Use of the Internet and Mobile-Based “Apps” for Sex-Seeking Among Men Who Have Sex With Men in New York City

Abstract

The Internet continues to be a popular venue for men who have sex with men (MSM) to seek sexual partners. Increased sexual risk behavior has been linked to MSM who use the Internet to seek partners. However, there has been little research on how new mobile-based social networking applications (“apps”) may affect sex-seeking and sexual risk behaviors of MSM. One hundred twenty-six MSM in New York City were recruited to complete an online survey on the use of the Internet and other technology for sex-seeking. The authors collected data on variables of interest including sexual sensation seeking, gay identity affirmation, internalized homophobia, Internet/app use, and sexual behavior with men met via the Internet and mobile apps. Results indicated that men who use both the Internet and mobile apps to seek partners were younger and reported higher incomes (p < .05) than men who used the Internet only. There were no significant differences between the groups with regard to race, sexual sensation seeking, gay identity affirmation, or internalized homophobia, indicating that the use of mobile-based apps for sex-seeking may be simply an example of evolving technologies. These findings may provide insight for the feasibility and planning of effective mobile app-based HIV/STI programs for MSM.

Keywords

MSM mobile apps HIV technology

Introduction

In the United States, men who have sex with men (MSM) are disproportionally at greater risk for HIV transmission than other men. Specifically, young men who have sex with men (YMSM) ages 13 to 24 years account for more than one quarter of all new infections in the United States and more than two thirds of new infections among all 13- to 29-year-olds (Centers for Disease Control and Prevention [CDC], 2011). Although MSM represent a relatively small subset of the U.S. population, accounting for approximately 2% of people, they comprised the majority of all new HIV infections annually from 2006 to 2009 (CDC, 2011) and accounted for 61% of all new U.S. HIV infections in 2009.

The HIV epidemic is pervasive in New York City (NYC). With the largest population of MSM in the United States, approximately 5% of all sexually active men in NYC are MSM and accounted for nearly 43% of all new HIV diagnoses in 2009 (New York City Department of Health and Mental Hygiene [NYCDOHMH], 2012a). Furthermore, NYC YMSM (men under 30) are disproportionately affected by HIV, and in 2011, 80.9% of new HIV diagnoses among males were in young MSM aged 20 to 29 years (NYCDOHMH, 2012b). Nationally, although the largest share of newly diagnosed MSM under age 30 is Caucasian, almost 80% of newly diagnosed YMSM in NYC are African American or Hispanic (NYCDOHMH, 2012a).

Internet Use and Sex-Seeking Among MSM

MSM were early adopters of using the Internet to meet sexual partners (Liau, Millett, & Marks, 2006; Rosser et al., 2011). The literature indicates that the Internet has become an increasingly popular method for MSM to communicate with and to meet sex partners (Bull, McFarlane, & Rietmeijer, 2001; Garofalo, Herrick, & Mustanski, 2007; Liau et al., 2006; Mustanski, 2007; Mustanski, Lyons, & Garcia, 2011). A meta-analysis of the literature on MSM and Internet use conducted by Liau et al. (2006) concluded that approximately 35% to 45% of MSM use the Internet to search for sexual partners. However, these analyses included MSM recruited solely in offline spaces and, given the time period in which the data were collected, may drastically underestimate the current prevalence. In fact, it is estimated that up to 85% of MSM report some recent Internet use for the purpose of meeting other men (Rosser et al., 2011). Rosser et al.’s (2011) study of 650 MSM who completed self-reported, venue-based surveys identified that up to 94% of men indicated that they had ever had sex with a partner met online, and up to 78% indicate having sex with a partner met online within the past 6 months.

The literature points to several ideas for the burgeoning popularity of the Internet for sex seeking among MSM. One thought is that the Internet provides MSM with the ability to conveniently and quickly locate sex partners in comparison to other offline venues (Rosser et al., 2011). The Internet may also allow men to sort by partner preferences (e.g., age, sexual positioning, race, scene, HIV status, etc.; Bull, McFarlane, Lloyd, & Rietmeijer, 2004). Additionally, the Internet provides individuals an outlet to be themselves without overexposing the truth about their sexual preferences and behaviors (Brown, Maycock, & Burns, 2005). Negative attitudes about homosexuality can lead to rejection of MSM by friends and family, discriminatory acts, and bullying and violence. Consequently, some MSM hide their same-sex behaviors with others to protect themselves from homophobia, stigma, and discrimination. This can be stressful, limit social support, and negatively affect health. The Internet is a vehicle for these men to become part of a new community, gain social support, and find sexual partners without the negative reaction from family and friends (Benotsch, Kalichman, & Cage, 2002; Brown et al., 2005).

