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Figure 1.
PRISMA Flow Diagram Detailing the Search Strategy
PRISMA Flow Diagram Detailing the Search Strategy

PRISMA indicates Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Figure 2.
Forest Plot of the Effect Sizes for Each Study Included in the Meta-analysis on the Prevalence of Sending a Sext
Forest Plot of the Effect Sizes for Each Study Included in the Meta-analysis on the Prevalence of Sending a Sext

Shown is a forest plot of studies3,4,6-9,25,26,28-36,38,40-53,55,56 included in the meta-analysis. The overall summary estimate for sending a sext was 14.8% (95% CI, 12.8%-16.8%). NCPTUP indicates National Campaign to Prevent Teen and Unplanned Pregnancy.

Figure 3.
Forest Plot of the Effect Sizes for Each Study Included in the Meta-analysis on the Prevalence of Receiving a Sext
Forest Plot of the Effect Sizes for Each Study Included in the Meta-analysis on the Prevalence of Receiving a Sext

Shown is a forest plot of studies2,4,6,9,26-29,31,35-37,40-43,48,51,54,55 included in the meta-analysis. The overall summary estimate for receiving a sext was 27.4% (95% CI, 23.1%-31.7%).

Table.  
Characteristics of All Studies Included in the Meta-analysis on Youth Sexting
Characteristics of All Studies Included in the Meta-analysis on Youth Sexting
1.
Benotsch  EG, Snipes  DJ, Martin  AM, Bull  SS.  Sexting, substance use, and sexual risk behavior in young adults.  J Adolesc Health. 2013;52(3):307-313.PubMedGoogle ScholarCrossref
2.
Mitchell  KJ, Finkelhor  D, Jones  LM, Wolak  J.  Prevalence and characteristics of youth sexting: a national study.  Pediatrics. 2012;129(1):13-20.PubMedGoogle ScholarCrossref
3.
Temple  JR, Paul  JA, van den Berg  P, Le  VD, McElhany  A, Temple  BW.  Teen sexting and its association with sexual behaviors.  Arch Pediatr Adolesc Med. 2012;166(9):828-833.PubMedGoogle ScholarCrossref
4.
Lenhart  A.  Teens and sexting. http://www.pewinternet.org/2009/12/15/teens-and-sexting/. Published December 15, 2009. Accessed September 30, 2016.
5.
O’Connor  TG, Dunn  J, Jenkins  JM, Pickering  K, Rasbash  J.  Family settings and children’s adjustment: differential adjustment within and across families.  Br J Psychiatry. 2001;179:110-115.PubMedGoogle ScholarCrossref
6.
Cox Communications. Youth online & wireless safety survey: cyberbullying, sexting, and parental controls. http://www.cox.com/wcm/en/aboutus/datasheet/takecharge/2009-teen-survey.pdf. Published May 2009. Accessed September 9, 2016.
7.
Dake  JA, Price  JH, Maziarz  L, Ward  B.  Prevalence and correlates of sexting behavior in adolescents.  Am J Sex Educ. 2012;7(1):1-15.Google ScholarCrossref
8.
Rice  E, Rhoades  H, Winetrobe  H,  et al.  Sexually explicit cell phone messaging associated with sexual risk among adolescents.  Pediatrics. 2012;130(4):667-673.PubMedGoogle ScholarCrossref
9.
Strassberg  DS, McKinnon  RK, Sustaíta  MA, Rullo  J.  Sexting by high school students: an exploratory and descriptive study.  Arch Sex Behav. 2013;42(1):15-21.PubMedGoogle ScholarCrossref
10.
Klettke  B, Hallford  DJ, Mellor  DJ.  Sexting prevalence and correlates: a systematic literature review.  Clin Psychol Rev. 2014;34(1):44-53.PubMedGoogle ScholarCrossref
11.
Sanderson  S, Tatt  ID, Higgins  JP.  Tools for assessing quality and susceptibility to bias in observational studies in epidemiology: a systematic review and annotated bibliography.  Int J Epidemiol. 2007;36(3):666-676.PubMedGoogle ScholarCrossref
12.
Tsang  TW, Lucas  BR, Carmichael Olson  H, Pinto  RZ, Elliott  EJ.  Prenatal alcohol exposure, FASD, and child behavior: a meta-analysis.  Pediatrics. 2016;137(3):e20152542.PubMedGoogle ScholarCrossref
13.
Hoy  D, Brooks  P, Woolf  A,  et al.  Assessing risk of bias in prevalence studies: modification of an existing tool and evidence of interrater agreement.  J Clin Epidemiol. 2012;65(9):934-939.PubMedGoogle ScholarCrossref
14.
Thomas  R, Sanders  S, Doust  J, Beller  E, Glasziou  P.  Prevalence of attention-deficit/hyperactivity disorder: a systematic review and meta-analysis.  Pediatrics. 2015;135(4):e994-e1001.PubMedGoogle ScholarCrossref
15.
Comprehensive Meta-Analysis (CMA) [computer program]. Version 3. Englewood, NJ: Biostat; 2014.
16.
Viechtbauer  W, Cheung  MW.  Outlier and influence diagnostics for meta-analysis.  Res Synth Methods. 2010;1(2):112-125.PubMedGoogle ScholarCrossref
17.
Pereda  N, Guilera  G, Forns  M, Gómez-Benito  J.  The prevalence of child sexual abuse in community and student samples: a meta-analysis.  Clin Psychol Rev. 2009;29(4):328-338.PubMedGoogle ScholarCrossref
18.
Egger  M, Davey Smith  G, Schneider  M, Minder  C.  Bias in meta-analysis detected by a simple, graphical test.  BMJ. 1997;315(7109):629-634.PubMedGoogle ScholarCrossref
19.
Sterne  JA, Egger  M.  Funnel plots for detecting bias in meta-analysis: guidelines on choice of axis.  J Clin Epidemiol. 2001;54(10):1046-1055.PubMedGoogle ScholarCrossref
20.
Borenstein  M, Hedges  LV, Higgins  J, Rothstein  H.  Introduction to Meta-analysis. Hoboken, NJ: John Wiley & Sons; 2011.
21.
Higgins  JP, Thompson  SG, Deeks  JJ, Altman  DG.  Measuring inconsistency in meta-analyses.  BMJ. 2003;327(7414):557-560.PubMedGoogle ScholarCrossref
22.
