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And yet, just this week, a brand new investigation from Michigan State University found that online dating leads to 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 heap of studies that have come before it. Actually, this latest proclamation on the state of contemporary love joins a 2010 study that found more couples meet online than at schools, taverns or parties. Free Sex Dating in Granada. And a 2012 study that found dating site algorithms are not effective. And a 2013 paper that implied Internet access is boosting marriage speeds. Plus an entire slew of doubtful data, surveys and case studies from dating giants like eHarmony and , who assert --- insist, even!! --- that online dating works."

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 remain up-to-date as it pertains to accelerated changing dating approaches and sero-adaptive behaviours (such as viral sorting and pre exposure prophylaxis). With each new way of dating and preventative chances, the rules of battles will change. Our data are 8years old and internet-based dating has developed since then. Nevertheless these results are useful, as they show how internet-based partner acquisition can result in more information on the sex partner, and this might influence on the frequency of UAI.

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Dating online may offer other opportunities for communicating on HIV status than dating in physical surroundings. Easing more on-line HIV status disclosure during partner seeking makes serosorting simpler. 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 unaware MSM and arouse timely HIV testing (i.e., after risk events or when experiencing symptoms of seroconversion illness) as well as routine testing when sexually active.

Because decisions on UAI seem to be partially based on sensed HIV concordance, precise knowledge of one's own and the partner's HIV status is important. In HIV negative men and HIV status-unaware men, decisions 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 the HIV window phase during which people can transmit HIV but cannot be diagnosed with the commonly used HIV tests. So serosorting cannot be regarded as a very powerful method of averting 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 effect of dating place on UAI did not change by adding partner characteristics, but it improved when adding lifestyle and drug use. It's hard to assess the real risk for HIV for these men: do they act as HIV negative guys who are trying to shield themselves from HIV infection, or as HIV positive men 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 debatable if they are HIV positive and participate in UAI with HIV negative partners 12 Formerly Matser et al. reported that 1.7% of the unaware and sensed HIV negative MSM were examined HIV-positive. The study population comprised the MSM reported in this study 15

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Online dating was not correlated with UAI among HIV-negative men, a finding in agreement with some previous studies, mostly among young men 21 , but in comparison with other studies 1 - 5 This may be because of the reality that most earlier studies compared sexual behaviour of two groups of MSM rather than comparing two sexual behaviour patterns within one group of guys. Granada Alberta Free Sex Dating. However it could also reflect secular changes; maybe 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 utilize the Net for dating.

Free sex dating nearby Granada, Alberta. A key strength of this study was that it investigated the relation between online dating and UAI among MSM who had recent sexual contact with both online and offline casual partners. This averted bias due to potential differences between men just dating online and those simply dating offline, a weakness of numerous previous studies. Free sex dating near Granada Alberta Canada. By recruiting participants at the greatest STI outpatient clinic in the Netherlands we could include a lot of MSM, and avoid potential differences in men sampled through Internet or face to face interviewing, weaknesses in certain previous studies 3 , 11

Among HIV positive men, in univariate analysis UAI was reported significantly more often with online partners than with offline associates. Free sex dating near me Granada Alberta. When correcting for associate features, the effect of online/offline dating on UAI among HIV-positive MSM became somewhat smaller and became non-significant; this implies that differences in partnership factors between online and offline partnerships are accountable for the increased UAI in online established ventures. This may be due to a mediating effect of more information on partners, (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 the multivariate models. Among HIV-unaware men, online dating was connected with UAI but only essential when adding associate and venture variants to the model.

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

The number of sex partners in the preceding 6months of the index was also connected 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 occurred in the partnership (OR = 16.29 95 % CI 7.07-37.52 for >10 sex acts within the partnership compared to only one sex act). Other variables significantly associated with UAI were group sex within the partnership, and sex-related multiple drug use within partnership.

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

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In univariate analysis, UAI was significantly more inclined to occur in online than in offline partnerships (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 impact of dating place on UAI differed by HIV status, as can be seen best in Table 5 Table 5 shows the organization of online dating using three distinct reference types, one for each HIV status. Among HIV-positive guys, UAI was more common in online in comparison to 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 when compared with offline partnerships, though not statistically significant (OR = 1.65 95 % CI 0.79-3.44).