The research on how community engagement and involvement, either through traditional settings or through new technological mediums, has affected sexual risk behavior is unclear and often conflicting. Research has identified that the Internet, for example, is a space in which both risk factors (e.g., assumptions of trust and honesty) and protective factors (e.g., partner selection based on expectations, rules, and safer sex negotiation) can occur simultaneously (Brown et al., 2005). Some studies have identified that both traditional methods (e.g., gay bar attendance) and the use of newer methods (the Internet) to meet potential partners are linked to increased risky sexual behaviors and may increase the opportunity for HIV and sexually transmitted infection (STI) transmission (Carballo-Diéguez et al., 2006; Menza, Kerani, Handsfield, & Golden, 2011; Rhodes, DiClemente, Cecil, Hergenrather, & Yee, 2002). Other research has established that identity affirmation and community involvement have been linked to good mental health, decreased reports of unprotected anal intercourse (UAI), and reduced odds of HIV infection (Lauby et al., 2012; Peterson et al., 1992). How the Internet (and other emerging social networking technologies) has affected community involvement and sexual identity affirmation is unclear. The relations between sexual identities, social support, and sexual risk behavior are understudied.

Brown et al. (2005) argue that while men on the Internet (or technologically based) are likely to be engaging in risk assessments differently than in other more traditional environments, as with any social or sexual venue, the environment may influence but does not determine risk behavior. Thus, the way in which assumptions, norms, and choices are reached in technologically driven sexual environments may be different than in traditional face-to-face venues. Researchers have recommended exploring these relationships in venues beyond the bar setting (such as the Internet and mobile apps), focusing on the ways in which community norms are developed with regard to risk reduction strategies and behaviors (Flores, Mansergh, Marks, Guzman, & Colfax, 2009; Grov, Ventuneac, Rendina, Jimenez, & Parsons, 2012).

Sexual Sensation Seeking and the Internet

There may be differences in men who use the Internet to seek sexual partners compared to men who do not. For example, the venue by which men meet their sexual partners may explain rising HIV infection rates. Specifically, it is thought that choice of venue may relate to greater sexual risk-taking. Research demonstrates that men who use the Internet to meet sexual partners may participate in more risky sexual behavior (Bolding, Davis, Hart, Sherr, & Elford, 2005; Hirshfield, Remien, Humberstone, Walavalkar, & Chiasson, 2004). Nonetheless, there is tension in the literature. It is unclear if the Internet facilitates risky behavior or if riskier MSM simply choose to use these particular technological venues.

The public health and sexuality literature suggests a positive relation between sexual sensation seeking (often also referred to as sexual adventurism) and sexual risk-taking among men (Dudley, Rostosky, Korfhage, & Zimmerman, 2004; Nguyen et al., 2012). There is evidence that men who use the Internet to seek potential sex partners exhibit higher levels of sexual adventurism (Grosskopf, Harris, Wallace, & Nanin, 2011; Liau et al., 2006), which has been linked to a UAI and sexual compulsivity (Halkitis & Parsons, 2003; Matarelli, 2013). One explanation for this phenomenon is that the Internet may offer a false sense of protection that makes people less inhibited and more willing to engage in sexual fantasies and risky behavior. This virtual space may also give MSM greater access to individuals who will participate in mutually desired activities including risky behaviors such as UAI while remaining hidden from exposure to HIV prevention messages traditionally delivered in community-based settings where men congregate (e.g., bars, community centers, etc.; Adam, Murphy, & de Wit, 2011). Limited data exist to understand if there are differences between men who use the Internet, men who use mobile technologies, and men who use both to meet sexual partners.

Mobile “Apps”: A New Medium for Sexual Risk

American adults, especially those ages 18 to 29 years, are particularly tech savvy. On average, 93% of adults own a cell phone, 65% of which are smartphones (phones that use Internet and GPS technology to communicate beyond standard dial-up; Duggan & Brenner, 2013). These advances in technology have created new outlets for MSM to meet sexual partners. In 2009, several location-based mobile apps, also known as Geosocial Networking Applications, geared toward gay and bisexual men were launched. These mobile apps employ the GPS capabilities of smartphones to facilitate connections with other users based on their current location. These enable users to use their smartphones or tablets to scan for nearby men, chat with them, and meet, sometimes for sexual encounters. Thus, these new media capabilities have potentially generated quicker and easier modes for MSM to meet potential partners based on shared interests, attraction, and physical proximity (Rice et al., 2012). For example, Grindr, a social networking site and smartphone application used by MSM, currently has more than 4 million users in 192 nations (PR Newswire, 2012), merely 4 years after its launch.