Thompson  SG, Higgins  JP.  How should meta-regression analyses be undertaken and interpreted?  Stat Med. 2002;21(11):1559-1573.PubMedGoogle ScholarCrossref
23.
Haq  AHB, Raihana  PA, Purwandari  E. Exploring cyberbullying among high school students in Surakarta. Paper presented at: The 2nd International Conference on Science, Technology, and Humanity; August 1, 2016; Yogyakarta, Indonesia.
24.
Rood  C, Thackeray  J, Letson  M, Leder  R, Berlan  E. Prevalence of sexting, online solicitations, and offline meetings among adolescents of a large child advocacy center with suspected child abuse. Paper presented at: NASPAG 29th Annual Clinical Research Meeting, Issues and Answers in Pediatric and Adolescent Gynecology; April 16, 2015; Orlando, FL.
25.
Baumgartner  SE, Sumter  SR, Peter  J, Valkenburg  PM, Livingstone  S.  Does country context matter? investigating the predictors of teen sexting across Europe.  Comput Human Behav. 2014;34:157-164.Google ScholarCrossref
26.
Campbell  SW, Park  YJ.  Predictors of mobile sexting among teens: toward a new explanatory framework.  Mobile Media Commun. 2014;2(1):20-39.Google ScholarCrossref
27.
Dowdell  EB, Burgess  AW, Flores  JR.  Original research: online social networking patterns among adolescents, young adults, and sexual offenders.  Am J Nurs. 2011;111(7):28-36.PubMedGoogle ScholarCrossref
28.
Fleschler Peskin  M, Markham  CM, Addy  RC, Shegog  R, Thiel  M, Tortolero  SR.  Prevalence and patterns of sexting among ethnic minority urban high school students.  Cyberpsychol Behav Soc Netw. 2013;16(6):454-459.PubMedGoogle ScholarCrossref
29.
Harris  AJ, Davidson  J, Letourneau  E, Paternite  C, Tusinski Miofsky  K. Building a prevention framework to address teen “sexting” behaviors. https://www.ncjrs.gov/pdffiles1/ojjdp/grants/244001.pdf. Published October 30, 2013. Accessed October 20, 2016.
30.
Houck  CD, Barker  D, Rizzo  C, Hancock  E, Norton  A, Brown  LK.  Sexting and sexual behavior in at-risk adolescents.  Pediatrics. 2014;133(2):e276-e282.PubMedGoogle ScholarCrossref
31.
Kerstens J, Stol W. Receiving online sexual requests and producing online sexual images: the multifaceted and dialogic nature of adolescents’ online sexual interactions. Cyberpsychology (Brno). 2014;8(1):Article 8. 10.5817/CP2014-1-8. Accessed January 7, 2018.
32.
Kopecký  K.  Sexting among Slovak pubescents and adolescent children.  Procedia Soc Behav Sci. 2015;203:244-250.Google ScholarCrossref
33.
Kopecký  K.  Cyberbullying and sexting between children and adolescents: comparative study.  Procedia Soc Behav Sci. 2014;149:467-471.Google ScholarCrossref
34.
Lee  CH, Moak  S, Walker  JT.  Effects of self-control, social control, and social learning on sexting behavior among South Korean youths.  Youth Soc. 2016;48(2):242-264.Google ScholarCrossref
35.
Lee  M, Crofts  T, McGovern  A, Milivojevic  S. Sexting among young people: perceptions and practices. http://www.aic.gov.au/publications/current%20series/tandi/501-520/tandi508.html. Published December 2015. Accessed September 30, 2016.
36.
Lippman  JR, Campbell  SW.  Damned if you do, damned if you don’t...if you’re a girl: relational and normative contexts of adolescent sexting in the United States.  J Child Media. 2014;8(4):371-386.Google ScholarCrossref
37.
Livingstone  S, Gorzig  A.  When adolescents receive sexual messages on the internet: explaining experiences of risk and harm.  Comput Hum Behav. 2014;33:8-15.Google ScholarCrossref
38.
Marcum  CD, Higgins  GE, Ricketts  ML.  Sexting behaviors among adolescents in rural North Carolina: a theoretical examination of low self-control and deviant peer association.  Int J Cyber Criminology. 2014;8(2):68-78.Google Scholar
39.
Mishna  F, Cook  C, Gadalla  T, Daciuk  J, Solomon  S.  Cyber bullying behaviors among middle and high school students.  Am J Orthopsychiatry. 2010;80(3):362-374.PubMedGoogle ScholarCrossref
40.
Murray  DL. A survey of the practices and perceptions of students in one Catholic high school on the use of the internet in relation to safety, cyberbullying, and sexting [dissertation]. https://repository.usfca.edu/diss/89/. Published 2014. Accessed September 30, 2016.
41.
O’Sullivan  LF.  Linking online sexual activities to health outcomes among teens.  New Dir Child Adolesc Dev. 2014;2014(144):37-51.PubMedGoogle ScholarCrossref
42.
Patrick  K, Heywood  W, Pitts  MK, Mitchell  A.  Demographic and behavioural correlates of six sexting behaviours among Australian secondary school students.  Sex Health. 2015;12(6):480-487.PubMedGoogle ScholarCrossref
43.
Rice  E, Gibbs  J, Winetrobe  H,  et al.  Sexting and sexual behavior among middle school students.  Pediatrics. 2014;134(1):e21-e28.PubMedGoogle ScholarCrossref
44.
Ricketts  ML, Maloney  C, Marcum  CD, Higgins  GE.  The effect of Internet related problems on the sexting behaviors of juveniles.  Am J Crim Justice. 2015;40(2):270-284.Google ScholarCrossref
45.
Schloms-Madlener KC. The Prevalence and Characteristics of Sexting Behaviours Among Adolescents and Adults in Cape Town, South Africa [dissertation]. Cape Town, South Africa: University of Cape Town; 2013.
46.
Ševčíková  A.  Girls’ and boys’ experience with teen sexting in early and late adolescence.  J Adolesc. 2016;51:156-162.PubMedGoogle ScholarCrossref
47.
