MJ, Bayes R, Arno A, Balague M, et al
. Prospective randomized two-arm controlled study to determine the efficacy of a specific intervention to improve long-term adherence to highly active antiretroviral therapy. J Acquir Immune Defic Syndr 2000; 25:221–228.
23. Berkman LF. The relationship of social networks and social support to morbidity and mortality. In: Cohen S, Syme SL, editors. Social Support and Health. San Diego, CA: Academic Press; 1985. pp. 241–262.
24. Cohen S. Psychosocial models of the role of social support in the etiology of physical disease. Health Psychol 1988; 7:269–297.
25. DiMatteo MR. Social support and patient adherence to medical treatment: a meta-analysis. Health Psychol 2004; 23:207–218.
26. DiMatteo MR, Hays R. Social support and serious illness. In: Gottlieb BA, editor. Social Networks and Social Support in Community Mental Health. Beverly Hills, CA: Sage; 1981. pp. 117–148.
27. Kaplan RM, Toshima MT. The function effects of social relationships on chronic illnesses and disability
. In: Social Support: An Interactional View
. Edited by Sarason IG, Sarason BR, Pierce GR. New York: Wiley; 1990; pp. 427–453.
28. Anderson CM, Griffin S, Rossi A, Pagonis I, Holder DP, Treiber R. A comparative study of the impact of education vs. process groups for families of patients with affective disorders. Fam Process 1986; 25:185–205.
29. Jacob M, Frank E, Kupfer DJ, Cornes C, Carpenter LL. A psychoeducational workshop for depressed patients, family, and friends: description and evaluation. Hosp Community Psychiatry 1987; 38:968–972.
30. Rea MM, Miklowitz DJ, Tompson MC, Goldstein MJ, Hwang S, Mintz J. Family-focused treatment versus individual treatment for bipolar disorder: results of a randomized clinical trial. J Consult Clin Psychol 2003; 71:482–492.
31. Cutter CG, Cutter HS. Adult children of alcoholics. J Stud Alcohol 1987; 48:29–32.
32. Wermuth L, Scheidt S. Enlisting family support in drug treatment. Fam Process 1986; 25:25–33.
33. Edelstein J, Linn MW. The influence of family on control of diabetes. Soc Sci Med 1985; 21:541–545.
34. Mitrowski CA. Social work intervention with geriatric cancer patients and their children. Soc Casework 1985; 66:242–245.
35. Nathanson MN, Monaco GP. Meeting the educational and psychosocial needs produced by diagnosis of pediatric/adolescent cancer. Health Educ Q 1984; S10:67–75.
36. Remien RH, Chrisopher R. A family psychoeducational model for longterm rehabilitation: prevention and treatment. Phys Occup Ther Geriatr 1996; 14:2.
37. Ewart CK. Social action theory for a public health psychology. Am Psychol 1991; 46:931–946.
38. Bangsberg DR, Hecht FM, Charlebois E, 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.
39. Rubin D. Multiple imputation for nonresponse in surveys
. New York: Wiley; 1987.
40. Goujard C, Bernard N, Sohier N, Peyramond D, Lancon F, Chwalow J, et al
. Impact of a patient education program on adherence to HIV medication: a randomized clinical trial. J Acquir Immune Defic Syndr 2003; 34:191–194.
41. Hooper J, Ferguson NM, Donnelly CA, Lapins D, Hamel E, Anderson RM. Modeling the interaction between adherence and the evolution of antiviral resistance
. Eighth Conference on Retroviruses and Opportunistic Infections
. Chicago, February 2001 [abstract 482].
42. Stansell J, Holtzer C, Mayer S, de Guzman C, Hamel E, Lapins D. Factors affecting treatment outcomes in a medication event monitoring system (MEMS) clinical trial on PI-containing HAART
. Eighth Conference on Retroviruses and Opportunistic Infections
. Chicago, February 2001 [abstract 478].
43. Vrijens B, Mayer SL, Rode R, Bertz R, Urquhart J. Dose-timing information improves the clinical explanatory power of data on patient adherence to antiretroviral drug regimens
. Twelfth Population Approach Group Europe Meeting
. Verona, June 2003.
44. Mannheimer S, Friedland G, Matts J, Child C, Chesney M, for the Terry Beirn Community Programs for Clinical Research on AIDS. The consistency of adherence to antiretroviral therapy predicts biologic outcomes for human immunodeficiency virus-infected persons in clinical trials. Clin Infect Dis 2002; 34:1115–1121.
45. Bangsberg DR, Moss AR, Deeks SG. Paradoxes of adherence and drug resistance to HIV antiretroviral therapy. J Antimicrob Chemother 2004; 53:696–699.
46. Deeks SG. International perspectives on antiretroviral resistance: nonnucleoside reverse transcriptase inhibitor resistance. J Acquir Immune Defic Syndr 2001; 26:25–33.
47. Gallego O, de Mendoza C, Perez-Elias MJ, Guardiola JM, Pedreira J, Dalmau D, et al
. Drug resistance in persons experiencing early virological failure under a triple combination including indinavir. AIDS 2001; 15:1701–1706.
48. Walsh JC, Pozniak AL, Nelson MR, Mandalia S, Gazzard BG. Virologic rebound on HAART in the context of low treatment adherence is associated with a low prevalence of antiretroviral drug resistance. J Acquir Immune Defic Syndr 2002; 30:278–287.
Keywords:© 2005 Lippincott Williams & Wilkins, Inc.
HIV; antiretroviral; adherence; intervention; randomized controlled trial; MEMS; serodiscordant couples