Whether or not the introduction of smartphones equipped with mobile social networking apps has facilitated riskier (e.g., increased number of sex partners or increased UAI) sexual partnering for MSM is unclear. Grindr and other mobile networking websites use GPS technology to help connect their users often discretely, conveniently, and for free. There is some concern that mobile networking apps that promote dating and sex partner locating may facilitate sexual risk behaviors. While there is support in the literature on Internet-based sex seeking and sexual sensation seeking that points to an increase in risky behavior among MSM (Chng & Geliga-Vargas, 2000; Grosskopf et al., 2011), research specifically on how these growing mobile-based technologies affect the sex-seeking and sexual risk taking behaviors of MSM is sparse.

In a recent study conducted by Landovitz et al. (2012), the authors collected data on 375 YMSM who used Grindr in metropolitan Los Angeles. They reported that participants had high rates of sexual partnering and UAI (46.1%) in the past 3 months. Interestingly, 70% of the men who reported having UAI with a partner met on Grindr have low perception of HIV acquisition risk, and more than half of the participants did not always ask their sexual partners about their HIV status. Conversely, a recent study explored the sexual risk-taking behaviors of YMSM who use Grindr and other traditional means such as bars and friends to meet other men (Rice et al., 2012). Among 195 YMSM (aged 19-24) recruited using Grindr, the researchers indicated that 75% of participants reported sexual encounters with partners met on the app with significantly higher rates of condom use with Grindr partners (58.8%) compared to partners met in other venues. Only 14.7% of participants in the Rice et al. (2012) study reported having UAI with their last Grindr partner.

In another study conducted by Burrell et al. (2012), the authors compared demographics and sexual risk behaviors of 105 MSM recruited through Grindr and through traditional media outlets. The researchers reported that Grindr participants were younger (mean age more White identified, and had more sex partners in the previous 14 days than other recruits; Burrell et al., 2012). Rendina, Jimenez, Grov, Ventuneac, and Parsons (2014) indicated that among 1,351 Grindr users in the NYC-metro area, older age, reporting an HIV-negative status, and recent UAI were independently associated with lifetime HIV testing. In fact, testing rates among men in the sample exceeded those of the general population in NYC. Since NYC has the largest MSM population in the United States, extremely high HIV infection rates, and is the second most represented metropolitan area (behind London) to use Grindr, further research exploring the characteristics of MSM who use mobile apps and the Internet to seek sexual partners is needed.

To that end, this study explored survey data on MSM’s use of the Internet websites (e.g., Manhunt, Craigslist, Adam4Adam) as well as mobile-based apps (e.g., Grindr, Jack’d, Scruff, etc.) to seek sexual partners. Specifically, three research questions guided this study:

Research Question 1: What are the characteristics of participants who use the Internet to meet men compared to those who use the mobile technology (apps) to meet men?

Research Question 2: To what extent are Internet and mobile app methods used to meet men associated with gay identity affirmation and sexual sensation seeking?

Research Question 3: To what extent is risky sexual behavior associated with newer and older social networking technologies?

Method

Participants and Procedure

As part of a larger study, Project EPOCH (Education and Prevention for Online Community Health), data were collected on a select sample of MSM from November 2009 through February 2011. Participants were recruited using both passive and active online/offline methods to complete an Internet based-survey at a later time. Eligibility requirements to participate in the parent study included the following: (a) being at least 18 years of age; (b) self-identifying as male; (c) sex with a man in the past 3 months; (d) having logged into a website, chat room, or other social networking site where men go to meet other men; and (e) current residence in NYC (by zip code). While the sample size for the parent study was larger, participant data for these analyses only included men who reported having logged into a website, chat room, or other social networking site for the explicit purpose of seeking a male sex partner.

For online recruitment, a modified time-space sampling technique (MacKellar, Valleroy, Karon, Lemp, & Janssen, 1996; Sudman, Sirken, & Cowan, 1988) was used to construct an exhaustive sampling frame that reflected a diverse number of social networking websites where MSM go to meet other men. The frame also included days/times of greatest user activity on the site. With permission of the sites and using a random digit generator to select the day, time, and site, members of the research team logged onto these sites and posted passive recruitment materials.

In an effort to supplement online strategies, the research team again used time-space sampling to recruit men offline, again developing an exhaustive list of bars and clubs (and busiest days/times) in the five NYC boroughs. Venues were then randomly selected for recruitment. With the permission of bar/club management, members of the study recruitment team then randomly approached every nth participant in the venue and asked him to screen for the online study. Screening questions included the aforementioned eligibility requirements. Potential participants who screened eligible were alerted to their eligibility, were briefly informed of the study’s purpose, and were asked to visit the study website to complete the full survey.