National Campaign to Prevent Teen and Unplanned Pregnancy. Sex and tech: results from a survey of teens and young adults. https://www.drvc.org/pdf/protecting_children/sextech_summary.pdf. Published 2008. Accessed September 30, 2016.
48.
Van Ouytsel  J, Van Gool  E, Ponnet  K, Walrave  M.  Brief report: the association between adolescents’ characteristics and engagement in sexting.  J Adolesc. 2014;37(8):1387-1391.PubMedGoogle ScholarCrossref
49.
Van Ouytsel  J, Ponnet  K, Walrave  M.  The associations between adolescents’ consumption of pornography and music videos and their sexting behavior.  Cyberpsychol Behav Soc Netw. 2014;17(12):772-778.PubMedGoogle ScholarCrossref
50.
Vanden Abeele  M, Campbell  SW, Eggermont  S, Roe  K.  Sexting, mobile porn use, and peer group dynamics: boys’ and girls’ self-perceived popularity, need for popularity, and perceived peer pressure.  Media Psychol. 2014;17(1):6-33.Google ScholarCrossref
51.
Velarde V. The Exchange of Sexually Explicit Cell Phone Pictures (Sexting) Among High School Students [senior honors thesis]. Salt Lake City: University of Utah; 2014.
52.
Walrave  M, Heirman  W, Hallam  L.  Under pressure to sext? applying the theory of planned behaviour to adolescent sexting [published online October 2, 2013].  Behav Inform Technol. doi:10.1080/0144929X.2013.837099Google Scholar
53.
Walrave  M, Ponnet  K, Van Ouytsel  J, Van Gool  E, Heirman  W, Verbeek  A.  Whether or not to engage in sexting: explaining adolescent sexting behaviour by applying the prototype willingness model.  Telemat Inform. 2015;32(4):796-808.Google ScholarCrossref
54.
Wolfe  SE, Marcum  CD, Higgins  GE, Ricketts  ML.  Routine cell phone activity and exposure to sext messages: extending the generality of routine activity theory and exploring the etiology of a risky teenage behavior.  Crime Delinq. 2016;62(5):614-644.Google ScholarCrossref
55.
Wood  M, Barter  C, Stanley  N, Aghtaie  N, Larkins  C.  Images across Europe: the sending and receiving of sexual images and associations with interpersonal violence in young people’s relationships.  Child Youth Serv Rev. 2015;59:149-160.Google ScholarCrossref
56.
Ybarra  ML, Mitchell  KJ.  “Sexting” and its relation to sexual activity and sexual risk behavior in a national survey of adolescents.  J Adolesc Health. 2014;55(6):757-764. PubMedGoogle ScholarCrossref
57.
Drouin  M, Vogel  KN, Surbey  A, Stills  JR.  Let’s talk about sexting, baby: computer-mediated sexual behaviors among young adults.  Comput Hum Behav. 2013;29(5):A25-A30.Google ScholarCrossref
58.
Ott  MA, Pfeiffer  EJ.  “That’s nasty” to curiosity: early adolescent cognitions about sexual abstinence.  J Adolesc Health. 2009;44(6):575-581.PubMedGoogle ScholarCrossref
59.
Temple  JR, Choi  H.  Longitudinal association between teen sexting and sexual behavior.  Pediatrics. 2014;134(5):e1287-e1292.PubMedGoogle ScholarCrossref
60.
Influence Central. Kids & tech: the evolution of today’s digital natives. http://influence-central.com/kids-tech-the-evolution-of-todays-digital-natives/. Published 2016. Accessed September 13, 2017.
61.
Zimmer-Gembeck  MJ, Siebenbruner  J, Collins  WA.  Diverse aspects of dating: associations with psychosocial functioning from early to middle adolescence.  J Adolesc. 2001;24(3):313-336.PubMedGoogle ScholarCrossref
62.
Arnett  JJ.  Emerging adulthood: what is it, and what is it good for?  Child Dev Perspect. 2007;1(2):68-73.Google ScholarCrossref
63.
Don't get sextorted campaign [press release]. https://www.protectchildren.ca/app/en/media_release_201705_dontgetsextorted_campaign. Published May 23, 2017. Accessed August 1, 2017.
64.
Kugler  KC, Vasilenko  SA, Butera  NM, Coffman  DL.  Long-term consequences of early sexual initiation on young adult health: a causal inference approach.  J Early Adolesc. 2017;37(5):662-676.PubMedGoogle ScholarCrossref
65.
Dir  AL, Coskunpinar  A, Steiner  JL, Cyders  MA.  Understanding differences in sexting behaviors across gender, relationship status, and sexual identity, and the role of expectancies in sexting.  Cyberpsychol Behav Soc Netw. 2013;16(8):568-574.PubMedGoogle ScholarCrossref
66.
Choi  H, Van Ouytsel  J, Temple  JR.  Association between sexting and sexual coercion among female adolescents.  J Adolesc. 2016;53:164-168.PubMedGoogle ScholarCrossref
67.
CTV News. Timeline: Amanda Todd investigation [press release]. https://www.ctvnews.ca/canada/timeline-amanda-todd-investigation-1.1782168. Published April 18, 2014. Accessed October 20, 2016.
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Moreno  MA.  Your child’s first cell phone.  JAMA Pediatr. 2017;171(6):608.PubMedGoogle ScholarCrossref
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American Academy of Pediatrics. Talking to kids and teens about social media and sexting: tips from the American Academy of Pediatrics. https://www.aap.org/en-us/about-the-aap/aap-press-room/news-features-and-safety-tips/Pages/Talking-to-Kids-and-Teens-About-Social-Media-and-Sexting.aspx. Updated May 31, 2013. Accessed September 4, 2017.
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71.
Dir  AL, Cyders  MA.  Risks, risk factors, and outcomes associated with phone and internet sexting among university students in the United States.  Arch Sex Behav. 2015;44(6):1675-1684.PubMedGoogle ScholarCrossref
Original Investigation
April 2018

