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  • Research article
  • Open Access
  • Open Peer Review

The use of geosocial networking smartphone applications and the risk of sexually transmitted infections among men who have sex with men: a systematic review and meta-analysis

BMC Public Health201818:1178

https://doi.org/10.1186/s12889-018-6092-3

  • Received: 7 April 2018
  • Accepted: 4 October 2018
  • Published:
Open Peer Review reports

Abstract

Background

Geosocial networking smartphone applications (apps) are popular tools for seeking sexual partners among men who have sex with men (MSM). We evaluated app use and risk of sexually transmitted infections (STIs) in app-using MSM (app-users) by a systematic review and meta-analysis.

Methods

A literature search for relevant studies was performed. We extracted date of STIs (ever being diagnosed with human immunodeficiency virus [HIV], syphilis, gonorrhea and chlamydia) and sexual behavior (e.g., number of app-met partners, unprotected anal/oral sex, HIV testing) from the eligible studies. Pooled proportions and odds ratios (ORs) with 95% confidence intervals (95% CIs) were estimated.

Results

Twenty-five studies were included. The self-reported prevalence of prior diagnosis of HIV among app-users ranged from 2.2 to 37.7%, and the pooled prevalence of HIV infection was 6% (95% CI, 4–11%). Compared with non-users, app-users were more likely to have gonorrhea (OR = 2.36; 95% CI, 2.07–2.70) and chlamydia (OR = 2.22; 95% CI, 1.92–2.56). The two groups were similar in terms of diagnoses of HIV (OR = 0.89, 95% CI, 0.68–1.16) and syphilis (OR = 1.92; 95% CI, 0.91–4.03). However, when one study that caused substantial heterogeneity was omitted, the pooled OR for app-users to contract syphilis became 3.00 (95% CI, 1.84–4.91) .

Conclusions

MSM who seek sexual partners using apps may be more likely to have STIs as than are non-users.

Keywords

  • Geosocial networking application
  • App
  • MSM
  • HIV
  • Sexually transmitted infection

Background

The prevalence of men who have sex with men (MSM)-related human immunodeficiency virus (HIV) infection is increasing worldwide [1, 2]. Advances in communication technology now offer MSM different opportunities to meet sexual partners. In recent years, a number of global positioning system (GPS)-equipped smart phone applications (geosocial networking smartphone applications; apps) have been developed (e.g., Jack’d, Scruff, Blued, and Grindr) that are popular tools in the MSM community [3]. These apps allow subscribers to create individualized profiles, share photos, and send their location. Users can also send instant messages to other users who are in close (or least identified) proximity, effectively allowing MSM to arrange sexual encounters. From 2009 to 2013, these apps have been used increasingly among MSM. Approximately 40% of MSM reported using these apps to seek sex partners in 2013 [4]. The first of these apps, Grindr (launched in 2009), reported it had reached approximately 6 million users around the world in 2013, with an estimated 8000 new users every day [3, 5].

With the proliferation of apps, increased use of these apps may facilitate finding casual sexual partners, resulting in unsafe sexual practices [6]. Prior work has shown that MSM who use these apps (app-users) tend to have more sexual encounters, more frequent anal intercourse, more unprotected sex, and a larger number of sexual partners known to have HIV and other STIs [710]. This increases their risk for HIV and STIs acquisition/transmission, compared with MSM who used different channels to seek sex partners (non-users) [11, 12]. However, evidence among these studies is inconsistent. Some studies suggested that app-users may be more likely to practice safer sex with these partners than are non-users [5, 13], and that use of apps was not associated with increased risky behavior for STIs transmission [14]. Therefore, a comprehensive summary of apps usage and their associated effects on sexual health is warranted.

The aims of the present study were to: (1) examine the characteristics of app-users; (2) summarize the existing evidence on the use of apps and associated sexual behaviors among app-users; and (3) compare STIs diagnoses in apps-users with those of non-users.

Methods

Literature search

This meta-analysis report followed the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) [15]. We identified relevant articles published up to 9th October, 2017 by a systematic search of MEDLINE via PubMed, using the key words “homosexual”, “gay”, “bisexual”, “men who have sex with men”, “MSM”, “applications”, “apps”, “phone”, “smartphone”, “mobile phone”, “cellphone”, “Grindr”, “Jack’d”, “Scruff”, “Hornet”, “Blued”, “SpaceFinder”, “GSN”. In order to identify additional potentially relevant articles, the reference lists of included articles were manually searched by researchers.

Study selection

All articles that reported use of apps and their associated effects on sexual health and/or sexual behaviors among MSM were assessed. We selected articles adhering to the following criteria: (1) focus on app-using MSM or studies involving both app-users and non-users; (2) reported data for sexual health or sexual behaviors; and (3) full texts were available. Only English-language studies were considered.

Exclusion criteria were as follows: (1) studies that reported non-users only; and (2) studies that lacked required data on outcomes of interest.