Online Survey

The online survey was housed on a secure server, and the address was masked with a unique study URL (www.projectepoch.org) to promote easy access. Potential participants who accessed the site, on giving informed consent, were asked a series of questions to determine eligibility (even if they had been previously screened in the field). Eligible participants were then directed to the full survey, which took an approximate 15 to 20 minutes to complete. Participants who completed the full survey were eligible to receive a $5 Starbucks gift card. All study procedures and materials were reviewed and approved by the IRB at the primary author’s institution.

Measures

Using previous literature on MSM, Internet sex-seeking, and subsequent sexual behavior as a basis, several instruments were used to assess variables of interest including sexual sensation seeking, sexual behavior, use of the Internet and mobile apps to meet other men, and gay identity affirmation.

Sexual Sensation Seeking Scale

The Sexual Sensation Seeking Scale is a nine-item scale based on the most widely used measure of sensation seeking (Kalichman et al., 1994). Sensation seeking, the propensity to prefer exciting, optimal, and novel stimulation or arousal, has been reported to be a potential moderating factor in risky sexual behavior among MSM (Grosskopf et al., 2011; Horvath, Beadnell, & Bowen, 2006; Newcomb, Clerkin, & Mustanski, 2011). Items on the scale are measured on a 4-point scale, from 1 (Not at all like me) to 4 (Very much like me). Higher scores indicate greater levels of sexual sensation seeking. The nine items on the scale are added together to make one composite score for sexual sensation seeking, which is then used in analysis. The following ranges are often used in studies of sexual sensation seeking: 11 to 19 = low sexual sensation seeking, 20 to 27 = moderate sexual sensation seeking, 28 to 38 = high sexual sensation seeking (Grosskopf et al., 2011). An original Cronbach’s α level of .75 was reported for this scale (Kalichman et al., 1994). Similarly, the instrument was reliable with this sample (α = .77).

Gay Identity and Affirmation Scale

Eight items were used to assess participants’ association with a gay identity. The Gay Identity Affirmation and Self-Homophobia Subscales are part of a larger battery of questions on gay community and gay activity (Flores et al., 2009). The following variables were assessed: gay identity importance (four items) and self-homophobia (four items). The eight items are scored on a 5-point scale from 1 (Strongly disagree) to 5 (Strongly agree). A composite score is computed for each variable that contains multiple items with higher scores indicating (a) greater perceived importance of a gay identity and (b) increased internalized homophobia. Scores for each subscale can range from 4 to 20. The original identity affirmation and self-homophobia subscales reliability coefficients were acceptable (α = .55 and α = .86, respectively; Bartholow et al., 1994; Bell, Weinberg, & Hammersmith, 1981). The instrument was highly reliable with this sample (identity affirmation α = .85; self-homophobia α = .90).

Sexual Practices Assessment Schedule-MSM (SPAS-MSM)

The SPAS-MSM was used to measure the frequency of sexual behaviors as well as the continuum of sexual risk among study participants (Carballo-Diéguez, Remien, Dolezal, & Wagner, 1999). The SPAS-MSM is a 16-item instrument that assesses the number of monogamous (e.g., “partner” or “lover”) and casual (e.g., “one night stands” or “fuck buddies”) partners and associated sex behaviors with each kind of partner during the past 3 months. Items include a range of behaviors on a sexual risk continuum such as “On how many of these occasions did you masturbate him?” to “On how many occasions did he ejaculate (come) inside you?” Originally developed for one-on-one interviews with participants, the instrument was adapted for use in this study. Moreover, participants were asked to distinguish between partners met online and those met using mobile apps.

Original Internet/Mobile Application Use Questionnaire

Based on the Internet Use section of the Internet and HIV Questionnaire (Elford, Bolding, Davis, Sherr, & Hart, 2004) an original Internet and mobile application use questionnaire was developed to assess participants’ use of the Internet and mobile apps. The instrument includes 19 items ranging from questions on access to and frequency of visits/use of Internet and mobile-based MSM social networking websites and mobile apps (e.g., Grindr, Jack’d, Growlr, Scruff, etc.), sex-seeking behaviors on these sites and apps (in conjunction with the SPAS-MSM, including subsequent face-to-face meetings after first meeting on the Internet or an app), and preference of venue for meeting men (Internet vs. mobile apps).

Demographic Questionnaire

A five-item demographic questionnaire assessing age, employment status, education level, self-identified race, and annual household income was used. The instrument is an abbreviated version of the demographic questionnaire used in the New York City Department of Health and Mental Hygiene’s Community Health Survey (NYCDOHMH, 2006).