Prevalence of Multiple Forms of Sexting Behavior Among Youth: A Systematic Review and Meta-analysis

Author Affiliations
  • 1Department of Psychology, University of Calgary, Calgary, Alberta, Canada
  • 2Alberta Children’s Hospital Research Institute, Calgary, Alberta, Canada
  • 3Department of Communication Studies, Universiteit Antwerpen, Antwerp, Belgium
  • 4Department of Obstetrics and Gynecology, The University of Texas Medical Branch at Galveston
JAMA Pediatr. 2018;172(4):327-335. doi:10.1001/jamapediatrics.2017.5314
Key Points

Question  What is the prevalence of sexting behavior among youth?

Findings  Among 39 studies (with 110 380 participants) in this meta-analysis, the mean prevalences for sending and receiving sexts were 14.8% and 27.4%, respectively, with prevalence rates increasing in recent years and as youth age. The prevalences of forwarding a sext without consent and having a sext forwarded without consent were 12.0% and 8.4%, respectively.

Meaning  Sexting is becoming a more common practice among youth; therefore, age-specific information on sexting and its potential consequences should regularly be provided as a component of sex education.

Abstract

Importance  The existing literature on sexting among youth shows that sexting is a predictor of sexual behavior and may be associated with other health outcomes and risky behaviors. However, there remains a lack of consensus on the prevalence of sexting, which is needed to inform future research, intervention, and policy development.

Objective  To provide a meta-analytic synthesis of studies examining the prevalence of multiple forms of sexting behavior, analyzed by age, sex, geography, and method of sexting.

Data Sources  In an academic setting, electronic searches in MEDLINE, PsycINFO, EMBASE, and Web of Science were conducted for the period January 1990 to June 2016, yielding 1147 nonduplicate records.

Study Selection  Studies were included if participants were younger than 18 years and the prevalence of sexting explicit images, videos, or messages was reported.

Data Extraction and Synthesis  Literature review and data extraction followed established PRISMA guidelines. Two independent reviewers extracted all relevant data. Random-effects meta-analyses were used to derive the mean prevalence rates. Thirty-nine studies met final inclusion criteria.

Main Outcomes and Measures  Meta-analyses of the prevalence of sending, receiving, and forwarding without consent, as well as having one’s sext forwarded without consent.

Results  Among 39 included studies, there were 110 380 participants; the mean age was 15.16 years (age range, 11.9-17.0 years), and on average 47.2% were male. Studies were available for sending (n = 34), receiving (n = 20), forwarding without consent (n = 5), and having a sext forwarded without consent (n = 4). The mean prevalences for sending and receiving sexts were 14.8% (95% CI, 12.8%-16.8%) and 27.4% (95% CI, 23.1%-31.7%), respectively. Moderator analyses revealed that effect sizes varied as a function of child age (prevalence increased with age), year of data collection (prevalence increased over time), and sexting method (higher prevalence on mobile devices compared with computers). The prevalence of forwarding a sext without consent was 12.0% (95% CI, 8.4%-15.6%), and the prevalence of having a sext forwarded without consent was 8.4% (95% CI, 4.7%-12.0%).

