INTRODUCTION
Antiretroviral therapy (ART) reduces morbidity and mortality related to HIV infection. Sustained adherence to ART improves individual outcomes and reduces secondary transmission, because low viral load (VL) is associated with reduced HIV transmission1–3 4 5 6 7 8–10
Scalable strategies are needed to optimize ART adherence and to reduce secondary transmission of HIV. Meta-analyses show that interventions to support ART adherence11,12 13,14 15,16 17,18
We hypothesized that a computer-delivered intervention could support ART adherence and reduce HIV transmission risk by PLWH. We evaluated such an intervention called Computer Assessment & Rx Education for HIV-positive people (CARE+).
METHODS
Participants
Study participants were recruited from a university-affiliated public HIV clinic and a large AIDS service organization in Seattle, WA. Eligibility criteria included age ≥18 years, on ART, able to understand spoken English; exclusions included thought disorders and current participation in ART adherence or prevention-with-positives studies. Written consent was obtained before randomized assignment. All procedures were approved by the University of Washington Human Subjects Division, 06-1198-C. Participants received $20 at the first 3 sessions and $40 at the final session.
Intervention
Design
The computerized-counseling intervention was evaluated in a prospective 2-arm randomized controlled trial. The study sample of n = 240 was assigned through an automated pseudo-random number generation algorithm, disallowing any exposure to intervention by controls. The experimental group received CARE+ (audio-narrated assessment, tailored feedback, skill-building videos, health plan, and printout) on a tablet computer and standard care, whereas controls received assessment only on tablets and standard care. Each group underwent 4 sessions specific to assigned arm at 3-month intervals over 9 months. Sessions were scheduled on same day as clinic visits wherever possible.
CARE+ is a NET-based (Microsoft, Redmond, WA) custom software application on touch-screen computers. Intervention content is based on theoretical frameworks: information–motivation–behavior including “importance” and confidence' scales around ART use and transmission risk-reduction,19 20 21 22 23,24 25 26 27
Figure 1 summarizes the CARE+ session. Users received tailored feedback based on risk assessment responses and viewed video versions for heterosexuals or for men who have sex with men showcasing skills around HIV disclosure, ART adherence, safer sex, substance abuse, male/female condoms, condom use negotiation, working with providers, and HIV natural history and ART mechanisms. Users develop a plan for ART adherence or safer sex (user choice at the first session and switched at the third session). A personalized printout summarized feedback, health plan, and referral phone numbers.
FIGURE 1: CARE+ intervention session content.
The control condition comprised computerized risk-assessment only (sexual behaviors, substance use, mental health, ART regimen, side effects, adherence in last 7 and 30 days).
In both study arms, the tool flagged reports of severe depression by Patient Health Questionnaire (PHQ-9 score of ≥20),28 29
Outcome Measures
The primary biological outcome was HIV-1 RNA VL, determined using a 500 mL plasma specimen in a TaqMan real-time polymerase chain reaction assay with 30 copies per microliter as the lower limit of quantification for detectable HIV-1. HIV-1 VL was assessed using specimens drawn on day of study interview or as part of patient care within a month before study visit. The same laboratory was used for all study and clinical VLs and was determined by personnel blinded to study arm. Primary behavioral outcome measures collected by self-report in both arms consisted of a composite variable of sexual transmission risk—no condom use (unprotected sex) or condom use with problems/errors (ie, vaginal or anal sex either without a condom or where a condom was used but HIV exposure may have occurred due to mechanical or user failure)—and ART adherence by 30-day visual analog scale (VAS).30,31
Statistical Methods
Fisher exact and Wilcoxon rank-sum tests assessed differences between intervention and control groups and between study sites in baseline study population characteristics. We used generalized estimating equation (GEE) models with a Gaussian link and an unstructured correlation structure to compare changes in VL (log10 -transformed) and ART adherence (30-day VAS) between intervention and control groups and from baseline to 9-month follow-up. GEE models with a logit link and an unstructured correlation structure compared odds of undetectable VL and sexual transmission risks between intervention and control groups and from baseline to 9-month follow-up. All GEE models included main effects for intervention condition and linear trend, as well as an interaction between these terms to capture differences in change between intervention conditions. The analysis was intent-to-treat. Covariates in the models included likely depression diagnosis by PHQ-9 because this was the only variable that differed significantly between study arms at baseline, as well as education, condom use with main partner at baseline, and study site. These analyses were performed for the whole sample and for the subgroup that had detectable VL at baseline. Estimates for these “detectables” were obtained by including relevant main and interaction effects for an indicator variable specifying whether each participant had detectable VL at baseline. When modeling odds of VL being undetectable within this subgroup, only the 3 follow-up assessments were included. Analyses were performed using R,32 33 34 35
RESULTS
We approached 301 individuals at 2 study sites; 240 enrolled (80% acceptance), 239 completed baseline, and 87% (209/239) were retained for 9-month study duration (Fig. 2 ). Participants were consented, enrolled, and then randomized. The 30 lost to follow-up had similar numbers and reasons across arms, suggesting nondifferential dropout (P = 0.56 for attrition by arm).
