Evidence map›Paper›PMID 34941560›Full record

ArticleJournal of medical Internet research2021

Condomless Anal Sex Associated With Heterogeneous Profiles Of HIV Pre-Exposure Prophylaxis Use and Sexual Activities Among Men Who Have Sex With Men: A Latent Class Analysis Using Sex Diary Data on a Mobile App.

Yi-Fang Yu, Huei-Jiuan Wu, Stephane Wen-Wei Ku, Po-Hsien Huang, Chia-Wen Li, Poyao Huang, Carol Strong

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 41% 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

4 citing papers in PubMed, 5 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

7 authors at 5 institutions in 2 countries.

Yi-Fang YuDepartment of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan.ORCID 0000-0002-8284-6880
Huei-Jiuan WuThe Kirby Institute, University of New South Wales, Sydney, Australia.ORCID 0000-0002-5085-1976
Stephane Wen-Wei KuDivision of Infectious Diseases, Department of Medicine, Taipei City Hospital Renai Branch, Taipei, Taiwan.ORCID 0000-0001-7399-5852
Po-Hsien HuangDepartment of Psychology, National Chengchi University, Taipei, Taiwan.ORCID 0000-0002-4820-1245
Chia-Wen LiCenter for Infection Control and Department of Internal Medicine, National Cheng Kung University Hospital, Tainan, Taiwan.ORCID 0000-0002-4834-5935
Poyao HuangInstitute of Health Behavior and Community Sciences, National Taiwan University, Taipei, Taiwan.ORCID 0000-0002-3319-5145
Carol StrongDepartment of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan.ORCID 0000-0002-2934-5382
National Cheng Kung University · TWNational Taiwan University · TWNational Cheng Kung University Hospital · TWTaipei City Hospital · TWUNSW Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNew innovative technologies, such as mobile apps, have been developed to increase pre-exposure prophylaxis (PrEP) adherence and the use of log sex diaries. The contiguity of mobile apps reduces the recall bias that generally affects reported condom and PrEP use. However, none of the currently used mobile apps were designed for event-driven PrEP users, and few studies have demonstrated the potential usage of sex diary data to facilitate the understanding of the different HIV risks among heterogeneous profiles of sex diaries and PrEP use.

objectiveWe aim to discriminate the heterogeneous profiles of sex events and PrEP use and examine the risk of condomless anal sex among different types of sex events.

methodsWe recruited 35 adult men who have sex with men from two medical centers in Taiwan since May 2020 and followed up for four months. Participants were on PrEP or willing to take PrEP. They were asked to log their sex events, PrEP use, and dosing regimens on a mobile app to improve their PrEP adherence. Latent class analysis was used to distinguish profiles of sex events and PrEP use. Indicators included correct intake of PrEP for each sex event, participants' sexual positioning, partner's HIV status, and age.

resultsA total of 551 sex events were classified into three classes by latent class analysis: PrEP nonadherent flip-flopping (234/551, 42%), PrEP imperfect-adherent power bottoming (284/551, 52%), and PrEP adherent serodiscordant topping (33/551, 6%). "PrEP nonadherent flip-flopping" sex events were more likely to involve condomless anal sex than "PrEP imperfect-adherent power bottoming" (OR 1.83, 95% CI 1.03-3.25) after considering random intercepts for individuals, and this class needed to increase their PrEP adherence and use of condoms. "PrEP imperfect-adherent power bottoming" realized their own risk and packaged PrEP with condoms to protect themselves. Up to 99% (32/33) of sex events in "PrEP adherent serodiscordant topping" were protected by PrEP, but all of the sex events in this group were condomless.

conclusionsUsing the sex diary data could advance the capacity to identify high-risk groups. HIV prevention strategy should be more flexible and combine PrEP with condom use for future HIV prevention.

Indexed as

Anti-HIV AgentsHIV InfectionsMobile ApplicationsPre-Exposure ProphylaxisSexual and Gender MinoritiesAdultHomosexuality, MaleHumansLatent Class AnalysisMaleSexual BehaviorAnti-HIV AgentsadherenceappcondomdiaryHIVlatent class analysismen who have sex with menmobile appsMSMpre-exposure prophylaxisPrEPpreventionrisksexsex diariessexual healthTaiwan

Identifiers

PMID34941560
PMCPMC8738983
OpenAlexW4200170477

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.