Data Analyses

Descriptive statistics were used to examine the demographic and behavioral characteristics of the sample and bivariate inferential statistics (chi-square and t test) to determine which of the demographic and behavioral variables were significantly associated with the use of the Internet and/or mobile apps to meet partners. For analyses in which there were an insufficient number of cases per cell, categories were collapsed or Fisher’s exact test was used. All analyses were performed using SPSS 21.0.

Results

Research Question 1: What are the characteristics of participants who use the Internet to meet men compared to those who use the mobile technology (apps) to meet men?

The sample (n = 126) ranged from 19 to 50 years of age (Mdn = 27.13). Men self-identified as White (n = 77, 61.1%), Black (n = 21, 16.7%), Latino (n = 16, 12.7%), and Asian/Pacific Islander (n = 12, 9.5%). The majority (n = 82, 78.1%) reported being employed for wages/salary during the past 3 months, and more than half (n = 85, 80.9%) reported completing a bachelor’s degree or higher. The most commonly reported annual household income from all sources was >$75,000 (n = 32, 31.7%).

In comparing demographic characteristics between men who have sought sex partners on the Internet in the past 3 months and men who used both the Internet and mobile apps to seek partners, statistically significant differences were identified in median age and income. Although significant, there was not a large difference in age, with the median age of men who had looked for partners on the Internet being 27.75 years old, whereas those who used both the Internet and mobile apps to meet other men were 24.83 years old, t(124) = 2.02, p < .05. Respondents reporting use of both the Internet and mobile apps were more likely to report annual incomes of $50,000 or higher, χ²(1, n = 101) = 3.91, p < .05. Employment status and race were nearly significant, χ²(1, n = 105) = 3.73, p = .08, and χ²(3, n = 126) = 6.45, p = .09, respectively, with users of both the Internet and mobile apps reporting higher rates of employment (n = 32, 88.9%) and more likely to self-identify as White (n = 30, 76.9%). It is important to note that these nearly significant results were interpreted a priori based on recently published demographics on smartphone and mobile app use. There were no significant differences in education, χ²(1, n = 105) = .95, p = .33 (see Table 1).

Table 1.

Demographics, Sexual Sensation Seeking, Gay Identity Affirmation, and Self-Homophobia of (A) Men Who Used Only the Internet and (B) Men Who Used Both the Internet and Mobile “Apps” to Seek Sex Partners in the Past 3 Months.

	Overall (n = 126)		(A) Internet only (n = 87)		(B) Internet & mobile apps (n = 39)
Characteristic	n	%	n	%	n	%	χ²	df	p
Race							6.45	3	.09^a
White	77	61.2	47	54	30	76.9
Black	21	16.7	17	19.5	4	10.3
Latino	16	12.7	14	16.1	2	5.1
Asian/Pacific Islander	12	9.5	9	10.3	3	7.7
Education							.95	1	.33
Less than college degree	20	19	15	21.7	5	13.9
College degree or more	85	81	54	78.3	31	86.1
Employment							3.73	1	.08^a
Employed	82	78.1	50	72.5	32	88.9
Not employed	23	21.9	19	27.5	4	11.1
Income							3.92	1	<.05
Less than $50,00	47	46.5	35	53.8	12	33.3
More than $50,000	54	53.5	30	46.2	24	66.8
	Mdn	Range	Mdn	Range	Mdn	Range		df	p
Age	27.13	19-50	27.75	19-50	24.83	19-48		124	<.05
Sexual sensation seeking	22.14	13-36	22.11	13-36	22.25	15-36		121	.58
Gay identity affirmation	16	4-20	15.5	4-20	17	4-20		123	.82
Self-homophobia	7.5	4-20	7.5	4-20	7	4-20		124	.65

Expected counts fall below 5 in one or more cells; chi-square statistic should be interpreted with caution. Differences are nonsignificant using Fisher’s exact test.

The majority of the sample (n = 69, 55.2%) reported logging on at least daily to Internet social networking websites geared specifically for MSM. While also nearly significant, men who used the only the Internet preferred using the Internet versus other mediums to meet potential partners, χ²(2, n = 125) = 4.18, p = .12. The majority of men in the sample reported both having met a man face-to-face and having subsequent sex with him after first meeting online or on a mobile app in the past 3 months (n = 110, 96.5%, and n = 110, 96.5%, respectively; see Table 2).

Table 2.

Attitudes and Behaviors of (A) Men Who Used Only the Internet and (B) Men Who Used Both the Internet and Mobile “Apps” to Seek Sex Partners in the Past 3 Months.