Features of online and offline partners and partnerships are shown in Table 2 The median age of the partners was 34years (IQR 28-40). Free sex dating near Granada. 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 frequently reported as understood (61.4% vs. 49.4%; P 0.001), and in on-line ventures, perceived HIV concordance was higher (49.0% vs. 39.8%; P 0.001). Participants reported that their online partners more often knew the HIV status of the participant than offline partners (38.8% vs. 27.2%; P 0.001). Participants more frequently reported multiple sexual contacts with online partners (50.9% vs. 41.3%; P 0.001). Sex-related substance use, alcohol use, and group sex were less frequently reported with online partners.

To be able to examine the possible mediating effect of more information on partners (including perceived HIV status) on UAI, we developed three variant models. In version 1, we adjusted the organization between online/offline dating place and UAI for characteristics of the participant: age, ethnicity, number of sex partners in the preceding 6months, and self-perceived HIV status. In model 2 we added the partnership features (age difference, ethnic concordance, lifestyle concordance, and HIV concordance). In version 3, we adjusted additionally for venture sexual risk behavior (i.e., sex-associated drug use and sex frequency) and venture sort (i.e., casual or anonymous). Granada Free Sex Dating. 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. Free sex dating in Alberta. For clarity, the effects of online/offline dating on UAI are also presented separately for HIV-negative, HIV-positive, and HIV-unaware guys. We performed a sensitivity analysis confined 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 miss potentially important associations. As a rather big number of statistical tests were done and reported, this strategy does lead to an elevated risk of one or more false positive organizations. Analyses were done using the statistical programme STATA, version 13 (STATA Intercooled, College Station, TX, USA).

Before the evaluations 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; on-line partner acquisition), others were considered as confounders (participants' age, participants' ethnicity, and no. of male sex partners in preceding 6months), and some were assumed to be on the causal pathway between the principal exposure of interest and result (age difference between participant and partner; ethnic concordance; concordance in life styles; HIV concordance; partnership type; sex frequency within venture; group sex with partner; sex-related substance use in partnership).

We compared characteristics of participants by self-reported HIV status (using 2-evaluations for dichotomous and categorical variables and using rank sum test for continuous variables). We compared characteristics of participants, partners, and venture sexual conduct by online or offline partnership, and computed 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 measure the importance of a variable in a model.

In order to investigate possible disclosure of HIV status we additionally asked the participant whether the casual sex partner understood the HIV status of the participant, together with the answer choices: (1) no, (2) possibly, (3) yes. Free Sex Dating closest to Alberta. Sexual behaviour with each partner was dichotomised as: (1) no anal intercourse or merely shielded anal intercourse, and (2) unprotected anal intercourse. To determine the subculture, we asked whether the participant characterised himself or his partners as belonging to one or more of the subsequent subcultures/lifestyles: casual, formal, alternate, drag, leather, military, sports, trendy, punk/skinhead, rubber/lycra, gothic, bear, jeans, skater, or, if none of these characteristics were appropriate, other. Concordant lifestyle was categorised as: (1) concordant; (2) discordant. Accidental partner sort was categorised by the participants into (1) known traceable and (2) anonymous partners.

HIV status of the participant was obtained by asking the question 'Do you know whether you're HIV infected?', with five answer alternatives: (1) I 'm certainly not HIV-infected; (2) I believe that I'm not HIV-contaminated; (3) I do not know; (4) I believe I may be HIV-infected; (5) I know for sure that I am HIV-infected. Free sex dating in Granada, Canada. We categorised this into HIV negative (1,2), unknown (3), and HIV-positive (4,5) status. The survey enquired about the HIV status of each sex partner with all the question: 'Do you understand whether this partner is HIV-contaminated?' with similar reply options as above. Perceived concordance in HIV status within ventures was categorised as; (1) concordant; (2) discordant; (3) unknown. The final class represents all partnerships where the participant didn't understand his own status, or the status of his partner, or both. In this study the HIV status of the participant is self-reported and self-perceived. The HIV status of the sexual partner is as perceived by the participant.

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