Geosocial Networking App Use Associated With Sexual Risk Behavior and Pre-exposure Prophylaxis Use Among Gay, Bisexual, and Other Men Who Have Sex With Men: Cross-sectional Web-Based Survey
Bibliographic record
Abstract
BACKGROUND: In the United States, geosocial networking (GSN) apps (ie, mobile dating apps) have become central to dating and sexual interactions in recent years. Among gay, bisexual, and other men who have sex with men (GBM), these apps play an important role in reducing barriers and facilitating partner seeking. However, despite these benefits, there are concerns that these apps may facilitate risky sexual behavior and transmission of sexually transmitted infections (STIs) among GBM. OBJECTIVE: This study aimed to examine the association between GSN app use and sexual risk in a US sample of GBM. METHODS: Using a cross-sectional design, respondents (N=223) completed a web-based survey assessing their use of GSN apps, sexual risk and protective behaviors, HIV serostatus, and previous STI diagnoses. RESULTS: Respondents were aged 21-78 (mean 31.90, SD 10.06) years and 69.5% (155/223) were non-Hispanic White. The sample included respondents from 40 states and the District of Columbia. Nearly half (104/223, 47%) of the participants reported using GSN apps. GSN users were more likely to report past-year condomless anal intercourse (P<.001), 3 or more sexual partners in the previous year (P<.001), and a previous STI diagnosis (P=.001) than nonusers. GSN users also reported more frequent use of recreational drugs before sex (P=.001), alcohol use before sex (P<.001), and cannabis use before sex (P=.01). Interestingly, GSN users were also more likely to report having ever taken an HIV test (P<.001) and using pre-exposure prophylaxis (P=.03). The rates of HIV seropositivity did not differ significantly between GSN users and nonusers (P=.53). Among the subset of GSN users, 38 participants reported using only GBM-specific GSN apps (eg, Grindr), whereas 27 participants reported using only sexuality nonspecific GSN apps (eg, Tinder). Exclusive users of GBM-specific apps reported more frequent recreational drug use before sex (P=.01) and were also more likely to report past-year condomless anal intercourse (P<.001), 3 or more sexual partners in the previous year (P=.004), a previous STI diagnosis (P=.002), and HIV testing (P=.003). Alcohol use before sex, cannabis use before sex, pre-exposure prophylaxis use, and HIV rates were similar between both groups (P>.11). CONCLUSIONS: The findings suggest that GSN apps may be a useful pathway for interventions aimed at reducing STI risk in GBM. Future prospective studies should examine how risk levels change after the initiation of GSN app use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".