Three authors (HW, LZ, YZ) independently assessed the retrieved records. The study selection process was conducted in two steps: first, titles and abstracts were analyzed and preselected according to inclusion and exclusion criteria; second, full texts of potentially eligible articles were retrieved for further evaluation. Disagreements were resolved by consensus.

Data extraction

Three authors (HW, KW, XZ) independently reviewed the full text of eligible studies and extracted the following information: (1) study details: first author, year of publication, study location, study period, recruitment method, main study objective; (2) characteristics of the study population: age, sexual orientation, education, race/ethnicity; and (3) outcomes of interest: HIV/STIs diagnoses (ever being diagnosed with HIV, syphilis, gonorrhea and chlamydia) and app related sexual behaviors (e.g., number of sex partners found through the platform). Disagreements were resolved by consensus.

Statistical analysis

Meta-analysis was performed using R software with the Meta package (version 3.2.0). The Higgins I2 statistic was used to test for heterogeneity among studies, with I2 < 25% considered low heterogeneity, 25–75% considered medium heterogeneity, and > 75% considered high heterogeneity [16]. If middle or high heterogeneity existed among studies, a DerSimonian-Laird random-effects model was used to calculate pooled proportion or odds ratios (ORs) and corresponding 95% confidence intervals (95% CIs) [17]. A Mantel-Haenszel fixed-effects model was used in the absence of heterogeneity [18, 19]. If there was heterogeneity, we performed sensitivity analysis to test the reliability of the results. In addition, Begg’s and Egger’s tests were used to evaluate publication bias, with P > 0.05 indicating no publication bias [20].

Results

Characteristics of included studies

A total of 421 articles were identified from the database search; six additional papers was found through a reference check; 53 potentially eligible articles were retrieved for more detailed analysis. Ultimately, 25 studies were included for the meta-analysis [36, 13, 2145]. The flow diagram of the study selection process is shown in Fig. 1.
Fig. 1
Fig. 1

Flow diagram of the study selection process

The characteristics of the included studies are summarized in Additional file 1: Table S1. All studies were cross-sectional; 17 were conducted in the United States, five in China, and one each in Australia, Thailand and India. Ten studies recruited MSM through apps [5, 21, 22, 26, 30, 34, 36, 38, 42, 43]. Other studies applied a variety of recruitment methods, including gay websites, fixed venues, and social service organizations serving MSM. Data collection year of MSM ranged from 2009 to 2015. Most studies (n = 20, 80.0%) evaluated sexual behaviors/characteristics of app-users [36, 13, 2123, 2631, 33, 3542].

Demographic characteristics

More than half of these studies (n = 15/25, 60.0%) recruited app-users aged were 18 or above years of age [5, 13, 2127, 29, 32, 34, 3638, 41, 4345], and showed a predominance of young adults (18 to 30 years old; n = 8/15, 53%) [5, 13, 21, 22, 25, 36, 38, 41, 4345]. According to the available data, 4427 (54.2%) app-users were white, 5754 (78.2%) were gay-identified and 6420 (71.3%) had at least college education. 1748 (71.3%) non-users were white, 2575 (47.9%) were gay-identified and 5791 (74.3%) had at least college education (Table 1).
Table 1

Demographic characteristics of app users and non-app users

First author (Year)

Age Mean ± SD or n (%)

Sexual orientation n (%)

Race/ethnicity n (%)

Education n (%)

Group

app users

non-app users

Group

app users

non-app users

Group

app users

non-app users

Group

app users

non-app users

Goedel (2015)

18–66

31.73 ± 10.7

NR

Gay

71 (77.2)

NR

White

58 (63.0)

NR

<College

45 (48.9)

NR

Other

21 (22.8)

Other

34 (37.0)

≥College

47 (51.1)

Goedel (2016)

Duncan (2016)

18–30

≥31

98 (56.6)

NR

Gay

146 (84.9)

NR

White

69 (39.9)

NR

<College

96 (55.5)

NR

75 (43.4)

Other

26 (15.1)

Other

104 (60.1)

≥College

77 (44.5)

Phillips (2014)

18–34

160 (66.4)

70 (50.7)

Gay

220 (91.7)

111 (81.6)

White

120 (49.8)

60 (43.5)

<College

44 (18.3)

22 (15.9)

≥35

81 (33.6)

68 (49.3)

Other

20 (8.3)

25 (18.4)

Other

121 (50.2)

78 (56.5)

≥College

197 (81.7)

116 (84.1)

Rhoton (2016)

≥18

29.46 ± 8.20

NR

Gay

2 (0.4)

NR

NR

<College

384 (87.0)

NR

Other

406 (90.4)

≥College

57 (12.0)

Holloway (2015)

Holloway (2015)

≥25

30.66 ± 6.68

NR

Gay

265 (90.1)

NR

White

152 (51.5)

NR

<College

33 (11.2)

NR

Other

29 (9.9)

Other

143 (48.5)

≥College

262 (88.8)

Ko (2016)