	Overall (n = 126)		(A) Internet only (n = 87)		(B) Internet & mobile apps (n = 49)
Attitude/behavior	n	%	n	%	n	%	χ²	df	p
Prefer meeting men on the Internet versus other venues							4.18	2	.12
Yes	59	47.2	45	52.3	14	35.9
No	34	27.2	19	22.1	15	38.5
No preference	32	25.6	22	25.6	10	25.6
Met a man from Internet/apps after first meeting online							.08	1	.77^a
Yes	110	96.5	75	96.2	35	97.2
No	4	3.5	3	3.8	1	2.8
Sex with a man met on the Internet/app							.08	1	.77^a
Yes	110	96.5	75	96.2	35	97.2
No	4	3.5	3	3.8	1	2.8
UAI with a man met on the Internet/app							2.47	1	.12
Yes	56	57.7	34	53.1	22	66.7
No	41	42.3	30	46.9	11	33.3
Only oral or manual sex with a man met on the Internet/app							1.23	1	.27
Yes	31	44.3	20	42.6	11	47.8
No	39	55.7	27	57.4	12	52.2
Frequency of logging onto MSM-geared sites/apps							2.56	4	.13^a
More than once a day	36	28.8	22	25.6	14	35.9
Daily	33	26.4	28	32.6	5	12.8
A few days a week	35	28	22	25.6	13	33.3
Once a week	10	8	8	9.3	2	5.1
Less than once a week	11	8.8	6	7	5	12.8
	Mdn	Range	Mdn	Range	Mdn	Range		df	p
Hours spent online/on apps geared toward MSM	4.84	0-100	5	0-100	4.7	0-60		122	.53

Note. MSM = men who have sex with men; UAI = unprotected anal intercourse.

Expected counts fall below 5 in one or more cells; chi-square statistic should be interpreted with caution. Differences are nonsignificant using Fisher’s exact test.

Research Question 2: To what extent are Internet and mobile app methods used to meet men associated with gay identity affirmation and sexual sensation seeking?

On average, participants’ level of sexual sensation seeking was moderate (Mdn = 22.14; Range = 13-36). There was no significant difference between mean sexual sensation seeking scores for men seeking partners on the Internet only when compared with those who used both the Internet and mobile apps to seek partners, t(121) = −.55, p = .58. On average, participants reported high levels of gay identity importance (Mdn = 16; Range = 4-20) and low levels of internalized homophobia (Mdn = 7.5; Range = 4-20). Internal consistency for both the gay identity importance (α= .86) and self-homophobia (α = .90) was also highly acceptable with the sample. Again, there was no significant difference between gay identity affirmation and internalized homophobia scores for the Internet only and Internet and mobile app groups, t(123) = .23, p = .82, and t(124) = .65, p = .52.

Research Question 3: To what extent is risky sexual behavior associated with newer and older social networking technologies?

The SPAS-MSM was used to calculate average frequencies of sexual behaviors on a continuum of HIV risk. For the purposes of this study, any reported frequencies of UAI, receptive or insertive, with both regular–casual sex partners and one-time casual sex partners were collapsed into a risky sex behavior binary variable (“yes” and “no”). Regular monogamous partners/lovers were not included in these analyses. Any reported UAI in the past 3 months with any regular or one-time casual partner was compared between (a) men who use the Internet but not mobile apps to meet potential partners and (b) men who use both the Internet and mobile apps to meet partners. Table 2 illustrates that while there were nearly significant differences observed in risky sexual behavior between the groups, χ²(1, n = 97) = 2.47, p = .12, with men who use both the Internet and mobile apps reporting greater instances of UAI, the majority of men in the sample reported at least one act of UAI in the past 3 months (n = 56, 57.7%). Forty-four percent (n = 31) of the sample reported only having had oral sex and/or manual sex (e.g., mutual masturbation) with partners met on the Internet and/or on mobile apps.

Discussion

Through an online survey of MSM who use Internet-based social networking websites as well as mobile apps that target same-sex attracted men, this study sought to explore the reasons for using these virtual social networking venues, meeting potential partners, sexual sensation seeking, gay identity affirmation, self-homophobia, and risky sexual behavior. As one of the first to focus on both the use of the Internet and mobile technologies for meeting potential partners, this study assists in providing a descriptive snapshot of the diffusion of technological innovation, specifically describing the potential role of older versus newer technological mediums in shaping the HIV epidemic among MSM in NYC.