Conclusions and Relevance  The prevalence of sexting has increased in recent years and increases as youth age. Further research focusing on nonconsensual sexting is necessary to appropriately target and inform intervention, education, and policy efforts.

Introduction

Sexting—the sharing of sexually explicit images, videos, or messages through electronic means—has received mounting attention from the popular press and an accumulating amount of attention in the empirical literature. However, the true public health importance of youth sexting is unclear at present because the field is handicapped by inconsistent information regarding its prevalence. With the published rate of youth sexting ranging from 1.3% to 60%,1-3 the extent to which health care professionals, school personnel, policymakers, and parents should be concerned about this behavior is unknown.

One of the first published studies on youth sexting was conducted in 2009 before the current prolific use of smartphones among youth.4 Among youth aged 12 to 17 years, results indicated that 4% reported sending and 15% reported receiving nude or seminude images. A 2012 study2 revealed a low prevalence of sexting among participants aged 10 to 17 years, with 2.5% and 7.1% of predominantly older youth sending and receiving sexts, respectively. That study had notable strengths, including a nationally representative sample, an explicit definition of sexting, and a wide age range. However, several methodological limitations likely resulted in the underreporting of sexting, including the use of landlines to conduct the survey and interviews with youth in the presence of parents. Recent studies reveal that sexting is an increasingly common practice, with the prevalence increasing each year until youth reach the age of 18 years.3

While it is becoming clear that a sizable number of adolescent boys and girls participate in sexting, research examining sex differences has been inconsistent. A few studies2,5,6 have found that female youth were more likely to send a sext than their male counterparts, while other studies3,7,8 have not revealed any sex differences with respect to sending sexts. Some evidence suggests that adolescent boys are more likely than girls to receive9 and request3 sexts.

Although research on sexting is no longer in its infancy, there is a lack of consensus on the prevalence of sexting behaviors, which is critically important to informing future research and policy. We aim to extend the literature by examining the mean prevalence of sending and receiving sexts, as well as the rate of the nonconsensual forwarding of sexts. Moreover, we aim to determine whether prevalence rates vary as a function of sex, age, and time, as well as other potential moderators.

Methods
Search Strategy and Study Selection

This meta-analysis was conducted in an academic setting following the recommendations and standards set by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A medical librarian conducted an electronic search in MEDLINE, PsycINFO, EMBASE, and Web of Science (January 1990 to June 2016) using the following combination of keywords: (youth*, adolescen* youth* child* girl* boy*, young people OR student*) and (image*, photo* or picture*, messag*) or (sext or sexting), or (sex*, nude, or explicit), or (cyber or internet or online). No language or publication restrictions were applied. In addition, references of all articles meeting study inclusion were reviewed for additional studies, and online reports were also searched.

Studies met inclusion criteria if (1) participants were younger than 18 years; (2) the study reported the prevalence of 1 or more act of sending, receiving, or forwarding without consent or having one’s sext forwarded without consent; (3) the definition of sexting was consistent with the sending, receiving, or forwarding of sexually explicit images, videos, and/or messages, a definition consistent with previous literature reviews10; (4) both prevalence and sample size were provided; and (5) the study was available in English. Two of us (A.L. and C.R.) reviewed the titles and abstracts of all studies identified in the search strategy.

Data Extraction

Extracted data included prevalence and sample size, as well as potential moderators, including the following: (1) age, examined continuously as a mean; (2) sex, examined as the percentage of boys in the sample; (3) earliest year of data collection; (4) method of sexting (mobile device vs computer); and (5) message content (images only, images/videos, or images/videos/explicit messaging). In addition, we extracted (6) study location (United States, Europe, or other) and (7) publication status (published in peer-reviewed journal vs dissertation or report). To avoid oversampling effect size estimates from individual studies, only the total study’s sexting prevalences of sending, receiving, and/or forwarding sexts are represented (rather than sexting data stratified by age, sex, or country). For the nonconsensual forwarding of sext, data were extracted based on the total sample of youth in the study (as opposed to sexting youth only). When data from more than one wave of data collection were provided or when data from one sample were presented across multiple publications, we selected the wave or publication with the largest sample size and the most comprehensive data extraction information. To ensure accuracy and reliability, all studies were double coded, and discrepancies were resolved by consensus.

Study Quality

To examine methodological quality and validity of findings, a 9-point critical appraisal assessment tool was developed based on previous meta-analyses.11-13 The coding criteria for the quality scoring of all studies meeting inclusion criteria are listed in eTable 1 and eTable 2 in the Supplement. Articles were given a score of 0 (no) or 1 (yes) for each criterion and summed to give a total score out of 9. The classification system used identified studies of low (≤2), moderate (3-5), or high (≥6) quality.14

Calculation of Effect Sizes

All data were extracted and entered into Comprehensive Meta-Analysis (CMA) software (version 3; Biostat).15 A series of meta-analyses were conducted for each type of sexting behavior, presented as a mean prevalence, with associated 95% CIs around the estimate. Comprehensive Meta-Analysis software transforms the prevalence rate into a logit event rate effect size with a computed standard error. Subsequently, effect sizes are weighted by the inverse of their variance, giving greater weight to studies with larger sample sizes and thus more precise estimates. Finally, logits are retransformed into proportions to facilitate ease of interpretation. Random-effects models were selected to calculate effect sizes because they represent a more conservative estimate of the mean prevalence.