18–54

27.3 ± 6.8

26.5 ± 6.6

NR

NR

<College

69 (17.3)

120 (18.2)

≥College

331 (82.7)

540 (81.8)

Beymer (2014)

≤29

1287 (49.7)

1823 (27.9)

NR

White

1366 (52.8)

2198 (47.8)

<College

287 (11.1)

753 (16.4)

≥30

1302 (50.3)

2772 (72.1)

Other

1223 (47.2)

2397 (52.2)

≥College

2302 (88.9)

3842 (83.6)

Beymer (2016)

NR

NR

White

109 (74.7)

NR

<College

17 (11.6)

NR

Other

37 (25.3)

≥College

129 (88.4)

Yeo (2016)

17–26

21.52 ± 2.29

NR

Gay

159 (74.6)

NR

Chinese

206 (96.7)

NR

<College

47 (22.2)

NR

Other

54 (25.4)

Other

7 (3.3)

≥College

165 (77.8)

Winetrobe (2014)

Rice (2012)

18–24

21.8 ± 1.7

NR

Gay

168 (86.2)

NR

White

76 (39.0)

NR

<College

30 (15.4)

NR

Other

27 (13.8)

Other

119 (61.0)

≥College

165 (84.6)

Tang (2016)

≤29

680 (82.5)

424 (70.7)

Gay

626 (76.0)

412 (68.7)

NR

<College

186 (22.6)

183 (30.5)

≥30

144 (17.5)

176 (29.3)

Other

198 (24.0)

188 (31.3)

≥College

638 (77.4)

417 (69.5)

Muessig (2013)

LeGrand (2014)

18–30

24 ± 3.0

NR

NR

Black

22 (100)

NR

NR

Chow (2016)

Chow (2017)

NR

NR

NR

NR

Allen (2017)

18–29

≥30

65 (34.6)

212 (37.8)

Gay

164 (87.2)

434 (77.4)

Black

86 (45.7)

270 (48.1)

<College

125 (66.5)

389 (69.3)

123 (65.4)

349 (62.2)

Other

24 (12.8)

127 (22.6)

Hispanic

102 (54.3)

291 (51.9)

≥College

63 (33.5)

172 (30.7)

Bien (2015)

16–25

≥26

156 (28.6)

161 (20.2)

Gay

428 (78.7)

543 (69.1)

NR

<College

224 (41.5)

396 (50.1)

389 (71.4)

636 (79.8)

Other

116 (21.3)

243 (30.9)

≥College

316 (58.5)

395 (49.9)

Rendina (2014)

≥18

30.1 ± 9.1

NR

Gay

1162 (86.0)

NR

White

666 (49.3)

NR

NR

Other

189 (14.0)

Other

685 (50.7)

Grosskopf (2014)

NR

Mdn 24.83

Mdn 27.75

NR

White

30 (76.9)

47 (54)

<College

5 (13.9)

15 (21.7)

Other

9 (23.1)

40 (46)

≥College

31 (86.1)

54 (78.3)

Goedel (2016)

18–30

≥31

94 (62.7)

NR

Gay

126 (84.0)

NR

White

66 (44.0)

NR

<College

64 (42.7)

NR

56 (37.3)

Other

24 (16.0)

Other

84 (56.0)

≥College

86 (57.3)

Lehmiller (2014)

NR

30.7 ± 10.1

28.9 ± 11.7

Gay

(86.9)

(73.1)

White

(86.7)

(86.0)

NR

Other

(13.1)

(26.9)

Other

(13.3)

(14.0)

Goedel (2017)

18–30

≥31

78 (38.6)

NR

Gay

176 (87.1)

NR

White

143 (72.2)

NR

NR

124 (61.4)

Other

26 (12.9)

Other

55 (27.8)

Landovitz (2013)

18–29

≥30

349 (93.1)

NR

NR

White

159 (42.4)

NR

NR

26 (6.9)

Other

216 (57.6)

Burrell (2012)

18–30

(56.0)

(18.8)

NR

White

(44.0)

(30.4)

≥College

(68.0)

(40.3)

Cao (2017)

≤29

393 (80.7)

241 (63.9)

Gay

373 (76.6)

257 (68.2)

NR

<College

119 (24.4)

122 (32.4)

≥30

94 (19.3)

136 (36.1)

Other

114 (23.4)

120 (31.8)

≥College

368 (75.6)

255 (67.6)

PhillipsII (2015)

18–29

18–29

NR

Gay

1668 (83.6)

NR

White

1207 (63.7)

NR

<College

809 (40.6)

NR

Other

327 (16.4)

Other

688 (36.3)

≥College

1186 (59.4)

Weiss (2017)

15–29

163 (47.2)

NR

NR

NR

NR

≥30

182 (52.8)

Abbreviations: SD Standard Deviation; <College, Less than college; ≥College, College or above; Mdn Median age, NR not reported

