Evidence map›Paper›PMID 33847597›Full record

Trial reportJMIR mHealth and uHealth2021

Mobile Phone Intervention Based on an HIV Risk Prediction Tool for HIV Prevention Among Men Who Have Sex With Men in China: Randomized Controlled Trial.

Ke Yun, Zhenxing Chu, Jing Zhang, Wenqing Geng, Yongjun Jiang, Willa Dong, Hong Shang, Junjie Xu

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in JMIR mHealth and uHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 3 pooled it
1.2field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 3 syntheses or guidelines pooled it, 24 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 2 countries.

Ke YunNHC Key Laboratory of AIDS Immunology (China Medical University), National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, China.ORCID 0000-0003-4030-4490
Zhenxing ChuNHC Key Laboratory of AIDS Immunology (China Medical University), National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, China.ORCID 0000-0002-6398-2107
Jing ZhangNHC Key Laboratory of AIDS Immunology (China Medical University), National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, China.ORCID 0000-0002-1759-1041
Wenqing GengNHC Key Laboratory of AIDS Immunology (China Medical University), National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, China.ORCID 0000-0002-7008-9974
Yongjun JiangNHC Key Laboratory of AIDS Immunology (China Medical University), National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, China.ORCID 0000-0003-0166-7846
Willa DongDepartment of Health Behavior, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.ORCID 0000-0001-5614-6293
Hong Shang *NHC Key Laboratory of AIDS Immunology (China Medical University), National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, China.ORCID 0000-0001-5333-8943
Junjie Xu *NHC Key Laboratory of AIDS Immunology (China Medical University), National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, China.ORCID 0000-0003-4303-7295
Chinese Academy of Medical Sciences & Peking Union Medical College · CNUniversity of North Carolina at Chapel Hill · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundeHealth interventions based on risk stratification have not been extensively applied for HIV behavioral interventions among HIV-negative men who have sex with men (MSM).

objectiveThis study aimed to evaluate the efficacy of a mobile phone intervention based on an HIV risk prediction tool in promoting HIV testing and reducing high-risk behavior among HIV-negative MSM in China.

methodsWe performed a mobile phone-based randomized controlled clinical trial for 12 weeks. A comprehensive intervention package deployed on Jinshuju-an online survey platform-was developed and consisted of 4 components: (1) a validated HIV risk prediction tool that provides information on personalized risk reduction interventions; (2) a map of individualized HIV testing facilities based on their geographic location; (3) a QR code for free resources on HIV prevention, including condoms and HIV self-testing kits; and (4) general resources for HIV health education. MSM participants recruited from WeChat/QQ groups were randomly assigned to the intervention or control group at a 1:1 ratio. The staff sent the QR code for the comprehensive intervention package to MSM in the intervention group over WeChat and sent the QR code only for the resources on HIV health education to those in the control group. At baseline and 12-week follow-up, data on HIV-related risk behavior and HIV testing behavior were collected through the Jinshuju online survey platform.

resultsIn total, 192 MSM were recruited and assigned to the intervention or control group (n=96 each). At week 12, the total clinical trial retention rate was 87.5%. The number of male sexual partners of the MSM in the past 3 months was significantly lower in the intervention group than in the control group (3.51, SD 4.1 vs 6.01, SD 11.4, respectively; mean difference -2.5; 95% CI -5.12 to 0.12; P=.05); the rate of condom use with casual sexual partners was higher in the intervention group than in the control group (87%, n=66/76 vs 70%, n=54/77 respectively; odds ratio 2.81, 95% CI 1.23-6.39; P=.01). The proportion of individuals intending to undergo HIV testing after in the following 30 days was marginally higher in the intervention group than in the control group (90%, n=77/86 vs 79%, n=65/82 respectively; odds ratio 2.20, 95% CI 0.90-5.35; P=.07). The incremental cost-effectiveness ratio of eHealth intervention was US $131.60 on reducing 1 sexual partner and US $19.70 for a 1% increment in condom usage with casual partners.

conclusionsA comprehensive intervention based on an HIV risk prediction tool can reduce the number of male sexual partners among MSM and increase the rate of condom use with casual partners. Hence, this intervention is a very promising preventive strategy for HIV among MSM, especially in areas with a prominent HIV epidemic.

trial registrationChinese Clinical Trial Registry ChiCTR1800017268; http://www.chictr.org.cn/showprojen.aspx?proj=29271.

Indexed as

Cell PhoneHIV InfectionsSexual and Gender MinoritiesChinaHomosexuality, MaleHumansMaleeHealth interventionhigh-risk behavior interventionHIV risk predictionmen who have sex with men

Identifiers

PMID33847597
PMCPMC8080142
OpenAlexW3134221822

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.