Sensitivity Analysis and Publication Bias

Outlier detection was used to determine if the mean prevalence of each sexting behavior was affected by extreme values.16 Inspection of box plots17 derived in SPSS (version 23.0; IBM Corporation) were examined, and detected outliers were removed from the calculation of the effect size if prevalence rates were affected by these values. Publication bias was examined using inspection of funnel plots and the Egger test.18,19

Assessment of Statistical Heterogeneity and Subgroups

The Q and I2 statistics were computed to assess for statistical heterogeneity of effect sizes.20,21 A significant Q statistic suggests that study variability in effect size estimates is greater than sampling error, and moderators should be explored. The I2 statistic examines the rate of variability across studies due to heterogeneity rather than chance. The I2 statistic ranges from 0% to 100% and can be interpreted as no (0%) and maximal (100%) heterogeneity. Between-study heterogeneity was examined using the Q statistic (categorical moderators) and meta-regressions.15,22

Results

As shown in the PRISMA flow diagram (Figure 1), our electronic search yielded 1147 nonduplicate records. A total of 122 articles were identified as potentially meeting inclusion criteria, and full-text articles were retrieved. On review of all full-text articles, 41 studies met inclusion criteria.

Study Quality Evaluation

The mean study quality score across the 41 articles meeting inclusion criteria was 6.2 (eTables 1 and 2 in the Supplement). Two studies23,24 (4.9%) fell in the low-quality range, and 10 studies (24.4%) were in the moderate-quality range, with the remaining 29 studies (70.7%) in the high-quality range. The 2 studies deemed to have low methodological quality were removed from analyses. Therefore, the remaining 39 studies were used in subsequent meta-analyses.

Sample Characteristics of Included Studies

A total of 39 studies met all study and methodological inclusion criteria. Studies were available for sending (n = 34), receiving (n = 20), forwarding without consent (n = 5), or having a sext forwarded without consent (n = 4). The Table summarizes included studies. In total across the 39 studies, 110 380 participants were included, with a mean age of 15.16 years (age range, 11.9-17.0 years). On average, 47.2% of participants were male. Twenty-two studies (56.4%) were from the United States, 12 studies (30.8%) were from Europe, 2 studies (5.1%) were from Australia, 1 study (2.6%) was from Canada, 1 study (2.6%) was from South Africa (2.6%), and 1 study (2.6%) was from South Korea. Eighteen studies (46.2%) examined sexting using mobile devices and computers, with 6 studies (15.4%) using computers only, 14 studies (35.9%) via mobile devices only, and one study (2.6%) providing insufficient information for determination. Eleven studies (28.2%) asked participants about sexting via images, 14 studies (35.9%) via images or videos, 7 studies (17.9%) via images and/or explicit messages, and 7 studies (17.9%) via images, videos, and/or explicit messages. Note that no study examined sexting via sexually explicit messaging alone. Finally, 31 studies (79.5%) were published in peer-reviewed journals, and 8 studies (20.5%) were unpublished.

Combined Prevalence of Sending a Sext

The random-effects analysis of the 34 studies on sending a sext yielded a mean prevalence of 14.8% (95% CI, 12.8%-16.8%) (Figure 2). The Egger test provided evidence that studies with smaller sample sizes had more extreme prevalence estimates (eFigure 1 in the Supplement). A sensitivity analysis was conducted to determine the presence of potential outliers, and one study was identified. Heterogeneity of effect sizes remained present with (Q = 3765.13, P < .001, I2 = 99.04%) and without (Q = 3699.53, P < .001, I2 = 99.08%) the outlying study; therefore, potential moderators were explored with all studies included, and results are summarized in eTable 3 in the Supplement.

A meta-regression analysis revealed a linear increase in prevalence as age increased (b = 0.037; 95% CI, 0.024-0.050). Effect sizes were also moderated by year of study data collection, with a demonstrated increase in the prevalence of sending a sext over time (b = 0.026; 95% CI, 0.012-0.039). Messaging outlet also explained between-study variability, with a higher prevalence of sexting on mobile devices (k = 13; 13.4%; 95% CI, 9.0%-17.7%) compared with computers (k = 4; 5.5%; 95% CI, 2.3%-8.6%), where k indicates the number of studies. Prevalence was not moderated by sex, geographical location, message content, or publication status.

Combined Prevalence of Receiving a Sext

The random-effects analysis of the 20 studies on receiving a sext yielded a mean prevalence of 27.4% (95% CI, 23.1%-31.7%) (Figure 3). The Egger test provided evidence that studies with smaller sample sizes had more extreme prevalence estimates (eFigure 2 in the Supplement). No outliers were detected. Heterogeneity of effect sizes was present (Q = 1415.99, P < .001, I2 = 98.66%), and results of moderator analyses are summarized in eTable 4 in the Supplement.