App usage and sexual behaviors among app-users

App usage and sexual behavior is summarized in Table 2. Among app-users, Muessig and LeGrand found 50% reported using the phone to find sexual partners [44, 45]. Ko found 88% had online sex partners in the previous 3 months [30]. Winetrobe reported that the average number of Grindr-met partners in the past 1 month was 1.84 (Standard Deviation [SD] = 2.92) [5, 13]. Goedel and Duncan reported that the average number of app-met insertive and receptive anal intercourse partners was 1.46 (SD = 6.27) and 1.07 (SD = 2.45), respectively [25, 36]. The study from Tang et al..... found 66.7% of app-users did not ask for HIV status of the last gay app partner before meeting in person [4].
Table 2

The use of apps and sexual behaviors among app-users

First author (Year)

Sexual behaviors

APP users

N/Mean

%/SD

Goedel (2016)

Duncan (2016)

App-met IAI partners, P3M

1.46

6.27

App-met RAI partners, P3M

1.07

2.45

Rhoton (2016)

HIV status on GSN app

2.98

8.96

Ko (2016)

Had online sex partners, P3M

352

88.0

Unprotected anal sex online sexual partners, P6M

228

64.8

Unprotected oral sex online sexual partners, P6M

325

88.8

Yeo (2016)

Sexual partnering via apps

  

0

86

40.4

1–3

91

42.7

> 3

36

16.9

Winetrobe (2014)

Rice (2012)

Number of Grindr-met partners, P1M

1.84

2.92

Ever had sex with a partner met on Grindr

147

75.4

Tang (2016)

Number of sex partners found through gay app, P6M

  

1–6

680

82.5

> 6

144

17.5

Number of IAI with partners met through gay app, P6M

  

0–5

629

76.3

> 6

195

23.7

Condomless anal sex with the last partner met through gay app

338

41.0

Not asked for HIV status of the last gay app partner before met in person

550

66.7

Muessig (2013)

LeGrand (2014)

Use phone to find sex partners

11

50.0

Chow (2016)

Chow (2017)

Meeting partners via mobile apps

723

55.0

Grosskopf (2014)

Sex with a man met on the app

35

97.9

UAI with a man met on the app

22

66.7

Only oral or manual sex with a man met on the app

11

47.8

Cao (2017)

No. of sex partners found through the platform, P6M

  

Single

151

31.0

Multiple

336

69.0

Abbreviations: IAI Insertive anal intercourse, RAI Receptive anal intercourse, GSN Geosocial networking, P1M In the past 1 month, P3M In the past 3 months, P6M In the past 6 months, UAI Unprotected anal intercourse

Substance use

Two studies reported prevalence of recreational drug use among app-users (16.9% and 50.2%) [23, 34]. Two studies reported prevalence of injectable drug use (2.1% and 5.4%) [23, 31]. Goedel et al found 38.6% app-users reported having had five or more drinks containing alcohol in the previous 3 months [18]. Phillips et al reported that the prevalence of recreational drug and injectable drug use among non-users was 43.1% and 4.1%, respectively [23].

HIV testing

The lifetime rate of HIV testing among app-users ranged from 49.1 to 96.7% [5, 23, 26, 33, 40], and ranged from 50.1 to 97.1% among non-users [23, 33, 40]. The rate of HIV testing in the preceding 12 months among app-users ranged from 10.8 to 83.2% [5, 23, 26, 27, 38], and ranged from 37.4 to 58.0% among non-users [23, 27].

HIV prevalence

HIV prevalence was reported in sixteen studies among app-users. The range was 2.2% to 37.7%. The pooled prevalence was 6.0% (95% CI 4.0–11.0%, I2 = 97%, Pheterogeneity < 0.01), but with high heterogeneity (Additional file 2: Figure S1).

Comparisons of ever being diagnosed with HIV/STIs between app-users and non-users

Eight studies assessed self-reported HIV diagnosis [3, 4, 6, 23, 27, 32, 33, 38]. Since we found medium heterogeneity (I2 = 45%, Pheterogeneity = 0.08) among these studies, a random-effects model was used to pool the OR. The pooled OR of 0.89 (95% CI, 0.68–1.16) for HIV diagnosis suggested no significant difference in HIV infection between app-users and non-users (Fig. 2). We applied a sensitivity analysis to explore the factors contributing to heterogeneity. Sensitivity analysis showed omitting one study in each group did not substantially change the pooled OR.
Fig. 2
Fig. 2

Forest plots of HIV/STI diagnosis by app-users versus non-users. Squares indicate odds ratio in each study; square size is proportional to the weight of the corresponding study in the meta-analysis; the length of the horizontal lines represents the 95% confidence interval; the diamond indicates the pooled odds ratio and 95% confidence interval

For self-reported syphilis diagnosis, we first used a fixed-effect model to pool the available data [3, 6, 32], We found that app-users were more likely to have syphilis (OR = 1.88; 95% CI, 1.37–2.59). However, we detected medium heterogeneity (I2 = 70%, Pheterogeneity = 0.04) among these studies. Therefore, we employed a random-effects model to calculate the pooled OR and found that there was no significant difference between apps-users and non-users (OR = 1.92; 95% CI, 0.91–4.03) (Fig. 2). We found that Beymer et al. contributed substantially to heterogeneity according to the results of sensitivity analysis. When this study was omitted, the pooled OR for syphilis infection became 3.00 (95% CI, 1.84–4.91, I2 = 0%, Pheterogeneity = 0.36) suggesting app-users were more likely to report syphilis infection.

