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And yet, just this week, a new investigation from Michigan State University found that online dating results in fewer committed relationships than offline dating does --- that it doesn't work, in other words. That, in the words of its own author, contradicts a load of studies that have come before it. In reality, this latest proclamation on the state of contemporary love joins a 2010 study that found more couples meet online than at schools, bars or parties. And a 2012 study that found dating site algorithms are not effective. And a 2013 paper that suggested Internet access is boosting marriage rates. Plus a whole host of doubtful statistics, surveys and case studies from dating giants like eHarmony and , who assert --- insist, even!! --- that online dating works." Adult hookups in Victoria Australia.

AMC, Academic Medical Center; aOR, adjusted odds ratio; CI, confidence interval; CINIMA, Center for Infection and Immunology Amsterdam; DAG, directed acyclic graph; HIV, human immuno deficiency virus; i.e., id est, it's, for example; IQR, interquartile range; MEC, Medical Ethics Committee; MSM, men who have sex with men; OR, odds ratio; RIVM, National Institute of Public Health and the Environment, Centre for Infectious Disease Control; STI, sexually transmitted infection; UAI, unprotected anal intercourse; UMCU, University Medical Center Utrecht

New research should stay up-to-date in regards to fast changing dating approaches as well as sero-adaptive behaviours (such as viral sorting and pre exposure prophylaxis). With every new way of dating and preventative chances, the rules of battles will vary. Our data are 8years old and internet-based dating has developed since then. Nevertheless these results are useful, as they reveal how net-based partner acquisition may lead to more info on the sex partner, and this might impact on the frequency of UAI.

Relationship online may offer other chances for communication on HIV status than dating in physical environments. Easing more on-line HIV status disclosure during partner seeking makes serosorting easier. Yet, serosorting may raise the weight of other STI and will not prevent HIV disease completely. Interventions to prevent HIV transmission should especially be directed at HIV negative and oblivious MSM and spark timely HIV testing (i.e., after risk occasions or when experiencing symptoms of seroconversion illness) as well as regular testing when sexually active.

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Because conclusions on UAI seem to be partly based on sensed HIV concordance, accurate knowledge of one's own and the partner's HIV status is important. In HIV negative men and HIV status-unaware guys, conclusions on UAI will not only be based on perceived HIV status of the partner but in addition on one's own negative status. HIV serosorting is challenged by the frequency of HIV testing and also the HIV window phase during which people can transmit HIV but cannot be diagnosed with the commonly used HIV tests. Therefore serosorting cannot be regarded as an extremely successful way of preventing HIV transmission 22 Besides interventions to stimulate the uptake of HIV and STI testing in sexually active men, interventions to warn against UAI based on sensed HIV-negative concordant status are in order, irrespective of whether this concerns online or offline dating.

For HIV-unaware guys the impact of dating place on UAI did not change by adding partner features, but it increased when adding lifestyle and drug use. It's hard to assess the real risk for HIV for these guys: do they behave as HIV negative guys that are trying to shield themselves from HIV infection, or as HIV-positive guys attempting to protect their HIV-negative partner from HIV infection? A study by Horvath et al. reported that 72% of men who were never tested for HIV, profiled themselves online as being HIV-negative, which might be problematic if they are HIV positive and participate in UAI with HIV-negative partners 12 Previously Matser et al. reported that 1.7% of the oblivious and sensed HIV negative MSM were analyzed HIV-positive. The study population comprised the MSM reported in this study 15

Online dating was not associated with UAI among HIV negative guys, a finding in agreement with some previous studies, mainly among young men 21 , but in comparison with other studies 1 - 5 This may be due to the fact that most earlier studies compared sexual behaviour of two groups of MSM rather than comparing two sexual behavior patterns within one group of guys. Adult Hookups nearest Richmond. Adult hookups in Richmond VIC. Nevertheless it can also reflect secular changes; perhaps in the beginning of online dating a more high-risk group of guys used the Internet, and over time online dating normalized and less high-risk MSM today also make use of the Web for dating.

A key strength of the study was that it explored the relation between online dating and UAI among MSM who had recent sexual contact with both online and also offline casual partners. Adult Hookups Near Me Melbourne Victoria. This averted prejudice caused by potential differences between men just dating online and those only dating offline, a weakness of several previous studies. By recruiting participants at the greatest STI outpatient clinic in the Netherlands we could comprise a high number of MSM, and prevent potential differences in guys sampled through Internet or face-to-face interviewing, weaknesses in some previous studies 3 , 11

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Among HIV-positive guys, in univariate analysis UAI was reported significantly more often with online partners than with offline associates. When adjusting for partner characteristics, the effect of online/offline dating on UAI among HIV-positive MSM became somewhat smaller and became non significant; this indicates that differences in partnership factors between online and also offline partnerships are accountable for the increased UAI in online established ventures. This may be because of a mediating effect of more information on associates, (including perceived HIV status) on UAI, or to other factors. Among HIV-negative men no effect of online dating on UAI was observed, either in univariate or in some of the multivariate models. Among HIV-unaware guys, online dating was associated with UAI but just important when adding associate and partnership variants to the model.

In this large study among MSM attending the STI clinic in Amsterdam, we found no evidence that online dating was independently associated with a higher danger of UAI than offline dating. For HIV negative men this dearth of assocation was clear (aOR = 0.94 95 % CI 0.59-1.48); among HIV-positive men there was a non significant association between online dating and UAI (aOR = 1.62 95 % CI 0.96-2.72). Only among guys who indicated they were not aware of their HIV status (a little group in this study), UAI was more common with on-line than offline partners.