FIGURE 2: CARE+ computerized counseling intervention trial, 4 sessions over 9 months.
Table 1 shows participant characteristics at baseline by study arm. At baseline intervention, participants were less likely than controls to obtain a positive screening result for likely depression diagnosis (n = 14 vs. 25, P = 0.032). There were several significant differences at baseline between clinic- and organization-recruited participants, including proportion of men who have sex with men, proportion incarcerated more than 1 night, condom use, self-reported ART adherence (VAS), proportion with resistant virus, and proportion victimized by IPV (see Table S1, Supplemental Digital Content, https://links.lww.com/QAI/A504
TABLE 1: Baseline Characteristics of CARE+ Intervention and Control Conditions and by Academic and Community-Based Study Sites
Figure 3 shows 95% confidence intervals (CIs) for outcome means by time and study condition. Detailed GEE results are in Supplemental Digital Content Tables S2-S4 (see https://links.lww.com/QAI/A504 Table S2, Supplemental Digital Content, https://links.lww.com/QAI/A504 Table S1, Supplemental Digital Content, https://links.lww.com/QAI/A504 Table S4, Supplemental Digital Content, https://links.lww.com/QAI/A504
FIGURE 3: Adjusted mean values* by time and treatment condition. *For these independent 95% CIs, nonoverlap indicates
P < 0.05, but overlap does not reliably indicate that
P > 0.05.
36 See
Figure 4 for contrasts. Detectable BL: had detectable HIV1 viral load at first baseline (BL) session. Full sample: All participants regardless of viral load status at baseline.
Figure 4 summarizes main outcomes of interest. The first 4 contrasts in each figure are intervention versus control at each time point, whereas the last 2 contrasts in each figure compare baseline with 9-month follow-up within each study arm.
FIGURE 4: Comparison of viral load, ART adherence, and sexual transmission risk for each follow-up point and changes within each treatment condition.
HIV-1 VL Effect
Figure 4A shows 95% CIs for log10 VL point-in-time study condition mean differences and change within each group from baseline to 9-month follow-up. Figure 4B shows 95% CIs for point-in-time study condition differences and change within each group from baseline to 9-month follow-up in the odds of having undetectable VL. There were marginally significant differences in change from baseline to the 9-month follow-up between study arms in log10 VL (P = 0.053) and in the odds of having undetectable VL (P = 0.090). CARE+ intervention participants overall had an average decrease of 0.17 log10 VL (P = 0.112; 95% CI: −0.39 to 0.04), whereas control participants had an increase of 0.13 (P = 0.250; 95% CI: −0.09 to 0.35) (Fig. 4A , right). Relative to baseline, the odds of having undetectable VL at the 9-month follow-up were increased in the CARE+ condition [odds ratio (OR) = 1.57; P = 0.037; 95% CI: 1.03 to 2.39] but reduced in the control condition (OR = 0.98; P = 0.925; 95% CI: 0.71 to 1.37) (Fig. 4B , right). At the 9-month follow-up, CARE+ intervention participants were lower than controls in log10 VL (−0.06; P = 0.741; 95% CI: −0.4 to 0.30) and had increased odds of undetectable VL (OR = 1.03; P = 0.920; 95% CI: 0.58 to 1.81), but neither of these differences were significant.