Three studies assessed self-reported gonorrhea and chlamydia diagnoses [3, 6, 32]. As there was no heterogeneity for either gonorrhea (I2 = 0%, Pheterogeneity = 0.80) or chlamydia (I2 = 0%, Pheterogeneity = 0.88) diagnoses, we employed a fixed-effect model to pool the OR. The pooled OR showed app-users were more likely to report gonorrhea (OR = 2.36; 95% CI, 2.07–2.70) (Fig. 2) and chlamydia (OR = 2.22; 95% CI, 1.92–2.56) (Fig. 2) infections.

Publication bias

We found no publication bias for these analyses by Begg’s test (all P > 0.05) or Egger’s test (all P > 0.05).

Discussion

This was a quantitative study estimating the prevalence of HIV infection among app-users and non-users, and comparing self-reported STIs diagnoses between the two groups. In addition, we investigated app use and sexual behaviors of app-users. The prevalence of HIV infection, substance use, and HIV testing varied widely among app-users and non-users. We found that app-users engaged in several unsafe sexual behaviors. Our meta-analysis demonstrated that app-users were more likely to have syphilis, gonorrhea and chlamydia diagnosis than were non-users.

Since the early 2000s, researchers have noted that partnerships pursued through online interactions were different from venue-based interactions. Advances in communication technology may affect the sexual partnership [14]. Many studies have reported that because apps provide MSM with more efficient ways to seek sexual partners compared with other methods, apps were more likely to facilitate higher risk sexual behaviors [35, 38]. Our study was not exceptional for finding that risky sexual behaviors (e.g., greater number of sexual partners, unprotected sex) were common among app-users. Apps potentially foster risky behavior because users could carry their smart phones with them at all times. A notable finding was that app-users were more inclined to be diagnosed with STIs than were non-users. Higher prevalence of risky sexual behaviors and higher risk of STIs infections may put app-users at greater risk for HIV transmission. However, we found that the two groups were similar with respect to reported HIV diagnosis. Ko et al. found that HIV-positive MSM were more likely to use apps to seek partners. HIV-positive MSM were concerned with maintaining his attractiveness in apps, and therefore implied his serostatus in his app profile. Therefore, apps might provide these men easier channels to hide HIV positive status [27]. Taken together, the advancements in apps and the increase in MSM using these apps may produce more adverse effects on sexual health. The data demonstrate the need for increased app-based prevention interventions among MSM.

Our study found high prevalence of recreational drug use among app-users. Substance use and misuse are prevalent among MSM [46], especially alcohol and recreational drugs [47]. The National HIV Behavioral Surveillance showed that 42% of MSM used substances recreationally [48]. It has been reported that the use of substances was associated with HIV-related sexual risk behaviors [25, 46]. Therefore, substance use may a strong predictor of sexual risk behaviors.

We found a high rate of lifetime HIV testing among both app-users and non-users, and a slightly higher rate of HIV testing in the previous 12 months among app-users compared with non-users. It appears that app-users may be likely to utilize health resources, because MSM engaging in risky behaviors may recognize the need for HIV testing [49]. As has been validated by several studies, app-users were more likely to engage in unsafe sex [79]. Therefore, for app-users, frequent testing might be associated with high-risk sexual behaviors. Nevertheless, we cannot verify this association in the present study. In fact, several studies reported that many app-users never underwent HIV testing [33, 40]. A study conducted in Peru reported that 60% of MSM with newly diagnosed HIV infection had not been tested within 12 months [49], suggesting that non-testers might be at high risk for infection. This is a significant issue, because infected non-testers can unknowingly transmit HIV to their partners [50, 51], resulting in an increasing rate of HIV infection. This suggests that, integrating HIV testing into routine medical care might increase testing in high-risk MSM.

Our study had a few limitations. First, most studies were descriptive, without a comparable group (referred to non-users). This presented an obstacle for making comparisons between app-users and non-users. Second, the association between app use and sexual risk behaviors/STIs may not imply a causal relation.

Conclusions

Increased app use among MSM has been linked to casual sexual partners and unsafe sex. We suspect that app based dating offers avenues for more discreet dating that offers a possibility of increases in STIs. This situation has important implications for HIV prevention. Our analyses support the notion that MSM who seek sexual partners using apps may be more likely to have STIs infections than are non-users. As smartphone use increases, acceptable mobile platforms for HIV prevention are recommended. In addition, more studies, especially longitudinal studies, are needed to confirm the relative risk between app-user and non-user..

