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Shi H, Du J, Jin G, Yang H, Guo H, Yuan G, Zhu Z, Xu W, Wang S, Guo H, Jiang K, Hao J, Sun Y, Su P, Zhang Z. Effectiveness of eHealth interventions for HIV prevention, testing and management: An umbrella review. Int J STD AIDS 2024; 35:752-774. [PMID: 38733263 DOI: 10.1177/09564624241252457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2024]
Abstract
BACKGROUND Human immunodeficiency virus (HIV) infection has become a major contributor to the global burden of disease. Globally, the number of cases of HIV continues to increase. Electronic health (eHealth) interventions have emerged as promising tools to support disease self-management among people living with HIV. The purpose of this umbrella review is to systematically evaluate and summarize the evidence and results of published systematic reviews and meta-analyses on the effectiveness of eHealth interventions for HIV prevention, testing and management. METHODS PubMed, Embase and the Cochrane Library were searched for reviews. The methodological quality of the included studies was assessed using AMSTAR-2. RESULTS A total of 22 systematic reviews were included. The methodological quality of the reviews was low or critically low. EHealth interventions range from Internet, computer, or mobile interventions to websites, programs, applications, email, video, games, telemedicine, texting, and social media, or a combination of them. The majority of the reviews showed evidence of effectiveness (including increased participation in HIV management behaviours, successfully changed HIV testing behaviours, and reduced risk behaviours). EHealth interventions were effective in the short term. CONCLUSIONS Ehealth interventions have the potential to improve HIV prevention, HIV testing and disease management. Due to the limitations of the low methodological quality of the currently available systematic reviews, more high-quality evidence is needed to develop clear and robust recommendations.
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Affiliation(s)
- Haiyan Shi
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Jun Du
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Guifang Jin
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Huayu Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Haiyun Guo
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Guojing Yuan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Zhihui Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Wenzhuo Xu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Sainan Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Hao Guo
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Kele Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Jiahu Hao
- Department of Maternal, Child & Adolescent Health, School of Public Health, Anhui Medical University, Hefei, China
| | - Ying Sun
- Department of Maternal, Child & Adolescent Health, School of Public Health, Anhui Medical University, Hefei, China
| | - Puyu Su
- Department of Maternal, Child & Adolescent Health, School of Public Health, Anhui Medical University, Hefei, China
| | - Zhihua Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
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Assessment of a viral load result-triggered automated differentiated service delivery model for people taking ART in Lesotho (the VITAL study): Study protocol of a cluster-randomized trial. PLoS One 2022; 17:e0268100. [PMID: 35511950 PMCID: PMC9071137 DOI: 10.1371/journal.pone.0268100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 03/29/2022] [Indexed: 01/19/2023] Open
Abstract
INTRODUCTION To sustainably provide good quality care to increasing numbers of people living with HIV (PLHIV) receiving antiretroviral therapy (ART) in resource-limited settings, care delivery must shift from a "one-size-fits-all" approach to differentiated service delivery models. Such models should reallocate resources from PLHIV who are doing well to groups of PLHIV who may need more attention, such as those with treatment failure. The VIral load Triggered ART care Lesotho (VITAL) trial assesses a viral load (VL)-, participant's preference-informed, electronic health (eHealth)-supported, automated differentiated service delivery model (VITAL model). With VITAL, we aim to assess if the VITAL model is at least non-inferior to the standard of care in the proportion of participants engaged in care with viral suppression at 24 months follow-up and if it is cost-saving. METHODS The VITAL trial is a pragmatic, multicenter, cluster-randomized, non-blinded, non-inferiority trial with 1:1 allocation conducted at 18 nurse-led, rural health facilities in two districts of northern Lesotho, enrolling adult PLHIV taking ART. In intervention clinics, providers are trained to implement the VITAL model and are guided by a clinical decision support tool, the VITALapp. VITAL differentiates care according to VL results, clinical characteristics, sub-population and participants' and health care providers' preferences. EXPECTED OUTCOMES Evidence on the effect of differentiated service delivery for PLHIV on treatment outcomes is still limited. This pragmatic cluster-randomized trial will assess if the VITAL model is at least non-inferior to the standard of care and if it is cost saving. TRIAL REGISTRATION The study has been registered with clinicaltrials.gov (Registration number NCT04527874; August 27, 2020).
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