Among the subgroup that had detectable VL at baseline, CARE+ intervention participants had an average decrease of 0.60 log10 VL (P = 0.004; 95% CI: −1.01 to −0.19) (Fig. 4A , left), whereas control participants had an increase of 0.15 log10 VL (P = 0.641; 95% CI: −0.48 to 0.78). At the 9-month follow-up, CARE+ intervention participants were lower than controls in log10 VL (−0.73; 95% CI: −1.42 to −0.03), a significant difference (P = 0.041) (Fig. 4A , left). CARE+ intervention participants also had higher odds of undetectable VL than controls at the 9-month follow-up (OR = 2.32; 95% CI: 0.85 to 6.34), although this difference was only marginally significant (P = 0.101) (Fig. 4B , left). For the subgroup with detectable VL at baseline, in Figure 4B we do not show ORs for the baseline time point nor changes from baseline to the 9-month follow-up time point within each group, because all of the participants in this subgroup had detectable VL at baseline. For the log10 VL outcome, the 3-way interaction between baseline detectable VL, study arm, and time was significant (P = 0.034), indicating CARE+ was more effective than control for those with detectable VL at baseline [Supp. 6].
ART Adherence Effect
Figure 4C shows 95% CIs for point-in-time VAS mean differences between groups and mean change within each group from baseline to 9-month follow-up. There was a statistically significant difference in change from baseline to the 9-month follow-up between study arms (P = 0.046) in self-reported ART adherence by 30-day VAS (see Table S2, Supplemental Digital Content, https://links.lww.com/QAI/A504 P = 0.014; 95% CI: 0.95 to 8.48), whereas control participants had a decrease of 1.39 points (P = 0.556; 95% CI: −6.03 to 3.24) (Fig. 4C , right). At the 9-month follow-up, CARE+ intervention participants were higher than controls in ART adherence (4.77; 95% CI: −0.79 to 10.33), but this difference was not significant (P = 0.093) (Fig. 4C , right).
Among those with detectable VL at baseline, CARE+ intervention participants had an average VAS adherence increase of 8.00 points (P = 0.040; 95% CI: 0.37 to 15.62), whereas control participants had a decrease of 1.53 points (P = 0.822; 95% CI: −14.84 to 11.78) (Fig. 4C , left). At the 9-month follow-up, CARE+ intervention participants were higher than controls in ART adherence (13.44; 95% CI: 0.73 to 26.14), a significant difference (P = 0.038) (Fig. 4C , left).
Secondary HIV Transmission Risk Effect
Figure 4D shows CIs for point-in-time study condition differences and change within each group from baseline to 9-month follow-up in self-reported transmission risks, defined as sex without a condom or condom use with errors. There was a statistically significant difference in change from baseline to the 9-month follow-up between study arms in self-reported transmission risks (P = 0.040). Among CARE+ intervention participants, the odds of transmission risks were 0.55 times lower at the 9-month follow-up than at baseline (P = 0.020; 95% CI: 0.34 to 0.91), whereas for control participants, the odds of transmission risks increased over time (OR = 1.10; P = 0.664; 95% CI: 0.72 to 1.67) (Fig. 4D , right). At the 9-month follow-up, CARE+ intervention participants had a reduced odds of transmission risks when compared with controls (OR = 0.46; 95% CI: 0.25 to 0.84), a significant difference (P = 0.012) (Fig. 4D , right).
Clinic Site and Detectable VL at Baseline as Effect Modifiers
None of the 3-way interactions involving study arm, linear trend, and clinic site were statistically significant, suggesting similar intervention effects in the university-affiliated and community-based organization sites (see Table S5, Supplemental Digital Content, https://links.lww.com/QAI/A504 10 VL, as indicated by a significant 3-way interaction between study arm, linear trend, and detectable VL at baseline (P = 0.034). Three-way interactions involving study arm, linear trend, and detectable VL at baseline were not statistically significant for other outcomes (see Table S6, Supplemental Digital Content, https://links.lww.com/QAI/A504
Health Promotion Behavior Plans
CARE+ intervention participants made a concrete plan for ART adherence or transmission risk reduction (controls did not make plans). Many individuals (78%) indicated at baseline that they had an approach that was working for them, which they detailed with specific steps in the CARE+ session; 12% made a new plan. Common plans for ART adherence were to “keep doing what I am doing” (n = 32) “use reminders” (31), and “get support” (25). Common plans for transmission risk reduction were to “not have sex” (31), “use condoms” (27), “have fewer or only 1 sex partner(s)” (22), or “only have sex with people who are also positive (7).”