The prevalence of receiving a sext increased as age increased (b = 0.068; 95% CI, 0.035-0.100). Meta-regression analyses revealed that year of study data collection explained between-study heterogeneity. Specifically, the prevalence of receiving a sext has increased over time (b = 0.060; 95% CI, 0.032-0.088). Effect sizes also varied as a function of messaging outlet, with a higher prevalence of sexting on mobile devices (k = 9; 27.6%; 95% CI, 20.7%-34.6%) compared with computers (k = 4; 13.6%; 95% CI, 9.8%-17.4%). Prevalence was not moderated by sex, message content, geographical location, or publication status.

Forwarding a Sext Without Consent

The random-effects analysis of the 5 studies on forwarding a sext without consent yielded a mean prevalence of 12.0% (95% CI, 8.4%-15.6%) (eFigure 3 in the Supplement). No publication bias (eFigure 4 in the Supplement) or outliers were detected. Heterogeneity of effect sizes was present (Q = 33.64, P < .001, I2 = 88.11%), and moderators were explored. Neither age (b = −0.051; 95% CI, −0.113 to 0.105) nor sex (b = 0.002; 95% CI, −0.005 to 0.009) moderated prevalence. No other moderators could be explored due to limited studies at each level of the moderators.

Having a Sext Forwarded Without Consent

The random-effects analysis of the 4 studies on having a sext forwarded without consent yielded a mean prevalence of 8.4% (95% CI, 4.7%-12.0%) (eFigure 5 in the Supplement). No publication bias (eFigure 6 in the Supplement) or outliers were detected. Heterogeneity of effect sizes was present (Q = 151.26, P < .001, I2 = 98.02%), and moderators were explored. Neither age (b = −0.017; 95% CI, −0.077 to 0.042) nor sex (b = 0.010; 95% CI, −0.005 to 0.025) moderated prevalence rates. No other moderators could be explored due to limited studies at each level of the moderators.

Discussion

The heightened media attention over youth sexting has portrayed widespread involvement in this phenomenon, which in turn has created alarm in the public domain. However, the documented prevalence of youth sexting in emerging research varies considerably, creating difficulty in interpreting the composite of findings to either support or refute media portrayals. The present meta-analysis established that a sizable minority of youth engage in sexting (1 in 7 sends sexts, while 1 in 4 receives sexts), with rates varying as a function of age, year of data collection, and method of sexting. Of particular concern is the prevalence of nonconsensual sexting, with 12.5% (1 in 8) of youth reporting that they have forwarded a sext.

The meta-analysis revealed that the prevalence of receiving sexts was higher than the prevalence of sending sexts. Because the methods of assessing sexting typically use analogous items to measure both sending and receiving of sexts,2,57 the source of this discrepancy is likely not methodological in nature. Klettke et al10 suggest that this discrepancy may occur for several reasons: some respondents may underreport their active engagement in sexting, some sexters may send the same picture to multiple people, and/or those who receive a sext might not reciprocate the message.

Youth were more likely to send and receive sexts with increasing age. A higher rate among older youth is expected and generally corresponds to the age of sexual identity and exploration,58 which lends credence to the notion that youth sexting may be an emerging, and potentially normal, component of sexual behavior and development.59 Moreover, the increase in prevalence rates with age is commensurate with older youth having greater access to and/or owning smartphones compared with younger youth.4 That said, there is a growing trend for tweens to have access to smartphones: in 2016, the mean age of first smartphone possession was estimated to be 10.3 years.60 However, there is limited knowledge of sexting in youth under the age of 12 years. To our knowledge, the only existing study on sexting in youth younger than 12 years is by Mitchell et al,2 who reported that 1% of youth aged 10 to 11 years appeared in, created, or received nude images or videos. However, because these data were collected in 2010-2011, this finding is likely outdated given the proliferation of smartphones and the trend for earlier age at first smartphone possession. Relationships among tweens are often transient,61,62 which may make them more vulnerable to having sexts forwarded without consent. Moreover, given their relative cognitive naïveté, tweens may be particularly vulnerable to sextortion (ie, nude images and/or videos are used as a form of threat or blackmail)63 and, like youth who report early sexual debut, may be at risk for a host of risky behaviors and negative consequences.64

With smartphone ownership becoming near ubiquitous in recent years,4 our finding that the prevalence of youth sexting was higher in more recent studies was not surprising. The finding that rates of sexting were also higher via mobile devices relative to computers was expected because mobile phones are a portable, convenient technology that allows for immediate, rapid, and seemingly private communication. These latter 2 findings help explain the low prevalence found in many early studies on sexting.2,4 Moreover, in recent years, smartphone apps have been developed that may (seemingly) facilitate privacy in the sharing and storing of videos/images, which may have increased both awareness of and motivation for engaging in sexting. For example, the 4% prevalence of sexting found in the Pew Research Center study4 in 2009 is often cited as evidence that youth sexting is not common; however, that study took place before many youth had access to mobile devices. Therefore, inclusion of these earlier studies in our meta-analyses may have underestimated the mean prevalence of youth sexting.

It has been suggested that female youth and young adults may be more likely to sext due to perceived pressure by male peers to send nude images.65,66 Indeed, media portrayals of sexting often implicate adolescent girls as the senders of naked photographs and adolescent boys as the requesters. However, this popular belief and empirical proposition were not supported by the present meta-analysis, which found no significant sex differences in the rate of sending or receiving sexts.