Abbreviations

App: 

Geosocial networking smartphone application

App-users: 

App-using MSM

CI: 

Confidence interval

GPS: 

Global positioning system

HIV: 

Human immunodeficiency virus

MSM: 

Men who have sex with men

Non-users: 

MSM who used different channels to seek sex partners

OR: 

Odds ratio

SD: 

Standard Deviation

STI: 

Sexually transmitted infection

Declarations

Acknowledgements

The authors thank Robert P Lindeman for his revision of grammar mistakes.

Funding

There is no funding for this study.

Availability of data and materials

The data is included in the manuscript and tables.

Authors’ contributions

JW and GW were responsible for the study concept and design. HW, LZ, YZ, KW and XZ acquired data. HW performed the statistical analysis. HW drafted the report. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Authors’ Affiliations

(1)
School of Public Health, North China University of Science and Technology, No. 21 Bohai Road, Tangshan Bay Eco City, Caofeidian District, Tangshan, 063210, People’s Republic of China

References

  1. Ostermann J, Kumar V, Pence BW, Whetten K. Trends in HIV testing and differences between planned and actual testing in the United States, 2000-2005. Arch Intern Med. 2007;167(19):2128–35.View ArticleGoogle Scholar
  2. CA MNDSM. Trends in HIV/AIDS diagnoses among men who have sex with men--33 states, 2001-2006. MMWR Morb Mortal Wkly Rep. 2008;57(25):681–6.Google Scholar
  3. Beymer MR, Weiss RE, Bolan RK, Rudy ET, Bourque LB, Rodriguez JP, Morisky DE. Sex on demand: geosocial networking phone apps and risk of sexually transmitted infections among a cross-sectional sample of men who have sex with men in Los Angeles County. Sex Transm Infect. 2014;90(7):567–72.View ArticleGoogle Scholar
  4. Tang W, Best J, Zhang Y, Liu FY, Tso LS, Huang S, Yang B, Wei C, Tucker JD. Gay mobile apps and the evolving virtual risk environment: a cross-sectional online survey among men who have sex with men in China. Sex Transm Infect. 2016;92(7):508–14.View ArticleGoogle Scholar
  5. Winetrobe H, Rice E, Bauermeister J, Petering R, Holloway IW. Associations of unprotected anal intercourse with Grindr-met partners among Grindr-using young men who have sex with men in Los Angeles. AIDS Care. 2014;26(10):1303–8.View ArticleGoogle Scholar
  6. Lehmiller JJ, Ioerger M. Social networking smartphone applications and sexual health outcomes among men who have sex with men. PLoS One. 2014;9(1):e86603.View ArticleGoogle Scholar
  7. Chow JY, Konda KA, Calvo GM, Klausner JD, Caceres CF. Demographics, behaviors, and sexual health characteristics of high risk men who have sex with men and transgender women who use social media to meet sex Partners in Lima, Peru. Sex Transm Dis. 2017;44(3):143–8.PubMedGoogle Scholar
  8. Tang W, Tang S, Qin Y, Zhang Y, Zhang W, Liu C, Tso LS, Wei C, Yang L, Huang S, et al. Will gay sex-seeking Mobile phone applications facilitate group sex? A cross-sectional online survey among men who have sex with men in China. PLoS One. 2016;11(11):e0167238.View ArticleGoogle Scholar
  9. Hull P, Mao L, Prestage G, Zablotska I, de Wit J, Holt M. The use of mobile phone apps by Australian gay and bisexual men to meet sex partners: an analysis of sex-seeking repertoires and risks for HIV and STIs using behavioural surveillance data. Sex Transm Infect. 2016;92(7):502–7.View ArticleGoogle Scholar
  10. Card KG, Lachowsky NJ, Cui Z, Shurgold S, Gislason M, Forrest JI, Rich AJ, Moore D, Roth E, Hogg RS. Exploring the role of sex-seeking apps and websites in the social and sexual lives of gay, bisexual and other men who have sex with men: a cross-sectional study. Sex Health. 2016;14(3):229–37.Google Scholar
  11. Elford J, Bolding G, Sherr L. Seeking sex on the internet and sexual risk behaviour among gay men using London gyms. AIDS. 2001;15(11):1409–15.View ArticleGoogle Scholar
  12. Evans AR, Wiggins RD, Mercer CH, Bolding GJ, Elford J. Men who have sex with men in Great Britain: comparison of a self-selected internet sample with a national probability sample. Sex Transm Infect. 2007;83(3):200–5 discussion 205.View ArticleGoogle Scholar
  13. Rice E, Holloway I, Winetrobe H, Rhoades H, Barmanadhikari A, Gibbs J, Carranza A, Dent D, Dunlap S. Sex risk among young men who have sex with men who use Grindr, a smartphone geosocial networking application. Hiv Clinical Trials. 2012;84(S4):1–6.Google Scholar