Intervention participants' confidence in their plan success increased over time, from 66% at 3 months to 80% at 9 months (McNemar χ2 P = 0.02). Confidence in their ability to not transmit HIV increased over time: 0–10 ascending scale for confidence, mean 8.43 (SD, 2.27) at baseline and 9.14 (SD, 1.53) at 9 months, P = 0.02.
At baseline, 41 referrals were made for intervention participants and 52 for controls for reported severe depression (37%), IPV (9%), or suicidal ideation (53%); at 9 months total referrals for these conditions were 21 and 32, respectively (P = 0.10).
Intervention Acceptability
Nearly all (97%) CARE+ intervention participants found the tool easy to use; 99% rated session length as “just right”; 97% felt they had “enough privacy” during the session. Most (93%) felt the CARE+ session helped them as much or more than face-to-face counseling with a staff person, and 75% said they would prefer the computer over a human counselor in the future. No harms or unintended effects were noted in either arm of the study.
DISCUSSION
We found that a computerized counseling tool was effective at helping PLWH improve ART adherence and reduce HIV transmission risk behaviors, as measured by improvement in self-reported adherence, reduction in VL, and improvement in reported correct and consistent condom use, compared with controls receiving usual care. The adherence effect was most pronounced among those whose plasma HIV-1 was not suppressed at baseline. The reduced VL and fewer sexual transmission risk behaviors seen among those undergoing the intervention both may contribute to decreasing HIV transmission to sexual partners.
In our study population area, chart audits found that fewer than half of HIV-positive clients were assessed for sexual risks, STD testing, or referral.37 38 39
Multiple studies have used computers to assess ART nonadherence40–42 43–49 50,51 52,53 54 55–57 58
Economic evaluation models have found that adherence interventions with modest effectiveness may provide survival benefit to patients and be cost effective.59
Study limitations include the fact that two-thirds of our population already had suppressed VL at baseline and 60% did not engage in sexual activities at any time point. Both limitations present conservative biases to the null. Mirroring the Seattle HIV epidemic, the sample was predominantly male. This makes extension of these results to females, or to those living outside the United States, less generalizable. These were heavily treatment- and intervention-experienced populations. Half the sample was from an HIV clinic whose approach to ART adherence support was itself found to reduce log10 VL.60 61 10 change in VL. However, given that this is one study, examining intervention effects in additional diverse HIV-positive samples would contribute to important next steps of replication and generalization. Future research could include examination of the interplay of multiple HIV behaviors such as nonadherence and sexual risk. The intervention may have influenced risk behavior reporting, though computerized approaches can reduce social desirability bias62 63 64
The study was strengthened by including community- and clinic-based samples, which had similar intervention effects, increasing generalizability.
CONCLUSIONS
Computer-delivered counseling had a modest, but significant, positive impact on HIV-1 VL—a primary driver of morbidity and genital compartment infectivity65 10 a reduction that has implications for the person's own health and infectiousness. Their reported ART adherence increased by around 10% (76% at baseline to 85% at 9 months), whereas controls started at 74% mean adherence and showed no improvement over time. The intervention's relatively modest absolute changes were enough to get this vulnerable group into better ranges of medication adherence, as seen by VL impact. The importance of supporting treatment adherence has been highlighted by Gardner and others who have shown how few HIV-positive individuals even in care are virally suppressed in the United States.66 64
As far as we know, this is the first ART adherence and secondary HIV transmission risk intervention to find biological effect (VL) and behavioral impact among PLWH. The computer format was highly acceptable and facilitated delivery in busy settings. Such an approach warrants further evaluation to determine utility in improving HIV treatment outcomes and reducing secondary HIV transmission among PLWH.
ACKNOWLEDGMENTS
The authors acknowledge Jim Larkin and Tycen Hopkins of Resources Online, CARE+ software developers; and J. Dennis Fortenberry, MD, MS and C. Kevin Malotte, DrPH for their contribution to the original CARE platform. The authors appreciate the assistance of Carol Glenn, RN, Robert D. Harrington, MD, and Thomas M. Hooton, MD of the Harborview Medical Center HIV Clinic affiliated with the University of Washington; David Richart and Hal Garcia-Smith of Lifelong AIDS Alliance; and Peter Tarczy-Hornoch, University of Washington Biomedical and Health Informatics. The authors thank Nok Chhun for her expert assistance with article production.
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