Results of this meta-analysis reveal that 12.0% and 8.4% of youth have forwarded a sext (perpetrator of nonconsensual sexting) or have had their sext forwarded (recipient of nonconsensual sexting) without consent, respectively. Neither age nor sex appeared to affect the prevalence of this phenomenon. The negative outcomes of this behavior have increasingly gained attention in the media as a growing number of cases highlight how the nonconsensual forwarding of sexts can lead to harassment by peers, cyberbullying, or blackmailing. In extreme cases, the deleterious effects of the nonconsensual forwarding of explicit photographs have been implicated in youth suicide.67 Moreover, nonconsensual sexting may be a prelude to or a marker of in-person sexual assault.66 An important caveat is that the sample sizes for the meta-analyses on nonconsensual sexting were small, warranting additional research in this area.

Public Health and Policy Implications

There are several public health and policy implications of our findings reported herein. A sizable minority of youth are sexting. It is possible that this behavior may be a normal part of sexual behavior and identity formation in the digital age. Consequently, efforts and resources to criminalize sexts should be redirected to educational programs on digital citizenship and healthy relationships. Moreover, given that the mean age of first smartphone acquisition is 10.3 years,60 it is important for middle school educators, pediatricians, and parents to have ongoing conversations with tweens regarding sexting and digital citizenship. Several criterion-standard resources for engaging in conversations regarding mobile phone use and responsibilities, as well as sexting behavior, are available.68,69

The rate of nonconsensual sexting among younger youth is concerning and, with respect to legislation on sexting, should continue to be a primary concern for policymakers. As parents, health care professionals, school administrators, and law enforcement authorities continue to grapple with educating youth on nonconsensual forwarding of sexts, it is promising to see that policymakers are responding to this problem by introducing and amending legislation (eg, laws against revenge porn) that makes it a criminal offense to share intimate images of a person without the person’s consent.70 However, because many existing laws were intended to punish adult behavior, policymakers must be aware of the implications of these laws for adolescent offenders while not introducing legal loopholes for adult offenders.

Limitations

Although this meta-analysis includes a robust number of studies on sending and receiving sexts, there are comparatively fewer studies on nonconsensual sexting. Moreover, there were too few studies to examine the solicitation of sexts. A larger sample size in a meta-analytic inquiry leads to greater precision in estimations of the prevalence and increased capacity to detect factors that increase or decrease the mean prevalence rates. In the nonconsensual sexting meta-analyses, we could not adequately assess for potential moderators due to small sample sizes and, accordingly, a lack of statistical power. This meta-analysis is also limited in that it focuses solely on prevalence rates and not on variables that predict a proclivity for engaging in sexting behavior. Studies are devoting more attention to attitudinal and behavioral risks for sexting by examining motivations for sexting,65 perceived risks associated with sexting,57,71 and negative experiences resulting from sexting.65 These studies contribute to a more nuanced understanding of factors that motivate youth to sext, and their results can inform the development and delivery of educational interventions. Finally, meta-analyses are reliant on the methods used in individual studies. A notable limitation of sexting research in general is variability in definitions and sampling techniques.10 For example, while some studies define sexting as the sharing of sexually explicit images, videos, and/or text messages, others define sexting as the sharing of nude images or videos only. Moreover, many studies examined herein reported a combined prevalence rate for images, videos, and/or messages, rendering it impossible to parse out the prevalence of each digital method. The field of sexting would benefit from a uniform definition of sexting, and future research should strive toward the methodological practice of providing prevalence rates for each messaging method (images, videos, and texts) to better understand the nuances of youth sexting. As noted by Mitchell et al,2 this methodological clarity is also important for policymakers seeking to draw on the existing literature to create or amend policies on the nonconsensual sharing of nude images/videos in particular.

Conclusions

Contrary to some earlier findings,4 our results indicated that consensual sexting is becoming a more common practice among youth, with 14.8% and 27.4% of youth sending and receiving sexts, respectively. Moreover, higher prevalence rates were found in more recent studies, with older youth, and with youth using a mobile device to sext. Troublingly, approximately 1 in 8 youth reports either forwarding or having a sext forwarded without their consent. An important area of future inquiry will be the identification of variables associated with nonconsensual sexting, as well as the evaluation of the effectiveness of educational campaigns and legal policies striving to mitigate nonconsensual sexting in youth.

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Article Information

Accepted for Publication: November 27, 2017.

Corresponding Author: Sheri Madigan, PhD, Department of Psychology, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada (sheri.madigan@ucalgary.ca).

Correction: This article was corrected for a repeated typographical error in the Results section on April 2, 2018. The 8 values preceded by β should have been preceded by lowercase italicized b.

Published Online: February 26, 2018. doi:10.1001/jamapediatrics.2017.5314

Author Contributions: Dr Madigan had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Madigan, Temple.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Madigan, Ly, Van Ouytsel, Temple.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Madigan.

Obtained funding: Madigan.

Administrative, technical, or material support: Madigan, Ly, Van Ouytsel, Temple.

Study supervision: Madigan.

Conflict of Interest Disclosures: None reported.

Funding/Support: This work was supported by the Alberta Children’s Hospital Foundation and the Canada Research Chairs Program.

Role of the Funder/Support: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The opinions, results, and conclusions reported in this article are those of the authors and are independent from the funding sources.

Additional Contributions: Cheri Nickel, MLIS (University of Calgary), conducted the literature search. Elena Buliga (University of Calgary) assisted in the abstract review. Both received compensation for their work.

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