The amount of sex partners in the preceding 6months of the index was also associated with UAI (OR = 6.79 95 % CI 2.86-16.13 for those with 50 or more recent sex partners compared to those with fewer than 5 recent sex partners). UAI was significantly more likely if more sex acts had happened in the partnership (OR = 16.29 95 % CI 7.07-37.52 for >10 sex acts within the venture compared to only one sex act). Other factors significantly associated with UAI were group sex within the partnership, and sex-related multiple drug use within venture.

In multivariate model 3 (Tables 4 and 5 ), additionally including variants concerning sexual behaviour in the venture (sex-related multiple drug use, sex frequency and partner type), the separate effect of online dating location on UAI became somewhat more powerful (though not significant) for the HIV positive men (aOR = 1.62 95 % CI; 0.96-2.72), but remained similar for HIV negative guys (aOR = 0.94 95 % CI 0.59-1.48). Adult Hookups nearest Richmond VIC. The effect of online dating on UAI became stronger (and significant) for HIV-unaware guys (aOR = 2.55 95 % CI 1.11-5.86) (Table 5 ).

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In univariate analysis, UAI was significantly more prone to happen in on-line than in offline ventures (OR = 1.36 95 % CI 1.03-1.81) (Table 4 ). The self-perceived HIV status of the participant was strongly associated with UAI (OR = 11.70 95 % CI 7.40-18.45). The result of dating location on UAI differed by HIV status, as can be seen best in Table 5 Table 5 shows the association of online dating using three different reference classes, one for each HIV status. Among HIV positive men, UAI was more common in online when compared with offline ventures (OR = 1.61 95 % CI 1.03-2.50). Among HIV-negative guys no association was evident between UAI and internet partnerships (OR = 1.07 95 % CI 0.71-1.62). Among HIV-unaware men, UAI was more common in online in comparison to offline ventures, though not statistically significant (OR = 1.65 95 % CI 0.79-3.44).

Features of online and offline partners and ventures are shown in Table 2 The median age of the partners was 34years (IQR 28-40). Compared to offline partners, more online partners were Dutch (61.3% vs. 54.0%; P 0.001) and were defined as a known partner (77.7% vs. 54.4%; P 0.001). The HIV status of online partners was more often reported as known (61.4% vs. 49.4%; P 0.001), and in on-line partnerships, perceived HIV concordance was higher (49.0% vs. 39.8%; P 0.001). Participants reported that their on-line partners more frequently knew the HIV status of the participant than offline partners (38.8% vs. 27.2%; P 0.001). Participants more often reported multiple sexual contacts with internet partners (50.9% vs. 41.3%; P 0.001). Sex-associated material use, alcohol use, and group sex were less frequently reported with online partners.

To be able to examine the potential mediating effect of more information on partners (including perceived HIV status) on UAI, we developed three variant models. In model 1, we adjusted the organization between online/offline dating place and UAI for features of the participant: age, ethnicity, number of sex partners in the preceding 6months, and self-perceived HIV status. In model 2 we added the venture characteristics (age difference, ethnic concordance, lifestyle concordance, and HIV concordance). In model 3, we adapted additionally for partnership sexual risk behaviour (i.e., sex-related drug use and sex frequency) and partnership sort (i.e., casual or anonymous). As we assumed a differential effect of dating place for HIV-positive, HIV negative and HIV status unknown MSM, an interaction between HIV status of the participant and dating place was contained in all three models by making a new six-category variable. For clarity, the effects of online/offline dating on UAI are also presented separately for HIV-negative, HIV-positive, and HIV-unaware men. We performed a sensitivity analysis restricted to partnerships in which only one sexual contact occurred. Statistical significance was defined as P 0.05. No adjustments for multiple comparisons were made, in order not to lose potentially important organizations. As a fairly large number of statistical tests were done and reported, this approach does lead to an increased risk of one or more false-positive organizations. Evaluations were done utilizing the statistical programme STATA, version 13 (STATA Intercooled, College Station, TX, USA).

Before the investigations we developed a directed acyclic graph (DAG) representing a causal model of UAI. In this model some variants were putative causes (self-reported HIV status; online partner acquisition), others were considered as confounders (participants' age, participants' ethnicity, and no. of male sex partners in preceding 6months), and some were presumed to be on the causal pathway between the principal exposure of interest and results (age difference between participant and partner; ethnic concordance; concordance in life styles; HIV concordance; partnership kind; sex frequency within venture; group sex with partner; sex-related material use in partnership).

We compared characteristics of participants by self-reported HIV status (using 2-tests for dichotomous and categorical variables and using rank sum test for continuous variables). We compared features of participants, partners, and partnership sexual behaviour by on-line or offline partnership, and calculated P values based on logistic regression with robust standard errors, accounting for related data. Continuous variables (i.e., age, amount of sex partners) are reported as medians with an interquartile range (IQR), and were categorised for inclusion in multivariate models. Random effects logistic regression models were used to analyze the association between dating location (online versus offline) and UAI. Odds ratio tests were used to evaluate the significance of a variable in a model.

As a way to explore possible disclosure of HIV status we also asked the participant whether the casual sex partner understood the HIV status of the participant, with the response options: (1) no, (2) possibly, (3) yes. Sexual behavior with each partner was dichotomised as: (1) no anal intercourse or only shielded anal intercourse, and (2) unprotected anal intercourse. To ascertain the subculture, we asked whether the participant characterised himself or his partners as belonging to one or more of the following subcultures/lifestyles: casual, formal, substitute, drag, leather, military, sports, fashionable, punk/skinhead, rubber/lycra, gothic, bear, jeans, skater, or, if not one of these features were applicable, other. Adult Hookups Near Me Blackburn Victoria. Concordant lifestyle was categorised as: (1) concordant; (2) discordant. Adult Hookups near Richmond. Chance partner sort was categorised by the participants into (1) known traceable and (2) anonymous partners.