  14. Lee SS, Lam AN, Lee CK, Wong NS. Virtual versus physical channel for sex networking in men having sex with men of sauna customers in the City of Hong Kong. PLoS One. 2012;7(2):e31072.View ArticleGoogle Scholar
  15. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Int J Surg. 2010;8(5):336–41.View ArticleGoogle Scholar
  16. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–58.View ArticleGoogle Scholar
  17. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–88.View ArticleGoogle Scholar
  18. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539.View ArticleGoogle Scholar
  19. Dersimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177.View ArticleGoogle Scholar
  20. Stuck AE, Rubenstein LZ, Wieland D. Bias in meta-analysis detected by a simple, graphical test. Asymmetry detected in funnel plot was probably due to true heterogeneity. BMJ (Clinical research ed). 1998;316(7129):469 author reply 470–461.View ArticleGoogle Scholar
  21. Goedel WC, Duncan DT. Geosocial-Networking App Usage Patterns of Gay, Bisexual, and Other Men Who Have Sex With Men: Survey Among Users of Grindr, A Mobile Dating App. JMIR Public Health Surveill. 2015;1(1):e4.View ArticleGoogle Scholar
  22. Goedel WC, Duncan DT. Contextual factors in geosocial-networking smartphone application use and engagement in condomless anal intercourse among gay, bisexual, and other men who have sex with men who use Grindr. Sex Health. 2016;13(6):549–54.View ArticleGoogle Scholar
  23. Phillips G 2nd, Magnus M, Kuo I, Rawls A, Peterson J, Jia Y, Opoku J, Greenberg AE. Use of geosocial networking (GSN) mobile phone applications to find men for sex by men who have sex with men (MSM) in Washington, DC. AIDS Behav. 2014;18(9):1630–7.View ArticleGoogle Scholar
  24. Rhoton J, Wilkerson JM. Sexual Preferences and Presentation on Geosocial Networking Apps by Indian Men Who Have Sex With Men in Maharashtra. JMIR Mhealth Uhealth. 2016;4(4):e120.View ArticleGoogle Scholar
  25. Duncan DT, Goedel WC, Stults CB, Brady WJ, Brooks FA, Blakely JS, Hagen D. A study of intimate partner violence, substance abuse, and sexual risk behaviors among gay, bisexual, and other men who have sex with men in a sample of geosocial-networking smartphone application users. Am J Mens Health. 2016;12(2):292–301.View ArticleGoogle Scholar
  26. Holloway IW, Pulsipher CA, Gibbs J, Barman-Adhikari A, Rice E. Network influences on the sexual risk behaviors of gay, bisexual and other men who have sex with men using geosocial networking applications. AIDS Behav. 2015;19(Suppl 2):112–22.View ArticleGoogle Scholar
  27. Ko NY, Tseng PC, Huang YC, Chen YC, Hsu ST. Seeking sex partners through the internet and mobile phone applications among men who have sex with men in Taiwan. AIDS Care. 2016;28(7):927–31.View ArticleGoogle Scholar
  28. Beymer MR, Rossi AD, Shu SB. Assessing self-control and geosocial networking app behavior among an online sample of men who have sex with men. J Urban Health. 2016;93(4):698–708.View ArticleGoogle Scholar
  29. Holloway IW. Substance use homophily among geosocial networking application using gay, bisexual, and other men who have sex with men. Arch Sex Behav. 2015;44(7):1799–811.View ArticleGoogle Scholar
  30. Yeo TE, Ng YL. Sexual risk behaviors among apps-using young men who have sex with men in Hong Kong. AIDS Care. 2016;28(3):314–8.View ArticleGoogle Scholar
  31. Chow EP. Saliva use in sex: associations with use of smartphone dating applications in men who have sex with men. JMIR human factors. 2017;29(4):362–66.View ArticleGoogle Scholar
  32. Allen JE, Mansergh G, Mimiaga MJ, Holman J, Herbst JH. Mobile phone and internet use mostly for sex-seeking and associations with sexually transmitted infections and sample characteristics among black/African American and Hispanic/Latino men who have sex with men in 3 US cities. JMIR mHealth and uHealth. 2017;44(5):284–9.Google Scholar
  33. Bien CH, Best JM, Muessig KE, Wei C, Han L, Tucker JD. Gay apps for seeking sex Partners in China: implications for MSM sexual health. AIDS Behav. 2015;19(6):941–6.View ArticleGoogle Scholar
  34. Rendina HJ, Jimenez RH, Grov C, Ventuneac A, Parsons JT. Patterns of lifetime and recent HIV testing among men who have sex with men in new York City who use Grindr. AIDS Behav. 2014;18(1):41–9.View ArticleGoogle Scholar
  35. Grosskopf NA, LeVasseur MT, Glaser DB. Use of the internet and mobile-based “apps” for sex-seeking among men who have sex with men in new York City. Am J Mens Health. 2014;8(6):510–20.View ArticleGoogle Scholar
