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].
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).
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, http://links.lww.com/QAI/A504). Detectable VL at baseline was a modifier of study arm effects on log10 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, http://links.lww.com/QAI/A504).
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).
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.
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 Another study assessing 26 HIV clinics across the United States found that providers reported delivering prevention-with-positives counseling at 67% of initial visits but only 53% of subsequent regular visits.38 Computerized counseling may lack some advantages offered by a highly skilled human counselor, but it is delivered consistently with fidelity,39 without need for staff time or training. In our study, it proved highly acceptable and had an efficacious impact on priority behaviors and objective measures of VL response.
Economic evaluation models have found that adherence interventions with modest effectiveness may provide survival benefit to patients and be cost effective.59 The intervention we tested that does not require staff time, training, and monitoring may be easier to introduce into busy practice settings.
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 The potential for detecting intervention effect, and magnitude of effect, may be greater in populations with lower ART adherence or higher sexual risk at baseline.61 The study was well powered to detect a clinically meaningful intervention effect, that is, a mean half-log10 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 and the VL differences seen is consistent with differences in reported adherence behavior. In previous work with the CARE platform, users reported that it was easier to be honest with the computer and that the session allowed them to reflect on recent risk behaviors.63 Self-reports of sexual transmission risk outcomes and of ART adherence are limitations. Follow-up of 9 months did not allow evaluation of longer-term impact or effect duration. Though the study was conducted in a period when there were fewer ART regimens available, inconsistent ART adherence even to current simplified regimens continues to be a major challenge.64
The study was strengthened by including community- and clinic-based samples, which had similar intervention effects, increasing generalizability.
Computer-delivered counseling had a modest, but significant, positive impact on HIV-1 VL—a primary driver of morbidity and genital compartment infectivity65—and on self-reported HIV transmission risks. This was particularly the case for those who had nonsuppression of VL at baseline—precisely the highest-need group in whom an intervention can have impact. This group's average VL at baseline declined by a clinically meaningful reduction of approximately 0.5 log10 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 Interventions to support adherence have tended to show relatively small effects, highlighting the need for efficacious interventions that can be implemented without straining health system resources,64 as is the promise of computerized counseling tools such as CARE+.
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.
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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