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Background Following the successful scale-up of antiretroviral therapy (ART), the focus is now on ensuring good quality of life (QoL) and sustained viral suppression in people living with HIV. The access to mobile technology in the most burdened countries is increasing rapidly, and therefore, mobile health (mHealth) technologies could be leveraged to improve QoL in people living with HIV. However, data on the impact of mHealth tools on the QoL in people living with HIV are limited to the evaluation of SMS text messaging; these are infeasible in high-illiteracy settings. Objective The primary and secondary outcomes were to determine the impact of interactive voice response (IVR) technology on Medical Outcomes Study HIV QoL scores and viral suppression at 12 months, respectively. Methods Within the Call for Life study, ART-experienced and ART-naïve people living with HIV commencing ART were randomized (1:1 ratio) to the control (no IVR support) or intervention arm (daily adherence and pre-appointment reminders, health information tips, and option to report symptoms). The software evaluated was Call for Life Uganda, an IVR technology that is based on the Mobile Technology for Community Health open-source software. Eligibility criteria for participation included access to a phone, fluency in local languages, and provision of consent. The differences in differences (DIDs) were computed, adjusting for baseline HIV RNA and CD4. Results Overall, 600 participants (413 female, 68.8%) were enrolled and followed-up for 12 months. In the intervention arm of 300 participants, 298 (99.3%) opted for IVR and 2 (0.7%) chose SMS text messaging as the mode of receiving reminders and health tips. At 12 months, there was no overall difference in the QoL between the intervention and control arms (DID=0.0; P=.99) or HIV RNA (DID=0.01; P=.94). At 12 months, 124 of the 256 (48.4%) active participants had picked up at least 50% of the calls. In the active intervention participants, high users (received >75% of reminders) had overall higher QoL compared to low users (received <25% of reminders) (92.2 versus 87.8, P=.02). Similarly, high users also had higher QoL scores in the mental health domain (93.1 versus 86.8, P=.008) and better appointment keeping. Similarly, participants with moderate use (51%-75%) had better viral suppression at 12 months (80/94, 85% versus 11/19, 58%, P=.006). Conclusions Overall, there was high uptake and acceptability of the IVR tool. While we found no overall difference in the QoL and viral suppression between study arms, people living with HIV with higher usage of the tool showed greater improvements in QoL, viral suppression, and appointment keeping. With the declining resources available to HIV programs and the increasing number of people living with HIV accessing ART, IVR technology could be used to support patient care. The tool may be helpful in situations where physical consultations are infeasible, including the current COVID epidemic. Trial Registration ClinicalTrials.gov NCT02953080; https://clinicaltrials.gov/ct2/show/NCT02953080

Introduction Cardiovascular disease (CVD) is the leading cause of death globally, representing 31% of all global deaths. HIV and long term anti-retroviral therapy (ART) are risk factors for development of CVD in populations of people living with HIV (PLHIV). CVD risk assessment tools are currently being applied to SSA populations, but there are questions about accuracy as well as implementation challenges of these tools in lower resource setting populations. We aimed to assess the level of agreement between the various cardiovascular screening tools (Data collection on Adverse effects of anti-HIV Drugs (D:A:D), Framingham risk score, WHO risk score and The Atherosclerotic Cardiovascular Disease Score) when applied to an HIV ART experienced population in Sub-Saharan Africa. Methods This study was undertaken in an Anti-Retroviral Long Term (ALT) Cohort of 1000 PLHIV in care who have been on ART for at least 10 years in urban Uganda. A systematic review was undertaken to find the most frequently used screening tools from SSA PLHIV populations; these were applied to the ALT cohort. Levels of agreement between the resulting scores (those including lipids and non-lipids based, as well as HIV-specific and non-HIV specific) as applied to our cohort were compared. Prevalence Bias Adjusted Kappa was used to evaluate agreement between tools. Results Overall, PLHIV in ALT cohort had a median score of 1.1–1.4% risk of a CVD event over 5 years and 1.7–2.5% risk of a CVD event over 10 years. There was no statistical difference in the risk scores obtained for this population when comparing the different tools, including comparisons of those with lipids and non-lipids, and HIV specific vs non-HIV specific. Conclusion The various tools yielded similar results, but those not including lipids are more feasible to apply in our setting. Long-term cohorts of PLHIV in SSA should in future provide longitudinal data to evaluate existing CVD risk prediction tools for these populations. Inclusion of HIV and ART history factors to existing scoring systems may improve accuracy without adding the expense and technical difficulty of lipid testing.

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