  36. Goedel WC, Halkitis PN, Duncan DT. Behavior- and partner-based HIV risk perception and sexual risk behaviors in men who have sex with men (MSM) who use geosocial-networking smartphone applications in new York City. J Urban Health. 2016;93(2):400–6.View ArticleGoogle Scholar
  37. Goedel WC, Duncan DT. Correlates of engagement in group sex events among men who have sex with men in London who use geosocial-networking smartphone applications. AIDS Behav. 2017;29(3):244–50.View ArticleGoogle Scholar
  38. Landovitz RJ, Tseng CH, Weissman M, Haymer M, Mendenhall B, Rogers K, Veniegas R, Gorbach PM, Reback CJ, Shoptaw S. Epidemiology, sexual risk behavior, and HIV prevention practices of men who have sex with men using GRINDR in Los Angeles, California. J Urban Health. 2013;90(4):729–39.View ArticleGoogle Scholar
  39. Chow EP, Cornelisse VJ, Read TR, Hocking JS, Walker S, Chen MY, Bradshaw CS, Fairley CK. Risk practices in the era of smartphone apps for meeting partners: a cross-sectional study among men who have sex with men in Melbourne, Australia. AIDS Patient Care STDs. 2016;30(4):151–4.View ArticleGoogle Scholar
  40. Cao B, Liu C, Stein G, Tang W, Best J, Zhang Y, Yang B, Huang S, Wei C, Tucker JD. Faster and riskier? Online context of sex seeking among men who have sex with men in China. Sex Transm Dis. 2017;44(4):239–44.View ArticleGoogle Scholar
  41. Phillips G, Grov C, Mustanski B. Engagement in group sex among geosocial networking mobile application-using men who have sex with men. Sex Health. 2015;12(6):495–500.View ArticleGoogle Scholar
  42. Weiss KM, Jonas KJ, Guadamuz TE. Playing and never testing: human immunodeficiency virus and sexually transmitted infection testing among app-using MSM in Southeast Asia. Sex Transm Dis. 2017;44(7):406–11.View ArticleGoogle Scholar
  43. Burrell ER, Pines HA, Robbie E, Coleman L, Murphy RD, Hess KL, Anton P, Gorbach PM. Use of the location-based social networking application GRINDR as a recruitment tool in rectal microbicide development research. AIDS Behav. 2012;16(7):1816–20.View ArticleGoogle Scholar
  44. LeGrand S, Muessig KE, Pike EC, Baltierra N, Hightow-Weidman LB. If you build it will they come? Addressing social isolation within a technology-based HIV intervention for young black men who have sex with men. AIDS Care. 2014;26(9):1194–200.View ArticleGoogle Scholar
  45. Muessig KE, Pike EC, Fowler B, LeGrand S, Parsons JT, Bull SS, Wilson PA, Wohl DA, Hightow-Weidman LB. Putting prevention in their pockets: developing mobile phone-based HIV interventions for black men who have sex with men. AIDS Patient Care STDs. 2013;27(4):211–22.View ArticleGoogle Scholar
  46. Santos GM, Coffin PO, Vittinghoff E, DeMicco E, Das M, Matheson T, Raiford JL, Carry M, Colfax G, Herbst JH, et al. Substance use and drinking outcomes in personalized cognitive counseling randomized trial for episodic substance-using men who have sex with men. Drug Alcohol Depend. 2014;138:234–9.View ArticleGoogle Scholar
  47. Colfax G, Vittinghoff E, Husnik MJ, McKirnan D, Buchbinder S, Koblin B, Celum C, Chesney M, Huang Y, Mayer K, et al. Substance use and sexual risk: a participant- and episode-level analysis among a cohort of men who have sex with men. Am J Epidemiol. 2004;159(10):1002–12.View ArticleGoogle Scholar
  48. Sanchez T, Finlayson T, Drake A, Behel S, Cribbin M, Dinenno E, Hall T, Kramer S, Lansky A. Human immunodeficiency virus (HIV) risk, prevention, and testing behaviors--United States, national HIV behavioral surveillance system: men who have sex with men, November 2003-April 2005. Morb Mortal Wkly Rep Surveill Summ. 2006;55(6):1–16.Google Scholar
  49. Lee SW, Deiss RG, Segura ER, Clark JL, Lake JE, Konda KA, Coates TJ, Caceres CF. A cross-sectional study of low HIV testing frequency and high-risk behaviour among men who have sex with men and transgender women in Lima, Peru. BMC Public Health. 2015;15:408.View ArticleGoogle Scholar
  50. Shi TX, Zhang BC, Li XF, Xu JX, Wang N, Yu ZZ, Wang YF. Study on the high risk behaviors related to AIDS among men who having sex with men in the floating population. Zhonghua Liu Xing Bing Xue Za Zhi. 2009;30(7):668–71.PubMedGoogle Scholar
  51. Huang Y, Zhang Y, Li K, Zhao J. Changes in prevalence of HIV or syphilis among male sex workers and non-commercial men who have sex with men in Shenzhen, China: results of a second survey. PLoS One. 2016;11(12):e0167619.View ArticleGoogle Scholar

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