The participants in this study tended to be younger, identified as White, were well-educated, and reported above-average incomes. These findings are consistent with previous studies, which investigated the role of the Internet and other technological mediums for meeting potential sex partners (Chiasson et al., 2007; Grosskopf et al., 2011; Hirshfield et al., 2004). Men who used both the Internet and mobile apps to meet other men were significantly younger and reported higher incomes than those who used only the Internet. This is of particular concern given the recent endemic rates of HIV infection among YMSM in NYC. However, this may be reflective of the behavior of early adopters. In the Pew Research Center’s Internet & American Life Project Tracking Survey (2013), 80% (n = 527) of participants aged 18 to 34 years reported owning smartphones versus 45.25% of those 35 and older, 70% (n = 834) of smartphone users report a college degree or higher, and 78% (n = 582) reported an annual income of $75,000 or higher (the median income for NYC according to the 2010 U.S. Census was approximately $56,000). Additionally, 68% of smartphone owners reported, on average, using their phone for social networking with 50% of them accessing a social networking app daily.

An overwhelming number of participants (n =100, 96.5%) in this study reported sex with a man after having met him online or on a mobile app in the past 3 months. Moreover, the majority of the sample (n = 56, 57.7%) reported UAI with a casual partner met on the Internet or via an app in the past 3 months. Nonetheless, nearly half the participants (n = 33, 44.3%) reported only manual or oral sex with men in the past 3 months. These findings indicate that although most men in the sample who reported using both older and newer technologies to meet potential partners ended up engaging in sex acts with these partners, nearly half of the men also reported using risk reduction strategies, whether intentionally or unintentionally. These findings are supported in the literature (Grosskopf et al., 2011; Grov & Crow, 2012). Although reports of risky sex behaviors remained high, it is encouraging that nearly half of the men in the sample reported lower-risk sex behaviors.

Some scholars have posited that because the Internet and other tech-based social networking mediums provide a bridge between sexual fantasies, desires, and the traditional ways of expressing these, men who prefer these venues for meeting potential partners may exhibit higher levels of sexual sensation seeking while feeling less connected to the community (and a gay identity) due to the allure and anonymity that these technologies provide (Ross, 2005). However, participants, on average, reported high levels of gay identity importance and a relatively low-level of self-homophobia. This signifies that the men in this sample possessed a positive sexual self-identity—an indicator of good mental health—as well as a continued connection to the gay community. Moreover, among participants, the average sensation seeking score was moderate. Although participants did report extremely high instances of sex with men met both online and via mobile apps, there were no significance differences in risky sex behaviors between men who used the Internet to meet potential partners versus those who used both the Internet and mobile apps. This suggests that although MSM may use technology expressively for seeking sexual partners (vs. more traditional, community-based venues), there may be few differences between those who are using older forms of technology versus those who have adopted more innovative mediums. While the modes in which men use technology to access potential partners to whom they may have not otherwise had contact continue to grow and will likely remain highly sexualized, these exploratory results suggest that mobile-based social networking may be, in essence, an extension of traditional Internet-based venues.

Limitations

The leading limitation of this study was its sampling strategy and subsequent sample size. While both an online and offline time-space sampling approach was taken to increase the generalizability of the study data, MSM often congregate in a variety of venues, which include not only bars and clubs, social networking, and other websites geared toward MSM but also public (e.g., bathhouses and parks) and private sex environments (e.g., private sex parties; Pollock & Halkitis, 2009). Moreover, the smaller sample size may be simply a result of research “burn out” among members of the community, highlighting the need for the development of cost-effective, novel recruitment techniques and methodologies. Finally, it is often difficult to estimate recruitment response rates, especially for follow-up Internet-based surveys (Rhodes, Bowie, & Hergenrather, 2003). Data for this study were collected from a self-selecting sample of MSM who chose to participate and thus cannot be generalized to all MSM. Biases in self-report and recall may also exist.

Unfortunately, men of color were underrepresented in this study, which has been identified as a common limitation in many Internet-based researches (Sullivan et al., 2011). Furthermore, because these data were drawn from a larger study, eligibility for this report was limited to men who reported a primary residence in NYC, to those who reported having logged into an Internet website geared toward MSM, and to those who had sex with a man in the past 3 months. Potentially important data from men who visit NYC regularly but do not reside there, recent sexually inactive men, and men who only use mobile-based and/or traditional methods to meet other men were not collected.

Another limitation was the time period during which the data were collected. Since these data were collected, smartphone use has grown exponentially, with 35% of Americans reporting smartphone use in mid-2011 to 56% in mid-2013 (Pew Research Center, 2013). Moreover, Grindr, one of the most popular apps among MSM, had approximately 1.5 million U.S. members by mid-2012. NYC is ranked #1 in the United States, with 127,000 users alone (PR Newswire, 2012). Thus, the men in this study may include early adopters of either smartphones or social networking apps like Grindr or both.

Conclusion and Recommendations

It is estimated that over the next decade, smartphones will be broadly dispersed, connecting billions of people to the Internet (Lester & Karanja, 2008). New social networking apps that use GPS technology to locate potential partners who are closest and other emerging forms of mobile-based communication are replacing or enhancing older technological communication methods, specifically among YMSM.

To date, there has been very little attention paid in the literature to the impact of mobile app technologies on these men’s sexuality, certainly not to the extent that MSM’s use of the Internet has been documented. Additionally, there has been very little research published on the specific uses of apps for sex seeking and implications for app-based HIV prevention interventions. Nevertheless, many scholars who have published recent research on the use of the Internet and other context-specific venues for sex-seeking among MSM have identified exploration on the use of mobile-based networking apps and potential intervention programming as recommended areas for future research (Grov & Crow, 2012; Grov et al., 2012; Rosser et al., 2011).

Some research has identified ways Internet sex-seeking can facilitate greater communication prior to meeting, promote trust and honesty, and encourage greater communication regarding disclosure of HIV status, HIV testing history and encourage the prescreening of potential partners (St. De Lore et al., 2012). In the future, studies of this nature should focus on the distinct differences between the Internet and mobile app technologies, primarily the role mobile access to these mediums and geographic location is associated with sex-seeking and decision making, subsequent face-to-face meeting, and sex behaviors. Exploring how, when, why, and where men use apps (e.g., logging in to browse the local gallery of users) could yield important public health data. For example, future research could explore whether neighborhoods with endemic rates of HIV see increased app use or whether or not the sheer number of users in a given zip code increases the likelihood that one might meet a potential partner face-to-face. Researchers should consider how users perceive and use different apps and the communities to which they cater (e.g., Growlr for the bear community vs. Recon for the leather community) as well as exploring how men use profile text and images to negotiate preferences and behaviors (e.g., friends, sex partners, etc., relationship status, sexual positioning, HIV/STI status).

In a review of existing mobile apps on various operating systems and platforms (e.g., iPhone, Android, Blackberry, etc.), Gkatzidou, Hone, Estcourt, Sadiq, and Sonnenberg (2013) indicate that, currently, there are no functional apps that fully support the user in the sharing of information, education, diagnosis, care, and self-management of STIs. Exploring strategies to effectively use these novel technologies to recruit, connect, and retain men in not only public health research but also STI/HIV prevention programming, counseling, testing, and treatment is important in fostering positive health outcomes among MSM. These perspectives should be integrated into the current and growing research on venues in which MSM seek potential partners, the social norms of these venues, and subsequent, contextual HIV risk behavior.

As smartphone use increases, the number and types of social networking apps are sure to grow. Because of the proprietary nature of app development, it may be difficult for researchers and public health practitioners to glean the kinds of data that may be most useful in describing the ways mobile apps influence the health of MSM. Many apps (as Internet sites have done in the past) require those wishing to recruit for a study or market new health promotion programming to buy ad space, which can leave junior researchers and those with limited funding without many options. In future endeavors, it may be beneficial for researchers to consider a collaborative approach. It is likely that app developers want to be seen as supporting the health of their users, and the exchange of information from such a partnership could yield extreme insight.

In an attempt to harness the power of mobile-based technologies, the small majority of recent literature published on the use of mobile-based apps in public health has focused on formative, yet innovative ways to use these new technologies in health promotion (e.g., diet and exercise tracking, self-monitoring of alcohol consumption patterns, etc.; Atienza & Patrick, 2011; Cohn, Hunter-Reel, Hagman, & Mitchell, 2011). Although reflecting on the implications of the data from this and similar studies when designing mobile-based public health programming for MSM are important, future research should not only explore the determinants of HIV-related and other health behaviors but also the feasibility of the development and delivery of app-based interventions. Research should focus on the design of appropriate studies to assess the need for such programming, as well as evaluate the capacity for agencies to implement these interventions, including cost–benefit and cost-effectiveness analyses. Such knowledge and understanding is critical to enriching the development and delivery of effective public health programming through mobile apps and social media networks, particularly, in light of the increasing number of new HIV infections among YMSM of color.

Footnotes

Acknowledgements

The authors would like to thank the contributing members of the Collaborative Research Group on Health Policy and Promotion at York College of The City University of New York.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received financial support for the research, authorship, and/or publication of this article from: a PSC-CUNY Award, jointly funded by The Professional Staff Congress and The City University of New York. (Award # 60165-39 40).

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